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.

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.

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.

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

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

Estimating multivariate similarity between neuroimaging datasets with sparse canonical correlation analysis: an application to perfusion imaging.

PubMed

Rosa, Maria J; Mehta, Mitul A; Pich, Emilio M; Risterucci, Celine; Zelaya, Fernando; Reinders, Antje A T S; Williams, Steve C R; Dazzan, Paola; Doyle, Orla M; Marquand, Andre F

2015-01-01

An increasing number of neuroimaging studies are based on either combining more than one data modality (inter-modal) or combining more than one measurement from the same modality (intra-modal). To date, most intra-modal studies using multivariate statistics have focused on differences between datasets, for instance relying on classifiers to differentiate between effects in the data. However, to fully characterize these effects, multivariate methods able to measure similarities between datasets are needed. One classical technique for estimating the relationship between two datasets is canonical correlation analysis (CCA). However, in the context of high-dimensional data the application of CCA is extremely challenging. A recent extension of CCA, sparse CCA (SCCA), overcomes this limitation, by regularizing the model parameters while yielding a sparse solution. In this work, we modify SCCA with the aim of facilitating its application to high-dimensional neuroimaging data and finding meaningful multivariate image-to-image correspondences in intra-modal studies. In particular, we show how the optimal subset of variables can be estimated independently and we look at the information encoded in more than one set of SCCA transformations. We illustrate our framework using Arterial Spin Labeling data to investigate multivariate similarities between the effects of two antipsychotic drugs on cerebral blood flow.

When Does Neoadjuvant Chemotherapy Really Avoid Radiotherapy? Clinical Predictors of Adjuvant Radiotherapy in Cervical Cancer.

PubMed

Papadia, Andrea; Bellati, Filippo; Bogani, Giorgio; Ditto, Antonino; Martinelli, Fabio; Lorusso, Domenica; Donfrancesco, Cristina; Gasparri, Maria Luisa; Raspagliesi, Francesco

2015-12-01

The aim of this study was to identify clinical variables that may predict the need for adjuvant radiotherapy after neoadjuvant chemotherapy (NACT) and radical surgery in locally advanced cervical cancer patients. A retrospective series of cervical cancer patients with International Federation of Gynecology and Obstetrics (FIGO) stages IB2-IIB treated with NACT followed by radical surgery was analyzed. Clinical predictors of persistence of intermediate- and/or high-risk factors at final pathological analysis were investigated. Statistical analysis was performed using univariate and multivariate analysis and using a model based on artificial intelligence known as artificial neuronal network (ANN) analysis. Overall, 101 patients were available for the analyses. Fifty-two (51 %) patients were considered at high risk secondary to parametrial, resection margin and/or lymph node involvement. When disease was confined to the cervix, four (4 %) patients were considered at intermediate risk. At univariate analysis, FIGO grade 3, stage IIB disease at diagnosis and the presence of enlarged nodes before NACT predicted the presence of intermediate- and/or high-risk factors at final pathological analysis. At multivariate analysis, only FIGO grade 3 and tumor diameter maintained statistical significance. The specificity of ANN models in evaluating predictive variables was slightly superior to conventional multivariable models. FIGO grade, stage, tumor diameter, and histology are associated with persistence of pathological intermediate- and/or high-risk factors after NACT and radical surgery. This information is useful in counseling patients at the time of treatment planning with regard to the probability of being subjected to pelvic radiotherapy after completion of the initially planned treatment.

Esophageal wall dose-surface maps do not improve the predictive performance of a multivariable NTCP model for acute esophageal toxicity in advanced stage NSCLC patients treated with intensity-modulated (chemo-)radiotherapy.

PubMed

Dankers, Frank; Wijsman, Robin; Troost, Esther G C; Monshouwer, René; Bussink, Johan; Hoffmann, Aswin L

2017-05-07

In our previous work, a multivariable normal-tissue complication probability (NTCP) model for acute esophageal toxicity (AET) Grade  ⩾2 after highly conformal (chemo-)radiotherapy for non-small cell lung cancer (NSCLC) was developed using multivariable logistic regression analysis incorporating clinical parameters and mean esophageal dose (MED). Since the esophagus is a tubular organ, spatial information of the esophageal wall dose distribution may be important in predicting AET. We investigated whether the incorporation of esophageal wall dose-surface data with spatial information improves the predictive power of our established NTCP model. For 149 NSCLC patients treated with highly conformal radiation therapy esophageal wall dose-surface histograms (DSHs) and polar dose-surface maps (DSMs) were generated. DSMs were used to generate new DSHs and dose-length-histograms that incorporate spatial information of the dose-surface distribution. From these histograms dose parameters were derived and univariate logistic regression analysis showed that they correlated significantly with AET. Following our previous work, new multivariable NTCP models were developed using the most significant dose histogram parameters based on univariate analysis (19 in total). However, the 19 new models incorporating esophageal wall dose-surface data with spatial information did not show improved predictive performance (area under the curve, AUC range 0.79-0.84) over the established multivariable NTCP model based on conventional dose-volume data (AUC  =  0.84). For prediction of AET, based on the proposed multivariable statistical approach, spatial information of the esophageal wall dose distribution is of no added value and it is sufficient to only consider MED as a predictive dosimetric parameter.

Esophageal wall dose-surface maps do not improve the predictive performance of a multivariable NTCP model for acute esophageal toxicity in advanced stage NSCLC patients treated with intensity-modulated (chemo-)radiotherapy

NASA Astrophysics Data System (ADS)

Dankers, Frank; Wijsman, Robin; Troost, Esther G. C.; Monshouwer, René; Bussink, Johan; Hoffmann, Aswin L.

