Comparison of connectivity analyses for resting state EEG data

NASA Astrophysics Data System (ADS)

Olejarczyk, Elzbieta; Marzetti, Laura; Pizzella, Vittorio; Zappasodi, Filippo

2017-06-01

Objective. In the present work, a nonlinear measure (transfer entropy, TE) was used in a multivariate approach for the analysis of effective connectivity in high density resting state EEG data in eyes open and eyes closed. Advantages of the multivariate approach in comparison to the bivariate one were tested. Moreover, the multivariate TE was compared to an effective linear measure, i.e. directed transfer function (DTF). Finally, the existence of a relationship between the information transfer and the level of brain synchronization as measured by phase synchronization value (PLV) was investigated. Approach. The comparison between the connectivity measures, i.e. bivariate versus multivariate TE, TE versus DTF, TE versus PLV, was performed by means of statistical analysis of indexes based on graph theory. Main results. The multivariate approach is less sensitive to false indirect connections with respect to the bivariate estimates. The multivariate TE differentiated better between eyes closed and eyes open conditions compared to DTF. Moreover, the multivariate TE evidenced non-linear phenomena in information transfer, which are not evidenced by the use of DTF. We also showed that the target of information flow, in particular the frontal region, is an area of greater brain synchronization. Significance. Comparison of different connectivity analysis methods pointed to the advantages of nonlinear methods, and indicated a relationship existing between the flow of information and the level of synchronization of the brain.

Multivariate statistical process control (MSPC) using Raman spectroscopy for in-line culture cell monitoring considering time-varying batches synchronized with correlation optimized warping (COW).

PubMed

Liu, Ya-Juan; André, Silvère; Saint Cristau, Lydia; Lagresle, Sylvain; Hannas, Zahia; Calvosa, Éric; Devos, Olivier; Duponchel, Ludovic

2017-02-01

Multivariate statistical process control (MSPC) is increasingly popular as the challenge provided by large multivariate datasets from analytical instruments such as Raman spectroscopy for the monitoring of complex cell cultures in the biopharmaceutical industry. However, Raman spectroscopy for in-line monitoring often produces unsynchronized data sets, resulting in time-varying batches. Moreover, unsynchronized data sets are common for cell culture monitoring because spectroscopic measurements are generally recorded in an alternate way, with more than one optical probe parallelly connecting to the same spectrometer. Synchronized batches are prerequisite for the application of multivariate analysis such as multi-way principal component analysis (MPCA) for the MSPC monitoring. Correlation optimized warping (COW) is a popular method for data alignment with satisfactory performance; however, it has never been applied to synchronize acquisition time of spectroscopic datasets in MSPC application before. In this paper we propose, for the first time, to use the method of COW to synchronize batches with varying durations analyzed with Raman spectroscopy. In a second step, we developed MPCA models at different time intervals based on the normal operation condition (NOC) batches synchronized by COW. New batches are finally projected considering the corresponding MPCA model. We monitored the evolution of the batches using two multivariate control charts based on Hotelling's T 2 and Q. As illustrated with results, the MSPC model was able to identify abnormal operation condition including contaminated batches which is of prime importance in cell culture monitoring We proved that Raman-based MSPC monitoring can be used to diagnose batches deviating from the normal condition, with higher efficacy than traditional diagnosis, which would save time and money in the biopharmaceutical industry. Copyright © 2016 Elsevier B.V. All rights reserved.

Analysis and differentiation of paper samples by capillary electrophoresis and multivariate analysis.

PubMed

Fernández de la Ossa, Ma Ángeles; Ortega-Ojeda, Fernando; García-Ruiz, Carmen

2014-11-01

This work reports an investigation for the analysis of different paper samples using CE with laser-induced detection. Papers from four different manufactures (white-copy paper) and four different paper sources (white and recycled-copy papers, adhesive yellow paper notes and restaurant serviettes) were pulverized by scratching with a surgical scalpel prior to their derivatization with a fluorescent labeling agent, 8-aminopyrene-1,3,6-trisulfonic acid. Methodological conditions were evaluated, specifically the derivatization conditions with the aim to achieve the best S/N signals and the separation conditions in order to obtain optimum values of sensitivity and reproducibility. The best conditions, in terms of fastest, and easiest sample preparation procedure, minimal sample consumption, as well as the use of the simplest and fastest CE-procedure for obtaining the best analytical parameters, were applied to the analysis of the different paper samples. The registered electropherograms were pretreated (normalized and aligned) and subjected to multivariate analysis (principal component analysis). A successful discrimination among paper samples without entanglements was achieved. To the best of our knowledge, this work presents the first approach to achieve a successful differentiation among visually similar white-copy paper samples produced by different manufactures and paper from different paper sources through their direct analysis by CE-LIF and subsequent comparative study of the complete cellulose electropherogram by chemometric tools. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A multiplexed analysis approach identifies new association of inflammatory proteins in patients with overactive bladder

PubMed Central

Ma, Emily; Vetter, Joel; Bliss, Laura; Lai, H. Henry; Mysorekar, Indira U.

2016-01-01

Overactive bladder (OAB) is a common debilitating bladder condition with unknown etiology and limited diagnostic modalities. Here, we explored a novel high-throughput and unbiased multiplex approach with cellular and molecular components in a well-characterized patient cohort to identify biomarkers that could be reliably used to distinguish OAB from controls or provide insights into underlying etiology. As a secondary analysis, we determined whether this method could discriminate between OAB and other chronic bladder conditions. We analyzed plasma samples from healthy volunteers (n = 19) and patients diagnosed with OAB, interstitial cystitis/bladder pain syndrome (IC/BPS), or urinary tract infections (UTI; n = 51) for proinflammatory, chemokine, cytokine, angiogenesis, and vascular injury factors using Meso Scale Discovery (MSD) analysis and urinary cytological analysis. Wilcoxon rank-sum tests were used to perform univariate and multivariate comparisons between patient groups (controls, OAB, IC/BPS, and UTI). Multivariate logistic regression models were fit for each MSD analyte on 1) OAB patients and controls, 2) OAB and IC/BPS patients, and 3) OAB and UTI patients. Age, race, and sex were included as independent variables in all multivariate analysis. Receiver operating characteristic (ROC) curves were generated to determine the diagnostic potential of a given analyte. Our findings demonstrate that five analytes, i.e., interleukin 4, TNF-α, macrophage inflammatory protein-1β, serum amyloid A, and Tie2 can reliably differentiate OAB relative to controls and can be used to distinguish OAB from the other conditions. Together, our pilot study suggests a molecular imbalance in inflammatory proteins may contribute to OAB pathogenesis. PMID:27029431

Combining Correlation Matrices: Simulation Analysis of Improved Fixed-Effects Methods

ERIC Educational Resources Information Center

Hafdahl, Adam R.

2007-01-01

The originally proposed multivariate meta-analysis approach for correlation matrices--analyze Pearson correlations, with each study's observed correlations replacing their population counterparts in its conditional-covariance matrix--performs poorly. Two refinements are considered: Analyze Fisher Z-transformed correlations, and substitute better…

Univariate and multivariate skewness and kurtosis for measuring nonnormality: Prevalence, influence and estimation.

PubMed

Cain, Meghan K; Zhang, Zhiyong; Yuan, Ke-Hai

2017-10-01

Nonnormality of univariate data has been extensively examined previously (Blanca et al., Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, 9(2), 78-84, 2013; Miceeri, Psychological Bulletin, 105(1), 156, 1989). However, less is known of the potential nonnormality of multivariate data although multivariate analysis is commonly used in psychological and educational research. Using univariate and multivariate skewness and kurtosis as measures of nonnormality, this study examined 1,567 univariate distriubtions and 254 multivariate distributions collected from authors of articles published in Psychological Science and the American Education Research Journal. We found that 74 % of univariate distributions and 68 % multivariate distributions deviated from normal distributions. In a simulation study using typical values of skewness and kurtosis that we collected, we found that the resulting type I error rates were 17 % in a t-test and 30 % in a factor analysis under some conditions. Hence, we argue that it is time to routinely report skewness and kurtosis along with other summary statistics such as means and variances. To facilitate future report of skewness and kurtosis, we provide a tutorial on how to compute univariate and multivariate skewness and kurtosis by SAS, SPSS, R and a newly developed Web application.

Fitting and Testing Conditional Multinormal Partial Credit Models

ERIC Educational Resources Information Center

Hessen, David J.

2012-01-01

A multinormal partial credit model for factor analysis of polytomously scored items with ordered response categories is derived using an extension of the Dutch Identity (Holland in "Psychometrika" 55:5-18, 1990). In the model, latent variables are assumed to have a multivariate normal distribution conditional on unweighted sums of item…

Multivariate statistical analysis of diffusion imaging parameters using partial least squares: Application to white matter variations in Alzheimer's disease.

PubMed

Konukoglu, Ender; Coutu, Jean-Philippe; Salat, David H; Fischl, Bruce

2016-07-01

Diffusion magnetic resonance imaging (dMRI) is a unique technology that allows the noninvasive quantification of microstructural tissue properties of the human brain in healthy subjects as well as the probing of disease-induced variations. Population studies of dMRI data have been essential in identifying pathological structural changes in various conditions, such as Alzheimer's and Huntington's diseases (Salat et al., 2010; Rosas et al., 2006). The most common form of dMRI involves fitting a tensor to the underlying imaging data (known as diffusion tensor imaging, or DTI), then deriving parametric maps, each quantifying a different aspect of the underlying microstructure, e.g. fractional anisotropy and mean diffusivity. To date, the statistical methods utilized in most DTI population studies either analyzed only one such map or analyzed several of them, each in isolation. However, it is most likely that variations in the microstructure due to pathology or normal variability would affect several parameters simultaneously, with differing variations modulating the various parameters to differing degrees. Therefore, joint analysis of the available diffusion maps can be more powerful in characterizing histopathology and distinguishing between conditions than the widely used univariate analysis. In this article, we propose a multivariate approach for statistical analysis of diffusion parameters that uses partial least squares correlation (PLSC) analysis and permutation testing as building blocks in a voxel-wise fashion. Stemming from the common formulation, we present three different multivariate procedures for group analysis, regressing-out nuisance parameters and comparing effects of different conditions. We used the proposed procedures to study the effects of non-demented aging, Alzheimer's disease and mild cognitive impairment on the white matter. Here, we present results demonstrating that the proposed PLSC-based approach can differentiate between effects of different conditions in the same region as well as uncover spatial variations of effects across the white matter. The proposed procedures were able to answer questions on structural variations such as: "are there regions in the white matter where Alzheimer's disease has a different effect than aging or similar effect as aging?" and "are there regions in the white matter that are affected by both mild cognitive impairment and Alzheimer's disease but with differing multivariate effects?" Copyright © 2016 Elsevier Inc. All rights reserved.

Multivariate Statistical Analysis of Diffusion Imaging Parameters using Partial Least Squares: Application to White Matter Variations in Alzheimer’s Disease

PubMed Central

Konukoglu, Ender; Coutu, Jean-Philippe; Salat, David H.; Fischl, Bruce

2016-01-01

Diffusion magnetic resonance imaging (dMRI) is a unique technology that allows the noninvasive quantification of microstructural tissue properties of the human brain in healthy subjects as well as the probing of disease-induced variations. Population studies of dMRI data have been essential in identifying pathological structural changes in various conditions, such as Alzheimer’s and Huntington’s diseases1,2. The most common form of dMRI involves fitting a tensor to the underlying imaging data (known as Diffusion Tensor Imaging, or DTI), then deriving parametric maps, each quantifying a different aspect of the underlying microstructure, e.g. fractional anisotropy and mean diffusivity. To date, the statistical methods utilized in most DTI population studies either analyzed only one such map or analyzed several of them, each in isolation. However, it is most likely that variations in the microstructure due to pathology or normal variability would affect several parameters simultaneously, with differing variations modulating the various parameters to differing degrees. Therefore, joint analysis of the available diffusion maps can be more powerful in characterizing histopathology and distinguishing between conditions than the widely used univariate analysis. In this article, we propose a multivariate approach for statistical analysis of diffusion parameters that uses partial least squares correlation (PLSC) analysis and permutation testing as building blocks in a voxel-wise fashion. Stemming from the common formulation, we present three different multivariate procedures for group analysis, regressing-out nuisance parameters and comparing effects of different conditions. We used the proposed procedures to study the effects of non-demented aging, Alzheimer’s disease and mild cognitive impairment on the white matter. Here, we present results demonstrating that the proposed PLSC-based approach can differentiate between effects of different conditions in the same region as well as uncover spatial variations of effects across the white matter. The proposed procedures were able to answer questions on structural variations such as: “are there regions in the white matter where Alzheimer’s disease has a different effect than aging or similar effect as aging?” and “are there regions in the white matter that are affected by both mild cognitive impairment and Alzheimer’s disease but with differing multivariate effects?” PMID:27103138

Atomic-scale phase composition through multivariate statistical analysis of atom probe tomography data.

PubMed

Keenan, Michael R; Smentkowski, Vincent S; Ulfig, Robert M; Oltman, Edward; Larson, David J; Kelly, Thomas F

2011-06-01

We demonstrate for the first time that multivariate statistical analysis techniques can be applied to atom probe tomography data to estimate the chemical composition of a sample at the full spatial resolution of the atom probe in three dimensions. Whereas the raw atom probe data provide the specific identity of an atom at a precise location, the multivariate results can be interpreted in terms of the probabilities that an atom representing a particular chemical phase is situated there. When aggregated to the size scale of a single atom (∼0.2 nm), atom probe spectral-image datasets are huge and extremely sparse. In fact, the average spectrum will have somewhat less than one total count per spectrum due to imperfect detection efficiency. These conditions, under which the variance in the data is completely dominated by counting noise, test the limits of multivariate analysis, and an extensive discussion of how to extract the chemical information is presented. Efficient numerical approaches to performing principal component analysis (PCA) on these datasets, which may number hundreds of millions of individual spectra, are put forward, and it is shown that PCA can be computed in a few seconds on a typical laptop computer.

Copula Multivariate analysis of Gross primary production and its hydro-environmental driver; A BIOME-BGC model applied to the Antisana páramos

NASA Astrophysics Data System (ADS)

Minaya, Veronica; Corzo, Gerald; van der Kwast, Johannes; Galarraga, Remigio; Mynett, Arthur

2014-05-01

Simulations of carbon cycling are prone to uncertainties from different sources, which in general are related to input data, parameters and the model representation capacities itself. The gross carbon uptake in the cycle is represented by the gross primary production (GPP), which deals with the spatio-temporal variability of the precipitation and the soil moisture dynamics. This variability associated with uncertainty of the parameters can be modelled by multivariate probabilistic distributions. Our study presents a novel methodology that uses multivariate Copulas analysis to assess the GPP. Multi-species and elevations variables are included in a first scenario of the analysis. Hydro-meteorological conditions that might generate a change in the next 50 or more years are included in a second scenario of this analysis. The biogeochemical model BIOME-BGC was applied in the Ecuadorian Andean region in elevations greater than 4000 masl with the presence of typical vegetation of páramo. The change of GPP over time is crucial for climate scenarios of the carbon cycling in this type of ecosystem. The results help to improve our understanding of the ecosystem function and clarify the dynamics and the relationship with the change of climate variables. Keywords: multivariate analysis, Copula, BIOME-BGC, NPP, páramos

Comparison of pure laparoscopic versus open left hemihepatectomy by multivariate analysis: a retrospective cohort study.

PubMed

Cho, Hwui-Dong; Kim, Ki-Hun; Hwang, Shin; Ahn, Chul-Soo; Moon, Deok-Bog; Ha, Tae-Yong; Song, Gi-Won; Jung, Dong-Hwan; Park, Gil-Chun; Lee, Sung-Gyu

2018-02-01

To compare the outcomes of pure laparoscopic left hemihepatectomy (LLH) versus open left hemihepatectomy (OLH) for benign and malignant conditions using multivariate analysis. All consecutive cases of LLH and OLH between October 2007 and December 2013 in a tertiary referral hospital were enrolled in this retrospective cohort study. All surgical procedures were performed by one surgeon. The LLH and OLH groups were compared in terms of patient demographics, preoperative data, clinical perioperative outcomes, and tumor characteristics in patients with malignancy. Multivariate analysis of the prognostic factors associated with severe complications was then performed. The LLH group (n = 62) had a significantly shorter postoperative hospital stay than the OLH group (n = 118) (9.53 ± 3.30 vs 14.88 ± 11.36 days, p < 0.001). Multivariate analysis revealed that the OLH group had >4 times the risk of the LLH group in terms of developing severe complications (Clavien-Dindo grade ≥III) (odds ratio 4.294, 95% confidence intervals 1.165-15.832, p = 0.029). LLH was a safe and feasible procedure for selected patients. LLH required shorter hospital stay and resulted in less operative blood loss. Multivariate analysis revealed that LLH was associated with a lower risk of severe complications compared to OLH. The authors suggest that LLH could be a reasonable treatment option for selected patients.

Chemometric and multivariate statistical analysis of time-of-flight secondary ion mass spectrometry spectra from complex Cu-Fe sulfides.

PubMed

Kalegowda, Yogesh; Harmer, Sarah L

2012-03-20

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) spectra of mineral samples are complex, comprised of large mass ranges and many peaks. Consequently, characterization and classification analysis of these systems is challenging. In this study, different chemometric and statistical data evaluation methods, based on monolayer sensitive TOF-SIMS data, have been tested for the characterization and classification of copper-iron sulfide minerals (chalcopyrite, chalcocite, bornite, and pyrite) at different flotation pulp conditions (feed, conditioned feed, and Eh modified). The complex mass spectral data sets were analyzed using the following chemometric and statistical techniques: principal component analysis (PCA); principal component-discriminant functional analysis (PC-DFA); soft independent modeling of class analogy (SIMCA); and k-Nearest Neighbor (k-NN) classification. PCA was found to be an important first step in multivariate analysis, providing insight into both the relative grouping of samples and the elemental/molecular basis for those groupings. For samples exposed to oxidative conditions (at Eh ~430 mV), each technique (PCA, PC-DFA, SIMCA, and k-NN) was found to produce excellent classification. For samples at reductive conditions (at Eh ~ -200 mV SHE), k-NN and SIMCA produced the most accurate classification. Phase identification of particles that contain the same elements but a different crystal structure in a mixed multimetal mineral system has been achieved.

Multivariate analysis of the cotton seed ionome reveals integrated genetic signatures of abiotic stress-response

USDA-ARS?s Scientific Manuscript database

To mitigate the effects of heat and drought stress, an understanding of the genetic control of physiological responses to these environmental conditions is needed. To this end, we evaluated an upland cotton (Gossypium hirsutum L.) mapping population under water-limited and well-watered conditions in...

Variation of heavy metals in recent sediments from Piratininga Lagoon (Brazil): interpretation of geochemical data with the aid of multivariate analysis

NASA Astrophysics Data System (ADS)

Huang, W.; Campredon, R.; Abrao, J. J.; Bernat, M.; Latouche, C.

1994-06-01

In the last decade, the Atlantic coast of south-eastern Brazil has been affected by increasing deforestation and anthropogenic effluents. Sediments in the coastal lagoons have recorded the process of such environmental change. Thirty-seven sediment samples from three cores in Piratininga Lagoon, Rio de Janeiro, were analyzed for their major components and minor element concentrations in order to examine geochemical characteristics and the depositional environment and to investigate the variation of heavy metals of environmental concern. Two multivariate analysis methods, principal component analysis and cluster analysis, were performed on the analytical data set to help visualize the sample clusters and the element associations. On the whole, the sediment samples from each core are similar and the sample clusters corresponding to the three cores are clearly separated, as a result of the different conditions of sedimentation. Some changes in the depositional environment are recognized using the results of multivariate analysis. The enrichment of Pb, Cu, and Zn in the upper parts of cores is in agreement with increasing anthropogenic influx (pollution).

Performance of Modified Test Statistics in Covariance and Correlation Structure Analysis under Conditions of Multivariate Nonnormality.

ERIC Educational Resources Information Center

Fouladi, Rachel T.

2000-01-01

Provides an overview of standard and modified normal theory and asymptotically distribution-free covariance and correlation structure analysis techniques and details Monte Carlo simulation results on Type I and Type II error control. Demonstrates through the simulation that robustness and nonrobustness of structure analysis techniques vary as a…

A need for a standardization in anaerobic digestion experiments? Let's get some insight from meta-analysis and multivariate analysis.

PubMed

Lavergne, Céline; Jeison, David; Ortega, Valentina; Chamy, Rolando; Donoso-Bravo, Andrés

2018-09-15

An important variability in the experimental results in anaerobic digestion lab test has been reported. This study presents a meta-analysis coupled with multivariate analysis aiming to assess the impact of this experimental variability in batch and continuous operation at mesophilic and thermophilic anaerobic digestion of waste activated sludge. An analysis of variance showed that there was no significant difference between mesophilic and thermophilic conditions in both continuous and batch conditions. Concerning the operation mode, the values of methane yield were significantly higher in batch experiment than in continuous reactors. According to the PCA, for both cases, the methane yield is positive correlated to the temperature rises. Interestingly, in the batch experiments, the higher the volatile solids in the substrate was, the lowest was the methane production, which is correlated to experimental flaws when setting up those tests. In continuous mode, unlike the batch test, the methane yield is strongly (positively) correlated to the organic content of the substrate. Experimental standardization, above all, in batch conditions are urgently necessary or move to continuous experiments for reporting results. The modeling can also be a source of disturbance in batch test. Copyright © 2018 Elsevier Ltd. All rights reserved.

Time-varying nonstationary multivariate risk analysis using a dynamic Bayesian copula

NASA Astrophysics Data System (ADS)

Sarhadi, Ali; Burn, Donald H.; Concepción Ausín, María.; Wiper, Michael P.

2016-03-01

A time-varying risk analysis is proposed for an adaptive design framework in nonstationary conditions arising from climate change. A Bayesian, dynamic conditional copula is developed for modeling the time-varying dependence structure between mixed continuous and discrete multiattributes of multidimensional hydrometeorological phenomena. Joint Bayesian inference is carried out to fit the marginals and copula in an illustrative example using an adaptive, Gibbs Markov Chain Monte Carlo (MCMC) sampler. Posterior mean estimates and credible intervals are provided for the model parameters and the Deviance Information Criterion (DIC) is used to select the model that best captures different forms of nonstationarity over time. This study also introduces a fully Bayesian, time-varying joint return period for multivariate time-dependent risk analysis in nonstationary environments. The results demonstrate that the nature and the risk of extreme-climate multidimensional processes are changed over time under the impact of climate change, and accordingly the long-term decision making strategies should be updated based on the anomalies of the nonstationary environment.

New robust bilinear least squares method for the analysis of spectral-pH matrix data.

PubMed

Goicoechea, Héctor C; Olivieri, Alejandro C

2005-07-01

A new second-order multivariate method has been developed for the analysis of spectral-pH matrix data, based on a bilinear least-squares (BLLS) model achieving the second-order advantage and handling multiple calibration standards. A simulated Monte Carlo study of synthetic absorbance-pH data allowed comparison of the newly proposed BLLS methodology with constrained parallel factor analysis (PARAFAC) and with the combination multivariate curve resolution-alternating least-squares (MCR-ALS) technique under different conditions of sample-to-sample pH mismatch and analyte-background ratio. The results indicate an improved prediction ability for the new method. Experimental data generated by measuring absorption spectra of several calibration standards of ascorbic acid and samples of orange juice were subjected to second-order calibration analysis with PARAFAC, MCR-ALS, and the new BLLS method. The results indicate that the latter method provides the best analytical results in regard to analyte recovery in samples of complex composition requiring strict adherence to the second-order advantage. Linear dependencies appear when multivariate data are produced by using the pH or a reaction time as one of the data dimensions, posing a challenge to classical multivariate calibration models. The presently discussed algorithm is useful for these latter systems.

Beer fermentation: monitoring of process parameters by FT-NIR and multivariate data analysis.

PubMed

Grassi, Silvia; Amigo, José Manuel; Lyndgaard, Christian Bøge; Foschino, Roberto; Casiraghi, Ernestina

2014-07-15

This work investigates the capability of Fourier-Transform near infrared (FT-NIR) spectroscopy to monitor and assess process parameters in beer fermentation at different operative conditions. For this purpose, the fermentation of wort with two different yeast strains and at different temperatures was monitored for nine days by FT-NIR. To correlate the collected spectra with °Brix, pH and biomass, different multivariate data methodologies were applied. Principal component analysis (PCA), partial least squares (PLS) and locally weighted regression (LWR) were used to assess the relationship between FT-NIR spectra and the abovementioned process parameters that define the beer fermentation. The accuracy and robustness of the obtained results clearly show the suitability of FT-NIR spectroscopy, combined with multivariate data analysis, to be used as a quality control tool in the beer fermentation process. FT-NIR spectroscopy, when combined with LWR, demonstrates to be a perfectly suitable quantitative method to be implemented in the production of beer. Copyright © 2014 Elsevier Ltd. All rights reserved.

New strategy to identify radicals in a time evolving EPR data set by multivariate curve resolution-alternating least squares.

PubMed

Fadel, Maya Abou; de Juan, Anna; Vezin, Hervé; Duponchel, Ludovic

2016-12-01

Electron paramagnetic resonance (EPR) spectroscopy is a powerful technique that is able to characterize radicals formed in kinetic reactions. However, spectral characterization of individual chemical species is often limited or even unmanageable due to the severe kinetic and spectral overlap among species in kinetic processes. Therefore, we applied, for the first time, multivariate curve resolution-alternating least squares (MCR-ALS) method to EPR time evolving data sets to model and characterize the different constituents in a kinetic reaction. Here we demonstrate the advantage of multivariate analysis in the investigation of radicals formed along the kinetic process of hydroxycoumarin in alkaline medium. Multiset analysis of several EPR-monitored kinetic experiments performed in different conditions revealed the individual paramagnetic centres as well as their kinetic profiles. The results obtained by MCR-ALS method demonstrate its prominent potential in analysis of EPR time evolved spectra. Copyright © 2016 Elsevier B.V. All rights reserved.

Farseer-NMR: automatic treatment, analysis and plotting of large, multi-variable NMR data.

PubMed

Teixeira, João M C; Skinner, Simon P; Arbesú, Miguel; Breeze, Alexander L; Pons, Miquel

2018-05-11

We present Farseer-NMR ( https://git.io/vAueU ), a software package to treat, evaluate and combine NMR spectroscopic data from sets of protein-derived peaklists covering a range of experimental conditions. The combined advances in NMR and molecular biology enable the study of complex biomolecular systems such as flexible proteins or large multibody complexes, which display a strong and functionally relevant response to their environmental conditions, e.g. the presence of ligands, site-directed mutations, post translational modifications, molecular crowders or the chemical composition of the solution. These advances have created a growing need to analyse those systems' responses to multiple variables. The combined analysis of NMR peaklists from large and multivariable datasets has become a new bottleneck in the NMR analysis pipeline, whereby information-rich NMR-derived parameters have to be manually generated, which can be tedious, repetitive and prone to human error, or even unfeasible for very large datasets. There is a persistent gap in the development and distribution of software focused on peaklist treatment, analysis and representation, and specifically able to handle large multivariable datasets, which are becoming more commonplace. In this regard, Farseer-NMR aims to close this longstanding gap in the automated NMR user pipeline and, altogether, reduce the time burden of analysis of large sets of peaklists from days/weeks to seconds/minutes. We have implemented some of the most common, as well as new, routines for calculation of NMR parameters and several publication-quality plotting templates to improve NMR data representation. Farseer-NMR has been written entirely in Python and its modular code base enables facile extension.

Multivariate and geo-spatial approach for seawater quality of Chidiyatappu Bay, south Andaman Islands, India.

PubMed

Jha, Dilip Kumar; Vinithkumar, Nambali Valsalan; Sahu, Biraja Kumar; Dheenan, Palaiya Sukumaran; Das, Apurba Kumar; Begum, Mehmuna; Devi, Marimuthu Prashanthi; Kirubagaran, Ramalingam

2015-07-15

Chidiyatappu Bay is one of the least disturbed marine environments of Andaman & Nicobar Islands, the union territory of India. Oceanic flushing from southeast and northwest direction is prevalent in this bay. Further, anthropogenic activity is minimal in the adjoining environment. Considering the pristine nature of this bay, seawater samples collected from 12 sampling stations covering three seasons were analyzed. Principal Component Analysis (PCA) revealed 69.9% of total variance and exhibited strong factor loading for nitrite, chlorophyll a and phaeophytin. In addition, analysis of variance (ANOVA-one way), regression analysis, box-whisker plots and Geographical Information System based hot spot analysis further simplified and supported multivariate results. The results obtained are important to establish reference conditions for comparative study with other similar ecosystems in the region. Copyright © 2015 Elsevier Ltd. All rights reserved.

Time-frequency analysis of neuronal populations with instantaneous resolution based on noise-assisted multivariate empirical mode decomposition.

PubMed

Alegre-Cortés, J; Soto-Sánchez, C; Pizá, Á G; Albarracín, A L; Farfán, F D; Felice, C J; Fernández, E

2016-07-15

Linear analysis has classically provided powerful tools for understanding the behavior of neural populations, but the neuron responses to real-world stimulation are nonlinear under some conditions, and many neuronal components demonstrate strong nonlinear behavior. In spite of this, temporal and frequency dynamics of neural populations to sensory stimulation have been usually analyzed with linear approaches. In this paper, we propose the use of Noise-Assisted Multivariate Empirical Mode Decomposition (NA-MEMD), a data-driven template-free algorithm, plus the Hilbert transform as a suitable tool for analyzing population oscillatory dynamics in a multi-dimensional space with instantaneous frequency (IF) resolution. The proposed approach was able to extract oscillatory information of neurophysiological data of deep vibrissal nerve and visual cortex multiunit recordings that were not evidenced using linear approaches with fixed bases such as the Fourier analysis. Texture discrimination analysis performance was increased when Noise-Assisted Multivariate Empirical Mode plus Hilbert transform was implemented, compared to linear techniques. Cortical oscillatory population activity was analyzed with precise time-frequency resolution. Similarly, NA-MEMD provided increased time-frequency resolution of cortical oscillatory population activity. Noise-Assisted Multivariate Empirical Mode Decomposition plus Hilbert transform is an improved method to analyze neuronal population oscillatory dynamics overcoming linear and stationary assumptions of classical methods. Copyright © 2016 Elsevier B.V. All rights reserved.

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.

The prevalence of anxiety and depression in patients with or without hyperhidrosis (HH).

PubMed

Bahar, Rayeheh; Zhou, Pingyu; Liu, Yudan; Huang, Yuanshen; Phillips, Arlie; Lee, Tim K; Su, Mingwan; Yang, Sen; Kalia, Sunil; Zhang, Xuejun; Zhou, Youwen

2016-12-01

There are conflicting data about the correlation between hyperhidrosis (HH) and anxiety and depression. We sought to determine the prevalence of anxiety and depression in patients with or without HH. We examined 2017 consecutive dermatology outpatients from Vancouver, British Columbia, Canada, and Shanghai, China, using Patient Health Questionnaire-9 and Generalized Anxiety Disorder-7 scales for anxiety and depression assessments. Multivariable logistic regression analysis was performed to evaluate if the impact of HH on anxiety and depression is dependent on demographic factors and diagnoses of the patients' presenting skin conditions. The prevalence of anxiety and depression was 21.3% and 27.2% in patients with HH, respectively, and 7.5% and 9.7% in patients without HH, respectively (P value <.001 for both). There were positive correlations between HH severity and the prevalence of anxiety and depression. Multivariable analysis showed that HH-associated increase in anxiety and depression prevalence is independent of demographic factors and presenting skin conditions. The data from the questionnaires relied on the accuracy of patients' self-reports. Both single variant and multivariable analyses showed a significant association between HH and the prevalence of anxiety and depression in a HH severity-dependent manner. Copyright © 2016 American Academy of Dermatology, Inc. Published by Elsevier Inc. All rights reserved.

Interpretability of Multivariate Brain Maps in Linear Brain Decoding: Definition, and Heuristic Quantification in Multivariate Analysis of MEG Time-Locked Effects.

PubMed

Kia, Seyed Mostafa; Vega Pons, Sandro; Weisz, Nathan; Passerini, Andrea

2016-01-01

Brain decoding is a popular multivariate approach for hypothesis testing in neuroimaging. Linear classifiers are widely employed in the brain decoding paradigm to discriminate among experimental conditions. Then, the derived linear weights are visualized in the form of multivariate brain maps to further study spatio-temporal patterns of underlying neural activities. It is well known that the brain maps derived from weights of linear classifiers are hard to interpret because of high correlations between predictors, low signal to noise ratios, and the high dimensionality of neuroimaging data. Therefore, improving the interpretability of brain decoding approaches is of primary interest in many neuroimaging studies. Despite extensive studies of this type, at present, there is no formal definition for interpretability of multivariate brain maps. As a consequence, there is no quantitative measure for evaluating the interpretability of different brain decoding methods. In this paper, first, we present a theoretical definition of interpretability in brain decoding; we show that the interpretability of multivariate brain maps can be decomposed into their reproducibility and representativeness. Second, as an application of the proposed definition, we exemplify a heuristic for approximating the interpretability in multivariate analysis of evoked magnetoencephalography (MEG) responses. Third, we propose to combine the approximated interpretability and the generalization performance of the brain decoding into a new multi-objective criterion for model selection. Our results, for the simulated and real MEG data, show that optimizing the hyper-parameters of the regularized linear classifier based on the proposed criterion results in more informative multivariate brain maps. More importantly, the presented definition provides the theoretical background for quantitative evaluation of interpretability, and hence, facilitates the development of more effective brain decoding algorithms in the future.

Interpretability of Multivariate Brain Maps in Linear Brain Decoding: Definition, and Heuristic Quantification in Multivariate Analysis of MEG Time-Locked Effects

PubMed Central

Kia, Seyed Mostafa; Vega Pons, Sandro; Weisz, Nathan; Passerini, Andrea

2017-01-01

Brain decoding is a popular multivariate approach for hypothesis testing in neuroimaging. Linear classifiers are widely employed in the brain decoding paradigm to discriminate among experimental conditions. Then, the derived linear weights are visualized in the form of multivariate brain maps to further study spatio-temporal patterns of underlying neural activities. It is well known that the brain maps derived from weights of linear classifiers are hard to interpret because of high correlations between predictors, low signal to noise ratios, and the high dimensionality of neuroimaging data. Therefore, improving the interpretability of brain decoding approaches is of primary interest in many neuroimaging studies. Despite extensive studies of this type, at present, there is no formal definition for interpretability of multivariate brain maps. As a consequence, there is no quantitative measure for evaluating the interpretability of different brain decoding methods. In this paper, first, we present a theoretical definition of interpretability in brain decoding; we show that the interpretability of multivariate brain maps can be decomposed into their reproducibility and representativeness. Second, as an application of the proposed definition, we exemplify a heuristic for approximating the interpretability in multivariate analysis of evoked magnetoencephalography (MEG) responses. Third, we propose to combine the approximated interpretability and the generalization performance of the brain decoding into a new multi-objective criterion for model selection. Our results, for the simulated and real MEG data, show that optimizing the hyper-parameters of the regularized linear classifier based on the proposed criterion results in more informative multivariate brain maps. More importantly, the presented definition provides the theoretical background for quantitative evaluation of interpretability, and hence, facilitates the development of more effective brain decoding algorithms in the future. PMID:28167896

A systematic uncertainty analysis for liner impedance eduction technology

NASA Astrophysics Data System (ADS)

Zhou, Lin; Bodén, Hans

2015-11-01

The so-called impedance eduction technology is widely used for obtaining acoustic properties of liners used in aircraft engines. The measurement uncertainties for this technology are still not well understood though it is essential for data quality assessment and model validation. A systematic framework based on multivariate analysis is presented in this paper to provide 95 percent confidence interval uncertainty estimates in the process of impedance eduction. The analysis is made using a single mode straightforward method based on transmission coefficients involving the classic Ingard-Myers boundary condition. The multivariate technique makes it possible to obtain an uncertainty analysis for the possibly correlated real and imaginary parts of the complex quantities. The results show that the errors in impedance results at low frequency mainly depend on the variability of transmission coefficients, while the mean Mach number accuracy is the most important source of error at high frequencies. The effect of Mach numbers used in the wave dispersion equation and in the Ingard-Myers boundary condition has been separated for comparison of the outcome of impedance eduction. A local Mach number based on friction velocity is suggested as a way to reduce the inconsistencies found when estimating impedance using upstream and downstream acoustic excitation.

Community-Acquired Pneumonia Hospitalization among Children with Neurologic Disorders.

PubMed

Millman, Alexander J; Finelli, Lyn; Bramley, Anna M; Peacock, Georgina; Williams, Derek J; Arnold, Sandra R; Grijalva, Carlos G; Anderson, Evan J; McCullers, Jonathan A; Ampofo, Krow; Pavia, Andrew T; Edwards, Kathryn M; Jain, Seema

2016-06-01

To describe and compare the clinical characteristics, outcomes, and etiology of pneumonia among children hospitalized with community-acquired pneumonia (CAP) with neurologic disorders, non-neurologic underlying conditions, and no underlying conditions. Children <18 years old hospitalized with clinical and radiographic CAP were enrolled at 3 US children's hospitals. Neurologic disorders included cerebral palsy, developmental delay, Down syndrome, epilepsy, non-Down syndrome chromosomal abnormalities, and spinal cord abnormalities. We compared the epidemiology, etiology, and clinical outcomes of CAP in children with neurologic disorders with those with non-neurologic underlying conditions, and those with no underlying conditions using bivariate, age-stratified, and multivariate logistic regression analyses. From January 2010-June 2012, 2358 children with radiographically confirmed CAP were enrolled; 280 (11.9%) had a neurologic disorder (52.1% of these individuals also had non-neurologic underlying conditions), 934 (39.6%) had non-neurologic underlying conditions only, and 1144 (48.5%) had no underlying conditions. Children with neurologic disorders were older and more likely to require intensive care unit (ICU) admission than children with non-neurologic underlying conditions and children with no underlying conditions; similar proportions were mechanically ventilated. In age-stratified analysis, children with neurologic disorders were less likely to have a pathogen detected than children with non-neurologic underlying conditions. In multivariate analysis, having a neurologic disorder was associated with ICU admission for children ≥2 years of age. Children with neurologic disorders hospitalized with CAP were less likely to have a pathogen detected and more likely to be admitted to the ICU than children without neurologic disorders. Published by Elsevier Inc.

Comparison of univariate and multivariate calibration for the determination of micronutrients in pellets of plant materials by laser induced breakdown spectrometry

NASA Astrophysics Data System (ADS)

Braga, Jez Willian Batista; Trevizan, Lilian Cristina; Nunes, Lidiane Cristina; Rufini, Iolanda Aparecida; Santos, Dário, Jr.; Krug, Francisco José

2010-01-01

The application of laser induced breakdown spectrometry (LIBS) aiming the direct analysis of plant materials is a great challenge that still needs efforts for its development and validation. In this way, a series of experimental approaches has been carried out in order to show that LIBS can be used as an alternative method to wet acid digestions based methods for analysis of agricultural and environmental samples. The large amount of information provided by LIBS spectra for these complex samples increases the difficulties for selecting the most appropriated wavelengths for each analyte. Some applications have suggested that improvements in both accuracy and precision can be achieved by the application of multivariate calibration in LIBS data when compared to the univariate regression developed with line emission intensities. In the present work, the performance of univariate and multivariate calibration, based on partial least squares regression (PLSR), was compared for analysis of pellets of plant materials made from an appropriate mixture of cryogenically ground samples with cellulose as the binding agent. The development of a specific PLSR model for each analyte and the selection of spectral regions containing only lines of the analyte of interest were the best conditions for the analysis. In this particular application, these models showed a similar performance, but PLSR seemed to be more robust due to a lower occurrence of outliers in comparison to the univariate method. Data suggests that efforts dealing with sample presentation and fitness of standards for LIBS analysis must be done in order to fulfill the boundary conditions for matrix independent development and validation.

A multivariate spatial mixture model for areal data: examining regional differences in standardized test scores

PubMed Central

Neelon, Brian; Gelfand, Alan E.; Miranda, Marie Lynn

2013-01-01

Summary Researchers in the health and social sciences often wish to examine joint spatial patterns for two or more related outcomes. Examples include infant birth weight and gestational length, psychosocial and behavioral indices, and educational test scores from different cognitive domains. We propose a multivariate spatial mixture model for the joint analysis of continuous individual-level outcomes that are referenced to areal units. The responses are modeled as a finite mixture of multivariate normals, which accommodates a wide range of marginal response distributions and allows investigators to examine covariate effects within subpopulations of interest. The model has a hierarchical structure built at the individual level (i.e., individuals are nested within areal units), and thus incorporates both individual- and areal-level predictors as well as spatial random effects for each mixture component. Conditional autoregressive (CAR) priors on the random effects provide spatial smoothing and allow the shape of the multivariate distribution to vary flexibly across geographic regions. We adopt a Bayesian modeling approach and develop an efficient Markov chain Monte Carlo model fitting algorithm that relies primarily on closed-form full conditionals. We use the model to explore geographic patterns in end-of-grade math and reading test scores among school-age children in North Carolina. PMID:26401059

Vibration-based structural health monitoring using adaptive statistical method under varying environmental condition

NASA Astrophysics Data System (ADS)

Jin, Seung-Seop; Jung, Hyung-Jo

2014-03-01

It is well known that the dynamic properties of a structure such as natural frequencies depend not only on damage but also on environmental condition (e.g., temperature). The variation in dynamic characteristics of a structure due to environmental condition may mask damage of the structure. Without taking the change of environmental condition into account, false-positive or false-negative damage diagnosis may occur so that structural health monitoring becomes unreliable. In order to address this problem, an approach to construct a regression model based on structural responses considering environmental factors has been usually used by many researchers. The key to success of this approach is the formulation between the input and output variables of the regression model to take into account the environmental variations. However, it is quite challenging to determine proper environmental variables and measurement locations in advance for fully representing the relationship between the structural responses and the environmental variations. One alternative (i.e., novelty detection) is to remove the variations caused by environmental factors from the structural responses by using multivariate statistical analysis (e.g., principal component analysis (PCA), factor analysis, etc.). The success of this method is deeply depending on the accuracy of the description of normal condition. Generally, there is no prior information on normal condition during data acquisition, so that the normal condition is determined by subjective perspective with human-intervention. The proposed method is a novel adaptive multivariate statistical analysis for monitoring of structural damage detection under environmental change. One advantage of this method is the ability of a generative learning to capture the intrinsic characteristics of the normal condition. The proposed method is tested on numerically simulated data for a range of noise in measurement under environmental variation. A comparative study with conventional methods (i.e., fixed reference scheme) demonstrates the superior performance of the proposed method for structural damage detection.

Classification of functional interactions from multi-electrodes data using conditional modularity analysis

NASA Astrophysics Data System (ADS)

Makhtar, Siti Noormiza; Senik, Mohd Harizal

2018-02-01

The availability of massive amount of neuronal signals are attracting widespread interest in functional connectivity analysis. Functional interactions estimated by multivariate partial coherence analysis in the frequency domain represent the connectivity strength in this study. Modularity is a network measure for the detection of community structure in network analysis. The discovery of community structure for the functional neuronal network was implemented on multi-electrode array (MEA) signals recorded from hippocampal regions in isoflurane-anaesthetized Lister-hooded rats. The analysis is expected to show modularity changes before and after local unilateral kainic acid (KA)-induced epileptiform activity. The result is presented using color-coded graphic of conditional modularity measure for 19 MEA nodes. This network is separated into four sub-regions to show the community detection within each sub-region. The results show that classification of neuronal signals into the inter- and intra-modular nodes is feasible using conditional modularity analysis. Estimation of segregation properties using conditional modularity analysis may provide further information about functional connectivity from MEA data.

Mapping the Diversity among Runaways: A Descriptive Multivariate Analysis of Selected Social Psychological Background Conditions.

ERIC Educational Resources Information Center

Brennan, Tim

1980-01-01

A review of prior classification systems of runaways is followed by a descriptive taxonomy of runaways developed using cluster-analytic methods. The empirical types illustrate patterns of weakness in bonds between runaways and families, schools, or peer relationships. (Author)

Integrated biomarker response in catfish Hypostomus ancistroides by multivariate analysis in the Pirapó River, southern Brazil.

PubMed

Ghisi, Nédia C; Oliveira, Elton C; Mendonça Mota, Thais F; Vanzetto, Guilherme V; Roque, Aliciane A; Godinho, Jayson P; Bettim, Franciele Lima; Silva de Assis, Helena Cristina da; Prioli, Alberto J

2016-10-01

Aquatic pollutants produce multiple consequences in organisms, populations, communities and ecosystems, affecting the function of organs, reproductive state, population size, species survival and even biodiversity. In order to monitor the health of aquatic organisms, biomarkers have been used as effective tools in environmental risk assessment. The aim of this study is to evaluate, through a multivariate and integrative analysis, the response of the native species Hypostomus ancistroides over a pollution gradient in the main water supply body of northwestern Paraná state (Brazil). The condition factor, micronucleus test and erythrocyte nuclear abnormalities (ENA), comet assay, measurement of the cerebral and muscular enzyme acetylcholinesterase (AChE), and histopathological analysis of liver and gill were evaluated in fishes from three sites of the Pirapó River during the dry and rainy seasons. The multivariate general result showed that the interaction between the seasons and the sites was significant: there are variations in the rates of alterations in the biological parameters, depending on the time of year researched at each site. In general, the best results were observed for the site nearest the spring, and alterations in the parameters at the intermediate and downstream sites. In sum, the results of this study showed the necessity of a multivariate analysis, evaluating several biological parameters, to obtain an integrated response to the effects of the environmental pollutants on the organisms. Copyright © 2016 Elsevier Ltd. All rights reserved.

A Quality by Design approach to investigate tablet dissolution shift upon accelerated stability by multivariate methods.

PubMed

Huang, Jun; Goolcharran, Chimanlall; Ghosh, Krishnendu

2011-05-01

This paper presents the use of experimental design, optimization and multivariate techniques to investigate root-cause of tablet dissolution shift (slow-down) upon stability and develop control strategies for a drug product during formulation and process development. The effectiveness and usefulness of these methodologies were demonstrated through two application examples. In both applications, dissolution slow-down was observed during a 4-week accelerated stability test under 51°C/75%RH storage condition. In Application I, an experimental design was carried out to evaluate the interactions and effects of the design factors on critical quality attribute (CQA) of dissolution upon stability. The design space was studied by design of experiment (DOE) and multivariate analysis to ensure desired dissolution profile and minimal dissolution shift upon stability. Multivariate techniques, such as multi-way principal component analysis (MPCA) of the entire dissolution profiles upon stability, were performed to reveal batch relationships and to evaluate the impact of design factors on dissolution. In Application II, an experiment was conducted to study the impact of varying tablet breaking force on dissolution upon stability utilizing MPCA. It was demonstrated that the use of multivariate methods, defined as Quality by Design (QbD) principles and tools in ICH-Q8 guidance, provides an effective means to achieve a greater understanding of tablet dissolution upon stability. Copyright © 2010 Elsevier B.V. All rights reserved.

Salting-out assisted liquid-liquid extraction and partial least squares regression to assay low molecular weight polycyclic aromatic hydrocarbons leached from soils and sediments

NASA Astrophysics Data System (ADS)

Bressan, Lucas P.; do Nascimento, Paulo Cícero; Schmidt, Marcella E. P.; Faccin, Henrique; de Machado, Leandro Carvalho; Bohrer, Denise

2017-02-01

A novel method was developed to determine low molecular weight polycyclic aromatic hydrocarbons in aqueous leachates from soils and sediments using a salting-out assisted liquid-liquid extraction, synchronous fluorescence spectrometry and a multivariate calibration technique. Several experimental parameters were controlled and the optimum conditions were: sodium carbonate as the salting-out agent at concentration of 2 mol L- 1, 3 mL of acetonitrile as extraction solvent, 6 mL of aqueous leachate, vortexing for 5 min and centrifuging at 4000 rpm for 5 min. The partial least squares calibration was optimized to the lowest values of root mean squared error and five latent variables were chosen for each of the targeted compounds. The regression coefficients for the true versus predicted concentrations were higher than 0.99. Figures of merit for the multivariate method were calculated, namely sensitivity, multivariate detection limit and multivariate quantification limit. The selectivity was also evaluated and other polycyclic aromatic hydrocarbons did not interfere in the analysis. Likewise, high performance liquid chromatography was used as a comparative methodology, and the regression analysis between the methods showed no statistical difference (t-test). The proposed methodology was applied to soils and sediments of a Brazilian river and the recoveries ranged from 74.3% to 105.8%. Overall, the proposed methodology was suitable for the targeted compounds, showing that the extraction method can be applied to spectrofluorometric analysis and that the multivariate calibration is also suitable for these compounds in leachates from real samples.

Changes in Concurrent Risk of Warm and Dry Years under Impact of Climate Change

NASA Astrophysics Data System (ADS)

Sarhadi, A.; Wiper, M.; Touma, D. E.; Ausín, M. C.; Diffenbaugh, N. S.

2017-12-01

Anthropogenic global warming has changed the nature and the risk of extreme climate phenomena. The changing concurrence of multiple climatic extremes (warm and dry years) may result in intensification of undesirable consequences for water resources, human and ecosystem health, and environmental equity. The present study assesses how global warming influences the probability that warm and dry years co-occur in a global scale. In the first step of the study a designed multivariate Mann-Kendall trend analysis is used to detect the areas in which the concurrence of warm and dry years has increased in the historical climate records and also climate models in the global scale. The next step investigates the concurrent risk of the extremes under dynamic nonstationary conditions. A fully generalized multivariate risk framework is designed to evolve through time under dynamic nonstationary conditions. In this methodology, Bayesian, dynamic copulas are developed to model the time-varying dependence structure between the two different climate extremes (warm and dry years). The results reveal an increasing trend in the concurrence risk of warm and dry years, which are in agreement with the multivariate trend analysis from historical and climate models. In addition to providing a novel quantification of the changing probability of compound extreme events, the results of this study can help decision makers develop short- and long-term strategies to prepare for climate stresses now and in the future.

A framework for multivariate data-based at-site flood frequency analysis: Essentiality of the conjugal application of parametric and nonparametric approaches

NASA Astrophysics Data System (ADS)

Vittal, H.; Singh, Jitendra; Kumar, Pankaj; Karmakar, Subhankar

2015-06-01

In watershed management, flood frequency analysis (FFA) is performed to quantify the risk of flooding at different spatial locations and also to provide guidelines for determining the design periods of flood control structures. The traditional FFA was extensively performed by considering univariate scenario for both at-site and regional estimation of return periods. However, due to inherent mutual dependence of the flood variables or characteristics [i.e., peak flow (P), flood volume (V) and flood duration (D), which are random in nature], analysis has been further extended to multivariate scenario, with some restrictive assumptions. To overcome the assumption of same family of marginal density function for all flood variables, the concept of copula has been introduced. Although, the advancement from univariate to multivariate analyses drew formidable attention to the FFA research community, the basic limitation was that the analyses were performed with the implementation of only parametric family of distributions. The aim of the current study is to emphasize the importance of nonparametric approaches in the field of multivariate FFA; however, the nonparametric distribution may not always be a good-fit and capable of replacing well-implemented multivariate parametric and multivariate copula-based applications. Nevertheless, the potential of obtaining best-fit using nonparametric distributions might be improved because such distributions reproduce the sample's characteristics, resulting in more accurate estimations of the multivariate return period. Hence, the current study shows the importance of conjugating multivariate nonparametric approach with multivariate parametric and copula-based approaches, thereby results in a comprehensive framework for complete at-site FFA. Although the proposed framework is designed for at-site FFA, this approach can also be applied to regional FFA because regional estimations ideally include at-site estimations. The framework is based on the following steps: (i) comprehensive trend analysis to assess nonstationarity in the observed data; (ii) selection of the best-fit univariate marginal distribution with a comprehensive set of parametric and nonparametric distributions for the flood variables; (iii) multivariate frequency analyses with parametric, copula-based and nonparametric approaches; and (iv) estimation of joint and various conditional return periods. The proposed framework for frequency analysis is demonstrated using 110 years of observed data from Allegheny River at Salamanca, New York, USA. The results show that for both univariate and multivariate cases, the nonparametric Gaussian kernel provides the best estimate. Further, we perform FFA for twenty major rivers over continental USA, which shows for seven rivers, all the flood variables followed nonparametric Gaussian kernel; whereas for other rivers, parametric distributions provide the best-fit either for one or two flood variables. Thus the summary of results shows that the nonparametric method cannot substitute the parametric and copula-based approaches, but should be considered during any at-site FFA to provide the broadest choices for best estimation of the flood return periods.

Information spreading by a combination of MEG source estimation and multivariate pattern classification.

PubMed

Sato, Masashi; Yamashita, Okito; Sato, Masa-Aki; Miyawaki, Yoichi

2018-01-01

To understand information representation in human brain activity, it is important to investigate its fine spatial patterns at high temporal resolution. One possible approach is to use source estimation of magnetoencephalography (MEG) signals. Previous studies have mainly quantified accuracy of this technique according to positional deviations and dispersion of estimated sources, but it remains unclear how accurately MEG source estimation restores information content represented by spatial patterns of brain activity. In this study, using simulated MEG signals representing artificial experimental conditions, we performed MEG source estimation and multivariate pattern analysis to examine whether MEG source estimation can restore information content represented by patterns of cortical current in source brain areas. Classification analysis revealed that the corresponding artificial experimental conditions were predicted accurately from patterns of cortical current estimated in the source brain areas. However, accurate predictions were also possible from brain areas whose original sources were not defined. Searchlight decoding further revealed that this unexpected prediction was possible across wide brain areas beyond the original source locations, indicating that information contained in the original sources can spread through MEG source estimation. This phenomenon of "information spreading" may easily lead to false-positive interpretations when MEG source estimation and classification analysis are combined to identify brain areas that represent target information. Real MEG data analyses also showed that presented stimuli were able to be predicted in the higher visual cortex at the same latency as in the primary visual cortex, also suggesting that information spreading took place. These results indicate that careful inspection is necessary to avoid false-positive interpretations when MEG source estimation and multivariate pattern analysis are combined.

Information spreading by a combination of MEG source estimation and multivariate pattern classification

PubMed Central

Sato, Masashi; Yamashita, Okito; Sato, Masa-aki

2018-01-01

To understand information representation in human brain activity, it is important to investigate its fine spatial patterns at high temporal resolution. One possible approach is to use source estimation of magnetoencephalography (MEG) signals. Previous studies have mainly quantified accuracy of this technique according to positional deviations and dispersion of estimated sources, but it remains unclear how accurately MEG source estimation restores information content represented by spatial patterns of brain activity. In this study, using simulated MEG signals representing artificial experimental conditions, we performed MEG source estimation and multivariate pattern analysis to examine whether MEG source estimation can restore information content represented by patterns of cortical current in source brain areas. Classification analysis revealed that the corresponding artificial experimental conditions were predicted accurately from patterns of cortical current estimated in the source brain areas. However, accurate predictions were also possible from brain areas whose original sources were not defined. Searchlight decoding further revealed that this unexpected prediction was possible across wide brain areas beyond the original source locations, indicating that information contained in the original sources can spread through MEG source estimation. This phenomenon of “information spreading” may easily lead to false-positive interpretations when MEG source estimation and classification analysis are combined to identify brain areas that represent target information. Real MEG data analyses also showed that presented stimuli were able to be predicted in the higher visual cortex at the same latency as in the primary visual cortex, also suggesting that information spreading took place. These results indicate that careful inspection is necessary to avoid false-positive interpretations when MEG source estimation and multivariate pattern analysis are combined. PMID:29912968

Exploring High-D Spaces with Multiform Matrices and Small Multiples

PubMed Central

MacEachren, Alan; Dai, Xiping; Hardisty, Frank; Guo, Diansheng; Lengerich, Gene

2011-01-01

We introduce an approach to visual analysis of multivariate data that integrates several methods from information visualization, exploratory data analysis (EDA), and geovisualization. The approach leverages the component-based architecture implemented in GeoVISTA Studio to construct a flexible, multiview, tightly (but generically) coordinated, EDA toolkit. This toolkit builds upon traditional ideas behind both small multiples and scatterplot matrices in three fundamental ways. First, we develop a general, MultiForm, Bivariate Matrix and a complementary MultiForm, Bivariate Small Multiple plot in which different bivariate representation forms can be used in combination. We demonstrate the flexibility of this approach with matrices and small multiples that depict multivariate data through combinations of: scatterplots, bivariate maps, and space-filling displays. Second, we apply a measure of conditional entropy to (a) identify variables from a high-dimensional data set that are likely to display interesting relationships and (b) generate a default order of these variables in the matrix or small multiple display. Third, we add conditioning, a kind of dynamic query/filtering in which supplementary (undisplayed) variables are used to constrain the view onto variables that are displayed. Conditioning allows the effects of one or more well understood variables to be removed from the analysis, making relationships among remaining variables easier to explore. We illustrate the individual and combined functionality enabled by this approach through application to analysis of cancer diagnosis and mortality data and their associated covariates and risk factors. PMID:21947129

Simultaneous quantitative analysis of olmesartan, amlodipine and hydrochlorothiazide in their combined dosage form utilizing classical and alternating least squares based chemometric methods.

PubMed

Darwish, Hany W; Bakheit, Ahmed H; Abdelhameed, Ali S

2016-03-01

Simultaneous spectrophotometric analysis of a multi-component dosage form of olmesartan, amlodipine and hydrochlorothiazide used for the treatment of hypertension has been carried out using various chemometric methods. Multivariate calibration methods include classical least squares (CLS) executed by net analyte processing (NAP-CLS), orthogonal signal correction (OSC-CLS) and direct orthogonal signal correction (DOSC-CLS) in addition to multivariate curve resolution-alternating least squares (MCR-ALS). Results demonstrated the efficiency of the proposed methods as quantitative tools of analysis as well as their qualitative capability. The three analytes were determined precisely using the aforementioned methods in an external data set and in a dosage form after optimization of experimental conditions. Finally, the efficiency of the models was validated via comparison with the partial least squares (PLS) method in terms of accuracy and precision.

Bayesian inference on risk differences: an application to multivariate meta-analysis of adverse events in clinical trials.

PubMed

Chen, Yong; Luo, Sheng; Chu, Haitao; Wei, Peng

2013-05-01

Multivariate meta-analysis is useful in combining evidence from independent studies which involve several comparisons among groups based on a single outcome. For binary outcomes, the commonly used statistical models for multivariate meta-analysis are multivariate generalized linear mixed effects models which assume risks, after some transformation, follow a multivariate normal distribution with possible correlations. In this article, we consider an alternative model for multivariate meta-analysis where the risks are modeled by the multivariate beta distribution proposed by Sarmanov (1966). This model have several attractive features compared to the conventional multivariate generalized linear mixed effects models, including simplicity of likelihood function, no need to specify a link function, and has a closed-form expression of distribution functions for study-specific risk differences. We investigate the finite sample performance of this model by simulation studies and illustrate its use with an application to multivariate meta-analysis of adverse events of tricyclic antidepressants treatment in clinical trials.

Statistical Modeling of the Individual: Rationale and Application of Multivariate Stationary Time Series Analysis

ERIC Educational Resources Information Center

Hamaker, Ellen L.; Dolan, Conor V.; Molenaar, Peter C. M.

2005-01-01

Results obtained with interindividual techniques in a representative sample of a population are not necessarily generalizable to the individual members of this population. In this article the specific condition is presented that must be satisfied to generalize from the interindividual level to the intraindividual level. A way to investigate…

Experiments with a three-dimensional statistical objective analysis scheme using FGGE data

NASA Technical Reports Server (NTRS)

Baker, Wayman E.; Bloom, Stephen C.; Woollen, John S.; Nestler, Mark S.; Brin, Eugenia

1987-01-01

A three-dimensional (3D), multivariate, statistical objective analysis scheme (referred to as optimum interpolation or OI) has been developed for use in numerical weather prediction studies with the FGGE data. Some novel aspects of the present scheme include: (1) a multivariate surface analysis over the oceans, which employs an Ekman balance instead of the usual geostrophic relationship, to model the pressure-wind error cross correlations, and (2) the capability to use an error correlation function which is geographically dependent. A series of 4-day data assimilation experiments are conducted to examine the importance of some of the key features of the OI in terms of their effects on forecast skill, as well as to compare the forecast skill using the OI with that utilizing a successive correction method (SCM) of analysis developed earlier. For the three cases examined, the forecast skill is found to be rather insensitive to varying the error correlation function geographically. However, significant differences are noted between forecasts from a two-dimensional (2D) version of the OI and those from the 3D OI, with the 3D OI forecasts exhibiting better forecast skill. The 3D OI forecasts are also more accurate than those from the SCM initial conditions. The 3D OI with the multivariate oceanic surface analysis was found to produce forecasts which were slightly more accurate, on the average, than a univariate version.

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

PubMed

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

2016-11-01

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

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

PubMed

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

2015-03-27

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

Racial/Ethnic Disparities in the Mental Health Care Utilization of Fifth Grade Children

PubMed Central

Coker, Tumaini R.; Elliott, Marc N.; Kataoka, Sheryl; Schwebel, David C.; Mrug, Sylvie; Grunbaum, Jo Anne; Cuccaro, Paula; Peskin, Melissa F.; Schuster, Mark A.

2015-01-01

Objective The aim of this study was to examine racial/ethnic differences in fifth grade children’s mental health care utilization. Methods We analyzed cross-sectional data from a study of 5147 fifth graders and their parents in 3 US metropolitan areas from 2004–06. Multivariate logistic regression was used to examine racial/ethnic differences in mental health care utilization. Results Nine percent of parents reported that their child had ever used mental health care services; fewer black (6%) and Hispanic (8%) children had used services than white children (14%). Fewer black and Hispanic children with recent symptoms of attention-deficit/hyperactivity disorder, oppositional defiant disorder, and conduct disorder, and fewer black children with symptoms of depression had ever utilized services compared with white children. In multivariate analyses controlling for demographic factors, parental mental health, social support, and symptoms of the 4 mental health conditions, we found that black children were less likely than white children to have ever used services (Odds ratio [OR] 0.3, 95% confidence interval [95% CI], 0.2–0.4, P <.001). The odds ratio for black children remained virtually unchanged when the analysis was restricted to children with symptoms of ≥1 mental health condition, and when the analysis was stratified by mental health condition. The difference in utilization for Hispanic compared with white children was fully explained by sociodemographics in all multivariate models. Conclusions Disparities exist in mental health care utilization for black and Hispanic children; the disparity for black children is independent of sociodemographics and child mental health need. Efforts to reduce this disparity may benefit from addressing not only access and diagnosis issues, but also parents’ help-seeking preferences for mental health care for their children. PMID:19329099

Bayesian multivariate Poisson abundance models for T-cell receptor data.

PubMed

Greene, Joshua; Birtwistle, Marc R; Ignatowicz, Leszek; Rempala, Grzegorz A

2013-06-07

A major feature of an adaptive immune system is its ability to generate B- and T-cell clones capable of recognizing and neutralizing specific antigens. These clones recognize antigens with the help of the surface molecules, called antigen receptors, acquired individually during the clonal development process. In order to ensure a response to a broad range of antigens, the number of different receptor molecules is extremely large, resulting in a huge clonal diversity of both B- and T-cell receptor populations and making their experimental comparisons statistically challenging. To facilitate such comparisons, we propose a flexible parametric model of multivariate count data and illustrate its use in a simultaneous analysis of multiple antigen receptor populations derived from mammalian T-cells. The model relies on a representation of the observed receptor counts as a multivariate Poisson abundance mixture (m PAM). A Bayesian parameter fitting procedure is proposed, based on the complete posterior likelihood, rather than the conditional one used typically in similar settings. The new procedure is shown to be considerably more efficient than its conditional counterpart (as measured by the Fisher information) in the regions of m PAM parameter space relevant to model T-cell data. Copyright © 2013 Elsevier Ltd. All rights reserved.

Technology-enhanced Interactive Teaching of Marginal, Joint and Conditional Probabilities: The Special Case of Bivariate Normal Distribution

PubMed Central

Dinov, Ivo D.; Kamino, Scott; Bhakhrani, Bilal; Christou, Nicolas

2014-01-01

Summary Data analysis requires subtle probability reasoning to answer questions like What is the chance of event A occurring, given that event B was observed? This generic question arises in discussions of many intriguing scientific questions such as What is the probability that an adolescent weighs between 120 and 140 pounds given that they are of average height? and What is the probability of (monetary) inflation exceeding 4% and housing price index below 110? To address such problems, learning some applied, theoretical or cross-disciplinary probability concepts is necessary. Teaching such courses can be improved by utilizing modern information technology resources. Students’ understanding of multivariate distributions, conditional probabilities, correlation and causation can be significantly strengthened by employing interactive web-based science educational resources. Independent of the type of a probability course (e.g. majors, minors or service probability course, rigorous measure-theoretic, applied or statistics course) student motivation, learning experiences and knowledge retention may be enhanced by blending modern technological tools within the classical conceptual pedagogical models. We have designed, implemented and disseminated a portable open-source web-application for teaching multivariate distributions, marginal, joint and conditional probabilities using the special case of bivariate Normal distribution. A real adolescent height and weight dataset is used to demonstrate the classroom utilization of the new web-application to address problems of parameter estimation, univariate and multivariate inference. PMID:25419016

Technology-enhanced Interactive Teaching of Marginal, Joint and Conditional Probabilities: The Special Case of Bivariate Normal Distribution.

PubMed

Dinov, Ivo D; Kamino, Scott; Bhakhrani, Bilal; Christou, Nicolas

2013-01-01

Data analysis requires subtle probability reasoning to answer questions like What is the chance of event A occurring, given that event B was observed? This generic question arises in discussions of many intriguing scientific questions such as What is the probability that an adolescent weighs between 120 and 140 pounds given that they are of average height? and What is the probability of (monetary) inflation exceeding 4% and housing price index below 110? To address such problems, learning some applied, theoretical or cross-disciplinary probability concepts is necessary. Teaching such courses can be improved by utilizing modern information technology resources. Students' understanding of multivariate distributions, conditional probabilities, correlation and causation can be significantly strengthened by employing interactive web-based science educational resources. Independent of the type of a probability course (e.g. majors, minors or service probability course, rigorous measure-theoretic, applied or statistics course) student motivation, learning experiences and knowledge retention may be enhanced by blending modern technological tools within the classical conceptual pedagogical models. We have designed, implemented and disseminated a portable open-source web-application for teaching multivariate distributions, marginal, joint and conditional probabilities using the special case of bivariate Normal distribution. A real adolescent height and weight dataset is used to demonstrate the classroom utilization of the new web-application to address problems of parameter estimation, univariate and multivariate inference.

Enhancing e-waste estimates: Improving data quality by multivariate Input–Output Analysis

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

Wang, Feng, E-mail: fwang@unu.edu; Design for Sustainability Lab, Faculty of Industrial Design Engineering, Delft University of Technology, Landbergstraat 15, 2628CE Delft; Huisman, Jaco

2013-11-15

Highlights: • A multivariate Input–Output Analysis method for e-waste estimates is proposed. • Applying multivariate analysis to consolidate data can enhance e-waste estimates. • We examine the influence of model selection and data quality on e-waste estimates. • Datasets of all e-waste related variables in a Dutch case study have been provided. • Accurate modeling of time-variant lifespan distributions is critical for estimate. - Abstract: Waste electrical and electronic equipment (or e-waste) is one of the fastest growing waste streams, which encompasses a wide and increasing spectrum of products. Accurate estimation of e-waste generation is difficult, mainly due to lackmore » of high quality data referred to market and socio-economic dynamics. This paper addresses how to enhance e-waste estimates by providing techniques to increase data quality. An advanced, flexible and multivariate Input–Output Analysis (IOA) method is proposed. It links all three pillars in IOA (product sales, stock and lifespan profiles) to construct mathematical relationships between various data points. By applying this method, the data consolidation steps can generate more accurate time-series datasets from available data pool. This can consequently increase the reliability of e-waste estimates compared to the approach without data processing. A case study in the Netherlands is used to apply the advanced IOA model. As a result, for the first time ever, complete datasets of all three variables for estimating all types of e-waste have been obtained. The result of this study also demonstrates significant disparity between various estimation models, arising from the use of data under different conditions. It shows the importance of applying multivariate approach and multiple sources to improve data quality for modelling, specifically using appropriate time-varying lifespan parameters. Following the case study, a roadmap with a procedural guideline is provided to enhance e-waste estimation studies.« less

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.

A hybrid clustering approach for multivariate time series - A case study applied to failure analysis in a gas turbine.

PubMed

Fontes, Cristiano Hora; Budman, Hector

2017-11-01

A clustering problem involving multivariate time series (MTS) requires the selection of similarity metrics. This paper shows the limitations of the PCA similarity factor (SPCA) as a single metric in nonlinear problems where there are differences in magnitude of the same process variables due to expected changes in operation conditions. A novel method for clustering MTS based on a combination between SPCA and the average-based Euclidean distance (AED) within a fuzzy clustering approach is proposed. Case studies involving either simulated or real industrial data collected from a large scale gas turbine are used to illustrate that the hybrid approach enhances the ability to recognize normal and fault operating patterns. This paper also proposes an oversampling procedure to create synthetic multivariate time series that can be useful in commonly occurring situations involving unbalanced data sets. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Gas-water two-phase flow characterization with Electrical Resistance Tomography and Multivariate Multiscale Entropy analysis.