2017-05-01

In our previous work, a multivariable normal-tissue complication probability (NTCP) model for acute esophageal toxicity (AET) Grade  ⩾2 after highly conformal (chemo-)radiotherapy for non-small cell lung cancer (NSCLC) was developed using multivariable logistic regression analysis incorporating clinical parameters and mean esophageal dose (MED). Since the esophagus is a tubular organ, spatial information of the esophageal wall dose distribution may be important in predicting AET. We investigated whether the incorporation of esophageal wall dose-surface data with spatial information improves the predictive power of our established NTCP model. For 149 NSCLC patients treated with highly conformal radiation therapy esophageal wall dose-surface histograms (DSHs) and polar dose-surface maps (DSMs) were generated. DSMs were used to generate new DSHs and dose-length-histograms that incorporate spatial information of the dose-surface distribution. From these histograms dose parameters were derived and univariate logistic regression analysis showed that they correlated significantly with AET. Following our previous work, new multivariable NTCP models were developed using the most significant dose histogram parameters based on univariate analysis (19 in total). However, the 19 new models incorporating esophageal wall dose-surface data with spatial information did not show improved predictive performance (area under the curve, AUC range 0.79-0.84) over the established multivariable NTCP model based on conventional dose-volume data (AUC  =  0.84). For prediction of AET, based on the proposed multivariable statistical approach, spatial information of the esophageal wall dose distribution is of no added value and it is sufficient to only consider MED as a predictive dosimetric parameter.

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.

HEPDOOP: High-Energy Physics Analysis using Hadoop

NASA Astrophysics Data System (ADS)

Bhimji, W.; Bristow, T.; Washbrook, A.

2014-06-01

We perform a LHC data analysis workflow using tools and data formats that are commonly used in the "Big Data" community outside High Energy Physics (HEP). These include Apache Avro for serialisation to binary files, Pig and Hadoop for mass data processing and Python Scikit-Learn for multi-variate analysis. Comparison is made with the same analysis performed with current HEP tools in ROOT.

Selective sensing of vapors of similar dielectric constants using peptide-capped gold nanoparticles on individual multivariable transducers.

PubMed

Nagraj, Nandini; Slocik, Joseph M; Phillips, David M; Kelley-Loughnane, Nancy; Naik, Rajesh R; Potyrailo, Radislav A

2013-08-07

Peptide-capped AYSSGAPPMPPF gold nanoparticles were demonstrated for highly selective chemical vapor sensing using individual multivariable inductor-capacitor-resistor (LCR) resonators. Their multivariable response was achieved by measuring their resonance impedance spectra followed by multivariate spectral analysis. Detection of model toxic vapors and chemical agent simulants, such as acetonitrile, dichloromethane and methyl salicylate, was performed. Dichloromethane (dielectric constant εr = 9.1) and methyl salicylate (εr = 9.0) were discriminated using a single sensor. These sensing materials coupled to multivariable transducers can provide numerous opportunities for tailoring the vapor response selectivity based on the diversity of the amino acid composition of the peptides, and by the modulation of the nature of peptide-nanoparticle interactions through designed combinations of hydrophobic and hydrophilic amino acids.

High-throughput quantitative biochemical characterization of algal biomass by NIR spectroscopy; multiple linear regression and multivariate linear regression analysis.

PubMed

Laurens, L M L; Wolfrum, E J

2013-12-18

One of the challenges associated with microalgal biomass characterization and the comparison of microalgal strains and conversion processes is the rapid determination of the composition of algae. We have developed and applied a high-throughput screening technology based on near-infrared (NIR) spectroscopy for the rapid and accurate determination of algal biomass composition. We show that NIR spectroscopy can accurately predict the full composition using multivariate linear regression analysis of varying lipid, protein, and carbohydrate content of algal biomass samples from three strains. We also demonstrate a high quality of predictions of an independent validation set. A high-throughput 96-well configuration for spectroscopy gives equally good prediction relative to a ring-cup configuration, and thus, spectra can be obtained from as little as 10-20 mg of material. We found that lipids exhibit a dominant, distinct, and unique fingerprint in the NIR spectrum that allows for the use of single and multiple linear regression of respective wavelengths for the prediction of the biomass lipid content. This is not the case for carbohydrate and protein content, and thus, the use of multivariate statistical modeling approaches remains necessary.

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.

Multivariate statistical analysis of the polyphenolic constituents in kiwifruit juices to trace fruit varieties and geographical origins.

PubMed

Guo, Jing; Yuan, Yahong; Dou, Pei; Yue, Tianli

2017-10-01

Fifty-one kiwifruit juice samples of seven kiwifruit varieties from five regions in China were analyzed to determine their polyphenols contents and to trace fruit varieties and geographical origins by multivariate statistical analysis. Twenty-one polyphenols belonging to four compound classes were determined by ultra-high-performance liquid chromatography coupled with ultra-high-resolution TOF mass spectrometry. (-)-Epicatechin, (+)-catechin, procyanidin B1 and caffeic acid derivatives were the predominant phenolic compounds in the juices. Principal component analysis (PCA) allowed a clear separation of the juices according to kiwifruit varieties. Stepwise linear discriminant analysis (SLDA) yielded satisfactory categorization of samples, provided 100% success rate according to kiwifruit varieties and 92.2% success rate according to geographical origins. The result showed that polyphenolic profiles of kiwifruit juices contain enough information to trace fruit varieties and geographical origins. Copyright © 2017 Elsevier Ltd. All rights reserved.

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.

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

Assessing signal-to-noise in quantitative proteomics: multivariate statistical analysis in DIGE experiments.

PubMed

Friedman, David B

2012-01-01

All quantitative proteomics experiments measure variation between samples. When performing large-scale experiments that involve multiple conditions or treatments, the experimental design should include the appropriate number of individual biological replicates from each condition to enable the distinction between a relevant biological signal from technical noise. Multivariate statistical analyses, such as principal component analysis (PCA), provide a global perspective on experimental variation, thereby enabling the assessment of whether the variation describes the expected biological signal or the unanticipated technical/biological noise inherent in the system. Examples will be shown from high-resolution multivariable DIGE experiments where PCA was instrumental in demonstrating biologically significant variation as well as sample outliers, fouled samples, and overriding technical variation that would not be readily observed using standard univariate tests.

Multivariate data analysis and metabolic profiling of artemisinin and related compounds in high yielding varieties of Artemisia annua field-grown in Madagascar.

PubMed

Suberu, John; Gromski, Piotr S; Nordon, Alison; Lapkin, Alexei

2016-01-05

An improved liquid chromatography-tandem mass spectrometry (LC-MS/MS) protocol for rapid analysis of co-metabolites of A. annua in raw extracts was developed and extensively characterized. The new method was used to analyse metabolic profiles of 13 varieties of A. annua from an in-field growth programme in Madagascar. Several multivariate data analysis techniques consistently show the association of artemisinin with dihydroartemisinic acid. These data support the hypothesis of dihydroartemisinic acid being the late stage precursor to artemisinin in its biosynthetic pathway. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

The MIDAS processor. [Multivariate Interactive Digital Analysis System for multispectral scanner data

NASA Technical Reports Server (NTRS)

Kriegler, F. J.; Gordon, M. F.; Mclaughlin, R. H.; Marshall, R. E.

1975-01-01

The MIDAS (Multivariate Interactive Digital Analysis System) processor is a high-speed processor designed to process multispectral scanner data (from Landsat, EOS, aircraft, etc.) quickly and cost-effectively to meet the requirements of users of remote sensor data, especially from very large areas. MIDAS consists of a fast multipipeline preprocessor and classifier, an interactive color display and color printer, and a medium scale computer system for analysis and control. The system is designed to process data having as many as 16 spectral bands per picture element at rates of 200,000 picture elements per second into as many as 17 classes using a maximum likelihood decision rule.

Multivariate two-part statistics for analysis of correlated mass spectrometry data from multiple biological specimens.

PubMed

Taylor, Sandra L; Ruhaak, L Renee; Weiss, Robert H; Kelly, Karen; Kim, Kyoungmi

2017-01-01

High through-put mass spectrometry (MS) is now being used to profile small molecular compounds across multiple biological sample types from the same subjects with the goal of leveraging information across biospecimens. Multivariate statistical methods that combine information from all biospecimens could be more powerful than the usual univariate analyses. However, missing values are common in MS data and imputation can impact between-biospecimen correlation and multivariate analysis results. We propose two multivariate two-part statistics that accommodate missing values and combine data from all biospecimens to identify differentially regulated compounds. Statistical significance is determined using a multivariate permutation null distribution. Relative to univariate tests, the multivariate procedures detected more significant compounds in three biological datasets. In a simulation study, we showed that multi-biospecimen testing procedures were more powerful than single-biospecimen methods when compounds are differentially regulated in multiple biospecimens but univariate methods can be more powerful if compounds are differentially regulated in only one biospecimen. We provide R functions to implement and illustrate our method as supplementary information CONTACT: sltaylor@ucdavis.eduSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Exploring image data assimilation in the prospect of high-resolution satellite oceanic observations

NASA Astrophysics Data System (ADS)

Durán Moro, Marina; Brankart, Jean-Michel; Brasseur, Pierre; Verron, Jacques

2017-07-01

Satellite sensors increasingly provide high-resolution (HR) observations of the ocean. They supply observations of sea surface height (SSH) and of tracers of the dynamics such as sea surface salinity (SSS) and sea surface temperature (SST). In particular, the Surface Water Ocean Topography (SWOT) mission will provide measurements of the surface ocean topography at very high-resolution (HR) delivering unprecedented information on the meso-scale and submeso-scale dynamics. This study investigates the feasibility to use these measurements to reconstruct meso-scale features simulated by numerical models, in particular on the vertical dimension. A methodology to reconstruct three-dimensional (3D) multivariate meso-scale scenes is developed by using a HR numerical model of the Solomon Sea region. An inverse problem is defined in the framework of a twin experiment where synthetic observations are used. A true state is chosen among the 3D multivariate states which is considered as a reference state. In order to correct a first guess of this true state, a two-step analysis is carried out. A probability distribution of the first guess is defined and updated at each step of the analysis: (i) the first step applies the analysis scheme of a reduced-order Kalman filter to update the first guess probability distribution using SSH observation; (ii) the second step minimizes a cost function using observations of HR image structure and a new probability distribution is estimated. The analysis is extended to the vertical dimension using 3D multivariate empirical orthogonal functions (EOFs) and the probabilistic approach allows the update of the probability distribution through the two-step analysis. Experiments show that the proposed technique succeeds in correcting a multivariate state using meso-scale and submeso-scale information contained in HR SSH and image structure observations. It also demonstrates how the surface information can be used to reconstruct the ocean state below the surface.