PubMed

Tan, Chao; Zhao, Jia; Dong, Feng

2015-03-01

Flow behavior characterization is important to understand gas-liquid two-phase flow mechanics and further establish its description model. An Electrical Resistance Tomography (ERT) provides information regarding flow conditions at different directions where the sensing electrodes implemented. We extracted the multivariate sample entropy (MSampEn) by treating ERT data as a multivariate time series. The dynamic experimental results indicate that the MSampEn is sensitive to complexity change of flow patterns including bubbly flow, stratified flow, plug flow and slug flow. MSampEn can characterize the flow behavior at different direction of two-phase flow, and reveal the transition between flow patterns when flow velocity changes. The proposed method is effective to analyze two-phase flow pattern transition by incorporating information of different scales and different spatial directions. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Correlative and multivariate analysis of increased radon concentration in underground laboratory.

PubMed

Maletić, Dimitrije M; Udovičić, Vladimir I; Banjanac, Radomir M; Joković, Dejan R; Dragić, Aleksandar L; Veselinović, Nikola B; Filipović, Jelena

2014-11-01

The results of analysis using correlative and multivariate methods, as developed for data analysis in high-energy physics and implemented in the Toolkit for Multivariate Analysis software package, of the relations of the variation of increased radon concentration with climate variables in shallow underground laboratory is presented. Multivariate regression analysis identified a number of multivariate methods which can give a good evaluation of increased radon concentrations based on climate variables. The use of the multivariate regression methods will enable the investigation of the relations of specific climate variable with increased radon concentrations by analysis of regression methods resulting in 'mapped' underlying functional behaviour of radon concentrations depending on a wide spectrum of climate variables. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Effects of Social Class and School Conditions on Educational Enrollment and Achievement of Boys and Girls in Rural Viet Nam

ERIC Educational Resources Information Center

Nguyen, Phuong L.

2006-01-01

This study examines the effects of parental SES, school quality, and community factors on children's enrollment and achievement in rural areas in Viet Nam, using logistic regression and ordered logistic regression. Multivariate analysis reveals significant differences in educational enrollment and outcomes by level of household expenditures and…

Health Related Quality of Life among Insulin-Dependent Diabetics: Disease-Related and Psychosocial Correlates.

ERIC Educational Resources Information Center

Aalto, Anna-Mari; Uutela, Antti; Aro, Arja R.

1997-01-01

The associations of health and psychosocial factors with the Health Related Quality of Life Questionnaire were examined in adult type 1 diabetic patients (N=385). The most important factors from multivariate analysis were self-efficacy and diabetes-related social support, especially among those in good physical condition. Diabetes-specific factors…

Multivariate Methods for Meta-Analysis of Genetic Association Studies.

PubMed

Dimou, Niki L; Pantavou, Katerina G; Braliou, Georgia G; Bagos, Pantelis G

2018-01-01

Multivariate meta-analysis of genetic association studies and genome-wide association studies has received a remarkable attention as it improves the precision of the analysis. Here, we review, summarize and present in a unified framework methods for multivariate meta-analysis of genetic association studies and genome-wide association studies. Starting with the statistical methods used for robust analysis and genetic model selection, we present in brief univariate methods for meta-analysis and we then scrutinize multivariate methodologies. Multivariate models of meta-analysis for a single gene-disease association studies, including models for haplotype association studies, multiple linked polymorphisms and multiple outcomes are discussed. The popular Mendelian randomization approach and special cases of meta-analysis addressing issues such as the assumption of the mode of inheritance, deviation from Hardy-Weinberg Equilibrium and gene-environment interactions are also presented. All available methods are enriched with practical applications and methodologies that could be developed in the future are discussed. Links for all available software implementing multivariate meta-analysis methods are also provided.

Estimation and Psychometric Analysis of Component Profile Scores via Multivariate Generalizability Theory

ERIC Educational Resources Information Center

Grochowalski, Joseph H.

2015-01-01

Component Universe Score Profile analysis (CUSP) is introduced in this paper as a psychometric alternative to multivariate profile analysis. The theoretical foundations of CUSP analysis are reviewed, which include multivariate generalizability theory and constrained principal components analysis. Because CUSP is a combination of generalizability…

Assessment of Coastal and Urban Flooding Hazards Applying Extreme Value Analysis and Multivariate Statistical Techniques: A Case Study in Elwood, Australia

NASA Astrophysics Data System (ADS)

Guimarães Nobre, Gabriela; Arnbjerg-Nielsen, Karsten; Rosbjerg, Dan; Madsen, Henrik

2016-04-01

Traditionally, flood risk assessment studies have been carried out from a univariate frequency analysis perspective. However, statistical dependence between hydrological variables, such as extreme rainfall and extreme sea surge, is plausible to exist, since both variables to some extent are driven by common meteorological conditions. Aiming to overcome this limitation, multivariate statistical techniques has the potential to combine different sources of flooding in the investigation. The aim of this study was to apply a range of statistical methodologies for analyzing combined extreme hydrological variables that can lead to coastal and urban flooding. The study area is the Elwood Catchment, which is a highly urbanized catchment located in the city of Port Phillip, Melbourne, Australia. The first part of the investigation dealt with the marginal extreme value distributions. Two approaches to extract extreme value series were applied (Annual Maximum and Partial Duration Series), and different probability distribution functions were fit to the observed sample. Results obtained by using the Generalized Pareto distribution demonstrate the ability of the Pareto family to model the extreme events. Advancing into multivariate extreme value analysis, first an investigation regarding the asymptotic properties of extremal dependence was carried out. As a weak positive asymptotic dependence between the bivariate extreme pairs was found, the Conditional method proposed by Heffernan and Tawn (2004) was chosen. This approach is suitable to model bivariate extreme values, which are relatively unlikely to occur together. The results show that the probability of an extreme sea surge occurring during a one-hour intensity extreme precipitation event (or vice versa) can be twice as great as what would occur when assuming independent events. Therefore, presuming independence between these two variables would result in severe underestimation of the flooding risk in the study area.

Clinical characterisation of pneumonia caused by atypical pathogens combining classic and novel predictors.

PubMed

Masiá, M; Gutiérrez, F; Padilla, S; Soldán, B; Mirete, C; Shum, C; Hernández, I; Royo, G; Martin-Hidalgo, A

2007-02-01

The aim of this study was to characterise community-acquired pneumonia (CAP) caused by atypical pathogens by combining distinctive clinical and epidemiological features and novel biological markers. A population-based prospective study of consecutive patients with CAP included investigation of biomarkers of bacterial infection, e.g., procalcitonin, C-reactive protein and lipopolysaccharide-binding protein (LBP) levels. Clinical, radiological and laboratory data for patients with CAP caused by atypical pathogens were compared by univariate and multivariate analysis with data for patients with typical pathogens and patients from whom no organisms were identified. Two predictive scoring models were developed with the most discriminatory variables from multivariate analysis. Of 493 patients, 94 had CAP caused by atypical pathogens. According to multivariate analysis, patients with atypical pneumonia were more likely to have normal white blood cell counts, have repetitive air-conditioning exposure, be aged <65 years, have elevated aspartate aminotransferase levels, have been exposed to birds, and have lower serum levels of LBP. Two different scoring systems were developed that predicted atypical pathogens with sensitivities of 35.2% and 48.8%, and specificities of 93% and 91%, respectively. The combination of selected patient characteristics and laboratory data identified up to half of the cases of atypical pneumonia with high specificity, which should help clinicians to optimise initial empirical therapy for CAP.

Fourier Transform Infrared Spectroscopy and Multivariate Analysis for Online Monitoring of Dibutyl Phosphate Degradation Product in Tributyl Phosphate/n-Dodecane/Nitric Acid Solvent

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

Tatiana G. Levitskaia; James M. Peterson; Emily L. Campbell

2013-12-01

In liquid–liquid extraction separation processes, accumulation of organic solvent degradation products is detrimental to the process robustness, and frequent solvent analysis is warranted. Our research explores the feasibility of online monitoring of the organic solvents relevant to used nuclear fuel reprocessing. This paper describes the first phase of developing a system for monitoring the tributyl phosphate (TBP)/n-dodecane solvent commonly used to separate used nuclear fuel. In this investigation, the effect of extraction of nitric acid from aqueous solutions of variable concentrations on the quantification of TBP and its major degradation product dibutylphosphoric acid (HDBP) was assessed. Fourier transform infrared (FTIR)more » spectroscopy was used to discriminate between HDBP and TBP in the nitric acid-containing TBP/n-dodecane solvent. Multivariate analysis of the spectral data facilitated the development of regression models for HDBP and TBP quantification in real time, enabling online implementation of the monitoring system. The predictive regression models were validated using TBP/n-dodecane solvent samples subjected to high-dose external ?-irradiation. The predictive models were translated to flow conditions using a hollow fiber FTIR probe installed in a centrifugal contactor extraction apparatus, demonstrating the applicability of the FTIR technique coupled with multivariate analysis for the online monitoring of the organic solvent degradation products.« less

Fourier Transform Infrared Spectroscopy and Multivariate Analysis for Online Monitoring of Dibutyl Phosphate Degradation Product in Tributyl Phosphate /n-Dodecane/Nitric Acid Solvent

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

Levitskaia, Tatiana G.; Peterson, James M.; Campbell, Emily L.

2013-11-05

In liquid-liquid extraction separation processes, accumulation of organic solvent degradation products is detrimental to the process robustness and frequent solvent analysis is warranted. Our research explores feasibility of online monitoring of the organic solvents relevant to used nuclear fuel reprocessing. This paper describes the first phase of developing a system for monitoring the tributyl phosphate (TBP)/n-dodecane solvent commonly used to separate used nuclear fuel. In this investigation, the effect of extraction of nitric acid from aqueous solutions of variable concentrations on the quantification of TBP and its major degradation product dibutyl phosphoric acid (HDBP) was assessed. Fourier Transform Infrared Spectroscopymore » (FTIR) spectroscopy was used to discriminate between HDBP and TBP in the nitric acid-containing TBP/n-dodecane solvent. Multivariate analysis of the spectral data facilitated the development of regression models for HDBP and TBP quantification in real time, enabling online implementation of the monitoring system. The predictive regression models were validated using TBP/n-dodecane solvent samples subjected to the high dose external gamma irradiation. The predictive models were translated to flow conditions using a hollow fiber FTIR probe installed in a centrifugal contactor extraction apparatus demonstrating the applicability of the FTIR technique coupled with multivariate analysis for the online monitoring of the organic solvent degradation products.« less

[Quality evaluation of American ginseng using UPLC coupled with multivariate analysis].

PubMed

Tang, Yan; Yan, Shu-Mo; Wang, Jing-Jing; Yuan, Yuan; Yang, Bin

2016-05-01

An ultra performance liquid chromatography (UPLC)method combined with multivariate data analysis was developed to evaluate the quality of American ginseng by simultaneously determining the concentrations of six ginsenosides (Rg₁, Re, Rb₁, Rc, Ro and Rd)in the samples. For UPLC, acetonitrile with 0.01% formic acid and water with 0.01% formic acid were used as the mobile phase with gradient elution. Under the established chromatographic conditions, the six ginsenosides could be well separated and the results of linearity, stability, precision, repeatability, and recovery rate all reached the requirement of quantification analysis, respectively. The total contents of Rg₁, Re, and Rb₁ in 57 samples all reached the requirement of the 2015 edition of Chinese Pharmacopoeia. At the same time, the experimental data were analyzed by principle component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). The crude drugs and the decoction pieces can be discriminated by a PCA method and the samples with different age can be distinguished by a PLS-DA method. Copyright© by the Chinese Pharmaceutical Association.

Simultaneous Evaluation of Life Cycle Dynamics between a Host Paramecium and the Endosymbionts of Paramecium bursaria Using Capillary Flow Cytometry.

PubMed

Takahashi, Toshiyuki

2016-08-17

Endosymbioses are driving forces underlying cell evolution. The endosymbiosis exhibited by Paramecium bursaria is an excellent model with which to study symbiosis. A single-cell microscopic analysis of P. bursaria reveals that endosymbiont numbers double when the host is in the division phase. Consequently, endosymbionts must arrange their cell cycle schedule if the culture-condition-dependent change delays the generation time of P. bursaria. However, it remains poorly understood whether endosymbionts keep pace with the culture-condition-dependent behaviors of P. bursaria, or not. Using microscopy and flow cytometry, this study investigated the life cycle behaviors occurring between endosymbionts and the host. To establish a connection between the host cell cycle and endosymbionts comprehensively, multivariate analysis was applied. The multivariate analysis revealed important information related to regulation between the host and endosymbionts. Results show that dividing endosymbionts underwent transition smoothly from the division phase to interphase, when the host was in the logarithmic phase. In contrast, endosymbiont division stagnated when the host was in the stationary phase. This paper explains that endosymbionts fine-tune their cell cycle pace with their host and that a synchronous life cycle between the endosymbionts and the host is guaranteed in the symbiosis of P. bursaria.

Simultaneous Evaluation of Life Cycle Dynamics between a Host Paramecium and the Endosymbionts of Paramecium bursaria Using Capillary Flow Cytometry

PubMed Central

Takahashi, Toshiyuki

2016-01-01

Endosymbioses are driving forces underlying cell evolution. The endosymbiosis exhibited by Paramecium bursaria is an excellent model with which to study symbiosis. A single-cell microscopic analysis of P. bursaria reveals that endosymbiont numbers double when the host is in the division phase. Consequently, endosymbionts must arrange their cell cycle schedule if the culture-condition-dependent change delays the generation time of P. bursaria. However, it remains poorly understood whether endosymbionts keep pace with the culture-condition-dependent behaviors of P. bursaria, or not. Using microscopy and flow cytometry, this study investigated the life cycle behaviors occurring between endosymbionts and the host. To establish a connection between the host cell cycle and endosymbionts comprehensively, multivariate analysis was applied. The multivariate analysis revealed important information related to regulation between the host and endosymbionts. Results show that dividing endosymbionts underwent transition smoothly from the division phase to interphase, when the host was in the logarithmic phase. In contrast, endosymbiont division stagnated when the host was in the stationary phase. This paper explains that endosymbionts fine-tune their cell cycle pace with their host and that a synchronous life cycle between the endosymbionts and the host is guaranteed in the symbiosis of P. bursaria. PMID:27531180

Fludarabine Melphalan reduced-intensity conditioning allotransplanation provides similar disease control in lymphoid and myeloid malignancies: analysis of 344 patients.

PubMed

Bryant, A; Nivison-Smith, I; Pillai, E S; Kennedy, G; Kalff, A; Ritchie, D; George, B; Hertzberg, M; Patil, S; Spencer, A; Fay, K; Cannell, P; Berkahn, L; Doocey, R; Spearing, R; Moore, J

2014-01-01

This was an Australasian Bone Marrow Transplant Recipient Registry (ABMTRR)-based retrospective study assessing the outcome of Fludarabine Melphalan (FluMel) reduced-intensity conditioning between 1998 and 2008. Median follow-up was 3.4 years. There were 344 patients with a median age of 54 years (18-68). In all, 234 patients had myeloid malignancies, with AML (n=166) being the commonest indication. There were 110 lymphoid patients with non-hodgkins lymphoma (NHL) (n=64) the main indication. TRM at day 100 was 14% with no significant difference between the groups. OS and disease-free survival (DFS) were similar between myeloid and lymphoid patients (57 and 50% at 3 years, respectively). There was no difference in cumulative incidence of relapse or GVHD between groups. Multivariate analysis revealed four significant adverse risk factors for DFS: donor other than HLA-identical sibling donor, not in remission at transplant, previous autologous transplant and recipient CMV positive. Chronic GVHD was associated with improved DFS in multivariate analysis predominantly due to a marked reduction in relapse (HR:0.44, P=0.003). This study confirms that FluMel provides durable and equivalent remissions in both myeloid and lymphoid malignancies. Disease stage and chronic GVHD remain important determinants of outcome for FluMel allografting.

Multivariate pattern analysis reveals subtle brain anomalies relevant to the cognitive phenotype in neurofibromatosis type 1.

PubMed

Duarte, João V; Ribeiro, Maria J; Violante, Inês R; Cunha, Gil; Silva, Eduardo; Castelo-Branco, Miguel

2014-01-01

Neurofibromatosis Type 1 (NF1) is a common genetic condition associated with cognitive dysfunction. However, the pathophysiology of the NF1 cognitive deficits is not well understood. Abnormal brain structure, including increased total brain volume, white matter (WM) and grey matter (GM) abnormalities have been reported in the NF1 brain. These previous studies employed univariate model-driven methods preventing detection of subtle and spatially distributed differences in brain anatomy. Multivariate pattern analysis allows the combination of information from multiple spatial locations yielding a discriminative power beyond that of single voxels. Here we investigated for the first time subtle anomalies in the NF1 brain, using a multivariate data-driven classification approach. We used support vector machines (SVM) to classify whole-brain GM and WM segments of structural T1 -weighted MRI scans from 39 participants with NF1 and 60 non-affected individuals, divided in children/adolescents and adults groups. We also employed voxel-based morphometry (VBM) as a univariate gold standard to study brain structural differences. SVM classifiers correctly classified 94% of cases (sensitivity 92%; specificity 96%) revealing the existence of brain structural anomalies that discriminate NF1 individuals from controls. Accordingly, VBM analysis revealed structural differences in agreement with the SVM weight maps representing the most relevant brain regions for group discrimination. These included the hippocampus, basal ganglia, thalamus, and visual cortex. This multivariate data-driven analysis thus identified subtle anomalies in brain structure in the absence of visible pathology. Our results provide further insight into the neuroanatomical correlates of known features of the cognitive phenotype of NF1. Copyright © 2012 Wiley Periodicals, Inc.

Probabilistic structural analysis methods and applications

NASA Technical Reports Server (NTRS)

Cruse, T. A.; Wu, Y.-T.; Dias, B.; Rajagopal, K. R.

1988-01-01

An advanced algorithm for simulating the probabilistic distribution of structural responses due to statistical uncertainties in loads, geometry, material properties, and boundary conditions is reported. The method effectively combines an advanced algorithm for calculating probability levels for multivariate problems (fast probability integration) together with a general-purpose finite-element code for stress, vibration, and buckling analysis. Application is made to a space propulsion system turbine blade for which the geometry and material properties are treated as random variables.

Multivariate Multiscale Analysis

DTIC Science & Technology

1990-11-08

The conditions on k in the second half of the statement of the proposition can be somewhat relaxed. In the cases n = 2 and n = 3 the details are given...of Mathematical Func- lions, Dover, New York, N.Y., 1965. [2] Bray and D. C. Solmon, The horocycle transform and harmonic analysis on the Poincare disk...H. Izen, Inversion of the k- plane transform by orthogonal function series expansions, Inverse Problems, 5 (1989), 181-202. [20] J. V. Leahy, K. T

Multivariate information-theoretic measures reveal directed information structure and task relevant changes in fMRI connectivity.

PubMed

Lizier, Joseph T; Heinzle, Jakob; Horstmann, Annette; Haynes, John-Dylan; Prokopenko, Mikhail

2011-02-01

The human brain undertakes highly sophisticated information processing facilitated by the interaction between its sub-regions. We present a novel method for interregional connectivity analysis, using multivariate extensions to the mutual information and transfer entropy. The method allows us to identify the underlying directed information structure between brain regions, and how that structure changes according to behavioral conditions. This method is distinguished in using asymmetric, multivariate, information-theoretical analysis, which captures not only directional and non-linear relationships, but also collective interactions. Importantly, the method is able to estimate multivariate information measures with only relatively little data. We demonstrate the method to analyze functional magnetic resonance imaging time series to establish the directed information structure between brain regions involved in a visuo-motor tracking task. Importantly, this results in a tiered structure, with known movement planning regions driving visual and motor control regions. Also, we examine the changes in this structure as the difficulty of the tracking task is increased. We find that task difficulty modulates the coupling strength between regions of a cortical network involved in movement planning and between motor cortex and the cerebellum which is involved in the fine-tuning of motor control. It is likely these methods will find utility in identifying interregional structure (and experimentally induced changes in this structure) in other cognitive tasks and data modalities.

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. Copyright © 2013 Elsevier Ltd. All rights reserved.

A simplified parsimonious higher order multivariate Markov chain model with new convergence condition

NASA Astrophysics Data System (ADS)

Wang, Chao; Yang, Chuan-sheng

2017-09-01

In this paper, we present a simplified parsimonious higher-order multivariate Markov chain model with new convergence condition. (TPHOMMCM-NCC). Moreover, estimation method of the parameters in TPHOMMCM-NCC is give. Numerical experiments illustrate the effectiveness of TPHOMMCM-NCC.

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. Copyright © 2011 John Wiley & Sons, Ltd.

High-throughput investigation of single and binary protein adsorption isotherms in anion exchange chromatography employing multivariate analysis.

PubMed

Field, Nicholas; Konstantinidis, Spyridon; Velayudhan, Ajoy

2017-08-11

The combination of multi-well plates and automated liquid handling is well suited to the rapid measurement of the adsorption isotherms of proteins. Here, single and binary adsorption isotherms are reported for BSA, ovalbumin and conalbumin on a strong anion exchanger over a range of pH and salt levels. The impact of the main experimental factors at play on the accuracy and precision of the adsorbed protein concentrations is quantified theoretically and experimentally. In addition to the standard measurement of liquid concentrations before and after adsorption, the amounts eluted from the wells are measured directly. This additional measurement corroborates the calculation based on liquid concentration data, and improves precision especially under conditions of weak or moderate interaction strength. The traditional measurement of multicomponent isotherms is limited by the speed of HPLC analysis; this analytical bottleneck is alleviated by careful multivariate analysis of UV spectra. Copyright © 2017. Published by Elsevier B.V.

Discrimination of three Pegaga (Centella) varieties and determination of growth-lighting effects on metabolites content based on the chemometry of 1H nuclear magnetic resonance spectroscopy.

PubMed

H, Maulidiani; Khatib, Alfi; Shaari, Khozirah; Abas, Faridah; Shitan, Mahendran; Kneer, Ralf; Neto, Victor; Lajis, Nordin H

2012-01-11

The metabolites of three species of Apiaceae, also known as Pegaga, were analyzed utilizing (1)H NMR spectroscopy and multivariate data analysis. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) resolved the species, Centella asiatica, Hydrocotyle bonariensis, and Hydrocotyle sibthorpioides, into three clusters. The saponins, asiaticoside and madecassoside, along with chlorogenic acids were the metabolites that contributed most to the separation. Furthermore, the effects of growth-lighting condition to metabolite contents were also investigated. The extracts of C. asiatica grown in full-day light exposure exhibited a stronger radical scavenging activity and contained more triterpenes (asiaticoside and madecassoside), flavonoids, and chlorogenic acids as compared to plants grown in 50% shade. This study established the potential of using a combination of (1)H NMR spectroscopy and multivariate data analyses in differentiating three closely related species and the effects of growth lighting, based on their metabolite contents and identification of the markers contributing to their differences.

Assessment of cardio-respiratory interactions in preterm infants by bivariate autoregressive modeling and surrogate data analysis.

PubMed

Indic, Premananda; Bloch-Salisbury, Elisabeth; Bednarek, Frank; Brown, Emery N; Paydarfar, David; Barbieri, Riccardo

2011-07-01

Cardio-respiratory interactions are weak at the earliest stages of human development, suggesting that assessment of their presence and integrity may be an important indicator of development in infants. Despite the valuable research devoted to infant development, there is still a need for specifically targeted standards and methods to assess cardiopulmonary functions in the early stages of life. We present a new methodological framework for the analysis of cardiovascular variables in preterm infants. Our approach is based on a set of mathematical tools that have been successful in quantifying important cardiovascular control mechanisms in adult humans, here specifically adapted to reflect the physiology of the developing cardiovascular system. We applied our methodology in a study of cardio-respiratory responses for 11 preterm infants. We quantified cardio-respiratory interactions using specifically tailored multivariate autoregressive analysis and calculated the coherence as well as gain using causal approaches. The significance of the interactions in each subject was determined by surrogate data analysis. The method was tested in control conditions as well as in two different experimental conditions; with and without use of mild mechanosensory intervention. Our multivariate analysis revealed a significantly higher coherence, as confirmed by surrogate data analysis, in the frequency range associated with eupneic breathing compared to the other ranges. Our analysis validates the models behind our new approaches, and our results confirm the presence of cardio-respiratory coupling in early stages of development, particularly during periods of mild mechanosensory intervention, thus encouraging further application of our approach. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Geographically Sourcing Cocaine's Origin - Delineation of the Nineteen Major Coca Growing Regions in South America.

PubMed

Mallette, Jennifer R; Casale, John F; Jordan, James; Morello, David R; Beyer, Paul M

2016-03-23

Previously, geo-sourcing to five major coca growing regions within South America was accomplished. However, the expansion of coca cultivation throughout South America made sub-regional origin determinations increasingly difficult. The former methodology was recently enhanced with additional stable isotope analyses ((2)H and (18)O) to fully characterize cocaine due to the varying environmental conditions in which the coca was grown. An improved data analysis method was implemented with the combination of machine learning and multivariate statistical analysis methods to provide further partitioning between growing regions. Here, we show how the combination of trace cocaine alkaloids, stable isotopes, and multivariate statistical analyses can be used to classify illicit cocaine as originating from one of 19 growing regions within South America. The data obtained through this approach can be used to describe current coca cultivation and production trends, highlight trafficking routes, as well as identify new coca growing regions.

Geographically Sourcing Cocaine’s Origin - Delineation of the Nineteen Major Coca Growing Regions in South America

NASA Astrophysics Data System (ADS)

Mallette, Jennifer R.; Casale, John F.; Jordan, James; Morello, David R.; Beyer, Paul M.

2016-03-01

Previously, geo-sourcing to five major coca growing regions within South America was accomplished. However, the expansion of coca cultivation throughout South America made sub-regional origin determinations increasingly difficult. The former methodology was recently enhanced with additional stable isotope analyses (2H and 18O) to fully characterize cocaine due to the varying environmental conditions in which the coca was grown. An improved data analysis method was implemented with the combination of machine learning and multivariate statistical analysis methods to provide further partitioning between growing regions. Here, we show how the combination of trace cocaine alkaloids, stable isotopes, and multivariate statistical analyses can be used to classify illicit cocaine as originating from one of 19 growing regions within South America. The data obtained through this approach can be used to describe current coca cultivation and production trends, highlight trafficking routes, as well as identify new coca growing regions.

Analysis of National Trends in Hospital Acquired Conditions Following Major Urological Surgery Before and After Implementation of the Hospital Acquired Condition Reduction Program,,✰✰✰.

PubMed

Rude, Tope L; Donin, Nicholas M; Cohn, Matthew R; Meeks, William; Gulig, Scott; Patel, Samir N; Wysock, James S; Makarov, Danil V; Bjurlin, Marc A

2018-06-07

To define the rates of common Hospital Acquired Conditions (HACs) in patients undergoing major urological surgery over a period of time encompassing the implementation of the Hospital Acquired Condition Reduction program, and to evaluate whether implementation of the HAC reimbursement penalties in 2008 was associated with a change in the rate of HACs. Using American College of Surgeons National Surgical Quality Improvement Program (NSQIP) data, we determined rates of HACs in patients undergoing major inpatient urological surgery from 2005 to 2012. Rates were stratified by procedure type and approach (open vs. laparoscopic/robotic). Multivariable logistic regression was used to determine the association between year of surgery and HACs. We identified 39,257 patients undergoing major urological surgery, of whom 2300 (5.9%) had at least one hospital acquired condition. Urinary tract infection (UTI, 2.6%) was the most common, followed by surgical site infection (SSI, 2.5%) and venous thrombotic events (VTE, 0.7%). Multivariable logistic regression analysis demonstrated that open surgical approach, diabetes, congestive heart failure, chronic obstructive pulmonary disease, weight loss, and ASA class were among the variables associated with higher likelihood of HAC. We observed a non-significant secular trend of decreasing rates of HAC from 7.4% to 5.8% HACs during the study period, which encompassed the implementation of the Hospital Acquired Condition Reduction Program. HACs occurred at a rate of 5.9% after major urological surgery, and are significantly affected by procedure type and patient health status. The rate of HAC appeared unaffected by national reduction program in this cohort. Better understanding of the factors associated with HACs is critical in developing effective reduction programs. Copyright © 2018. Published by Elsevier Inc.

Multivariable passive RFID vapor sensors: roll-to-roll fabrication on a flexible substrate.

PubMed

Potyrailo, Radislav A; Burns, Andrew; Surman, Cheryl; Lee, D J; McGinniss, Edward

2012-06-21

We demonstrate roll-to-roll (R2R) fabrication of highly selective, battery-free radio frequency identification (RFID) sensors on a flexible polyethylene terephthalate (PET) polymeric substrate. Selectivity of our developed RFID sensors is provided by measurements of their resonance impedance spectra, followed by the multivariate analysis of spectral features, and correlation of these spectral features to the concentrations of vapors of interest. The multivariate analysis of spectral features also provides the ability for the rejection of ambient interferences. As a demonstration of our R2R fabrication process, we employed polyetherurethane (PEUT) as a "classic" sensing material, extruded this sensing material as 25, 75, and 125-μm thick films, and thermally laminated the films onto RFID inlays, rapidly producing approximately 5000 vapor sensors. We further tested these RFID vapor sensors for their response selectivity toward several model vapors such as toluene, acetone, and ethanol as well as water vapor as an abundant interferent. Our RFID sensing concept features 16-bit resolution provided by the sensor reader, granting a highly desired independence from costly proprietary RFID memory chips with a low-resolution analog input. Future steps are being planned for field-testing of these sensors in numerous conditions.

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…

Non-parametric directionality analysis - Extension for removal of a single common predictor and application to time series.

PubMed

Halliday, David M; Senik, Mohd Harizal; Stevenson, Carl W; Mason, Rob

2016-08-01

The ability to infer network structure from multivariate neuronal signals is central to computational neuroscience. Directed network analyses typically use parametric approaches based on auto-regressive (AR) models, where networks are constructed from estimates of AR model parameters. However, the validity of using low order AR models for neurophysiological signals has been questioned. A recent article introduced a non-parametric approach to estimate directionality in bivariate data, non-parametric approaches are free from concerns over model validity. We extend the non-parametric framework to include measures of directed conditional independence, using scalar measures that decompose the overall partial correlation coefficient summatively by direction, and a set of functions that decompose the partial coherence summatively by direction. A time domain partial correlation function allows both time and frequency views of the data to be constructed. The conditional independence estimates are conditioned on a single predictor. The framework is applied to simulated cortical neuron networks and mixtures of Gaussian time series data with known interactions. It is applied to experimental data consisting of local field potential recordings from bilateral hippocampus in anaesthetised rats. The framework offers a non-parametric approach to estimation of directed interactions in multivariate neuronal recordings, and increased flexibility in dealing with both spike train and time series data. The framework offers a novel alternative non-parametric approach to estimate directed interactions in multivariate neuronal recordings, and is applicable to spike train and time series data. Copyright © 2016 Elsevier B.V. All rights reserved.

Multivariate Analysis of the Cotton Seed Ionome Reveals a Shared Genetic Architecture

PubMed Central

Pauli, Duke; Ziegler, Greg; Ren, Min; Jenks, Matthew A.; Hunsaker, Douglas J.; Zhang, Min; Baxter, Ivan; Gore, Michael A.

2018-01-01

To mitigate the effects of heat and drought stress, a better understanding of the genetic control of physiological responses to these environmental conditions is needed. To this end, we evaluated an upland cotton (Gossypium hirsutum L.) mapping population under water-limited and well-watered conditions in a hot, arid environment. The elemental concentrations (ionome) of seed samples from the population were profiled in addition to those of soil samples taken from throughout the field site to better model environmental variation. The elements profiled in seeds exhibited moderate to high heritabilities, as well as strong phenotypic and genotypic correlations between elements that were not altered by the imposed irrigation regimes. Quantitative trait loci (QTL) mapping results from a Bayesian classification method identified multiple genomic regions where QTL for individual elements colocalized, suggesting that genetic control of the ionome is highly interrelated. To more fully explore this genetic architecture, multivariate QTL mapping was implemented among groups of biochemically related elements. This analysis revealed both additional and pleiotropic QTL responsible for coordinated control of phenotypic variation for elemental accumulation. Machine learning algorithms that utilized only ionomic data predicted the irrigation regime under which genotypes were evaluated with very high accuracy. Taken together, these results demonstrate the extent to which the seed ionome is genetically interrelated and predictive of plant physiological responses to adverse environmental conditions. PMID:29437829

A bispectral q-hypergeometric basis for a class of quantum integrable models

NASA Astrophysics Data System (ADS)

Baseilhac, Pascal; Martin, Xavier

2018-01-01

For the class of quantum integrable models generated from the q-Onsager algebra, a basis of bispectral multivariable q-orthogonal polynomials is exhibited. In the first part, it is shown that the multivariable Askey-Wilson polynomials with N variables and N + 3 parameters introduced by Gasper and Rahman [Dev. Math. 13, 209 (2005)] generate a family of infinite dimensional modules for the q-Onsager algebra, whose fundamental generators are realized in terms of the multivariable q-difference and difference operators proposed by Iliev [Trans. Am. Math. Soc. 363, 1577 (2011)]. Raising and lowering operators extending those of Sahi [SIGMA 3, 002 (2007)] are also constructed. In the second part, finite dimensional modules are constructed and studied for a certain class of parameters and if the N variables belong to a discrete support. In this case, the bispectral property finds a natural interpretation within the framework of tridiagonal pairs. In the third part, eigenfunctions of the q-Dolan-Grady hierarchy are considered in the polynomial basis. In particular, invariant subspaces are identified for certain conditions generalizing Nepomechie's relations. In the fourth part, the analysis is extended to the special case q = 1. This framework provides a q-hypergeometric formulation of quantum integrable models such as the open XXZ spin chain with generic integrable boundary conditions (q ≠ 1).

Environmental Influences on Daily Emergency Admissions in Sickle-Cell Disease Patients

PubMed Central

Mekontso Dessap, Armand; Contou, Damien; Dandine-Roulland, Claire; Hemery, François; Habibi, Anoosha; Charles-Nelson, Anaïs; Galacteros, Frederic; Brun-Buisson, Christian; Maitre, Bernard; Katsahian, Sandrine

2014-01-01

Abstract Previous reports have suggested a role for weather conditions and air pollution on the variability of sickle cell disease (SCD) severity, but large-scale comprehensive epidemiological studies are lacking. In order to evaluate the influence of air pollution and climatic factors on emergency hospital admissions (EHA) in SCD patients, we conducted an 8-year observational retrospective study in 22 French university hospitals in Paris conurbation, using distributed lag non-linear models, a methodology able to flexibly describe simultaneously non-linear and delayed associations, with a multivariable approach. During the 2922 days of the study, there were 17,710 EHA, with a mean daily number of 6.1 ± 2.8. Most environmental factors were significantly correlated to each other. The risk of EHA was significantly associated with higher values of nitrogen dioxide, atmospheric particulate matters, and daily mean wind speed; and with lower values of carbon monoxide, ozone, sulfur dioxide, daily temperature (minimal, maximal, mean, and range), day-to-day mean temperature change, daily bright sunshine, and occurrence of storm. There was a lag effect for 12 of 15 environmental factors influencing hospitalization rate. Multivariate analysis identified carbon monoxide, day-to-day temperature change, and mean wind speed, along with calendar factors (weekend, summer season, and year) as independent factors associated with EHA. In conclusion, most weather conditions and air pollutants assessed were correlated to each other and influenced the rate of EHA in SCD patients. In multivariate analysis, lower carbon monoxide concentrations, day-to-day mean temperature drop and higher wind speed were associated with increased risk of EHA. PMID:25546672

Environmental influences on daily emergency admissions in sickle-cell disease patients.

PubMed

Mekontso Dessap, Armand; Contou, Damien; Dandine-Roulland, Claire; Hemery, François; Habibi, Anoosha; Charles-Nelson, Anaïs; Galacteros, Frederic; Brun-Buisson, Christian; Maitre, Bernard; Katsahian, Sandrine

2014-12-01

Previous reports have suggested a role for weather conditions and air pollution on the variability of sickle cell disease (SCD) severity, but large-scale comprehensive epidemiological studies are lacking. In order to evaluate the influence of air pollution and climatic factors on emergency hospital admissions (EHA) in SCD patients, we conducted an 8-year observational retrospective study in 22 French university hospitals in Paris conurbation, using distributed lag non-linear models, a methodology able to flexibly describe simultaneously non-linear and delayed associations, with a multivariable approach. During the 2922 days of the study, there were 17,710 EHA, with a mean daily number of 6.1 ± 2.8. Most environmental factors were significantly correlated to each other. The risk of EHA was significantly associated with higher values of nitrogen dioxide, atmospheric particulate matters, and daily mean wind speed; and with lower values of carbon monoxide, ozone, sulfur dioxide, daily temperature (minimal, maximal, mean, and range), day-to-day mean temperature change, daily bright sunshine, and occurrence of storm. There was a lag effect for 12 of 15 environmental factors influencing hospitalization rate. Multivariate analysis identified carbon monoxide, day-to-day temperature change, and mean wind speed, along with calendar factors (weekend, summer season, and year) as independent factors associated with EHA. In conclusion, most weather conditions and air pollutants assessed were correlated to each other and influenced the rate of EHA in SCD patients. In multivariate analysis, lower carbon monoxide concentrations, day-to-day mean temperature drop and higher wind speed were associated with increased risk of EHA.

Deconstructing multivariate decoding for the study of brain function.

PubMed

Hebart, Martin N; Baker, Chris I

2017-08-04

Multivariate decoding methods were developed originally as tools to enable accurate predictions in real-world applications. The realization that these methods can also be employed to study brain function has led to their widespread adoption in the neurosciences. However, prior to the rise of multivariate decoding, the study of brain function was firmly embedded in a statistical philosophy grounded on univariate methods of data analysis. In this way, multivariate decoding for brain interpretation grew out of two established frameworks: multivariate decoding for predictions in real-world applications, and classical univariate analysis based on the study and interpretation of brain activation. We argue that this led to two confusions, one reflecting a mixture of multivariate decoding for prediction or interpretation, and the other a mixture of the conceptual and statistical philosophies underlying multivariate decoding and classical univariate analysis. Here we attempt to systematically disambiguate multivariate decoding for the study of brain function from the frameworks it grew out of. After elaborating these confusions and their consequences, we describe six, often unappreciated, differences between classical univariate analysis and multivariate decoding. We then focus on how the common interpretation of what is signal and noise changes in multivariate decoding. Finally, we use four examples to illustrate where these confusions may impact the interpretation of neuroimaging data. We conclude with a discussion of potential strategies to help resolve these confusions in interpreting multivariate decoding results, including the potential departure from multivariate decoding methods for the study of brain function. Copyright © 2017. Published by Elsevier Inc.

Support for voluntary and nonvoluntary euthanasia: what roles do conditions of suffering and the identity of the terminally ill play?

PubMed

Ho, Robert; Chantagul, Natalie

2015-01-01

This study investigated the level of support for voluntary and nonvoluntary euthanasia under three conditions of suffering (pain; debilitated nature of the body; burden on the family) experienced by oneself, a significant other, and a person in general. The sample consisted of 1,897 Thai adults (719 males, 1,178 females) who voluntarily filled in the study's questionnaire. Initial multivariate analysis of variance indicated significant group (oneself, significant other, person in general) differences in level of support for voluntary and nonvoluntary euthanasia and under the three conditions of suffering. Multigroup path analysis conducted on the posited euthanasia model showed that the three conditions of suffering exerted differential direct and indirect influences on the support of voluntary and nonvoluntary euthanasia as a function of the identity of the person for whom euthanasia was being considered. The implications of these findings are discussed.

Exploring the effects of tape-recording on personality assessment.

PubMed

Lichton, A I; Waehler, C A

1999-06-01

This study examined the possible influence of audio and video recording of personality assessment measures on anxiety. Undergraduate students in psychology were randomly assigned to Audiotape, Videotape, or Control conditions and given the State-Trait Anxiety Inventory and Rorschach Inkblot Method. A one-way multivariate analysis of variance indicated no significant differences among these conditions on the Spielberger, et al. State-Trait Anxiety Inventory, A-State scale, and five Rorschach measures of situational anxiety. Tape-recording itself did not seem to affect the anxiety indices of these frequently used personality assessments.

A conditional Granger causality model approach for group analysis in functional MRI

PubMed Central

Zhou, Zhenyu; Wang, Xunheng; Klahr, Nelson J.; Liu, Wei; Arias, Diana; Liu, Hongzhi; von Deneen, Karen M.; Wen, Ying; Lu, Zuhong; Xu, Dongrong; Liu, Yijun

2011-01-01

Granger causality model (GCM) derived from multivariate vector autoregressive models of data has been employed for identifying effective connectivity in the human brain with functional MR imaging (fMRI) and to reveal complex temporal and spatial dynamics underlying a variety of cognitive processes. In the most recent fMRI effective connectivity measures, pairwise GCM has commonly been applied based on single voxel values or average values from special brain areas at the group level. Although a few novel conditional GCM methods have been proposed to quantify the connections between brain areas, our study is the first to propose a viable standardized approach for group analysis of an fMRI data with GCM. To compare the effectiveness of our approach with traditional pairwise GCM models, we applied a well-established conditional GCM to pre-selected time series of brain regions resulting from general linear model (GLM) and group spatial kernel independent component analysis (ICA) of an fMRI dataset in the temporal domain. Datasets consisting of one task-related and one resting-state fMRI were used to investigate connections among brain areas with the conditional GCM method. With the GLM detected brain activation regions in the emotion related cortex during the block design paradigm, the conditional GCM method was proposed to study the causality of the habituation between the left amygdala and pregenual cingulate cortex during emotion processing. For the resting-state dataset, it is possible to calculate not only the effective connectivity between networks but also the heterogeneity within a single network. Our results have further shown a particular interacting pattern of default mode network (DMN) that can be characterized as both afferent and efferent influences on the medial prefrontal cortex (mPFC) and posterior cingulate cortex (PCC). These results suggest that the conditional GCM approach based on a linear multivariate vector autoregressive (MVAR) model can achieve greater accuracy in detecting network connectivity than the widely used pairwise GCM, and this group analysis methodology can be quite useful to extend the information obtainable in fMRI. PMID:21232892

Multivariate meta-analysis: Potential and promise

PubMed Central

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

2011-01-01

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. Copyright © 2011 John Wiley & Sons, Ltd. PMID:21268052

Experimental variability and data pre-processing as factors affecting the discrimination power of some chemometric approaches (PCA, CA and a new algorithm based on linear regression) applied to (+/-)ESI/MS and RPLC/UV data: Application on green tea extracts.

PubMed

Iorgulescu, E; Voicu, V A; Sârbu, C; Tache, F; Albu, F; Medvedovici, A

2016-08-01

The influence of the experimental variability (instrumental repeatability, instrumental intermediate precision and sample preparation variability) and data pre-processing (normalization, peak alignment, background subtraction) on the discrimination power of multivariate data analysis methods (Principal Component Analysis -PCA- and Cluster Analysis -CA-) as well as a new algorithm based on linear regression was studied. Data used in the study were obtained through positive or negative ion monitoring electrospray mass spectrometry (+/-ESI/MS) and reversed phase liquid chromatography/UV spectrometric detection (RPLC/UV) applied to green tea extracts. Extractions in ethanol and heated water infusion were used as sample preparation procedures. The multivariate methods were directly applied to mass spectra and chromatograms, involving strictly a holistic comparison of shapes, without assignment of any structural identity to compounds. An alternative data interpretation based on linear regression analysis mutually applied to data series is also discussed. Slopes, intercepts and correlation coefficients produced by the linear regression analysis applied on pairs of very large experimental data series successfully retain information resulting from high frequency instrumental acquisition rates, obviously better defining the profiles being compared. Consequently, each type of sample or comparison between samples produces in the Cartesian space an ellipsoidal volume defined by the normal variation intervals of the slope, intercept and correlation coefficient. Distances between volumes graphically illustrates (dis)similarities between compared data. The instrumental intermediate precision had the major effect on the discrimination power of the multivariate data analysis methods. Mass spectra produced through ionization from liquid state in atmospheric pressure conditions of bulk complex mixtures resulting from extracted materials of natural origins provided an excellent data basis for multivariate analysis methods, equivalent to data resulting from chromatographic separations. The alternative evaluation of very large data series based on linear regression analysis produced information equivalent to results obtained through application of PCA an CA. Copyright © 2016 Elsevier B.V. All rights reserved.

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 demonstrate that multivariate analyses can substantially increase power to detect associations. Software implementing our multivariate analysis methods is available at http://stephenslab.uchicago.edu/software.html. PMID:25898129

Multivariate Longitudinal Analysis with Bivariate Correlation Test

PubMed Central

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

2016-01-01

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

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.

Information transfer and information modification to identify the structure of cardiovascular and cardiorespiratory networks.

PubMed

Faes, Luca; Nollo, Giandomenico; Krohova, Jana; Czippelova, Barbora; Turianikova, Zuzana; Javorka, Michal

2017-07-01

To fully elucidate the complex physiological mechanisms underlying the short-term autonomic regulation of heart period (H), systolic and diastolic arterial pressure (S, D) and respiratory (R) variability, the joint dynamics of these variables need to be explored using multivariate time series analysis. This study proposes the utilization of information-theoretic measures to measure causal interactions between nodes of the cardiovascular/cardiorespiratory network and to assess the nature (synergistic or redundant) of these directed interactions. Indexes of information transfer and information modification are extracted from the H, S, D and R series measured from healthy subjects in a resting state and during postural stress. Computations are performed in the framework of multivariate linear regression, using bootstrap techniques to assess on a single-subject basis the statistical significance of each measure and of its transitions across conditions. We find patterns of information transfer and modification which are related to specific cardiovascular and cardiorespiratory mechanisms in resting conditions and to their modification induced by the orthostatic stress.

Is there a relationship between periodontal conditions and number of medications among the elderly?

PubMed

Natto, Zuhair S; Aladmawy, Majdi; Alshaeri, Heba K; Alasqah, Mohammed; Papas, Athena

2016-03-01

To investigate possible correlations of clinical attachment level and pocket depth with number of medications in elderly individuals. Intra-oral examinations for 139 patients visiting Tufts dental clinic were done. Periodontal assessments were performed with a manual UNC-15 periodontal probe to measure probing depth (PD) and clinical attachment level (CAL) at 6 sites. Complete lists of patients' medications were obtained during the examinations. Statistical analysis involved Kruskal-Wallis, chi square and multivariate logistic regression analyses. Age and health status attained statistical significance (p< 0.05), in contingency table analysis with number of medications. Number of medications had an effect on CAL: increased attachment loss was observed when 4 or more medications were being taken by the patient. Number of medications did not have any effect on periodontal PD. In multivariate logistic regression analysis, 6 or more medications had a higher risk of attachment loss (>3mm) when compared to the no-medication group, in crude OR (1.20, 95% CI:0.22-6.64), and age adjusted (OR=1.16, 95% CI:0.21-6.45), but not with the multivariate model (OR=0.71, 95% CI:0.11-4.39). CAL seems to be more sensitive to the number of medications taken, when compared to PD. However, it is not possible to discriminate at exactly what number of drug combinations the breakdown in CAL will happen. We need to do further analysis, including more subjects, to understand the possible synergistic mechanisms for different drug and periodontal responses.

Integration of multivariate empirical mode decomposition and independent component analysis for fetal ECG separation from abdominal signals.

PubMed

Thanaraj, Palani; Roshini, Mable; Balasubramanian, Parvathavarthini

2016-11-14

The fetal electrocardiogram (FECG) signals are essential to monitor the health condition of the baby. Fetal heart rate (FHR) is commonly used for diagnosing certain abnormalities in the formation of the heart. Usually, non-invasive abdominal electrocardiogram (AbECG) signals are obtained by placing surface electrodes in the abdomen region of the pregnant woman. AbECG signals are often not suitable for the direct analysis of fetal heart activity. Moreover, the strength and magnitude of the FECG signals are low compared to the maternal electrocardiogram (MECG) signals. The MECG signals are often superimposed with the FECG signals that make the monitoring of FECG signals a difficult task. Primary goal of the paper is to separate the fetal electrocardiogram (FECG) signals from the unwanted maternal electrocardiogram (MECG) signals. A multivariate signal processing procedure is proposed here that combines the Multivariate Empirical Mode Decomposition (MEMD) and Independent Component Analysis (ICA). The proposed method is evaluated with clinical abdominal signals taken from three pregnant women (N= 3) recorded during the 38-41 weeks of the gestation period. The number of fetal R-wave detected (NEFQRS), the number of unwanted maternal peaks (NMQRS), the number of undetected fetal R-wave (NUFQRS) and the FHR detection accuracy quantifies the performance of our method. Clinical investigation with three test subjects shows an overall detection accuracy of 92.8%. Comparative analysis with benchmark signal processing method such as ICA suggests the noteworthy performance of our method.

Multivariate random-parameters zero-inflated negative binomial regression model: an application to estimate crash frequencies at intersections.

PubMed

Dong, Chunjiao; Clarke, David B; Yan, Xuedong; Khattak, Asad; Huang, Baoshan

2014-09-01

Crash data are collected through police reports and integrated with road inventory data for further analysis. Integrated police reports and inventory data yield correlated multivariate data for roadway entities (e.g., segments or intersections). Analysis of such data reveals important relationships that can help focus on high-risk situations and coming up with safety countermeasures. To understand relationships between crash frequencies and associated variables, while taking full advantage of the available data, multivariate random-parameters models are appropriate since they can simultaneously consider the correlation among the specific crash types and account for unobserved heterogeneity. However, a key issue that arises with correlated multivariate data is the number of crash-free samples increases, as crash counts have many categories. In this paper, we describe a multivariate random-parameters zero-inflated negative binomial (MRZINB) regression model for jointly modeling crash counts. The full Bayesian method is employed to estimate the model parameters. Crash frequencies at urban signalized intersections in Tennessee are analyzed. The paper investigates the performance of MZINB and MRZINB regression models in establishing the relationship between crash frequencies, pavement conditions, traffic factors, and geometric design features of roadway intersections. Compared to the MZINB model, the MRZINB model identifies additional statistically significant factors and provides better goodness of fit in developing the relationships. The empirical results show that MRZINB model possesses most of the desirable statistical properties in terms of its ability to accommodate unobserved heterogeneity and excess zero counts in correlated data. Notably, in the random-parameters MZINB model, the estimated parameters vary significantly across intersections for different crash types. Copyright © 2014 Elsevier Ltd. All rights reserved.

Categorical speech processing in Broca's area: an fMRI study using multivariate pattern-based analysis.

PubMed

Lee, Yune-Sang; Turkeltaub, Peter; Granger, Richard; Raizada, Rajeev D S

2012-03-14

Although much effort has been directed toward understanding the neural basis of speech processing, the neural processes involved in the categorical perception of speech have been relatively less studied, and many questions remain open. In this functional magnetic resonance imaging (fMRI) study, we probed the cortical regions mediating categorical speech perception using an advanced brain-mapping technique, whole-brain multivariate pattern-based analysis (MVPA). Normal healthy human subjects (native English speakers) were scanned while they listened to 10 consonant-vowel syllables along the /ba/-/da/ continuum. Outside of the scanner, individuals' own category boundaries were measured to divide the fMRI data into /ba/ and /da/ conditions per subject. The whole-brain MVPA revealed that Broca's area and the left pre-supplementary motor area evoked distinct neural activity patterns between the two perceptual categories (/ba/ vs /da/). Broca's area was also found when the same analysis was applied to another dataset (Raizada and Poldrack, 2007), which previously yielded the supramarginal gyrus using a univariate adaptation-fMRI paradigm. The consistent MVPA findings from two independent datasets strongly indicate that Broca's area participates in categorical speech perception, with a possible role of translating speech signals into articulatory codes. The difference in results between univariate and multivariate pattern-based analyses of the same data suggest that processes in different cortical areas along the dorsal speech perception stream are distributed on different spatial scales.

Genetic association between milk yield, stayability, and mastitis in Holstein cows under tropical conditions.

PubMed

Irano, Natalia; Bignardi, Annaiza Braga; El Faro, Lenira; Santana, Mário Luiz; Cardoso, Vera Lúcia; Albuquerque, Lucia Galvão

2014-03-01

The objective of this study was to estimate genetic parameters for milk yield, stayability, and the occurrence of clinical mastitis in Holstein cows, as well as studying the genetic relationship between them, in order to provide subsidies for the genetic evaluation of these traits. Records from 5,090 Holstein cows with calving varying from 1991 to 2010, were used in the analysis. Two standard multivariate analyses were carried out, one containing the trait of accumulated 305-day milk yields in the first lactation (MY1), stayability (STAY) until the third lactation, and clinical mastitis (CM), as well as the other traits, considering accumulated 305-day milk yields (Y305), STAY, and CM, including the first three lactations as repeated measures for Y305 and CM. The covariance components were obtained by a Bayesian approach. The heritability estimates obtained by multivariate analysis with MY1 were 0.19, 0.28, and 0.13 for MY1, STAY, and CM, respectively, whereas using the multivariate analysis with the Y305, the estimates were 0.19, 0.31, and 0.14, respectively. The genetic correlations between MY1 and STAY, MY1 and CM, and STAY and CM, respectively, were 0.38, 0.12, and -0.49. The genetic correlations between Y305 and STAY, Y305 and CM, and STAY and CM, respectively, were 0.66, -0.25, and -0.52.

The role of chemometrics in single and sequential extraction assays: a review. Part II. Cluster analysis, multiple linear regression, mixture resolution, experimental design and other techniques.

PubMed

Giacomino, Agnese; Abollino, Ornella; Malandrino, Mery; Mentasti, Edoardo

2011-03-04

Single and sequential extraction procedures are used for studying element mobility and availability in solid matrices, like soils, sediments, sludge, and airborne particulate matter. In the first part of this review we reported an overview on these procedures and described the applications of chemometric uni- and bivariate techniques and of multivariate pattern recognition techniques based on variable reduction to the experimental results obtained. The second part of the review deals with the use of chemometrics not only for the visualization and interpretation of data, but also for the investigation of the effects of experimental conditions on the response, the optimization of their values and the calculation of element fractionation. We will describe the principles of the multivariate chemometric techniques considered, the aims for which they were applied and the key findings obtained. The following topics will be critically addressed: pattern recognition by cluster analysis (CA), linear discriminant analysis (LDA) and other less common techniques; modelling by multiple linear regression (MLR); investigation of spatial distribution of variables by geostatistics; calculation of fractionation patterns by a mixture resolution method (Chemometric Identification of Substrates and Element Distributions, CISED); optimization and characterization of extraction procedures by experimental design; other multivariate techniques less commonly applied. Copyright © 2010 Elsevier B.V. All rights reserved.

Multivariate Statistical Approach Applied to Sediment Source Tracking Through Quantification and Mineral Identification, Cheyenne River, South Dakota

NASA Astrophysics Data System (ADS)

Valder, J.; Kenner, S.; Long, A.

2008-12-01

Portions of the Cheyenne River are characterized as impaired by the U.S. Environmental Protection Agency because of water-quality exceedences. The Cheyenne River watershed includes the Black Hills National Forest and part of the Badlands National Park. Preliminary analysis indicates that the Badlands National Park is a major contributor to the exceedances of the water-quality constituents for total dissolved solids and total suspended solids. Water-quality data have been collected continuously since 2007, and in the second year of collection (2008), monthly grab and passive sediment samplers are being used to collect total suspended sediment and total dissolved solids in both base-flow and runoff-event conditions. In addition, sediment samples from the river channel, including bed, bank, and floodplain, have been collected. These samples are being analyzed at the South Dakota School of Mines and Technology's X-Ray Diffraction Lab to quantify the mineralogy of the sediments. A multivariate statistical approach (including principal components, least squares, and maximum likelihood techniques) is applied to the mineral percentages that were characterized for each site to identify the contributing source areas that are causing exceedances of sediment transport in the Cheyenne River watershed. Results of the multivariate analysis demonstrate the likely sources of solids found in the Cheyenne River samples. A further refinement of the methods is in progress that utilizes a conceptual model which, when applied with the multivariate statistical approach, provides a better estimate for sediment sources.

Comparative evaluation of spectroscopic models using different multivariate statistical tools in a multicancer scenario

NASA Astrophysics Data System (ADS)

Ghanate, A. D.; Kothiwale, S.; Singh, S. P.; Bertrand, Dominique; Krishna, C. Murali

2011-02-01

Cancer is now recognized as one of the major causes of morbidity and mortality. Histopathological diagnosis, the gold standard, is shown to be subjective, time consuming, prone to interobserver disagreement, and often fails to predict prognosis. Optical spectroscopic methods are being contemplated as adjuncts or alternatives to conventional cancer diagnostics. The most important aspect of these approaches is their objectivity, and multivariate statistical tools play a major role in realizing it. However, rigorous evaluation of the robustness of spectral models is a prerequisite. The utility of Raman spectroscopy in the diagnosis of cancers has been well established. Until now, the specificity and applicability of spectral models have been evaluated for specific cancer types. In this study, we have evaluated the utility of spectroscopic models representing normal and malignant tissues of the breast, cervix, colon, larynx, and oral cavity in a broader perspective, using different multivariate tests. The limit test, which was used in our earlier study, gave high sensitivity but suffered from poor specificity. The performance of other methods such as factorial discriminant analysis and partial least square discriminant analysis are at par with more complex nonlinear methods such as decision trees, but they provide very little information about the classification model. This comparative study thus demonstrates not just the efficacy of Raman spectroscopic models but also the applicability and limitations of different multivariate tools for discrimination under complex conditions such as the multicancer scenario.

Conditional survival estimates improve over time for patients with advanced melanoma: results from a population-based analysis.

PubMed

Xing, Yan; Chang, George J; Hu, Chung-Yuan; Askew, Robert L; Ross, Merrick I; Gershenwald, Jeffrey E; Lee, Jeffrey E; Mansfield, Paul F; Lucci, Anthony; Cormier, Janice N

2010-05-01

Conditional survival (CS) has emerged as a clinically relevant measure of prognosis for cancer survivors. The objective of this analysis was to provide melanoma-specific CS estimates to help clinicians promote more informed patient decision making. Patients with melanoma and at least 5 years of follow-up were identified from the Surveillance Epidemiology and End Results registry (1988-2000). By using the methods of Kaplan and Meier, stage-specific, 5-year CS estimates were independently calculated for survivors for each year after diagnosis. Stage-specific multivariate Cox regression models including baseline survivor functions were used to calculate adjusted melanoma-specific CS for different subgroups of patients further stratified by age, gender, race, marital status, anatomic tumor location, and tumor histology. Five-year CS estimates for patients with stage I disease remained constant at 97% annually, while for patients with stages II, III, and IV disease, 5-year CS estimates from time 0 (diagnosis) to 5 years improved from 72% to 86%, 51% to 87%, and 19% to 84%, respectively. Multivariate CS analysis revealed that differences in stages II through IV CS based on age, gender, and race decreased over time. Five-year melanoma-specific CS estimates improve dramatically over time for survivors with advanced stages of disease. These prognostic data are critical to patients for both treatment and nontreatment related life decisions. (c) 2010 American Cancer Society.

The microbiological profile and presence of bloodstream infection influence mortality rates in necrotizing fasciitis

PubMed Central

2011-01-01

Introduction Necrotizing fasciitis (NF) is a life threatening infectious disease with a high mortality rate. We carried out a microbiological characterization of the causative pathogens. We investigated the correlation of mortality in NF with bloodstream infection and with the presence of co-morbidities. Methods In this retrospective study, we analyzed 323 patients who presented with necrotizing fasciitis at two different institutions. Bloodstream infection (BSI) was defined as a positive blood culture result. The patients were categorized as survivors and non-survivors. Eleven clinically important variables which were statistically significant by univariate analysis were selected for multivariate regression analysis and a stepwise logistic regression model was developed to determine the association between BSI and mortality. Results Univariate logistic regression analysis showed that patients with hypotension, heart disease, liver disease, presence of Vibrio spp. in wound cultures, presence of fungus in wound cultures, and presence of Streptococcus group A, Aeromonas spp. or Vibrio spp. in blood cultures, had a significantly higher risk of in-hospital mortality. Our multivariate logistic regression analysis showed a higher risk of mortality in patients with pre-existing conditions like hypotension, heart disease, and liver disease. Multivariate logistic regression analysis also showed that presence of Vibrio spp in wound cultures, and presence of Streptococcus Group A in blood cultures were associated with a high risk of mortality while debridement > = 3 was associated with improved survival. Conclusions Mortality in patients with necrotizing fasciitis was significantly associated with the presence of Vibrio in wound cultures and Streptococcus group A in blood cultures. PMID:21693053

"L"-Bivariate and "L"-Multivariate Association Coefficients. Research Report. ETS RR-08-40

ERIC Educational Resources Information Center

Kong, Nan; Lewis, Charles

2008-01-01

Given a system of multiple random variables, a new measure called the "L"-multivariate association coefficient is defined using (conditional) entropy. Unlike traditional correlation measures, the L-multivariate association coefficient measures the multiassociations or multirelations among the multiple variables in the given system; that…

Multivariate Regression Analysis and Slaughter Livestock,

DTIC Science & Technology

AGRICULTURE, *ECONOMICS), (*MEAT, PRODUCTION), MULTIVARIATE ANALYSIS, REGRESSION ANALYSIS , ANIMALS, WEIGHT, COSTS, PREDICTIONS, STABILITY, MATHEMATICAL MODELS, STORAGE, BEEF, PORK, FOOD, STATISTICAL DATA, ACCURACY

Self-injurious behavior among Greek male prisoners: prevalence and risk factors.

PubMed

Sakelliadis, E I; Papadodima, S A; Sergentanis, T N; Giotakos, O; Spiliopoulou, C A

2010-04-01

Self-harm among prisoners is a common phenomenon. This study aims to estimate the prevalence of self-injurious behavior (SIB) among Greek male prisoners, record their motives and determine independent risk factors. A self-administered, anonymous questionnaire was administered to 173 male prisoners in the Chalkida prison, Greece. The questionnaire included items on self-harm/SIB, demographic parameters, childhood history, family history, physical and mental disease, lifestyle and smoking habits, alcohol dependence (CAGE questionnaire), illicit substance use, aggression (Buss-Perry Aggression Questionnaire [BPAQ] and Lifetime History of Aggression [LTHA]), impulsivity (Barrat Impulsivity Scale-11) and suicidal ideation (Spectrum of Suicidal Behavior Scale). Univariate nonparametric statistics and multivariate ordinal logistic regression were performed. Of all the participants, 49.4% (95% CI: 41.5-57.3%) disclosed self-harm (direct or indirect). The prevalence of SIB was equal to 34.8% (95% CI: 27.5-42.6%). Most frequently, SIB coexisted with indirect self-harm (80.7%). The most common underlying motives were to obtain emotional release (31.6%) and to release anger (21.1%). At the univariate analysis, SIB was positively associated with a host of closely related factors: low education, physical/sexual abuse in childhood, parental neglect, parental divorce, alcoholism in family, psychiatric condition in family, recidivism, age, sentence already served, impulsivity, aggression, alcohol dependence, self-reported diagnosed psychiatric condition and illicit substance use. Childhood variables were particularly associated with the presence of diagnosed psychiatric condition. At the multivariate analysis, however, only three parameters were proven independent risk factors: self-reported diagnosed psychiatric condition, illicit substance use and aggression (BPAQ scale). The prevalence of SIB is particularly high. Psychiatric condition, illicit substance use and aggression seem to be the most meaningful risk factors; childhood events seem only to act indirectly. Copyright (c) 2009 Elsevier Masson SAS. All rights reserved.

Multivariate Generalizations of Student's t-Distribution. ONR Technical Report. [Biometric Lab Report No. 90-3.

ERIC Educational Resources Information Center

Gibbons, Robert D.; And Others

In the process of developing a conditionally-dependent item response theory (IRT) model, the problem arose of modeling an underlying multivariate normal (MVN) response process with general correlation among the items. Without the assumption of conditional independence, for which the underlying MVN cdf takes on comparatively simple forms and can be…

Modeling the predictive value of pain intensity on costs and resources utilization in patients with peripheral neuropathic pain.

PubMed

Pérez, Concepción; Navarro, Ana; Saldaña, María T; Wilson, Koo; Rejas, Javier

2015-03-01

The aim of the present analysis was to model the association and predictive value of pain intensity on cost and resource utilization in patients with chronic peripheral neuropathic pain (PNP) treated in routine clinical practice settings in Spain. We performed a secondary economic analysis based on data from a multicenter, observational, and prospective cost-of-illness study in patients with chronic PNP that is refractory to prior treatment. Pain intensity was measured using the Short-Form McGill Pain Questionnaire. Univariate and multivariate linear regression models were fitted to identify independent predictors of cost and health care/non-health care resource utilization. A total of 1703 patients were included in the current analysis. Pain intensity was an independent predictor of total costs ([total costs]=35.6 [pain intensity]+214.5; coefficient of determination [R(2)]=0.19, P<0.001), direct costs ([direct costs]=10.8 [pain intensity]+257.7; R=0.06, P<0.001), and indirect costs ([indirect costs]=24.8 [pain intensity]-43.4; R(2)=0.20, P<0.001) related to chronic PNP in the univariate analysis. Pain intensity remains significantly associated with total costs, direct costs, and indirect costs after adjustment by other covariates in the multivariate analysis (P<0.001). None of the other variables considered in the multivariate analysis were predictors of resource utilization. Pain intensity predicts the health care and non-health care resource utilization, and costs related to chronic PNP. Management of patients with drugs associated with a higher reduction of pain intensity may have a greater impact on the economic burden of that condition.

Multivariate calibration in Laser-Induced Breakdown Spectroscopy quantitative analysis: The dangers of a 'black box' approach and how to avoid them

NASA Astrophysics Data System (ADS)

Safi, A.; Campanella, B.; Grifoni, E.; Legnaioli, S.; Lorenzetti, G.; Pagnotta, S.; Poggialini, F.; Ripoll-Seguer, L.; Hidalgo, M.; Palleschi, V.

2018-06-01

The introduction of multivariate calibration curve approach in Laser-Induced Breakdown Spectroscopy (LIBS) quantitative analysis has led to a general improvement of the LIBS analytical performances, since a multivariate approach allows to exploit the redundancy of elemental information that are typically present in a LIBS spectrum. Software packages implementing multivariate methods are available in the most diffused commercial and open source analytical programs; in most of the cases, the multivariate algorithms are robust against noise and operate in unsupervised mode. The reverse of the coin of the availability and ease of use of such packages is the (perceived) difficulty in assessing the reliability of the results obtained which often leads to the consideration of the multivariate algorithms as 'black boxes' whose inner mechanism is supposed to remain hidden to the user. In this paper, we will discuss the dangers of a 'black box' approach in LIBS multivariate analysis, and will discuss how to overcome them using the chemical-physical knowledge that is at the base of any LIBS quantitative analysis.

PROM and Labour Effects on Urinary Metabolome: A Pilot Study

PubMed Central

Meloni, Alessandra; Palmas, Francesco; Mereu, Rossella; Deiana, Sara Francesca; Fais, Maria Francesca; Mussap, Michele; Ragusa, Antonio; Pintus, Roberta; Fanos, Vassilios; Melis, Gian Benedetto

2018-01-01

Since pathologies and complications occurring during pregnancy and/or during labour may cause adverse outcomes for both newborns and mothers, there is a growing interest in metabolomic applications on pregnancy investigation. In fact, metabolomics has proved to be an efficient strategy for the description of several perinatal conditions. In particular, this study focuses on premature rupture of membranes (PROM) in pregnancy at term. For this project, urine samples were collected at three different clinical conditions: out of labour before PROM occurrence (Ph1), out of labour with PROM (Ph2), and during labour with PROM (Ph3). GC-MS analysis, followed by univariate and multivariate statistical analysis, was able to discriminate among the different classes, highlighting the metabolites most involved in the discrimination. PMID:29511388

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.

Multivariate Cluster Analysis.

ERIC Educational Resources Information Center

McRae, Douglas J.

Procedures for grouping students into homogeneous subsets have long interested educational researchers. The research reported in this paper is an investigation of a set of objective grouping procedures based on multivariate analysis considerations. Four multivariate functions that might serve as criteria for adequate grouping are given and…

Multivariate statistical analysis strategy for multiple misfire detection in internal combustion engines

NASA Astrophysics Data System (ADS)

Hu, Chongqing; Li, Aihua; Zhao, Xingyang

2011-02-01

This paper proposes a multivariate statistical analysis approach to processing the instantaneous engine speed signal for the purpose of locating multiple misfire events in internal combustion engines. The state of each cylinder is described with a characteristic vector extracted from the instantaneous engine speed signal following a three-step procedure. These characteristic vectors are considered as the values of various procedure parameters of an engine cycle. Therefore, determination of occurrence of misfire events and identification of misfiring cylinders can be accomplished by a principal component analysis (PCA) based pattern recognition methodology. The proposed algorithm can be implemented easily in practice because the threshold can be defined adaptively without the information of operating conditions. Besides, the effect of torsional vibration on the engine speed waveform is interpreted as the presence of super powerful cylinder, which is also isolated by the algorithm. The misfiring cylinder and the super powerful cylinder are often adjacent in the firing sequence, thus missing detections and false alarms can be avoided effectively by checking the relationship between the cylinders.

Multivariate Analysis for Quantification of Plutonium(IV) in Nitric Acid Based on Absorption Spectra

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

Lines, Amanda M.; Adami, Susan R.; Sinkov, Sergey I.