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

PubMed

Hakimzadeh, Neda; Parastar, Hadi; Fattahi, Mohammad

2014-01-24

In this study, multivariate curve resolution (MCR) and multivariate classification methods are proposed to develop a new chemometric strategy for comprehensive analysis of high-performance liquid chromatography-diode array absorbance detection (HPLC-DAD) fingerprints of sixty Salvia reuterana samples from five different geographical regions. Different chromatographic problems occurred during HPLC-DAD analysis of S. reuterana samples, such as baseline/background contribution and noise, low signal-to-noise ratio (S/N), asymmetric peaks, elution time shifts, and peak overlap are handled using the proposed strategy. In this way, chromatographic fingerprints of sixty samples are properly segmented to ten common chromatographic regions using local rank analysis and then, the corresponding segments are column-wise augmented for subsequent MCR analysis. Extended multivariate curve resolution-alternating least squares (MCR-ALS) is used to obtain pure component profiles in each segment. In general, thirty-one chemical components were resolved using MCR-ALS in sixty S. reuterana samples and the lack of fit (LOF) values of MCR-ALS models were below 10.0% in all cases. Pure spectral profiles are considered for identification of chemical components by comparing their resolved spectra with the standard ones and twenty-four components out of thirty-one components were identified. Additionally, pure elution profiles are used to obtain relative concentrations of chemical components in different samples for multivariate classification analysis by principal component analysis (PCA) and k-nearest neighbors (kNN). Inspection of the PCA score plot (explaining 76.1% of variance accounted for three PCs) showed that S. reuterana samples belong to four clusters. The degree of class separation (DCS) which quantifies the distance separating clusters in relation to the scatter within each cluster is calculated for four clusters and it was in the range of 1.6-5.8. These results are then confirmed by kNN. In addition, according to the PCA loading plot and kNN dendrogram of thirty-one variables, five chemical constituents of luteolin-7-o-glucoside, salvianolic acid D, rosmarinic acid, lithospermic acid and trijuganone A are identified as the most important variables (i.e., chemical markers) for clusters discrimination. Finally, the effect of different chemical markers on samples differentiation is investigated using counter-propagation artificial neural network (CP-ANN) method. It is concluded that the proposed strategy can be successfully applied for comprehensive analysis of chromatographic fingerprints of complex natural samples. Copyright © 2013 Elsevier B.V. All rights reserved.

Multivariate pattern analysis for MEG: A comparison of dissimilarity measures.

PubMed

Guggenmos, Matthias; Sterzer, Philipp; Cichy, Radoslaw Martin

2018-06-01

Multivariate pattern analysis (MVPA) methods such as decoding and representational similarity analysis (RSA) are growing rapidly in popularity for the analysis of magnetoencephalography (MEG) data. However, little is known about the relative performance and characteristics of the specific dissimilarity measures used to describe differences between evoked activation patterns. Here we used a multisession MEG data set to qualitatively characterize a range of dissimilarity measures and to quantitatively compare them with respect to decoding accuracy (for decoding) and between-session reliability of representational dissimilarity matrices (for RSA). We tested dissimilarity measures from a range of classifiers (Linear Discriminant Analysis - LDA, Support Vector Machine - SVM, Weighted Robust Distance - WeiRD, Gaussian Naïve Bayes - GNB) and distances (Euclidean distance, Pearson correlation). In addition, we evaluated three key processing choices: 1) preprocessing (noise normalisation, removal of the pattern mean), 2) weighting decoding accuracies by decision values, and 3) computing distances in three different partitioning schemes (non-cross-validated, cross-validated, within-class-corrected). Four main conclusions emerged from our results. First, appropriate multivariate noise normalization substantially improved decoding accuracies and the reliability of dissimilarity measures. Second, LDA, SVM and WeiRD yielded high peak decoding accuracies and nearly identical time courses. Third, while using decoding accuracies for RSA was markedly less reliable than continuous distances, this disadvantage was ameliorated by decision-value-weighting of decoding accuracies. Fourth, the cross-validated Euclidean distance provided unbiased distance estimates and highly replicable representational dissimilarity matrices. Overall, we strongly advise the use of multivariate noise normalisation as a general preprocessing step, recommend LDA, SVM and WeiRD as classifiers for decoding and highlight the cross-validated Euclidean distance as a reliable and unbiased default choice for RSA. Copyright © 2018 Elsevier Inc. All rights reserved.

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.

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

PubMed

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

2017-02-01

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

Extracting chemical information from high-resolution Kβ X-ray emission spectroscopy

NASA Astrophysics Data System (ADS)

Limandri, S.; Robledo, J.; Tirao, G.

2018-06-01

High-resolution X-ray emission spectroscopy allows studying the chemical environment of a wide variety of materials. Chemical information can be obtained by fitting the X-ray spectra and observing the behavior of some spectral features. Spectral changes can also be quantified by means of statistical parameters calculated by considering the spectrum as a probability distribution. Another possibility is to perform statistical multivariate analysis, such as principal component analysis. In this work the performance of these procedures for extracting chemical information in X-ray emission spectroscopy spectra for mixtures of Mn2+ and Mn4+ oxides are studied. A detail analysis of the parameters obtained, as well as the associated uncertainties is shown. The methodologies are also applied for Mn oxidation state characterization of double perovskite oxides Ba1+xLa1-xMnSbO6 (with 0 ≤ x ≤ 0.7). The results show that statistical parameters and multivariate analysis are the most suitable for the analysis of this kind of spectra.

Multivariate evaluation of Thyroid Imaging Reporting and Data System (TI-RADS) in diagnosis malignant thyroid nodule: application to PCA and PLS-DA analysis.

PubMed

Zhang, Tan; Li, Fangxuan; Mu, Jiali; Liu, Juntian; Zhang, Sheng

2017-06-01

To explore the significance of ultrasonic features in differential diagnosis of thyroid nodules via combining the thyroid imaging reporting and data system (TI-RADS) and multivariate statistical analysis. Patients who received surgical treatment and was diagnosed with single thyroid nodule by postoperative pathology and preoperative ultrasound were enrolled in this study. Multivariate analysis was applied to assess the significant ultrasonic features which correlated with identifying benign or malignance and grading the TI-RADS classification of thyroid nodule. There were significant differences in the nodule size, aspect ratio, internal, echogenicity, boundary, presence or absence of calcifications, calcification type and CDFI between benign and malignant thyroid nodules. Multivariate analysis showed clear-cut distinction both between benign and malignance and among different TI-RADS categories of malignancy nodules. The shape and calcification of the nodule were important factors for distinguish the benign and malignance. Height of the nodule, aspect and calcification was important factors for grading TI-RADS categories of malignancy thyroid nodules. Ill-defined boundary, irregular shape and presence of calcification related with highly malignant risk for thyroid nodule. The larger height and aspect and presence of calcification related with higher TI-RADS classification of malignancy thyroid nodule.

Multivariate analysis of heavy metal contamination using river sediment cores of Nankan River, northern Taiwan

NASA Astrophysics Data System (ADS)

Lee, An-Sheng; Lu, Wei-Li; Huang, Jyh-Jaan; Chang, Queenie; Wei, Kuo-Yen; Lin, Chin-Jung; Liou, Sofia Ya Hsuan

2016-04-01

Through the geology and climate characteristic in Taiwan, generally rivers carry a lot of suspended particles. After these particles settled, they become sediments which are good sorbent for heavy metals in river system. Consequently, sediments can be found recording contamination footprint at low flow energy region, such as estuary. Seven sediment cores were collected along Nankan River, northern Taiwan, which is seriously contaminated by factory, household and agriculture input. Physico-chemical properties of these cores were derived from Itrax-XRF Core Scanner and grain size analysis. In order to interpret these complex data matrices, the multivariate statistical techniques (cluster analysis, factor analysis and discriminant analysis) were introduced to this study. Through the statistical determination, the result indicates four types of sediment. One of them represents contamination event which shows high concentration of Cu, Zn, Pb, Ni and Fe, and low concentration of Si and Zr. Furthermore, three possible contamination sources of this type of sediment were revealed by Factor Analysis. The combination of sediment analysis and multivariate statistical techniques used provides new insights into the contamination depositional history of Nankan River and could be similarly applied to other river systems to determine the scale of anthropogenic contamination.

Application of multivariate statistical techniques in microbial ecology

PubMed Central

Paliy, O.; Shankar, V.

2016-01-01

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

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.

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

PubMed Central

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

2016-01-01

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

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

PubMed

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

2016-01-01

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

Multivariate meta-analysis of individual participant data helped externally validate the performance and implementation of a prediction model.

PubMed

Snell, Kym I E; Hua, Harry; Debray, Thomas P A; Ensor, Joie; Look, Maxime P; Moons, Karel G M; Riley, Richard D

2016-01-01

Our aim was to improve meta-analysis methods for summarizing a prediction model's performance when individual participant data are available from multiple studies for external validation. We suggest multivariate meta-analysis for jointly synthesizing calibration and discrimination performance, while accounting for their correlation. The approach estimates a prediction model's average performance, the heterogeneity in performance across populations, and the probability of "good" performance in new populations. This allows different implementation strategies (e.g., recalibration) to be compared. Application is made to a diagnostic model for deep vein thrombosis (DVT) and a prognostic model for breast cancer mortality. In both examples, multivariate meta-analysis reveals that calibration performance is excellent on average but highly heterogeneous across populations unless the model's intercept (baseline hazard) is recalibrated. For the cancer model, the probability of "good" performance (defined by C statistic ≥0.7 and calibration slope between 0.9 and 1.1) in a new population was 0.67 with recalibration but 0.22 without recalibration. For the DVT model, even with recalibration, there was only a 0.03 probability of "good" performance. Multivariate meta-analysis can be used to externally validate a prediction model's calibration and discrimination performance across multiple populations and to evaluate different implementation strategies. Crown Copyright © 2016. Published by Elsevier Inc. All rights reserved.

Multivariate pattern analysis of MEG and EEG: A comparison of representational structure in time and space.

PubMed

Cichy, Radoslaw Martin; Pantazis, Dimitrios

2017-09-01

Multivariate pattern analysis of magnetoencephalography (MEG) and electroencephalography (EEG) data can reveal the rapid neural dynamics underlying cognition. However, MEG and EEG have systematic differences in sampling neural activity. This poses the question to which degree such measurement differences consistently bias the results of multivariate analysis applied to MEG and EEG activation patterns. To investigate, we conducted a concurrent MEG/EEG study while participants viewed images of everyday objects. We applied multivariate classification analyses to MEG and EEG data, and compared the resulting time courses to each other, and to fMRI data for an independent evaluation in space. We found that both MEG and EEG revealed the millisecond spatio-temporal dynamics of visual processing with largely equivalent results. Beyond yielding convergent results, we found that MEG and EEG also captured partly unique aspects of visual representations. Those unique components emerged earlier in time for MEG than for EEG. Identifying the sources of those unique components with fMRI, we found the locus for both MEG and EEG in high-level visual cortex, and in addition for MEG in low-level visual cortex. Together, our results show that multivariate analyses of MEG and EEG data offer a convergent and complimentary view on neural processing, and motivate the wider adoption of these methods in both MEG and EEG research. Copyright © 2017 Elsevier Inc. All rights reserved.

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…

imDEV: a graphical user interface to R multivariate analysis tools in Microsoft Excel.

PubMed

Grapov, Dmitry; Newman, John W

2012-09-01

Interactive modules for Data Exploration and Visualization (imDEV) is a Microsoft Excel spreadsheet embedded application providing an integrated environment for the analysis of omics data through a user-friendly interface. Individual modules enables interactive and dynamic analyses of large data by interfacing R's multivariate statistics and highly customizable visualizations with the spreadsheet environment, aiding robust inferences and generating information-rich data visualizations. This tool provides access to multiple comparisons with false discovery correction, hierarchical clustering, principal and independent component analyses, partial least squares regression and discriminant analysis, through an intuitive interface for creating high-quality two- and a three-dimensional visualizations including scatter plot matrices, distribution plots, dendrograms, heat maps, biplots, trellis biplots and correlation networks. Freely available for download at http://sourceforge.net/projects/imdev/. Implemented in R and VBA and supported by Microsoft Excel (2003, 2007 and 2010).