Development of more effective, reliable, and fast methods for monitoring process streams is a growing opportunity for analytical applications. Many fields can benefit from on-line monitoring, including the nuclear fuel cycle where improved methods for monitoring radioactive materials will facilitate maintenance of proper safeguards and ensure safe and efficient processing of materials. On-line process monitoring with a focus on optical spectroscopy can provide a fast, non-destructive method for monitoring chemical species. However, identification and quantification of species can be hindered by the complexity of the solutions if bands overlap or show condition-dependent spectral features. Plutonium (IV) is one example ofmore » a species which displays significant spectral variation with changing nitric acid concentration. Single variate analysis (i.e. Beer’s Law) is difficult to apply to the quantification of Pu(IV) unless the nitric acid concentration is known and separate calibration curves have been made for all possible acid strengths. Multivariate, or chemometric, analysis is an approach that allows for the accurate quantification of Pu(IV) without a priori knowledge of nitric acid concentration.« less

Controlled pattern imputation for sensitivity analysis of longitudinal binary and ordinal outcomes with nonignorable dropout.

PubMed

Tang, Yongqiang

2018-04-30

The controlled imputation method refers to a class of pattern mixture models that have been commonly used as sensitivity analyses of longitudinal clinical trials with nonignorable dropout in recent years. These pattern mixture models assume that participants in the experimental arm after dropout have similar response profiles to the control participants or have worse outcomes than otherwise similar participants who remain on the experimental treatment. In spite of its popularity, the controlled imputation has not been formally developed for longitudinal binary and ordinal outcomes partially due to the lack of a natural multivariate distribution for such endpoints. In this paper, we propose 2 approaches for implementing the controlled imputation for binary and ordinal data based respectively on the sequential logistic regression and the multivariate probit model. Efficient Markov chain Monte Carlo algorithms are developed for missing data imputation by using the monotone data augmentation technique for the sequential logistic regression and a parameter-expanded monotone data augmentation scheme for the multivariate probit model. We assess the performance of the proposed procedures by simulation and the analysis of a schizophrenia clinical trial and compare them with the fully conditional specification, last observation carried forward, and baseline observation carried forward imputation methods. Copyright © 2018 John Wiley & Sons, Ltd.

Incidence of retinopathy of prematurity in the United States: 1997 through 2005.

PubMed

Lad, Eleonora M; Hernandez-Boussard, Tina; Morton, John M; Moshfeghi, Darius M

2009-09-01

To determine the incidence of retinopathy of prematurity (ROP) based on a national database and to identify baseline characteristics, demographic information, comorbidities, and surgical interventions. Retrospective study based on the National Inpatient Sample from 1997 through 2005. The National Inpatient Sample was queried for all newborn infants with and without ROP. Multivariate logistic regression was used to predict risk factors for ROP. Thirty-four million live births were recorded during the study period. The total ROP incidence was 0.17% overall and 15.58% for premature infants with length of stay of more than 28 days. Our results conclusively demonstrated the importance of low birth weight as a risk for ROP development in infants with length of stay of more than 28 days, as well as association with respiratory conditions, fetal hemorrhage, intraventricular hemorrhage, and blood transfer. An interesting finding was the protective effect conferred by hypoxia, necrotizing enterocolitis, and hemolytic disease of the newborn. Infants with ROP had a higher incidence of undergoing laser photocoagulation therapy, pars plana vitrectomy, and scleral buckle surgery. The current study represents a large, retrospective analysis of newborns with ROP. The multivariate analysis emphasizes the role of birth weight in extended-stay infants, as well as respiratory conditions, fetal hemorrhage, intraventricular hemorrhage, and blood transfer.

Fault Detection and Diagnosis In Hall-Héroult Cells Based on Individual Anode Current Measurements Using Dynamic Kernel PCA

NASA Astrophysics Data System (ADS)

Yao, Yuchen; Bao, Jie; Skyllas-Kazacos, Maria; Welch, Barry J.; Akhmetov, Sergey

2018-04-01

Individual anode current signals in aluminum reduction cells provide localized cell conditions in the vicinity of each anode, which contain more information than the conventionally measured cell voltage and line current. One common use of this measurement is to identify process faults that can cause significant changes in the anode current signals. While this method is simple and direct, it ignores the interactions between anode currents and other important process variables. This paper presents an approach that applies multivariate statistical analysis techniques to individual anode currents and other process operating data, for the detection and diagnosis of local process abnormalities in aluminum reduction cells. Specifically, since the Hall-Héroult process is time-varying with its process variables dynamically and nonlinearly correlated, dynamic kernel principal component analysis with moving windows is used. The cell is discretized into a number of subsystems, with each subsystem representing one anode and cell conditions in its vicinity. The fault associated with each subsystem is identified based on multivariate statistical control charts. The results show that the proposed approach is able to not only effectively pinpoint the problematic areas in the cell, but also assess the effect of the fault on different parts of the cell.

Creation and validation of a novel body condition scoring method for the magellanic penguin (Spheniscus magellanicus) in the zoo setting.

PubMed

Clements, Julie; Sanchez, Jessica N

2015-11-01

This research aims to validate a novel, visual body scoring system created for the Magellanic penguin (Spheniscus magellanicus) suitable for the zoo practitioner. Magellanics go through marked seasonal fluctuations in body mass gains and losses. A standardized multi-variable visual body condition guide may provide a more sensitive and objective assessment tool compared to the previously used single variable method. Accurate body condition scores paired with seasonal weight variation measurements give veterinary and keeper staff a clearer understanding of an individual's nutritional status. San Francisco Zoo staff previously used a nine-point body condition scale based on the classic bird standard of a single point of keel palpation with the bird restrained in hand, with no standard measure of reference assigned to each scoring category. We created a novel, visual body condition scoring system that does not require restraint to assesses subcutaneous fat and muscle at seven body landmarks using illustrations and descriptive terms. The scores range from one, the least robust or under-conditioned, to five, the most robust, or over-conditioned. The ratio of body weight to wing length was used as a "gold standard" index of body condition and compared to both the novel multi-variable and previously used single-variable body condition scores. The novel multi-variable scale showed improved agreement with weight:wing ratio compared to the single-variable scale, demonstrating greater accuracy, and reliability when a trained assessor uses the multi-variable body condition scoring system. Zoo staff may use this tool to manage both the colony and the individual to assist in seasonally appropriate Magellanic penguin nutrition assessment. © 2015 Wiley Periodicals, Inc.

Performance characteristics of LOX-H2, tangential-entry, swirl-coaxial, rocket injectors

NASA Technical Reports Server (NTRS)

Howell, Doug; Petersen, Eric; Clark, Jim

1993-01-01

Development of a high performing swirl-coaxial injector requires an understanding of fundamental performance characteristics. This paper addresses the findings of studies on cold flow atomic characterizations which provided information on the influence of fluid properties and element operating conditions on the produced droplet sprays. These findings are applied to actual rocket conditions. The performance characteristics of swirl-coaxial injection elements under multi-element hot-fire conditions were obtained by analysis of combustion performance data from three separate test series. The injection elements are described and test results are analyzed using multi-variable linear regression. A direct comparison of test results indicated that reduced fuel injection velocity improved injection element performance through improved propellant mixing.

Comparative forensic soil analysis of New Jersey state parks using a combination of simple techniques with multivariate statistics.

PubMed

Bonetti, Jennifer; Quarino, Lawrence

2014-05-01

This study has shown that the combination of simple techniques with the use of multivariate statistics offers the potential for the comparative analysis of soil samples. Five samples were obtained from each of twelve state parks across New Jersey in both the summer and fall seasons. Each sample was examined using particle-size distribution, pH analysis in both water and 1 M CaCl2 , and a loss on ignition technique. Data from each of the techniques were combined, and principal component analysis (PCA) and canonical discriminant analysis (CDA) were used for multivariate data transformation. Samples from different locations could be visually differentiated from one another using these multivariate plots. Hold-one-out cross-validation analysis showed error rates as low as 3.33%. Ten blind study samples were analyzed resulting in no misclassifications using Mahalanobis distance calculations and visual examinations of multivariate plots. Seasonal variation was minimal between corresponding samples, suggesting potential success in forensic applications. © 2014 American Academy of Forensic Sciences.

Quantifying the impact of between-study heterogeneity in multivariate meta-analyses

PubMed Central

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

2012-01-01

Measures that quantify the impact of heterogeneity in univariate meta-analysis, including the very popular I2 statistic, are now well established. Multivariate meta-analysis, where studies provide multiple outcomes that are pooled in a single analysis, is also becoming more commonly used. The question of how to quantify heterogeneity in the multivariate setting is therefore raised. It is the univariate R2 statistic, the ratio of the variance of the estimated treatment effect under the random and fixed effects models, that generalises most naturally, so this statistic provides our basis. This statistic is then used to derive a multivariate analogue of I2, which we call . We also provide a multivariate H2 statistic, the ratio of a generalisation of Cochran's heterogeneity statistic and its associated degrees of freedom, with an accompanying generalisation of the usual I2 statistic, . Our proposed heterogeneity statistics can be used alongside all the usual estimates and inferential procedures used in multivariate meta-analysis. We apply our methods to some real datasets and show how our statistics are equally appropriate in the context of multivariate meta-regression, where study level covariate effects are included in the model. Our heterogeneity statistics may be used when applying any procedure for fitting the multivariate random effects model. Copyright © 2012 John Wiley & Sons, Ltd. PMID:22763950

Analyzing Multiple Outcomes in Clinical Research Using Multivariate Multilevel Models

PubMed Central

Baldwin, Scott A.; Imel, Zac E.; Braithwaite, Scott R.; Atkins, David C.

2014-01-01

Objective Multilevel models have become a standard data analysis approach in intervention research. Although the vast majority of intervention studies involve multiple outcome measures, few studies use multivariate analysis methods. The authors discuss multivariate extensions to the multilevel model that can be used by psychotherapy researchers. Method and Results Using simulated longitudinal treatment data, the authors show how multivariate models extend common univariate growth models and how the multivariate model can be used to examine multivariate hypotheses involving fixed effects (e.g., does the size of the treatment effect differ across outcomes?) and random effects (e.g., is change in one outcome related to change in the other?). An online supplemental appendix provides annotated computer code and simulated example data for implementing a multivariate model. Conclusions Multivariate multilevel models are flexible, powerful models that can enhance clinical research. PMID:24491071

Differentiating organically and conventionally grown oregano using ultraperformance liquid chromatography mass spectrometry (UPLC-MS), headspace gas chromatography with flame ionization detection (headspace-GC-FID), and flow injection mass spectrum (FIMS) fingerprints combined with multivariate data analysis.

PubMed

Gao, Boyan; Qin, Fang; Ding, Tingting; Chen, Yineng; Lu, Weiying; Yu, Liangli Lucy

2014-08-13

Ultraperformance liquid chromatography mass spectrometry (UPLC-MS), flow injection mass spectrometry (FIMS), and headspace gas chromatography (headspace-GC) combined with multivariate data analysis techniques were examined and compared in differentiating organically grown oregano from that grown conventionally. It is the first time that headspace-GC fingerprinting technology is reported in differentiating organically and conventionally grown spice samples. The results also indicated that UPLC-MS, FIMS, and headspace-GC-FID fingerprints with OPLS-DA were able to effectively distinguish oreganos under different growing conditions, whereas with PCA, only FIMS fingerprint could differentiate the organically and conventionally grown oregano samples. UPLC fingerprinting provided detailed information about the chemical composition of oregano with a longer analysis time, whereas FIMS finished a sample analysis within 1 min. On the other hand, headspace GC-FID fingerprinting required no sample pretreatment, suggesting its potential as a high-throughput method in distinguishing organically and conventionally grown oregano samples. In addition, chemical components in oregano were identified by their molecular weight using QTOF-MS and headspace-GC-MS.

Combining fibre optic Raman spectroscopy and tactile resonance measurement for tissue characterization

NASA Astrophysics Data System (ADS)

Candefjord, Stefan; Nyberg, Morgan; Jalkanen, Ville; Ramser, Kerstin; Lindahl, Olof A.

2010-12-01

Tissue characterization is fundamental for identification of pathological conditions. Raman spectroscopy (RS) and tactile resonance measurement (TRM) are two promising techniques that measure biochemical content and stiffness, respectively. They have potential to complement the golden standard--histological analysis. By combining RS and TRM, complementary information about tissue content can be obtained and specific drawbacks can be avoided. The aim of this study was to develop a multivariate approach to compare RS and TRM information. The approach was evaluated on measurements at the same points on porcine abdominal tissue. The measurement points were divided into five groups by multivariate analysis of the RS data. A regression analysis was performed and receiver operating characteristic (ROC) curves were used to compare the RS and TRM data. TRM identified one group efficiently (area under ROC curve 0.99). The RS data showed that the proportion of saturated fat was high in this group. The regression analysis showed that stiffness was mainly determined by the amount of fat and its composition. We concluded that RS provided additional, important information for tissue identification that was not provided by TRM alone. The results are promising for development of a method combining RS and TRM for intraoperative tissue characterization.

Regression analysis for LED color detection of visual-MIMO system

NASA Astrophysics Data System (ADS)

Banik, Partha Pratim; Saha, Rappy; Kim, Ki-Doo

2018-04-01

Color detection from a light emitting diode (LED) array using a smartphone camera is very difficult in a visual multiple-input multiple-output (visual-MIMO) system. In this paper, we propose a method to determine the LED color using a smartphone camera by applying regression analysis. We employ a multivariate regression model to identify the LED color. After taking a picture of an LED array, we select the LED array region, and detect the LED using an image processing algorithm. We then apply the k-means clustering algorithm to determine the number of potential colors for feature extraction of each LED. Finally, we apply the multivariate regression model to predict the color of the transmitted LEDs. In this paper, we show our results for three types of environmental light condition: room environmental light, low environmental light (560 lux), and strong environmental light (2450 lux). We compare the results of our proposed algorithm from the analysis of training and test R-Square (%) values, percentage of closeness of transmitted and predicted colors, and we also mention about the number of distorted test data points from the analysis of distortion bar graph in CIE1931 color space.

Multivariate Optimization for Extraction of Pyrethroids in Milk and Validation for GC-ECD and CG-MS/MS Analysis

PubMed Central

Zanchetti Meneghini, Leonardo; Rübensam, Gabriel; Claudino Bica, Vinicius; Ceccon, Amanda; Barreto, Fabiano; Flores Ferrão, Marco; Bergold, Ana Maria

2014-01-01

A simple and inexpensive method based on solvent extraction followed by low temperature clean-up was applied for determination of seven pyrethroids residues in bovine raw milk using gas chromatography coupled to tandem mass spectrometry (GC-MS/MS) and gas chromatography with electron-capture detector (GC-ECD). Sample extraction procedure was established through the evaluation of seven different extraction protocols, evaluated in terms of analyte recovery and cleanup efficiency. Sample preparation optimization was based on Doehlert design using fifteen runs with three different variables. Response surface methodologies and polynomial analysis were used to define the best extraction conditions. Method validation was carried out based on SANCO guide parameters and assessed by multivariate analysis. Method performance was considered satisfactory since mean recoveries were between 87% and 101% for three distinct concentrations. Accuracy and precision were lower than ±20%, and led to no significant differences (p < 0.05) between results obtained by GC-ECD and GC-MS/MS techniques. The method has been applied to routine analysis for determination of pyrethroid residues in bovine raw milk in the Brazilian National Residue Control Plan since 2013, in which a total of 50 samples were analyzed. PMID:25380457

Atmospheric conditions, lunar phases, and childbirth: a multivariate analysis

NASA Astrophysics Data System (ADS)

Ochiai, Angela Megumi; Gonçalves, Fabio Luiz Teixeira; Ambrizzi, Tercio; Florentino, Lucia Cristina; Wei, Chang Yi; Soares, Alda Valeria Neves; De Araujo, Natalucia Matos; Gualda, Dulce Maria Rosa

2012-07-01

Our objective was to assess extrinsic influences upon childbirth. In a cohort of 1,826 days containing 17,417 childbirths among them 13,252 spontaneous labor admissions, we studied the influence of environment upon the high incidence of labor (defined by 75th percentile or higher), analyzed by logistic regression. The predictors of high labor admission included increases in outdoor temperature (odds ratio: 1.742, P = 0.045, 95%CI: 1.011 to 3.001), and decreases in atmospheric pressure (odds ratio: 1.269, P = 0.029, 95%CI: 1.055 to 1.483). In contrast, increases in tidal range were associated with a lower probability of high admission (odds ratio: 0.762, P = 0.030, 95%CI: 0.515 to 0.999). Lunar phase was not a predictor of high labor admission ( P = 0.339). Using multivariate analysis, increases in temperature and decreases in atmospheric pressure predicted high labor admission, and increases of tidal range, as a measurement of the lunar gravitational force, predicted a lower probability of high admission.

Geographically Sourcing Cocaine’s Origin – Delineation of the Nineteen Major Coca Growing Regions in South America

PubMed Central

Mallette, Jennifer R.; Casale, John F.; Jordan, James; Morello, David R.; Beyer, Paul M.

2016-01-01

Previously, geo-sourcing to five major coca growing regions within South America was accomplished. However, the expansion of coca cultivation throughout South America made sub-regional origin determinations increasingly difficult. The former methodology was recently enhanced with additional stable isotope analyses (2H and 18O) to fully characterize cocaine due to the varying environmental conditions in which the coca was grown. An improved data analysis method was implemented with the combination of machine learning and multivariate statistical analysis methods to provide further partitioning between growing regions. Here, we show how the combination of trace cocaine alkaloids, stable isotopes, and multivariate statistical analyses can be used to classify illicit cocaine as originating from one of 19 growing regions within South America. The data obtained through this approach can be used to describe current coca cultivation and production trends, highlight trafficking routes, as well as identify new coca growing regions. PMID:27006288

Implementation of the Iterative Proportion Fitting Algorithm for Geostatistical Facies Modeling

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

Li Yupeng, E-mail: yupeng@ualberta.ca; Deutsch, Clayton V.

2012-06-15

In geostatistics, most stochastic algorithm for simulation of categorical variables such as facies or rock types require a conditional probability distribution. The multivariate probability distribution of all the grouped locations including the unsampled location permits calculation of the conditional probability directly based on its definition. In this article, the iterative proportion fitting (IPF) algorithm is implemented to infer this multivariate probability. Using the IPF algorithm, the multivariate probability is obtained by iterative modification to an initial estimated multivariate probability using lower order bivariate probabilities as constraints. The imposed bivariate marginal probabilities are inferred from profiles along drill holes or wells.more » In the IPF process, a sparse matrix is used to calculate the marginal probabilities from the multivariate probability, which makes the iterative fitting more tractable and practical. This algorithm can be extended to higher order marginal probability constraints as used in multiple point statistics. The theoretical framework is developed and illustrated with estimation and simulation example.« less

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

Cullen, David A; Koestner, Roland; Kukreja, Ratan

Improved conditions for imaging and spectroscopic mapping of thin perfluorosulfonic acid (PFSA) ionomer layers in fuel cell electrodes by scanning transmission electron microscopy (STEM) have been investigated. These conditions are first identified on model systems of Nafion ionomer-coated nanostructured thin films and nanoporous Si. The optimized conditions are then applied in a quantitative study of the ionomer through-layer loading for two typical electrode catalyst coatings using electron energy loss and energy dispersive X-ray spectroscopy in the transmission electron microscope. The e-beam induced damage to the perfluorosulfonic acid (PFSA) ionomer is quantified by following the fluorine mass loss with electron exposuremore » and is then mitigated by a few orders of magnitude using cryogenic specimen cooling and a higher incident electron voltage. Multivariate statistical analysis is also applied to the analysis of spectrum images for data denoising and unbiased separation of independent components related to the catalyst, ionomer, and support.« less

Stability Study of Algerian Nigella sativa Seeds Stored under Different Conditions

PubMed Central

Ahamad Bustamam, Muhammad Safwan; Hadithon, Kamarul Arifin; Rukayadi, Yaya; Lajis, Nordin

2017-01-01

In a study to determine the stability of the main volatile constituents of Nigella sativa seeds stored under several conditions, eight storage conditions were established, based on the ecological abiotic effects of air, heat, and light. Six replicates each were prepared and analyzed with Headspace-Gas Chromatography-Mass Spectrometry (HS-GC-MS) for three time points at the initial (1st day (0)), 14th (1), and 28th (2) day of storage. A targeted multivariate analysis of Principal Component Analysis revealed that the stability of the main volatile constituents of the whole seeds was better than that of the ground seeds. Exposed seeds, whole or ground, were observed to experience higher decrement of the volatile composition. These ecofactors of air, heat, and light are suggested to be directly responsible for the loss of volatiles in N. sativa seeds, particularly of the ground seeds. PMID:28255502

A Model-Based Analysis of Chemical and Temporal Patterns of Cuticular Hydrocarbons in Male Drosophila melanogaster

PubMed Central

Kent, Clement; Azanchi, Reza; Smith, Ben; Chu, Adrienne; Levine, Joel

2007-01-01

Drosophila Cuticular Hydrocarbons (CH) influence courtship behaviour, mating, aggregation, oviposition, and resistance to desiccation. We measured levels of 24 different CH compounds of individual male D. melanogaster hourly under a variety of environmental (LD/DD) conditions. Using a model-based analysis of CH variation, we developed an improved normalization method for CH data, and show that CH compounds have reproducible cyclic within-day temporal patterns of expression which differ between LD and DD conditions. Multivariate clustering of expression patterns identified 5 clusters of co-expressed compounds with common chemical characteristics. Turnover rate estimates suggest CH production may be a significant metabolic cost. Male cuticular hydrocarbon expression is a dynamic trait influenced by light and time of day; since abundant hydrocarbons affect male sexual behavior, males may present different pheromonal profiles at different times and under different conditions. PMID:17896002

Influence of shifting cultivation practices on soil-plant-beetle interactions.

PubMed

Ibrahim, Kalibulla Syed; Momin, Marcy D; Lalrotluanga, R; Rosangliana, David; Ghatak, Souvik; Zothansanga, R; Kumar, Nachimuthu Senthil; Gurusubramanian, Guruswami

2016-08-01

Shifting cultivation (jhum) is a major land use practice in Mizoram. It was considered as an eco-friendly and efficient method when the cycle duration was long (15-30 years), but it poses the problem of land degradation and threat to ecology when shortened (4-5 years) due to increased intensification of farming systems. Studying beetle community structure is very helpful in understanding how shifting cultivation affects the biodiversity features compared to natural forest system. The present study examines the beetle species diversity and estimates the effects of shifting cultivation practices on the beetle assemblages in relation to change in tree species composition and soil nutrients. Scarabaeidae and Carabidae were observed to be the dominant families in the land use systems studied. Shifting cultivation practice significantly (P < 0.05) affected the beetle and tree species diversity as well as the soil nutrients as shown by univariate (one-way analysis of variance (ANOVA), correlation and regression, diversity indices) and multivariate (cluster analysis, principal component analysis (PCA), detrended correspondence analysis (DCA), canonical variate analysis (CVA), permutational multivariate analysis of variance (PERMANOVA), permutational multivariate analysis of dispersion (PERMDISP)) statistical analyses. Besides changing the tree species composition and affecting the soil fertility, shifting cultivation provides less suitable habitat conditions for the beetle species. Bioindicator analysis categorized the beetle species into forest specialists, anthropogenic specialists (shifting cultivation habitat specialist), and habitat generalists. Molecular analysis of bioindicator beetle species was done using mitochondrial cytochrome oxidase subunit I (COI) marker to validate the beetle species and describe genetic variation among them in relation to heterogeneity, transition/transversion bias, codon usage bias, evolutionary distance, and substitution pattern. The present study revealed the fact that shifting cultivation practice significantly affects the beetle species in terms of biodiversity pattern as well as evolutionary features. Spatiotemporal assessment of soil-plant-beetle interactions in shifting cultivation system and their influence in land degradation and ecology will be helpful in making biodiversity conservation decisions in the near future.

Determinants of Adiponectin Levels in Patients with Chronic Systolic Heart Failure

PubMed Central

Biolo, Andreia; Shibata, Rei; Ouchi, Noriyuki; Kihara, Shinji; Sonoda, Mina; Walsh, Kenneth; Sam, Flora

2010-01-01

Adiponectin, an adipocytokine, is secreted by adipocytes and mediates anti-hypertrophic and anti-inflammatory effects in the heart. Plasma concentrations of adiponectin are decreased in obesity, insulin resistance and obesity-associated conditions such as hypertension and coronary heart disease. However, a paradoxical increase in adiponectin levels is observed in human systolic heart failure (HF). We sought to investigate the determinants of adiponectin levels in patients with chronic systolic HF. Total adiponectin levels were measured in 99 patients with stable HF and left ventricular (LV) ejection fraction (EF) <40%. Determinants of adiponectin levels by univariate analysis were included in a multivariate linear regression model. At baseline patients were 62% black, 63% male, mean age of 60±13 years, LVEF of 21±9% and a body mass index (BMI) of 30.6±6.7kg/m2. Mean adiponectin levels were 15.8±15µg/ml. Beta-blocker use, BMI, and blood urea nitrogen (BUN) were significant determinants of adiponectin levels by multivariate analysis. LV mass, structure, and LVEF were not related to adiponectin levels by multivariate analysis. Interestingly, the effect of beta-blocker therapy was most marked in non-obese patients with BMI < 30kg/m2. In conclusion, in chronic systolic HF patients, beta-blocker therapy is correlated with lower adiponectin levels, especially in non-obese patients. This relation should be taken into account when studying the complex role of adiponectin in chronic systolic HF. PMID:20381668

Characterization of regional cold-hydrothermal inflows enriched in arsenic and associated trace-elements in the southern part of the Duero Basin (Spain), by multivariate statistical analysis.

PubMed

Giménez-Forcada, Elena; Vega-Alegre, Marisol; Timón-Sánchez, Susana

2017-09-01

Naturally occurring arsenic in groundwater exceeding the limit for potability has been reported along the southern edge of the Cenozoic Duero Basin (CDB) near its contact with the Spanish Central System (SCS). In this area, spatial variability of arsenic is high, peaking at 241μg/L. Forty-seven percent of samples collected contained arsenic above the maximum allowable concentration for drinking water (10μg/L). Correlations of As with other hydrochemical variables were investigated using multivariate statistical analysis (Hierarchical Cluster Analysis, HCA and Principal Component Analysis, PCA). It was found that As, V, Cr and pH are closely related and that there were also close correlations with temperature and Na + . The highest concentrations of arsenic and other associated Potentially Toxic Geogenic Trace Elements (PTGTE) are linked to alkaline NaHCO 3 waters (pH≈9), moderate oxic conditions and temperatures of around 18°C-19°C. The most plausible hypothesis to explain the high arsenic concentrations is the contribution of deeper regional flows with a significant hydrothermal component (cold-hydrothermal waters), flowing through faults in the basement rock. Water mixing and water-rock interactions occur both in the fissured aquifer media (igneous and metasedimentary bedrock) and in the sedimentary environment of the CDB, where agricultural pollution phenomena are also active. A combination of multivariate statistical tools and hydrochemical analysis enabled the distribution pattern of dissolved As and other PTGTE in groundwaters in the study area to be interpreted, and their most likely origin to be established. This methodology could be applied to other sedimentary areas with similar characteristics and problems. Copyright © 2017 Elsevier B.V. All rights reserved.

Analysis techniques for multivariate root loci. [a tool in linear control systems

NASA Technical Reports Server (NTRS)

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

1980-01-01

Analysis and techniques are developed for the multivariable root locus and the multivariable optimal root locus. The generalized eigenvalue problem is used to compute angles and sensitivities for both types of loci, and an algorithm is presented that determines the asymptotic properties of the optimal root locus.

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.

A Primer on Multivariate Analysis of Variance (MANOVA) for Behavioral Scientists

ERIC Educational Resources Information Center

Warne, Russell T.

2014-01-01

Reviews of statistical procedures (e.g., Bangert & Baumberger, 2005; Kieffer, Reese, & Thompson, 2001; Warne, Lazo, Ramos, & Ritter, 2012) show that one of the most common multivariate statistical methods in psychological research is multivariate analysis of variance (MANOVA). However, MANOVA and its associated procedures are often not…

Fast computation of the multivariable stability margin for real interrelated uncertain parameters

NASA Technical Reports Server (NTRS)

Sideris, Athanasios; Sanchez Pena, Ricardo S.

1988-01-01

A novel algorithm for computing the multivariable stability margin for checking the robust stability of feedback systems with real parametric uncertainty is proposed. This method eliminates the need for the frequency search involved in another given algorithm by reducing it to checking a finite number of conditions. These conditions have a special structure, which allows a significant improvement on the speed of computations.

[Mortality in early-stage, surgically resected non-small cell lung cancer less than 3 cm of size: Competing risk analysis].

PubMed

Jordá Aragón, Carlos; Peñalver Cuesta, Juan Carlos; Mancheño Franch, Nuria; de Aguiar Quevedo, Karol; Vera Sempere, Francisco; Padilla Alarcón, José

2015-09-07

Survival studies of non-small cell lung cancer (NSCLC) are usually based on the Kaplan-Meier method. However, other factors not covered by this method may modify the observation of the event of interest. There are models of cumulative incidence (CI), that take into account these competing risks, enabling more accurate survival estimates and evaluation of the risk of death from other causes. We aimed to evaluate these models in resected early-stage NSCLC patients. This study included 263 patients with resected NSCLC whose diameter was ≤ 3 cm without node involvement (N0). Demographic, clinical, morphopathological and surgical variables, TNM classification and long-term evolution were analysed. To analyse CI, death by another cause was considered to be competitive event. For the univariate analysis, Gray's method was used, while Fine and Gray's method was employed for the multivariate analysis. Mortality by NSCLC was 19.4% at 5 years and 14.3% by another cause. Both curves crossed at 6.3 years, and probability of death by another cause became greater from this point. In multivariate analysis, cancer mortality was conditioned by visceral pleural invasion (VPI) (P=.001) and vascular invasion (P=.020), with age>50 years (P=.034), smoking (P=.009) and the Charlson index ≥ 2 (P=.000) being by no cancer. By the method of CI, VPI and vascular invasion conditioned cancer death in NSCLC >3 cm, while non-tumor causes of long-term death were determined. Copyright © 2014 Elsevier España, S.L.U. All rights reserved.

Analysis of heavy metals from water, sediment, and tissues of Labeo angra (Hamilton, 1822), from an Ox-box lake- an wetland site from Assam, India.

PubMed

Das, Suchismita; Choudhury, Shamim Sultana

2016-01-01

The aim of this study was to assess the regional impacts of heavy metals (Mn, Fe, Mg, Ca, Cu, Zn, Cd, Cr, Pb and Ni) on water, sediment and a native, teleost fish species, Labeo angra, inhabiting a flood plain wetland of Barak River in Assam, India. Heavy metal concentrations in the water, sediments and fish were measured; bioaccumulation factor, metal pollution index as well as condition indices were calculated, to assess the pollution load and health status of the fish. Multivariate statistical analysis was used on wetland water and sediment heavy metals to ascertain the possible sources and seasonal variations of the pollutants. Results showed that most heavy metals in the wetland water and sediments exceeded the water (drinking and irrigation) and sediment quality guidelines, respectively. Seasonal variations were observed for geogenic heavy metals, Mn, Fe, Mg and Ca while no seasonal variations were observed for anthropogenic heavy metals, Cu, Cd, Cr, Pb and Ni. Multivariate statistical analysis showed that there was strong correlation between geogenic and anthropogenic heavy metals in water and sediment, both originating from the common anthropogenic sources. Accumulation of most of the metals in all the tissues was above the safe limits as recommended by the Food and Agriculture Organization. High bioaccumulation factors and metal pollution index for these metals in the different tissues revealed that metals were extensively bio-accumulated and bioconcentrated. Condition indices in fish from the wetland suggested metabolic abnormalities.

Correlation analysis between work-related musculoskeletal disorders and the nursing practice environment, quality of life, and social support in the nursing professionals

PubMed Central

Yan, Ping; Yang, Yi; Zhang, Li; Li, Fuye; Huang, Amei; Wang, Yanan; Dai, Yali; Yao, Hua

2018-01-01

Abstract We aim to analyze the correlated influential factors between work-related musculoskeletal disorders (WMSDs) and nursing practice environment and quality of life and social support. From January 2015 to October 2015, cluster sampling was performed on the nurses from 12 hospitals in the 6 areas in Xinjiang. The questionnaires including the modified Nordic Musculoskeletal Questionnaire, Practice Environment Scale (PES), the Mos 36-item Short Form Health Survey, and Social Support Rating Scale were used to investigate. Multivariate logistic regression analysis was used to explore the influential factors of WMSDs. The total prevalence of WMSDs was 79.52% in the nurses ever since the working occupation, which was mainly involved waist (64.83%), neck (61.83%), and shoulder (52.36%). Multivariate logistic regression analysis indicated age (≥26 years), working in the Department of Surgery, Department of Critical Care, Outpatient Department, and Department of Anesthesia, working duration of >40 hours per week were the risk factors of WMSDs in the nurses. The physiological function (PF), body pain, total healthy condition, adequate working force and financial support, and social support were the protective factors of WMSDs. The prevalence of WMSDs in the nurses in Xinjiang Autonomous Region was high. PF, bodily pain, total healthy condition, having adequate staff and support resources to provide quality patient care, and social support were the protective factors of WMSDs in the nurses. PMID:29489648

Factors affecting plant species composition of hedgerows: relative importance and hierarchy

NASA Astrophysics Data System (ADS)

Deckers, Bart; Hermy, Martin; Muys, Bart

2004-07-01

Although there has been a clear quantitative and qualitative decline in traditional hedgerow network landscapes during last century, hedgerows are crucial for the conservation of rural biodiversity, functioning as an important habitat, refuge and corridor for numerous species. To safeguard this conservation function, insight in the basic organizing principles of hedgerow plant communities is needed. The vegetation composition of 511 individual hedgerows situated within an ancient hedgerow network landscape in Flanders, Belgium was recorded, in combination with a wide range of explanatory variables, including a selection of spatial variables. Non-parametric statistics in combination with multivariate data analysis techniques were used to study the effect of individual explanatory variables. Next, variables were grouped in five distinct subsets and the relative importance of these variable groups was assessed by two related variation partitioning techniques, partial regression and partial canonical correspondence analysis, taking into account explicitly the existence of intercorrelations between variables of different factor groups. Most explanatory variables affected significantly hedgerow species richness and composition. Multivariate analysis showed that, besides adjacent land use, hedgerow management, soil conditions, hedgerow type and origin, the role of other factors such as hedge dimensions, intactness, etc., could certainly not be neglected. Furthermore, both methods revealed the same overall ranking of the five distinct factor groups. Besides a predominant impact of abiotic environmental conditions, it was found that management variables and structural aspects have a relatively larger influence on the distribution of plant species in hedgerows than their historical background or spatial configuration.

A data fusion-based drought index

NASA Astrophysics Data System (ADS)

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

2016-03-01

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

Correlation analysis between work-related musculoskeletal disorders and the nursing practice environment, quality of life, and social support in the nursing professionals.

PubMed

Yan, Ping; Yang, Yi; Zhang, Li; Li, Fuye; Huang, Amei; Wang, Yanan; Dai, Yali; Yao, Hua

2018-03-01

We aim to analyze the correlated influential factors between work-related musculoskeletal disorders (WMSDs) and nursing practice environment and quality of life and social support.From January 2015 to October 2015, cluster sampling was performed on the nurses from 12 hospitals in the 6 areas in Xinjiang. The questionnaires including the modified Nordic Musculoskeletal Questionnaire, Practice Environment Scale (PES), the Mos 36-item Short Form Health Survey, and Social Support Rating Scale were used to investigate. Multivariate logistic regression analysis was used to explore the influential factors of WMSDs.The total prevalence of WMSDs was 79.52% in the nurses ever since the working occupation, which was mainly involved waist (64.83%), neck (61.83%), and shoulder (52.36%). Multivariate logistic regression analysis indicated age (≥26 years), working in the Department of Surgery, Department of Critical Care, Outpatient Department, and Department of Anesthesia, working duration of >40 hours per week were the risk factors of WMSDs in the nurses. The physiological function (PF), body pain, total healthy condition, adequate working force and financial support, and social support were the protective factors of WMSDs.The prevalence of WMSDs in the nurses in Xinjiang Autonomous Region was high. PF, bodily pain, total healthy condition, having adequate staff and support resources to provide quality patient care, and social support were the protective factors of WMSDs in the nurses.

Simultaneous determination of estrogens (ethinylestradiol and norgestimate) concentrations in human and bovine serum albumin by use of fluorescence spectroscopy and multivariate regression analysis.

PubMed

Hordge, LaQuana N; McDaniel, Kiara L; Jones, Derick D; Fakayode, Sayo O

2016-05-15

The endocrine disruption property of estrogens necessitates the immediate need for effective monitoring and development of analytical protocols for their analyses in biological and human specimens. This study explores the first combined utility of a steady-state fluorescence spectroscopy and multivariate partial-least-square (PLS) regression analysis for the simultaneous determination of two estrogens (17α-ethinylestradiol (EE) and norgestimate (NOR)) concentrations in bovine serum albumin (BSA) and human serum albumin (HSA) samples. The influence of EE and NOR concentrations and temperature on the emission spectra of EE-HSA EE-BSA, NOR-HSA, and NOR-BSA complexes was also investigated. The binding of EE with HSA and BSA resulted in increase in emission characteristics of HSA and BSA and a significant blue spectra shift. In contrast, the interaction of NOR with HSA and BSA quenched the emission characteristics of HSA and BSA. The observed emission spectral shifts preclude the effective use of traditional univariate regression analysis of fluorescent data for the determination of EE and NOR concentrations in HSA and BSA samples. Multivariate partial-least-squares (PLS) regression analysis was utilized to correlate the changes in emission spectra with EE and NOR concentrations in HSA and BSA samples. The figures-of-merit of the developed PLS regression models were excellent, with limits of detection as low as 1.6×10(-8) M for EE and 2.4×10(-7) M for NOR and good linearity (R(2)>0.994985). The PLS models correctly predicted EE and NOR concentrations in independent validation HSA and BSA samples with a root-mean-square-percent-relative-error (RMS%RE) of less than 6.0% at physiological condition. On the contrary, the use of univariate regression resulted in poor predictions of EE and NOR in HSA and BSA samples, with RMS%RE larger than 40% at physiological conditions. High accuracy, low sensitivity, simplicity, low-cost with no prior analyte extraction or separation required makes this method promising, compelling, and attractive alternative for the rapid determination of estrogen concentrations in biomedical and biological specimens, pharmaceuticals, or environmental samples. Published by Elsevier B.V.

Multivariate Analysis and Machine Learning in Cerebral Palsy Research

PubMed Central

Zhang, Jing

2017-01-01

Cerebral palsy (CP), a common pediatric movement disorder, causes the most severe physical disability in children. Early diagnosis in high-risk infants is critical for early intervention and possible early recovery. In recent years, multivariate analytic and machine learning (ML) approaches have been increasingly used in CP research. This paper aims to identify such multivariate studies and provide an overview of this relatively young field. Studies reviewed in this paper have demonstrated that multivariate analytic methods are useful in identification of risk factors, detection of CP, movement assessment for CP prediction, and outcome assessment, and ML approaches have made it possible to automatically identify movement impairments in high-risk infants. In addition, outcome predictors for surgical treatments have been identified by multivariate outcome studies. To make the multivariate and ML approaches useful in clinical settings, further research with large samples is needed to verify and improve these multivariate methods in risk factor identification, CP detection, movement assessment, and outcome evaluation or prediction. As multivariate analysis, ML and data processing technologies advance in the era of Big Data of this century, it is expected that multivariate analysis and ML will play a bigger role in improving the diagnosis and treatment of CP to reduce mortality and morbidity rates, and enhance patient care for children with CP. PMID:29312134

Multivariate Analysis and Machine Learning in Cerebral Palsy Research.

PubMed

Zhang, Jing

2017-01-01

Cerebral palsy (CP), a common pediatric movement disorder, causes the most severe physical disability in children. Early diagnosis in high-risk infants is critical for early intervention and possible early recovery. In recent years, multivariate analytic and machine learning (ML) approaches have been increasingly used in CP research. This paper aims to identify such multivariate studies and provide an overview of this relatively young field. Studies reviewed in this paper have demonstrated that multivariate analytic methods are useful in identification of risk factors, detection of CP, movement assessment for CP prediction, and outcome assessment, and ML approaches have made it possible to automatically identify movement impairments in high-risk infants. In addition, outcome predictors for surgical treatments have been identified by multivariate outcome studies. To make the multivariate and ML approaches useful in clinical settings, further research with large samples is needed to verify and improve these multivariate methods in risk factor identification, CP detection, movement assessment, and outcome evaluation or prediction. As multivariate analysis, ML and data processing technologies advance in the era of Big Data of this century, it is expected that multivariate analysis and ML will play a bigger role in improving the diagnosis and treatment of CP to reduce mortality and morbidity rates, and enhance patient care for children with CP.

An Empirical Study of the Dominating Predictive Features of Spoken Language in a Representative Sample of School Pupils: A Multivariate Description and Analysis of Oral Language Development.

ERIC Educational Resources Information Center

Marascuilo, Leonard A.; Loban, Walter

To determine whether language behavior represents an early conditioned verbal response or whether it changes with age and experience was the purpose of this study which attempted to define unique isolates of language on the basis of actual language produced by young children. Tape recorded data were collected for 12 years from 211 children in…

Transcriptome and Multivariable Data Analysis of Corynebacterium glutamicum under Different Dissolved Oxygen Conditions in Bioreactors.

PubMed

Sun, Yang; Guo, Wenwen; Wang, Fen; Peng, Feng; Yang, Yankun; Dai, Xiaofeng; Liu, Xiuxia; Bai, Zhonghu

2016-01-01

Dissolved oxygen (DO) is an important factor in the fermentation process of Corynebacterium glutamicum, which is a widely used aerobic microbe in bio-industry. Herein, we described RNA-seq for C. glutamicum under different DO levels (50%, 30% and 0%) in 5 L bioreactors. Multivariate data analysis (MVDA) models were used to analyze the RNA-seq and metabolism data to investigate the global effect of DO on the transcriptional distinction of the substance and energy metabolism of C. glutamicum. The results showed that there were 39 and 236 differentially expressed genes (DEGs) under the 50% and 0% DO conditions, respectively, compared to the 30% DO condition. Key genes and pathways affected by DO were analyzed, and the result of the MVDA and RNA-seq revealed that different DO levels in the fermenter had large effects on the substance and energy metabolism and cellular redox balance of C. glutamicum. At low DO, the glycolysis pathway was up-regulated, and TCA was shunted by the up-regulation of the glyoxylate pathway and over-production of amino acids, including valine, cysteine and arginine. Due to the lack of electron-acceptor oxygen, 7 genes related to the electron transfer chain were changed, causing changes in the intracellular ATP content at 0% and 30% DO. The metabolic flux was changed to rebalance the cellular redox. This study applied deep sequencing to identify a wealth of genes and pathways that changed under different DO conditions and provided an overall comprehensive view of the metabolism of C. glutamicum. The results provide potential ways to improve the oxygen tolerance of C. glutamicum and to modify the metabolic flux for amino acid production and heterologous protein expression.

Similar outcome of allogeneic stem cell transplantation after myeloablative and sequential conditioning regimen in patients with refractory or relapsed acute myeloid leukemia: A study from the Société Francophone de Greffe de Moelle et de Thérapie Cellulaire.

PubMed

Decroocq, Justine; Itzykson, Raphaël; Vigouroux, Stéphane; Michallet, Mauricette; Yakoub-Agha, Ibrahim; Huynh, Anne; Beckerich, Florence; Suarez, Felipe; Chevallier, Patrice; Nguyen-Quoc, Stéphanie; Ledoux, Marie-Pierre; Clement, Laurence; Hicheri, Yosr; Guillerm, Gaëlle; Cornillon, Jérôme; Contentin, Nathalie; Carre, Martin; Maillard, Natacha; Mercier, Mélanie; Mohty, Mohamad; Beguin, Yves; Bourhis, Jean-Henri; Charbonnier, Amandine; Dauriac, Charles; Bay, Jacques-Olivier; Blaise, Didier; Deconinck, Eric; Jubert, Charlotte; Raus, Nicole; Peffault de Latour, Regis; Dhedin, Nathalie

2018-03-01

Patients with acute myeloid leukemia (AML) in relapse or refractory to induction therapy have a dismal prognosis. Allogeneic hematopoietic stem cell transplantation is the only curative option. In these patients, we aimed to compare the results of a myeloablative transplant versus a sequential approach consisting in a cytoreductive chemotherapy followed by a reduced intensity conditioning regimen and prophylactic donor lymphocytes infusions. We retrospectively analyzed 99 patients aged 18-50 years, transplanted for a refractory (52%) or a relapsed AML not in remission (48%). Fifty-eight patients received a sequential approach and 41 patients a myeloablative conditioning regimen. Only 6 patients received prophylactic donor lymphocytes infusions. With a median follow-up of 48 months, 2-year overall survival was 39%, 95% confidence interval (CI) (24-53) in the myeloablative group versus 33%, 95% CI (21-45) in the sequential groups (P = .39), and 2-year cumulative incidence of relapse (CIR) was 57% versus 50% respectively (P = .99). Nonrelapse mortality was not higher in the myeloablative group (17% versus 15%, P = .44). In multivariate analysis, overall survival, CIR and nonrelapse mortality remained similar between the two groups. However, in multivariate analysis, sequential conditioning led to fewer acute grade II-IV graft versus host disease (GVHD) (HR for sequential approach = 0.37; 95% CI: 0.21-0.65; P < .001) without a significant impact on chronic GVHD (all grades and extensive). In young patients with refractory or relapsed AML, myeloablative transplant and sequential approach offer similar outcomes except for a lower incidence of acute GvHD after a sequential transplant. © 2018 Wiley Periodicals, Inc.

Transcriptome and Multivariable Data Analysis of Corynebacterium glutamicum under Different Dissolved Oxygen Conditions in Bioreactors

PubMed Central

Sun, Yang; Guo, Wenwen; Wang, Fen; Peng, Feng; Yang, Yankun; Dai, Xiaofeng; Liu, Xiuxia; Bai, Zhonghu

2016-01-01

Dissolved oxygen (DO) is an important factor in the fermentation process of Corynebacterium glutamicum, which is a widely used aerobic microbe in bio-industry. Herein, we described RNA-seq for C. glutamicum under different DO levels (50%, 30% and 0%) in 5 L bioreactors. Multivariate data analysis (MVDA) models were used to analyze the RNA-seq and metabolism data to investigate the global effect of DO on the transcriptional distinction of the substance and energy metabolism of C. glutamicum. The results showed that there were 39 and 236 differentially expressed genes (DEGs) under the 50% and 0% DO conditions, respectively, compared to the 30% DO condition. Key genes and pathways affected by DO were analyzed, and the result of the MVDA and RNA-seq revealed that different DO levels in the fermenter had large effects on the substance and energy metabolism and cellular redox balance of C. glutamicum. At low DO, the glycolysis pathway was up-regulated, and TCA was shunted by the up-regulation of the glyoxylate pathway and over-production of amino acids, including valine, cysteine and arginine. Due to the lack of electron-acceptor oxygen, 7 genes related to the electron transfer chain were changed, causing changes in the intracellular ATP content at 0% and 30% DO. The metabolic flux was changed to rebalance the cellular redox. This study applied deep sequencing to identify a wealth of genes and pathways that changed under different DO conditions and provided an overall comprehensive view of the metabolism of C. glutamicum. The results provide potential ways to improve the oxygen tolerance of C. glutamicum and to modify the metabolic flux for amino acid production and heterologous protein expression. PMID:27907077

Occurrence of mastitis and associated risk factors in lactating goats under pastoral management in Borana, Southern Ethiopia.

PubMed

Megersa, Bekele; Tadesse, Chala; Abunna, Fufa; Regassa, Alemayehu; Mekibib, Berhanu; Debela, Etana

2010-08-01

Mastitis prevalence and related risk factors were studied in 1,072 udder halves of 536 lactating goats from October, 2008 to February, 2009. Clinical and subclinical mastitis were prevalent in 4.3% (95% CI = 2.8, 6.5) and 11.2% (95% CI = 8.7, 14.3) of the studied animals, respectively, resulting in an overall prevalence of 15.5% (95% CI = 12.6, 18.9). Univariate analysis of the potential risk factors has depicted that mastitis was more prevalent in does with previous mastitis history, increased parity, poor body conditions, increased milk production, late lactation stage, long teat, and housed goats. Furthermore, prevalence was significantly higher (p < 0.05) during the wet period of October to November than the dry periods of January to February. No significant variations (p > 0.05) were observed in mastitis prevalence with udder tick infestation, mixing goat with sheep and flock size. With multivariable analysis, lactation stage, teat length, body condition, and season (wet months) have showed significant association with mastitis prevalence, and these factors maintained significant in the stepwise elimination of multivariable logistic regression model. As a result, does in late stage of lactation (OR = 4.3, 1.8, 10.4), poor body condition (OR = 5.0, 1.7, 10.0), long teats (OR = 2.2, 95% CI = 1.1, 4.2) and does examined in wet period were at higher risk of udder infections than early lactation, good body condition, short teat, and examined in dry period, respectively. The study showed occurrence of mastitis and associated risk factors in studied goats, which suggests the need for control intervention. Further investigations into pathogens involved in goat mastitis will optimize our knowledge of causative agents and control interventions.

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.

Characterization of the volatile components in green tea by IRAE-HS-SPME/GC-MS combined with multivariate analysis.

PubMed

Yang, Yan-Qin; Yin, Hong-Xu; Yuan, Hai-Bo; Jiang, Yong-Wen; Dong, Chun-Wang; Deng, Yu-Liang

2018-01-01

In the present work, a novel infrared-assisted extraction coupled to headspace solid-phase microextraction (IRAE-HS-SPME) followed by gas chromatography-mass spectrometry (GC-MS) was developed for rapid determination of the volatile components in green tea. The extraction parameters such as fiber type, sample amount, infrared power, extraction time, and infrared lamp distance were optimized by orthogonal experimental design. Under optimum conditions, a total of 82 volatile compounds in 21 green tea samples from different geographical origins were identified. Compared with classical water-bath heating, the proposed technique has remarkable advantages of considerably reducing the analytical time and high efficiency. In addition, an effective classification of green teas based on their volatile profiles was achieved by partial least square-discriminant analysis (PLS-DA) and hierarchical clustering analysis (HCA). Furthermore, the application of a dual criterion based on the variable importance in the projection (VIP) values of the PLS-DA models and on the category from one-way univariate analysis (ANOVA) allowed the identification of 12 potential volatile markers, which were considered to make the most important contribution to the discrimination of the samples. The results suggest that IRAE-HS-SPME/GC-MS technique combined with multivariate analysis offers a valuable tool to assess geographical traceability of different tea varieties.

Characterization of the volatile components in green tea by IRAE-HS-SPME/GC-MS combined with multivariate analysis

PubMed Central

Yin, Hong-Xu; Yuan, Hai-Bo; Jiang, Yong-Wen; Dong, Chun-Wang; Deng, Yu-Liang

2018-01-01

In the present work, a novel infrared-assisted extraction coupled to headspace solid-phase microextraction (IRAE-HS-SPME) followed by gas chromatography-mass spectrometry (GC-MS) was developed for rapid determination of the volatile components in green tea. The extraction parameters such as fiber type, sample amount, infrared power, extraction time, and infrared lamp distance were optimized by orthogonal experimental design. Under optimum conditions, a total of 82 volatile compounds in 21 green tea samples from different geographical origins were identified. Compared with classical water-bath heating, the proposed technique has remarkable advantages of considerably reducing the analytical time and high efficiency. In addition, an effective classification of green teas based on their volatile profiles was achieved by partial least square-discriminant analysis (PLS-DA) and hierarchical clustering analysis (HCA). Furthermore, the application of a dual criterion based on the variable importance in the projection (VIP) values of the PLS-DA models and on the category from one-way univariate analysis (ANOVA) allowed the identification of 12 potential volatile markers, which were considered to make the most important contribution to the discrimination of the samples. The results suggest that IRAE-HS-SPME/GC-MS technique combined with multivariate analysis offers a valuable tool to assess geographical traceability of different tea varieties. PMID:29494626

ToF-SIMS and principal component analysis of lipids and amino acids from inflamed and dysplastic human colonic mucosa.

PubMed

Urbini, Marco; Petito, Valentina; de Notaristefani, Francesco; Scaldaferri, Franco; Gasbarrini, Antonio; Tortora, Luca

2017-10-01

Here, time of flight secondary ion mass spectrometry (ToF-SIMS) and multivariate analysis were combined to study the role of ulcerative colitis (UC), a type of inflammatory bowel disease (IBD), in the colon cancer progression. ToF-SIMS was used to obtain mass spectra and chemical maps from the mucosal surface of human normal (NC), inflamed (IC), and dysplastic (DC) colon tissues. Chemical mapping with a lateral resolution of ≈ 1 μm allowed to evaluate zonation of fatty acids and amino acids as well as the morphological condition of the intestinal glands. High mass resolution ToF-SIMS spectra showed chemical differences in lipid and amino acid composition as a function of pathological state. In positive ion mode, mono- (MAG), di- (DAG), and triacylglycerol (TAG) signals were detected in NC tissues, while in IC and DC tissues, the only cholesterol was present as lipid class representative. Signals from fatty acids, collected in negative ion mode, were subjected to principal component analysis (PCA). PCA showed a strict correlation between IC and DC samples, due to an increase of stearic, arachidonic, and linoleic acid. In the same way, differences in the amino acid composition were highlighted through multivariate analysis. PCA revealed that glutamic acid, leucine/isoleucine, and valine fragments are related to IC tissues. On the other hand, tyrosine, methionine, and tryptophan peaks contributed highly to the separation of DC tissues. Finally, a classification of NC, IC, and DC patients was also achieved through hierarchical cluster analysis of amino acid fragments. In this case, human colonic inflammation showed a stronger relationship with normal than dysplastic condition. Graphical Abstract ᅟ.

Multivariate probability distribution for sewer system vulnerability assessment under data-limited conditions.

PubMed

Del Giudice, G; Padulano, R; Siciliano, D

2016-01-01

The lack of geometrical and hydraulic information about sewer networks often excludes the adoption of in-deep modeling tools to obtain prioritization strategies for funds management. The present paper describes a novel statistical procedure for defining the prioritization scheme for preventive maintenance strategies based on a small sample of failure data collected by the Sewer Office of the Municipality of Naples (IT). Novelty issues involve, among others, considering sewer parameters as continuous statistical variables and accounting for their interdependences. After a statistical analysis of maintenance interventions, the most important available factors affecting the process are selected and their mutual correlations identified. Then, after a Box-Cox transformation of the original variables, a methodology is provided for the evaluation of a vulnerability map of the sewer network by adopting a joint multivariate normal distribution with different parameter sets. The goodness-of-fit is eventually tested for each distribution by means of a multivariate plotting position. The developed methodology is expected to assist municipal engineers in identifying critical sewers, prioritizing sewer inspections in order to fulfill rehabilitation requirements.

Estimating an Effect Size in One-Way Multivariate Analysis of Variance (MANOVA)

ERIC Educational Resources Information Center

Steyn, H. S., Jr.; Ellis, S. M.

2009-01-01

When two or more univariate population means are compared, the proportion of variation in the dependent variable accounted for by population group membership is eta-squared. This effect size can be generalized by using multivariate measures of association, based on the multivariate analysis of variance (MANOVA) statistics, to establish whether…

Dangers in Using Analysis of Covariance Procedures.

ERIC Educational Resources Information Center

Campbell, Kathleen T.

Problems associated with the use of analysis of covariance (ANCOVA) as a statistical control technique are explained. Three problems relate to the use of "OVA" methods (analysis of variance, analysis of covariance, multivariate analysis of variance, and multivariate analysis of covariance) in general. These are: (1) the wasting of information when…

NIH disease funding levels and burden of disease.

PubMed

Gillum, Leslie A; Gouveia, Christopher; Dorsey, E Ray; Pletcher, Mark; Mathers, Colin D; McCulloch, Charles E; Johnston, S Claiborne

2011-02-24

An analysis of NIH funding in 1996 found that the strongest predictor of funding, disability-adjusted life-years (DALYs), explained only 39% of the variance in funding. In 1998, Congress requested that the Institute of Medicine (IOM) evaluate priority-setting criteria for NIH funding; the IOM recommended greater consideration of disease burden. We examined whether the association between current burden and funding has changed since that time. We analyzed public data on 2006 NIH funding for 29 common conditions. Measures of US disease burden in 2004 were obtained from the World Health Organization's Global Burden of Disease study and national databases. We assessed the relationship between disease burden and NIH funding dollars in univariate and multivariable log-linear models that evaluated all measures of disease burden. Sensitivity analyses examined associations with future US burden, current and future measures of world disease burden, and a newly standardized NIH accounting method. In univariate and multivariable analyses, disease-specific NIH funding levels increased with burden of disease measured in DALYs (p = 0.001), which accounted for 33% of funding level variation. No other factor predicted funding in multivariable models. Conditions receiving the most funding greater than expected based on disease burden were AIDS ($2474 M), diabetes mellitus ($390 M), and perinatal conditions ($297 M). Depression ($719 M), injuries ($691 M), and chronic obstructive pulmonary disease ($613 M) were the most underfunded. Results were similar using estimates of future US burden, current and future world disease burden, and alternate NIH accounting methods. Current levels of NIH disease-specific research funding correlate modestly with US disease burden, and correlation has not improved in the last decade.

Conditional Survival in Anal Carcinoma Using the National Population-Based Survey of Epidemiology and End Results Database (1988-2012).

PubMed

Kim, Ellen; Kim, Jong S; Choi, Mehee; Thomas, Charles R

2016-04-01

Conditional survival can provide valuable information for both patients and healthcare providers about the changing prognosis in surviving patients over time. This study estimated conditional survival for patients with anal cancer in the United States through analysis of a national population-based cancer registry. Log-rank test identified significant covariates of cause-specific survival (defined as time from diagnosis until death from anal cancer). Significant covariates were considered in the multivariable regression of cause-specific survival using Cox proportional hazards models. Covariates included cancer stage and demographic variables. Patients in Surveillance, Epidemiology, and End Results regions diagnosed with anal squamous cell carcinoma as their first and only cancer diagnosis from 1988 to 2012 were selected from this database, and 5145 patients were included in the retrospective cohort study. Five-year conditional survival stratified by each variable in the final Cox models was measured : The final multivariable models of overall and cause-specific survivals included stage, grade, sex, age, race, and relationship status. Over the first 6 years after diagnosis, conditional survival of distant stage increased from 37% to 89%, whereas regional stage increased from 65% to 93% and localized stage increased from 84% to 96%. The other variables had increasing prognosis as well, but the subgroups increased at a more similar rate over time. The data source used does not include information on chemotherapy treatment, patient comorbidities, or socioeconomic status. Conditional survival showed improvement over time. Patients with advanced stage had the greatest improvement in conditional survival. This is the first study to provide specific conditional survival probabilities for patients with anal cancer.

Probabilistic estimates of drought impacts on agricultural production

NASA Astrophysics Data System (ADS)

Madadgar, Shahrbanou; AghaKouchak, Amir; Farahmand, Alireza; Davis, Steven J.

2017-08-01

Increases in the severity and frequency of drought in a warming climate may negatively impact agricultural production and food security. Unlike previous studies that have estimated agricultural impacts of climate condition using single-crop yield distributions, we develop a multivariate probabilistic model that uses projected climatic conditions (e.g., precipitation amount or soil moisture) throughout a growing season to estimate the probability distribution of crop yields. We demonstrate the model by an analysis of the historical period 1980-2012, including the Millennium Drought in Australia (2001-2009). We find that precipitation and soil moisture deficit in dry growing seasons reduced the average annual yield of the five largest crops in Australia (wheat, broad beans, canola, lupine, and barley) by 25-45% relative to the wet growing seasons. Our model can thus produce region- and crop-specific agricultural sensitivities to climate conditions and variability. Probabilistic estimates of yield may help decision-makers in government and business to quantitatively assess the vulnerability of agriculture to climate variations. We develop a multivariate probabilistic model that uses precipitation to estimate the probability distribution of crop yields. The proposed model shows how the probability distribution of crop yield changes in response to droughts. During Australia's Millennium Drought precipitation and soil moisture deficit reduced the average annual yield of the five largest crops.

The Association of Combined GSTM1 and CYP2C9 Genotype Status with the Occurrence of Hemorrhagic Cystitis in Pediatric Patients Receiving Myeloablative Conditioning Regimen Prior to Allogeneic Hematopoietic Stem Cell Transplantation

PubMed Central

Uppugunduri, Chakradhara Rao S.; Storelli, Flavia; Mlakar, Vid; Huezo-Diaz Curtis, Patricia; Rezgui, Aziz; Théorêt, Yves; Marino, Denis; Doffey-Lazeyras, Fabienne; Chalandon, Yves; Bader, Peter; Daali, Youssef; Bittencourt, Henrique; Krajinovic, Maja; Ansari, Marc

2017-01-01

Hemorrhagic cystitis (HC) is one of the complications of busulfan-cyclophosphamide (BU-CY) conditioning regimen during allogeneic hematopoietic stem cell transplantation (HSCT) in children. Identifying children at high risk of developing HC in a HSCT setting could facilitate the evaluation and implementation of effective prophylactic measures. In this retrospective analysis genotyping of selected candidate gene variants was performed in 72 children and plasma Sulfolane (Su, water soluble metabolite of BU) levels were measured in 39 children following treatment with BU-CY regimen. The cytotoxic effects of Su and acrolein (Ac, water soluble metabolite of CY) were tested on human urothelial cells (HUCs). The effect of Su was also tested on cytochrome P 450 (CYP) function in HepaRG hepatic cells. Cumulative incidences of HC before day 30 post HSCT were estimated using Kaplan–Meier curves and log-rank test was used to compare the difference between groups in a univariate analysis. Multivariate Cox regression was used to estimate hazard ratios with 95% confidence intervals (CIs). Multivariate analysis included co-variables that were significantly associated with HC in a univariate analysis. Cumulative incidence of HC was 15.3%. In the univariate analysis, HC incidence was significantly (p < 0.05) higher in children older than 10 years (28.6 vs. 6.8%) or in children with higher Su levels (>40 vs. <11%) or in carriers of both functional GSTM1 and CYP2C9 (33.3 vs. 6.3%) compared to the other group. In a multivariate analysis, combined GSTM1 and CYP2C9 genotype status was associated with HC occurrence with a hazards ratio of 4.8 (95% CI: 1.3–18.4; p = 0.02). Ac was found to be toxic to HUC cells at lower concentrations (33 μM), Su was not toxic to HUC cells at concentrations below 1 mM and did not affect CYP function in HepaRG cells. Our observations suggest that pre-emptive genotyping of CYP2C9 and GSTM1 may aid in selection of more effective prophylaxis to reduce HC development in pediatric patients undergoing allogeneic HSCT. Article summary: (1) Children carrying functional alleles in GSTM1 and CYP2C9 are at high risk for developing hemorrhagic cystitis following treatment with busulfan and cyclophosphamide based conditioning regimen. (2) Identification of children at high risk for developing hemorrhagic cystitis in an allogeneic HSCT setting will enable us to evaluate and implement optimal strategies for its prevention. Trial registration: This study is a part of the trail “clinicaltrials.gov identifier: NCT01257854.” PMID:28744217

The Association of Combined GSTM1 and CYP2C9 Genotype Status with the Occurrence of Hemorrhagic Cystitis in Pediatric Patients Receiving Myeloablative Conditioning Regimen Prior to Allogeneic Hematopoietic Stem Cell Transplantation.

PubMed

Uppugunduri, Chakradhara Rao S; Storelli, Flavia; Mlakar, Vid; Huezo-Diaz Curtis, Patricia; Rezgui, Aziz; Théorêt, Yves; Marino, Denis; Doffey-Lazeyras, Fabienne; Chalandon, Yves; Bader, Peter; Daali, Youssef; Bittencourt, Henrique; Krajinovic, Maja; Ansari, Marc

2017-01-01

Hemorrhagic cystitis (HC) is one of the complications of busulfan-cyclophosphamide (BU-CY) conditioning regimen during allogeneic hematopoietic stem cell transplantation (HSCT) in children. Identifying children at high risk of developing HC in a HSCT setting could facilitate the evaluation and implementation of effective prophylactic measures. In this retrospective analysis genotyping of selected candidate gene variants was performed in 72 children and plasma Sulfolane (Su, water soluble metabolite of BU) levels were measured in 39 children following treatment with BU-CY regimen. The cytotoxic effects of Su and acrolein (Ac, water soluble metabolite of CY) were tested on human urothelial cells (HUCs). The effect of Su was also tested on cytochrome P 450 (CYP) function in HepaRG hepatic cells. Cumulative incidences of HC before day 30 post HSCT were estimated using Kaplan-Meier curves and log-rank test was used to compare the difference between groups in a univariate analysis. Multivariate Cox regression was used to estimate hazard ratios with 95% confidence intervals (CIs). Multivariate analysis included co-variables that were significantly associated with HC in a univariate analysis. Cumulative incidence of HC was 15.3%. In the univariate analysis, HC incidence was significantly ( p < 0.05) higher in children older than 10 years (28.6 vs. 6.8%) or in children with higher Su levels (>40 vs. <11%) or in carriers of both functional GSTM1 and CYP2C9 (33.3 vs. 6.3%) compared to the other group. In a multivariate analysis, combined GSTM1 and CYP2C9 genotype status was associated with HC occurrence with a hazards ratio of 4.8 (95% CI: 1.3-18.4; p = 0.02). Ac was found to be toxic to HUC cells at lower concentrations (33 μM), Su was not toxic to HUC cells at concentrations below 1 mM and did not affect CYP function in HepaRG cells. Our observations suggest that pre-emptive genotyping of CYP2C9 and GSTM1 may aid in selection of more effective prophylaxis to reduce HC development in pediatric patients undergoing allogeneic HSCT. Article summary : (1) Children carrying functional alleles in GSTM1 and CYP2C9 are at high risk for developing hemorrhagic cystitis following treatment with busulfan and cyclophosphamide based conditioning regimen. (2) Identification of children at high risk for developing hemorrhagic cystitis in an allogeneic HSCT setting will enable us to evaluate and implement optimal strategies for its prevention. Trial registration : This study is a part of the trail "clinicaltrials.gov identifier: NCT01257854."

Assessment of Body Condition in African (Loxodonta africana) and Asian (Elephas maximus) Elephants in North American Zoos and Management Practices Associated with High Body Condition Scores.

PubMed

Morfeld, Kari A; Meehan, Cheryl L; Hogan, Jennifer N; Brown, Janine L

2016-01-01

Obesity has a negative effect on health and welfare of many species, and has been speculated to be a problem for zoo elephants. To address this concern, we assessed the body condition of 240 elephants housed in North American zoos based on a set of standardized photographs using a 5-point Body Condition Score index (1 = thinnest; 5 = fattest). A multi-variable regression analysis was then used to determine how demographic, management, housing, and social factors were associated with an elevated body condition score in 132 African (Loxodonta africana) and 108 Asian (Elephas maximus) elephants. The highest BCS of 5, suggestive of obesity, was observed in 34% of zoo elephants. In both species, the majority of elephants had elevated BCS, with 74% in the BCS 4 (40%) and 5 (34%) categories. Only 22% of elephants had BCS 3, and less than 5% of the population was assigned the lowest BCS categories (BCS 1 and 2). The strongest multi-variable model demonstrated that staff-directed walking exercise of 14 hours or more per week and highly unpredictable feeding schedules were associated with decreased risk of BCS 4 or 5, while increased diversity in feeding methods and being female was associated with increased risk of BCS 4 or 5. Our data suggest that high body condition is prevalent among North American zoo elephants, and management strategies that help prevent and mitigate obesity may lead to improvements in welfare of zoo elephants.

Assessment of Body Condition in African (Loxodonta africana) and Asian (Elephas maximus) Elephants in North American Zoos and Management Practices Associated with High Body Condition Scores

PubMed Central

Morfeld, Kari A.; Meehan, Cheryl L.; Hogan, Jennifer N.; Brown, Janine L.

2016-01-01

Obesity has a negative effect on health and welfare of many species, and has been speculated to be a problem for zoo elephants. To address this concern, we assessed the body condition of 240 elephants housed in North American zoos based on a set of standardized photographs using a 5-point Body Condition Score index (1 = thinnest; 5 = fattest). A multi-variable regression analysis was then used to determine how demographic, management, housing, and social factors were associated with an elevated body condition score in 132 African (Loxodonta africana) and 108 Asian (Elephas maximus) elephants. The highest BCS of 5, suggestive of obesity, was observed in 34% of zoo elephants. In both species, the majority of elephants had elevated BCS, with 74% in the BCS 4 (40%) and 5 (34%) categories. Only 22% of elephants had BCS 3, and less than 5% of the population was assigned the lowest BCS categories (BCS 1 and 2). The strongest multi-variable model demonstrated that staff-directed walking exercise of 14 hours or more per week and highly unpredictable feeding schedules were associated with decreased risk of BCS 4 or 5, while increased diversity in feeding methods and being female was associated with increased risk of BCS 4 or 5. Our data suggest that high body condition is prevalent among North American zoo elephants, and management strategies that help prevent and mitigate obesity may lead to improvements in welfare of zoo elephants. PMID:27415629

1 H-NMR with Multivariate Analysis for Automobile Lubricant Comparison.

PubMed

Kim, Siwon; Yoon, Dahye; Lee, Dong-Kye; Yoon, Changshin; Kim, Suhkmann

2017-07-01

Identification of suspected automobile-related lubricants could provide valuable information in forensic cases. We examined that automobile lubricants might exhibit the chemometric characteristics to their individual usages. To compare the degree of clustering in the plots, we co-plotted general industrial oils that were highly dissimilar with automobile lubricants in additive compositions. 1 H-NMR spectroscopy was used with multivariate statistics as a tool for grouping, clustering, and identification of automobile lubricants in laboratory conditions. We analyzed automobile lubricants including automobile engine oils, automobile transmission oils, automobile gear oils, and motorcycle oils. In contrast to the general industrial oils, automobile lubricants showed relatively high tendencies of clustering to their usages. Our pilot study demonstrated that the comparison of known and questioned samples to their usages might be possible in forensic fields. © 2017 American Academy of Forensic Sciences.