A Versatile Cell Death Screening Assay Using Dye-Stained Cells and Multivariate Image Analysis.

PubMed

Collins, Tony J; Ylanko, Jarkko; Geng, Fei; Andrews, David W

2015-11-01

A novel dye-based method for measuring cell death in image-based screens is presented. Unlike conventional high- and medium-throughput cell death assays that measure only one form of cell death accurately, using multivariate analysis of micrographs of cells stained with the inexpensive mix, red dye nonyl acridine orange, and a nuclear stain, it was possible to quantify cell death induced by a variety of different agonists even without a positive control. Surprisingly, using a single known cytotoxic agent as a positive control for training a multivariate classifier allowed accurate quantification of cytotoxicity for mechanistically unrelated compounds enabling generation of dose-response curves. Comparison with low throughput biochemical methods suggested that cell death was accurately distinguished from cell stress induced by low concentrations of the bioactive compounds Tunicamycin and Brefeldin A. High-throughput image-based format analyses of more than 300 kinase inhibitors correctly identified 11 as cytotoxic with only 1 false positive. The simplicity and robustness of this dye-based assay makes it particularly suited to live cell screening for toxic compounds.

A Versatile Cell Death Screening Assay Using Dye-Stained Cells and Multivariate Image Analysis

PubMed Central

Collins, Tony J.; Ylanko, Jarkko; Geng, Fei

2015-01-01

Abstract A novel dye-based method for measuring cell death in image-based screens is presented. Unlike conventional high- and medium-throughput cell death assays that measure only one form of cell death accurately, using multivariate analysis of micrographs of cells stained with the inexpensive mix, red dye nonyl acridine orange, and a nuclear stain, it was possible to quantify cell death induced by a variety of different agonists even without a positive control. Surprisingly, using a single known cytotoxic agent as a positive control for training a multivariate classifier allowed accurate quantification of cytotoxicity for mechanistically unrelated compounds enabling generation of dose–response curves. Comparison with low throughput biochemical methods suggested that cell death was accurately distinguished from cell stress induced by low concentrations of the bioactive compounds Tunicamycin and Brefeldin A. High-throughput image-based format analyses of more than 300 kinase inhibitors correctly identified 11 as cytotoxic with only 1 false positive. The simplicity and robustness of this dye-based assay makes it particularly suited to live cell screening for toxic compounds. PMID:26422066

SPReM: Sparse Projection Regression Model For High-dimensional Linear Regression *

PubMed Central

Sun, Qiang; Zhu, Hongtu; Liu, Yufeng; Ibrahim, Joseph G.

2014-01-01

The aim of this paper is to develop a sparse projection regression modeling (SPReM) framework to perform multivariate regression modeling with a large number of responses and a multivariate covariate of interest. We propose two novel heritability ratios to simultaneously perform dimension reduction, response selection, estimation, and testing, while explicitly accounting for correlations among multivariate responses. Our SPReM is devised to specifically address the low statistical power issue of many standard statistical approaches, such as the Hotelling’s T2 test statistic or a mass univariate analysis, for high-dimensional data. We formulate the estimation problem of SPREM as a novel sparse unit rank projection (SURP) problem and propose a fast optimization algorithm for SURP. Furthermore, we extend SURP to the sparse multi-rank projection (SMURP) by adopting a sequential SURP approximation. Theoretically, we have systematically investigated the convergence properties of SURP and the convergence rate of SURP estimates. Our simulation results and real data analysis have shown that SPReM out-performs other state-of-the-art methods. PMID:26527844

LIKELIHOOD RATIO TESTS OF HYPOTHESES ON MULTIVARIATE POPULATIONS, VOLUME II, TEST OF HYPOTHESIS--STATISTICAL MODELS FOR THE EVALUATION AND INTERPRETATION OF EDUCATIONAL CRITERIA. PART 4.

ERIC Educational Resources Information Center

SAW, J.G.

THIS PAPER DEALS WITH SOME TESTS OF HYPOTHESIS FREQUENTLY ENCOUNTERED IN THE ANALYSIS OF MULTIVARIATE DATA. THE TYPE OF HYPOTHESIS CONSIDERED IS THAT WHICH THE STATISTICIAN CAN ANSWER IN THE NEGATIVE OR AFFIRMATIVE. THE DOOLITTLE METHOD MAKES IT POSSIBLE TO EVALUATE THE DETERMINANT OF A MATRIX OF HIGH ORDER, TO SOLVE A MATRIX EQUATION, OR TO…

A Multitaper, Causal Decomposition for Stochastic, Multivariate Time Series: Application to High-Frequency Calcium Imaging Data.

PubMed

Sornborger, Andrew T; Lauderdale, James D

2016-11-01

Neural data analysis has increasingly incorporated causal information to study circuit connectivity. Dimensional reduction forms the basis of most analyses of large multivariate time series. Here, we present a new, multitaper-based decomposition for stochastic, multivariate time series that acts on the covariance of the time series at all lags, C ( τ ), as opposed to standard methods that decompose the time series, X ( t ), using only information at zero-lag. In both simulated and neural imaging examples, we demonstrate that methods that neglect the full causal structure may be discarding important dynamical information in a time series.

Evaluation of in-line Raman data for end-point determination of a coating process: Comparison of Science-Based Calibration, PLS-regression and univariate data analysis.

PubMed

Barimani, Shirin; Kleinebudde, Peter

2017-10-01

A multivariate analysis method, Science-Based Calibration (SBC), was used for the first time for endpoint determination of a tablet coating process using Raman data. Two types of tablet cores, placebo and caffeine cores, received a coating suspension comprising a polyvinyl alcohol-polyethylene glycol graft-copolymer and titanium dioxide to a maximum coating thickness of 80µm. Raman spectroscopy was used as in-line PAT tool. The spectra were acquired every minute and correlated to the amount of applied aqueous coating suspension. SBC was compared to another well-known multivariate analysis method, Partial Least Squares-regression (PLS) and a simpler approach, Univariate Data Analysis (UVDA). All developed calibration models had coefficient of determination values (R 2 ) higher than 0.99. The coating endpoints could be predicted with root mean square errors (RMSEP) less than 3.1% of the applied coating suspensions. Compared to PLS and UVDA, SBC proved to be an alternative multivariate calibration method with high predictive power. Copyright © 2017 Elsevier B.V. All rights reserved.

Association Between Treatment at High-Volume Facilities and Improved Overall Survival in Soft Tissue Sarcomas.

PubMed

Venigalla, Sriram; Nead, Kevin T; Sebro, Ronnie; Guttmann, David M; Sharma, Sonam; Simone, Charles B; Levin, William P; Wilson, Robert J; Weber, Kristy L; Shabason, Jacob E

2018-03-15

Soft tissue sarcomas (STS) are rare malignancies that require complex multidisciplinary management. Therefore, facilities with high sarcoma case volume may demonstrate superior outcomes. We hypothesized that STS treatment at high-volume (HV) facilities would be associated with improved overall survival (OS). Patients aged ≥18 years with nonmetastatic STS treated with surgery and radiation therapy at a single facility from 2004 through 2013 were identified from the National Cancer Database. Facilities were dichotomized into HV and low-volume (LV) cohorts based on total case volume over the study period. OS was assessed using multivariable Cox regression with propensity score-matching. Patterns of care were assessed using multivariable logistic regression analysis. Of 9025 total patients, 1578 (17%) and 7447 (83%) were treated at HV and LV facilities, respectively. On multivariable analysis, high educational attainment, larger tumor size, higher grade, and negative surgical margins were statistically significantly associated with treatment at HV facilities; conversely, black race and non-metropolitan residence were negative predictors of treatment at HV facilities. On propensity score-matched multivariable analysis, treatment at HV facilities versus LV facilities was associated with improved OS (hazard ratio, 0.87, 95% confidence interval, 0.80-0.95; P = .001). Older age, lack of insurance, greater comorbidity, larger tumor size, higher tumor grade, and positive surgical margins were associated with statistically significantly worse OS. In this observational cohort study using the National Cancer Database, receipt of surgery and radiation therapy at HV facilities was associated with improved OS in patients with STS. Potential sociodemographic disparities limit access to care at HV facilities for certain populations. Our findings highlight the importance of receipt of care at HV facilities for patients with STS and warrant further study into improving access to care at HV facilities. Copyright © 2017 Elsevier Inc. All rights reserved.

Factors related to clinical pregnancy after vitrified-warmed embryo transfer: a retrospective and multivariate logistic regression analysis of 2313 transfer cycles.

PubMed

Shi, Wenhao; Zhang, Silin; Zhao, Wanqiu; Xia, Xue; Wang, Min; Wang, Hui; Bai, Haiyan; Shi, Juanzi

2013-07-01

What factors does multivariate logistic regression show to be significantly associated with the likelihood of clinical pregnancy in vitrified-warmed embryo transfer (VET) cycles? Assisted hatching (AH) and if the reason to freeze embryos was to avoid the risk of ovarian hyperstimulation syndrome (OHSS) were significantly positively associated with a greater likelihood of clinical pregnancy. Single factor analysis has shown AH, number of embryos transferred and the reason of freezing for OHSS to be positively and damaged blastomere to be negatively significantly associated with the chance of clinical pregnancy after VET. It remains unclear what factors would be significant after multivariate analysis. The study was a retrospective analysis of 2313 VET cycles from 1481 patients performed between January 2008 and April 2012. A multivariate logistic regression analysis was performed to identify the factors to affect clinical pregnancy outcome of VET. There were 22 candidate variables selected based on clinical experiences and the literature. With the thresholds of α entry = α removal= 0.05 for both variable entry and variable removal, eight variables were chosen to contribute the multivariable model by the bootstrap stepwise variable selection algorithm (n = 1000). Eight variables were age at controlled ovarian hyperstimulation (COH), reason for freezing, AH, endometrial thickness, damaged blastomere, number of embryos transferred, number of good-quality embryos, and blood presence on transfer catheter. A descriptive comparison of the relative importance was accomplished by the proportion of explained variation (PEV). Among the reasons for freezing, the OHSS group showed a higher OR than the surplus embryo group when compared with other reasons for VET groups (OHSS versus Other, OR: 2.145; CI: 1.4-3.286; Surplus embryos versus Other, OR: 1.152; CI: 0.761-1.743) and high PEV (marginal 2.77%, P = 0.2911; partial 1.68%; CI of area under receptor operator characteristic curve (ROC): 0.5576-0.6000). AH also showed a high OR (OR: 2.105, CI: 1.554-2.85) and high PEV (marginal 1.97%; partial 1.02%; CI of area under ROC: 0.5344-0.5647). The number of good-quality embryos showed the highest marginal PEV and partial PEV (marginal 3.91%, partial 2.28%; CI of area under ROC: 0.5886-0.6343). This was a retrospective multivariate analysis of the data obtained in 5 years from a single IVF center. Repeated cycles in the same woman were treated as independent observations, which could introduce bias. Results are based on clinical pregnancy and not live births. Prospective analysis of a larger data set from a multicenter study based on live births is necessary to confirm the findings. Paying attention to the quality of embryos, the number of good embryos, AH and the reasons for freezing that are associated with clinical pregnancy after VET will assist the improvement of success rates.

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.