Non-uniform multivariate embedding to assess the information transfer in cardiovascular and cardiorespiratory variability series.

PubMed

Faes, Luca; Nollo, Giandomenico; Porta, Alberto

2012-03-01

The complexity of the short-term cardiovascular control prompts for the introduction of multivariate (MV) nonlinear time series analysis methods to assess directional interactions reflecting the underlying regulatory mechanisms. This study introduces a new approach for the detection of nonlinear Granger causality in MV time series, based on embedding the series by a sequential, non-uniform procedure, and on estimating the information flow from one series to another by means of the corrected conditional entropy. The approach is validated on short realizations of linear stochastic and nonlinear deterministic processes, and then evaluated on heart period, systolic arterial pressure and respiration variability series measured from healthy humans in the resting supine position and in the upright position after head-up tilt. Copyright © 2011 Elsevier Ltd. All rights reserved.

Multivariate Phylogenetic Comparative Methods: Evaluations, Comparisons, and Recommendations.

PubMed

Adams, Dean C; Collyer, Michael L

2018-01-01

Recent years have seen increased interest in phylogenetic comparative analyses of multivariate data sets, but to date the varied proposed approaches have not been extensively examined. Here we review the mathematical properties required of any multivariate method, and specifically evaluate existing multivariate phylogenetic comparative methods in this context. Phylogenetic comparative methods based on the full multivariate likelihood are robust to levels of covariation among trait dimensions and are insensitive to the orientation of the data set, but display increasing model misspecification as the number of trait dimensions increases. This is because the expected evolutionary covariance matrix (V) used in the likelihood calculations becomes more ill-conditioned as trait dimensionality increases, and as evolutionary models become more complex. Thus, these approaches are only appropriate for data sets with few traits and many species. Methods that summarize patterns across trait dimensions treated separately (e.g., SURFACE) incorrectly assume independence among trait dimensions, resulting in nearly a 100% model misspecification rate. Methods using pairwise composite likelihood are highly sensitive to levels of trait covariation, the orientation of the data set, and the number of trait dimensions. The consequences of these debilitating deficiencies are that a user can arrive at differing statistical conclusions, and therefore biological inferences, simply from a dataspace rotation, like principal component analysis. By contrast, algebraic generalizations of the standard phylogenetic comparative toolkit that use the trace of covariance matrices are insensitive to levels of trait covariation, the number of trait dimensions, and the orientation of the data set. Further, when appropriate permutation tests are used, these approaches display acceptable Type I error and statistical power. We conclude that methods summarizing information across trait dimensions, as well as pairwise composite likelihood methods should be avoided, whereas algebraic generalizations of the phylogenetic comparative toolkit provide a useful means of assessing macroevolutionary patterns in multivariate data. Finally, we discuss areas in which multivariate phylogenetic comparative methods are still in need of future development; namely highly multivariate Ornstein-Uhlenbeck models and approaches for multivariate evolutionary model comparisons. © The Author(s) 2017. Published by Oxford University Press on behalf of the Systematic Biology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Workload and quality of life of medical doctors in the field of oncology in Germany--a survey of the working group quality of life of the AIO for the study group of internal oncology.

PubMed

Hipp, Matthias; Pilz, Lothar; Al-Batran, Salah E; Hautmann, Matthias G; Hofheinz, Ralf-Dieter

2015-01-01

An increasing number of surveys have investigated professional stress and satisfaction among oncologists. Coevally, structural development has changed the oncological working environment. This survey investigated the quality of life and job stress among German oncological physicians. A 48-item questionnaire, which included the 'Stress questionnaire of physicians and nurses' (FBAS), was developed by the 'Quality of life' working group of the Internal oncology study group (AIO), and distributed anonymously at the annual meeting of the AIO working group in 2010. Descriptive statistics as well as univariate and multivariate analysis were performed. 261 oncologists, mostly male (64%), older than 40 years (38%), and medical specialists (78%), took part in the survey. 'Structural conditions' were identified as causing the highest mean stress levels, followed by 'professional and private life'. Female participants showed a significantly lower global quality of life than male participants (p = 0.020). 'Structural conditions' induced more stress among younger oncologists < 50 years old (p < 0.001). Qualification status was influenced by gender (p < 0.001); the multivariate analysis described the dependence of gender (p = 0.0045), working situation (p = 0.0317) and global stress (p = 0.0008). Structural conditions, age younger than 50 years and female gender were identified as stress risk factors among the AIO members, and showed that job stress is present in German oncology. Further research is warranted to develop evidence-based intervention strategies. © 2015 S. Karger GmbH, Freiburg.

Conflicting relationship between age-dependent disorders, valvular heart disease and coronary artery disease by covariance structure analysis: Possible contribution of natriuretic peptide.

PubMed

Fukumoto, Risa; Kawai, Makoto; Minai, Kosuke; Ogawa, Kazuo; Yoshida, Jun; Inoue, Yasunori; Morimoto, Satoshi; Tanaka, Toshikazu; Nagoshi, Tomohisa; Ogawa, Takayuki; Yoshimura, Michihiro

2017-01-01

It is conceivable that contemporary valvular heart disease (VHD) is affected largely by an age-dependent atherosclerotic process, which is similar to that observed in coronary artery disease (CAD). However, a comorbid condition of VHD and CAD has not been precisely examined. The first objective of this study was to examine a possible comorbid condition. Provided that there is no comorbidity, the second objective was to search for the possible reasons by using conventional risk factors and plasma B-type natriuretic peptide (BNP) because BNP has a potentiality to suppress atherosclerotic development. The study population consisted of 3,457 patients consecutively admitted to our institution. The possible comorbid condition of VHD and CAD and the factors that influence the comorbidity were examined by covariance structure analysis and multivariate analysis. The distribution of the patients with VHD and those with CAD in the histograms showed that the incidence of VHD and the severity of CAD rose with seniority in appearance. The real statistical analysis was planned by covariance structure analysis. The current path model revealed that aging was associated with VHD and CAD severity (P < 0.001 for each); however, as a notable result, there was an inverse association regarding the comorbid condition between VHD and CAD (Correlation coefficient [β]: -0.121, P < 0.001). As the second objective, to clarify the factors leading to this inverse association, the contribution of conventional risk factors, such as age, gender, hypertension, smoking, diabetes, obesity and dyslipidemia, to VHD and CAD were examined by multivariate analysis. However, these factors did not exert an opposing effect on VHD and CAD, and the inverse association defied explanation. Since different pathological mechanisms may contribute to the formation of VHD and CAD, a differentially proposed path model using plasma BNP revealed that an increase in plasma BNP being drawn by VHD suppressed the progression of CAD (β: -0.465, P < 0.001). The incidence of VHD and CAD showed a significant conflicting relationship. This result supported the likely presence of unknown diverse mechanisms on top of the common cascade of atherosclerosis. Among them, the continuous elevation of plasma BNP due to VHD might be one of the explicable factors suppressing the progression of CAD.

Chemical Discrimination of Cortex Phellodendri amurensis and Cortex Phellodendri chinensis by Multivariate Analysis Approach.

PubMed

Sun, Hui; Wang, Huiyu; Zhang, Aihua; Yan, Guangli; Han, Ying; Li, Yuan; Wu, Xiuhong; Meng, Xiangcai; Wang, Xijun

2016-01-01

As herbal medicines have an important position in health care systems worldwide, their current assessment, and quality control are a major bottleneck. Cortex Phellodendri chinensis (CPC) and Cortex Phellodendri amurensis (CPA) are widely used in China, however, how to identify species of CPA and CPC has become urgent. In this study, multivariate analysis approach was performed to the investigation of chemical discrimination of CPA and CPC. Principal component analysis showed that two herbs could be separated clearly. The chemical markers such as berberine, palmatine, phellodendrine, magnoflorine, obacunone, and obaculactone were identified through the orthogonal partial least squared discriminant analysis, and were identified tentatively by the accurate mass of quadruple-time-of-flight mass spectrometry. A total of 29 components can be used as the chemical markers for discrimination of CPA and CPC. Of them, phellodenrine is significantly higher in CPC than that of CPA, whereas obacunone and obaculactone are significantly higher in CPA than that of CPC. The present study proves that multivariate analysis approach based chemical analysis greatly contributes to the investigation of CPA and CPC, and showed that the identified chemical markers as a whole should be used to discriminate the two herbal medicines, and simultaneously the results also provided chemical information for their quality assessment. Multivariate analysis approach was performed to the investigate the herbal medicineThe chemical markers were identified through multivariate analysis approachA total of 29 components can be used as the chemical markers. UPLC-Q/TOF-MS-based multivariate analysis method for the herbal medicine samples Abbreviations used: CPC: Cortex Phellodendri chinensis, CPA: Cortex Phellodendri amurensis, PCA: Principal component analysis, OPLS-DA: Orthogonal partial least squares discriminant analysis, BPI: Base peaks ion intensity.

metaCCA: summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis.

PubMed

Cichonska, Anna; Rousu, Juho; Marttinen, Pekka; Kangas, Antti J; Soininen, Pasi; Lehtimäki, Terho; Raitakari, Olli T; Järvelin, Marjo-Riitta; Salomaa, Veikko; Ala-Korpela, Mika; Ripatti, Samuli; Pirinen, Matti

2016-07-01

A dominant approach to genetic association studies is to perform univariate tests between genotype-phenotype pairs. However, analyzing related traits together increases statistical power, and certain complex associations become detectable only when several variants are tested jointly. Currently, modest sample sizes of individual cohorts, and restricted availability of individual-level genotype-phenotype data across the cohorts limit conducting multivariate tests. We introduce metaCCA, a computational framework for summary statistics-based analysis of a single or multiple studies that allows multivariate representation of both genotype and phenotype. It extends the statistical technique of canonical correlation analysis to the setting where original individual-level records are not available, and employs a covariance shrinkage algorithm to achieve robustness.Multivariate meta-analysis of two Finnish studies of nuclear magnetic resonance metabolomics by metaCCA, using standard univariate output from the program SNPTEST, shows an excellent agreement with the pooled individual-level analysis of original data. Motivated by strong multivariate signals in the lipid genes tested, we envision that multivariate association testing using metaCCA has a great potential to provide novel insights from already published summary statistics from high-throughput phenotyping technologies. Code is available at https://github.com/aalto-ics-kepaco anna.cichonska@helsinki.fi or matti.pirinen@helsinki.fi Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

metaCCA: summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis

PubMed Central

Cichonska, Anna; Rousu, Juho; Marttinen, Pekka; Kangas, Antti J.; Soininen, Pasi; Lehtimäki, Terho; Raitakari, Olli T.; Järvelin, Marjo-Riitta; Salomaa, Veikko; Ala-Korpela, Mika; Ripatti, Samuli; Pirinen, Matti

2016-01-01

Motivation: A dominant approach to genetic association studies is to perform univariate tests between genotype-phenotype pairs. However, analyzing related traits together increases statistical power, and certain complex associations become detectable only when several variants are tested jointly. Currently, modest sample sizes of individual cohorts, and restricted availability of individual-level genotype-phenotype data across the cohorts limit conducting multivariate tests. Results: We introduce metaCCA, a computational framework for summary statistics-based analysis of a single or multiple studies that allows multivariate representation of both genotype and phenotype. It extends the statistical technique of canonical correlation analysis to the setting where original individual-level records are not available, and employs a covariance shrinkage algorithm to achieve robustness. Multivariate meta-analysis of two Finnish studies of nuclear magnetic resonance metabolomics by metaCCA, using standard univariate output from the program SNPTEST, shows an excellent agreement with the pooled individual-level analysis of original data. Motivated by strong multivariate signals in the lipid genes tested, we envision that multivariate association testing using metaCCA has a great potential to provide novel insights from already published summary statistics from high-throughput phenotyping technologies. Availability and implementation: Code is available at https://github.com/aalto-ics-kepaco Contacts: anna.cichonska@helsinki.fi or matti.pirinen@helsinki.fi Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27153689

The association between a body shape index and cardiovascular risk in overweight and obese children and adolescents.

PubMed

Mameli, Chiara; Krakauer, Nir Y; Krakauer, Jesse C; Bosetti, Alessandra; Ferrari, Chiara Matilde; Moiana, Norma; Schneider, Laura; Borsani, Barbara; Genoni, Teresa; Zuccotti, Gianvincenzo

2018-01-01

A Body Shape Index (ABSI) and normalized hip circumference (Hip Index, HI) have been recently shown to be strong risk factors for mortality and for cardiovascular disease in adults. We conducted an observational cross-sectional study to evaluate the relationship between ABSI, HI and cardiometabolic risk factors and obesity-related comorbidities in overweight and obese children and adolescents aged 2-18 years. We performed multivariate linear and logistic regression analyses with BMI, ABSI, and HI age and sex normalized z scores as predictors to examine the association with cardiometabolic risk markers (systolic and diastolic blood pressure, fasting glucose and insulin, total cholesterol and its components, transaminases, fat mass % detected by bioelectrical impedance analysis) and obesity-related conditions (including hepatic steatosis and metabolic syndrome). We recruited 217 patients (114 males), mean age 11.3 years. Multivariate linear regression showed a significant association of ABSI z score with 10 out of 15 risk markers expressed as continuous variables, while BMI z score showed a significant correlation with 9 and HI only with 1. In multivariate logistic regression to predict occurrence of obesity-related conditions and above-threshold values of risk factors, BMI z score was significantly correlated to 7 out of 12, ABSI to 5, and HI to 1. Overall, ABSI is an independent anthropometric index that was significantly associated with cardiometabolic risk markers in a pediatric population affected by overweight and obesity.

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

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

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

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

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

DOE PAGES

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

2017-12-28

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

Assessment of self-organizing maps to analyze sole-carbon source utilization profiles.

PubMed

Leflaive, Joséphine; Céréghino, Régis; Danger, Michaël; Lacroix, Gérard; Ten-Hage, Loïc

2005-07-01

The use of community-level physiological profiles obtained with Biolog microplates is widely employed to consider the functional diversity of bacterial communities. Biolog produces a great amount of data which analysis has been the subject of many studies. In most cases, after some transformations, these data were investigated with classical multivariate analyses. Here we provided an alternative to this method, that is the use of an artificial intelligence technique, the Self-Organizing Maps (SOM, unsupervised neural network). We used data from a microcosm study of algae-associated bacterial communities placed in various nutritive conditions. Analyses were carried out on the net absorbances at two incubation times for each substrates and on the chemical guild categorization of the total bacterial activity. Compared to Principal Components Analysis and cluster analysis, SOM appeared as a valuable tool for community classification, and to establish clear relationships between clusters of bacterial communities and sole-carbon sources utilization. Specifically, SOM offered a clear bidimensional projection of a relatively large volume of data and were easier to interpret than plots commonly obtained with multivariate analyses. They would be recommended to pattern the temporal evolution of communities' functional diversity.

Groundwater flow and hydrogeochemical evolution in the Jianghan Plain, central China

NASA Astrophysics Data System (ADS)

Gan, Yiqun; Zhao, Ke; Deng, Yamin; Liang, Xing; Ma, Teng; Wang, Yanxin

2018-05-01

Hydrogeochemical analysis and multivariate statistics were applied to identify flow patterns and major processes controlling the hydrogeochemistry of groundwater in the Jianghan Plain, which is located in central Yangtze River Basin (central China) and characterized by intensive surface-water/groundwater interaction. Although HCO3-Ca-(Mg) type water predominated in the study area, the 457 (21 surface water and 436 groundwater) samples were effectively classified into five clusters by hierarchical cluster analysis. The hydrochemical variations among these clusters were governed by three factors from factor analysis. Major components (e.g., Ca, Mg and HCO3) in surface water and groundwater originated from carbonate and silicate weathering (factor 1). Redox conditions (factor 2) influenced the geogenic Fe and As contamination in shallow confined groundwater. Anthropogenic activities (factor 3) primarily caused high levels of Cl and SO4 in surface water and phreatic groundwater. Furthermore, the factor score 1 of samples in the shallow confined aquifer gradually increased along the flow paths. This study demonstrates that enhanced information on hydrochemistry in complex groundwater flow systems, by multivariate statistical methods, improves the understanding of groundwater flow and hydrogeochemical evolution due to natural and anthropogenic impacts.

Elemental content of Vietnamese rice. Part 2. Multivariate data analysis.

PubMed

Kokot, S; Phuong, T D

1999-04-01

Rice samples were obtained from the Red River region and some other parts of Vietnam as well as from Yanco, Australia. These samples were analysed for 14 elements (P, K, Mg, Ca, Mn, Zn, Fe, Cu, Al, Na, Ni, As, Mo and Cd) by ICP-AES, ICP-MS and FAAS as described in Part 1. This data matrix was then submitted to multivariate data analysis by principal component analysis to investigate the influences of environmental and crop cultivation variables on the elemental content of rice. Results revealed that geographical location, grain variety, seasons and soil conditions are the most likely significant factors causing changes in the elemental content between the rice samples. To assess rice quality according to its elemental content and physio-biological properties, a multicriteria decision making method (PROMETHEE) was applied. With the Vietnamese rice, the sticky rice appeared to contain somewhat higher levels of nutritionally significant elements such as P, K and Mg than the non-sticky rice. Also, rice samples grown during the wet season have better levels of nutritionally significant mineral elements than those of the dry season, but in general, the wet season seemed to provide better overall elemental and physio-biological rice quality.

NMR metabolomics of ripened and developing oilseed rape (Brassica napus) and turnip rape (Brassica rapa).

PubMed

Kortesniemi, Maaria; Vuorinen, Anssi L; Sinkkonen, Jari; Yang, Baoru; Rajala, Ari; Kallio, Heikki

2015-04-01

The oilseeds of the commercially important oilseed rape (Brassica napus) and turnip rape (Brassica rapa) were investigated with (1)H NMR metabolomics. The compositions of ripened (cultivated in field trials) and developing seeds (cultivated in controlled conditions) were compared in multivariate models using principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA). Differences in the major lipids and the minor metabolites between the two species were found. A higher content of polyunsaturated fatty acids and sucrose were observed in turnip rape, while the overall oil content and sinapine levels were higher in oilseed rape. The genotype traits were negligible compared to the effect of the growing site and concomitant conditions on the oilseed metabolome. This study demonstrates the applicability of NMR-based analysis in determining the species, geographical origin, developmental stage, and quality of oilseed Brassicas. Copyright © 2014 Elsevier Ltd. All rights reserved.

Using Interactive Graphics to Teach Multivariate Data Analysis to Psychology Students

ERIC Educational Resources Information Center

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

2011-01-01

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

A power analysis for multivariate tests of temporal trend in species composition.

PubMed

Irvine, Kathryn M; Dinger, Eric C; Sarr, Daniel

2011-10-01

Long-term monitoring programs emphasize power analysis as a tool to determine the sampling effort necessary to effectively document ecologically significant changes in ecosystems. Programs that monitor entire multispecies assemblages require a method for determining the power of multivariate statistical models to detect trend. We provide a method to simulate presence-absence species assemblage data that are consistent with increasing or decreasing directional change in species composition within multiple sites. This step is the foundation for using Monte Carlo methods to approximate the power of any multivariate method for detecting temporal trends. We focus on comparing the power of the Mantel test, permutational multivariate analysis of variance, and constrained analysis of principal coordinates. We find that the power of the various methods we investigate is sensitive to the number of species in the community, univariate species patterns, and the number of sites sampled over time. For increasing directional change scenarios, constrained analysis of principal coordinates was as or more powerful than permutational multivariate analysis of variance, the Mantel test was the least powerful. However, in our investigation of decreasing directional change, the Mantel test was typically as or more powerful than the other models.

Metabolomic and Transcriptomic Comparison of Solid-State and Submerged Fermentation of Penicillium expansum KACC 40815

PubMed Central

Park, Hye Min; Singh, Digar; Lee, Choong Hwan

2016-01-01

Penicillium spp. are known to harbor a wide array of secondary metabolites with cryptic bioactivities. However, the metabolomics of these species is not well-understood in terms of different fermentation models and conditions. The present study involved metabolomics profiling and transcriptomic analysis of Penicillium expansum 40815 under solid-state fermentation (SSF) and submerged fermentation (SmF). Metabolite profiling was carried out using ultra-performance liquid chromatography quadruple time-of-flight mass spectrometry with multivariate analysis, followed by transcriptomic analyses of differentially expressed genes. In principal component analysis, the metabolite profiling data was studied under different experimental sets, including SSF and SmF. The significantly different metabolites such as polyketide metabolites (agonodepside B, rotiorin, verrucosidin, and ochrephilone) and corresponding gene transcripts (polyketide synthase, aromatic prenyltransferase, and terpenoid synthase) were primarily detected under SmF conditions. In contrast, the meroterpenoid compounds (andrastin A and C) and their genes transcripts were exclusively detected under SSF conditions. We demonstrated that the metabolite production and its corresponding gene expression levels in P. expansum 40815 were significantly influenced by the varying growth parameters and the immediate environment. This study further provides a foundation to produce specific metabolites by regulating fermentation conditions. PMID:26863302

Metabolomic and Transcriptomic Comparison of Solid-State and Submerged Fermentation of Penicillium expansum KACC 40815.

PubMed

Kim, Hyang Yeon; Heo, Do Yeon; Park, Hye Min; Singh, Digar; Lee, Choong Hwan

2016-01-01

Penicillium spp. are known to harbor a wide array of secondary metabolites with cryptic bioactivities. However, the metabolomics of these species is not well-understood in terms of different fermentation models and conditions. The present study involved metabolomics profiling and transcriptomic analysis of Penicillium expansum 40815 under solid-state fermentation (SSF) and submerged fermentation (SmF). Metabolite profiling was carried out using ultra-performance liquid chromatography quadruple time-of-flight mass spectrometry with multivariate analysis, followed by transcriptomic analyses of differentially expressed genes. In principal component analysis, the metabolite profiling data was studied under different experimental sets, including SSF and SmF. The significantly different metabolites such as polyketide metabolites (agonodepside B, rotiorin, verrucosidin, and ochrephilone) and corresponding gene transcripts (polyketide synthase, aromatic prenyltransferase, and terpenoid synthase) were primarily detected under SmF conditions. In contrast, the meroterpenoid compounds (andrastin A and C) and their genes transcripts were exclusively detected under SSF conditions. We demonstrated that the metabolite production and its corresponding gene expression levels in P. expansum 40815 were significantly influenced by the varying growth parameters and the immediate environment. This study further provides a foundation to produce specific metabolites by regulating fermentation conditions.

Targeted metabolomic profiling in rat tissues reveals sex differences.

PubMed

Ruoppolo, Margherita; Caterino, Marianna; Albano, Lucia; Pecce, Rita; Di Girolamo, Maria Grazia; Crisci, Daniela; Costanzo, Michele; Milella, Luigi; Franconi, Flavia; Campesi, Ilaria

2018-03-16

Sex differences affect several diseases and are organ-and parameter-specific. In humans and animals, sex differences also influence the metabolism and homeostasis of amino acids and fatty acids, which are linked to the onset of diseases. Thus, the use of targeted metabolite profiles in tissues represents a powerful approach to examine the intermediary metabolism and evidence for any sex differences. To clarify the sex-specific activities of liver, heart and kidney tissues, we used targeted metabolomics, linear discriminant analysis (LDA), principal component analysis (PCA), cluster analysis and linear correlation models to evaluate sex and organ-specific differences in amino acids, free carnitine and acylcarnitine levels in male and female Sprague-Dawley rats. Several intra-sex differences affect tissues, indicating that metabolite profiles in rat hearts, livers and kidneys are organ-dependent. Amino acids and carnitine levels in rat hearts, livers and kidneys are affected by sex: male and female hearts show the greatest sexual dimorphism, both qualitatively and quantitatively. Finally, multivariate analysis confirmed the influence of sex on the metabolomics profiling. Our data demonstrate that the metabolomics approach together with a multivariate approach can capture the dynamics of physiological and pathological states, which are essential for explaining the basis of the sex differences observed in physiological and pathological conditions.

Contraceptive Use among Women with Chronic Medical Conditions and Factors Associated with Its Non-Use in Malaysia

PubMed Central

Manaf, Rosliza Abdul; Ismail, Irmi Zarina; Latiff, Latiffah A.

2012-01-01

Introduction: Women with chronic medical conditions are at higher risk of adverse pregnancy outcomes, which may be minimized through optimal preconception care and appropriate contraceptive use. This study aimed to describe contraceptive use among women with chronic medical conditions and factors associated with its non-use. Methods: This study used cross-sectional data from a family planning survey among women with chronic medical conditions conducted in three health facilities in a southern state of Malaysia. A total of 450 married women in reproductive age (18-50 year) with intact uterus, and do not plan to conceive were analysed for contraceptive use. Both univariate and multivariate analysis was conducted to identify factors associated with contraceptive non-use among the study participants. Results: A total of 312 (69.3%) of the study participants did not use contraceptive. Contraceptive non-use was highest among the diabetics (71.2%), connective tissue disease patients (68.6%) and hypertensive patients (65.3%). Only 26.3% of women with heart disease did not use contraceptive. In the multivariate analysis, contraceptive non-use was significantly more common among women who received their medical treatment in the health clinics as compared to those who received treatment in the hospital (adjusted odds ratio [OR]=1.75, 95% confidence interval [CI]: 1.09, 2.79), being in older age group of 41-50 year (adjusted OR=2.31, 95% CI: 1.19, 4.48), having children (adjusted OR=4.57, 95% CI: 1.66, 12.57) and having lower education (adjusted OR=2.87, 95% CI: 1.43, 5.77). Conclusion: About two-third of women with chronic medical conditions who needed contraceptive did not use them despite the higher risk of pregnancy related complications. The high unmet need warrant an effective health promotion programme to encourage the uptake of contraceptives especially targeting women of older age group, low education and those who received their medical treatment at health clinics. PMID:22980381

Fourier Transform Infrared Spectroscopy (FTIR) and Multivariate Analysis for Identification of Different Vegetable Oils Used in Biodiesel Production

PubMed Central

Mueller, Daniela; Ferrão, Marco Flôres; Marder, Luciano; da Costa, Adilson Ben; de Cássia de Souza Schneider, Rosana

2013-01-01

The main objective of this study was to use infrared spectroscopy to identify vegetable oils used as raw material for biodiesel production and apply multivariate analysis to the data. Six different vegetable oil sources—canola, cotton, corn, palm, sunflower and soybeans—were used to produce biodiesel batches. The spectra were acquired by Fourier transform infrared spectroscopy using a universal attenuated total reflectance sensor (FTIR-UATR). For the multivariate analysis principal component analysis (PCA), hierarchical cluster analysis (HCA), interval principal component analysis (iPCA) and soft independent modeling of class analogy (SIMCA) were used. The results indicate that is possible to develop a methodology to identify vegetable oils used as raw material in the production of biodiesel by FTIR-UATR applying multivariate analysis. It was also observed that the iPCA found the best spectral range for separation of biodiesel batches using FTIR-UATR data, and with this result, the SIMCA method classified 100% of the soybean biodiesel samples. PMID:23539030

Multivariate meta-analysis for non-linear and other multi-parameter associations

PubMed Central

Gasparrini, A; Armstrong, B; Kenward, M G

2012-01-01

In this paper, we formalize the application of multivariate meta-analysis and meta-regression to synthesize estimates of multi-parameter associations obtained from different studies. This modelling approach extends the standard two-stage analysis used to combine results across different sub-groups or populations. The most straightforward application is for the meta-analysis of non-linear relationships, described for example by regression coefficients of splines or other functions, but the methodology easily generalizes to any setting where complex associations are described by multiple correlated parameters. The modelling framework of multivariate meta-analysis is implemented in the package mvmeta within the statistical environment R. As an illustrative example, we propose a two-stage analysis for investigating the non-linear exposure–response relationship between temperature and non-accidental mortality using time-series data from multiple cities. Multivariate meta-analysis represents a useful analytical tool for studying complex associations through a two-stage procedure. Copyright © 2012 John Wiley & Sons, Ltd. PMID:22807043

Dose-dependent effect of mammographic breast density on the risk of contralateral breast cancer.

PubMed

Chowdhury, Marzana; Euhus, David; O'Donnell, Maureen; Onega, Tracy; Choudhary, Pankaj K; Biswas, Swati

2018-07-01

Increased mammographic breast density is a significant risk factor for breast cancer. It is not clear if it is also a risk factor for the development of contralateral breast cancer. The data were obtained from Breast Cancer Surveillance Consortium and included women diagnosed with invasive breast cancer or ductal carcinoma in situ between ages 18 and 88 and years 1995 and 2009. Each case of contralateral breast cancer was matched with three controls based on year of first breast cancer diagnosis, race, and length of follow-up. A total of 847 cases and 2541 controls were included. The risk factors included in the study were mammographic breast density, age of first breast cancer diagnosis, family history of breast cancer, anti-estrogen treatment, hormone replacement therapy, menopausal status, and estrogen receptor status, all from the time of first breast cancer diagnosis. Both univariate analysis and multivariate conditional logistic regression analysis were performed. In the final multivariate model, breast density, family history of breast cancer, and anti-estrogen treatment remained significant with p values less than 0.01. Increasing breast density had a dose-dependent effect on the risk of contralateral breast cancer. Relative to 'almost entirely fat' category of breast density, the adjusted odds ratios (and p values) in the multivariate analysis for 'scattered density,' 'heterogeneously dense,' and 'extremely dense' categories were 1.65 (0.036), 2.10 (0.002), and 2.32 (0.001), respectively. Breast density is an independent and significant risk factor for development of contralateral breast cancer. This risk factor should contribute to clinical decision making.

MAVTgsa: An R Package for Gene Set (Enrichment) Analysis

DOE PAGES

Chien, Chih-Yi; Chang, Ching-Wei; Tsai, Chen-An; ...

2014-01-01

Gene semore » t analysis methods aim to determine whether an a priori defined set of genes shows statistically significant difference in expression on either categorical or continuous outcomes. Although many methods for gene set analysis have been proposed, a systematic analysis tool for identification of different types of gene set significance modules has not been developed previously. This work presents an R package, called MAVTgsa, which includes three different methods for integrated gene set enrichment analysis. (1) The one-sided OLS (ordinary least squares) test detects coordinated changes of genes in gene set in one direction, either up- or downregulation. (2) The two-sided MANOVA (multivariate analysis variance) detects changes both up- and downregulation for studying two or more experimental conditions. (3) A random forests-based procedure is to identify gene sets that can accurately predict samples from different experimental conditions or are associated with the continuous phenotypes. MAVTgsa computes the P values and FDR (false discovery rate) q -value for all gene sets in the study. Furthermore, MAVTgsa provides several visualization outputs to support and interpret the enrichment results. This package is available online.« less

The Potential of Multivariate Analysis in Assessing Students' Attitude to Curriculum Subjects

ERIC Educational Resources Information Center

Gaotlhobogwe, Michael; Laugharne, Janet; Durance, Isabelle

2011-01-01

Background: Understanding student attitudes to curriculum subjects is central to providing evidence-based options to policy makers in education. Purpose: We illustrate how quantitative approaches used in the social sciences and based on multivariate analysis (categorical Principal Components Analysis, Clustering Analysis and General Linear…

Two-sample tests and one-way MANOVA for multivariate biomarker data with nondetects.

PubMed

Thulin, M

2016-09-10

Testing whether the mean vector of a multivariate set of biomarkers differs between several populations is an increasingly common problem in medical research. Biomarker data is often left censored because some measurements fall below the laboratory's detection limit. We investigate how such censoring affects multivariate two-sample and one-way multivariate analysis of variance tests. Type I error rates, power and robustness to increasing censoring are studied, under both normality and non-normality. Parametric tests are found to perform better than non-parametric alternatives, indicating that the current recommendations for analysis of censored multivariate data may have to be revised. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

A non-iterative extension of the multivariate random effects meta-analysis.

PubMed

Makambi, Kepher H; Seung, Hyunuk

2015-01-01

Multivariate methods in meta-analysis are becoming popular and more accepted in biomedical research despite computational issues in some of the techniques. A number of approaches, both iterative and non-iterative, have been proposed including the multivariate DerSimonian and Laird method by Jackson et al. (2010), which is non-iterative. In this study, we propose an extension of the method by Hartung and Makambi (2002) and Makambi (2001) to multivariate situations. A comparison of the bias and mean square error from a simulation study indicates that, in some circumstances, the proposed approach perform better than the multivariate DerSimonian-Laird approach. An example is presented to demonstrate the application of the proposed approach.

A multivariable model for predicting the frictional behaviour and hydration of the human skin.

PubMed

Veijgen, N K; van der Heide, E; Masen, M A

2013-08-01

The frictional characteristics of skin-object interactions are important when handling objects, in the assessment of perception and comfort of products and materials and in the origins and prevention of skin injuries. In this study, based on statistical methods, a quantitative model is developed that describes the friction behaviour of human skin as a function of the subject characteristics, contact conditions, the properties of the counter material as well as environmental conditions. Although the frictional behaviour of human skin is a multivariable problem, in literature the variables that are associated with skin friction have been studied using univariable methods. In this work, multivariable models for the static and dynamic coefficients of friction as well as for the hydration of the skin are presented. A total of 634 skin-friction measurements were performed using a recently developed tribometer. Using a statistical analysis, previously defined potential influential variables were linked to the static and dynamic coefficient of friction and to the hydration of the skin, resulting in three predictive quantitative models that descibe the friction behaviour and the hydration of human skin respectively. Increased dynamic coefficients of friction were obtained from older subjects, on the index finger, with materials with a higher surface energy at higher room temperatures, whereas lower dynamic coefficients of friction were obtained at lower skin temperatures, on the temple with rougher contact materials. The static coefficient of friction increased with higher skin hydration, increasing age, on the index finger, with materials with a higher surface energy and at higher ambient temperatures. The hydration of the skin was associated with the skin temperature, anatomical location, presence of hair on the skin and the relative air humidity. Predictive models have been derived for the static and dynamic coefficient of friction using a multivariable approach. These two coefficients of friction show a strong correlation. Consequently the two multivariable models resemble, with the static coefficient of friction being on average 18% lower than the dynamic coefficient of friction. The multivariable models in this study can be used to describe the data set that was the basis for this study. Care should be taken when generalising these results. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Multivariate Autoregressive Modeling and Granger Causality Analysis of Multiple Spike Trains

PubMed Central

Krumin, Michael; Shoham, Shy

2010-01-01

Recent years have seen the emergence of microelectrode arrays and optical methods allowing simultaneous recording of spiking activity from populations of neurons in various parts of the nervous system. The analysis of multiple neural spike train data could benefit significantly from existing methods for multivariate time-series analysis which have proven to be very powerful in the modeling and analysis of continuous neural signals like EEG signals. However, those methods have not generally been well adapted to point processes. Here, we use our recent results on correlation distortions in multivariate Linear-Nonlinear-Poisson spiking neuron models to derive generalized Yule-Walker-type equations for fitting ‘‘hidden” Multivariate Autoregressive models. We use this new framework to perform Granger causality analysis in order to extract the directed information flow pattern in networks of simulated spiking neurons. We discuss the relative merits and limitations of the new method. PMID:20454705

A refined method for multivariate meta-analysis and meta-regression.

PubMed

Jackson, Daniel; Riley, Richard D

2014-02-20

Making inferences about the average treatment effect using the random effects model for meta-analysis is problematic in the common situation where there is a small number of studies. This is because estimates of the between-study variance are not precise enough to accurately apply the conventional methods for testing and deriving a confidence interval for the average effect. We have found that a refined method for univariate meta-analysis, which applies a scaling factor to the estimated effects' standard error, provides more accurate inference. We explain how to extend this method to the multivariate scenario and show that our proposal for refined multivariate meta-analysis and meta-regression can provide more accurate inferences than the more conventional approach. We explain how our proposed approach can be implemented using standard output from multivariate meta-analysis software packages and apply our methodology to two real examples. Copyright © 2013 John Wiley & Sons, Ltd.

Psychotic-like experiences and happiness in the English general population.

PubMed

Koyanagi, Ai

2017-11-01

Psychotic-like experiences (PLEs) have been associated with a variety of adverse outcomes but how they affect happiness in individuals with PLE is unknown. Thus, the aim of the study was to assess the association between PLEs and happiness, and the factors that may influence this association. Nationally representative data from the 2007 Adult Psychiatric Morbidity Survey including adults aged ≥ 16 years was analyzed. The Psychosis Screening Questionnaire was used to assess past 12-month PLE. Individuals who endorsed at least one of the following were considered to have any PLE: thought control, paranoia, strange experiences, auditory hallucinations. Happiness (3-point scale) was assessed with a validated question with higher scores indicating lower levels of happiness. The association between PLE and happiness was assessed by multivariable ordinal logistic regression. Mediation analysis was also performed. Among the 7363 individuals included in the analysis, the prevalence of any PLE increased with decreasing levels of happiness [very happy (2.3%), fairly happy (5.4%), not too happy (14.9%)]. This was also shown in the multivariable analysis adjusted for sociodemographic factors and stressful life events (from very happy to not too happy: OR = 2.41; 95%CI = 1.86-3.12). Mediation analysis showed that anxiety disorders explained the largest proportion of the association (38.8%) followed by depressive episode (28.5%), insomnia (21.9%), disability (16.5%), pain (12.5%), social support (10.0%), and physical health conditions (6.0%). The cross-sectional design limits causal inference. Interventions to identify and address conditions that may have a negative impact on happiness in individuals with PLE may be important to improve their well-being. Copyright © 2017 Elsevier B.V. All rights reserved.

Causes of death and competing risk analysis of the associated factors for non-small cell lung cancer using the Surveillance, Epidemiology, and End Results database.

PubMed

Wei, Shenhai; Tian, Jintao; Song, Xiaoping; Wu, Bingqun; Liu, Limin

2018-01-01

To investigate the probability of death (POD) from any causes by time after diagnosis of non-small cell lung cancer (NSCLC) and the factors associated with survival for NSCLC patients. A total of 202,914 patients with NSCLC from 2004 to 2013 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. The overall survival (OS) and lung cancer-specific survival (LCSS) were calculated and POD from any causes at different time periods after diagnosis was explored. The predictive factors for OS, LCSS and survival from non-lung cancer deaths were investigated using multivariate analysis with Cox proportional hazards regression and competing risk regression analysis. The 5- and 10-year OS were 20.4% and 11.5%, accordingly that for LCSS were 25.5% and 18.4%, respectively. Lung cancer contributed 88.3% (n = 128,402) of the deaths. The POD from lung cancer decreased with time after diagnosis. In multivariate analysis, advanced age and advanced stage of NSCLC were associated with decreased OS and LCSS. Comparing to no surgery, any kind of resection conferred lower risk of death from lung cancer and higher risk of dying from non-lung cancer conditions except lobectomy or bilobectomy, which was associated with lower risk of death from both lung cancer and non-lung cancer conditions. Most of the patients with NSCLC died from lung cancer. Rational surveillance and treatment policies should be made for them. Early stage and lobectomy or bilobectomy were associated with improved OS and LCSS. It is reasonable to focus on early detection and optimal surgical treatment for NSCLC.

Inorganic selenium speciation analysis in Allium and Brassica vegetables by ionic liquid assisted liquid-liquid microextraction with multivariate optimization.

PubMed

Castro Grijalba, Alexander; Martinis, Estefanía M; Wuilloud, Rodolfo G

2017-03-15

A highly sensitive vortex assisted liquid-liquid microextraction (VA-LLME) method was developed for inorganic Se [Se(IV) and Se(VI)] speciation analysis in Allium and Brassica vegetables. Trihexyl(tetradecyl)phosphonium decanoate phosphonium ionic liquid (IL) was applied for the extraction of Se(IV)-ammonium pyrrolidine dithiocarbamate (APDC) complex followed by Se determination with electrothermal atomic absorption spectrometry. A complete optimization of the graphite furnace temperature program was developed for accurate determination of Se in the IL-enriched extracts and multivariate statistical optimization was performed to define the conditions for the highest extraction efficiency. Significant factors of IL-VA-LLME method were sample volume, extraction pH, extraction time and APDC concentration. High extraction efficiency (90%), a 100-fold preconcentration factor and a detection limit of 5.0ng/L were achieved. The high sensitivity obtained with preconcentration and the non-chromatographic separation of inorganic Se species in complex matrix samples such as garlic, onion, leek, broccoli and cauliflower, are the main advantages of IL-VA-LLME. Copyright © 2016 Elsevier Ltd. All rights reserved.

Evaluation of the combined solar TiO2/photo-Fenton process using multivariate analysis.

PubMed

Nogueira, R F P; Trovó, A G; Paterlini, W C

2004-01-01

The effect of combining the photocatalytic processes using TiO2 and the photo-Fenton reaction with Fe3+ or ferrioxalate as a source of Fe2+ was investigated in the degradation of 4-chlorophenol (4CP) and dichloroacetic acid (DCA) using solar irradiation. Multivariate analysis was used to evaluate the role of three variables: iron, H2O2 and TiO2 concentrations. The results show that TiO2 plays a minor role when compared to iron and H2O2 in the solar degradation of 4CP and DCA in the studied conditions. However, its presence can improve TOC removal when H2O2 is totally consumed. Iron and peroxide play major roles, especially when Fe(NO3)3 is used in the degradation of 4CP. No significant synergistic effect was observed by the addition of TiO2 in this process. On the other hand, synergistic effects were observed between FeOx and TiO2 and between H2O2 and TiO2 in the degradation of DCA.

Using Boosting Decision Trees in Gravitational Wave Searches triggered by Gamma-ray Bursts

NASA Astrophysics Data System (ADS)

Zuraw, Sarah; LIGO Collaboration

2015-04-01

The search for gravitational wave bursts requires the ability to distinguish weak signals from background detector noise. Gravitational wave bursts are characterized by their transient nature, making them particularly difficult to detect as they are similar to non-Gaussian noise fluctuations in the detector. The Boosted Decision Tree method is a powerful machine learning algorithm which uses Multivariate Analysis techniques to explore high-dimensional data sets in order to distinguish between gravitational wave signal and background detector noise. It does so by training with known noise events and simulated gravitational wave events. The method is tested using waveform models and compared with the performance of the standard gravitational wave burst search pipeline for Gamma-ray Bursts. It is shown that the method is able to effectively distinguish between signal and background events under a variety of conditions and over multiple Gamma-ray Burst events. This example demonstrates the usefulness and robustness of the Boosted Decision Tree and Multivariate Analysis techniques as a detection method for gravitational wave bursts. LIGO, UMass, PREP, NEGAP.

Thiotepa-based conditioning versus total body irradiation as myeloablative conditioning prior to allogeneic stem cell transplantation for acute lymphoblastic leukemia: A matched-pair analysis from the Acute Leukemia Working Party of the European Society for Blood and Marrow Transplantation.

PubMed

Eder, Sandra; Canaani, Jonathan; Beohou, Eric; Labopin, Myriam; Sanz, Jaime; Arcese, William; Or, Reuven; Finke, Juergen; Cortelezzi, Agostino; Beelen, Dietrich; Passweg, Jakob; Socié, Gerard; Gurman, Gunhan; Aljurf, Mahmoud; Stelljes, Matthias; Giebel, Sebastian; Mohty, Mohamad; Nagler, Arnon

2017-10-01

The optimal conditioning regimen to employ before hematopoietic stem cell transplantation in acute lymphoblastic leukemia (ALL) is still undecided, and while cyclophosphamide/total body irradiation (Cy/TBI) is the most commonly used myeloablative regimen, there are concerns regarding long-term toxicity for patients conditioned with this regimen. Thiotepa-based conditioning is an emerging radiation-free regimen with recent publications indicative of comparable clinical outcomes to TBI-based conditioning. In this analysis of the acute leukemia working party of the EBMT, we performed a retrospective matched-pair analysis, evaluating the outcome of adult patients with ALL who received thiotepa-based conditioning (n = 180) with those receiving Cy/TBI conditioning (n = 540). The 2-year leukemia-free survival and overall survival (OS) rates of both conditioning regimens were comparable, 33% for thiotepa [95% confidence interval (CI): 26.4-42.8] versus 39% for Cy/TBI (95% CI: 34.8-44.5] (P = .33) and 46.5% [95% CI: 38.6-56.1] versus 48.8% [95% CI: 44.2-54] (P = .9), respectively. There was no significant difference between the two regimens in the incidence of either acute graft versus host disease (GVHD) or chronic GVHD. Multivariate analysis demonstrated increased relapse incidence for thiotepa conditioning compared to Cy/TBI (HR = 1.78, 95% CI, 1.07-2.95; P = .03) which did not affect OS. Our results indicate that thiotepa-based conditioning may not be inferior to Cy/TBI for adult patients with ALL. © 2017 Wiley Periodicals, Inc.

Multivariate missing data in hydrology - Review and applications

NASA Astrophysics Data System (ADS)

Ben Aissia, Mohamed-Aymen; Chebana, Fateh; Ouarda, Taha B. M. J.

2017-12-01

Water resources planning and management require complete data sets of a number of hydrological variables, such as flood peaks and volumes. However, hydrologists are often faced with the problem of missing data (MD) in hydrological databases. Several methods are used to deal with the imputation of MD. During the last decade, multivariate approaches have gained popularity in the field of hydrology, especially in hydrological frequency analysis (HFA). However, treating the MD remains neglected in the multivariate HFA literature whereas the focus has been mainly on the modeling component. For a complete analysis and in order to optimize the use of data, MD should also be treated in the multivariate setting prior to modeling and inference. Imputation of MD in the multivariate hydrological framework can have direct implications on the quality of the estimation. Indeed, the dependence between the series represents important additional information that can be included in the imputation process. The objective of the present paper is to highlight the importance of treating MD in multivariate hydrological frequency analysis by reviewing and applying multivariate imputation methods and by comparing univariate and multivariate imputation methods. An application is carried out for multiple flood attributes on three sites in order to evaluate the performance of the different methods based on the leave-one-out procedure. The results indicate that, the performance of imputation methods can be improved by adopting the multivariate setting, compared to mean substitution and interpolation methods, especially when using the copula-based approach.

Development of Pattern Recognition Techniques for the Evaluation of Toxicant Impacts to Multispecies Systems

DTIC Science & Technology

1993-06-18

the exception. In the Standardized Aquatic Microcosm and the Mixed Flask Culture (MFC) microcosms, multivariate analysis and clustering methods...rule rather than the exception. In the Standardized Aquatic Microcosm and the Mixed Flask Culture (MFC) microcosms, multivariate analysis and...experiments using two microcosm protocols. We use nonmetric clustering, a multivariate pattern recognition technique developed by Matthews and Heame (1991

Multivariate test power approximations for balanced linear mixed models in studies with missing data.

PubMed

Ringham, Brandy M; Kreidler, Sarah M; Muller, Keith E; Glueck, Deborah H

2016-07-30

Multilevel and longitudinal studies are frequently subject to missing data. For example, biomarker studies for oral cancer may involve multiple assays for each participant. Assays may fail, resulting in missing data values that can be assumed to be missing completely at random. Catellier and Muller proposed a data analytic technique to account for data missing at random in multilevel and longitudinal studies. They suggested modifying the degrees of freedom for both the Hotelling-Lawley trace F statistic and its null case reference distribution. We propose parallel adjustments to approximate power for this multivariate test in studies with missing data. The power approximations use a modified non-central F statistic, which is a function of (i) the expected number of complete cases, (ii) the expected number of non-missing pairs of responses, or (iii) the trimmed sample size, which is the planned sample size reduced by the anticipated proportion of missing data. The accuracy of the method is assessed by comparing the theoretical results to the Monte Carlo simulated power for the Catellier and Muller multivariate test. Over all experimental conditions, the closest approximation to the empirical power of the Catellier and Muller multivariate test is obtained by adjusting power calculations with the expected number of complete cases. The utility of the method is demonstrated with a multivariate power analysis for a hypothetical oral cancer biomarkers study. We describe how to implement the method using standard, commercially available software products and give example code. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

Multivariate analysis of gamma spectra to characterize used nuclear fuel

DOE PAGES

Coble, Jamie; Orton, Christopher; Schwantes, Jon

2017-01-17

The Multi-Isotope Process (MIP) Monitor provides an efficient means to monitor the process conditions in used nuclear fuel reprocessing facilities to support process verification and validation. The MIP Monitor applies multivariate analysis to gamma spectroscopy of key stages in the reprocessing stream in order to detect small changes in the gamma spectrum, which may indicate changes in process conditions. This research extends the MIP Monitor by characterizing a used fuel sample after initial dissolution according to the type of reactor of origin (pressurized or boiling water reactor; PWR and BWR, respectively), initial enrichment, burn up, and cooling time. Simulated gammamore » spectra were used in this paper to develop and test three fuel characterization algorithms. The classification and estimation models employed are based on the partial least squares regression (PLS) algorithm. A PLS discriminate analysis model was developed which perfectly classified reactor type for the three PWR and three BWR reactor designs studied. Locally weighted PLS models were fitted on-the-fly to estimate the remaining fuel characteristics. For the simulated gamma spectra considered, burn up was predicted with 0.1% root mean squared percent error (RMSPE) and both cooling time and initial enrichment with approximately 2% RMSPE. Finally, this approach to automated fuel characterization can be used to independently verify operator declarations of used fuel characteristics and to inform the MIP Monitor anomaly detection routines at later stages of the fuel reprocessing stream to improve sensitivity to changes in operational parameters that may indicate issues with operational control or malicious activities.« less

Multivariate analysis of gamma spectra to characterize used nuclear fuel

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

Coble, Jamie; Orton, Christopher; Schwantes, Jon

The Multi-Isotope Process (MIP) Monitor provides an efficient means to monitor the process conditions in used nuclear fuel reprocessing facilities to support process verification and validation. The MIP Monitor applies multivariate analysis to gamma spectroscopy of key stages in the reprocessing stream in order to detect small changes in the gamma spectrum, which may indicate changes in process conditions. This research extends the MIP Monitor by characterizing a used fuel sample after initial dissolution according to the type of reactor of origin (pressurized or boiling water reactor; PWR and BWR, respectively), initial enrichment, burn up, and cooling time. Simulated gammamore » spectra were used in this paper to develop and test three fuel characterization algorithms. The classification and estimation models employed are based on the partial least squares regression (PLS) algorithm. A PLS discriminate analysis model was developed which perfectly classified reactor type for the three PWR and three BWR reactor designs studied. Locally weighted PLS models were fitted on-the-fly to estimate the remaining fuel characteristics. For the simulated gamma spectra considered, burn up was predicted with 0.1% root mean squared percent error (RMSPE) and both cooling time and initial enrichment with approximately 2% RMSPE. Finally, this approach to automated fuel characterization can be used to independently verify operator declarations of used fuel characteristics and to inform the MIP Monitor anomaly detection routines at later stages of the fuel reprocessing stream to improve sensitivity to changes in operational parameters that may indicate issues with operational control or malicious activities.« less

Steroid Avoidance in Pediatric Heart Transplantation Results in Excellent Graft Survival

PubMed Central

Auerbach, Scott R.; Gralla, Jane; Campbell, David N.; Miyamoto, Shelley D.; Pietra, Biagio A.

2018-01-01

Background Maintenance steroid (MS) use in pediatric heart transplantation (HT) varies across centers. The purpose of this study was to evaluate the impact of steroid-free maintenance immunosuppression (SF) on graft outcomes in pediatric HT. Methods Patients younger than 18 years in the United States undergoing a first HT during 1990 to 2010 were analyzed for conditional 30-day graft loss (death or repeat HT) and death based on MS use by multivariable analysis. A propensity score was then given to each patient using a logistic model, and propensity matching was performed using pre-HT risk factors, induction therapy, and nonsteroid maintenance immunosuppression. Kaplan-Meier graft and patient survival probabilities by MS use were then calculated. Results Of 4894 patients, 3962 (81%) were taking MS and 932 (19%) SF. Of the 4530 alive at 30 days after HT, 3694 (82%) and 836 (18%) were in the MS and SF groups, respectively. Unmatched multivariable analysis showed no difference in 30-day conditional graft survival between MS and SF groups (hazard ratio=1.08, 95% confidence interval=0.93-1.24; P=0.33). Propensity matching resulted in 462 patients in each MS and SF group. Propensity-matched Kaplan-Meier survival analysis showed no difference in graft or patient survival between groups (P=0.3 and P=0.16, respectively). Conclusions We found no difference in graft survival between SF patients and those taking MS. An SF regimen in pediatric HT avoids potential complications of steroid use without compromising graft survival, even after accounting for pre-HT risk factors. PMID:24389908

Multivariate analysis for scanning tunneling spectroscopy data

NASA Astrophysics Data System (ADS)

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

2018-01-01

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

Mars approach for global sensitivity analysis of differential equation models with applications to dynamics of influenza infection.

PubMed

Lee, Yeonok; Wu, Hulin

2012-01-01

Differential equation models are widely used for the study of natural phenomena in many fields. The study usually involves unknown factors such as initial conditions and/or parameters. It is important to investigate the impact of unknown factors (parameters and initial conditions) on model outputs in order to better understand the system the model represents. Apportioning the uncertainty (variation) of output variables of a model according to the input factors is referred to as sensitivity analysis. In this paper, we focus on the global sensitivity analysis of ordinary differential equation (ODE) models over a time period using the multivariate adaptive regression spline (MARS) as a meta model based on the concept of the variance of conditional expectation (VCE). We suggest to evaluate the VCE analytically using the MARS model structure of univariate tensor-product functions which is more computationally efficient. Our simulation studies show that the MARS model approach performs very well and helps to significantly reduce the computational cost. We present an application example of sensitivity analysis of ODE models for influenza infection to further illustrate the usefulness of the proposed method.

Multivariate Analysis of Schools and Educational Policy.

ERIC Educational Resources Information Center

Kiesling, Herbert J.

This report describes a multivariate analysis technique that approaches the problems of educational production function analysis by (1) using comparable measures of output across large experiments, (2) accounting systematically for differences in socioeconomic background, and (3) treating the school as a complete system in which different…

A comparative analysis of heat waves and associated mortality in St. Louis, Missouri--1980 and 1995.

PubMed

Smoyer, K E

1998-08-01

This research investigates heat-related mortality during the 1980 and 1995 heat waves in St. Louis, Missouri. St. Louis has a long history of extreme summer weather, and heat-related mortality is a public health concern. Heat waves are defined as days with apparent temperatures exceeding 40.6 degrees C (105 degrees F). The study uses a multivariate analysis to investigate the relationship between mortality and heat wave intensity, duration, and timing within the summer season. The heat wave of 1980 was more severe and had higher associated mortality than that of 1995. To learn if changing population characteristics, in addition to weather conditions, contributed to this difference, changes in population vulnerability between 1980 and 1995 are evaluated under simulated heat wave conditions. The findings show that St. Louis remains at risk of heat wave mortality. In addition, there is evidence that vulnerability has increased despite increased air-conditioning penetration and public health interventions.

Multivariate statistical analysis: Principles and applications to coorbital streams of meteorite falls

NASA Technical Reports Server (NTRS)

Wolf, S. F.; Lipschutz, M. E.

1993-01-01

Multivariate statistical analysis techniques (linear discriminant analysis and logistic regression) can provide powerful discrimination tools which are generally unfamiliar to the planetary science community. Fall parameters were used to identify a group of 17 H chondrites (Cluster 1) that were part of a coorbital stream which intersected Earth's orbit in May, from 1855 - 1895, and can be distinguished from all other H chondrite falls. Using multivariate statistical techniques, it was demonstrated that a totally different criterion, labile trace element contents - hence thermal histories - or 13 Cluster 1 meteorites are distinguishable from those of 45 non-Cluster 1 H chondrites. Here, we focus upon the principles of multivariate statistical techniques and illustrate their application using non-meteoritic and meteoritic examples.

Multivariate EMD and full spectrum based condition monitoring for rotating machinery

NASA Astrophysics Data System (ADS)

Zhao, Xiaomin; Patel, Tejas H.; Zuo, Ming J.

2012-02-01

Early assessment of machinery health condition is of paramount importance today. A sensor network with sensors in multiple directions and locations is usually employed for monitoring the condition of rotating machinery. Extraction of health condition information from these sensors for effective fault detection and fault tracking is always challenging. Empirical mode decomposition (EMD) is an advanced signal processing technology that has been widely used for this purpose. Standard EMD has the limitation in that it works only for a single real-valued signal. When dealing with data from multiple sensors and multiple health conditions, standard EMD faces two problems. First, because of the local and self-adaptive nature of standard EMD, the decomposition of signals from different sources may not match in either number or frequency content. Second, it may not be possible to express the joint information between different sensors. The present study proposes a method of extracting fault information by employing multivariate EMD and full spectrum. Multivariate EMD can overcome the limitations of standard EMD when dealing with data from multiple sources. It is used to extract the intrinsic mode functions (IMFs) embedded in raw multivariate signals. A criterion based on mutual information is proposed for selecting a sensitive IMF. A full spectral feature is then extracted from the selected fault-sensitive IMF to capture the joint information between signals measured from two orthogonal directions. The proposed method is first explained using simple simulated data, and then is tested for the condition monitoring of rotating machinery applications. The effectiveness of the proposed method is demonstrated through monitoring damage on the vane trailing edge of an impeller and rotor-stator rub in an experimental rotor rig.

Diversity pattern in Sesamum mutants selected for a semi-arid cropping system.

PubMed

Murty, B R; Oropeza, F

1989-02-01

Due to the complex requirements of moisture stress, substantial genetic diversity with a wide array of character combinations and effective simultaneous selection for several variables is necessary for improving the productivity and adaptation of a component crop in order for it to fit into a cropping system under semi-arid tropical conditions. Sesamum indicum L. is grown in Venezuela after rice/sorghum/or maize under such conditions. A mutation breeding program was undertaken using six locally adapted varieties to develop genotypes suitable for the above system. The diversity pattern for nine variables was assessed by multivariate analysis in 301 M4 progenies. Analysis of the characteristic roots and principal components in three methods of selection, i.e., M2 bulks (A), individual plant selection throughout (B), and selection in M3 for single variable (C), revealed differences in the pattern of variation between varieties, selection methods, and varieties x methods interactions. Method B was superior to the others and gave 17 of the 21 best M5 progenies. 'Piritu' and 'CF' varieties yielded the most productive progenies in M5 and M6. Diversity was large and selection was effective for such developmental traits as earliness and synchrony, combined with multiple disease resistance, which could be related to their importance by multivariate analyses. Considerable differences in the variety of character combinations among the high yielding. M5 progenies of 'CF' and 'Piritu' suggested possible further yield improvement. The superior response of 'Piritu' and 'CF' over other varieties in yield and adaptation was due to major changes in plant type and character associations. Multilocation testing of M5 generations revealed that the mutant progenies had a 40%-100% yield superiority over the parents; this was combined with earliness, synchrony, and multiple disease resistance, and was confirmed in the M6 generation grown on a commercial scale. This study showed that multivariate analysis is an effective tool for assessing diversity patterns, choice of appropriate variety, and selection methodology in order to make rapid progress in meeting the complex requirements of semi-arid cropping systems.

Experts' perceptions on the entrepreneurial framework conditions

NASA Astrophysics Data System (ADS)

Correia, Aldina; e Silva, Eliana Costa; Lopes, I. Cristina; Braga, Alexandra; Braga, Vitor

2017-11-01

The Global Entrepreneurship Monitor is a large scale database for internationally comparative entrepreneurship. This database includes information of more than 100 countries concerning several aspects of entrepreneurship activities, perceptions, conditions, national and regional policy, among others, in two main sources of primary data: the Adult Population Survey and the National Expert Survey. In the present work the National Expert Survey datasets for 2011, 2012 and 2013 are analyzed with the purpose of studying the effects of different type of entrepreneurship expert specialization on the perceptions about the Entrepreneurial Framework Conditions (EFCs). The results of the multivariate analysis of variance for the 2013 data show significant differences of the entrepreneurship experts when compared the 2011 and 2012 surveys. For the 2013 data entrepreneur experts are less favorable then most of the other experts to the EFCs.

Assessment of benthic changes during 20 years of monitoring the Mexican Salina Cruz Bay.

PubMed

González-Macías, C; Schifter, I; Lluch-Cota, D B; Méndez-Rodríguez, L; Hernández-Vázquez, S

2009-02-01

In this work a non-parametric multivariate analysis was used to assess the impact of metals and organic compounds in the macro infaunal component of the mollusks benthic community using surface sediment data from several monitoring programs collected over 20 years in Salina Cruz Bay, Mexico. The data for benthic mollusks community characteristics (richness, abundance and diversity) were linked to multivariate environmental patterns, using the Alternating Conditional Expectations method to correlate the biological measurements of the mollusk community with the physicochemical properties of water and sediments. Mollusks community variation is related to environmental characteristics as well as lead content. Surface deposit feeders are increasing their relative density, while subsurface deposit feeders are decreasing with respect to time, these last are expected to be more related with sediment and more affected then by its quality. However gastropods with predatory carnivore as well as chemosymbiotic deposit feeder bivalves have maintained their relative densities along time.

Specific prognostic factors for secondary pancreatic infection in severe acute pancreatitis.

PubMed

Armengol-Carrasco, M; Oller, B; Escudero, L E; Roca, J; Gener, J; Rodríguez, N; del Moral, P; Moreno, P

1999-01-01

The aim of the present study was to investigate whether there are specific prognostic factors to predict the development of secondary pancreatic infection (SPI) in severe acute pancreatitis in order to perform a computed tomography-fine needle aspiration with bacteriological sampling at the right moment and confirm the diagnosis. Twenty-five clinical and laboratory parameters were determined sequentially in 150 patients with severe acute pancreatitis (SAP) and univariate, and multivariate regression analyses were done looking for correlation with the development of SPI. Only APACHE II score and C-reactive protein levels were related to the development of SPI in the multivariate analysis. A regression equation was designed using these two parameters, and empiric cut-off points defined the subgroup of patients at high risk of developing secondary pancreatic infection. The results showed that it is possible to predict SPI during SAP allowing bacteriological confirmation and early treatment of this severe condition.

Multivariate statistical analysis to investigate the subduction zone parameters favoring the occurrence of giant megathrust earthquakes

NASA Astrophysics Data System (ADS)

Brizzi, S.; Sandri, L.; Funiciello, F.; Corbi, F.; Piromallo, C.; Heuret, A.

2018-03-01

The observed maximum magnitude of subduction megathrust earthquakes is highly variable worldwide. One key question is which conditions, if any, favor the occurrence of giant earthquakes (Mw ≥ 8.5). Here we carry out a multivariate statistical study in order to investigate the factors affecting the maximum magnitude of subduction megathrust earthquakes. We find that the trench-parallel extent of subduction zones and the thickness of trench sediments provide the largest discriminating capability between subduction zones that have experienced giant earthquakes and those having significantly lower maximum magnitude. Monte Carlo simulations show that the observed spatial distribution of giant earthquakes cannot be explained by pure chance to a statistically significant level. We suggest that the combination of a long subduction zone with thick trench sediments likely promotes a great lateral rupture propagation, characteristic of almost all giant earthquakes.

Applying multivariate analysis as decision tool for evaluating sediment-specific remediation strategies.

PubMed

Pedersen, Kristine B; Lejon, Tore; Jensen, Pernille E; Ottosen, Lisbeth M

2016-05-01

Multivariate methodology was employed for finding optimum remediation conditions for electrodialytic remediation of harbour sediment from an Arctic location in Norway. The parts of the experimental domain in which both sediment- and technology-specific remediation objectives were met were identified. Objectives targeted were removal of the sediment-specific pollutants Cu and Pb, while minimising the effect on the sediment matrix by limiting the removal of naturally occurring metals while maintaining low energy consumption. Two different cell designs for electrochemical remediation were tested and final concentrations of Cu and Pb were below background levels in large parts of the experimental domain when operating at low current densities (<0.12 mA/cm(2)). However, energy consumption, remediation times and the effect on naturally occurring metals were different for the 2- and 3-compartment cells. Copyright © 2016 Elsevier Ltd. All rights reserved.

Evaluation of risk factors for perforated peptic ulcer.

PubMed

Yamamoto, Kazuki; Takahashi, Osamu; Arioka, Hiroko; Kobayashi, Daiki

2018-02-15

The aim of this study was to evaluate the prediction factors for perforated peptic ulcer (PPU). At St. Luke's International Hospital in Tokyo, Japan, a case control study was performed between August 2004 and March 2016. All patients diagnosed with PPU were included. As control subjects, patients with age, sex and date of CT scan corresponding to those of the PPU subjects were included in the study at a proportion of 2 controls for every PPU subject. All data such as past medical histories, physical findings, and laboratory data were collected through chart reviews. Univariate analyses and multivariate analyses with logistic regression were conducted, and receiver operating characteristic curves (ROCs) were calculated to show validity. Sensitivity analyses were performed to confirm results using a stepwise method and conditional logistic regression. A total of 408 patients were included in this study; 136 were a group of patients with PPU, and 272 were a control group. Univariate analysis showed statistical significance in many categories. Four different models of multivariate analyses were conducted, and significant differences were found for muscular defense and a history of peptic ulcer disease (PUD) in all models. The conditional forced-entry analysis of muscular defense showed an odds ratio (OR) of 23.8 (95% confidence interval [CI]: 5.70-100.0), and the analysis of PUD history showed an OR of 6.40 (95% CI: 1.13-36.2). The sensitivity analysis showed consistent results, with an OR of 23.8-366.2 for muscular defense and an OR of 3.67-7.81 for PUD history. The area under the curve (AUC) of all models was high enough to confirm the results. However, anticoagulants, known risk factors for PUD, did not increase the risk for PPU in our study. The conditional forced-entry analysis of anticoagulant use showed an OR of 0.85 (95% CI: 0.03-22.3). The evaluation of prediction factors and development of a prediction rule for PPU may help our decision making in performing a CT scan for patients with acute abdominal pain.

Prevalence and factors associated with rheumatic diseases and chronic joint symptoms in the elderly.

PubMed

Falsarella, Gláucia R; Coimbra, Ibsen B; Barcelos, Caroline C; Costallat, Lilian Tl; Carvalho, Olga Mf; Coimbra, Arlete Mv

2013-10-01

In the elderly population, rheumatic conditions are major causes of pain that restrict participation in activities and mobility, and cause difficulties in the execution of self-care tasks. The present study aimed to analyze the prevalence and factors associated with the self-reported rheumatic diseases and chronic joint symptoms of the elderly. This transversal epidemiological survey involved 2209 older adults (aged ≥ 60 years). The investigation included sociodemographic factors, anthropometrics, activities of daily living, chronic conditions, medication and quality of life. Univariate and multivariate regression analysis were used for statistical procedures, P ≤ 0.05. The prevalence of rheumatism was 22.7%. Multivariate analysis showed that rheumatism was correlated with the following: female sex (OR = 1.91), high income (OR = 2.34), cardiovascular disease (OR = 1.42), cataracts (OR = 1.39), glucocorticoids (OR = 5.24), other anti-inflammatory medications (OR = 2.24) and pain (OR = 0.983). After adjusting for age and glucocorticoids, an association between cataracts and rheumatism was detected (OR = 1.32). The prevalence of symptoms was 45.6%. Multivariate regression results for symptoms included the following: female sex (OR = 1.40), body mass index ≥ 30.0 kg/m(2) (OR = 3.31), functional capacity (OR = 0.990), general health (OR = 0.993) and pain (OR = 0.981). After adjustment for age and glucocorticoids, an association between cataracts and symptoms was detected (OR = 1.26). There was a significant association of rheumatism and symptoms with women and high incomes. Obesity was associated with joint symptoms, which in turn were associated with an impaired quality of life. Cataracts and cardiovascular disease were associated with rheumatism. The identification of these characteristics in the elderly will contribute to a better understanding of this systemic disease and should be used to plan effective preventive measures. © 2013 Japan Geriatrics Society.

Ethnic differences in self reported health in Malmö in southern Sweden

PubMed Central

Lindstrom, M; Sundquist, J; Ostergren, P

2001-01-01

STUDY OBJECTIVE—The aim of this study was to investigate ethnic differences in self reported health in the city of Malmö, Sweden, and whether these differences could be explained by psychosocial and economic conditions.
DESIGN/SETTING/PARTICIPANTS—The public health survey in Malmö 1994 was a cross sectional study. A total of 5600 people aged 20-80 years completed a postal questionnaire. The participation rate was 71%. The population was categorised according to country of origin: born in Sweden, other Western countries, Yugoslavia, Poland, Arabic speaking countries and all other countries. The multivariate analysis was performed using a logistic regression model in order to investigate the importance of possible confounders on the differences by country of origin in self reported health. Finally, variables measuring psychosocial and economic conditions were introduced into the model.
MAIN RESULTS—The odds ratios of having poor self reported health were significantly higher among men born in other Western countries, Yugoslavia, Arabic speaking countries and in the category all other countries, as well as among women born in Yugoslavia, Poland and all other countries, compared with men and women born in Sweden. The multivariate analysis including age and education did not change these results. A huge reduction of the odds ratios was observed for men and women born in Yugoslavia, Arabic speaking countries and all other countries, and for women born in Poland after the introduction of the social network, social support and economic factors into the multivariate model.
CONCLUSIONS—There were significant ethnic group differences in self reported health. These differences were greatly reduced by psychosocial and economic factors, which suggest that these factors may be important determinants of self rated health in certain minority groups.


Keywords: self reported health; social network; social support PMID:11154248

Multivariate factorial analysis to design a robust batch leaching test to assess the volcanic ash geochemical hazard.

PubMed

Ruggieri, Flavia; Gil, Raúl A; Fernandez-Turiel, Jose-Luis; Saavedra, Julio; Gimeno, Domingo; Lobo, Agustin; Martinez, Luis D; Rodriguez-Gonzalez, Alejandro

2012-04-30

A method to obtain robust information on short term leaching behaviour of volcanic ashes has been developed independently on the sample age. A mixed factorial design (MFD) was employed as a multivariate strategy for the evaluation of the effects of selected control factors and their interactions (amount of sample (A), contact time (B), and liquid to solid ratio or L/S (C)) on the leaching process of selected metals (Na, K, Mg, Ca, Si, Al, V, Mn, Fe, and Co) and anions (Cl(-) and SO(4)(2-)). Box plots of the data acquired were used to evaluate the reproducibility achieved at different experimental conditions. Both the amount of sample (A) and leaching time (B) had a significant effect on the element stripping whereas the L/S ratio influenced only few elements. The lowest dispersion values have been observed when 1.0 g was leached with an L/S ratio equal to 10, shaking during 4 h. The entire method is completed within few hours, and it is simple, feasible and reliable in laboratory conditions. Copyright © 2012 Elsevier B.V. All rights reserved.

Multivariate analysis in the pharmaceutical industry: enabling process understanding and improvement in the PAT and QbD era.

PubMed

Ferreira, Ana P; Tobyn, Mike

2015-01-01

In the pharmaceutical industry, chemometrics is rapidly establishing itself as a tool that can be used at every step of product development and beyond: from early development to commercialization. This set of multivariate analysis methods allows the extraction of information contained in large, complex data sets thus contributing to increase product and process understanding which is at the core of the Food and Drug Administration's Process Analytical Tools (PAT) Guidance for Industry and the International Conference on Harmonisation's Pharmaceutical Development guideline (Q8). This review is aimed at providing pharmaceutical industry professionals an introduction to multivariate analysis and how it is being adopted and implemented by companies in the transition from "quality-by-testing" to "quality-by-design". It starts with an introduction to multivariate analysis and the two methods most commonly used: principal component analysis and partial least squares regression, their advantages, common pitfalls and requirements for their effective use. That is followed with an overview of the diverse areas of application of multivariate analysis in the pharmaceutical industry: from the development of real-time analytical methods to definition of the design space and control strategy, from formulation optimization during development to the application of quality-by-design principles to improve manufacture of existing commercial products.

Input-output oriented computation algorithms for the control of large flexible structures

NASA Technical Reports Server (NTRS)

Minto, K. D.

1989-01-01

An overview is given of work in progress aimed at developing computational algorithms addressing two important aspects in the control of large flexible space structures; namely, the selection and placement of sensors and actuators, and the resulting multivariable control law design problem. The issue of sensor/actuator set selection is particularly crucial to obtaining a satisfactory control design, as clearly a poor choice will inherently limit the degree to which good control can be achieved. With regard to control law design, the researchers are driven by concerns stemming from the practical issues associated with eventual implementation of multivariable control laws, such as reliability, limit protection, multimode operation, sampling rate selection, processor throughput, etc. Naturally, the burden imposed by dealing with these aspects of the problem can be reduced by ensuring that the complexity of the compensator is minimized. Our approach to these problems is based on extensions to input/output oriented techniques that have proven useful in the design of multivariable control systems for aircraft engines. In particular, researchers are exploring the use of relative gain analysis and the condition number as a means of quantifying the process of sensor/actuator selection and placement for shape control of a large space platform.

Determination of variability in leaf biomass densities of conifers and mixed conifers under different environmental conditions in the San Joaquin Valley air basin. Final report

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

Temple, P.J.; Mutters, R.J.; Adams, C.

1995-06-01

Biomass sampling plots were established at 29 locations within the dominant vegetation zones of the study area. Estimates of foliar biomass were made for each plot by three independent methods: regression analysis on the basis of tree diameter, calculation of the amount of light intercepted by the leaf canopy, and extrapolation from branch leaf area. Multivariate regression analysis was used to relate these foliar biomass estimates for oak plots and conifer plots to several independent predictor variables, including elevation, slope, aspect, temperature, precipitation, and soil chemical characteristics.

Multi-variant study of obesity risk genes in African Americans: The Jackson Heart Study.

PubMed

Liu, Shijian; Wilson, James G; Jiang, Fan; Griswold, Michael; Correa, Adolfo; Mei, Hao

2016-11-30

Genome-wide association study (GWAS) has been successful in identifying obesity risk genes by single-variant association analysis. For this study, we designed steps of analysis strategy and aimed to identify multi-variant effects on obesity risk among candidate genes. Our analyses were focused on 2137 African American participants with body mass index measured in the Jackson Heart Study and 657 common single nucleotide polymorphisms (SNPs) genotyped at 8 GWAS-identified obesity risk genes. Single-variant association test showed that no SNPs reached significance after multiple testing adjustment. The following gene-gene interaction analysis, which was focused on SNPs with unadjusted p-value<0.10, identified 6 significant multi-variant associations. Logistic regression showed that SNPs in these associations did not have significant linear interactions; examination of genetic risk score evidenced that 4 multi-variant associations had significant additive effects of risk SNPs; and haplotype association test presented that all multi-variant associations contained one or several combinations of particular alleles or haplotypes, associated with increased obesity risk. Our study evidenced that obesity risk genes generated multi-variant effects, which can be additive or non-linear interactions, and multi-variant study is an important supplement to existing GWAS for understanding genetic effects of obesity risk genes. Copyright © 2016 Elsevier B.V. All rights reserved.

Beyond singular values and loop shapes

NASA Technical Reports Server (NTRS)

Stein, G.

1985-01-01

The status of singular value loop-shaping as a design paradigm for multivariable feedback systems is reviewed. It shows that this paradigm is an effective design tool whenever the problem specifications are spacially round. The tool can be arbitrarily conservative, however, when they are not. This happens because singular value conditions for robust performance are not tight (necessary and sufficient) and can severely overstate actual requirements. An alternate paradign is discussed which overcomes these limitations. The alternative includes a more general problem formulation, a new matrix function mu, and tight conditions for both robust stability and robust performance. The state of the art currently permits analysis of feedback systems within this new paradigm. Synthesis remains a subject of research.

Effect of Common Faults on the Performance of Different Types of Vapor Compression Systems

PubMed Central

Du, Zhimin; Domanski, Piotr A.; Payne, W. Vance

2016-01-01

The effect of faults on the cooling capacity, coefficient of performance, and sensible heat ratio, was analyzed and compared for five split and rooftop systems, which use different types of expansion devices, compressors and refrigerants. The study applied multivariable polynomial and normalized performance models, which were developed for the studied systems for both fault-free and faulty conditions based on measurements obtained in a laboratory under controlled conditions. The analysis indicated differences in responses and trends between the studied systems, which underscores the challenge to devise a universal FDD algorithm for all vapor compression systems and the difficulty to develop a methodology for rating the performance of different FDD algorithms. PMID:26929732

Effect of Common Faults on the Performance of Different Types of Vapor Compression Systems.

PubMed

Du, Zhimin; Domanski, Piotr A; Payne, W Vance

2016-04-05

The effect of faults on the cooling capacity, coefficient of performance, and sensible heat ratio, was analyzed and compared for five split and rooftop systems, which use different types of expansion devices, compressors and refrigerants. The study applied multivariable polynomial and normalized performance models, which were developed for the studied systems for both fault-free and faulty conditions based on measurements obtained in a laboratory under controlled conditions. The analysis indicated differences in responses and trends between the studied systems, which underscores the challenge to devise a universal FDD algorithm for all vapor compression systems and the difficulty to develop a methodology for rating the performance of different FDD algorithms.

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.

A Study of Effects of MultiCollinearity in the Multivariable Analysis

PubMed Central

Yoo, Wonsuk; Mayberry, Robert; Bae, Sejong; Singh, Karan; (Peter) He, Qinghua; Lillard, James W.

2015-01-01

A multivariable analysis is the most popular approach when investigating associations between risk factors and disease. However, efficiency of multivariable analysis highly depends on correlation structure among predictive variables. When the covariates in the model are not independent one another, collinearity/multicollinearity problems arise in the analysis, which leads to biased estimation. This work aims to perform a simulation study with various scenarios of different collinearity structures to investigate the effects of collinearity under various correlation structures amongst predictive and explanatory variables and to compare these results with existing guidelines to decide harmful collinearity. Three correlation scenarios among predictor variables are considered: (1) bivariate collinear structure as the most simple collinearity case, (2) multivariate collinear structure where an explanatory variable is correlated with two other covariates, (3) a more realistic scenario when an independent variable can be expressed by various functions including the other variables. PMID:25664257

A Study of Effects of MultiCollinearity in the Multivariable Analysis.

PubMed

Yoo, Wonsuk; Mayberry, Robert; Bae, Sejong; Singh, Karan; Peter He, Qinghua; Lillard, James W

2014-10-01

A multivariable analysis is the most popular approach when investigating associations between risk factors and disease. However, efficiency of multivariable analysis highly depends on correlation structure among predictive variables. When the covariates in the model are not independent one another, collinearity/multicollinearity problems arise in the analysis, which leads to biased estimation. This work aims to perform a simulation study with various scenarios of different collinearity structures to investigate the effects of collinearity under various correlation structures amongst predictive and explanatory variables and to compare these results with existing guidelines to decide harmful collinearity. Three correlation scenarios among predictor variables are considered: (1) bivariate collinear structure as the most simple collinearity case, (2) multivariate collinear structure where an explanatory variable is correlated with two other covariates, (3) a more realistic scenario when an independent variable can be expressed by various functions including the other variables.

Ethnic differences in daily smoking in Malmö, Sweden. Varying influence of psychosocial and economic factors.

PubMed

Lindström, Martin; Sundquist, Jan

2002-12-01

The aim was to investigate ethnic differences in daily smoking in Malmö, Sweden, and whether these differences could be explained by psychosocial and economic conditions. The public health survey in Malmö 1994 is a cross-sectional study. A total of 5,600 individuals aged 20-80 years were randomly chosen to respond to a postal questionnaire. The participation rate was 71%. The study population was divided into seven categories according to country of birth; Sweden, Denmark/Norway, other Western countries, former Yugoslavia, Poland, Arabic-speaking countries and all other countries. A multivariate analysis was performed using a logistic regression model in order to investigate the importance of possible confounders on the ethnic differences in daily smoking. Finally, variables measuring social network, social support and economic conditions were introduced. The prevalence of daily smoking was significantly higher among both men and women born in Denmark/Norway (39.1% and 37.0%), men born in other Western countries (32.9%), Poland (34.0%) and Arabic-speaking countries (36.4%) than among Swedish men (21.7%) and women (23.8%). Women born in Arabic-speaking countries had a significantly lower smoking prevalence (7.1%). The multivariate analysis, including age, education and snuff, did not affect these results. A reduction of the odds ratio of daily smoking was observed for men born in Arabic-speaking countries and Poland after the introduction of the psychosocial and economic factors in the model. Only small changes were observed for women. There were significant ethnic group differences in daily smoking. Psychosocial and economic conditions in Sweden may be of importance in some ethnic groups.

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.

Metabolomic differentiation of maca (Lepidium meyenii) accessions cultivated under different conditions using NMR and chemometric analysis.

PubMed

Zhao, Jianping; Avula, Bharathi; Chan, Michael; Clément, Céline; Kreuzer, Michael; Khan, Ikhlas A

2012-01-01

To gain insights on the effects of color type, cultivation history, and growing site on the composition alterations of maca (Lepidium meyenii Walpers) hypocotyls, NMR profiling combined with chemometric analysis was applied to investigate the metabolite variability in different maca accessions. Maca hypocotyls with different colors (yellow, pink, violet, and lead-colored) cultivated at different geographic sites and different areas were examined for differences in metabolite expression. Differentiations of the maca accessions grown under the different cultivation conditions were determined by principle component analyses (PCAs) which were performed on the datasets derived from their ¹H NMR spectra. A total of 16 metabolites were identified by NMR analysis, and the changes in metabolite levels in relation to the color types and growing conditions of maca hypocotyls were evaluated using univariate statistical analysis. In addition, the changes of the correlation pattern among the metabolites identified in the maca accessions planted at the two different sites were examined. The results from both multivariate and univariate analysis indicated that the planting site was the major determining factor with regards to metabolite variations in maca hypocotyls, while the color of maca accession seems to be of minor importance in this respect. © Georg Thieme Verlag KG Stuttgart · New York.

EEMD-based multiscale ICA method for slewing bearing fault detection and diagnosis

NASA Astrophysics Data System (ADS)

Žvokelj, Matej; Zupan, Samo; Prebil, Ivan

2016-05-01

A novel multivariate and multiscale statistical process monitoring method is proposed with the aim of detecting incipient failures in large slewing bearings, where subjective influence plays a minor role. The proposed method integrates the strengths of the Independent Component Analysis (ICA) multivariate monitoring approach with the benefits of Ensemble Empirical Mode Decomposition (EEMD), which adaptively decomposes signals into different time scales and can thus cope with multiscale system dynamics. The method, which was named EEMD-based multiscale ICA (EEMD-MSICA), not only enables bearing fault detection but also offers a mechanism of multivariate signal denoising and, in combination with the Envelope Analysis (EA), a diagnostic tool. The multiscale nature of the proposed approach makes the method convenient to cope with data which emanate from bearings in complex real-world rotating machinery and frequently represent the cumulative effect of many underlying phenomena occupying different regions in the time-frequency plane. The efficiency of the proposed method was tested on simulated as well as real vibration and Acoustic Emission (AE) signals obtained through conducting an accelerated run-to-failure lifetime experiment on a purpose-built laboratory slewing bearing test stand. The ability to detect and locate the early-stage rolling-sliding contact fatigue failure of the bearing indicates that AE and vibration signals carry sufficient information on the bearing condition and that the developed EEMD-MSICA method is able to effectively extract it, thereby representing a reliable bearing fault detection and diagnosis strategy.

Enhancing e-waste estimates: improving data quality by multivariate Input-Output Analysis.

PubMed

Wang, Feng; Huisman, Jaco; Stevels, Ab; Baldé, Cornelis Peter

2013-11-01

Waste electrical and electronic equipment (or e-waste) is one of the fastest growing waste streams, which encompasses a wide and increasing spectrum of products. Accurate estimation of e-waste generation is difficult, mainly due to lack of high quality data referred to market and socio-economic dynamics. This paper addresses how to enhance e-waste estimates by providing techniques to increase data quality. An advanced, flexible and multivariate Input-Output Analysis (IOA) method is proposed. It links all three pillars in IOA (product sales, stock and lifespan profiles) to construct mathematical relationships between various data points. By applying this method, the data consolidation steps can generate more accurate time-series datasets from available data pool. This can consequently increase the reliability of e-waste estimates compared to the approach without data processing. A case study in the Netherlands is used to apply the advanced IOA model. As a result, for the first time ever, complete datasets of all three variables for estimating all types of e-waste have been obtained. The result of this study also demonstrates significant disparity between various estimation models, arising from the use of data under different conditions. It shows the importance of applying multivariate approach and multiple sources to improve data quality for modelling, specifically using appropriate time-varying lifespan parameters. Following the case study, a roadmap with a procedural guideline is provided to enhance e-waste estimation studies. Copyright © 2013 Elsevier Ltd. All rights reserved.

Localization of genes involved in the metabolic syndrome using multivariate linkage analysis.

PubMed

Olswold, Curtis; de Andrade, Mariza

2003-12-31

There are no well accepted criteria for the diagnosis of the metabolic syndrome. However, the metabolic syndrome is identified clinically by the presence of three or more of these five variables: larger waist circumference, higher triglyceride levels, lower HDL-cholesterol concentrations, hypertension, and impaired fasting glucose. We use sets of two or three variables, which are available in the Framingham Heart Study data set, to localize genes responsible for this syndrome using multivariate quantitative linkage analysis. This analysis demonstrates the applicability of using multivariate linkage analysis and how its use increases the power to detect linkage when genes are involved in the same disease mechanism.

Activity-Limiting Musculoskeletal Conditions in US Veterans Compared to Non-Veterans: Results from the 2013 National Health Interview Survey

PubMed Central

Hinojosa, Ramon

2016-01-01

Past military service is associated with health outcomes, both positive and negative. In this study we use the 2013 National Health Interview Survey to examine the constellation of conditions referred to as musculoskeletal disorders (MSDs) for Veterans and non-veterans with health conditions that limit their daily activities. Multivariate logistic regression analysis reveal that Veterans are more likely to report MSDs like neck and back problems, fracture bone and joint problems as an activity limiting problem compared to non-veterans. The relationship between age and reports of activity limiting MSDs is moderated by Veteran status. Veterans in this sample report more activity limiting MSDs at younger ages compared to non-veterans and fewer MSDs at older ages. This research contributes to our understanding of potentially limiting health conditions at earlier ages for Veterans. PMID:28005905

Mixotrophic and heterotrophic production of lipids and carbohydrates by a locally isolated microalga using wastewater as a growth medium.

PubMed

Nzayisenga, Jean Claude; Eriksson, Karolina; Sellstedt, Anita

2018-06-01

The biomass production and changes in biochemical composition of a locally isolated microalga (Chlorella sp.) were investigated in autotrophic, mixotrophic and heterotrophic conditions, using glucose or glycerol as carbon sources and municipal wastewater as the growth medium. Both standard methods and Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) analysis of data acquired by Fourier-transform IR (FTIR) spectrometry showed that autotrophic and mixotrophic conditions promoted carbohydrate accumulation, while heterotrophic conditions with glycerol resulted in the highest lipid content and lowest carbohydrate content. Heterotrophic conditions with glycerol as a carbon source also resulted in high oleic acid (18:1) contents and low linolenic acid (18:3) contents, and thus increasing biodiesel quality. The results also show the utility of MCR-ALS for analyzing changes in microalgal biochemical composition. Copyright © 2018 Elsevier Ltd. All rights reserved.

Awareness, treatment, control of diabetes mellitus and the risk factors: survey results from northeast China.

PubMed

Wang, Chang; Yu, Yaqin; Zhang, Xiangyang; Li, Yong; Kou, Changgui; Li, Bo; Tao, Yuchun; Zhen, Qing; He, Huan; Kanu, Joseph Sam; Huang, Xufeng; Han, Mei; Liu, Yawen

2014-01-01

The awareness, treatment and control of diabetes mellitus (DM) can effectively reflect on the social status of diabetes conditions. Although several researchers have investigated the awareness, treatment and control rates of diabetes mellitus in China, little is known about their association with risk factors. This study aims to examine the relationship between risk factors and awareness, treatment and control of diabetes mellitus in northeast China. A cross-sectional survey was conducted in 2012. Multistage stratified random cluster sampling design was used to select participants aged 18 to 79 years old. The analysis was based on a representative sample of 1,854 adult subjects. Multivariable logistic regression analysis was used to examine socio-demographic factors associated with the levels of awareness, treatment and control of diabetes mellitus. The awareness, treatment, and control rates of diabetes mellitus were 64.1%, 52.9% and 44.2%, respectively. In the multivariable logistic regression analysis, family history of diabetes was significantly positively associated with awareness (OR, 2.145; 95% CI, 1.600-2.875) and treatment (OR, 2.021; 95% CI, 1.559-2.619) of diabetes mellitus, while negatively associated with control (OR, 0.671; 95% CI, 0.529-0.951). Cigarette smokers and alcohol drinkers were less likely than non-smokers and non-drinkers to be aware of their blood glucose levels (OR, 0.895, 0.614; 95% CI, 0.659-1.216, 0.446-0.844, respectively). Participants who frequently exercise were more likely to be aware of their diabetic conditions than people who never or rarely exercise (OR, 2.003; 95% CI, 1.513-2.651). We found that the awareness and treatment of diabetes mellitus were positively associated with age and were high in participants with a family history of diabetes and those who exercise frequently, but low for cigarette smokers and alcohol drinkers. Participants with a family history of diabetes had their diabetic condition poorly controlled.

Meta-analysis identifies gene-by-environment interactions as demonstrated in a study of 4,965 mice.

PubMed

Kang, Eun Yong; Han, Buhm; Furlotte, Nicholas; Joo, Jong Wha J; Shih, Diana; Davis, Richard C; Lusis, Aldons J; Eskin, Eleazar

2014-01-01

Identifying environmentally-specific genetic effects is a key challenge in understanding the structure of complex traits. Model organisms play a crucial role in the identification of such gene-by-environment interactions, as a result of the unique ability to observe genetically similar individuals across multiple distinct environments. Many model organism studies examine the same traits but under varying environmental conditions. For example, knock-out or diet-controlled studies are often used to examine cholesterol in mice. These studies, when examined in aggregate, provide an opportunity to identify genomic loci exhibiting environmentally-dependent effects. However, the straightforward application of traditional methodologies to aggregate separate studies suffers from several problems. First, environmental conditions are often variable and do not fit the standard univariate model for interactions. Additionally, applying a multivariate model results in increased degrees of freedom and low statistical power. In this paper, we jointly analyze multiple studies with varying environmental conditions using a meta-analytic approach based on a random effects model to identify loci involved in gene-by-environment interactions. Our approach is motivated by the observation that methods for discovering gene-by-environment interactions are closely related to random effects models for meta-analysis. We show that interactions can be interpreted as heterogeneity and can be detected without utilizing the traditional uni- or multi-variate approaches for discovery of gene-by-environment interactions. We apply our new method to combine 17 mouse studies containing in aggregate 4,965 distinct animals. We identify 26 significant loci involved in High-density lipoprotein (HDL) cholesterol, many of which are consistent with previous findings. Several of these loci show significant evidence of involvement in gene-by-environment interactions. An additional advantage of our meta-analysis approach is that our combined study has significantly higher power and improved resolution compared to any single study thus explaining the large number of loci discovered in the combined study.

Meta-Analysis Identifies Gene-by-Environment Interactions as Demonstrated in a Study of 4,965 Mice

PubMed Central

Joo, Jong Wha J.; Shih, Diana; Davis, Richard C.; Lusis, Aldons J.; Eskin, Eleazar

2014-01-01

Identifying environmentally-specific genetic effects is a key challenge in understanding the structure of complex traits. Model organisms play a crucial role in the identification of such gene-by-environment interactions, as a result of the unique ability to observe genetically similar individuals across multiple distinct environments. Many model organism studies examine the same traits but under varying environmental conditions. For example, knock-out or diet-controlled studies are often used to examine cholesterol in mice. These studies, when examined in aggregate, provide an opportunity to identify genomic loci exhibiting environmentally-dependent effects. However, the straightforward application of traditional methodologies to aggregate separate studies suffers from several problems. First, environmental conditions are often variable and do not fit the standard univariate model for interactions. Additionally, applying a multivariate model results in increased degrees of freedom and low statistical power. In this paper, we jointly analyze multiple studies with varying environmental conditions using a meta-analytic approach based on a random effects model to identify loci involved in gene-by-environment interactions. Our approach is motivated by the observation that methods for discovering gene-by-environment interactions are closely related to random effects models for meta-analysis. We show that interactions can be interpreted as heterogeneity and can be detected without utilizing the traditional uni- or multi-variate approaches for discovery of gene-by-environment interactions. We apply our new method to combine 17 mouse studies containing in aggregate 4,965 distinct animals. We identify 26 significant loci involved in High-density lipoprotein (HDL) cholesterol, many of which are consistent with previous findings. Several of these loci show significant evidence of involvement in gene-by-environment interactions. An additional advantage of our meta-analysis approach is that our combined study has significantly higher power and improved resolution compared to any single study thus explaining the large number of loci discovered in the combined study. PMID:24415945

Multivariate frequency domain analysis of protein dynamics

NASA Astrophysics Data System (ADS)

Matsunaga, Yasuhiro; Fuchigami, Sotaro; Kidera, Akinori

2009-03-01

Multivariate frequency domain analysis (MFDA) is proposed to characterize collective vibrational dynamics of protein obtained by a molecular dynamics (MD) simulation. MFDA performs principal component analysis (PCA) for a bandpass filtered multivariate time series using the multitaper method of spectral estimation. By applying MFDA to MD trajectories of bovine pancreatic trypsin inhibitor, we determined the collective vibrational modes in the frequency domain, which were identified by their vibrational frequencies and eigenvectors. At near zero temperature, the vibrational modes determined by MFDA agreed well with those calculated by normal mode analysis. At 300 K, the vibrational modes exhibited characteristic features that were considerably different from the principal modes of the static distribution given by the standard PCA. The influences of aqueous environments were discussed based on two different sets of vibrational modes, one derived from a MD simulation in water and the other from a simulation in vacuum. Using the varimax rotation, an algorithm of the multivariate statistical analysis, the representative orthogonal set of eigenmodes was determined at each vibrational frequency.

Imaging of polysaccharides in the tomato cell wall with Raman microspectroscopy

PubMed Central

2014-01-01

Background The primary cell wall of fruits and vegetables is a structure mainly composed of polysaccharides (pectins, hemicelluloses, cellulose). Polysaccharides are assembled into a network and linked together. It is thought that the percentage of components and of plant cell wall has an important influence on mechanical properties of fruits and vegetables. Results In this study the Raman microspectroscopy technique was introduced to the visualization of the distribution of polysaccharides in cell wall of fruit. The methodology of the sample preparation, the measurement using Raman microscope and multivariate image analysis are discussed. Single band imaging (for preliminary analysis) and multivariate image analysis methods (principal component analysis and multivariate curve resolution) were used for the identification and localization of the components in the primary cell wall. Conclusions Raman microspectroscopy supported by multivariate image analysis methods is useful in distinguishing cellulose and pectins in the cell wall in tomatoes. It presents how the localization of biopolymers was possible with minimally prepared samples. PMID:24917885

A refined method for multivariate meta-analysis and meta-regression

PubMed Central

Jackson, Daniel; Riley, Richard D

2014-01-01

Making inferences about the average treatment effect using the random effects model for meta-analysis is problematic in the common situation where there is a small number of studies. This is because estimates of the between-study variance are not precise enough to accurately apply the conventional methods for testing and deriving a confidence interval for the average effect. We have found that a refined method for univariate meta-analysis, which applies a scaling factor to the estimated effects’ standard error, provides more accurate inference. We explain how to extend this method to the multivariate scenario and show that our proposal for refined multivariate meta-analysis and meta-regression can provide more accurate inferences than the more conventional approach. We explain how our proposed approach can be implemented using standard output from multivariate meta-analysis software packages and apply our methodology to two real examples. © 2013 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd. PMID:23996351

A matrix-based method of moments for fitting the multivariate random effects model for meta-analysis and meta-regression

PubMed Central

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

2013-01-01

Multivariate meta-analysis is becoming more commonly used. Methods for fitting the multivariate random effects model include maximum likelihood, restricted maximum likelihood, Bayesian estimation and multivariate generalisations of the standard univariate method of moments. Here, we provide a new multivariate method of moments for estimating the between-study covariance matrix with the properties that (1) it allows for either complete or incomplete outcomes and (2) it allows for covariates through meta-regression. Further, for complete data, it is invariant to linear transformations. Our method reduces to the usual univariate method of moments, proposed by DerSimonian and Laird, in a single dimension. We illustrate our method and compare it with some of the alternatives using a simulation study and a real example. PMID:23401213

Biostatistics Series Module 10: Brief Overview of Multivariate Methods.

PubMed

Hazra, Avijit; Gogtay, Nithya

2017-01-01

Multivariate analysis refers to statistical techniques that simultaneously look at three or more variables in relation to the subjects under investigation with the aim of identifying or clarifying the relationships between them. These techniques have been broadly classified as dependence techniques, which explore the relationship between one or more dependent variables and their independent predictors, and interdependence techniques, that make no such distinction but treat all variables equally in a search for underlying relationships. Multiple linear regression models a situation where a single numerical dependent variable is to be predicted from multiple numerical independent variables. Logistic regression is used when the outcome variable is dichotomous in nature. The log-linear technique models count type of data and can be used to analyze cross-tabulations where more than two variables are included. Analysis of covariance is an extension of analysis of variance (ANOVA), in which an additional independent variable of interest, the covariate, is brought into the analysis. It tries to examine whether a difference persists after "controlling" for the effect of the covariate that can impact the numerical dependent variable of interest. Multivariate analysis of variance (MANOVA) is a multivariate extension of ANOVA used when multiple numerical dependent variables have to be incorporated in the analysis. Interdependence techniques are more commonly applied to psychometrics, social sciences and market research. Exploratory factor analysis and principal component analysis are related techniques that seek to extract from a larger number of metric variables, a smaller number of composite factors or components, which are linearly related to the original variables. Cluster analysis aims to identify, in a large number of cases, relatively homogeneous groups called clusters, without prior information about the groups. The calculation intensive nature of multivariate analysis has so far precluded most researchers from using these techniques routinely. The situation is now changing with wider availability, and increasing sophistication of statistical software and researchers should no longer shy away from exploring the applications of multivariate methods to real-life data sets.

Bayesian Estimation of Multivariate Latent Regression Models: Gauss versus Laplace

ERIC Educational Resources Information Center

Culpepper, Steven Andrew; Park, Trevor

2017-01-01

A latent multivariate regression model is developed that employs a generalized asymmetric Laplace (GAL) prior distribution for regression coefficients. The model is designed for high-dimensional applications where an approximate sparsity condition is satisfied, such that many regression coefficients are near zero after accounting for all the model…

Error Covariance Penalized Regression: A novel multivariate model combining penalized regression with multivariate error structure.

PubMed

Allegrini, Franco; Braga, Jez W B; Moreira, Alessandro C O; Olivieri, Alejandro C

2018-06-29

A new multivariate regression model, named Error Covariance Penalized Regression (ECPR) is presented. Following a penalized regression strategy, the proposed model incorporates information about the measurement error structure of the system, using the error covariance matrix (ECM) as a penalization term. Results are reported from both simulations and experimental data based on replicate mid and near infrared (MIR and NIR) spectral measurements. The results for ECPR are better under non-iid conditions when compared with traditional first-order multivariate methods such as ridge regression (RR), principal component regression (PCR) and partial least-squares regression (PLS). Copyright © 2018 Elsevier B.V. All rights reserved.

Depression and associated factors in older adults in South Africa.

PubMed

Peltzer, Karl; Phaswana-Mafuya, Nancy

2013-01-18

Late-life depression is an important public health problem because of its devastating consequences. The study aims to investigate the prevalence and associated factors of self-reported symptom-based depression in a national sample of older South Africans who participated in the Study of Global Ageing and Adult Health (SAGE wave 1) in 2008. We conducted a national population-based cross-sectional study with a probability sample of 3,840 individuals aged 50 years or above in South Africa in 2008. The questionnaire included socio-demographic characteristics, health variables, anthropometric and blood pressure measurements as well as questions on depression symptoms in the past 12 months. Multivariable regression analysis was performed to assess the association of socio-demographic factors, health variables, and depression. The overall prevalence of symptom-based depression in the past 12 months was 4.0%. In multivariable analysis, functional disability, lack of quality of life, and chronic conditions (angina, asthma, arthritis, and nocturnal sleep problems) were associated with self-reported depression symptoms in the past 12 months. Self-reported depression in older South Africans seems to be a public health problem calling for appropriate interventions to reduce occurrence. Factors identified to be associated with depression, including functional disability, lack of quality of life, and chronic conditions (angina, asthma, arthritis, and nocturnal sleep problems), can be used to guide interventions. The identified protective and risk factors can help in formulating public health care policies to improve quality of life among older adults.

Allogeneic transplantation provides durable remission in a subset of DLBCL patients relapsing after autologous transplantation.

PubMed

Fenske, Timothy S; Ahn, Kwang W; Graff, Tara M; DiGilio, Alyssa; Bashir, Qaiser; Kamble, Rammurti T; Ayala, Ernesto; Bacher, Ulrike; Brammer, Jonathan E; Cairo, Mitchell; Chen, Andy; Chen, Yi-Bin; Chhabra, Saurabh; D'Souza, Anita; Farooq, Umar; Freytes, Cesar; Ganguly, Siddhartha; Hertzberg, Mark; Inwards, David; Jaglowski, Samantha; Kharfan-Dabaja, Mohamed A; Lazarus, Hillard M; Nathan, Sunita; Pawarode, Attaphol; Perales, Miguel-Angel; Reddy, Nishitha; Seo, Sachiko; Sureda, Anna; Smith, Sonali M; Hamadani, Mehdi

2016-07-01

For diffuse large B-cell lymphoma (DLBCL) patients progressing after autologous haematopoietic cell transplantation (autoHCT), allogeneic HCT (alloHCT) is often considered, although limited information is available to guide patient selection. Using the Center for International Blood and Marrow Transplant Research (CIBMTR) database, we identified 503 patients who underwent alloHCT after disease progression/relapse following a prior autoHCT. The 3-year probabilities of non-relapse mortality, progression/relapse, progression-free survival (PFS) and overall survival (OS) were 30, 38, 31 and 37% respectively. Factors associated with inferior PFS on multivariate analysis included Karnofsky performance status (KPS) <80, chemoresistance, autoHCT to alloHCT interval <1-year and myeloablative conditioning. Factors associated with worse OS on multivariate analysis included KPS<80, chemoresistance and myeloablative conditioning. Three adverse prognostic factors were used to construct a prognostic model for PFS, including KPS<80 (4 points), autoHCT to alloHCT interval <1-year (2 points) and chemoresistant disease at alloHCT (5 points). This CIBMTR prognostic model classified patients into four groups: low-risk (0 points), intermediate-risk (2-5 points), high-risk (6-9 points) or very high-risk (11 points), predicting 3-year PFS of 40, 32, 11 and 6%, respectively, with 3-year OS probabilities of 43, 39, 19 and 11% respectively. In conclusion, the CIBMTR prognostic model identifies a subgroup of DLBCL patients experiencing long-term survival with alloHCT after a failed prior autoHCT. © 2016 John Wiley & Sons Ltd.

Dose-response effects for depression and Schizophrenia management on hospital utilization in Illinois Medicaid: a multivariate regression analysis.

PubMed

Berg, Gregory D; Donnelly, Shawn; Warnick, Kathleen; Medina, Wendie; Miller, Mary

2014-07-03

The prevalence of schizophrenia and depression in the United States is far higher among Medicaid recipients than in the general population. Individuals suffering from mental illness, including schizophrenia and depression, also have higher rates of emergency department utilization, which is costly and may not generate the positive health outcomes desired. Disease management programs strive to help individuals suffering from chronic illnesses better manage their condition(s) and seek health care in the appropriate settings. The objective of this manuscript is to estimate a dose-response impact on hospital inpatient and emergency room utilizations for any reason by Medicaid recipients with depression or schizophrenia who received disease management contacts. Multivariate regression analysis of panel data taken from administrative claims was conducted to test the hypothesis that increased contacts lower the likelihood of all-cause inpatient admissions and emergency room visits. Subjects included 6,274 members of Illinois' non-institutionalized Medicaid-only aged, blind or disabled population diagnosed with depression or schizophrenia. The statistical measure is the odds ratio. The odds ratio association is between the monthly utilization indicators and the number of contacts (doses) a member had for each particular disease management intervention. Higher numbers of intervention contacts for Medicaid recipients diagnosed with depression or schizophrenia were associated with statistically significant reductions in all-cause inpatient admissions and emergency room utilizations. There is a high correlation between depression and schizophrenia disease management contacts and lowered all-cause hospital inpatient and emergency room utilizations.

A multivariate analysis of biophysical parameters of tallgrass prairie among land management practices and years

USGS Publications Warehouse

Griffith, J.A.; Price, K.P.; Martinko, E.A.

2001-01-01

Six treatments of eastern Kansas tallgrass prairie - native prairie, hayed, mowed, grazed, burned and untreated - were studied to examine the biophysical effects of land management practices on grasslands. On each treatment, measurements of plant biomass, leaf area index, plant cover, leaf moisture and soil moisture were collected. In addition, measurements were taken of the Normalized Difference Vegetation Index (NDVI), which is derived from spectral reflectance measurements. Measurements were taken in mid-June, mid-July and late summer of 1990 and 1991. Multivariate analysis of variance was used to determine whether there were differences in the set of variables among treatments and years. Follow-up tests included univariate t-tests to determine which variables were contributing to any significant difference. Results showed a significant difference (p < 0.0005) among treatments in the composite of parameters during each of the months sampled. In most treatment types, there was a significant difference between years within each month. The univariate tests showed, however, that only some variables, primarily soil moisture, were contributing to this difference. We conclude that biomass and % plant cover show the best potential to serve as long-term indicators of grassland condition as they generally were sensitive to effects of different land management practices but not to yearly change in weather conditions. NDVI was insensitive to precipitation differences between years in July for most treatments, but was not in the native prairie. Choice of sampling time is important for these parameters to serve effectively as indicators.

Two models for evaluating landslide hazards

USGS Publications Warehouse

Davis, J.C.; Chung, C.-J.; Ohlmacher, G.C.

2006-01-01

Two alternative procedures for estimating landslide hazards were evaluated using data on topographic digital elevation models (DEMs) and bedrock lithologies in an area adjacent to the Missouri River in Atchison County, Kansas, USA. The two procedures are based on the likelihood ratio model but utilize different assumptions. The empirical likelihood ratio model is based on non-parametric empirical univariate frequency distribution functions under an assumption of conditional independence while the multivariate logistic discriminant model assumes that likelihood ratios can be expressed in terms of logistic functions. The relative hazards of occurrence of landslides were estimated by an empirical likelihood ratio model and by multivariate logistic discriminant analysis. Predictor variables consisted of grids containing topographic elevations, slope angles, and slope aspects calculated from a 30-m DEM. An integer grid of coded bedrock lithologies taken from digitized geologic maps was also used as a predictor variable. Both statistical models yield relative estimates in the form of the proportion of total map area predicted to already contain or to be the site of future landslides. The stabilities of estimates were checked by cross-validation of results from random subsamples, using each of the two procedures. Cell-by-cell comparisons of hazard maps made by the two models show that the two sets of estimates are virtually identical. This suggests that the empirical likelihood ratio and the logistic discriminant analysis models are robust with respect to the conditional independent assumption and the logistic function assumption, respectively, and that either model can be used successfully to evaluate landslide hazards. ?? 2006.

Searching for forcing signatures in decadal patterns of shoreline change

NASA Astrophysics Data System (ADS)

Burningham, H.; French, J.

2016-12-01

Analysis of shoreline position at spatial scales of the order 10 - 100 km and at a multi-decadal time-scale has the potential to reveal regional coherence (or lack of) in the primary controls on shoreline tendencies and trends. Such information is extremely valuable for the evaluation of climate forcing on coastal behaviour. Segmenting a coast into discrete behaviour units based on these types of analyses is often subjective, however, and in the context of pervasive human interventions and alongshore variability in ocean climate, determining the most important controls on shoreline dynamics can be challenging. Multivariate analyses provide one means to resolve common behaviours across shoreline position datasets, thereby underpinning a more objective evaluation of possible coupling between shorelines at different scales. In an analysis of the Suffolk coast (eastern England) we explore the use of multivariate statistics to understand and classify mesoscale coastal behaviour. Suffolk comprises a relatively linear shoreline that shifts from east-facing in the north to southeast-facing in the south. Although primarily formed of a beach foreshore backed by cliffs or shingle barrier, the shoreline is punctuated at 3 locations by narrow tidal inlets with offset entrances that imply a persistent north to south sediment transport direction. Tidal regime decreases south to north from mesotidal (3.6m STR) to microtidal (1.9m STR), and the bimodal wave climate (northeast and southwest modes) presents complex local-scale variability in nearshore conditions. Shorelines exhibit a range of decadal behaviours from rapid erosion (up to 4m/yr) to quasi-stability that cannot be directly explained by the spatial organisation of contemporary landforms or coastal defences. A multivariate statistical approach to shoreline change analysis helps to define the key modes of change and determine the most likely forcing factors.

Association between thoracic aortic disease and inguinal hernia.

PubMed

Olsson, Christian; Eriksson, Per; Franco-Cereceda, Anders

2014-08-21

The study hypothesis was that thoracic aortic disease (TAD) is associated with a higher-than-expected prevalence of inguinal hernia. Such an association has been reported for abdominal aortic aneurysm (AAA) and hernia. Unlike AAA, TAD is not necessarily detectable with clinical examination or ultrasound, and there are no population-based screening programs for TAD. Therefore, conditions associated with TAD, such as inguinal hernia, are of particular clinical relevance. The prevalence of inguinal hernia in subjects with TAD was determined from nation-wide register data and compared to a non-TAD group (patients with isolated aortic stenosis). Groups were balanced using propensity score matching. Multivariable statistical analysis (logistic regression) was performed to identify variables independently associated with hernia. Hernia prevalence was 110 of 750 (15%) in subjects with TAD versus 29 of 301 (9.6%) in non-TAD, P=0.03. This statistically significant difference remained after propensity score matching: 21 of 159 (13%) in TAD versus 14 of 159 (8.9%) in non-TAD, P<0.001. Variables independently associated with hernia in multivariable analysis were male sex (odds ratio [OR] with 95% confidence interval [95% CI]) 3.4 (2.1 to 5.4), P<0.001; increased age, OR 1.02/year (1.004 to 1.04), P=0.014; and TAD, OR 1.8 (1.1 to 2.8), P=0.015. The prevalence of inguinal hernia (15%) in TAD is higher than expected in a general population and higher in TAD, compared to non-TAD. TAD is independently associated with hernia in multivariable analysis. Presence or history of hernia may be of importance in detecting TAD, and the association warrants further study. © 2014 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell.

Multivariate Analysis of Factors Associated with the Koebner Phenomenon in Vitiligo: An Observational Study of 381 Patients

PubMed Central

Khurrum, Huma; Bedaiwi, Khalid M.; AlBalahi, Naif Meshael

2017-01-01

Background The Koebner phenomenon (KP) is a common entity observed in dermatological disorders. The reported incidence of KP in vitiligo varies widely. Although the KP is frequently observed in patients with viltiligo, the associated factors with KP has not been established yet. Objective The aim is to estimate the prevalence of KP in vitiligo patients and to investigate the associated factors with KP among vitiligo characteristics. Methods A cross-sectional observational study was conducted using 381 vitiligo patients. Demographic and clinical information was obtained via the completion of Vitiligo European Task Force (VETF) questionnaires. Patients with positive history of KP were extracted from this vitiligo database. Multivariate analysis was performed to assess associations with KP. Results The median age of cases was 24 years (range, 0.6~76). In total, 237 of the patients were male (62.2%). Vitiligo vulgaris was the most common type observed (152/381, 39.9%). Seventy-two percent (274/381) patients did not exhibit KP, whereas 28.1% (107/381) of patients exhibited this condition. Multivariable analysis showed the following to be independent factors with KP in patients with vitiligo: the progressive disease (odds ratio [OR], 1.82; 95% confidence interval [95% CI], 1.17~2.92; p=0.041), disease duration longer than 5 years (OR, 1.92; 95% CI, 1.22~2.11; p=0.003), and body surface area more than 2% (OR, 2.20; 95% CI, 1.26~3.24; p<0.001). Conclusion Our results suggest that KP may be used to evaluate disease activity and investigate different associations between the clinical profile and course of vitiligo. Further studies are needed to predict the relationship between KP and responsiveness to therapy. PMID:28566906

The Dissolution Behavior of Borosilicate Glasses in Far-From Equilibrium Conditions

DOE PAGES

Neeway, James J.; Rieke, Peter C.; Parruzot, Benjamin P.; ...

2018-02-10

An area of agreement in the waste glass corrosion community is that, at far-from-equilibrium conditions, the dissolution of borosilicate glasses used to immobilize nuclear waste is known to be a function of both temperature and pH. The aim of this work is to study the effects of temperature and pH on the dissolution rate of three model nuclear waste glasses (SON68, ISG, AFCI). The dissolution rate data are then used to parameterize a kinetic rate model based on Transition State Theory that has been developed to model glass corrosion behavior in dilute conditions. To do this, experiments were conducted atmore » temperatures of 23, 40, 70, and 90 °C and pH(22 °C) values of 9, 10, 11, and 12 with the single-pass flow-through (SPFT) test method. Both the absolute dissolution rates and the rate model parameters are compared with previous results. Rate model parameters for the three glasses studied here are nearly equivalent within error and in relative agreement with previous studies though quantifiable differences exist. The glass dissolution rates were analyzed with a linear multivariate regression (LMR) and a nonlinear multivariate regression performed with the use of the Glass Corrosion Modeling Tool (GCMT), with which a robust uncertainty analysis is performed. This robust analysis highlights the high degree of correlation of various parameters in the kinetic rate model. As more data are obtained on borosilicate glasses with varying compositions, a mathematical description of the effect of glass composition on the rate parameter values should be possible. This would allow for the possibility of calculating the forward dissolution rate of glass based solely on composition. In addition, the method of determination of parameter uncertainty and correlation provides a framework for other rate models that describe the dissolution rates of other amorphous and crystalline materials in a wide range of chemical conditions. As a result, the higher level of uncertainty analysis would provide a basis for comparison of different rate models and allow for a better means of quantifiably comparing the various models.« less

The dissolution behavior of borosilicate glasses in far-from equilibrium conditions

NASA Astrophysics Data System (ADS)

Neeway, James J.; Rieke, Peter C.; Parruzot, Benjamin P.; Ryan, Joseph V.; Asmussen, R. Matthew

2018-04-01

An area of agreement in the waste glass corrosion community is that, at far-from-equilibrium conditions, the dissolution of borosilicate glasses used to immobilize nuclear waste is known to be a function of both temperature and pH. The aim of this work is to study the effects of temperature and pH on the dissolution rate of three model nuclear waste glasses (SON68, ISG, AFCI). The dissolution rate data are then used to parameterize a kinetic rate model based on Transition State Theory that has been developed to model glass corrosion behavior in dilute conditions. To do this, experiments were conducted at temperatures of 23, 40, 70, and 90 °C and pH (22 °C) values of 9, 10, 11, and 12 with the single-pass flow-through (SPFT) test method. Both the absolute dissolution rates and the rate model parameters are compared with previous results. Rate model parameters for the three glasses studied here are nearly equivalent within error and in relative agreement with previous studies though quantifiable differences exist. The glass dissolution rates were analyzed with a linear multivariate regression (LMR) and a nonlinear multivariate regression performed with the use of the Glass Corrosion Modeling Tool (GCMT), with which a robust uncertainty analysis is performed. This robust analysis highlights the high degree of correlation of various parameters in the kinetic rate model. As more data are obtained on borosilicate glasses with varying compositions, a mathematical description of the effect of glass composition on the rate parameter values should be possible. This would allow for the possibility of calculating the forward dissolution rate of glass based solely on composition. In addition, the method of determination of parameter uncertainty and correlation provides a framework for other rate models that describe the dissolution rates of other amorphous and crystalline materials in a wide range of chemical conditions. The higher level of uncertainty analysis would provide a basis for comparison of different rate models and allow for a better means of quantifiably comparing the various models.

The Dissolution Behavior of Borosilicate Glasses in Far-From Equilibrium Conditions

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

Neeway, James J.; Rieke, Peter C.; Parruzot, Benjamin P.

An area of agreement in the waste glass corrosion community is that, at far-from-equilibrium conditions, the dissolution of borosilicate glasses used to immobilize nuclear waste is known to be a function of both temperature and pH. The aim of this work is to study the effects of temperature and pH on the dissolution rate of three model nuclear waste glasses (SON68, ISG, AFCI). The dissolution rate data are then used to parameterize a kinetic rate model based on Transition State Theory that has been developed to model glass corrosion behavior in dilute conditions. To do this, experiments were conducted atmore » temperatures of 23, 40, 70, and 90 °C and pH(22 °C) values of 9, 10, 11, and 12 with the single-pass flow-through (SPFT) test method. Both the absolute dissolution rates and the rate model parameters are compared with previous results. Rate model parameters for the three glasses studied here are nearly equivalent within error and in relative agreement with previous studies though quantifiable differences exist. The glass dissolution rates were analyzed with a linear multivariate regression (LMR) and a nonlinear multivariate regression performed with the use of the Glass Corrosion Modeling Tool (GCMT), with which a robust uncertainty analysis is performed. This robust analysis highlights the high degree of correlation of various parameters in the kinetic rate model. As more data are obtained on borosilicate glasses with varying compositions, a mathematical description of the effect of glass composition on the rate parameter values should be possible. This would allow for the possibility of calculating the forward dissolution rate of glass based solely on composition. In addition, the method of determination of parameter uncertainty and correlation provides a framework for other rate models that describe the dissolution rates of other amorphous and crystalline materials in a wide range of chemical conditions. As a result, the higher level of uncertainty analysis would provide a basis for comparison of different rate models and allow for a better means of quantifiably comparing the various models.« less

Multivariate time series analysis of neuroscience data: some challenges and opportunities.

PubMed

Pourahmadi, Mohsen; Noorbaloochi, Siamak

2016-04-01

Neuroimaging data may be viewed as high-dimensional multivariate time series, and analyzed using techniques from regression analysis, time series analysis and spatiotemporal analysis. We discuss issues related to data quality, model specification, estimation, interpretation, dimensionality and causality. Some recent research areas addressing aspects of some recurring challenges are introduced. Copyright © 2015 Elsevier Ltd. All rights reserved.

Data analysis techniques

NASA Technical Reports Server (NTRS)

Park, Steve

1990-01-01

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

Chemical structure of wood charcoal by infrared spectroscopy and multivariate analysis

Treesearch

Nicole Labbe; David Harper; Timothy Rials; Thomas Elder

2006-01-01

In this work, the effect of temperature on charcoal structure and chemical composition is investigated for four tree species. Wood charcoal carbonized at various temperatures is analyzed by mid infrared spectroscopy coupled with multivariate analysis and by thermogravimetric analysis to characterize the chemical composition during the carbonization process. The...

Characterization of cytochrome c as marker for retinal cell degeneration by uv/vis spectroscopic imaging

NASA Astrophysics Data System (ADS)

Hollmach, Julia; Schweizer, Julia; Steiner, Gerald; Knels, Lilla; Funk, Richard H. W.; Thalheim, Silko; Koch, Edmund

2011-07-01

Retinal diseases like age-related macular degeneration have become an important cause of visual loss depending on increasing life expectancy and lifestyle habits. Due to the fact that no satisfying treatment exists, early diagnosis and prevention are the only possibilities to stop the degeneration. The protein cytochrome c (cyt c) is a suitable marker for degeneration processes and apoptosis because it is a part of the respiratory chain and involved in the apoptotic pathway. The determination of the local distribution and oxidative state of cyt c in living cells allows the characterization of cell degeneration processes. Since cyt c exhibits characteristic absorption bands between 400 and 650 nm wavelength, uv/vis in situ spectroscopic imaging was used for its characterization in retinal ganglion cells. The large amount of data, consisting of spatial and spectral information, was processed by multivariate data analysis. The challenge consists in the identification of the molecular information of cyt c. Baseline correction, principle component analysis (PCA) and cluster analysis (CA) were performed in order to identify cyt c within the spectral dataset. The combination of PCA and CA reveals cyt c and its oxidative state. The results demonstrate that uv/vis spectroscopic imaging in conjunction with sophisticated multivariate methods is a suitable tool to characterize cyt c under in situ conditions.

Multivariate analysis: greater insights into complex systems

USDA-ARS?s Scientific Manuscript database

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 ...

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

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

Van Benthem, Mark Hilary; Mowry, Curtis Dale; Kotula, Paul Gabriel

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 varymore » 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 this multivariate analysis will enable superior differentiation capabilities. In addition, noise and system artifacts challenge the analysis of GC-MS data collected on lower cost equipment, ubiquitous in commercial laboratories. This research has the potential to affect many areas of analytical chemistry including materials analysis, medical testing, and environmental surveillance. It could also provide a method to measure adsorption parameters for chemical interactions on various surfaces by measuring desorption as a function of temperature for mixtures. We have presented results of a novel method for examining offgas products of a common PDMS material. Our method involves utilizing a stepped TD/GC-MS data acquisition scheme that may be almost totally automated, coupled with multivariate analysis schemes. This method of data generation and analysis can be applied to a number of materials aging and thermal degradation studies.« less

Sibling separation and psychological problems of double AIDS orphans in rural China - a comparison analysis.

PubMed

Gong, J; Li, X; Fang, X; Zhao, G; Lv, Y; Zhao, J; Lin, X; Zhang, L; Chen, X; Stanton, B

2009-07-01

We investigated the psychological impact of sibling separation among children who lost both of their parents to AIDS and were placed in group care or kinship care settings in rural China. Comparative analysis of cross-sectional survey data among 155 children among whom 96 experienced sibling separation. Trauma symptoms (Anxiety, Depression, Anger, Post-traumatic stress, Dissociation, Sexual concerns) were compared between the AIDS orphans who experienced sibling separation and those who did not using analysis of variance and multivariate analysis of covariance. Among the participants (47.7% girls) with an average age of 12.4 years, univariate and multivariate analyses showed that separation from siblings was associated with significantly higher scores in anxiety, depression, anger and dissociation before or after controlling for gender, age, care arrangement, number of household replacement, trusting relationship with the current caregivers and perceived quality of current living condition. Sibling separation among orphans was not associated with level of post-traumatic stress and sexual concerns. AIDS orphans separated from their siblings suffered from increased psychological distress compared with those who remained with their siblings. The data in the current study suggest that care arrangement for AIDS orphan should include accommodating the siblings together or providing them with opportunities for frequent contact and/or communication with each other. Appropriate psychological counselling should be given to those orphans experiencing sibling separation.

A study of pH-dependent photodegradation of amiloride by a multivariate curve resolution approach to combined kinetic and acid-base titration UV data.

PubMed

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

2012-11-21

Amiloride photostability at different pH values was studied in depth by applying Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) to the UV spectrophotometric data from drug solutions exposed to stressing irradiation. Resolution of all degradation photoproducts was possible by simultaneous spectrophotometric analysis of kinetic photodegradation and acid-base titration experiments. Amiloride photodegradation showed to be strongly dependent on pH. Two hard modelling constraints were sequentially used in MCR-ALS for the unambiguous resolution of all the species involved in the photodegradation process. An amiloride acid-base system was defined by using the equilibrium constraint, and the photodegradation pathway was modelled taking into account the kinetic constraint. The simultaneous analysis of photodegradation and titration experiments revealed the presence of eight different species, which were differently distributed according to pH and time. Concentration profiles of all the species as well as their pure spectra were resolved and kinetic rate constants were estimated. The values of rate constants changed with pH and under alkaline conditions the degradation pathway and photoproducts also changed. These results were compared to those obtained by LC-MS analysis from drug photodegradation experiments. MS analysis allowed the identification of up to five species and showed the simultaneous presence of more than one acid-base equilibrium.

Development of achiral and chiral 2D HPLC methods for analysis of albendazole metabolites in microsomal fractions using multivariate analysis for the in vitro metabolism.

PubMed

Belaz, Kátia Roberta A; Pereira-Filho, Edenir Rodrigues; Oliveira, Regina V

2013-08-01

In this work, the development of two multidimensional liquid chromatography methods coupled to a fluorescence detector is described for direct analysis of microsomal fractions obtained from rat livers. The chiral multidimensional method was then applied for the optimization of the in vitro metabolism of albendazole by experimental design. Albendazole was selected as a model drug because of its anthelmintics properties and recent potential for cancer treatment. The development of two fully automated achiral-chiral and chiral-chiral high performance liquid chromatography (HPLC) methods for the determination of albendazole (ABZ) and its metabolites albendazole sulphoxide (ABZ-SO), albendazole sulphone (ABZ-SO2) and albendazole 2-aminosulphone (ABZ-SO2NH2) in microsomal fractions are described. These methods involve the use of a phenyl (RAM-phenyl-BSA) or octyl (RAM-C8-BSA) restricted access media bovine serum albumin column for the sample clean-up, followed by an achiral phenyl column (15.0×0.46cmI.D.) or a chiral amylose tris(3,5-dimethylphenylcarbamate) column (15.0×0.46cmI.D.). The chiral 2D HPLC method was applied to the development of a compromise condition for the in vitro metabolism of ABZ by means of experimental design involving multivariate analysis. Copyright © 2013 Elsevier B.V. All rights reserved.

Integrated GIS and multivariate statistical analysis for regional scale assessment of heavy metal soil contamination: A critical review.

PubMed

Hou, Deyi; O'Connor, David; Nathanail, Paul; Tian, Li; Ma, Yan

2017-12-01

Heavy metal soil contamination is associated with potential toxicity to humans or ecotoxicity. Scholars have increasingly used a combination of geographical information science (GIS) with geostatistical and multivariate statistical analysis techniques to examine the spatial distribution of heavy metals in soils at a regional scale. A review of such studies showed that most soil sampling programs were based on grid patterns and composite sampling methodologies. Many programs intended to characterize various soil types and land use types. The most often used sampling depth intervals were 0-0.10 m, or 0-0.20 m, below surface; and the sampling densities used ranged from 0.0004 to 6.1 samples per km 2 , with a median of 0.4 samples per km 2 . The most widely used spatial interpolators were inverse distance weighted interpolation and ordinary kriging; and the most often used multivariate statistical analysis techniques were principal component analysis and cluster analysis. The review also identified several determining and correlating factors in heavy metal distribution in soils, including soil type, soil pH, soil organic matter, land use type, Fe, Al, and heavy metal concentrations. The major natural and anthropogenic sources of heavy metals were found to derive from lithogenic origin, roadway and transportation, atmospheric deposition, wastewater and runoff from industrial and mining facilities, fertilizer application, livestock manure, and sewage sludge. This review argues that the full potential of integrated GIS and multivariate statistical analysis for assessing heavy metal distribution in soils on a regional scale has not yet been fully realized. It is proposed that future research be conducted to map multivariate results in GIS to pinpoint specific anthropogenic sources, to analyze temporal trends in addition to spatial patterns, to optimize modeling parameters, and to expand the use of different multivariate analysis tools beyond principal component analysis (PCA) and cluster analysis (CA). Copyright © 2017 Elsevier Ltd. All rights reserved.

Professions and Working Conditions Associated With Community-Acquired Pneumonia.

PubMed

Almirall, Jordi; Serra-Prat, Mateu; Bolíbar, Ignasi; Palomera, Elisabet; Roig, Jordi; Boixeda, Ramon; Bartolomé, Maria; de la Torre, Mari; Parra, Olga; Torres, Antoni

2015-12-01

Community-acquired pneumonia (CAP) is not considered a professional disease, and the effect of different occupations and working conditions on susceptibility to CAP is unknown. The aim of this study is to determine whether different jobs and certain working conditions are risk factors for CAP. Over a 1-year period, all radiologically confirmed cases of CAP (n=1,336) and age- and sex-matched controls (n=1,326) were enrolled in a population-based case-control study. A questionnaire on CAP risk factors, including work-related questions, was administered to all participants during an in-person interview. The bivariate analysis showed that office work is a protective factor against CAP, while building work, contact with dust and sudden changes of temperature in the workplace were risk factors for CAP. The occupational factor disappeared when the multivariate analysis was adjusted for working conditions. Contact with dust (previous month) and sudden changes of temperature (previous 3 months) were risk factors for CAP, irrespective of the number of years spent working in these conditions, suggesting reversibility. Some recent working conditions such as exposure to dust and sudden changes of temperature in the workplace are risk factors for CAP. Both factors are reversible and preventable. Copyright © 2014 SEPAR. Published by Elsevier Espana. All rights reserved.

Association of comorbid mental health symptoms and physical health conditions with employee productivity.

PubMed

Parker, Kristin M; Wilson, Mark G; Vandenberg, Robert J; DeJoy, David M; Orpinas, Pamela

2009-10-01

This study tests the hypothesis that employees with comorbid physical health conditions and mental health symptoms are less productive than other employees. Self-reported health status and productivity measures were collected from 1723 employees of a national retail organization. chi2, analysis of variance, and linear contrast analyses were conducted to evaluate whether health status groups differed on productivity measures. Multivariate linear regression and multinomial logistic regression analyses were conducted to analyze how predictive health status was of productivity. Those with comorbidities were significantly less productive on all productivity measures compared with all other health status groups and those with only physical health conditions or mental health symptoms. Health status also significantly predicted levels of employee productivity. These findings provide evidence for the relationship between health statuses and productivity, which has potential programmatic implications.

Employment situation and risk of death among middle-aged Japanese women.

PubMed

Honjo, Kaori; Iso, Hiroyasu; Ikeda, Ai; Fujino, Yoshihisa; Tamakoshi, Akiko

2015-10-01

Few studies have examined the health effects of employment situation among women, taking social and economic conditions into consideration. The objective of this research was to investigate the association of employment situation (full-time or part-time employee and self-employed) with mortality risk in women over a 20-year follow-up period. Additionally, we examined whether the association between employment situation and mortality in women differed by education level and marital status. We investigated the association of employment situation with mortality among 16,692 women aged 40-59 years enrolled in the Japan Collaborative Cohort Study. Multivariate HRs and 95% CIs for total deaths by employment situation were calculated after adjustment for age, disease history, residential area, education level, marital status and number of children. We also conducted subgroup analysis by education level and marital status. Multivariate HRs for mortality of part-time employees and self-employed workers were 1.48 (95% CI, 1.25 to 1.75) and 1.44 (95% CI, 1.21 to 1.72), respectively, with reference to women working full-time. Subgroup analysis by education level indicated that health effects in women according to employment situation were likely to be more evident in the low education-level group. Subgroup analysis by marital status indicated that this factor also affected the association between employment situation and risk of death. Among middle-aged Japanese women, employment situation was associated with mortality risk. Health effects were likely to differ by household structure and socioeconomic conditions. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Impact of social capital on psychological distress and interaction with house destruction and displacement after the Great East Japan Earthquake of 2011.

PubMed

Tsuchiya, Naho; Nakaya, Naoki; Nakamura, Tomohiro; Narita, Akira; Kogure, Mana; Aida, Jun; Tsuji, Ichiro; Hozawa, Atsushi; Tomita, Hiroaki

2017-01-01

Social capital has been considered an important factor affecting mental-health outcomes, such as psychological distress in post-disaster settings. Although disaster-related house condition and displacement could affect both social capital and psychological distress, limited studies have investigated interactions. This study aimed to examine the association between social capital and psychological distress, taking into consideration the interaction of disaster-related house condition after the Great East Japan Earthquake of 2011. Using data from 3793 adults living in Shichigahama, Miyagi Prefecture, Japan, we examined the association between social capital measured by generalized trust and psychological distress measured by the Kessler 6 scale. We conducted stratified analysis to investigate an interaction of house destruction and displacement. Multivariate analyses taking into consideration the interaction were performed. In the crude analysis, low social capital (odds ratio [OR] 4.46; 95% confidence interval [CI], 3.27-6.07) and large-scale house destruction (OR 1.96; 95%CI, 1.47-2.62) were significantly associated with psychological distress. Stratified analyses detected an interaction with house destruction and displacement (P for interaction = 0.04). Multivariate analysis with interaction term revealed that individuals with low social capital, large-scale house damage, and displacement were at greater risk of psychological distress, corresponding to adjusted OR of 5.78 (95%CI, 3.48-9.60). In the post-disaster setting, low social capital increased the risk of psychological distress, especially among individuals who had large-scale house destruction. Among the participants with severe disaster damage, high social capital would play an important role in protecting mental health. © 2016 The Authors. Psychiatry and Clinical Neurosciences published by John Wiley & Sons Australia, Ltd on behalf of Japanese Society of Psychiatry and Neurology.

Dietary inflammatory index and risk of lung cancer and other respiratory conditions among heavy smokers in the COSMOS screening study.

PubMed

Maisonneuve, Patrick; Shivappa, Nitin; Hébert, James R; Bellomi, Massimo; Rampinelli, Cristiano; Bertolotti, Raffaella; Spaggiari, Lorenzo; Palli, Domenico; Veronesi, Giulia; Gnagnarella, Patrizia

2016-04-01

To test whether the inflammatory potential of diet, as measured using the dietary inflammatory index (DII), is associated with risk of lung cancer or other respiratory conditions and to compare results obtained with those based on the aMED score, an established dietary index that measures adherence to the traditional Mediterranean diet. In 4336 heavy smokers enrolled in a prospective, non-randomized lung cancer screening program, we measured participants' diets at baseline using a self-administered food frequency questionnaire from which dietary scores were calculated. Cox proportional hazards and logistic regression models were used to assess association between the dietary indices and lung cancer diagnosed during annual screening, and other respiratory outcomes that were recorded at baseline, respectively. In multivariable analysis, adjusted for baseline lung cancer risk (estimated from age, sex, smoking history, and asbestos exposure) and total energy, both DII and aMED scores were associated with dyspnoea (p trend = 0.046 and 0.02, respectively) and radiological evidence of emphysema (p trend = 0.0002 and 0.02). After mutual adjustment of the two dietary scores, only the association between DII and radiological evidence of emphysema (Q4 vs. Q1, OR 1.30, 95 % CI 1.01-1.67, p trend = 0.012) remained statistically significant. At univariate analysis, both DII and aMED were associated with lung cancer risk, but in fully adjusted multivariate analysis, only the association with aMED remained statistically significant (p trend = 0.04). Among heavy smokers, a pro-inflammatory diet, as indicated by increasing DII score, is associated with dyspnoea and radiological evidence of emphysema. A traditional Mediterranean diet, which is associated with a lower DII, may lower lung cancer risk.

Dietary inflammatory index and risk of lung cancer and other respiratory conditions among heavy smokers in the COSMOS screening study

PubMed Central

Shivappa, Nitin; Hébert, James R.; Bellomi, Massimo; Rampinelli, Cristiano; Bertolotti, Raffaella; Spaggiari, Lorenzo; Palli, Domenico; Veronesi, Giulia; Gnagnarella, Patrizia

2016-01-01

Purpose To test whether the inflammatory potential of diet, as measured using the dietary inflammatory index (DII), is associated with risk of lung cancer or other respiratory conditions and to compare results obtained with those based on the aMED score, an established dietary index that measures adherence to the traditional Mediterranean diet. Methods In 4336 heavy smokers enrolled in a prospective, non-randomized lung cancer screening program, we measured participants’ diets at baseline using a self-administered food frequency questionnaire from which dietary scores were calculated. Cox proportional hazards and logistic regression models were used to assess association between the dietary indices and lung cancer diagnosed during annual screening, and other respiratory outcomes that were recorded at baseline, respectively. Results In multivariable analysis, adjusted for baseline lung cancer risk (estimated from age, sex, smoking history, and asbestos exposure) and total energy, both DII and aMED scores were associated with dyspnoea (p trend = 0.046 and 0.02, respectively) and radiological evidence of emphysema (p trend = 0.0002 and 0.02). After mutual adjustment of the two dietary scores, only the association between DII and radiological evidence of emphysema (Q4 vs. Q1, OR 1.30, 95 % CI 1.01–1.67, p trend = 0.012) remained statistically significant. At univariate analysis, both DII and aMED were associated with lung cancer risk, but in fully adjusted multivariate analysis, only the association with aMED remained statistically significant (p trend = 0.04). Conclusions Among heavy smokers, a pro-inflammatory diet, as indicated by increasing DII score, is associated with dyspnoea and radiological evidence of emphysema. A traditional Mediterranean diet, which is associated with a lower DII, may lower lung cancer risk. PMID:25953452

MULTIVARIATE CURVE RESOLUTION OF NMR SPECTROSCOPY METABONOMIC DATA

EPA Science Inventory

Sandia National Laboratories is working with the EPA to evaluate and develop mathematical tools for analysis of the collected NMR spectroscopy data. Initially, we have focused on the use of Multivariate Curve Resolution (MCR) also known as molecular factor analysis (MFA), a tech...

Characterizing multivariate decoding models based on correlated EEG spectral features

PubMed Central

McFarland, Dennis J.

2013-01-01

Objective Multivariate decoding methods are popular techniques for analysis of neurophysiological data. The present study explored potential interpretative problems with these techniques when predictors are correlated. Methods Data from sensorimotor rhythm-based cursor control experiments was analyzed offline with linear univariate and multivariate models. Features were derived from autoregressive (AR) spectral analysis of varying model order which produced predictors that varied in their degree of correlation (i.e., multicollinearity). Results The use of multivariate regression models resulted in much better prediction of target position as compared to univariate regression models. However, with lower order AR features interpretation of the spectral patterns of the weights was difficult. This is likely to be due to the high degree of multicollinearity present with lower order AR features. Conclusions Care should be exercised when interpreting the pattern of weights of multivariate models with correlated predictors. Comparison with univariate statistics is advisable. Significance While multivariate decoding algorithms are very useful for prediction their utility for interpretation may be limited when predictors are correlated. PMID:23466267

Drunk driving detection based on classification of multivariate time series.

PubMed

Li, Zhenlong; Jin, Xue; Zhao, Xiaohua

2015-09-01

This paper addresses the problem of detecting drunk driving based on classification of multivariate time series. First, driving performance measures were collected from a test in a driving simulator located in the Traffic Research Center, Beijing University of Technology. Lateral position and steering angle were used to detect drunk driving. Second, multivariate time series analysis was performed to extract the features. A piecewise linear representation was used to represent multivariate time series. A bottom-up algorithm was then employed to separate multivariate time series. The slope and time interval of each segment were extracted as the features for classification. Third, a support vector machine classifier was used to classify driver's state into two classes (normal or drunk) according to the extracted features. The proposed approach achieved an accuracy of 80.0%. Drunk driving detection based on the analysis of multivariate time series is feasible and effective. The approach has implications for drunk driving detection. Copyright © 2015 Elsevier Ltd and National Safety Council. All rights reserved.

Cole-Cole, linear and multivariate modeling of capacitance data for on-line monitoring of biomass.

PubMed

Dabros, Michal; Dennewald, Danielle; Currie, David J; Lee, Mark H; Todd, Robert W; Marison, Ian W; von Stockar, Urs

2009-02-01

This work evaluates three techniques of calibrating capacitance (dielectric) spectrometers used for on-line monitoring of biomass: modeling of cell properties using the theoretical Cole-Cole equation, linear regression of dual-frequency capacitance measurements on biomass concentration, and multivariate (PLS) modeling of scanning dielectric spectra. The performance and robustness of each technique is assessed during a sequence of validation batches in two experimental settings of differing signal noise. In more noisy conditions, the Cole-Cole model had significantly higher biomass concentration prediction errors than the linear and multivariate models. The PLS model was the most robust in handling signal noise. In less noisy conditions, the three models performed similarly. Estimates of the mean cell size were done additionally using the Cole-Cole and PLS models, the latter technique giving more satisfactory results.

The choice of prior distribution for a covariance matrix in multivariate meta-analysis: a simulation study.

PubMed

Hurtado Rúa, Sandra M; Mazumdar, Madhu; Strawderman, Robert L

2015-12-30

Bayesian meta-analysis is an increasingly important component of clinical research, with multivariate meta-analysis a promising tool for studies with multiple endpoints. Model assumptions, including the choice of priors, are crucial aspects of multivariate Bayesian meta-analysis (MBMA) models. In a given model, two different prior distributions can lead to different inferences about a particular parameter. A simulation study was performed in which the impact of families of prior distributions for the covariance matrix of a multivariate normal random effects MBMA model was analyzed. Inferences about effect sizes were not particularly sensitive to prior choice, but the related covariance estimates were. A few families of prior distributions with small relative biases, tight mean squared errors, and close to nominal coverage for the effect size estimates were identified. Our results demonstrate the need for sensitivity analysis and suggest some guidelines for choosing prior distributions in this class of problems. The MBMA models proposed here are illustrated in a small meta-analysis example from the periodontal field and a medium meta-analysis from the study of stroke. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

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

The relative impact of chronic conditions and multimorbidity on health-related quality of life in Ontario long-stay home care clients.

PubMed

Mondor, Luke; Maxwell, Colleen J; Bronskill, Susan E; Gruneir, Andrea; Wodchis, Walter P

2016-10-01

To examine the relative impact of 16 common chronic conditions and increasing morbidity on health-related quality of life (HRQL) in a population-based sample of home care clients in Ontario, Canada. Participants were adult clients assessed with the Resident Assessment Instrument for Home Care (RAI-HC) between January and June 2009 and diagnosed with one (or more) of 16 common chronic conditions. HRQL was evaluated using the Minimum Data Set-Health Status Index (MDS-HSI), a preference-based measure derived from items captured in the RAI-HC. Multivariable linear regression models assessed the relative impact of each condition, and increasing number of diagnoses, on MDS-HSI scores. Mean (SD) MDS-HSI score in the study population (n = 106,159) was 0.524 (0.213). Multivariable analysis revealed a statistically significant (p < 0.05) and clinically important (difference ≥ 0.03) decrease in MDS-HSI scores associated with stroke (-0.056), osteoarthritis (-0.036), rheumatoid arthritis (-0.033) and congestive heart failure (CHF, -0.030). Differences by age and sex were observed; most notably, the negative impact associated with dementia was greater among men (-0.043) than among women (-0.019). Further, HRQL decreased incrementally with additional diagnoses. In all models, chronic conditions and number of diagnoses accounted for a relatively small proportion of the variance observed in MDS-HSI. Clinically important negative effects on HRQL were observed for clients with a previous diagnosis of stroke, osteo- and rheumatoid arthritis, or CHF, as well as with increasing levels of multimorbidity. Findings provide baseline preference-based HRQL scores for home care clients with different diagnoses and may be useful for identifying, targeting and evaluating care strategies toward populations with significant HRQL impairments.

Application of multivariable statistical techniques in plant-wide WWTP control strategies analysis.

PubMed

Flores, X; Comas, J; Roda, I R; Jiménez, L; Gernaey, K V

2007-01-01

The main objective of this paper is to present the application of selected multivariable statistical techniques in plant-wide wastewater treatment plant (WWTP) control strategies analysis. In this study, cluster analysis (CA), principal component analysis/factor analysis (PCA/FA) and discriminant analysis (DA) are applied to the evaluation matrix data set obtained by simulation of several control strategies applied to the plant-wide IWA Benchmark Simulation Model No 2 (BSM2). These techniques allow i) to determine natural groups or clusters of control strategies with a similar behaviour, ii) to find and interpret hidden, complex and casual relation features in the data set and iii) to identify important discriminant variables within the groups found by the cluster analysis. This study illustrates the usefulness of multivariable statistical techniques for both analysis and interpretation of the complex multicriteria data sets and allows an improved use of information for effective evaluation of control strategies.

Moving beyond Univariate Post-Hoc Testing in Exercise Science: A Primer on Descriptive Discriminate Analysis

ERIC Educational Resources Information Center

Barton, Mitch; Yeatts, Paul E.; Henson, Robin K.; Martin, Scott B.

2016-01-01

There has been a recent call to improve data reporting in kinesiology journals, including the appropriate use of univariate and multivariate analysis techniques. For example, a multivariate analysis of variance (MANOVA) with univariate post hocs and a Bonferroni correction is frequently used to investigate group differences on multiple dependent…

Multivariate statistical analysis to characterize/discriminate between anthropogenic and geogenic trace elements occurrence in the Campania Plain, Southern Italy.

PubMed

Busico, Gianluigi; Cuoco, Emilio; Kazakis, Nerantzis; Colombani, Nicolò; Mastrocicco, Micòl; Tedesco, Dario; Voudouris, Konstantinos

2018-03-01

Shallow aquifers are the most accessible reservoirs of potable groundwater; nevertheless, they are also prone to various sources of pollution and it is usually difficult to distinguish between human and natural sources at the watershed scale. The area chosen for this study (the Campania Plain) is characterized by high spatial heterogeneities both in geochemical features and in hydraulic properties. Groundwater mineralization is driven by many processes such as, geothermal activity, weathering of volcanic products and intense human activities. In such a landscape, multivariate statistical analysis has been used to differentiate among the main hydrochemical processes occurring in the area, using three different approaches of factor analysis: (i) major elements, (ii) trace elements, (iii) both major and trace elements. The elaboration of the factor analysis approaches has revealed seven distinct hydrogeochemical processes: i) Salinization (Cl - , Na + ); ii) Carbonate rocks dissolution; iii) Anthropogenic inputs (NO 3 - , SO 4 2- , U, V); iv) Reducing conditions (Fe 2+ , Mn 2+ ); v) Heavy metals contamination (Cr and Ni); vi) Geothermal fluids influence (Li + ); and vii) Volcanic products contribution (As, Rb). Results from this study highlight the need to separately apply factor analysis when a large data set of trace elements is available. In fact, the impact of geothermal fluids in the shallow aquifer was identified from the application of the factor analysis using only trace elements. This study also reveals that the factor analysis of major and trace elements can differentiate between anthropogenic and geogenic sources of pollution in intensively exploited aquifers. Copyright © 2017 Elsevier Ltd. All rights reserved.

MGAS: a powerful tool for multivariate gene-based genome-wide association analysis.

PubMed

Van der Sluis, Sophie; Dolan, Conor V; Li, Jiang; Song, Youqiang; Sham, Pak; Posthuma, Danielle; Li, Miao-Xin

2015-04-01

Standard genome-wide association studies, testing the association between one phenotype and a large number of single nucleotide polymorphisms (SNPs), are limited in two ways: (i) traits are often multivariate, and analysis of composite scores entails loss in statistical power and (ii) gene-based analyses may be preferred, e.g. to decrease the multiple testing problem. Here we present a new method, multivariate gene-based association test by extended Simes procedure (MGAS), that allows gene-based testing of multivariate phenotypes in unrelated individuals. Through extensive simulation, we show that under most trait-generating genotype-phenotype models MGAS has superior statistical power to detect associated genes compared with gene-based analyses of univariate phenotypic composite scores (i.e. GATES, multiple regression), and multivariate analysis of variance (MANOVA). Re-analysis of metabolic data revealed 32 False Discovery Rate controlled genome-wide significant genes, and 12 regions harboring multiple genes; of these 44 regions, 30 were not reported in the original analysis. MGAS allows researchers to conduct their multivariate gene-based analyses efficiently, and without the loss of power that is often associated with an incorrectly specified genotype-phenotype models. MGAS is freely available in KGG v3.0 (http://statgenpro.psychiatry.hku.hk/limx/kgg/download.php). Access to the metabolic dataset can be requested at dbGaP (https://dbgap.ncbi.nlm.nih.gov/). The R-simulation code is available from http://ctglab.nl/people/sophie_van_der_sluis. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press.

Multivariate meta-analysis using individual participant data

PubMed Central

Riley, R. D.; Price, M. J.; Jackson, D.; Wardle, M.; Gueyffier, F.; Wang, J.; Staessen, J. A.; White, I. R.

2016-01-01

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 that within-study correlations needed to fit the multivariate model are unknown from published reports. However, provision of individual participant data (IPD) allows them to be calculated directly. Here, we illustrate how to use IPD to estimate within-study correlations, using a joint linear regression for multiple continuous outcomes and bootstrapping methods for binary, survival and mixed outcomes. In a meta-analysis of 10 hypertension trials, we then show how these methods enable multivariate meta-analysis to address novel clinical questions about continuous, survival and binary outcomes; treatment–covariate interactions; adjusted risk/prognostic factor effects; longitudinal data; prognostic and multiparameter models; and multiple treatment comparisons. Both frequentist and Bayesian approaches are applied, with example software code provided to derive within-study correlations and to fit the models. PMID:26099484

1H NMR-based metabolic profiling for evaluating poppy seed rancidity and brewing.

PubMed

Jawień, Ewa; Ząbek, Adam; Deja, Stanisław; Łukaszewicz, Marcin; Młynarz, Piotr

2015-12-01

Poppy seeds are widely used in household and commercial confectionery. The aim of this study was to demonstrate the application of metabolic profiling for industrial monitoring of the molecular changes which occur during minced poppy seed rancidity and brewing processes performed on raw seeds. Both forms of poppy seeds were obtained from a confectionery company. Proton nuclear magnetic resonance (1H NMR) was applied as the analytical method of choice together with multivariate statistical data analysis. Metabolic fingerprinting was applied as a bioprocess control tool to monitor rancidity with the trajectory of change and brewing progressions. Low molecular weight compounds were found to be statistically significant biomarkers of these bioprocesses. Changes in concentrations of chemical compounds were explained relative to the biochemical processes and external conditions. The obtained results provide valuable and comprehensive information to gain a better understanding of the biology of rancidity and brewing processes, while demonstrating the potential for applying NMR spectroscopy combined with multivariate data analysis tools for quality control in food industries involved in the processing of oilseeds. This precious and versatile information gives a better understanding of the biology of these processes.

Multiscale entropy analysis of biological signals: a fundamental bi-scaling law

PubMed Central

Gao, Jianbo; Hu, Jing; Liu, Feiyan; Cao, Yinhe

2015-01-01

Since introduced in early 2000, multiscale entropy (MSE) has found many applications in biosignal analysis, and been extended to multivariate MSE. So far, however, no analytic results for MSE or multivariate MSE have been reported. This has severely limited our basic understanding of MSE. For example, it has not been studied whether MSE estimated using default parameter values and short data set is meaningful or not. Nor is it known whether MSE has any relation with other complexity measures, such as the Hurst parameter, which characterizes the correlation structure of the data. To overcome this limitation, and more importantly, to guide more fruitful applications of MSE in various areas of life sciences, we derive a fundamental bi-scaling law for fractal time series, one for the scale in phase space, the other for the block size used for smoothing. We illustrate the usefulness of the approach by examining two types of physiological data. One is heart rate variability (HRV) data, for the purpose of distinguishing healthy subjects from patients with congestive heart failure, a life-threatening condition. The other is electroencephalogram (EEG) data, for the purpose of distinguishing epileptic seizure EEG from normal healthy EEG. PMID:26082711

Effectively Transforming IMC Flight into VMC Flight: An SVS Case Study

NASA Technical Reports Server (NTRS)

Glaab, Louis J.; Hughes, Monic F.; Parrish, Russell V.; Takallu, Mohammad A.

2006-01-01

A flight-test experiment was conducted using the NASA LaRC Cessna 206 aircraft. Four primary flight and navigation display concepts, including baseline and Synthetic Vision System (SVS) concepts, were evaluated in the local area of Roanoke Virginia Airport, flying visual and instrument approach procedures. A total of 19 pilots, from 3 pilot groups reflecting the diverse piloting skills of the GA population, served as evaluation pilots. Multi-variable Discriminant Analysis was applied to three carefully selected and markedly different operating conditions with conventional instrumentation to provide an extension of traditional analysis methods as well as provide an assessment of the effectiveness of SVS displays to effectively transform IMC flight into VMC flight.

Cluster and Multiple Correspondence Analyses in Rheumatology: Paths to Uncovering Relationships in a Sea of Data.

PubMed

Han, Lu; Benseler, Susanne M; Tyrrell, Pascal N

2018-05-01

Rheumatic diseases encompass a wide range of conditions caused by inflammation and dysregulation of the immune system resulting in organ damage. Research in these heterogeneous diseases benefits from multivariate methods. The aim of this review was to describe and evaluate current literature in rheumatology regarding cluster analysis and correspondence analysis. A systematic review showed an increase in studies making use of these 2 methods. However, standardization in how these methods are applied and reported is needed. Researcher expertise was determined to be the main barrier to considering these approaches, whereas education and collaborating with a biostatistician were suggested ways forward. Copyright © 2018 Elsevier Inc. All rights reserved.

The dynamic correlation between policy uncertainty and stock market returns in China

NASA Astrophysics Data System (ADS)

Yang, Miao; Jiang, Zhi-Qiang

2016-11-01

The dynamic correlation is examined between government's policy uncertainty and Chinese stock market returns in the period from January 1995 to December 2014. We find that the stock market is significantly correlated to policy uncertainty based on the results of the Vector Auto Regression (VAR) and Structural Vector Auto Regression (SVAR) models. In contrast, the results of the Dynamic Conditional Correlation Generalized Multivariate Autoregressive Conditional Heteroscedasticity (DCC-MGARCH) model surprisingly show a low dynamic correlation coefficient between policy uncertainty and market returns, suggesting that the fluctuations of each variable are greatly influenced by their values in the preceding period. Our analysis highlights the understanding of the dynamical relationship between stock market and fiscal and monetary policy.

MANOVA for distinguishing experts' perceptions about entrepreneurship using NES data from GEM

NASA Astrophysics Data System (ADS)

Correia, Aldina; Costa e Silva, Eliana; Lopes, Isabel C.; Braga, Alexandra

2016-12-01

Global Entrepreneurship Monitor is a large scale database for internationally comparative entrepreneurship that includes information about many aspects of entrepreneurship activities, perceptions, conditions, national and regional policy, among others, of a large number of countries. This project has two main sources of primary data: the Adult Population Survey and the National Expert Survey. In this work the 2011 and 2012 National Expert Survey datasets are studied. Our goal is to analyze the effects of the different type of entrepreneurship expert specialization on the perceptions about the Entrepreneurial Framework Conditions. For this purpose the multivariate analysis of variance is used. Some similarities between the results obtained for the 2011 and 2012 datasets were found, however the differences between experts still exist.

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.

Multivariate generalized multifactor dimensionality reduction to detect gene-gene interactions

PubMed Central

2013-01-01

Background Recently, one of the greatest challenges in genome-wide association studies is to detect gene-gene and/or gene-environment interactions for common complex human diseases. Ritchie et al. (2001) proposed multifactor dimensionality reduction (MDR) method for interaction analysis. MDR is a combinatorial approach to reduce multi-locus genotypes into high-risk and low-risk groups. Although MDR has been widely used for case-control studies with binary phenotypes, several extensions have been proposed. One of these methods, a generalized MDR (GMDR) proposed by Lou et al. (2007), allows adjusting for covariates and applying to both dichotomous and continuous phenotypes. GMDR uses the residual score of a generalized linear model of phenotypes to assign either high-risk or low-risk group, while MDR uses the ratio of cases to controls. Methods In this study, we propose multivariate GMDR, an extension of GMDR for multivariate phenotypes. Jointly analysing correlated multivariate phenotypes may have more power to detect susceptible genes and gene-gene interactions. We construct generalized estimating equations (GEE) with multivariate phenotypes to extend generalized linear models. Using the score vectors from GEE we discriminate high-risk from low-risk groups. We applied the multivariate GMDR method to the blood pressure data of the 7,546 subjects from the Korean Association Resource study: systolic blood pressure (SBP) and diastolic blood pressure (DBP). We compare the results of multivariate GMDR for SBP and DBP to the results from separate univariate GMDR for SBP and DBP, respectively. We also applied the multivariate GMDR method to the repeatedly measured hypertension status from 5,466 subjects and compared its result with those of univariate GMDR at each time point. Results Results from the univariate GMDR and multivariate GMDR in two-locus model with both blood pressures and hypertension phenotypes indicate best combinations of SNPs whose interaction has significant association with risk for high blood pressures or hypertension. Although the test balanced accuracy (BA) of multivariate analysis was not always greater than that of univariate analysis, the multivariate BAs were more stable with smaller standard deviations. Conclusions In this study, we have developed multivariate GMDR method using GEE approach. It is useful to use multivariate GMDR with correlated multiple phenotypes of interests. PMID:24565370

Tunnelled haemodialysis catheter and haemodialysis outcomes: a retrospective cohort study in Zagreb, Croatia.

PubMed

Pašara, Vedran; Maksimović, Bojana; Gunjača, Mihaela; Mihovilović, Karlo; Lončar, Andrea; Kudumija, Boris; Žabić, Igor; Knotek, Mladen

2016-05-17

Studies have reported that the tunnelled dialysis catheter (TDC) is associated with inferior haemodialysis (HD) patient survival, in comparison with arteriovenous fistula (AVF). Since many cofactors may also affect survival of HD patients, it is unclear whether the greater risk for survival arises from TDC per se, or from associated conditions. Therefore, the aim of this study was to determine, in a multivariate analysis, the long-term outcome of HD patients, with respect to vascular access (VA). Retrospective cohort study. This retrospective cohort study included all 156 patients with a TDC admitted at University Hospital Merkur, from 2010 to 2012. The control group consisted of 97 patients dialysed via AVF. The groups were matched according to dialysis unit and time of VA placement. The site of choice for the placement of the TDC was the right jugular vein. Kaplan-Meier analysis with log-rank test was used to assess patient survival. Multivariate Cox regression analysis was used to determine independent variables associated with patient survival. Patient survival with respect to VA. The cumulative 1-year survival of patients who were dialysed exclusively via TDC was 86.4% and of those who were dialysed exclusively via AVF, survival was 97.1% (p=0.002). In multivariate Cox regression analysis, male sex and older age were independently negatively associated with the survival of HD patients, while shorter HD vintage before the creation of the observed VA, hypertensive renal disease and glomerulonephritis were positively associated with survival. TDC was an independent risk factor for survival of HD patients (HR 23.0, 95% CI 6.2 to 85.3). TDC may be an independent negative risk factor for HD patient survival. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Power analysis for multivariate and repeated measures designs: a flexible approach using the SPSS MANOVA procedure.

PubMed

D'Amico, E J; Neilands, T B; Zambarano, R

2001-11-01

Although power analysis is an important component in the planning and implementation of research designs, it is often ignored. Computer programs for performing power analysis are available, but most have limitations, particularly for complex multivariate designs. An SPSS procedure is presented that can be used for calculating power for univariate, multivariate, and repeated measures models with and without time-varying and time-constant covariates. Three examples provide a framework for calculating power via this method: an ANCOVA, a MANOVA, and a repeated measures ANOVA with two or more groups. The benefits and limitations of this procedure are discussed.

Multi-Sample Cluster Analysis Using Akaike’s Information Criterion.

DTIC Science & Technology

1982-12-20

of Likelihood Criteria for I)fferent Hypotheses," in P. A. Krishnaiah (Ed.), Multivariate Analysis-Il, New York: Academic Press. [5] Fisher, R. A...Methods of Simultaneous Inference in MANOVA," in P. R. Krishnaiah (Ed.), rultivariate Analysis-Il, New York: Academic Press. [8) Kendall, M. G. (1966...1982), Applied Multivariate Statisti- cal-Analysis, Englewood Cliffs: Prentice-Mall, Inc. [1U] Krishnaiah , P. R. (1969), "Simultaneous Test

Docking and multivariate methods to explore HIV-1 drug-resistance: a comparative analysis

NASA Astrophysics Data System (ADS)

Almerico, Anna Maria; Tutone, Marco; Lauria, Antonino

2008-05-01

In this paper we describe a comparative analysis between multivariate and docking methods in the study of the drug resistance to the reverse transcriptase and the protease inhibitors. In our early papers we developed a simple but efficient method to evaluate the features of compounds that are less likely to trigger resistance or are effective against mutant HIV strains, using the multivariate statistical procedures PCA and DA. In the attempt to create a more solid background for the prediction of susceptibility or resistance, we carried out a comparative analysis between our previous multivariate approach and molecular docking study. The intent of this paper is not only to find further support to the results obtained by the combined use of PCA and DA, but also to evidence the structural features, in terms of molecular descriptors, similarity, and energetic contributions, derived from docking, which can account for the arising of drug-resistance against mutant strains.

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...

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…

Bayesian inference for multivariate meta-analysis Box-Cox transformation models for individual patient data with applications to evaluation of cholesterol lowering drugs

PubMed Central

Kim, Sungduk; Chen, Ming-Hui; Ibrahim, Joseph G.; Shah, Arvind K.; Lin, Jianxin

2013-01-01

In this paper, we propose a class of Box-Cox transformation regression models with multidimensional random effects for analyzing multivariate responses for individual patient data (IPD) in meta-analysis. Our modeling formulation uses a multivariate normal response meta-analysis model with multivariate random effects, in which each response is allowed to have its own Box-Cox transformation. Prior distributions are specified for the Box-Cox transformation parameters as well as the regression coefficients in this complex model, and the Deviance Information Criterion (DIC) is used to select the best transformation model. Since the model is quite complex, a novel Monte Carlo Markov chain (MCMC) sampling scheme is developed to sample from the joint posterior of the parameters. This model is motivated by a very rich dataset comprising 26 clinical trials involving cholesterol lowering drugs where the goal is to jointly model the three dimensional response consisting of Low Density Lipoprotein Cholesterol (LDL-C), High Density Lipoprotein Cholesterol (HDL-C), and Triglycerides (TG) (LDL-C, HDL-C, TG). Since the joint distribution of (LDL-C, HDL-C, TG) is not multivariate normal and in fact quite skewed, a Box-Cox transformation is needed to achieve normality. In the clinical literature, these three variables are usually analyzed univariately: however, a multivariate approach would be more appropriate since these variables are correlated with each other. A detailed analysis of these data is carried out using the proposed methodology. PMID:23580436

Bayesian inference for multivariate meta-analysis Box-Cox transformation models for individual patient data with applications to evaluation of cholesterol-lowering drugs.

PubMed

Kim, Sungduk; Chen, Ming-Hui; Ibrahim, Joseph G; Shah, Arvind K; Lin, Jianxin

2013-10-15

In this paper, we propose a class of Box-Cox transformation regression models with multidimensional random effects for analyzing multivariate responses for individual patient data in meta-analysis. Our modeling formulation uses a multivariate normal response meta-analysis model with multivariate random effects, in which each response is allowed to have its own Box-Cox transformation. Prior distributions are specified for the Box-Cox transformation parameters as well as the regression coefficients in this complex model, and the deviance information criterion is used to select the best transformation model. Because the model is quite complex, we develop a novel Monte Carlo Markov chain sampling scheme to sample from the joint posterior of the parameters. This model is motivated by a very rich dataset comprising 26 clinical trials involving cholesterol-lowering drugs where the goal is to jointly model the three-dimensional response consisting of low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C), and triglycerides (TG) (LDL-C, HDL-C, TG). Because the joint distribution of (LDL-C, HDL-C, TG) is not multivariate normal and in fact quite skewed, a Box-Cox transformation is needed to achieve normality. In the clinical literature, these three variables are usually analyzed univariately; however, a multivariate approach would be more appropriate because these variables are correlated with each other. We carry out a detailed analysis of these data by using the proposed methodology. Copyright © 2013 John Wiley & Sons, Ltd.

Multivariate research in areas of phosphorus cast-iron brake shoes manufacturing using the statistical analysis and the multiple regression equations

NASA Astrophysics Data System (ADS)

Kiss, I.; Cioată, V. G.; Alexa, V.; Raţiu, S. A.

2017-05-01

The braking system is one of the most important and complex subsystems of railway vehicles, especially when it comes for safety. Therefore, installing efficient safe brakes on the modern railway vehicles is essential. Nowadays is devoted attention to solving problems connected with using high performance brake materials and its impact on thermal and mechanical loading of railway wheels. The main factor that influences the selection of a friction material for railway applications is the performance criterion, due to the interaction between the brake block and the wheel produce complex thermos-mechanical phenomena. In this work, the investigated subjects are the cast-iron brake shoes, which are still widely used on freight wagons. Therefore, the cast-iron brake shoes - with lamellar graphite and with a high content of phosphorus (0.8-1.1%) - need a special investigation. In order to establish the optimal condition for the cast-iron brake shoes we proposed a mathematical modelling study by using the statistical analysis and multiple regression equations. Multivariate research is important in areas of cast-iron brake shoes manufacturing, because many variables interact with each other simultaneously. Multivariate visualization comes to the fore when researchers have difficulties in comprehending many dimensions at one time. Technological data (hardness and chemical composition) obtained from cast-iron brake shoes were used for this purpose. In order to settle the multiple correlation between the hardness of the cast-iron brake shoes, and the chemical compositions elements several model of regression equation types has been proposed. Because a three-dimensional surface with variables on three axes is a common way to illustrate multivariate data, in which the maximum and minimum values are easily highlighted, we plotted graphical representation of the regression equations in order to explain interaction of the variables and locate the optimal level of each variable for maximal response. For the calculation of the regression coefficients, dispersion and correlation coefficients, the software Matlab was used.

Feasibility Study on the Use of On-line Multivariate Statistical Process Control for Safeguards Applications in Natural Uranium Conversion Plants

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

Ladd-Lively, Jennifer L

2014-01-01

The objective of this work was to determine the feasibility of using on-line multivariate statistical process control (MSPC) for safeguards applications in natural uranium conversion plants. Multivariate statistical process control is commonly used throughout industry for the detection of faults. For safeguards applications in uranium conversion plants, faults could include the diversion of intermediate products such as uranium dioxide, uranium tetrafluoride, and uranium hexafluoride. This study was limited to a 100 metric ton of uranium (MTU) per year natural uranium conversion plant (NUCP) using the wet solvent extraction method for the purification of uranium ore concentrate. A key component inmore » the multivariate statistical methodology is the Principal Component Analysis (PCA) approach for the analysis of data, development of the base case model, and evaluation of future operations. The PCA approach was implemented through the use of singular value decomposition of the data matrix where the data matrix represents normal operation of the plant. Component mole balances were used to model each of the process units in the NUCP. However, this approach could be applied to any data set. The monitoring framework developed in this research could be used to determine whether or not a diversion of material has occurred at an NUCP as part of an International Atomic Energy Agency (IAEA) safeguards system. This approach can be used to identify the key monitoring locations, as well as locations where monitoring is unimportant. Detection limits at the key monitoring locations can also be established using this technique. Several faulty scenarios were developed to test the monitoring framework after the base case or normal operating conditions of the PCA model were established. In all of the scenarios, the monitoring framework was able to detect the fault. Overall this study was successful at meeting the stated objective.« less

The influence of demographic characteristics, living conditions, and trauma exposure on the overall health of a conflict-affected population in Southern Sudan.

PubMed

Roberts, Bayard; Damundu, Eliaba Yona; Lomoro, Olivia; Sondorp, Egbert

2010-08-27

There remains limited evidence on how armed conflict affects overall physical and mental well-being rather than specific physical or mental health conditions. The aim of this study was to investigate the influence of demographic characteristics, living conditions, and violent and traumatic events on general physical and mental health in Southern Sudan which is emerging from 20 years of armed conflict. A cross-sectional survey of 1228 adults was conducted in November 2007 in the town of Juba, the capital of Southern Sudan. Multivariate linear regression analysis was used to investigate the associations and relative influence of variables in three models of demographic characteristics, living conditions, and trauma exposure, on general physical and mental health status. These models were run separately and also as a combined model. Data quality and the internal consistency of the health status instrument (SF-8) were assessed. The variables in the multivariate analysis (combined model) with negative coefficients of association with general physical health and mental health (i.e. worse health), respectively, were being female (coef. -2.47; -2.63), higher age (coef.-0.16; -0.17), absence of soap in the household (physical health coef. -2.24), and experiencing within the past 12 months a lack of food and/or water (coef. -1.46; -2.27) and lack of medical care (coef.-3.51; -3.17). A number of trauma variables and cumulative exposure to trauma showed an association with physical and mental health (see main text for data). There was limited variance in results when each of the three models were run separately and when they were combined, suggesting the pervasive influence of these variables. The SF-8 showed good data quality and internal consistency. This study provides evidence on the pervasive influence of demographic characteristics, living conditions, and violent and traumatic events on the general physical and mental health of a conflict-affected population in Southern Sudan, and highlights the importance of addressing all these influences on overall health.

The influence of demographic characteristics, living conditions, and trauma exposure on the overall health of a conflict-affected population in Southern Sudan

PubMed Central

2010-01-01

Background There remains limited evidence on how armed conflict affects overall physical and mental well-being rather than specific physical or mental health conditions. The aim of this study was to investigate the influence of demographic characteristics, living conditions, and violent and traumatic events on general physical and mental health in Southern Sudan which is emerging from 20 years of armed conflict. Methods A cross-sectional survey of 1228 adults was conducted in November 2007 in the town of Juba, the capital of Southern Sudan. Multivariate linear regression analysis was used to investigate the associations and relative influence of variables in three models of demographic characteristics, living conditions, and trauma exposure, on general physical and mental health status. These models were run separately and also as a combined model. Data quality and the internal consistency of the health status instrument (SF-8) were assessed. Results The variables in the multivariate analysis (combined model) with negative coefficients of association with general physical health and mental health (i.e. worse health), respectively, were being female (coef. -2.47; -2.63), higher age (coef.-0.16; -0.17), absence of soap in the household (physical health coef. -2.24), and experiencing within the past 12 months a lack of food and/or water (coef. -1.46; -2.27) and lack of medical care (coef.-3.51; -3.17). A number of trauma variables and cumulative exposure to trauma showed an association with physical and mental health (see main text for data). There was limited variance in results when each of the three models were run separately and when they were combined, suggesting the pervasive influence of these variables. The SF-8 showed good data quality and internal consistency. Conclusions This study provides evidence on the pervasive influence of demographic characteristics, living conditions, and violent and traumatic events on the general physical and mental health of a conflict-affected population in Southern Sudan, and highlights the importance of addressing all these influences on overall health. PMID:20799956

Stochastic modelling of temperatures affecting the in situ performance of a solar-assisted heat pump: The multivariate approach and physical interpretation

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

Loveday, D.L.; Craggs, C.

Box-Jenkins-based multivariate stochastic modeling is carried out using data recorded from a domestic heating system. The system comprises an air-source heat pump sited in the roof space of a house, solar assistance being provided by the conventional tile roof acting as a radiation absorber. Multivariate models are presented which illustrate the time-dependent relationships between three air temperatures - at external ambient, at entry to, and at exit from, the heat pump evaporator. Using a deterministic modeling approach, physical interpretations are placed on the results of the multivariate technique. It is concluded that the multivariate Box-Jenkins approach is a suitable techniquemore » for building thermal analysis. Application to multivariate Box-Jenkins approach is a suitable technique for building thermal analysis. Application to multivariate model-based control is discussed, with particular reference to building energy management systems. It is further concluded that stochastic modeling of data drawn from a short monitoring period offers a means of retrofitting an advanced model-based control system in existing buildings, which could be used to optimize energy savings. An approach to system simulation is suggested.« less

Recent applications of multivariate data analysis methods in the authentication of rice and the most analyzed parameters: A review.

PubMed

Maione, Camila; Barbosa, Rommel Melgaço

2018-01-24

Rice is one of the most important staple foods around the world. Authentication of rice is one of the most addressed concerns in the present literature, which includes recognition of its geographical origin and variety, certification of organic rice and many other issues. Good results have been achieved by multivariate data analysis and data mining techniques when combined with specific parameters for ascertaining authenticity and many other useful characteristics of rice, such as quality, yield and others. This paper brings a review of the recent research projects on discrimination and authentication of rice using multivariate data analysis and data mining techniques. We found that data obtained from image processing, molecular and atomic spectroscopy, elemental fingerprinting, genetic markers, molecular content and others are promising sources of information regarding geographical origin, variety and other aspects of rice, being widely used combined with multivariate data analysis techniques. Principal component analysis and linear discriminant analysis are the preferred methods, but several other data classification techniques such as support vector machines, artificial neural networks and others are also frequently present in some studies and show high performance for discrimination of rice.

Maternal conditions and perinatal characteristics associated with autism spectrum disorder and intellectual disability.

PubMed

Langridge, Amanda T; Glasson, Emma J; Nassar, Natasha; Jacoby, Peter; Pennell, Craig; Hagan, Ronald; Bourke, Jenny; Leonard, Helen; Stanley, Fiona J

2013-01-01

As well as being highly comorbid conditions, autism spectrum disorders (ASD) and intellectual disability (ID) share a number of clinically-relevant phenomena. This raises questions about similarities and overlap in diagnosis and aetiological pathways that may exist for both conditions. To examine maternal conditions and perinatal factors for children diagnosed with an ASD, with or without ID, and children with ID of unknown cause, compared with unaffected children. The study population comprised all live singleton births in Western Australia (WA) between January 1984 and December 1999 (N = 383,153). Univariate and multivariate multinomial logistic regression models were applied using a blocked modelling approach to assess the effect of maternal conditions, sociodemographic factors, labour and delivery characteristics and neonatal outcomes. In univariate analyses mild-moderate ID was associated with pregnancy hypertension, asthma, urinary tract infection, some types of ante-partum haemorrhage, any type of preterm birth, elective C-sections, breech presentation, poor fetal growth and need for resuscitation at birth, with all factors showing an increased risk. Severe ID was positively associated with poor fetal growth and need for resuscitation, as well as any labour or delivery complication. In the multivariate analysis no maternal conditions or perinatal factors were associated with an increased risk of ASD without ID. However, pregnancy hypertension and small head circumference were associated with a reduced risk (OR = 0.64, 95% CI: 0.43, 0.94; OR = 0.58, 95% CI: 0.34, 0.96, respectively). For ASD with ID, threatened abortion before 20 weeks gestation and poor fetal growth were associated with an increased risk. Findings show that indicators of a poor intrauterine environment are associated with an elevated risk of ID, while for ASD, and particularly ASD without ID, the associations are much weaker. As such, these findings highlight the importance of accounting for the absence or presence of ID when examining ASD, if we are to improve our understanding of the causal pathways associated with these conditions.

Multi-technique approach for qualitative and quantitative characterization of furazidin degradation kinetics under alkaline conditions.

PubMed

Bērziņš, Kārlis; Kons, Artis; Grante, Ilze; Dzabijeva, Diana; Nakurte, Ilva; Actiņš, Andris

2016-09-10

Degradation of drug furazidin was studied under different conditions of environmental pH (11-13) and temperature (30-60°C). The novel approach of hybrid hard- and soft-multivariate curve resolution-alternating least squares (HS-MCR-ALS) method was applied to UV-vis spectral data to determine a valid kinetic model and kinetic parameters of the degradation process. The system was found to be comprised of three main species and best characterized by two consecutive first-order reactions. Furazidin degradation rate was found to be highly dependent on the applied environmental conditions, showing more prominent differences between both degradation steps towards higher pH and temperature. Complimentary qualitative analysis of the degradation process was carried out using HPLC-DAD-TOF-MS. Based on the obtained chromatographic and mass spectrometric results, as well as additional computational analysis of the species (theoretical UV-vis spectra calculations utilizing TD-DFT methodology), the operating degradation mechanism was proposed to include formation of a 5-hydroxyfuran derivative, followed by complete hydrolysis of furazidin hydantoin ring. Copyright © 2016 Elsevier B.V. All rights reserved.

Medical risk factors for severe West Nile Virus disease, United States, 2008-2010.

PubMed

Lindsey, Nicole P; Staples, J Erin; Lehman, Jennifer A; Fischer, Marc

2012-07-01

We conducted enhanced surveillance to identify medical risk factors for severe illness (i.e., hospitalization or death) and neuroinvasive disease (i.e., encephalitis or meningitis) among all West Nile virus disease cases reported from selected states from 2008 to 2010. Of the 1,090 case-patients included in the analysis, 708 (65%) case-patients were hospitalized, 641 (59%) case-patients had neuroinvasive disease, and 55 (5%) case-patients died. Chronic renal disease (adjusted odds ratio [aOR] = 4.1; 95% confidence interval [CI] = 1.4-12.1), history of cancer (aOR = 3.7; 95% CI = 1.8-7.5), history of alcohol abuse (aOR = 3.0; 95% CI = 1.3-6.7), diabetes (aOR = 2.2; 95% CI = 1.4-3.4), and hypertension (aOR = 1.5; 95% CI = 1.1-2.1) were independently associated with severe illness on multivariable analysis. Although the same medical conditions were independently associated with encephalitis, only hypertension was associated with meningitis. The only condition independently associated with death was immune suppression. Prevention messages should be targeted to persons with these conditions.

A direct-gradient multivariate index of biotic condition

USGS Publications Warehouse

Miranda, Leandro E.; Aycock, J.N.; Killgore, K. J.

2012-01-01

Multimetric indexes constructed by summing metric scores have been criticized despite many of their merits. A leading criticism is the potential for investigator bias involved in metric selection and scoring. Often there is a large number of competing metrics equally well correlated with environmental stressors, requiring a judgment call by the investigator to select the most suitable metrics to include in the index and how to score them. Data-driven procedures for multimetric index formulation published during the last decade have reduced this limitation, yet apprehension remains. Multivariate approaches that select metrics with statistical algorithms may reduce the level of investigator bias and alleviate a weakness of multimetric indexes. We investigated the suitability of a direct-gradient multivariate procedure to derive an index of biotic condition for fish assemblages in oxbow lakes in the Lower Mississippi Alluvial Valley. Although this multivariate procedure also requires that the investigator identify a set of suitable metrics potentially associated with a set of environmental stressors, it is different from multimetric procedures because it limits investigator judgment in selecting a subset of biotic metrics to include in the index and because it produces metric weights suitable for computation of index scores. The procedure, applied to a sample of 35 competing biotic metrics measured at 50 oxbow lakes distributed over a wide geographical region in the Lower Mississippi Alluvial Valley, selected 11 metrics that adequately indexed the biotic condition of five test lakes. Because the multivariate index includes only metrics that explain the maximum variability in the stressor variables rather than a balanced set of metrics chosen to reflect various fish assemblage attributes, it is fundamentally different from multimetric indexes of biotic integrity with advantages and disadvantages. As such, it provides an alternative to multimetric procedures.

Risk of hemorrhagic transformation after ischemic stroke in patients with antiphospholipid antibody syndrome.

PubMed

Mehta, Tapan; Hussain, Mohammed; Sheth, Khushboo; Ding, Yuchuan; McCullough, Louise D

2017-06-01

Several rheumatologic conditions including systemic lupus erythematosus, antiphospholipid antibody (APS) syndrome, rheumatoid arthritis, and scleroderma are known risk factors for stroke. The risk of hemorrhagic transformation after an acute ischemic stroke (AIS) in these patients is not known. We queried the Nationwide Inpatient Sample (NIS) data between 2010 and 2012 with ICD 9 diagnostic codes for AIS. The primary outcome was the development of hemorrhagic transformation. Multivariate predictors for hemorrhagic transformation were identified with a logistic regression model. Using SAS 9.2, Survey procedures were used to accommodate for hierarchical two stage cluster design of NIS. APS (OR 2.57, 95% CI 1.14-5.81, p = 0.0228) independently predicted risk of hemorrhagic transformation in multivariate regression analysis. Similarly, in multivariate regression models for the outcome variables of total charges of the hospitalization and length of stay (LOS), patients with APS had the highest charges ($56,286, p = 0.0228) and LOS (3.87 days, p = 0.0164) compared to other co-variates. Univariate analysis showed increased mortality in the APS compared to the non-APS group (11.68% vs. 7.16%, p = 0.0024). APS is an independent risk factor for hemorrhagic transformation in both thrombolytic and non-thrombolytic treated patients. APS is also associated with longer length and cost of hospital stay. Further research is warranted to identify the unique risk factors in these patients to identify strategies to reduce the risk of hemorrhagic transformation in this subgroup of the population.

High local unemployment rates limit work after lung transplantation.

PubMed

Nau, Michael; Shrider, Emily A; Tobias, Joseph D; Hayes, Don; Tumin, Dmitry

2016-10-01

Most lung transplant (LTx) recipients recover sufficient functional status to resume working, yet unemployment is common after LTx. Weak local labor markets may limit employment opportunities for LTx recipients. United Network for Organ Sharing data on first-time LTx recipients 18-60 years old who underwent transplant between 2010 and 2014 were linked to American Community Survey data on unemployment rates at the ZIP Code level. Multivariable competing-risks regression modeled the influence of dichotomous (≥8%) and continuous local unemployment rates on employment after LTx, accounting for the competing risk of mortality. For comparison, analyses were duplicated in a cohort of heart transplant (HTx) recipients who underwent transplant during the same period. The analysis included 3,897 LTx and 5,577 HTx recipients. Work after LTx was reported by 300 (16.3%) residents of low-unemployment areas and 244 (11.9%) residents of high-unemployment areas (p < 0.001). Multivariable analysis of 3,626 LTx recipients with complete covariate data found that high local unemployment rates limited employment after LTx (sub-hazard ratio = 0.605; 95% confidence interval = 0.477, 0.768; p < 0.001), conditional on not working before transplant. Employment after HTx was higher compared with employment after LTx, and not associated with local unemployment rates in multivariable analyses. LTx recipients of working age exhibit exceptionally low employment rates. High local unemployment rates exacerbate low work participation after LTx, and may discourage job search in this population. Copyright © 2016 International Society for Heart and Lung Transplantation. Published by Elsevier Inc. All rights reserved.

Landscape Characterization and Representativeness Analysis for Understanding Sampling Network Coverage

DOE Data Explorer

Maddalena, Damian; Hoffman, Forrest; Kumar, Jitendra; Hargrove, William

2014-08-01

Sampling networks rarely conform to spatial and temporal ideals, often comprised of network sampling points which are unevenly distributed and located in less than ideal locations due to access constraints, budget limitations, or political conflict. Quantifying the global, regional, and temporal representativeness of these networks by quantifying the coverage of network infrastructure highlights the capabilities and limitations of the data collected, facilitates upscaling and downscaling for modeling purposes, and improves the planning efforts for future infrastructure investment under current conditions and future modeled scenarios. The work presented here utilizes multivariate spatiotemporal clustering analysis and representativeness analysis for quantitative landscape characterization and assessment of the Fluxnet, RAINFOR, and ForestGEO networks. Results include ecoregions that highlight patterns of bioclimatic, topographic, and edaphic variables and quantitative representativeness maps of individual and combined networks.

Multivariate control of plant species richness and community biomass in blackland prairie

USGS Publications Warehouse

Weiher, E.; Forbes, S.; Schauwecker, T.; Grace, J.B.

2004-01-01

Recent studies have shown that patterns of plant species richness and community biomass are best understood in a multivariate context. The objective of this study was to develop and evaluate a multivariate hypothesis about how herbaceous biomass and richness relate to gradients in soil conditions and woody plant cover in blackland prairies. Structural equation modeling was used to investigate how soil characteristics and shade by scattered Juniperus virginiana trees relate to standing biomass and species richness in 99 0.25 m2 quadrats collected in eastern Mississippi, USA. Analysis proceeded in two stages. In the first stage, we evaluated the hypothesis that correlations among soil parameters could be represented by two underlying (latent) soil factors, mineral content and organic content. In the second stage, we evaluated the hypothesis that richness and biomass were related to (1) soil properties, (2) tree canopy extent, and (3) each other (i.e. reciprocal effects between richness and biomass). With some modification to the details of the original model, it was found that soil properties could be represented as two latent variables. In the overall model, 51% and 53% of the observed variation in richness and biomass were explained. The order of importance for variables explaining variations in richness was (1) soil organic content, (2) soil mineral content, (3) community biomass, and (4) tree canopy extent. The order of importance for variables explaining biomass was (1) tree canopy and (2) soil organic content, with neither soil mineral content nor species richness explaining significant variation in biomass. Based on these findings, we conclude that variations in richness are uniquely related to both variations in soil conditions and variations in herbaceous biomass. We further conclude that there is no evidence in these data for effects of species richness on biomass.

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

PubMed

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

2015-11-04

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 ((1)H-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.

Nutritional Intervention: A Secondary Analysis of Its Effect on Malnourished Colombian Pre-Schoolers.

ERIC Educational Resources Information Center

Bejar, Isaac I.

1981-01-01

Effects of nutritional supplementation on physical development of malnourished children was analyzed by univariate and multivariate methods for the analysis of repeated measures. Results showed that the nutritional treatment was successful, but it was necessary to resort to the multivariate approach. (Author/GK)

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…

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…

Applied Statistics: From Bivariate through Multivariate Techniques [with CD-ROM

ERIC Educational Resources Information Center

Warner, Rebecca M.

2007-01-01

This book provides a clear introduction to widely used topics in bivariate and multivariate statistics, including multiple regression, discriminant analysis, MANOVA, factor analysis, and binary logistic regression. The approach is applied and does not require formal mathematics; equations are accompanied by verbal explanations. Students are asked…

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.

MULTIVARIATE ANALYSIS ON LEVELS OF SELECTED METALS, PARTICULATE MATTER, VOC, AND HOUSEHOLD CHARACTERISTICS AND ACTIVITIES FROM THE MIDWESTERN STATES NHEXAS

EPA Science Inventory

Microenvironmental and biological/personal monitoring information were collected during the National Human Exposure Assessment Survey (NHEXAS), conducted in the six states comprising U.S. EPA Region Five. They have been analyzed by multivariate analysis techniques with general ...

Multivariate meta-analysis: a robust approach based on the theory of U-statistic.

PubMed

Ma, Yan; Mazumdar, Madhu

2011-10-30

Meta-analysis is the methodology for combining findings from similar research studies asking the same question. When the question of interest involves multiple outcomes, multivariate meta-analysis is used to synthesize the outcomes simultaneously taking into account the correlation between the outcomes. Likelihood-based approaches, in particular restricted maximum likelihood (REML) method, are commonly utilized in this context. REML assumes a multivariate normal distribution for the random-effects model. This assumption is difficult to verify, especially for meta-analysis with small number of component studies. The use of REML also requires iterative estimation between parameters, needing moderately high computation time, especially when the dimension of outcomes is large. A multivariate method of moments (MMM) is available and is shown to perform equally well to REML. However, there is a lack of information on the performance of these two methods when the true data distribution is far from normality. In this paper, we propose a new nonparametric and non-iterative method for multivariate meta-analysis on the basis of the theory of U-statistic and compare the properties of these three procedures under both normal and skewed data through simulation studies. It is shown that the effect on estimates from REML because of non-normal data distribution is marginal and that the estimates from MMM and U-statistic-based approaches are very similar. Therefore, we conclude that for performing multivariate meta-analysis, the U-statistic estimation procedure is a viable alternative to REML and MMM. Easy implementation of all three methods are illustrated by their application to data from two published meta-analysis from the fields of hip fracture and periodontal disease. We discuss ideas for future research based on U-statistic for testing significance of between-study heterogeneity and for extending the work to meta-regression setting. Copyright © 2011 John Wiley & Sons, Ltd.

Prevalence of gestational diabetes mellitus in Europe: A meta-analysis.

PubMed

Eades, Claire E; Cameron, Dawn M; Evans, Josie M M

2017-07-01

Estimates of the prevalence of gestational diabetes vary widely. It is important to have a clear understanding of the prevalence of this condition to be able to plan interventions and health care provision. This paper describes a meta-analysis of primary research data reporting the prevalence of gestational diabetes mellitus in the general pregnant population of developed countries in Europe. Four electronic databases were systematically searched in May 2016. English language articles reporting gestational diabetes mellitus prevalence using universal screening in general pregnant population samples from developed countries in Europe were included. All papers identified by the search were screened by one author, and then half screened independently by a second author and half by a third author. Data were extracted by one author. Values for the measures of interest were combined using a random effects model and analysis of the effects of moderator variables was carried out. A total of 3258 abstracts were screened, with 40 studies included in the review. Overall prevalence of gestational diabetes mellitus was 5.4% (3.8-7.8). Maternal age, year of data collection, country, area of Europe, week of gestation at testing, and diagnostic criteria were found to have a significant univariate effect on GDM prevalence, and area, week of gestation at testing and year of data collection remained statistically significant in multivariate analysis. Quality category was significant in multivariate but not univariate analysis. This meta-analysis shows prevalence of GDM that is at the upper end of previous estimates in Europe. Copyright © 2017 Elsevier B.V. All rights reserved.

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.

Bayesian transformation cure frailty models with multivariate failure time data.

PubMed

Yin, Guosheng

2008-12-10

We propose a class of transformation cure frailty models to accommodate a survival fraction in multivariate failure time data. Established through a general power transformation, this family of cure frailty models includes the proportional hazards and the proportional odds modeling structures as two special cases. Within the Bayesian paradigm, we obtain the joint posterior distribution and the corresponding full conditional distributions of the model parameters for the implementation of Gibbs sampling. Model selection is based on the conditional predictive ordinate statistic and deviance information criterion. As an illustration, we apply the proposed method to a real data set from dentistry.

Testing for Granger Causality in the Frequency Domain: A Phase Resampling Method.

PubMed

Liu, Siwei; Molenaar, Peter

2016-01-01

This article introduces phase resampling, an existing but rarely used surrogate data method for making statistical inferences of Granger causality in frequency domain time series analysis. Granger causality testing is essential for establishing causal relations among variables in multivariate dynamic processes. However, testing for Granger causality in the frequency domain is challenging due to the nonlinear relation between frequency domain measures (e.g., partial directed coherence, generalized partial directed coherence) and time domain data. Through a simulation study, we demonstrate that phase resampling is a general and robust method for making statistical inferences even with short time series. With Gaussian data, phase resampling yields satisfactory type I and type II error rates in all but one condition we examine: when a small effect size is combined with an insufficient number of data points. Violations of normality lead to slightly higher error rates but are mostly within acceptable ranges. We illustrate the utility of phase resampling with two empirical examples involving multivariate electroencephalography (EEG) and skin conductance data.

Health-state utilities in a prisoner population: a cross-sectional survey

PubMed Central

Chong, Christopher AKY; Li, Sicong; Nguyen, Geoffrey C; Sutton, Andrew; Levy, Michael H; Butler, Tony; Krahn, Murray D; Thein, Hla-Hla

2009-01-01

Background Health-state utilities for prisoners have not been described. Methods We used data from a 1996 cross-sectional survey of Australian prisoners (n = 734). Respondent-level SF-36 data was transformed into utility scores by both the SF-6D and Nichol's method. Socio-demographic and clinical predictors of SF-6D utility were assessed in univariate analyses and a multivariate general linear model. Results The overall mean SF-6D utility was 0.725 (SD 0.119). When subdivided by various medical conditions, prisoner SF-6D utilities ranged from 0.620 for angina to 0.764 for those with none/mild depressive symptoms. Utilities derived by the Nichol's method were higher than SF-6D scores, often by more than 0.1. In multivariate analysis, significant independent predictors of worse utility included female gender, increasing age, increasing number of comorbidities and more severe depressive symptoms. Conclusion The utilities presented may prove useful for future economic and decision models evaluating prison-based health programs. PMID:19715571

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

Multivariate Statistical Modelling of Drought and Heat Wave Events C. Manning1,2, M. Widmann1, M. Vrac2, D. Maraun3, E. Bevaqua2,3 1. School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, UK 2. Laboratoire des Sciences du Climat et de l'Environnement, (LSCE-IPSL), Centre d'Etudes de Saclay, Gif-sur-Yvette, France 3. Wegener Center for Climate and Global Change, University of Graz, Brandhofgasse 5, 8010 Graz, Austria Compound extreme events are a combination of two or more contributing events which in themselves may not be extreme but through their joint occurrence produce an extreme impact. Compound events are noted in the latest IPCC report as an important type of extreme event that have been given little attention so far. As part of the CE:LLO project (Compound Events: muLtivariate statisticaL mOdelling) we are developing a multivariate statistical model to gain an understanding of the dependence structure of certain compound events. One focus of this project is on the interaction between drought and heat wave events. Soil moisture has both a local and non-local effect on the occurrence of heat waves where it strongly controls the latent heat flux affecting the transfer of sensible heat to the atmosphere. These processes can create a feedback whereby a heat wave maybe amplified or suppressed by the soil moisture preconditioning, and vice versa, the heat wave may in turn have an effect on soil conditions. An aim of this project is to capture this dependence in order to correctly describe the joint probabilities of these conditions and the resulting probability of their compound impact. We will show an application of Pair Copula Constructions (PCCs) to study the aforementioned compound event. PCCs allow in theory for the formulation of multivariate dependence structures in any dimension where the PCC is a decomposition of a multivariate distribution into a product of bivariate components modelled using copulas. A 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.

Characterizing multivariate decoding models based on correlated EEG spectral features.

PubMed

McFarland, Dennis J

2013-07-01

Multivariate decoding methods are popular techniques for analysis of neurophysiological data. The present study explored potential interpretative problems with these techniques when predictors are correlated. Data from sensorimotor rhythm-based cursor control experiments was analyzed offline with linear univariate and multivariate models. Features were derived from autoregressive (AR) spectral analysis of varying model order which produced predictors that varied in their degree of correlation (i.e., multicollinearity). The use of multivariate regression models resulted in much better prediction of target position as compared to univariate regression models. However, with lower order AR features interpretation of the spectral patterns of the weights was difficult. This is likely to be due to the high degree of multicollinearity present with lower order AR features. Care should be exercised when interpreting the pattern of weights of multivariate models with correlated predictors. Comparison with univariate statistics is advisable. While multivariate decoding algorithms are very useful for prediction their utility for interpretation may be limited when predictors are correlated. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Intravenous bisphosphonate-related osteonecrosis of the jaws: Influence of coadjuvant antineoplastic treatment and study of buccodental condition

PubMed Central

Bagán, José; Poveda-Roda, Rafael

2013-01-01

Objectives: To determine whether coadjuvant antineoplastic treatment can influence the number and size of bone exposures among patients with intravenous bisphosphonate-related osteonecrosis of the jaws (iBRONJ), and to analyze the buccodental condition of these patients. Material and methods: The study sample comprised 67 patients with iBRONJ, 53 patients without iBRONJ receiving treatment with intravenous bisphosphonates, and 36 healthy subjects. In all three groups, measurements were made of the CAO index and of resting whole saliva and stimulated whole saliva. In the patients with iBRONJ, the size (cm) and number of bone exposures were recorded. The data obtained were subjected to analysis of variance (ANOVA), the Mann-Whitney U-test, and multivariate logistic regression analysis. Results: A total of 57.6% of the patients presented single bone exposure, 25.4% presented two, and 17% more than two exposures. The mean exposure size was 2.3±1.9 cm. Neither the bivariate analysis nor the multivariate multiple regression analysis found coadjuvant antineoplastic treatment to exert a statistically significant effect upon the number and size of bone exposures. On the other hand, there were statistically significant differences among the three study groups in relation to the CAO index (p=0.02) and the number of missing teeth (p=0.00). The resting whole saliva and stimulated whole saliva levels were similar in the three groups, though the patients with osteonecrosis of the jaws showed comparatively lower SWS levels. Conclusions: Coadjuvant antineoplastic treatment alone appears to exert no influence upon the size and number of bone exposures in iBRONJ. The patients with this disease show a higher CAO index and a larger number of missing teeth. Key words:Osteonecrosis of the jaws, bisphosphonates, bone exposure, CAO index, resting whole saliva, stimulated whole saliva. PMID:23229272

Relating N2O emissions during biological nitrogen removal with operating conditions using multivariate statistical techniques.

PubMed

Vasilaki, V; Volcke, E I P; Nandi, A K; van Loosdrecht, M C M; Katsou, E

2018-04-26

Multivariate statistical analysis was applied to investigate the dependencies and underlying patterns between N 2 O emissions and online operational variables (dissolved oxygen and nitrogen component concentrations, temperature and influent flow-rate) during biological nitrogen removal from wastewater. The system under study was a full-scale reactor, for which hourly sensor data were available. The 15-month long monitoring campaign was divided into 10 sub-periods based on the profile of N 2 O emissions, using Binary Segmentation. The dependencies between operating variables and N 2 O emissions fluctuated according to Spearman's rank correlation. The correlation between N 2 O emissions and nitrite concentrations ranged between 0.51 and 0.78. Correlation >0.7 between N 2 O emissions and nitrate concentrations was observed at sub-periods with average temperature lower than 12 °C. Hierarchical k-means clustering and principal component analysis linked N 2 O emission peaks with precipitation events and ammonium concentrations higher than 2 mg/L, especially in sub-periods characterized by low N 2 O fluxes. Additionally, the highest ranges of measured N 2 O fluxes belonged to clusters corresponding with NO 3 -N concentration less than 1 mg/L in the upstream plug-flow reactor (middle of oxic zone), indicating slow nitrification rates. The results showed that the range of N 2 O emissions partially depends on the prior behavior of the system. The principal component analysis validated the findings from the clustering analysis and showed that ammonium, nitrate, nitrite and temperature explained a considerable percentage of the variance in the system for the majority of the sub-periods. The applied statistical methods, linked the different ranges of emissions with the system variables, provided insights on the effect of operating conditions on N 2 O emissions in each sub-period and can be integrated into N 2 O emissions data processing at wastewater treatment plants. Copyright © 2018. Published by Elsevier Ltd.

Quality of life in preoperative patients with sacroiliac joint dysfunction is at least as depressed as in other lumbar spinal conditions.

PubMed

Cher, Daniel Joseph; Reckling, W Carlton

2015-01-01

Pain from the sacroiliac joint (SIJ) is an under-recognized cause of low back pain. The degree to which SIJ pain decreases quality of life has not been directly compared to other more familiar conditions of the lumbar spine. Multivariate regression analysis of individual patient data from two prospective multicenter clinical trials of SIJ fusion and three prospective multicenter clinical trials of surgical treatments for degenerative lumbar spine conditions. Controlling for baseline demographic parameters as well as a validated disability score, quality of life scores (EuroQOL 5-D and SF-36) were, in most cases, lower in the SIJ cohorts compared to the three other spine surgery cohorts. Patients with SIJ dysfunction considering surgery have decrements in quality of life as or more severe compared to patients with degenerative spondylolisthesis, spinal stenosis, and intervertebral disc herniation.

A financial network perspective of financial institutions' systemic risk contributions

NASA Astrophysics Data System (ADS)

Huang, Wei-Qiang; Zhuang, Xin-Tian; Yao, Shuang; Uryasev, Stan

2016-08-01

This study considers the effects of the financial institutions' local topology structure in the financial network on their systemic risk contribution using data from the Chinese stock market. We first measure the systemic risk contribution with the Conditional Value-at-Risk (CoVaR) which is estimated by applying dynamic conditional correlation multivariate GARCH model (DCC-MVGARCH). Financial networks are constructed from dynamic conditional correlations (DCC) with graph filtering method of minimum spanning trees (MSTs). Then we investigate dynamics of systemic risk contributions of financial institution. Also we study dynamics of financial institution's local topology structure in the financial network. Finally, we analyze the quantitative relationships between the local topology structure and systemic risk contribution with panel data regression analysis. We find that financial institutions with greater node strength, larger node betweenness centrality, larger node closeness centrality and larger node clustering coefficient tend to be associated with larger systemic risk contributions.

Predictive Factors of Surgical Outcome in Frontal Lobe Epilepsy Explored with Stereoelectroencephalography.

PubMed

Bonini, Francesca; McGonigal, Aileen; Scavarda, Didier; Carron, Romain; Régis, Jean; Dufour, Henry; Péragut, Jean-Claude; Laguitton, Virginie; Villeneuve, Nathalie; Chauvel, Patrick; Giusiano, Bernard; Trébuchon, Agnès; Bartolomei, Fabrice

2017-06-29

Resective surgery established treatment for pharmacoresistant frontal lobe epilepsy (FLE), but seizure outcome and prognostic indicators are poorly characterized and vary between studies. To study long-term seizure outcome and identify prognostic factors. We retrospectively analyzed 42 FLE patients having undergone surgical resection, mostly preceded by invasive recordings with stereoelectroencephalography (SEEG). Postsurgical outcome up to 10-yr follow-up and prognostic indicators were analyzed using Kaplan-Meier analysis and multivariate and conditional inference procedures. At the time of last follow-up, 57.1% of patients were seizure-free. The estimated chance of seizure freedom was 67% (95% confidence interval [CI]: 54-83) at 6 mo, 59% (95% CI: 46-76) at 1 yr, 53% (95% CI: 40-71) at 2 yr, and 46% (95% CI: 32-66) at 5 yr. Most relapses (83%) occurred within the first 12 mo. Multivariate analysis showed that completeness of resection of the epileptogenic zone (EZ) as defined by SEEG was the main predictor of seizure outcome. According to conditional inference trees, in patients with complete resection of the EZ, focal cortical dysplasia as etiology and focal EZ were positive prognostic indicators. No difference in outcome was found in patients with positive vs negative magnetic resonance imaging. Surgical resection in drug-resistant FLE can be a successful therapeutic approach, even in the absence of neuroradiologically visible lesions. SEEG may be highly useful in both nonlesional and lesional FLE cases, because complete resection of the EZ as defined by SEEG is associated with better prognosis. Copyright © 2017 by the Congress of Neurological Surgeons

Time Series Model Identification by Estimating Information.

DTIC Science & Technology

1982-11-01

principle, Applications of Statistics, P. R. Krishnaiah , ed., North-Holland: Amsterdam, 27-41. Anderson, T. W. (1971). The Statistical Analysis of Time Series...E. (1969). Multiple Time Series Modeling, Multivariate Analysis II, edited by P. Krishnaiah , Academic Press: New York, 389-409. Parzen, E. (1981...Newton, H. J. (1980). Multiple Time Series Modeling, II Multivariate Analysis - V, edited by P. Krishnaiah , North Holland: Amsterdam, 181-197. Shibata, R

Genomic Analysis of Complex Microbial Communities in Wounds

DTIC Science & Technology

2012-01-01

thoroughly in the ecology literature. Permutation Multivariate Analysis of Variance ( PerMANOVA ). We used PerMANOVA to test the null-hypothesis of no...difference between the bacterial communities found within a single wound compared to those from different patients (α = 0.05). PerMANOVA is a...permutation-based version of the multivariate analysis of variance (MANOVA). PerMANOVA uses the distances between samples to partition variance and

In situ X-ray diffraction analysis of (CF x) n batteries: signal extraction by multivariate analysis

DOE PAGES

Rodriguez, Mark A.; Keenan, Michael R.; Nagasubramanian, Ganesan

2007-11-10

In this study, (CF x) n cathode reaction during discharge has been investigated using in situ X-ray diffraction (XRD). Mathematical treatment of the in situ XRD data set was performed using multivariate curve resolution with alternating least squares (MCR–ALS), a technique of multivariate analysis. MCR–ALS analysis successfully separated the relatively weak XRD signal intensity due to the chemical reaction from the other inert cell component signals. The resulting dynamic reaction component revealed the loss of (CF x) n cathode signal together with the simultaneous appearance of LiF by-product intensity. Careful examination of the XRD data set revealed an additional dynamicmore » component which may be associated with the formation of an intermediate compound during the discharge process.« less

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.

Differential effects of dietary diversity and maternal characteristics on linear growth of children aged 6-59 months in sub-Saharan Africa: a multi-country analysis.

PubMed

Amugsi, Dickson A; Dimbuene, Zacharie T; Kimani-Murage, Elizabeth W; Mberu, Blessing; Ezeh, Alex C

2017-04-01

To investigate the differential effects of dietary diversity (DD) and maternal characteristics on child linear growth at different points of the conditional distribution of height-for-age Z-score (HAZ) in sub-Saharan Africa. Secondary analysis of data from nationally representative cross-sectional samples of singleton children aged 0-59 months, born to mothers aged 15-49 years. The outcome variable was child HAZ. Quantile regression was used to perform the multivariate analysis. The most recent Demographic and Health Surveys from Ghana, Nigeria, Kenya, Mozambique and Democratic Republic of Congo (DRC). The present analysis was restricted to children aged 6-59 months (n 31 604). DD was associated positively with HAZ in the first four quantiles (5th, 10th, 25th and 50th) and the highest quantile (90th) in Nigeria. The largest effect occurred at the very bottom (5th quantile) and the very top (90th quantile) of the conditional HAZ distribution. In DRC, DD was significantly and positively associated with HAZ in the two lower quantiles (5th, 10th). The largest effects of maternal education occurred at the lower end of the conditional HAZ distribution in Ghana, Nigeria and DRC. Maternal BMI and height also had positive effects on HAZ at different points of the conditional distribution of HAZ. Our analysis shows that the association between DD and maternal factors and HAZ differs along the conditional HAZ distribution. Intervention measures need to take into account the heterogeneous effect of the determinants of child nutritional status along the different percentiles of the HAZ distribution.

Modeling Associations among Multivariate Longitudinal Categorical Variables in Survey Data: A Semiparametric Bayesian Approach

ERIC Educational Resources Information Center

Tchumtchoua, Sylvie; Dey, Dipak K.

2012-01-01

This paper proposes a semiparametric Bayesian framework for the analysis of associations among multivariate longitudinal categorical variables in high-dimensional data settings. This type of data is frequent, especially in the social and behavioral sciences. A semiparametric hierarchical factor analysis model is developed in which the…

Southeast Atlantic Cloud Properties in a Multivariate Statistical Model - How Relevant is Air Mass History for Local Cloud Properties?

NASA Astrophysics Data System (ADS)

Fuchs, Julia; Cermak, Jan; Andersen, Hendrik

2017-04-01

This study aims at untangling the impacts of external dynamics and local conditions on cloud properties in the Southeast Atlantic (SEA) by combining satellite and reanalysis data using multivariate statistics. The understanding of clouds and their determinants at different scales is important for constraining the Earth's radiative budget, and thus prominent in climate-system research. In this study, SEA stratocumulus cloud properties are observed not only as the result of local environmental conditions but also as affected by external dynamics and spatial origins of air masses entering the study area. In order to assess to what extent cloud properties are impacted by aerosol concentration, air mass history, and meteorology, a multivariate approach is conducted using satellite observations of aerosol and cloud properties (MODIS, SEVIRI), information on aerosol species composition (MACC) and meteorological context (ERA-Interim reanalysis). To account for the often-neglected but important role of air mass origin, information on air mass history based on HYSPLIT modeling is included in the statistical model. This multivariate approach is intended to lead to a better understanding of the physical processes behind observed stratocumulus cloud properties in the SEA.

Multivariate Analysis of Multi-tracer and Climatological Data in an Urbanizing, Drought-impacted Watershed

NASA Astrophysics Data System (ADS)

Creech, L. T.; Donahoe, R. J.

2009-12-01

This paper documents water quality conditions of the Lake Tuscaloosa, Alabama water-supply reservoir and its watershed under two end-members of hydrologic and climatic variability. These data afford the opportunity to view water quality in the context of both land use and drought, facilitating the development of coupled hydrologic and water-quality forecast models to guide watershed management decisions. This study demonstrates that even the region’s normal 10-year drought cycle holds the capacity to significantly impact water quality and should be incorporated into watershed models and decision-making. To accomplish the goals of this project, a multi-tracer approach has been adopted to assess solute sources and water-quality impairments induced by land use. The biogeochemical tracers include: Major- and minor-ions, trace metals, nutrient speciation and stable-isotope tracers at natural abundance levels. These tracers are also vital to understand the role of climate variability in the context of a heterogeneous landscape. Eight seasonal sampling events across 23 sample locations and two water years yield 184 discrete water-quality samples representative of a range of landscape variability and climatological conditions. Each sample was analyzed for 27 solute species and relevant indicators of water quality. Climatological data was obtained from public repositories (NCDC, USDA); hydrologic data from stream and precipitation gages within the watershed (USGS). Multivariate statistics are used to facilitate the numerical analysis and interpretation of the resulting data. Measurements of nitrogen speciation were collected to document patterns of nutrient loading and nitrogen cycling. These data are augmented by the analysis of nitrogen and oxygen isotopes of nitrate. These data clarify the extent to which nitrogen is being loaded in the non-growing season as well as the capacity of the lake to assimilate nutrients. Under drought conditions the lake becomes nitrogen-limited at most locations. Yet, despite these low concentrations of dissolved nitrogen, Diel measurements reveal that the lake achieves a eutrophic state (due to algal productivity and decomposition). This ecological state is also associated with elevated coliform bacteria in the lake, at times exceeding regulatory limits. Although the lake assimilates excess dissolved nitrogen via enhanced productivity, the process constitutes a water-resource impairment. In this context, the stable-isotope tracer component of the project both: 1) accounts for nitrogen sources and mixing, and 2) clarifies the relative importance of nitrogen assimilation vs. biogeochemical cycling. Multivariate analyses of nutrient data, plus that of metals and rock-weathering solutes further clarify the fate of nitrogen at times and locations that nitrogen flux is less than in most river basins, and less than existing models might predict. By extension, these data may also afford deeper understanding of the larger Mobile River Basin’s 'missing' nitrogen loads under variable flow conditions. This phenomenon offers a protective effect against even faster eutrophication rates (than already exist) in our coastal waters, yet is incompletely understood.

Periodontal inflamed surface area as a novel numerical variable describing periodontal conditions

PubMed Central

2017-01-01

Purpose A novel index, the periodontal inflamed surface area (PISA), represents the sum of the periodontal pocket depth of bleeding on probing (BOP)-positive sites. In the present study, we evaluated correlations between PISA and periodontal classifications, and examined PISA as an index integrating the discrete conventional periodontal indexes. Methods This study was a cross-sectional subgroup analysis of data from a prospective cohort study investigating the association between chronic periodontitis and the clinical features of ankylosing spondylitis. Data from 84 patients without systemic diseases (the control group in the previous study) were analyzed in the present study. Results PISA values were positively correlated with conventional periodontal classifications (Spearman correlation coefficient=0.52; P<0.01) and with periodontal indexes, such as BOP and the plaque index (PI) (r=0.94; P<0.01 and r=0.60; P<0.01, respectively; Pearson correlation test). Porphyromonas gingivalis (P. gingivalis) expression and the presence of serum P. gingivalis antibodies were significant factors affecting PISA values in a simple linear regression analysis, together with periodontal classification, PI, bleeding index, and smoking, but not in the multivariate analysis. In the multivariate linear regression analysis, PISA values were positively correlated with the quantity of current smoking, PI, and severity of periodontal disease. Conclusions PISA integrates multiple periodontal indexes, such as probing pocket depth, BOP, and PI into a numerical variable. PISA is advantageous for quantifying periodontal inflammation and plaque accumulation. PMID:29093989

Automated pre-processing and multivariate vibrational spectra analysis software for rapid results in clinical settings

NASA Astrophysics Data System (ADS)

Bhattacharjee, T.; Kumar, P.; Fillipe, L.

2018-02-01

Vibrational spectroscopy, especially FTIR and Raman, has shown enormous potential in disease diagnosis, especially in cancers. Their potential for detecting varied pathological conditions are regularly reported. However, to prove their applicability in clinics, large multi-center multi-national studies need to be undertaken; and these will result in enormous amount of data. A parallel effort to develop analytical methods, including user-friendly software that can quickly pre-process data and subject them to required multivariate analysis is warranted in order to obtain results in real time. This study reports a MATLAB based script that can automatically import data, preprocess spectra— interpolation, derivatives, normalization, and then carry out Principal Component Analysis (PCA) followed by Linear Discriminant Analysis (LDA) of the first 10 PCs; all with a single click. The software has been verified on data obtained from cell lines, animal models, and in vivo patient datasets, and gives results comparable to Minitab 16 software. The software can be used to import variety of file extensions, asc, .txt., .xls, and many others. Options to ignore noisy data, plot all possible graphs with PCA factors 1 to 5, and save loading factors, confusion matrices and other parameters are also present. The software can provide results for a dataset of 300 spectra within 0.01 s. We believe that the software will be vital not only in clinical trials using vibrational spectroscopic data, but also to obtain rapid results when these tools get translated into clinics.

Decreased tongue pressure is associated with sarcopenia and sarcopenic dysphagia in the elderly.

PubMed

Maeda, Keisuke; Akagi, Junji

2015-02-01

The aim of this study was to clarify the association between tongue pressure and factors related to sarcopenia such as aging, activities of daily living, nutritional state, and dysphagia. One-hundred-and-four patients without a history of treatment of stroke and without a diagnosis of neurodegenerative disease (36 men and 68 women), with a mean age of 84.1 ± 5.6 years, hospitalized from May 2013 to June 2013 were included in this study. Maximum voluntary tongue pressure against the palate (MTP) was measured by a device consisting of a disposable oral balloon probe. Nutritional and anthropometric parameters such as serum albumin concentration, Mini-Nutritional Assessment short form (MNA-SF), body mass index, arm muscle area (AMA), and others and presence of sarcopenia and dysphagia were analyzed to evaluate their relationships. Correlation analysis and univariate or multivariate analysis were performed. Simple correlation analysis showed that MTP correlated with Barthel index (BI), MNA-SF, serum albumin concentration, body mass index, and AMA. Univariate and multivariate analysis showed that sarcopenia, BI, MNA-SF, and age were the independent explanatory factors for decreased MTP, and the propensity score for dysphagia, including causes of primary or secondary sarcopenia, and the presence of sarcopenia were significantly associated with the presence of dysphagia. Decreased MTP and dysphagia were related to sarcopenia or the causes of sarcopenia in the studied population. Furthermore, the clinical condition of sarcopenic dysphagia may be partially interpreted as the presence of sarcopenia and causal factors for sarcopenia.

Use of Multivariate Linkage Analysis for Dissection of a Complex Cognitive Trait

PubMed Central

Marlow, Angela J.; Fisher, Simon E.; Francks, Clyde; MacPhie, I. Laurence; Cherny, Stacey S.; Richardson, Alex J.; Talcott, Joel B.; Stein, John F.; Monaco, Anthony P.; Cardon, Lon R.

2003-01-01

Replication of linkage results for complex traits has been exceedingly difficult, owing in part to the inability to measure the precise underlying phenotype, small sample sizes, genetic heterogeneity, and statistical methods employed in analysis. Often, in any particular study, multiple correlated traits have been collected, yet these have been analyzed independently or, at most, in bivariate analyses. Theoretical arguments suggest that full multivariate analysis of all available traits should offer more power to detect linkage; however, this has not yet been evaluated on a genomewide scale. Here, we conduct multivariate genomewide analyses of quantitative-trait loci that influence reading- and language-related measures in families affected with developmental dyslexia. The results of these analyses are substantially clearer than those of previous univariate analyses of the same data set, helping to resolve a number of key issues. These outcomes highlight the relevance of multivariate analysis for complex disorders for dissection of linkage results in correlated traits. The approach employed here may aid positional cloning of susceptibility genes in a wide spectrum of complex traits. PMID:12587094

The association between body mass index and severe biliary infections: a multivariate analysis.

PubMed

Stewart, Lygia; Griffiss, J McLeod; Jarvis, Gary A; Way, Lawrence W

2012-11-01

Obesity has been associated with worse infectious disease outcomes. It is a risk factor for cholesterol gallstones, but little is known about associations between body mass index (BMI) and biliary infections. We studied this using factors associated with biliary infections. A total of 427 patients with gallstones were studied. Gallstones, bile, and blood (as applicable) were cultured. Illness severity was classified as follows: none (no infection or inflammation), systemic inflammatory response syndrome (fever, leukocytosis), severe (abscess, cholangitis, empyema), or multi-organ dysfunction syndrome (bacteremia, hypotension, organ failure). Associations between BMI and biliary bacteria, bacteremia, gallstone type, and illness severity were examined using bivariate and multivariate analysis. BMI inversely correlated with pigment stones, biliary bacteria, bacteremia, and increased illness severity on bivariate and multivariate analysis. Obesity correlated with less severe biliary infections. BMI inversely correlated with pigment stones and biliary bacteria; multivariate analysis showed an independent correlation between lower BMI and illness severity. Most patients with severe biliary infections had a normal BMI, suggesting that obesity may be protective in biliary infections. This study examined the correlation between BMI and biliary infection severity. Published by Elsevier Inc.

Multivariate meta-analysis using individual participant data.

PubMed

Riley, R D; Price, M J; Jackson, D; Wardle, M; Gueyffier, F; Wang, J; Staessen, J A; White, I R

2015-06-01

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 that within-study correlations needed to fit the multivariate model are unknown from published reports. However, provision of individual participant data (IPD) allows them to be calculated directly. Here, we illustrate how to use IPD to estimate within-study correlations, using a joint linear regression for multiple continuous outcomes and bootstrapping methods for binary, survival and mixed outcomes. In a meta-analysis of 10 hypertension trials, we then show how these methods enable multivariate meta-analysis to address novel clinical questions about continuous, survival and binary outcomes; treatment-covariate interactions; adjusted risk/prognostic factor effects; longitudinal data; prognostic and multiparameter models; and multiple treatment comparisons. Both frequentist and Bayesian approaches are applied, with example software code provided to derive within-study correlations and to fit the models. © 2014 The Authors. Research Synthesis Methods published by John Wiley & Sons, Ltd.

Multivariate Analysis As a Support for Diagnostic Flowcharts in Allergic Bronchopulmonary Aspergillosis: A Proof-of-Concept Study.

PubMed

Vitte, Joana; Ranque, Stéphane; Carsin, Ania; Gomez, Carine; Romain, Thomas; Cassagne, Carole; Gouitaa, Marion; Baravalle-Einaudi, Mélisande; Bel, Nathalie Stremler-Le; Reynaud-Gaubert, Martine; Dubus, Jean-Christophe; Mège, Jean-Louis; Gaudart, Jean

2017-01-01

Molecular-based allergy diagnosis yields multiple biomarker datasets. The classical diagnostic score for allergic bronchopulmonary aspergillosis (ABPA), a severe disease usually occurring in asthmatic patients and people with cystic fibrosis, comprises succinct immunological criteria formulated in 1977: total IgE, anti- Aspergillus fumigatus ( Af ) IgE, anti- Af "precipitins," and anti- Af IgG. Progress achieved over the last four decades led to multiple IgE and IgG(4) Af biomarkers available with quantitative, standardized, molecular-level reports. These newly available biomarkers have not been included in the current diagnostic criteria, either individually or in algorithms, despite persistent underdiagnosis of ABPA. Large numbers of individual biomarkers may hinder their use in clinical practice. Conversely, multivariate analysis using new tools may bring about a better chance of less diagnostic mistakes. We report here a proof-of-concept work consisting of a three-step multivariate analysis of Af IgE, IgG, and IgG4 biomarkers through a combination of principal component analysis, hierarchical ascendant classification, and classification and regression tree multivariate analysis. The resulting diagnostic algorithms might show the way for novel criteria and improved diagnostic efficiency in Af -sensitized patients at risk for ABPA.

The impact of multiple endpoint dependency on Q and I(2) in meta-analysis.

PubMed

Thompson, Christopher Glen; Becker, Betsy Jane

2014-09-01

A common assumption in meta-analysis is that effect sizes are independent. When correlated effect sizes are analyzed using traditional univariate techniques, this assumption is violated. This research assesses the impact of dependence arising from treatment-control studies with multiple endpoints on homogeneity measures Q and I(2) in scenarios using the unbiased standardized-mean-difference effect size. Univariate and multivariate meta-analysis methods are examined. Conditions included different overall outcome effects, study sample sizes, numbers of studies, between-outcomes correlations, dependency structures, and ways of computing the correlation. The univariate approach used typical fixed-effects analyses whereas the multivariate approach used generalized least-squares (GLS) estimates of a fixed-effects model, weighted by the inverse variance-covariance matrix. Increased dependence among effect sizes led to increased Type I error rates from univariate models. When effect sizes were strongly dependent, error rates were drastically higher than nominal levels regardless of study sample size and number of studies. In contrast, using GLS estimation to account for multiple-endpoint dependency maintained error rates within nominal levels. Conversely, mean I(2) values were not greatly affected by increased amounts of dependency. Last, we point out that the between-outcomes correlation should be estimated as a pooled within-groups correlation rather than using a full-sample estimator that does not consider treatment/control group membership. Copyright © 2014 John Wiley & Sons, Ltd.

Parallel experimental design and multivariate analysis provides efficient screening of cell culture media supplements to improve biosimilar product quality.

PubMed

Brühlmann, David; Sokolov, Michael; Butté, Alessandro; Sauer, Markus; Hemberger, Jürgen; Souquet, Jonathan; Broly, Hervé; Jordan, Martin

2017-07-01

Rational and high-throughput optimization of mammalian cell culture media has a great potential to modulate recombinant protein product quality. We present a process design method based on parallel design-of-experiment (DoE) of CHO fed-batch cultures in 96-deepwell plates to modulate monoclonal antibody (mAb) glycosylation using medium supplements. To reduce the risk of losing valuable information in an intricate joint screening, 17 compounds were separated into five different groups, considering their mode of biological action. The concentration ranges of the medium supplements were defined according to information encountered in the literature and in-house experience. The screening experiments produced wide glycosylation pattern ranges. Multivariate analysis including principal component analysis and decision trees was used to select the best performing glycosylation modulators. Subsequent D-optimal quadratic design with four factors (three promising compounds and temperature shift) in shake tubes confirmed the outcome of the selection process and provided a solid basis for sequential process development at a larger scale. The glycosylation profile with respect to the specifications for biosimilarity was greatly improved in shake tube experiments: 75% of the conditions were equally close or closer to the specifications for biosimilarity than the best 25% in 96-deepwell plates. Biotechnol. Bioeng. 2017;114: 1448-1458. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

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. Copyright © 2016 John Wiley & Sons, Ltd.

Raman exfoliative cytology for oral precancer diagnosis

NASA Astrophysics Data System (ADS)

Sahu, Aditi; Gera, Poonam; Pai, Venkatesh; Dubey, Abhishek; Tyagi, Gunjan; Waghmare, Mandavi; Pagare, Sandeep; Mahimkar, Manoj; Murali Krishna, C.

2017-11-01

Oral premalignant lesions (OPLs) such as leukoplakia, erythroplakia, and oral submucous fibrosis, often precede oral cancer. Screening and management of these premalignant conditions can improve prognosis. Raman spectroscopy has previously demonstrated potential in the diagnosis of oral premalignant conditions (in vivo), detected viral infection, and identified cancer in both oral and cervical exfoliated cells (ex vivo). The potential of Raman exfoliative cytology (REC) in identifying premalignant conditions was investigated. Oral exfoliated samples were collected from healthy volunteers (n=20), healthy volunteers with tobacco habits (n=20), and oral premalignant conditions (n=27, OPL) using Cytobrush. Spectra were acquired using Raman microprobe. Spectral acquisition parameters were: λex: 785 nm, laser power: 40 mW, acquisition time: 15 s, and average: 3. Postspectral acquisition, cell pellet was subjected to Pap staining. Multivariate analysis was carried out using principal component analysis and principal component-linear discriminant analysis using both spectra- and patient-wise approaches in three- and two-group models. OPLs could be identified with ˜77% (spectra-wise) and ˜70% (patient-wise) sensitivity in the three-group model while with 86% (spectra-wise) and 83% (patient-wise) in the two-group model. Use of histopathologically confirmed premalignant cases and better sampling devices may help in development of improved standard models and also enhance the sensitivity of the method. Future longitudinal studies can help validate potential of REC in screening and monitoring high-risk populations and prognosis prediction of premalignant lesions.

Urban air quality assessment using monitoring data of fractionized aerosol samples, chemometrics and meteorological conditions.

PubMed

Yotova, Galina I; Tsitouridou, Roxani; Tsakovski, Stefan L; Simeonov, Vasil D

2016-01-01

The present article deals with assessment of urban air by using monitoring data for 10 different aerosol fractions (0.015-16 μm) collected at a typical urban site in City of Thessaloniki, Greece. The data set was subject to multivariate statistical analysis (cluster analysis and principal components analysis) and, additionally, to HYSPLIT back trajectory modeling in order to assess in a better way the impact of the weather conditions on the pollution sources identified. A specific element of the study is the effort to clarify the role of outliers in the data set. The reason for the appearance of outliers is strongly related to the atmospheric condition on the particular sampling days leading to enhanced concentration of pollutants (secondary emissions, sea sprays, road and soil dust, combustion processes) especially for ultra fine and coarse particles. It is also shown that three major sources affect the urban air quality of the location studied-sea sprays, mineral dust and anthropogenic influences (agricultural activity, combustion processes, and industrial sources). The level of impact is related to certain extent to the aerosol fraction size. The assessment of the meteorological conditions leads to defining of four downwind patterns affecting the air quality (Pelagic, Western and Central Europe, Eastern and Northeastern Europe and Africa and Southern Europe). Thus, the present study offers a complete urban air assessment taking into account the weather conditions, pollution sources and aerosol fractioning.

Estimating correlation between multivariate longitudinal data in the presence of heterogeneity.

PubMed

Gao, Feng; Philip Miller, J; Xiong, Chengjie; Luo, Jingqin; Beiser, Julia A; Chen, Ling; Gordon, Mae O

2017-08-17

Estimating correlation coefficients among outcomes is one of the most important analytical tasks in epidemiological and clinical research. Availability of multivariate longitudinal data presents a unique opportunity to assess joint evolution of outcomes over time. Bivariate linear mixed model (BLMM) provides a versatile tool with regard to assessing correlation. However, BLMMs often assume that all individuals are drawn from a single homogenous population where the individual trajectories are distributed smoothly around population average. Using longitudinal mean deviation (MD) and visual acuity (VA) from the Ocular Hypertension Treatment Study (OHTS), we demonstrated strategies to better understand the correlation between multivariate longitudinal data in the presence of potential heterogeneity. Conditional correlation (i.e., marginal correlation given random effects) was calculated to describe how the association between longitudinal outcomes evolved over time within specific subpopulation. The impact of heterogeneity on correlation was also assessed by simulated data. There was a significant positive correlation in both random intercepts (ρ = 0.278, 95% CI: 0.121-0.420) and random slopes (ρ = 0.579, 95% CI: 0.349-0.810) between longitudinal MD and VA, and the strength of correlation constantly increased over time. However, conditional correlation and simulation studies revealed that the correlation was induced primarily by participants with rapid deteriorating MD who only accounted for a small fraction of total samples. Conditional correlation given random effects provides a robust estimate to describe the correlation between multivariate longitudinal data in the presence of unobserved heterogeneity (NCT00000125).

A Statistical Discrimination Experiment for Eurasian Events Using a Twenty-Seven-Station Network

DTIC Science & Technology

1980-07-08

to test the effectiveness of a multivariate method of analysis for distinguishing earthquakes from explosions. The data base for the experiment...to test the effectiveness of a multivariate method of analysis for distinguishing earthquakes from explosions. The data base for the experiment...the weight assigned to each variable whenever a new one is added. Jennrich, R. I. (1977). Stepwise discriminant analysis , in Statistical Methods for

Is Heart Rate Variability Better Than Routine Vital Signs for Prehospital Identification of Major Hemorrhage

DTIC Science & Technology

2015-01-01

different PRBC transfusion volumes. We performed multivariate regression analysis using HRV metrics and routine vital signs to test the hypothesis that...study sponsors did not have any role in the study design, data collection, analysis and interpretation of data, report writing, or the decision to...primary outcome was hemorrhagic injury plus different PRBC transfusion volumes. We performed multivariate regression analysis using HRV metrics and

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.

Nonlinear multivariate and time series analysis by neural network methods

NASA Astrophysics Data System (ADS)

Hsieh, William W.

2004-03-01

Methods in multivariate statistical analysis are essential for working with large amounts of geophysical data, data from observational arrays, from satellites, or from numerical model output. In classical multivariate statistical analysis, there is a hierarchy of methods, starting with linear regression at the base, followed by principal component analysis (PCA) and finally canonical correlation analysis (CCA). A multivariate time series method, the singular spectrum analysis (SSA), has been a fruitful extension of the PCA technique. The common drawback of these classical methods is that only linear structures can be correctly extracted from the data. Since the late 1980s, neural network methods have become popular for performing nonlinear regression and classification. More recently, neural network methods have been extended to perform nonlinear PCA (NLPCA), nonlinear CCA (NLCCA), and nonlinear SSA (NLSSA). This paper presents a unified view of the NLPCA, NLCCA, and NLSSA techniques and their applications to various data sets of the atmosphere and the ocean (especially for the El Niño-Southern Oscillation and the stratospheric quasi-biennial oscillation). These data sets reveal that the linear methods are often too simplistic to describe real-world systems, with a tendency to scatter a single oscillatory phenomenon into numerous unphysical modes or higher harmonics, which can be largely alleviated in the new nonlinear paradigm.

The joint return period analysis of natural disasters based on monitoring and statistical modeling of multidimensional hazard factors.

PubMed

Liu, Xueqin; Li, Ning; Yuan, Shuai; Xu, Ning; Shi, Wenqin; Chen, Weibin

2015-12-15

As a random event, a natural disaster has the complex occurrence mechanism. The comprehensive analysis of multiple hazard factors is important in disaster risk assessment. In order to improve the accuracy of risk analysis and forecasting, the formation mechanism of a disaster should be considered in the analysis and calculation of multi-factors. Based on the consideration of the importance and deficiencies of multivariate analysis of dust storm disasters, 91 severe dust storm disasters in Inner Mongolia from 1990 to 2013 were selected as study cases in the paper. Main hazard factors from 500-hPa atmospheric circulation system, near-surface meteorological system, and underlying surface conditions were selected to simulate and calculate the multidimensional joint return periods. After comparing the simulation results with actual dust storm events in 54years, we found that the two-dimensional Frank Copula function showed the better fitting results at the lower tail of hazard factors and that three-dimensional Frank Copula function displayed the better fitting results at the middle and upper tails of hazard factors. However, for dust storm disasters with the short return period, three-dimensional joint return period simulation shows no obvious advantage. If the return period is longer than 10years, it shows significant advantages in extreme value fitting. Therefore, we suggest the multivariate analysis method may be adopted in forecasting and risk analysis of serious disasters with the longer return period, such as earthquake and tsunami. Furthermore, the exploration of this method laid the foundation for the prediction and warning of other nature disasters. Copyright © 2015 Elsevier B.V. All rights reserved.

Early and late fracture following extensive limb lengthening in patients with achondroplasia and hypochondroplasia.

PubMed

Kitoh, H; Mishima, K; Matsushita, M; Nishida, Y; Ishiguro, N

2014-09-01

Two types of fracture, early and late, have been reported following limb lengthening in patients with achondroplasia (ACH) and hypochondroplasia (HCH). We reviewed 25 patients with these conditions who underwent 72 segmental limb lengthening procedures involving the femur and/or tibia, between 2003 and 2011. Gender, age at surgery, lengthened segment, body mass index, the shape of the callus, the amount and percentage of lengthening and the healing index were evaluated to determine predictive factors for the occurrence of early (within three weeks after removal of the fixation pins) and late fracture (> three weeks after removal of the pins). The Mann‑Whitney U test and Pearson's chi-squared test for univariate analysis and stepwise regression model for multivariate analysis were used to identify the predictive factor for each fracture. Only one patient (two tibiae) was excluded from the analysis due to excessively slow formation of the regenerate, which required supplementary measures. A total of 24 patients with 70 limbs were included in the study. There were 11 early fractures in eight patients. The shape of the callus (lateral or central callus) was the only statistical variable related to the occurrence of early fracture in univariate and multivariate analyses. Late fracture was observed in six limbs and the mean time between removal of the fixation pins and fracture was 18.3 weeks (3.3 to 38.4). Lengthening of the tibia, larger healing index, and lateral or central callus were related to the occurrence of a late fracture in univariate analysis. A multivariate analysis demonstrated that the shape of the callus was the strongest predictor for late fracture (odds ratio: 19.3, 95% confidence interval: 2.91 to 128). Lateral or central callus had a significantly larger risk of fracture than fusiform, cylindrical, or concave callus. Radiological monitoring of the shape of the callus during distraction is important to prevent early and late fracture of lengthened limbs in patients with ACH or HCH. In patients with thin callus formation, some measures to stimulate bone formation should be considered as early as possible. ©2014 The British Editorial Society of Bone & Joint Surgery.

Analysis and assessment on heavy metal sources in the coastal soils developed from alluvial deposits using multivariate statistical methods.

PubMed

Li, Jinling; He, Ming; Han, Wei; Gu, Yifan

2009-05-30

An investigation on heavy metal sources, i.e., Cu, Zn, Ni, Pb, Cr, and Cd in the coastal soils of Shanghai, China, was conducted using multivariate statistical methods (principal component analysis, clustering analysis, and correlation analysis). All the results of the multivariate analysis showed that: (i) Cu, Ni, Pb, and Cd had anthropogenic sources (e.g., overuse of chemical fertilizers and pesticides, industrial and municipal discharges, animal wastes, sewage irrigation, etc.); (ii) Zn and Cr were associated with parent materials and therefore had natural sources (e.g., the weathering process of parent materials and subsequent pedo-genesis due to the alluvial deposits). The effect of heavy metals in the soils was greatly affected by soil formation, atmospheric deposition, and human activities. These findings provided essential information on the possible sources of heavy metals, which would contribute to the monitoring and assessment process of agricultural soils in worldwide regions.

Application of multivariate statistical techniques for differentiation of ripe banana flour based on the composition of elements.

PubMed

Alkarkhi, Abbas F M; Ramli, Saifullah Bin; Easa, Azhar Mat

2009-01-01

Major (sodium, potassium, calcium, magnesium) and minor elements (iron, copper, zinc, manganese) and one heavy metal (lead) of Cavendish banana flour and Dream banana flour were determined, and data were analyzed using multivariate statistical techniques of factor analysis and discriminant analysis. Factor analysis yielded four factors explaining more than 81% of the total variance: the first factor explained 28.73%, comprising magnesium, sodium, and iron; the second factor explained 21.47%, comprising only manganese and copper; the third factor explained 15.66%, comprising zinc and lead; while the fourth factor explained 15.50%, comprising potassium. Discriminant analysis showed that magnesium and sodium exhibited a strong contribution in discriminating the two types of banana flour, affording 100% correct assignation. This study presents the usefulness of multivariate statistical techniques for analysis and interpretation of complex mineral content data from banana flour of different varieties.

Market interdependence among commodity prices based on information transmission on the Internet

NASA Astrophysics Data System (ADS)

Ji, Qiang; Guo, Jian-Feng

2015-05-01

Human behaviour on the Internet has become a synchro-projection of real society. In this paper, we introduce the public concern derived from query volumes on the Web to empirically analyse the influence of information on commodity markets (e.g., crude oil, heating oil, corn and gold) using multivariate GARCH models based on dynamic conditional correlations. The analysis found that the changes of public concern on the Internet can well depict the changes of market prices, as the former has significant Granger causality effects on market prices. The findings indicate that the information of external shocks to commodity markets could be transmitted quickly, and commodity markets easily absorb the public concern of the information-sensitive traders. Finally, the conditional correlation among commodity prices varies dramatically over time.

Low-dimensional approximation searching strategy for transfer entropy from non-uniform embedding

PubMed Central

2018-01-01

Transfer entropy from non-uniform embedding is a popular tool for the inference of causal relationships among dynamical subsystems. In this study we present an approach that makes use of low-dimensional conditional mutual information quantities to decompose the original high-dimensional conditional mutual information in the searching procedure of non-uniform embedding for significant variables at different lags. We perform a series of simulation experiments to assess the sensitivity and specificity of our proposed method to demonstrate its advantage compared to previous algorithms. The results provide concrete evidence that low-dimensional approximations can help to improve the statistical accuracy of transfer entropy in multivariate causality analysis and yield a better performance over other methods. The proposed method is especially efficient as the data length grows. PMID:29547669

Conventional and advanced time series estimation: application to the Australian and New Zealand Intensive Care Society (ANZICS) adult patient database, 1993-2006.

PubMed

Moran, John L; Solomon, Patricia J

2011-02-01

Time series analysis has seen limited application in the biomedical Literature. The utility of conventional and advanced time series estimators was explored for intensive care unit (ICU) outcome series. Monthly mean time series, 1993-2006, for hospital mortality, severity-of-illness score (APACHE III), ventilation fraction and patient type (medical and surgical), were generated from the Australia and New Zealand Intensive Care Society adult patient database. Analyses encompassed geographical seasonal mortality patterns, series structural time changes, mortality series volatility using autoregressive moving average and Generalized Autoregressive Conditional Heteroscedasticity models in which predicted variances are updated adaptively, and bivariate and multivariate (vector error correction models) cointegrating relationships between series. The mortality series exhibited marked seasonality, declining mortality trend and substantial autocorrelation beyond 24 lags. Mortality increased in winter months (July-August); the medical series featured annual cycling, whereas the surgical demonstrated long and short (3-4 months) cycling. Series structural breaks were apparent in January 1995 and December 2002. The covariance stationary first-differenced mortality series was consistent with a seasonal autoregressive moving average process; the observed conditional-variance volatility (1993-1995) and residual Autoregressive Conditional Heteroscedasticity effects entailed a Generalized Autoregressive Conditional Heteroscedasticity model, preferred by information criterion and mean model forecast performance. Bivariate cointegration, indicating long-term equilibrium relationships, was established between mortality and severity-of-illness scores at the database level and for categories of ICUs. Multivariate cointegration was demonstrated for {log APACHE III score, log ICU length of stay, ICU mortality and ventilation fraction}. A system approach to understanding series time-dependence may be established using conventional and advanced econometric time series estimators. © 2010 Blackwell Publishing Ltd.

Uncertainty Modeling for Robustness Analysis of Control Upset Prevention and Recovery Systems

NASA Technical Reports Server (NTRS)

Belcastro, Christine M.; Khong, Thuan H.; Shin, Jong-Yeob; Kwatny, Harry; Chang, Bor-Chin; Balas, Gary J.

2005-01-01

Formal robustness analysis of aircraft control upset prevention and recovery systems could play an important role in their validation and ultimate certification. Such systems (developed for failure detection, identification, and reconfiguration, as well as upset recovery) need to be evaluated over broad regions of the flight envelope and under extreme flight conditions, and should include various sources of uncertainty. However, formulation of linear fractional transformation (LFT) models for representing system uncertainty can be very difficult for complex parameter-dependent systems. This paper describes a preliminary LFT modeling software tool which uses a matrix-based computational approach that can be directly applied to parametric uncertainty problems involving multivariate matrix polynomial dependencies. Several examples are presented (including an F-16 at an extreme flight condition, a missile model, and a generic example with numerous crossproduct terms), and comparisons are given with other LFT modeling tools that are currently available. The LFT modeling method and preliminary software tool presented in this paper are shown to compare favorably with these methods.

An application of bioassessment metrics and multivariate techniques to evaluate central Nebraska streams

USGS Publications Warehouse

Frenzel, S.A.

1996-01-01

Ninety-one stream sites in central Nebraska were classified into four clusters on the basis of a cluster analysis (TWINSPAN) of macroinvertebrate data. Rapid bioassessment protocol scores for macroinvertebrate species were significantly different among sites grouped by teh first division into two clusters. This division may have distinguished sites on the basis of water-quality imparement. Individual metrics that differed between clusters of sites were the Hilsenhoff Biotic Index, the number of Ephemeroptera, Plecoptera, and Trichoptera (EPT) taxa, and the ratio of individuals in EPT to Chironomidae taxa. Canonical correspondence analysis of 57 of 91 sites showed that stream width, site altitude, latitude, soil permeability, water temperature, and mean annual precipitation were the most important environmental variables describing variance in the species-environment relation. Stream width and soil permeability reflected streamflow characteristics of a site, whereas site altitude and latitude were factors related to general climatic conditions. Mean annual precipitation related to both streamflow and climatic conditions.

Perceptual aspects of reproduced sound in car cabin acoustics.

PubMed

Kaplanis, Neofytos; Bech, Søren; Tervo, Sakari; Pätynen, Jukka; Lokki, Tapio; van Waterschoot, Toon; Jensen, Søren Holdt

2017-03-01

An experiment was conducted to determine the perceptual effects of car cabin acoustics on the reproduced sound field. In-car measurements were conducted whilst the cabin's interior was physically modified. The captured sound fields were recreated in the laboratory using a three-dimensional loudspeaker array. A panel of expert assessors followed a rapid sensory analysis protocol, the flash profile, to perceptually characterize and evaluate 12 acoustical conditions of the car cabin using individually elicited attributes. A multivariate analysis revealed the panel's consensus and the identified perceptual constructs. Six perceptual constructs characterize the differences between the acoustical conditions of the cabin, related to bass, ambience, transparency, width and envelopment, brightness, and image focus. The current results indicate the importance of several acoustical properties of a car's interior on the perceived sound qualities. Moreover, they signify the capacity of the applied methodology in assessing spectral and spatial properties of automotive environments in laboratory settings using a time-efficient and flexible protocol.

[Health status of immigrants arrived to Italian coast].

PubMed

Firenze, Alberto; Restivo, Vincenzo; Bonanno, Valentina; Aleo, Nicola; Pace, Stella; Marsala, Maria Grazia Laura; Palermo, Mario

2014-01-01

To analyze the factors involved in access to Emergency Department (ED) of undocumented immigrants in Lampedusa according to the country of origin. This is a retrospective observational study, carried out on newly arrived undocumented immigrants transferred to ED. Data were collected from medical records of Lampedusa ED between January 2012 and May 2013 on 326 undocumented immigrants. The outcomes evaluated are demographics characteristics and health condition of undocumented immigrants. In multivariate analysis associated factors to ED visits are: other pathologies rather than traumatic diseases (OR 0.22; p <0.001), younger age (OR 0.9; p <0.001) and female sex (OR 12.49; p=0.017) for Somalis; gastroenterological diseases (OR 2.55; p=0.026) and older age (OR 1.6; p=0.004) for Eritrean; neurological disease (OR 5.33; p=0.048) and male sex (OR 5.45; p=0.032) for Tunisian. This analysis shows that undocumented immigrants cannot be considered as a single population, because they generate a diversified set of pathological conditions.

Adjusting Wavelet-based Multiresolution Analysis Boundary Conditions for Robust Long-term Streamflow Forecasting Model

NASA Astrophysics Data System (ADS)

Maslova, I.; Ticlavilca, A. M.; McKee, M.

2012-12-01

There has been an increased interest in wavelet-based streamflow forecasting models in recent years. Often overlooked in this approach are the circularity assumptions of the wavelet transform. We propose a novel technique for minimizing the wavelet decomposition boundary condition effect to produce long-term, up to 12 months ahead, forecasts of streamflow. A simulation study is performed to evaluate the effects of different wavelet boundary rules using synthetic and real streamflow data. A hybrid wavelet-multivariate relevance vector machine model is developed for forecasting the streamflow in real-time for Yellowstone River, Uinta Basin, Utah, USA. The inputs of the model utilize only the past monthly streamflow records. They are decomposed into components formulated in terms of wavelet multiresolution analysis. It is shown that the model model accuracy can be increased by using the wavelet boundary rule introduced in this study. This long-term streamflow modeling and forecasting methodology would enable better decision-making and managing water availability risk.

PYCHEM: a multivariate analysis package for python.

PubMed

Jarvis, Roger M; Broadhurst, David; Johnson, Helen; O'Boyle, Noel M; Goodacre, Royston

2006-10-15

We have implemented a multivariate statistical analysis toolbox, with an optional standalone graphical user interface (GUI), using the Python scripting language. This is a free and open source project that addresses the need for a multivariate analysis toolbox in Python. Although the functionality provided does not cover the full range of multivariate tools that are available, it has a broad complement of methods that are widely used in the biological sciences. In contrast to tools like MATLAB, PyChem 2.0.0 is easily accessible and free, allows for rapid extension using a range of Python modules and is part of the growing amount of complementary and interoperable scientific software in Python based upon SciPy. One of the attractions of PyChem is that it is an open source project and so there is an opportunity, through collaboration, to increase the scope of the software and to continually evolve a user-friendly platform that has applicability across a wide range of analytical and post-genomic disciplines. http://sourceforge.net/projects/pychem

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

2017-12-01

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).

Multivariate longitudinal data analysis with censored and intermittent missing responses.

PubMed

Lin, Tsung-I; Lachos, Victor H; Wang, Wan-Lun

2018-05-08

The multivariate linear mixed model (MLMM) has emerged as an important analytical tool for longitudinal data with multiple outcomes. However, the analysis of multivariate longitudinal data could be complicated by the presence of censored measurements because of a detection limit of the assay in combination with unavoidable missing values arising when subjects miss some of their scheduled visits intermittently. This paper presents a generalization of the MLMM approach, called the MLMM-CM, for a joint analysis of the multivariate longitudinal data with censored and intermittent missing responses. A computationally feasible expectation maximization-based procedure is developed to carry out maximum likelihood estimation within the MLMM-CM framework. Moreover, the asymptotic standard errors of fixed effects are explicitly obtained via the information-based method. We illustrate our methodology by using simulated data and a case study from an AIDS clinical trial. Experimental results reveal that the proposed method is able to provide more satisfactory performance as compared with the traditional MLMM approach. Copyright © 2018 John Wiley & Sons, Ltd.

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

PubMed Central

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

2016-01-01

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). PMID:26546254

Multilingualism and fMRI: Longitudinal Study of Second Language Acquisition

PubMed Central

Andrews, Edna; Frigau, Luca; Voyvodic-Casabo, Clara; Voyvodic, James; Wright, John

2013-01-01

BOLD fMRI is often used for the study of human language. However, there are still very few attempts to conduct longitudinal fMRI studies in the study of language acquisition by measuring auditory comprehension and reading. The following paper is the first in a series concerning a unique longitudinal study devoted to the analysis of bi- and multilingual subjects who are: (1) already proficient in at least two languages; or (2) are acquiring Russian as a second/third language. The focus of the current analysis is to present data from the auditory sections of a set of three scans acquired from April, 2011 through April, 2012 on a five-person subject pool who are learning Russian during the study. All subjects were scanned using the same protocol for auditory comprehension on the same General Electric LX 3T Signa scanner in Duke University Hospital. Using a multivariate analysis of covariance (MANCOVA) for statistical analysis, proficiency measurements are shown to correlate significantly with scan results in the Russian conditions over time. The importance of both the left and right hemispheres in language processing is discussed. Special attention is devoted to the importance of contextualizing imaging data with corresponding behavioral and empirical testing data using a multivariate analysis of variance. This is the only study to date that includes: (1) longitudinal fMRI data with subject-based proficiency and behavioral data acquired in the same time frame; and (2) statistical modeling that demonstrates the importance of covariate language proficiency data for understanding imaging results of language acquisition. PMID:24961428

Multilingualism and fMRI: Longitudinal Study of Second Language Acquisition.

PubMed

Andrews, Edna; Frigau, Luca; Voyvodic-Casabo, Clara; Voyvodic, James; Wright, John

2013-05-28

BOLD fMRI is often used for the study of human language. However, there are still very few attempts to conduct longitudinal fMRI studies in the study of language acquisition by measuring auditory comprehension and reading. The following paper is the first in a series concerning a unique longitudinal study devoted to the analysis of bi- and multilingual subjects who are: (1) already proficient in at least two languages; or (2) are acquiring Russian as a second/third language. The focus of the current analysis is to present data from the auditory sections of a set of three scans acquired from April, 2011 through April, 2012 on a five-person subject pool who are learning Russian during the study. All subjects were scanned using the same protocol for auditory comprehension on the same General Electric LX 3T Signa scanner in Duke University Hospital. Using a multivariate analysis of covariance (MANCOVA) for statistical analysis, proficiency measurements are shown to correlate significantly with scan results in the Russian conditions over time. The importance of both the left and right hemispheres in language processing is discussed. Special attention is devoted to the importance of contextualizing imaging data with corresponding behavioral and empirical testing data using a multivariate analysis of variance. This is the only study to date that includes: (1) longitudinal fMRI data with subject-based proficiency and behavioral data acquired in the same time frame; and (2) statistical modeling that demonstrates the importance of covariate language proficiency data for understanding imaging results of language acquisition.

Support vector machine learning-based fMRI data group analysis.

PubMed

Wang, Ze; Childress, Anna R; Wang, Jiongjiong; Detre, John A

2007-07-15

To explore the multivariate nature of fMRI data and to consider the inter-subject brain response discrepancies, a multivariate and brain response model-free method is fundamentally required. Two such methods are presented in this paper by integrating a machine learning algorithm, the support vector machine (SVM), and the random effect model. Without any brain response modeling, SVM was used to extract a whole brain spatial discriminance map (SDM), representing the brain response difference between the contrasted experimental conditions. Population inference was then obtained through the random effect analysis (RFX) or permutation testing (PMU) on the individual subjects' SDMs. Applied to arterial spin labeling (ASL) perfusion fMRI data, SDM RFX yielded lower false-positive rates in the null hypothesis test and higher detection sensitivity for synthetic activations with varying cluster size and activation strengths, compared to the univariate general linear model (GLM)-based RFX. For a sensory-motor ASL fMRI study, both SDM RFX and SDM PMU yielded similar activation patterns to GLM RFX and GLM PMU, respectively, but with higher t values and cluster extensions at the same significance level. Capitalizing on the absence of temporal noise correlation in ASL data, this study also incorporated PMU in the individual-level GLM and SVM analyses accompanied by group-level analysis through RFX or group-level PMU. Providing inferences on the probability of being activated or deactivated at each voxel, these individual-level PMU-based group analysis methods can be used to threshold the analysis results of GLM RFX, SDM RFX or SDM PMU.

Kernel canonical-correlation Granger causality for multiple time series

NASA Astrophysics Data System (ADS)

Wu, Guorong; Duan, Xujun; Liao, Wei; Gao, Qing; Chen, Huafu

2011-04-01

Canonical-correlation analysis as a multivariate statistical technique has been applied to multivariate Granger causality analysis to infer information flow in complex systems. It shows unique appeal and great superiority over the traditional vector autoregressive method, due to the simplified procedure that detects causal interaction between multiple time series, and the avoidance of potential model estimation problems. However, it is limited to the linear case. Here, we extend the framework of canonical correlation to include the estimation of multivariate nonlinear Granger causality for drawing inference about directed interaction. Its feasibility and effectiveness are verified on simulated data.

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.

Classification of time-of-flight secondary ion mass spectrometry spectra from complex Cu-Fe sulphides by principal component analysis and artificial neural networks.

PubMed

Kalegowda, Yogesh; Harmer, Sarah L

2013-01-08

Artificial neural network (ANN) and a hybrid principal component analysis-artificial neural network (PCA-ANN) classifiers have been successfully implemented for classification of static time-of-flight secondary ion mass spectrometry (ToF-SIMS) mass spectra collected from complex Cu-Fe sulphides (chalcopyrite, bornite, chalcocite and pyrite) at different flotation conditions. ANNs are very good pattern classifiers because of: their ability to learn and generalise patterns that are not linearly separable; their fault and noise tolerance capability; and high parallelism. In the first approach, fragments from the whole ToF-SIMS spectrum were used as input to the ANN, the model yielded high overall correct classification rates of 100% for feed samples, 88% for conditioned feed samples and 91% for Eh modified samples. In the second approach, the hybrid pattern classifier PCA-ANN was integrated. PCA is a very effective multivariate data analysis tool applied to enhance species features and reduce data dimensionality. Principal component (PC) scores which accounted for 95% of the raw spectral data variance, were used as input to the ANN, the model yielded high overall correct classification rates of 88% for conditioned feed samples and 95% for Eh modified samples. Copyright © 2012 Elsevier B.V. All rights reserved.

High Throughput Determination of Mercury in Tobacco and Mainstream Smoke from Little Cigars

PubMed Central

Fresquez, Mark R.; Gonzalez-Jimenez, Nathalie; Gray, Naudia; Watson, Clifford H.; Pappas, R. Steven

2015-01-01

A method was developed that utilizes a platinum trap for mercury from mainstream tobacco smoke which represents an improvement over traditional approaches that require impingers and long sample preparation procedures. In this approach, the trapped mercury is directly released for analysis by heating the trap in a direct mercury analyzer. The method was applied to the analysis of mercury in the mainstream smoke of little cigars. The mercury levels in little cigar smoke obtained under Health Canada Intense smoking machine conditions ranged from 7.1 × 10−3 mg/m3 to 1.2 × 10−2 mg/m3. These air mercury levels exceed the chronic inhalation Minimal Risk Level corrected for intermittent exposure to metallic mercury (e.g., 1 or 2 hours per day, 5 days per week) determined by the Agency for Toxic Substances and Disease Registry. Multivariate statistical analysis was used to assess associations between mercury levels and little cigar physical design properties. Filter ventilation was identified as the principal physical parameter influencing mercury concentrations in mainstream little cigar smoke generated under ISO machine smoking conditions. With filter ventilation blocked under Health Canada Intense smoking conditions, mercury concentrations in tobacco and puff number (smoke volume) were the primary physical parameters that influenced mainstream smoke mercury concentrations. PMID:26051388

Diagnosis and Prognostic of Wastewater Treatment System Based on Bayesian Network

NASA Astrophysics Data System (ADS)

Li, Dan; Yang, Haizhen; Liang, XiaoFeng

2010-11-01

Wastewater treatment is a complicated and dynamic process. The treatment effect can be influenced by many variables in microbial, chemical and physical aspects. These variables are always uncertain. Due to the complex biological reaction mechanisms, the highly time-varying and multivariable aspects, the diagnosis and prognostic of wastewater treatment system are still difficult in practice. Bayesian network (BN) is one of the best methods for dealing with uncertainty in the artificial intelligence field. Because of the powerful inference ability and convenient decision mechanism, BN can be employed into the model description and influencing factor analysis of wastewater treatment system with great flexibility and applicability.In this paper, taking modified sequencing batch reactor (MSBR) as an analysis object, BN model was constructed according to the influent water quality, operational condition and effluent effect data of MSBR, and then a novel approach based on BN is proposed to analyze the influencing factors of the wastewater treatment system. The approach presented gives an effective tool for diagnosing and predicting analysis of the wastewater treatment system. On the basis of the influent water quality and operational condition, effluent effect can be predicted. Moreover, according to the effluent effect, the influent water quality and operational condition also can be deduced.

Comparison of Optimum Interpolation and Cressman Analyses

NASA Technical Reports Server (NTRS)

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

1984-01-01

The objective of this investigation is to develop a state-of-the-art optimum interpolation (O/I) objective analysis procedure for use in numerical weather prediction studies. A three-dimensional multivariate O/I analysis scheme has been developed. Some characteristics of the GLAS O/I compared with those of the NMC and ECMWF systems are summarized. Some recent enhancements of the GLAS scheme include a univariate analysis of water vapor mixing ratio, a geographically dependent model prediction error correlation function and a multivariate oceanic surface analysis.

Comparison of Optimum Interpolation and Cressman Analyses

NASA Technical Reports Server (NTRS)

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

1985-01-01

The development of a state of the art optimum interpolation (O/I) objective analysis procedure for use in numerical weather prediction studies was investigated. A three dimensional multivariate O/I analysis scheme was developed. Some characteristics of the GLAS O/I compared with those of the NMC and ECMWF systems are summarized. Some recent enhancements of the GLAS scheme include a univariate analysis of water vapor mixing ratio, a geographically dependent model prediction error correlation function and a multivariate oceanic surface analysis.

Reflectance of vegetation, soil, and water

NASA Technical Reports Server (NTRS)

Wiegand, C. L. (Principal Investigator)

1973-01-01

There are no author-identified significant results in this report. This report deals with the selection of the best channels from the 24-channel aircraft data to represent crop and soil conditions. A three-step procedure has been developed that involves using univariate statistics and an F-ratio test to indicate the best 14 channels. From the 14, the 10 best channels are selected by a multivariate stochastic process. The third step involves the pattern recognition procedures developed in the data analysis plan. Indications are that the procedures in use are satsifactory and will extract the desired information from the data.

Tracking Problem Solving by Multivariate Pattern Analysis and Hidden Markov Model Algorithms

ERIC Educational Resources Information Center

Anderson, John R.

2012-01-01

Multivariate pattern analysis can be combined with Hidden Markov Model algorithms to track the second-by-second thinking as people solve complex problems. Two applications of this methodology are illustrated with a data set taken from children as they interacted with an intelligent tutoring system for algebra. The first "mind reading" application…

Functional Path Analysis as a Multivariate Technique in Developing a Theory of Participation in Adult Education.

ERIC Educational Resources Information Center

Martin, James L.

This paper reports on attempts by the author to construct a theoretical framework of adult education participation using a theory development process and the corresponding multivariate statistical techniques. Two problems are identified: the lack of theoretical framework in studying problems, and the limiting of statistical analysis to univariate…

Missing Data and Multiple Imputation in the Context of Multivariate Analysis of Variance

ERIC Educational Resources Information Center

Finch, W. Holmes

2016-01-01

Multivariate analysis of variance (MANOVA) is widely used in educational research to compare means on multiple dependent variables across groups. Researchers faced with the problem of missing data often use multiple imputation of values in place of the missing observations. This study compares the performance of 2 methods for combining p values in…

Web-Based Tools for Modelling and Analysis of Multivariate Data: California Ozone Pollution Activity

ERIC Educational Resources Information Center

Dinov, Ivo D.; Christou, Nicolas

2011-01-01

This article presents a hands-on web-based activity motivated by the relation between human health and ozone pollution in California. This case study is based on multivariate data collected monthly at 20 locations in California between 1980 and 2006. Several strategies and tools for data interrogation and exploratory data analysis, model fitting…

Bias and Precision of Measures of Association for a Fixed-Effect Multivariate Analysis of Variance Model

ERIC Educational Resources Information Center

Kim, Soyoung; Olejnik, Stephen

2005-01-01

The sampling distributions of five popular measures of association with and without two bias adjusting methods were examined for the single factor fixed-effects multivariate analysis of variance model. The number of groups, sample sizes, number of outcomes, and the strength of association were manipulated. The results indicate that all five…

Multivariate analysis of climate along the southern coast of Alaska—some forestry implications.

Treesearch

Wilbur A. Farr; John S. Hard

1987-01-01

A multivariate analysis of climate was used to delineate 10 significantly different groups of climatic stations along the southern coast of Alaska based on latitude, longitude, seasonal temperatures and precipitation, frost-free periods, and total number of growing degree days. The climatic stations were too few to delineate this rugged, mountainous region into...

Aspirin Use Is Associated With Reduced Risk of Occlusion of Metallic Biliary Stents.

PubMed

Jang, Sunguk; Stevens, Tyler; Parsi, Mansour A; Lopez, Rocio; Vargo, John J

2017-03-01

Biliary self-expandable metallic stents (SEMSs) are widely used to treat malignant and benign conditions of bile duct. Despite their lower rate of occlusion and longer patency than plastic stents, SEMSs still have significant rates of occlusion. We aimed to identify factors associated with occlusion of biliary SEMS. We performed a retrospective study of consecutive patients who underwent endoscopic retrograde cholangiopancreatography with biliary SEMS placement at the Cleveland Clinic Foundation from March 2011 to April 2016. We collected clinical, endoscopic, radiographic, and surgical data from medical records and performed multivariable analysis to identify factors associated with SEMS patency. Subjects that received minimal daily dose of 81 mg at the time of stent placement until the end of follow-up were assigned to the aspirin exposure group (n = 157) and compared with subjects with no aspirin exposure (n = 436). Patients were followed for a median 81 days. The primary outcome was hazard ratio for SEMS occlusion requiring an interventional maneuver for biliary drainage. We analyzed data from patients receiving a total of 593 biliary SEMS for treatment of malignant and benign conditions of bile duct. Stent occlusion was observed in 126 cases. Multivariable analysis showed that daily use of aspirin (81 mg or more) was associated with 51% lower risk of stent occlusion than in patients without daily use of aspirin (hazard ratio, 0.49; 95% confidence interval, 0.32-0.75). Furthermore, SEMSs had a longer duration of stent patency in patients in the aspirin exposure group (434.4 days) versus the no aspirin exposure group (339.9 days) (P < .001). Stricture location limited to distal bile duct (in comparison with strictures involving proximal extrahepatic duct) was associated with lower risk of stent occlusion (hazard ratio, 0.39; 95% CI, 0.22-0.71). In an analysis of a large cohort of subjects with metallic biliary stent placement for malignant and benign conditions of bile duct, we associated daily use of aspirin (81 mg or more) with lower risk of SEMS occlusion and longer stent patency duration. Copyright © 2017 AGA Institute. Published by Elsevier Inc. All rights reserved.

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

PubMed Central

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 in the endpoint are imputed with null effects and quite large variance. PMID:26196398

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 in the endpoint are imputed with null effects and quite large variance.

Quantitative analysis of NMR spectra with chemometrics

NASA Astrophysics Data System (ADS)

Winning, H.; Larsen, F. H.; Bro, R.; Engelsen, S. B.

2008-01-01

The number of applications of chemometrics to series of NMR spectra is rapidly increasing due to an emerging interest for quantitative NMR spectroscopy e.g. in the pharmaceutical and food industries. This paper gives an analysis of advantages and limitations of applying the two most common chemometric procedures, Principal Component Analysis (PCA) and Multivariate Curve Resolution (MCR), to a designed set of 231 simple alcohol mixture (propanol, butanol and pentanol) 1H 400 MHz spectra. The study clearly demonstrates that the major advantage of chemometrics is the visualisation of larger data structures which adds a new exploratory dimension to NMR research. While robustness and powerful data visualisation and exploration are the main qualities of the PCA method, the study demonstrates that the bilinear MCR method is an even more powerful method for resolving pure component NMR spectra from mixtures when certain conditions are met.

Prevalence and correlates of dizziness in community-dwelling older people: a cross sectional population based study.

PubMed

de Moraes, Suzana Albuquerque; Soares, Wuber Jefferson de Souza; Ferriolli, Eduardo; Perracini, Monica Rodrigues

2013-01-04

Dizziness is a common complaint among older adults and has been linked to a wide range of health conditions, psychological and social characteristics in this population. However a profile of dizziness is still uncertain which hampers clinical decision-making. We therefore sought to explore the relationship between dizziness and a comprehensive range of demographic data, diseases, health and geriatric conditions, and geriatric syndromes in a representative sample of community-dwelling older people. This is a cross-sectional, population-based study derived from FIBRA (Network for the Study of Frailty in Brazilian Elderly Adults), with 391 elderly adults, both men and women, aged 65 years and older. Elderly participants living at home in an urban area were enrolled through a process of random cluster sampling of census regions. The outcome variable was the self-report of dizziness in the last year. Several feelings of dizziness were investigated including vertigo, spinning, light or heavy headedness, floating, fuzziness, giddiness and instability. A multivariate logistic regression analysis was conducted to estimate the adjusted odds ratios and build the probability model for dizziness. The complaint of dizziness was reported by 45% of elderly adults, from which 71.6% were women (p=0.004). The multivariate regression analysis revealed that dizziness is associated with depressive symptoms (OR = 2.08; 95% CI 1.29-3.35), perceived fatigue (OR = 1.93; 95% CI 1.21-3.10), recurring falls (OR = 2.01; 95% CI 1.11-3.62) and excessive drowsiness (OR = 1.91; 95% CI 1.11-3.29). The discrimination of the final model was AUC = 0.673 (95% CI 0.619-0.727) (p< 0.001). The prevalence of dizziness in community-dwelling elderly adults is substantial. It is associated with other common geriatric conditions usually neglected in elderly adults, such as fatigue and drowsiness, supporting its possible multifactorial manifestation. Our findings demonstrate the need to expand the design in future studies, aiming to estimate risk and identify possible causal relations.

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...

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.

Comprehensive drought characteristics analysis based on a nonlinear multivariate drought index

NASA Astrophysics Data System (ADS)

Yang, Jie; Chang, Jianxia; Wang, Yimin; Li, Yunyun; Hu, Hui; Chen, Yutong; Huang, Qiang; Yao, Jun

2018-02-01

It is vital to identify drought events and to evaluate multivariate drought characteristics based on a composite drought index for better drought risk assessment and sustainable development of water resources. However, most composite drought indices are constructed by the linear combination, principal component analysis and entropy weight method assuming a linear relationship among different drought indices. In this study, the multidimensional copulas function was applied to construct a nonlinear multivariate drought index (NMDI) to solve the complicated and nonlinear relationship due to its dependence structure and flexibility. The NMDI was constructed by combining meteorological, hydrological, and agricultural variables (precipitation, runoff, and soil moisture) to better reflect the multivariate variables simultaneously. Based on the constructed NMDI and runs theory, drought events for a particular area regarding three drought characteristics: duration, peak, and severity were identified. Finally, multivariate drought risk was analyzed as a tool for providing reliable support in drought decision-making. The results indicate that: (1) multidimensional copulas can effectively solve the complicated and nonlinear relationship among multivariate variables; (2) compared with single and other composite drought indices, the NMDI is slightly more sensitive in capturing recorded drought events; and (3) drought risk shows a spatial variation; out of the five partitions studied, the Jing River Basin as well as the upstream and midstream of the Wei River Basin are characterized by a higher multivariate drought risk. In general, multidimensional copulas provides a reliable way to solve the nonlinear relationship when constructing a comprehensive drought index and evaluating multivariate drought characteristics.

A Randomized Controlled Trial Testing the Efficacy of the Creating Opportunities for Parent Empowerment Program for Parents of Children With Epilepsy and Other Chronic Neurological Conditions.

PubMed

Duffy, Lisa V; Vessey, Judith A

2016-06-01

Parents of children with epilepsy and other neurological conditions live with a feeling of constant uncertainty. The uncertainty associated with caring for a child with a neurological condition produces stress, which leads to decreased parental belief in caregiving skills, anxiety, and depression, ultimately altering parental functioning resulting in an increase in child behavioral problems. The stress associated with caring for children with neurological conditions is unlike caring for children with other chronic conditions. Neurological conditions are unpredictable, and there are often no warning signs before an acute event. This unpredictability accompanied with stigma results in social isolation and impacts family functioning. In addition, children with neurological conditions have a higher rate of psychological comorbidities and behavior problems when compared with children with other chronic conditions. This produces an additional burden on the parents and family. This randomized controlled trial tested the efficacy of the Creating Opportunities for Parent Empowerment intervention for parents of children with epilepsy and other neurological conditions. This intervention was administered at three intervals: (a) during hospital admission, (b) 3 days after hospital discharge by telephone, and (c) 4-6 weeks after hospital discharge. Forty-six parents of children admitted to the inpatient neuroscience unit at Boston Children's Hospital participated in the study. Several study limitations resulted in an inadequate sample size to obtain the power necessary to reach statistically significant results for most of the research questions. A one-between, one-within multivariate analysis of variance revealed that the main effect of time was significant for differences in state anxiety for both the usual care group and the intervention group, F(1, 20) = 9.86, p = .005, indicating that state anxiety for both groups combined was more pronounced during the hospitalization. A one-between, one-within multivariate analysis of variance showed that the effect of the interaction between time and group was significant for internalized behavior assessment system score only (p = .037) because the usual care group reported a significant decrease in internalizing behavior scores in their children over time. Findings from this study have significant implications for clinical practice and future research. Parents of children with neurological conditions often struggle to manage a constant feeling of uncertainty in their daily lives. Nurses possess the knowledge and expertise necessary to identify the psychosocial needs of these parents and provide education and support as needed. Future research should focus on designing interventions to meet the needs of these families and develop strategies to help improve the quality of life for both the parent and child living with a neurological condition.

Effect of Contact Damage on the Strength of Ceramic Materials.

DTIC Science & Technology

1982-10-01

variables that are important to erosion, and a multivariate , linear regression analysis is used to fit the data to the dimensional analysis. The...of Equations 7 and 8 by a multivariable regression analysis (room tem- perature data) Exponent Regression Standard error Computed coefficient of...1980) 593. WEAVER, Proc. Brit. Ceram. Soc. 22 (1973) 125. 39. P. W. BRIDGMAN, "Dimensional Analaysis ", (Yale 18. R. W. RICE, S. W. FREIMAN and P. F

An adaptive confidence limit for periodic non-steady conditions fault detection

NASA Astrophysics Data System (ADS)

Wang, Tianzhen; Wu, Hao; Ni, Mengqi; Zhang, Milu; Dong, Jingjing; Benbouzid, Mohamed El Hachemi; Hu, Xiong

2016-05-01

System monitoring has become a major concern in batch process due to the fact that failure rate in non-steady conditions is much higher than in steady ones. A series of approaches based on PCA have already solved problems such as data dimensionality reduction, multivariable decorrelation, and processing non-changing signal. However, if the data follows non-Gaussian distribution or the variables contain some signal changes, the above approaches are not applicable. To deal with these concerns and to enhance performance in multiperiod data processing, this paper proposes a fault detection method using adaptive confidence limit (ACL) in periodic non-steady conditions. The proposed ACL method achieves four main enhancements: Longitudinal-Standardization could convert non-Gaussian sampling data to Gaussian ones; the multiperiod PCA algorithm could reduce dimensionality, remove correlation, and improve the monitoring accuracy; the adaptive confidence limit could detect faults under non-steady conditions; the fault sections determination procedure could select the appropriate parameter of the adaptive confidence limit. The achieved result analysis clearly shows that the proposed ACL method is superior to other fault detection approaches under periodic non-steady conditions.

A Framework for Establishing Standard Reference Scale of Texture by Multivariate Statistical Analysis Based on Instrumental Measurement and Sensory Evaluation.

PubMed

Zhi, Ruicong; Zhao, Lei; Xie, Nan; Wang, Houyin; Shi, Bolin; Shi, Jingye

2016-01-13

A framework of establishing standard reference scale (texture) is proposed by multivariate statistical analysis according to instrumental measurement and sensory evaluation. Multivariate statistical analysis is conducted to rapidly select typical reference samples with characteristics of universality, representativeness, stability, substitutability, and traceability. The reasonableness of the framework method is verified by establishing standard reference scale of texture attribute (hardness) with Chinese well-known food. More than 100 food products in 16 categories were tested using instrumental measurement (TPA test), and the result was analyzed with clustering analysis, principal component analysis, relative standard deviation, and analysis of variance. As a result, nine kinds of foods were determined to construct the hardness standard reference scale. The results indicate that the regression coefficient between the estimated sensory value and the instrumentally measured value is significant (R(2) = 0.9765), which fits well with Stevens's theory. The research provides reliable a theoretical basis and practical guide for quantitative standard reference scale establishment on food texture characteristics.

Understanding perception of active noise control system through multichannel EEG analysis.

PubMed

Bagha, Sangeeta; Tripathy, R K; Nanda, Pranati; Preetam, C; Das, Debi Prasad

2018-06-01

In this Letter, a method is proposed to investigate the effect of noise with and without active noise control (ANC) on multichannel electroencephalogram (EEG) signal. The multichannel EEG signal is recorded during different listening conditions such as silent, music, noise, ANC with background noise and ANC with both background noise and music. The multiscale analysis of EEG signal of each channel is performed using the discrete wavelet transform. The multivariate multiscale matrices are formulated based on the sub-band signals of each EEG channel. The singular value decomposition is applied to the multivariate matrices of multichannel EEG at significant scales. The singular value features at significant scales and the extreme learning machine classifier with three different activation functions are used for classification of multichannel EEG signal. The experimental results demonstrate that, for ANC with noise and ANC with noise and music classes, the proposed method has sensitivity values of 75.831% ( p < 0.001 ) and 99.31% ( p < 0.001 ), respectively. The method has an accuracy value of 83.22% for the classification of EEG signal with music and ANC with music as stimuli. The important finding of this study is that by the introduction of ANC, music can be better perceived by the human brain.

The prevalence of posttraumatic stress disorder among adult earthquake survivors in Peru.

PubMed

Cairo, Javier B; Dutta, Suparna; Nawaz, Haq; Hashmi, Shahrukh; Kasl, Stanislav; Bellido, Edgar

2010-03-01

To estimate the prevalence of posttraumatic stress disorder (PTSD) and to assess the relationships between PTSD and demographic and disaster-related factors. Five months after a magnitude 8.0 earthquake struck the city of Pisco, Peru, we conducted a cross-sectional study using demographic questions, the PTSD Checklist, and a translated version of the Harvard Trauma Questionnaire. We used stratified sampling to randomly enroll subjects in Pisco and its annexes. We then used bivariate and multivariate analyses to find correlations between PTSD and demographic and disaster-related factors. We interviewed 298 adult earthquake survivors and detected 75 cases of PTSD (prevalence 25.2%; 95% confidence interval, 20.2%-30.1%). In the bivariate analysis, PTSD was significantly associated with female sex, loss of church, food and water shortages immediately after the earthquake, joblessness, injuries, loss of a relative or friend, lack of clean drinking water or appropriate sleeping conditions 5 months after the earthquake, and low levels of perceived support from family and friends. In the multivariate analysis, only female sex, food and water shortages, loss of church, injuries, and low levels of perceived support from family and friends were independently associated with PTSD. PTSD affected about a quarter of Pisco's population. Its impact was moderate to severe when compared with other disasters worldwide and in Latin America.

Noninvasive assessment of the risk of tobacco abuse in oral mucosa using fluorescence spectroscopy: a clinical approach

NASA Astrophysics Data System (ADS)

Nazeer, Shaiju S.; Asish, Rajashekharan; Venugopal, Chandrashekharan; Anita, Balan; Gupta, Arun Kumar; Jayasree, Ramapurath S.

2014-05-01

Tobacco abuse and alcoholism cause cancer, emphysema, and heart disease, which contribute to high death rates, globally. Society pays a significant cost for these habits whose first demonstration in many cases is in the oral cavity. Oral cavity disorders are highly curable if a screening procedure is available to diagnose them in the earliest stages. The aim of the study is to identify the severity of tobacco abuse, in oral cavity, as reflected by the emission from endogenous fluorophores and the chromophore hemoglobin. A group who had no tobacco habits and another with a history of tobacco abuse were included in this study. To compare the results with a pathological condition, a group of leukoplakia patients were also included. Emission from porphyrin and the spectral filtering modulation effect of hemoglobin were collected from different sites. Multivariate analysis strengthened the spectral features with a sensitivity of 60% to 100% and a specificity of 76% to 100% for the discrimination. Total hemoglobin and porphyrin levels of habitués and leukoplakia groups were comparable, indicating the alarming situation about the risk of tobacco abuse. Results prove that fluorescence spectroscopy along with multivariate analysis is an effective noninvasive tool for the early diagnosis of pathological changes due to tobacco abuse.

Fourier-transform infrared spectroscopy as a novel approach to providing effect-based endpoints in duckweed toxicity testing.

PubMed

Hu, Li-Xin; Ying, Guang-Guo; Chen, Xiao-Wen; Huang, Guo-Yong; Liu, You-Sheng; Jiang, Yu-Xia; Pan, Chang-Gui; Tian, Fei; Martin, Francis L

2017-02-01

Traditional duckweed toxicity tests only measure plant growth inhibition as an endpoint, with limited effects-based data. The present study aimed to investigate whether Fourier-transform infrared (FTIR) spectroscopy could enhance the duckweed (Lemna minor L.) toxicity test. Four chemicals (Cu, Cd, atrazine, and acetochlor) and 4 metal-containing industrial wastewater samples were tested. After exposure of duckweed to the chemicals, standard toxicity endpoints (frond number and chlorophyll content) were determined; the fronds were also interrogated using FTIR spectroscopy under optimized test conditions. Biochemical alterations associated with each treatment were assessed and further analyzed by multivariate analysis. The results showed that comparable x% of effective concentration (ECx) values could be achieved based on FTIR spectroscopy in comparison with those based on traditional toxicity endpoints. Biochemical alterations associated with different doses of toxicant were mainly attributed to lipid, protein, nucleic acid, and carbohydrate structural changes, which helped to explain toxic mechanisms. With the help of multivariate analysis, separation of clusters related to different exposure doses could be achieved. The present study is the first to show successful application of FTIR spectroscopy in standard duckweed toxicity tests with biochemical alterations as new endpoints. Environ Toxicol Chem 2017;36:346-353. © 2016 SETAC. © 2016 SETAC.

Genetic Structure of Bluefin Tuna in the Mediterranean Sea Correlates with Environmental Variables

PubMed Central

Riccioni, Giulia; Stagioni, Marco; Landi, Monica; Ferrara, Giorgia; Barbujani, Guido; Tinti, Fausto

2013-01-01

Background Atlantic Bluefin Tuna (ABFT) shows complex demography and ecological variation in the Mediterranean Sea. Genetic surveys have detected significant, although weak, signals of population structuring; catch series analyses and tagging programs identified complex ABFT spatial dynamics and migration patterns. Here, we tested the hypothesis that the genetic structure of the ABFT in the Mediterranean is correlated with mean surface temperature and salinity. Methodology We used six samples collected from Western and Central Mediterranean integrated with a new sample collected from the recently identified easternmost reproductive area of Levantine Sea. To assess population structure in the Mediterranean we used a multidisciplinary framework combining classical population genetics, spatial and Bayesian clustering methods and a multivariate approach based on factor analysis. Conclusions FST analysis and Bayesian clustering methods detected several subpopulations in the Mediterranean, a result also supported by multivariate analyses. In addition, we identified significant correlations of genetic diversity with mean salinity and surface temperature values revealing that ABFT is genetically structured along two environmental gradients. These results suggest that a preference for some spawning habitat conditions could contribute to shape ABFT genetic structuring in the Mediterranean. However, further studies should be performed to assess to what extent ABFT spawning behaviour in the Mediterranean Sea can be affected by environmental variation. PMID:24260341

Reasons for job separations in a cohort of workers with psychiatric disabilities.

PubMed

Cook, Judith A; Burke-Miller, Jane K

2015-01-01

We explored the relative effects of adverse working conditions, job satisfaction, wages, worker characteristics, and local labor markets in explaining voluntary job separations (quits) among employed workers with psychiatric disabilities. Data come from the Employment Intervention Demonstration Program in which 2,086 jobs were ended by 892 workers during a 24 mo observation period. Stepped multivariable logistic regression analysis examined the effect of variables on the likelihood of quitting. Over half (59%) of all job separations were voluntary while 41% were involuntary, including firings (17%), temporary job endings (14%), and layoffs (10%). In multivariable analysis, workers were more likely to quit positions at which they were employed for 20 h/wk or less, those with which they were dissatisfied, low-wage jobs, non-temporary positions, and jobs in the structural (construction) occupations. Voluntary separation was less likely for older workers, members of racial and ethnic minority groups, and those residing in regions with lower unemployment rates. Patterns of job separations for workers with psychiatric disabilities mirrored some findings regarding job leaving in the general labor force but contradicted others. Job separation antecedents reflect the concentration of jobs for workers with psychiatric disabilities in the secondary labor market, characterized by low-salaried, temporary, and part-time employment.

Medicaid Participation among Liver Transplant Candidates after the Affordable Care Act Medicaid Expansion.

PubMed

Tumin, Dmitry; Beal, Eliza W; Mumtaz, Khalid; Hayes, Don; Tobias, Joseph D; Pawlik, Timothy M; Washburn, W Kenneth; Black, Sylvester M

2017-08-01

The 2014 Medicaid expansion in participating states increased insurance coverage among people with chronic health conditions, but its implications for access to surgical care remain unclear. We investigated how Medicaid expansion influenced the insurance status of candidates for liver transplantation (LT) and transplant center payor mix. Data on LT candidates aged 18 to 64 years, in 2012 to 2013 (pre-expansion) and 2014 to 2015 (post-expansion), were obtained from the United Network for Organ Sharing registry. Change between the 2 periods in the percent of LT candidates using Medicaid was compared between expansion and nonexpansion states. Multivariable logistic regression was used to determine how Medicaid expansion influenced individual LT candidates' likelihood of using Medicaid insurance. The study included 33,017 LT candidates, of whom 29,666 had complete data for multivariable analysis. Medicaid enrollment increased by 4% after Medicaid expansion in participating states. One-quarter of the transplant centers in these states experienced ≥10% increase in the proportion of LT candidates using Medicaid insurance. Multivariable analysis confirmed that Medicaid expansion was associated with increased odds of LT candidates using Medicaid insurance (odds ratio 1.49; 95% CI 1.34, 1.66; p < 0.001). However, the absolute number and demographic characteristics of patients listed for LT did not change in Medicaid expansion states during the post-expansion period. Candidates for LT became more likely to use Medicaid after the 2014 Medicaid expansion policy came into effect. Enactment of this policy did not appear to increase access to LT or address socioeconomic and demographic disparities in access to the LT wait list. Copyright © 2017 American College of Surgeons. Published by Elsevier Inc. All rights reserved.

HIV mono-infection is associated with FIB-4 - A noninvasive index of liver fibrosis - in women.

PubMed

Blackard, Jason T; Welge, Jeffrey A; Taylor, Lynn E; Mayer, Kenneth H; Klein, Robert S; Celentano, David D; Jamieson, Denise J; Gardner, Lytt; Sherman, Kenneth E

2011-03-01

FIB-4 represents a noninvasive, composite index that is a validated measure of hepatic fibrosis, which is an important indicator of liver disease. To date, there are limited data regarding hepatic fibrosis in women. FIB-4 was evaluated in a cohort of 1227 women, and associations were evaluated in univariate and multivariate regression models among 4 groups of subjects classified by their human immunodeficiency virus (HIV) and hepatitis C virus (HCV) infection status. The median FIB-4 scores were 0.60 in HIV-/HCV- women, 0.83 in HIV-/HCV+ women, 0.86 in HIV+/HCV- women, and 1.30 in HIV+/HCV+ women. In the HIV/HCV co-infected group, multivariate analysis showed that CD4(+) cell count and albumin level were negatively associated with FIB-4 (P <.0001), whereas antiretroviral therapy (ART) was positively associated with FIB-4 score (P =.0008). For the HIV mono-infected group, multivariate analysis showed that CD4(+) cell count (P <.0001) and albumin level (P =.0019) were negatively correlated with FIB-4 score, ART was positively associated with FIB-4 score (P =.0008), and plasma HIV RNA level was marginally associated with FIB-4 score (P =.080). In 72 HIV mono-infected women who were also hepatitis B surface antigen negative, ART naive, and reported no recent alcohol intake, plasma HIV RNA level was associated with increased FIB-4 score (P =.030). HIV RNA level was associated with increased FIB-4 score in the absence of hepatitis B, hepatitis C, ART, or alcohol use, suggesting a potential relationship between HIV infection and hepatic fibrosis in vivo. A better understanding of the various demographic and virologic variables that contribute to hepatic fibrosis may lead to more effective treatment of HIV infection and its co-morbid conditions.

DasPy – Open Source Multivariate Land Data Assimilation Framework with High Performance Computing

NASA Astrophysics Data System (ADS)

Han, Xujun; Li, Xin; Montzka, Carsten; Kollet, Stefan; Vereecken, Harry; Hendricks Franssen, Harrie-Jan

2015-04-01

Data assimilation has become a popular method to integrate observations from multiple sources with land surface models to improve predictions of the water and energy cycles of the soil-vegetation-atmosphere continuum. In recent years, several land data assimilation systems have been developed in different research agencies. Because of the software availability or adaptability, these systems are not easy to apply for the purpose of multivariate land data assimilation research. Multivariate data assimilation refers to the simultaneous assimilation of observation data for multiple model state variables into a simulation model. Our main motivation was to develop an open source multivariate land data assimilation framework (DasPy) which is implemented using the Python script language mixed with C++ and Fortran language. This system has been evaluated in several soil moisture, L-band brightness temperature and land surface temperature assimilation studies. The implementation allows also parameter estimation (soil properties and/or leaf area index) on the basis of the joint state and parameter estimation approach. LETKF (Local Ensemble Transform Kalman Filter) is implemented as the main data assimilation algorithm, and uncertainties in the data assimilation can be represented by perturbed atmospheric forcings, perturbed soil and vegetation properties and model initial conditions. The CLM4.5 (Community Land Model) was integrated as the model operator. The CMEM (Community Microwave Emission Modelling Platform), COSMIC (COsmic-ray Soil Moisture Interaction Code) and the two source formulation were integrated as observation operators for assimilation of L-band passive microwave, cosmic-ray soil moisture probe and land surface temperature measurements, respectively. DasPy is parallelized using the hybrid MPI (Message Passing Interface) and OpenMP (Open Multi-Processing) techniques. All the input and output data flow is organized efficiently using the commonly used NetCDF file format. Online 1D and 2D visualization of data assimilation results is also implemented to facilitate the post simulation analysis. In summary, DasPy is a ready to use open source parallel multivariate land data assimilation framework.

A Course in... Multivariable Control Methods.

ERIC Educational Resources Information Center

Deshpande, Pradeep B.

1988-01-01

Describes an engineering course for graduate study in process control. Lists four major topics: interaction analysis, multiloop controller design, decoupling, and multivariable control strategies. Suggests a course outline and gives information about each topic. (MVL)

Generation of multivariate near shore extreme wave conditions based on an extreme value copula for offshore boundary conditions.

NASA Astrophysics Data System (ADS)

Leyssen, Gert; Mercelis, Peter; De Schoesitter, Philippe; Blanckaert, Joris

2013-04-01

Near shore extreme wave conditions, used as input for numerical wave agitation simulations and for the dimensioning of coastal defense structures, need to be determined at a harbour entrance situated at the French North Sea coast. To obtain significant wave heights, the numerical wave model SWAN has been used. A multivariate approach was used to account for the joint probabilities. Considered variables are: wind velocity and direction, water level and significant offshore wave height and wave period. In a first step a univariate extreme value distribution has been determined for the main variables. By means of a technique based on the mean excess function, an appropriate member of the GPD is selected. An optimal threshold for peak over threshold selection is determined by maximum likelihood optimization. Next, the joint dependency structure for the primary random variables is modeled by an extreme value copula. Eventually the multivariate domain of variables was stratified in different classes, each of which representing a combination of variable quantiles with a joint probability, which are used for model simulation. The main variable is the wind velocity, as in the area of concern extreme wave conditions are wind driven. The analysis is repeated for 9 different wind directions. The secondary variable is water level. In shallow waters extreme waves will be directly affected by water depth. Hence the joint probability of occurrence for water level and wave height is of major importance for design of coastal defense structures. Wind velocity and water levels are only dependent for some wind directions (wind induced setup). Dependent directions are detected using a Kendall and Spearman test and appeared to be those with the longest fetch. For these directions, wind velocity and water level extreme value distributions are multivariately linked through a Gumbel Copula. These distributions are stratified into classes of which the frequency of occurrence can be calculated. For the remaining directions the univariate extreme wind velocity distribution is stratified, each class combined with 5 high water levels. The wave height at the model boundaries was taken into account by a regression with the extreme wind velocity at the offshore location. The regression line and the 95% confidence limits where combined with each class. Eventually the wave period is computed by a new regression with the significant wave height. This way 1103 synthetic events were selected and simulated with the SWAN wave model, each of which a frequency of occurrence is calculated for. Hence near shore significant wave heights are obtained with corresponding frequencies. The statistical distribution of the near shore wave heights is determined by sorting the model results in a descending order and accumulating the corresponding frequencies. This approach allows determination of conditional return periods. For example, for the imposed univariate design return periods of 100 years for significant wave height and 30 years for water level, the joint return period for a simultaneous exceedance of both conditions can be computed as 4000 years. Hence, this methodology allows for a probabilistic design of coastal defense structures.

Anal sphincter injury in vaginal deliveries complicated by shoulder dystocia.

PubMed

Hehir, Mark P; Rubeo, Zachary; Flood, Karen; Mardy, Anne H; O'Herlihy, Colm; Boylan, Peter C; D'Alton, Mary E

2018-03-01

Shoulder dystocia is an obstetric emergency that occurs in 0.2-3% of all cephalic vaginal deliveries. We hypothesized that because of the difficult nature of deliveries complicated by shoulder dystocia, the condition may be associated with anal sphincter injury. We sought to identify risk factors for obstetric anal sphincter injury in women with shoulder dystocia. This retrospective analysis included all cases of shoulder dystocia from 2007 to 2011 at two large tertiary referral centers, in the USA and Ireland. Details of maternal demographics, intrapartum characteristics, and delivery outcomes in cases of shoulder dystocia were analyzed. Univariate and multivariate analyses were used to describe the association between shoulder dystocia and anal sphincter injury. There were 685 cases of shoulder dystocia, and the rate of shoulder dystocia was similar at both institutions. The incidence of anal sphincter injury was 8.8% (60 out of 685). The rate was 14% (45 out of 324) in nulliparas and 4.2% (15 out of 361) in multiparas. Women with sphincter injury were more likely to be nulliparous (75% [45 out of 60] vs 45% [279 out of 625]; p < 0.0001), have had an operative vaginal delivery (50% [30 out of 60] vs 36% [226 out of 625]; p = 0.03) and require internal maneuvers (50% [30 out of 60] vs 32% [198 out of 625], p = 0.004) than those with an intact sphincter. On multivariate regression analysis, these predictors of sphincter injury remained significant when adjusted for other risk factors. Episiotomy was negatively associated with sphincter injury on multivariate regression analysis. In a retrospective cohort of 685 women with shoulder dystocia, the risk of anal sphincter injury is 9%. Risk factors include nulliparity, operative vaginal delivery, and use of internal maneuvers, whereas episiotomy was found to have a protective effect against anal sphincter injury during cases of shoulder dystocia.

A systematic review of the relationship factor between women and health professionals within the multivariant analysis of maternal satisfaction.

PubMed

Macpherson, Ignacio; Roqué-Sánchez, María V; Legget Bn, Finola O; Fuertes, Ferran; Segarra, Ignacio

2016-10-01

personalised support provided to women by health professionals is one of the prime factors attaining women's satisfaction during pregnancy and childbirth. However the multifactorial nature of 'satisfaction' makes difficult to assess it. Statistical multivariate analysis may be an effective technique to obtain in depth quantitative evidence of the importance of this factor and its interaction with the other factors involved. This technique allows us to estimate the importance of overall satisfaction in its context and suggest actions for healthcare services. systematic review of studies that quantitatively measure the personal relationship between women and healthcare professionals (gynecologists, obstetricians, nurse, midwifes, etc.) regarding maternity care satisfaction. The literature search focused on studies carried out between 1970 and 2014 that used multivariate analyses and included the woman-caregiver relationship as a factor of their analysis. twenty-four studies which applied various multivariate analysis tools to different periods of maternity care (antenatal, perinatal, post partum) were selected. The studies included discrete scale scores and questionnaires from women with low-risk pregnancies. The "personal relationship" factor appeared under various names: care received, personalised treatment, professional support, amongst others. The most common multivariate techniques used to assess the percentage of variance explained and the odds ratio of each factor were principal component analysis and logistic regression. the data, variables and factor analysis suggest that continuous, personalised care provided by the usual midwife and delivered within a family or a specialised setting, generates the highest level of satisfaction. In addition, these factors foster the woman's psychological and physiological recovery, often surpassing clinical action (e.g. medicalization and hospital organization) and/or physiological determinants (e.g. pain, pathologies, etc.). Copyright © 2016 Elsevier Ltd. All rights reserved.

Classification of Malaysia aromatic rice using multivariate statistical analysis

NASA Astrophysics Data System (ADS)

Abdullah, A. H.; Adom, A. H.; Shakaff, A. Y. Md; Masnan, M. J.; Zakaria, A.; Rahim, N. A.; Omar, O.

2015-05-01

Aromatic rice (Oryza sativa L.) is considered as the best quality premium rice. The varieties are preferred by consumers because of its preference criteria such as shape, colour, distinctive aroma and flavour. The price of aromatic rice is higher than ordinary rice due to its special needed growth condition for instance specific climate and soil. Presently, the aromatic rice quality is identified by using its key elements and isotopic variables. The rice can also be classified via Gas Chromatography Mass Spectrometry (GC-MS) or human sensory panels. However, the uses of human sensory panels have significant drawbacks such as lengthy training time, and prone to fatigue as the number of sample increased and inconsistent. The GC-MS analysis techniques on the other hand, require detailed procedures, lengthy analysis and quite costly. This paper presents the application of in-house developed Electronic Nose (e-nose) to classify new aromatic rice varieties. The e-nose is used to classify the variety of aromatic rice based on the samples odour. The samples were taken from the variety of rice. The instrument utilizes multivariate statistical data analysis, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and K-Nearest Neighbours (KNN) to classify the unknown rice samples. The Leave-One-Out (LOO) validation approach is applied to evaluate the ability of KNN to perform recognition and classification of the unspecified samples. The visual observation of the PCA and LDA plots of the rice proves that the instrument was able to separate the samples into different clusters accordingly. The results of LDA and KNN with low misclassification error support the above findings and we may conclude that the e-nose is successfully applied to the classification of the aromatic rice varieties.

Diagonal dominance for the multivariable Nyquist array using function minimization

NASA Technical Reports Server (NTRS)

Leininger, G. G.

1977-01-01

A new technique for the design of multivariable control systems using the multivariable Nyquist array method was developed. A conjugate direction function minimization algorithm is utilized to achieve a diagonal dominant condition over the extended frequency range of the control system. The minimization is performed on the ratio of the moduli of the off-diagonal terms to the moduli of the diagonal terms of either the inverse or direct open loop transfer function matrix. Several new feedback design concepts were also developed, including: (1) dominance control parameters for each control loop; (2) compensator normalization to evaluate open loop conditions for alternative design configurations; and (3) an interaction index to determine the degree and type of system interaction when all feedback loops are closed simultaneously. This new design capability was implemented on an IBM 360/75 in a batch mode but can be easily adapted to an interactive computer facility. The method was applied to the Pratt and Whitney F100 turbofan engine.

The typical structure of tornado proximity soundings

NASA Astrophysics Data System (ADS)

Schaefer, Joseph T.; Livingston, Richard L.

1988-05-01

An objective scheme based on empirical orthogonal function analysis to detect patterns in a single or multivariate data set is developed and applied to rawinsonde observations taken in the near-tornado environment. If only temperature data are considered, two distinct categories, differentiated chiefly by the tropopause height, are found. When moisture observations are included with the temperatures, the separation between categories becomes less distinct. However, it is noted that within the near-tornado environment there is an inverse relationship between the degree of observed convective and conditional instability. Analysis of only the winds shows that a low-level veering with height is the rule. However, the strength of the veering can vary considerably. When the temperature, moisture, and winds are treated in concert, two categories again appear. One group occurs with strong winds and a low tropopause, while the other group features weak winds and a high tropopause. These groups correspond to "springtime" and "summertime" synoptic situations, respectively. Comparisons of the various analyses indicate that the near-tornado environment typically features a balance between the strength of the veering of the winds and the amount of conditional instability present. Summer tornadoes feature strong conditional instability and weak winds, while springtime tornadoes occur with stronger veering and convective instability. The strongest tornadoes occur with springtime-type conditions.

Independent Predictors of Prognosis Based on Oral Cavity Squamous Cell Carcinoma Surgical Margins.

PubMed

Buchakjian, Marisa R; Ginader, Timothy; Tasche, Kendall K; Pagedar, Nitin A; Smith, Brian J; Sperry, Steven M

2018-05-01

Objective To conduct a multivariate analysis of a large cohort of oral cavity squamous cell carcinoma (OCSCC) cases for independent predictors of local recurrence (LR) and overall survival (OS), with emphasis on the relationship between (1) prognosis and (2) main specimen permanent margins and intraoperative tumor bed frozen margins. Study Design Retrospective cohort study. Setting Tertiary academic head and neck cancer program. Subjects and Methods This study included 426 patients treated with OCSCC resection between 2005 and 2014 at University of Iowa Hospitals and Clinics. Patients underwent excision of OCSCC with intraoperative tumor bed frozen margin sampling and main specimen permanent margin assessment. Multivariate analysis of the data set to predict LR and OS was performed. Results Independent predictors of LR included nodal involvement, histologic grade, and main specimen permanent margin status. Specifically, the presence of a positive margin (odds ratio, 6.21; 95% CI, 3.3-11.9) or <1-mm/carcinoma in situ margin (odds ratio, 2.41; 95% CI, 1.19-4.87) on the main specimen was an independent predictor of LR, whereas intraoperative tumor bed margins were not predictive of LR on multivariate analysis. Similarly, independent predictors of OS on multivariate analysis included nodal involvement, extracapsular extension, and a positive main specimen margin. Tumor bed margins did not independently predict OS. Conclusion The main specimen margin is a strong independent predictor of LR and OS on multivariate analysis. Intraoperative tumor bed frozen margins do not independently predict prognosis. We conclude that emphasis should be placed on evaluating the main specimen margins when estimating prognosis after OCSCC resection.

Improved modeling of in vivo confocal Raman data using multivariate curve resolution (MCR) augmentation of ordinary least squares models.

PubMed

Hancewicz, Thomas M; Xiao, Chunhong; Zhang, Shuliang; Misra, Manoj

2013-12-01

In vivo confocal Raman spectroscopy has become the measurement technique of choice for skin health and skin care related communities as a way of measuring functional chemistry aspects of skin that are key indicators for care and treatment of various skin conditions. Chief among these techniques are stratum corneum water content, a critical health indicator for severe skin condition related to dryness, and natural moisturizing factor components that are associated with skin protection and barrier health. In addition, in vivo Raman spectroscopy has proven to be a rapid and effective method for quantifying component penetration in skin for topically applied skin care formulations. The benefit of such a capability is that noninvasive analytical chemistry can be performed in vivo in a clinical setting, significantly simplifying studies aimed at evaluating product performance. This presumes, however, that the data and analysis methods used are compatible and appropriate for the intended purpose. The standard analysis method used by most researchers for in vivo Raman data is ordinary least squares (OLS) regression. The focus of work described in this paper is the applicability of OLS for in vivo Raman analysis with particular attention given to use for non-ideal data that often violate the inherent limitations and deficiencies associated with proper application of OLS. We then describe a newly developed in vivo Raman spectroscopic analysis methodology called multivariate curve resolution-augmented ordinary least squares (MCR-OLS), a relatively simple route to addressing many of the issues with OLS. The method is compared with the standard OLS method using the same in vivo Raman data set and using both qualitative and quantitative comparisons based on model fit error, adherence to known data constraints, and performance against calibration samples. A clear improvement is shown in each comparison for MCR-OLS over standard OLS, thus supporting the premise that the MCR-OLS method is better suited for general-purpose multicomponent analysis of in vivo Raman spectral data. This suggests that the methodology is more readily adaptable to a wide range of component systems and is thus more generally applicable than standard OLS.

Differences in passenger car and large truck involved crash frequencies at urban signalized intersections: an exploratory analysis.

PubMed

Dong, Chunjiao; Clarke, David B; Richards, Stephen H; Huang, Baoshan

2014-01-01

The influence of intersection features on safety has been examined extensively because intersections experience a relatively large proportion of motor vehicle conflicts and crashes. Although there are distinct differences between passenger cars and large trucks-size, operating characteristics, dimensions, and weight-modeling crash counts across vehicle types is rarely addressed. This paper develops and presents a multivariate regression model of crash frequencies by collision vehicle type using crash data for urban signalized intersections in Tennessee. In addition, the performance of univariate Poisson-lognormal (UVPLN), multivariate Poisson (MVP), and multivariate Poisson-lognormal (MVPLN) regression models in establishing the relationship between crashes, traffic factors, and geometric design of roadway intersections is investigated. Bayesian methods are used to estimate the unknown parameters of these models. The evaluation results suggest that the MVPLN model possesses most of the desirable statistical properties in developing the relationships. Compared to the UVPLN and MVP models, the MVPLN model better identifies significant factors and predicts crash frequencies. The findings suggest that traffic volume, truck percentage, lighting condition, and intersection angle significantly affect intersection safety. Important differences in car, car-truck, and truck crash frequencies with respect to various risk factors were found to exist between models. The paper provides some new or more comprehensive observations that have not been covered in previous studies. Copyright © 2013 Elsevier Ltd. All rights reserved.

Multivariate analysis of cytokine profiles in pregnancy complications.

PubMed

Azizieh, Fawaz; Dingle, Kamaludin; Raghupathy, Raj; Johnson, Kjell; VanderPlas, Jacob; Ansari, Ali

2018-03-01

The immunoregulation to tolerate the semiallogeneic fetus during pregnancy includes a harmonious dynamic balance between anti- and pro-inflammatory cytokines. Several earlier studies reported significantly different levels and/or ratios of several cytokines in complicated pregnancy as compared to normal pregnancy. However, as cytokines operate in networks with potentially complex interactions, it is also interesting to compare groups with multi-cytokine data sets, with multivariate analysis. Such analysis will further examine how great the differences are, and which cytokines are more different than others. Various multivariate statistical tools, such as Cramer test, classification and regression trees, partial least squares regression figures, 2-dimensional Kolmogorov-Smirmov test, principal component analysis and gap statistic, were used to compare cytokine data of normal vs anomalous groups of different pregnancy complications. Multivariate analysis assisted in examining if the groups were different, how strongly they differed, in what ways they differed and further reported evidence for subgroups in 1 group (pregnancy-induced hypertension), possibly indicating multiple causes for the complication. This work contributes to a better understanding of cytokines interaction and may have important implications on targeting cytokine balance modulation or design of future medications or interventions that best direct management or prevention from an immunological approach. © 2018 The Authors. American Journal of Reproductive Immunology Published by John Wiley & Sons Ltd.

Characterization of Interfacial Chemistry of Adhesive/Dentin Bond Using FTIR Chemical Imaging With Univariate and Multivariate Data Processing

PubMed Central

Wang, Yong; Yao, Xiaomei; Parthasarathy, Ranganathan

2008-01-01

Fourier transform infrared (FTIR) chemical imaging can be used to investigate molecular chemical features of the adhesive/dentin interfaces. However, the information is not straightforward, and is not easily extracted. The objective of this study was to use multivariate analysis methods, principal component analysis and fuzzy c-means clustering, to analyze spectral data in comparison with univariate analysis. The spectral imaging data collected from both the adhesive/healthy dentin and adhesive/caries-affected dentin specimens were used and compared. The univariate statistical methods such as mapping of intensities of specific functional group do not always accurately identify functional group locations and concentrations due to more or less band overlapping in adhesive and dentin. Apart from the ease with which information can be extracted, multivariate methods highlight subtle and often important changes in the spectra that are difficult to observe using univariate methods. The results showed that the multivariate methods gave more satisfactory, interpretable results than univariate methods and were conclusive in showing that they can discriminate and classify differences between healthy dentin and caries-affected dentin within the interfacial regions. It is demonstrated that the multivariate FTIR imaging approaches can be used in the rapid characterization of heterogeneous, complex structure. PMID:18980198

Multivariate Analysis of Longitudinal Rates of Change

PubMed Central

Bryan, Matthew; Heagerty, Patrick J.

2016-01-01

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 by Roy and Lin [1]; Proust-Lima, Letenneur and Jacqmin-Gadda [2]; and Gray and Brookmeyer [3] among others. Current methods for characterizing the relationship between covariates and the rate of change in multivariate outcomes are limited to select models. For example, Gray and Brookmeyer [3] introduce an “accelerated time” method which assumes 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. PMID:27417129

Cooperative control theory and integrated flight and propulsion control

NASA Technical Reports Server (NTRS)

Schmidt, David K.; Schierman, John D.

1995-01-01

The major contribution of this research was the exposition of the fact that airframe and engine interactions could be present, and their effects could include loss of stability and performance of the control systems. Also, the significance of two directional, as opposed to one-directional, coupling was identified and explained. A multivariable stability and performance analysis methodology was developed, and applied to several candidate aircraft configurations. In these example evaluations, the significance of these interactions was underscored. Also exposed was the fact that with interactions present along with some integrated control approaches, the engine command/limiting logic (which represents an important nonlinear component of the engine control system) can impact closed-loop airframe/engine system stability. Finally, a brief investigation of control-law synthesis techniques appropriate for the class of systems was pursued, and it was determined that multivariable techniques, including model-following formulations of LQG and/or H infinity methods, showed promise. However, for practical reasons, decentralized control architectures are preferred, which is an architecture incompatible with these synthesis methods. The major contributions of the second phase of the grant was the development of conditions under which no decentralized controller could achieve closed loop system requirements on stability and/or performance. Sought were conditions that depended only on properties of the plant and the requirement, and independent of any particular control law or synthesis approach. Therefore, they could be applied a priori, before synthesis of a candidate control law. Under this grant, such conditions were found regarding stability, and encouraging initial results were obtained regarding performance.

Exploring the Structure of Library and Information Science Web Space Based on Multivariate Analysis of Social Tags

ERIC Educational Resources Information Center

Joo, Soohyung; Kipp, Margaret E. I.

2015-01-01

Introduction: This study examines the structure of Web space in the field of library and information science using multivariate analysis of social tags from the Website, Delicious.com. A few studies have examined mathematical modelling of tags, mainly examining tagging in terms of tripartite graphs, pattern tracing and descriptive statistics. This…

Multivariate Analysis of High Through-Put Adhesively Bonded Single Lap Joints: Experimental and Workflow Protocols

DTIC Science & Technology

2016-06-01

unlimited. v List of Tables Table 1 Single-lap-joint experimental parameters ..............................................7 Table 2 Survey ...Joints: Experimental and Workflow Protocols by Robert E Jensen, Daniel C DeSchepper, and David P Flanagan Approved for...TR-7696 ● JUNE 2016 US Army Research Laboratory Multivariate Analysis of High Through-Put Adhesively Bonded Single Lap Joints: Experimental

A Multivariate Model for the Meta-Analysis of Study Level Survival Data at Multiple Times

ERIC Educational Resources Information Center

Jackson, Dan; Rollins, Katie; Coughlin, Patrick

2014-01-01

Motivated by our meta-analytic dataset involving survival rates after treatment for critical leg ischemia, we develop and apply a new multivariate model for the meta-analysis of study level survival data at multiple times. Our data set involves 50 studies that provide mortality rates at up to seven time points, which we model simultaneously, and…

Evaluation of the microscopic distribution of florfenicol in feed pellets for salmon by Fourier Transform infrared imaging and multivariate analysis.

PubMed

Bastidas, Camila Y; von Plessing, Carlos; Troncoso, José; Del P Castillo, Rosario

2018-04-15

Fourier Transform infrared imaging and multivariate analysis were used to identify, at the microscopic level, the presence of florfenicol (FF), a heavily-used antibiotic in the salmon industry, supplied to fishes in feed pellets for the treatment of salmonid rickettsial septicemia (SRS). The FF distribution was evaluated using Principal Component Analysis (PCA) and Augmented Multivariate Curve Resolution with Alternating Least Squares (augmented MCR-ALS) on the spectra obtained from images with pixel sizes of 6.25 μm × 6.25 μm and 1.56 μm × 1.56 μm, in different zones of feed pellets. Since the concentration of the drug was 3.44 mg FF/g pellet, this is the first report showing the powerful ability of the used of spectroscopic techniques and multivariate analysis, especially the augmented MCR-ALS, to describe the FF distribution in both the surface and inner parts of feed pellets at low concentration, in a complex matrix and at the microscopic level. The results allow monitoring the incorporation of the drug into the feed pellets. Copyright © 2018 Elsevier B.V. All rights reserved.

Risk factors for incidental durotomy during lumbar surgery: a retrospective study by multivariate analysis.

PubMed

Chen, Zhixiang; Shao, Peng; Sun, Qizhao; Zhao, Dong

2015-03-01

The purpose of the present study was to use a prospectively collected data to evaluate the rate of incidental durotomy (ID) during lumbar surgery and determine the associated risk factors by using univariate and multivariate analysis. We retrospectively reviewed 2184 patients who underwent lumbar surgery from January 1, 2009 to December 31, 2011 at a single hospital. Patients with ID (n=97) were compared with the patients without ID (n=2019). The influences of several potential risk factors that might affect the occurrence of ID were assessed using univariate and multivariate analyses. The overall incidence of ID was 4.62%. Univariate analysis demonstrated that older age, diabetes, lumbar central stenosis, posterior approach, revision surgery, prior lumber surgery and minimal invasive surgery are risk factors for ID during lumbar surgery. However, multivariate analysis identified older age, prior lumber surgery, revision surgery, and minimally invasive surgery as independent risk factors. Older age, prior lumber surgery, revision surgery, and minimal invasive surgery were independent risk factors for ID during lumbar surgery. These findings may guide clinicians making future surgical decisions regarding ID and aid in the patient counseling process to alleviate risks and complications. Copyright © 2015 Elsevier B.V. All rights reserved.

Non-fragile multivariable PID controller design via system augmentation

NASA Astrophysics Data System (ADS)

Liu, Jinrong; Lam, James; Shen, Mouquan; Shu, Zhan

2017-07-01

In this paper, the issue of designing non-fragile H∞ multivariable proportional-integral-derivative (PID) controllers with derivative filters is investigated. In order to obtain the controller gains, the original system is associated with an extended system such that the PID controller design can be formulated as a static output-feedback control problem. By taking the system augmentation approach, the conditions with slack matrices for solving the non-fragile H∞ multivariable PID controller gains are established. Based on the results, linear matrix inequality -based iterative algorithms are provided to compute the controller gains. Simulations are conducted to verify the effectiveness of the proposed approaches.

Multivariate reference technique for quantitative analysis of fiber-optic tissue Raman spectroscopy.

PubMed

Bergholt, Mads Sylvest; Duraipandian, Shiyamala; Zheng, Wei; Huang, Zhiwei

2013-12-03

We report a novel method making use of multivariate reference signals of fused silica and sapphire Raman signals generated from a ball-lens fiber-optic Raman probe for quantitative analysis of in vivo tissue Raman measurements in real time. Partial least-squares (PLS) regression modeling is applied to extract the characteristic internal reference Raman signals (e.g., shoulder of the prominent fused silica boson peak (~130 cm(-1)); distinct sapphire ball-lens peaks (380, 417, 646, and 751 cm(-1))) from the ball-lens fiber-optic Raman probe for quantitative analysis of fiber-optic Raman spectroscopy. To evaluate the analytical value of this novel multivariate reference technique, a rapid Raman spectroscopy system coupled with a ball-lens fiber-optic Raman probe is used for in vivo oral tissue Raman measurements (n = 25 subjects) under 785 nm laser excitation powers ranging from 5 to 65 mW. An accurate linear relationship (R(2) = 0.981) with a root-mean-square error of cross validation (RMSECV) of 2.5 mW can be obtained for predicting the laser excitation power changes based on a leave-one-subject-out cross-validation, which is superior to the normal univariate reference method (RMSE = 6.2 mW). A root-mean-square error of prediction (RMSEP) of 2.4 mW (R(2) = 0.985) can also be achieved for laser power prediction in real time when we applied the multivariate method independently on the five new subjects (n = 166 spectra). We further apply the multivariate reference technique for quantitative analysis of gelatin tissue phantoms that gives rise to an RMSEP of ~2.0% (R(2) = 0.998) independent of laser excitation power variations. This work demonstrates that multivariate reference technique can be advantageously used to monitor and correct the variations of laser excitation power and fiber coupling efficiency in situ for standardizing the tissue Raman intensity to realize quantitative analysis of tissue Raman measurements in vivo, which is particularly appealing in challenging Raman endoscopic applications.

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. Copyright © 2014 American College of Foot and Ankle Surgeons. Published by Elsevier Inc. All rights reserved.

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.

Socioeconomic Drought in a Changing Climate: Modeling and Management

NASA Astrophysics Data System (ADS)

AghaKouchak, Amir; Mehran, Ali; Mazdiyasni, Omid

2016-04-01

Drought is typically defined based on meteorological, hydrological and land surface conditions. However, in many parts of the world, anthropogenic changes and water management practices have significantly altered local water availability. Socioeconomic drought refers to conditions whereby the available water supply cannot satisfy the human and environmental water needs. Surface water reservoirs provide resilience against local climate variability (e.g., droughts), and play a major role in regional water management. This presentation focuses on a framework for describing socioeconomic drought based on both water supply and demand information. We present a multivariate approach as a measure of socioeconomic drought, termed Multivariate Standardized Reliability and Resilience Index (MSRRI; Mehran et al., 2015). This model links the information on inflow and surface reservoir storage to water demand. MSRRI integrates a "top-down" and a "bottom-up" approach for describing socioeconomic drought. The "top-down" component describes processes that cannot be simply controlled or altered by local decision-makers and managers (e.g., precipitation, climate variability, climate change), whereas the "bottom-up" component focuses on the local resilience, and societal capacity to respond to droughts. The two components (termed, Inflow-Demand Reliability (IDR) indicator and Water Storage Resilience (WSR) indicator) are integrated using a nonparametric multivariate approach. We use this framework to assess the socioeconomic drought during the Australian Millennium Drought (1998-2010) and the 2011-2014 California Droughts. MSRRI provides additional information on socioeconomic drought onset, development and termination based on local resilience and human demand that cannot be obtained from the commonly used drought indicators. We show that MSRRI can be used for water management scenario analysis (e.g., local water availability based on different human water demands scenarios). Finally, we provide examples of using the proposed modeling framework for analyzing water availability in a changing climate considering local conditions. Reference: Mehran A., Mazdiyasni O., AghaKouchak A., 2015, A Hybrid Framework for Assessing Socioeconomic Drought: Linking Climate Variability, Local Resilience, and Demand, Journal of Geophysical Research, 120 (15), 7520-7533, doi: 10.1002/2015JD023147

Multiscale Characterization of PM2.5 in Southern Taiwan based on Noise-assisted Multivariate Empirical Mode Decomposition and Time-dependent Intrinsic Correlation

NASA Astrophysics Data System (ADS)

Hsiao, Y. R.; Tsai, C.

2017-12-01

As the WHO Air Quality Guideline indicates, ambient air pollution exposes world populations under threat of fatal symptoms (e.g. heart disease, lung cancer, asthma etc.), raising concerns of air pollution sources and relative factors. This study presents a novel approach to investigating the multiscale variations of PM2.5 in southern Taiwan over the past decade, with four meteorological influencing factors (Temperature, relative humidity, precipitation and wind speed),based on Noise-assisted Multivariate Empirical Mode Decomposition(NAMEMD) algorithm, Hilbert Spectral Analysis(HSA) and Time-dependent Intrinsic Correlation(TDIC) method. NAMEMD algorithm is a fully data-driven approach designed for nonlinear and nonstationary multivariate signals, and is performed to decompose multivariate signals into a collection of channels of Intrinsic Mode Functions (IMFs). TDIC method is an EMD-based method using a set of sliding window sizes to quantify localized correlation coefficients for multiscale signals. With the alignment property and quasi-dyadic filter bank of NAMEMD algorithm, one is able to produce same number of IMFs for all variables and estimates the cross correlation in a more accurate way. The performance of spectral representation of NAMEMD-HSA method is compared with Complementary Empirical Mode Decomposition/ Hilbert Spectral Analysis (CEEMD-HSA) and Wavelet Analysis. The nature of NAMAMD-based TDICC analysis is then compared with CEEMD-based TDIC analysis and the traditional correlation analysis.

High levels of comorbidity and disability cancel out the dementia effect in predictions of long-term mortality after discharge in the very old.

PubMed

Zekry, Dina; Herrmann, François R; Graf, Christophe E; Giannelli, Sandra; Michel, Jean-Pierre; Gold, Gabriel; Krause, Karl-Heinz

2011-01-01

The relative weight of various etiologies of dementia as predictors of long-term mortality after other risk factors have been taken into account remains unclear. We investigated the 5-year mortality risk associated with dementia in elderly people after discharge from acute care, taking into account comorbid conditions and functionality. A prospective cohort study of 444 patients (mean age: 85 years; 74% female) discharged from the acute geriatric unit of Geneva University Hospitals. On admission, each subject underwent a standardized diagnostic evaluation: demographic variables, cognitive, comorbid medical conditions and functional assessment. Patients were followed yearly by the same team. Predictors of survival at 5 years were evaluated by Cox proportional hazards models. The univariate model showed that being older and male, and having vascular and severe dementia, comorbidity and functional disability, were predictive of shorter survival. However, in the full multivariate model adjusted for age and sex, the effect of dementia type or severity completely disappeared when all the variables were added. In multivariate analysis, the best predictor was higher comorbidity score, followed by functional status (R(2) = 23%). The identification of comorbidity and functional impairment effects as predictive factors for long-term mortality independent of cognitive status may increase the accuracy of long-term discharge planning. Copyright © 2011 S. Karger AG, Basel.

Association between Periodontal Condition and Nutritional Status of Brazilian Adolescents: A Population-based Study.

PubMed

Cavalcanti, Alessandro L; Ramos, Ianny A; Cardoso, Andreia M R; Fernandes, Liege Helena F; Aragão, Amanda S; Santos, Fábio G; Aguiar, Yêska P C; Carvalho, Danielle F; Medeiros, Carla C M; De S C Soares, Renata; Castro, Ricardo D

2016-12-01

Obesity is a serious problem of public health and affects all socio-economic groups, irrespective of age, sex or ethnicity. The aim of this study was to evaluate the association between periodontal condition and nutritional status of adolescents. This was a cross-sectional study using a probability cluster sampling, and the sample was defined by statistical criterion, consisting of 559 students aged 15-19 yr enrolled in public schools of adolescents of Campina Grande, PB, Brazil in 2012. Socioeconomic characteristics were analyzed, as well as self-reported general and oral health, anthropometric data and periodontal condition (CPI and OHI-S). Descriptive and analytical analysis from bivariate and multivariate Poisson regression analysis with 5% significance level was performed. Of the 559 adolescents, 18.6% were overweight and 98.4% had some form of periodontal changes such as: bleeding (34.3%), calculus (38.8%), shallow pocket (22.9%) and deep pocket (2.3%). There was association between presence of periodontal changes with obesity ( P <0.05; CI 95%: 0.99 [0.98 - 0.99]). The association between presence of periodontal changes and obesity status in adolescents was indicated.

Association between Periodontal Condition and Nutritional Status of Brazilian Adolescents: A Population-based Study

PubMed Central

CAVALCANTI, Alessandro L.; RAMOS, Ianny A.; CARDOSO, Andreia M. R.; FERNANDES, Liege Helena F.; ARAGÃO, Amanda S.; SANTOS, Fábio G.; AGUIAR, Yêska P. C.; CARVALHO, Danielle F.; MEDEIROS, Carla C. M.; De S. C. SOARES, Renata; CASTRO, Ricardo D.

2016-01-01

Background: Obesity is a serious problem of public health and affects all socio-economic groups, irrespective of age, sex or ethnicity. The aim of this study was to evaluate the association between periodontal condition and nutritional status of adolescents. Methods: This was a cross-sectional study using a probability cluster sampling, and the sample was defined by statistical criterion, consisting of 559 students aged 15–19 yr enrolled in public schools of adolescents of Campina Grande, PB, Brazil in 2012. Socioeconomic characteristics were analyzed, as well as self-reported general and oral health, anthropometric data and periodontal condition (CPI and OHI-S). Descriptive and analytical analysis from bivariate and multivariate Poisson regression analysis with 5% significance level was performed. Results: Of the 559 adolescents, 18.6% were overweight and 98.4% had some form of periodontal changes such as: bleeding (34.3%), calculus (38.8%), shallow pocket (22.9%) and deep pocket (2.3%). There was association between presence of periodontal changes with obesity (P<0.05; CI 95%: 0.99 [0.98 – 0.99]). Conclusion: The association between presence of periodontal changes and obesity status in adolescents was indicated. PMID:28053924

Physiological and pathological clinical conditions and light scattering in brain

NASA Astrophysics Data System (ADS)

Kurata, Tsuyoshi; Iwata, Sachiko; Tsuda, Kennosuke; Kinoshita, Masahiro; Saikusa, Mamoru; Hara, Naoko; Oda, Motoki; Ohmae, Etsuko; Araki, Yuko; Sugioka, Takashi; Takashima, Sachio; Iwata, Osuke

2016-08-01

MRI of preterm infants at term commonly reveals subtle brain lesions such as diffuse white matter injury, which are linked with later cognitive impairments. The timing and mechanism of such injury remains unclear. The reduced scattering coefficient of near-infrared light (μs’) has been shown to correlate linearly with gestational age in neonates. To identify clinical variables associated with brain μs’, 60 preterm and full-term infants were studied within 7 days of birth. Dependence of μs’ obtained from the frontal head on clinical variables was assessed. In the univariate analysis, smaller μs’ was associated with antenatal glucocorticoid, emergency Caesarean section, requirement for mechanical ventilation, smaller gestational age, smaller body sizes, low 1- and 5-minute Apgar scores, higher cord blood pH and PO2, and higher blood HCO3- at the time of study. Multivariate analysis revealed that smaller gestational age, requirement for mechanical ventilation, and higher HCO3- at the time of study were correlated with smaller μs’. Brain μs’ depended on variables associated with physiological maturation and pathological conditions of the brain. Further longitudinal studies may help identify pathological events and clinical conditions responsible for subtle brain injury and subsequent cognitive impairments following preterm birth.

Drug and alcohol use and family characteristics: a study among Brazilian high-school students.

PubMed

Carvalho, V; Pinsky, I; De Souza e Silva, R; Carlini-Cotrim, B

1995-01-01

The present work employs a multivariate analysis technique to study, simultaneously, family relations and alcohol/drug consumption among 16,378 Brazilian high-school students. The analysis is centered on the relation between subjective or objective family situations and consumption. Subjective situations are measured by adolescents' perception of their families, that is, the family's environmental "climate"--whether violent situations occur at home, whether there is frequent dialogue about the youngsters' problems, and whether they perceive interest on the part of parents. Objective situations refer to the conjugal status of parents. Results pointed to family violence as the factor most frequently associated with alcohol/drug use behavior. It was also found that the family's environmental climate constitutes a more important factor than the conjugal status of parents, when it comes to the development of drug use behavior. Therefore, the impact of this last variable (whether parents are living together) is determined by environmental conditions: when those conditions are favorable (no violence, problems habitually talked about, parents concerned with their offspring) the fact that parents were effectively living together meant a smaller probability of alcohol/drug use; when these conditions were unfavorable, the same fact was associated with a greater probability of consumption.

Parsing the roles of the frontal lobes and basal ganglia in task control using multivoxel pattern analysis

PubMed Central

Kehagia, Angie A.; Ye, Rong; Joyce, Dan W.; Doyle, Orla M.; Rowe, James B.; Robbins, Trevor W.

2017-01-01

Cognitive control has traditionally been associated with the prefrontal cortex, based on observations of deficits in patients with frontal lesions. However, evidence from patients with Parkinson’s disease (PD) indicates that subcortical regions also contribute to control under certain conditions. We scanned 17 healthy volunteers while they performed a task switching paradigm that previously dissociated performance deficits arising from frontal lesions in comparison with PD, as a function of the abstraction of the rules that are switched. From a multivoxel pattern analysis by Gaussian Process Classification (GPC), we then estimated the forward (generative) model to infer regional patterns of activity that predict Switch / Repeat behaviour between rule conditions. At 1000 permutations, Switch / Repeat classification accuracy for concrete rules was significant in the basal ganglia, but at chance in the frontal lobe. The inverse pattern was obtained for abstract rules, whereby the conditions were successfully discriminated in the frontal lobe but not in the basal ganglia. This double dissociation highlights the difference between cortical and subcortical contributions to cognitive control and demonstrates the utility of multivariate approaches in investigations of functions that rely on distributed and overlapping neural substrates. PMID:28387585

Analysis/forecast experiments with a multivariate statistical analysis scheme using FGGE data

NASA Technical Reports Server (NTRS)

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

1985-01-01

A three-dimensional, multivariate, statistical analysis method, optimal interpolation (OI) is described for modeling meteorological data from widely dispersed sites. The model was developed to analyze FGGE data at the NASA-Goddard Laboratory of Atmospherics. The model features a multivariate surface analysis over the oceans, including maintenance of the Ekman balance and a geographically dependent correlation function. Preliminary comparisons are made between the OI model and similar schemes employed at the European Center for Medium Range Weather Forecasts and the National Meteorological Center. The OI scheme is used to provide input to a GCM, and model error correlations are calculated for forecasts of 500 mb vertical water mixing ratios and the wind profiles. Comparisons are made between the predictions and measured data. The model is shown to be as accurate as a successive corrections model out to 4.5 days.

A multivariate test of disease risk reveals conditions leading to disease amplification.

PubMed

Halliday, Fletcher W; Heckman, Robert W; Wilfahrt, Peter A; Mitchell, Charles E

2017-10-25

Theory predicts that increasing biodiversity will dilute the risk of infectious diseases under certain conditions and will amplify disease risk under others. Yet, few empirical studies demonstrate amplification. This contrast may occur because few studies have considered the multivariate nature of disease risk, which includes richness and abundance of parasites with different transmission modes. By combining a multivariate statistical model developed for biodiversity-ecosystem-multifunctionality with an extensive field manipulation of host (plant) richness, composition and resource supply to hosts, we reveal that (i) host richness alone could not explain most changes in disease risk, and (ii) shifting host composition allowed disease amplification, depending on parasite transmission mode. Specifically, as predicted from theory, the effect of host diversity on parasite abundance differed for microbes (more density-dependent transmission) and insects (more frequency-dependent transmission). Host diversity did not influence microbial parasite abundance, but nearly doubled insect parasite abundance, and this amplification effect was attributable to variation in host composition. Parasite richness was reduced by resource addition, but only in species-rich host communities. Overall, this study demonstrates that multiple drivers, related to both host community and parasite characteristics, can influence disease risk. Furthermore, it provides a framework for evaluating multivariate disease risk in other systems. © 2017 The Author(s).

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

PubMed

Stamate, Mirela Cristina; Todor, Nicolae; Cosgarea, Marcel

2015-01-01

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. 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. 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 otoacoustic emission test presented high values of area under the curve, suggesting that implementing a multivariate approach to evaluate the performances of each otoacoustic emission test would serve to increase the accuracy in identifying the normal and impaired ears. We encountered the highest area under the curve value for the combined multivariate analysis suggesting that both otoacoustic emission tests should be used in assessing hearing status. Our multivariate analyses revealed that age is a constant predictor factor of the auditory status for both ears, but the presence of tinnitus was the most important predictor for the hearing level, only for the left ear. Age presented similar coefficients, but tinnitus coefficients, by their high value, produced the highest variations of the logistic scores, only for the left ear group, thus increasing the risk of hearing loss. We did not find gender differences between ears for any otoacoustic emission tests, but studies still debate this question as the results are contradictory. Neither gender, nor environment origin had any predictive value for the hearing status, according to the results of our study. Like any other audiological test, using otoacoustic emissions to identify hearing loss is not without error. Even when applying multivariate analysis, perfect test performance is never achieved. Although most studies demonstrated the benefit of using the multivariate analysis, it has not been incorporated into clinical decisions maybe because of the idiosyncratic nature of multivariate solutions or because of the lack of the validation studies.

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 otoacoustic emission test presented high values of area under the curve, suggesting that implementing a multivariate approach to evaluate the performances of each otoacoustic emission test would serve to increase the accuracy in identifying the normal and impaired ears. We encountered the highest area under the curve value for the combined multivariate analysis suggesting that both otoacoustic emission tests should be used in assessing hearing status. Our multivariate analyses revealed that age is a constant predictor factor of the auditory status for both ears, but the presence of tinnitus was the most important predictor for the hearing level, only for the left ear. Age presented similar coefficients, but tinnitus coefficients, by their high value, produced the highest variations of the logistic scores, only for the left ear group, thus increasing the risk of hearing loss. We did not find gender differences between ears for any otoacoustic emission tests, but studies still debate this question as the results are contradictory. Neither gender, nor environment origin had any predictive value for the hearing status, according to the results of our study. Conclusion Like any other audiological test, using otoacoustic emissions to identify hearing loss is not without error. Even when applying multivariate analysis, perfect test performance is never achieved. Although most studies demonstrated the benefit of using the multivariate analysis, it has not been incorporated into clinical decisions maybe because of the idiosyncratic nature of multivariate solutions or because of the lack of the validation studies. PMID:26733749

Novel in silico multivariate mapping of intrinsic and anticorrelated connectivity to neurocognitive functional maps supports the maturational hypothesis of ADHD.

PubMed

de Lacy, Nina; Kodish, Ian; Rachakonda, Srinivas; Calhoun, Vince D

2018-04-22

From childhood to adolescence, strengthened coupling in frontal, striatal and parieto-temporal regions associated with cognitive control, and increased anticorrelation between task-positive and task-negative circuits, subserve the reshaping of behavior. ADHD is a common condition peaking in adolescence and regressing in adulthood, with a wide variety of cognitive control deficits. Alternate hypotheses of ADHD emphasize lagging circuitry refinement versus categorical differences in network function. However, quantifying the individual circuit contributions to behavioral findings, and relative roles of maturational versus categorical effects, is challenging in vivo or in meta-analyses using task-based paradigms within the same pipeline, given the multiplicity of neurobehavioral functions implicated. To address this, we analyzed 46 positively-correlated and anticorrelated circuits in a multivariate model in resting-state data from 504 age- and gender-matched youth, and created a novel in silico method to map individual quantified effects to reverse inference maps of 8 neurocognitive functions consistently implicated in ADHD, as well as dopamine and hyperactivity. We identified only age- and gender-related effects in intrinsic connectivity, and found that maturational refinement of circuits in youth with ADHD occupied 3-10x more brain locations than in typical development, with the footprint, effect size and contribution of individual circuits varying substantially. Our analysis supports the maturational hypothesis of ADHD, suggesting lagging connectivity reorganization within specific subnetworks of fronto-parietal control, ventral attention, cingulo-opercular, temporo-limbic and cerebellar sub-networks contribute across neurocognitive findings present in this complex condition. We present the first analysis of anti-correlated connectivity in ADHD and suggest new directions for exploring residual and non-responsive symptoms. © 2018 Wiley Periodicals, Inc.

Influence of socioeconomic aspects on lymphatic filariasis: A case-control study in Andhra Pradesh, India.

PubMed

Mutheneni, Srinivasa Rao; Upadhyayula, Suryanaryana Murty; Kumaraswamy, Sriram; Kadiri, Madhusudhan Rao; Nagalla, Balakrishna

2016-01-01

Lymphatic filariasis (LF) is a major public health problem in India. The objective of the study was to assess the impact of socioeconomic conditions on LF in Chittoor district of Andhra Pradesh, India. A survey was carried out from 2004 to 2007 during which, an epidemiological and socioeconomic data were collected and analysed. The microfilaria (mf) positive samples were taken as cases and matched with control group by sex and age (1:1) for case-control study. Bivariate and multivariate logistic regression was used to identify the potential risk factors for filariasis. Using principal component analysis (PCA), a socioeconomic index was developed and the data/scores were classified into low, medium and high categories. In total 5,133 blood smears were collected, of which 77 samples were found positive for microfilaria (1.52%). Multivariate analysis showed that the risk of filariasis was higher in groups of people with income < ₹1000 per month [OR = 2.752 (95%CI, 0.435-17.429)]; ₹ 1000-3000 per month [3.079 (0.923-0.275)]; people living in tiled house structure [1.641 (0.534-5.048)], with kutcha (uncemented) drainage system [19.427 (2.985- 126.410)], respondents who did not implemented mosquito avoidance measures [1.737 (0.563-5.358)]; and in people who were not aware about prevention and control of filariasis [1.042 (0.368-2.956)]. PCA showed that respondents with low (41.6%) and medium (33.8%) socioeconomic status are more prone to filariasis (p=0.036). The cross sectional study showed that the population with low and medium socioeconomic status are at higher risk of filariasis. The identified socioeconomic risk factors can be used as a guideline for improving the conditions for effective management of filariasis.

The association of fatigue, comorbidity burden, disease activity, disability and gross domestic product in patients with rheumatoid arthritis. Results from 34 countries participating in the Quest-RA program.

PubMed

Grøn, Kathrine Lederballe; Ornbjerg, Lykke Midtbøll; Hetland, Merete Lund; Aslam, Fawad; Khan, Nasim A; Jacobs, Johannes W G; Henrohn, Dan; Rasker, J J; Kauppi, Markku J; Lang, Hui-Chu; Mota, Licia M H; Aggarwal, Amita; Yamanaka, Hisahi; Badsha, Humeira; Gossec, Laure; Cutolo, Maurizio; Ferraccioli, Gianfranco; Gremese, Elisa; Bong Lee, Eun; Inanc, Nevsun; Direskeneli, Haner; Taylor, Peter; Huisman, Margriet; Alten, Rieke; Pohl, Christoph; Oyoo, Omondi; Stropuviene, Sigita; Drosos, Alexandrosos A; Kerzberg, Eduardo; Ancuta, Codorina; Mofti, Ayman; Bergman, Martin; Detert, Jaqueline; Selim, Zaraa I; Abda, Essam A; Rexhepi, Blerta; Sokka, Tuulikki

2014-01-01

The aim is to assess the prevalence of comorbidities and to further analyse to which degree fatigue can be explained by comorbidity burden, disease activity, disability and gross domestic product (GDP) in patients with rheumatoid arthritis (RA). Nine thousands eight hundred seventy-four patients from 34 countries, 16 with high GDP (>24.000 US dollars [USD] per capita) and 18 low-GDP countries (<24.000 USD) participated in the Quantitative Standard monitoring of Patients with RA (QUEST-RA) study. The prevalence of 31 comorbid conditions, fatigue (0-10 cm visual analogue scale [VAS] [10=worst]), disease activity in 28 joints (DAS28), and physical disability (Health Assessment Questionnaire score [HAQ]) were assessed. Univariate and multivariate linear regression analyses were performed to assess the association between fatigue and comorbidities, disease activity, disability and GDP. Overall, patients reported a median of 2 comorbid conditions of which hypertension (31.5%), osteoporosis (17.6%), osteoarthritis (15.5%) and hyperlipidaemia (14.2%) were the most prevalent. The majority of comorbidities were more common in high-GDP countries. The median fatigue score was 4.4 (4.8 in low-GDP countries and 3.8 in high-GDP countries, p<0.001). In low-GDP countries 25.4% of the patients had a high level of fatigue (>6.6) compared with 23.0% in high-GDP countries (p<0.001). In univariate analysis, fatigue increased with increasing number of comorbidities, disease activity and disability in both high- and low-GDP countries. In multivariate analysis of all countries, these 3 variables explained 29.4% of the variability, whereas GDP was not significant. Fatigue is a widespread problem associated with high comorbidity burden, disease activity and disability regardless of GDP.

[Common mental disorders and the use of psychoactive drugs: the impact of socioeconomic conditions].

PubMed

Lima, Maria Cristina Pereira; Menezes, Paulo Rossi; Carandina, Luana; Cesar, Chester Luiz Galvão; Barros, Marilisa Berti de Azevedo; Goldbaum, Moisés

2008-08-01

To evaluate the influence of socioeconomic conditions on the association between common mental disorders and the use of health services and psychoactive drugs. This was a population-based cross-sectional study conducted in the city of Botucatu, Southeastern Brazil. The sample was probabilistic, stratified and cluster-based. Interviews with 1,023 subjects aged 15 years or over were held in their homes between 2001 and 2002. Common mental disorders were evaluated using the Self-Reporting Questionnaire (SRQ-20). The use of services was investigated in relation to the fortnight preceding the interview and the use of psychotropic drugs, over the preceding three days. Logistic regression was used for multivariable analysis, and the design effect was taken into consideration. Out of the whole sample, 13.4% (95% CI: 10.7;16.0) had sought health services over the fortnight preceding the interview. Seeking health services was associated with female gender (OR=2.0) and the presence of common mental disorders (OR=2.2). 13.3% of the sample (95% CI: 9.2;17.5) said they had used at least one psychotropic drug, especially antidepressives (5.0%) and benzodiazepines (3.1%). In the multivariable analysis, female gender and the presence of common mental disorders remained associated with the use of benzodiazepines. Per capita income presented a direct and independent association with the use of psychoactive drugs: the greater the income, the greater the use of these drugs was. Lower income was associated with the presence of common mental disorders, but not with the use of psychotropic drugs. The association of common mental disorders and the use of psychotropic drugs in relation to higher income strengthens the hypothesis that inequality of access to medical services exists among this population.

Racial Differences in Circulating Natriuretic Peptide Levels: The Atherosclerosis Risk in Communities Study

PubMed Central

Gupta, Deepak K; Claggett, Brian; Wells, Quinn; Cheng, Susan; Li, Man; Maruthur, Nisa; Selvin, Elizabeth; Coresh, Josef; Konety, Suma; Butler, Kenneth R; Mosley, Thomas; Boerwinkle, Eric; Hoogeveen, Ron; Ballantyne, Christie M; Solomon, Scott D

2015-01-01

Background Natriuretic peptides promote natriuresis, diuresis, and vasodilation. Experimental deficiency of natriuretic peptides leads to hypertension (HTN) and cardiac hypertrophy, conditions more common among African Americans. Hospital-based studies suggest that African Americans may have reduced circulating natriuretic peptides, as compared to Caucasians, but definitive data from community-based cohorts are lacking. Methods and Results We examined plasma N-terminal pro B-type natriuretic peptide (NTproBNP) levels according to race in 9137 Atherosclerosis Risk in Communities (ARIC) Study participants (22% African American) without prevalent cardiovascular disease at visit 4 (1996–1998). Multivariable linear and logistic regression analyses were performed adjusting for clinical covariates. Among African Americans, percent European ancestry was determined from genetic ancestry informative markers and then examined in relation to NTproBNP levels in multivariable linear regression analysis. NTproBNP levels were significantly lower in African Americans (median, 43 pg/mL; interquartile range [IQR], 18, 88) than Caucasians (median, 68 pg/mL; IQR, 36, 124; P<0.0001). In multivariable models, adjusted log NTproBNP levels were 40% lower (95% confidence interval [CI], −43, −36) in African Americans, compared to Caucasians, which was consistent across subgroups of age, gender, HTN, diabetes, insulin resistance, and obesity. African-American race was also significantly associated with having nondetectable NTproBNP (adjusted OR, 5.74; 95% CI, 4.22, 7.80). In multivariable analyses in African Americans, a 10% increase in genetic European ancestry was associated with a 7% (95% CI, 1, 13) increase in adjusted log NTproBNP. Conclusions African Americans have lower levels of plasma NTproBNP than Caucasians, which may be partially owing to genetic variation. Low natriuretic peptide levels in African Americans may contribute to the greater risk for HTN and its sequalae in this population. PMID:25999400

Spectral compression algorithms for the analysis of very large multivariate images

DOEpatents

Keenan, Michael R.

2007-10-16

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

Esophageal cancer detection based on tissue surface-enhanced Raman spectroscopy and multivariate analysis

NASA Astrophysics Data System (ADS)

Feng, Shangyuan; Lin, Juqiang; Huang, Zufang; Chen, Guannan; Chen, Weisheng; Wang, Yue; Chen, Rong; Zeng, Haishan

2013-01-01

The capability of using silver nanoparticle based near-infrared surface enhanced Raman scattering (SERS) spectroscopy combined with principal component analysis (PCA) and linear discriminate analysis (LDA) to differentiate esophageal cancer tissue from normal tissue was presented. Significant differences in Raman intensities of prominent SERS bands were observed between normal and cancer tissues. PCA-LDA multivariate analysis of the measured tissue SERS spectra achieved diagnostic sensitivity of 90.9% and specificity of 97.8%. This exploratory study demonstrated great potential for developing label-free tissue SERS analysis into a clinical tool for esophageal cancer detection.

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.

Risk of Inhospital Stroke or Death Is Associated With Age But Not Sex in Patients Treated With Carotid Endarterectomy for Asymptomatic or Symptomatic Stenosis in Routine Practice: Secondary Data Analysis of the Nationwide German Statutory Quality Assurance Database From 2009 to 2014.

PubMed

Schmid, Sofie; Tsantilas, Pavlos; Knappich, Christoph; Kallmayer, Michael; König, Thomas; Breitkreuz, Thorben; Zimmermann, Alexander; Kuehnl, Andreas; Eckstein, Hans-Henning

2017-03-13

Guideline recommendations on carotid endarterectomy are based predominantly on randomized, controlled trials, in which women or elderly patients are often under-represented. This study analyzed the association of age and sex with the risk of in-hospital stroke or death following carotid endarterectomy under routine conditions in Germany. Secondary data analysis using the Statutory German Quality Assurance Database on all carotid endarterectomy procedures (n=142 074) performed between 2009 and 2014. Primary outcome was any stroke or death until discharge; secondary outcomes were any in-hospital stroke (alone), and death (alone). Descriptive statistics and multilevel multivariable regression analyses were applied. Patients were predominately male (68%), with mean age 71 years. Carotid stenosis was symptomatic in 40%. Primary outcome occurred in 1.8% of women and 1.9% of men. Multivariable regression analysis revealed that more-advanced age was associated with a higher primary outcome rate (relative risk [RR] per 10-year increase: 1.19; 95% CI, 1.14-1.24). Risk of death (alone) was associated with age (RR, 1.68; 95% CI, 1.54-1.84). Age was associated with the risk of stroke (alone; RR, 1.05; 95% CI, 1.00-1.11). Sex was not associated with primary outcome rate (1.01; 95% CI, 0.93-1.10), nor did it significantly modify the age effect. This study shows that increasing age, but not sex, is associated with a higher risk of in-hospital stroke or death following carotid endarterectomy under everyday conditions in Germany. Whereas the risk of death (alone) is significantly associated with age, the association between age and the risk of stroke (alone) can be considered of minor importance. © 2017 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell.

Experimental and statistical determination of indicator parameters for the evaluation of fly ash and boiler ash PCDD/PCDF concentration from municipal solid waste incinerators.

PubMed

Streibel, T; Nordsieck, H; Neuer-Etscheidt, K; Schnelle-Kreis, J; Zimmermann, R

2007-04-01

On-line detectable indicator parameters in the flue gas of municipal solid waste incinerators (MSWI) such as chlorinated benzenes (PCBz) are well known surrogate compounds for gas-phase PCDD/PCDF concentration. In the here presented work derivation of indicators is broadened to the detection of fly and boiler ash fractions with increased PCDD/PCDF content. Subsequently these fractions could be subject to further treatment such as recirculation in the combustion chamber to destroy their PCDD/PCDF and other organic pollutants' content. Aim of this work was to detect suitable on-line detectable indicator parameters in the gas phase, which are well correlated to PCDD/PCDF concentration in the solid residues. For this, solid residues and gas-phase samples were taken at three MSWI plants in Bavaria. Analysis of the ash content from different plants yielded a broad variation range of PCDD/PCDF concentrations especially after disturbed combustion conditions. Even during normal operation conditions significantly increased PCDD/PCDF concentrations may occur after unanticipated disturbances. Statistical evaluation of gas phase and ash measurements was carried out by means of principal component analysis, uni- and multivariate correlation analysis. Surprisingly, well known indicators for gas-phase PCDD/PCDF concentration such as polychlorinated benzenes and phenols proved to be insufficiently correlated to PCDD/PCDF content of the solid residues. Moreover, no single parameter alone was found appropriate to describe the PCDD/PCDF content of fly and boiler ashes. On the other hand, multivariate fitting of three or four parameters yielded convenient correlation coefficients of at least r=0.8 for every investigated case. Thereby, comprehension of plant operation parameters such as temperatures and air flow alongside concentrations of inorganic compounds in the gas phase (HCl, CO, SO2, NOx) gave the best results. However, the suitable set of parameters suited best for estimation of PCDD/PCDF concentration in solid residues has to be derived anew for each individual plant and type of ash.

Testing Mean Differences among Groups: Multivariate and Repeated Measures Analysis with Minimal Assumptions

PubMed Central

Bathke, Arne C.; Friedrich, Sarah; Pauly, Markus; Konietschke, Frank; Staffen, Wolfgang; Strobl, Nicolas; Höller, Yvonne

2018-01-01

ABSTRACT To date, there is a lack of satisfactory inferential techniques for the analysis of multivariate data in factorial designs, when only minimal assumptions on the data can be made. Presently available methods are limited to very particular study designs or assume either multivariate normality or equal covariance matrices across groups, or they do not allow for an assessment of the interaction effects across within-subjects and between-subjects variables. We propose and methodologically validate a parametric bootstrap approach that does not suffer from any of the above limitations, and thus provides a rather general and comprehensive methodological route to inference for multivariate and repeated measures data. As an example application, we consider data from two different Alzheimer’s disease (AD) examination modalities that may be used for precise and early diagnosis, namely, single-photon emission computed tomography (SPECT) and electroencephalogram (EEG). These data violate the assumptions of classical multivariate methods, and indeed classical methods would not have yielded the same conclusions with regards to some of the factors involved. PMID:29565679

Development of multivariate exposure and fatal accident involvement rates for 1977

DOT National Transportation Integrated Search

1985-10-01

The need for multivariate accident involvement rates is often encounted in : accident analysis. The FARS (Fatal Accident Reporting System) files contain : records of fatal involvements characterized by many variables while NPTS : (National Personal T...

Bayesian multivariate hierarchical transformation models for ROC analysis.

PubMed

O'Malley, A James; Zou, Kelly H

2006-02-15

A Bayesian multivariate hierarchical transformation model (BMHTM) is developed for receiver operating characteristic (ROC) curve analysis based on clustered continuous diagnostic outcome data with covariates. Two special features of this model are that it incorporates non-linear monotone transformations of the outcomes and that multiple correlated outcomes may be analysed. The mean, variance, and transformation components are all modelled parametrically, enabling a wide range of inferences. The general framework is illustrated by focusing on two problems: (1) analysis of the diagnostic accuracy of a covariate-dependent univariate test outcome requiring a Box-Cox transformation within each cluster to map the test outcomes to a common family of distributions; (2) development of an optimal composite diagnostic test using multivariate clustered outcome data. In the second problem, the composite test is estimated using discriminant function analysis and compared to the test derived from logistic regression analysis where the gold standard is a binary outcome. The proposed methodology is illustrated on prostate cancer biopsy data from a multi-centre clinical trial.

Bayesian multivariate hierarchical transformation models for ROC analysis

PubMed Central

O'Malley, A. James; Zou, Kelly H.

2006-01-01

SUMMARY A Bayesian multivariate hierarchical transformation model (BMHTM) is developed for receiver operating characteristic (ROC) curve analysis based on clustered continuous diagnostic outcome data with covariates. Two special features of this model are that it incorporates non-linear monotone transformations of the outcomes and that multiple correlated outcomes may be analysed. The mean, variance, and transformation components are all modelled parametrically, enabling a wide range of inferences. The general framework is illustrated by focusing on two problems: (1) analysis of the diagnostic accuracy of a covariate-dependent univariate test outcome requiring a Box–Cox transformation within each cluster to map the test outcomes to a common family of distributions; (2) development of an optimal composite diagnostic test using multivariate clustered outcome data. In the second problem, the composite test is estimated using discriminant function analysis and compared to the test derived from logistic regression analysis where the gold standard is a binary outcome. The proposed methodology is illustrated on prostate cancer biopsy data from a multi-centre clinical trial. PMID:16217836

A Novel Approach to Detect Accelerated Aged and Surface-Mediated Degradation in Explosives by UPLC-ESI-MS.

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

Beppler, Christina L

2015-12-01

A new approach was created for studying energetic material degradation. This approach involved detecting and tentatively identifying non-volatile chemical species by liquid chromatography-mass spectrometry (LC-MS) with multivariate statistical data analysis that form as the CL-20 energetic material thermally degraded. Multivariate data analysis showed clear separation and clustering of samples based on sample group: either pristine or aged material. Further analysis showed counter-clockwise trends in the principal components analysis (PCA), a type of multivariate data analysis, Scores plots. These trends may indicate that there was a discrete shift in the chemical markers as the went from pristine to aged material, andmore » then again when the aged CL-20 mixed with a potentially incompatible material was thermally aged for 4, 6, or 9 months. This new approach to studying energetic material degradation should provide greater knowledge of potential degradation markers in these materials.« less

Complex numbers in chemometrics: examples from multivariate impedance measurements on lipid monolayers.

PubMed

Geladi, Paul; Nelson, Andrew; Lindholm-Sethson, Britta

2007-07-09

Electrical impedance gives multivariate complex number data as results. Two examples of multivariate electrical impedance data measured on lipid monolayers in different solutions give rise to matrices (16x50 and 38x50) of complex numbers. Multivariate data analysis by principal component analysis (PCA) or singular value decomposition (SVD) can be used for complex data and the necessary equations are given. The scores and loadings obtained are vectors of complex numbers. It is shown that the complex number PCA and SVD are better at concentrating information in a few components than the naïve juxtaposition method and that Argand diagrams can replace score and loading plots. Different concentrations of Magainin and Gramicidin A give different responses and also the role of the electrolyte medium can be studied. An interaction of Gramicidin A in the solution with the monolayer over time can be observed.