Avoiding hard chromatographic segmentation: A moving window approach for the automated resolution of gas chromatography-mass spectrometry-based metabolomics signals by multivariate methods.

PubMed

Domingo-Almenara, Xavier; Perera, Alexandre; Brezmes, Jesus

2016-11-25

Gas chromatography-mass spectrometry (GC-MS) produces large and complex datasets characterized by co-eluted compounds and at trace levels, and with a distinct compound ion-redundancy as a result of the high fragmentation by the electron impact ionization. Compounds in GC-MS can be resolved by taking advantage of the multivariate nature of GC-MS data by applying multivariate resolution methods. However, multivariate methods have to be applied in small regions of the chromatogram, and therefore chromatograms are segmented prior to the application of the algorithms. The automation of this segmentation process is a challenging task as it implies separating between informative data and noise from the chromatogram. This study demonstrates the capabilities of independent component analysis-orthogonal signal deconvolution (ICA-OSD) and multivariate curve resolution-alternating least squares (MCR-ALS) with an overlapping moving window implementation to avoid the typical hard chromatographic segmentation. Also, after being resolved, compounds are aligned across samples by an automated alignment algorithm. We evaluated the proposed methods through a quantitative analysis of GC-qTOF MS data from 25 serum samples. The quantitative performance of both moving window ICA-OSD and MCR-ALS-based implementations was compared with the quantification of 33 compounds by the XCMS package. Results shown that most of the R 2 coefficients of determination exhibited a high correlation (R 2 >0.90) in both ICA-OSD and MCR-ALS moving window-based approaches. Copyright © 2016 Elsevier B.V. All rights reserved.

Logistic regression analysis of factors associated with avascular necrosis of the femoral head following femoral neck fractures in middle-aged and elderly patients.

PubMed

Ai, Zi-Sheng; Gao, You-Shui; Sun, Yuan; Liu, Yue; Zhang, Chang-Qing; Jiang, Cheng-Hua

2013-03-01

Risk factors for femoral neck fracture-induced avascular necrosis of the femoral head have not been elucidated clearly in middle-aged and elderly patients. Moreover, the high incidence of screw removal in China and its effect on the fate of the involved femoral head require statistical methods to reflect their intrinsic relationship. Ninety-nine patients older than 45 years with femoral neck fracture were treated by internal fixation between May 1999 and April 2004. Descriptive analysis, interaction analysis between associated factors, single factor logistic regression, multivariate logistic regression, and detailed interaction analysis were employed to explore potential relationships among associated factors. Avascular necrosis of the femoral head was found in 15 cases (15.2 %). Age × the status of implants (removal vs. maintenance) and gender × the timing of reduction were interactive according to two-factor interactive analysis. Age, the displacement of fractures, the quality of reduction, and the status of implants were found to be significant factors in single factor logistic regression analysis. Age, age × the status of implants, and the quality of reduction were found to be significant factors in multivariate logistic regression analysis. In fine interaction analysis after multivariate logistic regression analysis, implant removal was the most important risk factor for avascular necrosis in 56-to-85-year-old patients, with a risk ratio of 26.00 (95 % CI = 3.076-219.747). The middle-aged and elderly have less incidence of avascular necrosis of the femoral head following femoral neck fractures treated by cannulated screws. The removal of cannulated screws can induce a significantly high incidence of avascular necrosis of the femoral head in elderly patients, while a high-quality reduction is helpful to reduce avascular necrosis.

A mixed-effects regression model for longitudinal multivariate ordinal data.

PubMed

Liu, Li C; Hedeker, Donald

2006-03-01

A mixed-effects item response theory model that allows for three-level multivariate ordinal outcomes and accommodates multiple random subject effects is proposed for analysis of multivariate ordinal outcomes in longitudinal studies. This model allows for the estimation of different item factor loadings (item discrimination parameters) for the multiple outcomes. The covariates in the model do not have to follow the proportional odds assumption and can be at any level. Assuming either a probit or logistic response function, maximum marginal likelihood estimation is proposed utilizing multidimensional Gauss-Hermite quadrature for integration of the random effects. An iterative Fisher scoring solution, which provides standard errors for all model parameters, is used. An analysis of a longitudinal substance use data set, where four items of substance use behavior (cigarette use, alcohol use, marijuana use, and getting drunk or high) are repeatedly measured over time, is used to illustrate application of the proposed model.

Simultaneous determination of rifampicin, isoniazid and pyrazinamide in tablet preparations by multivariate spectrophotometric calibration.

PubMed

Goicoechea, H C; Olivieri, A C

1999-08-01

The use of multivariate spectrophotometric calibration is presented for the simultaneous determination of the active components of tablets used in the treatment of pulmonary tuberculosis. The resolution of ternary mixtures of rifampicin, isoniazid and pyrazinamide has been accomplished by using partial least squares (PLS-1) regression analysis. Although the components show an important degree of spectral overlap, they have been simultaneously determined with high accuracy and precision, rapidly and with no need of nonaqueous solvents for dissolving the samples. No interference has been observed from the tablet excipients. A comparison is presented with the related multivariate method of classical least squares (CLS) analysis, which is shown to yield less reliable results due to the severe spectral overlap among the studied compounds. This is highlighted in the case of isoniazid, due to the small absorbances measured for this component.

SOCR Motion Charts: An Efficient, Open-Source, Interactive and Dynamic Applet for Visualizing Longitudinal Multivariate Data

PubMed Central

Al-Aziz, Jameel; Christou, Nicolas; Dinov, Ivo D.

2011-01-01

The amount, complexity and provenance of data have dramatically increased in the past five years. Visualization of observed and simulated data is a critical component of any social, environmental, biomedical or scientific quest. Dynamic, exploratory and interactive visualization of multivariate data, without preprocessing by dimensionality reduction, remains a nearly insurmountable challenge. The Statistics Online Computational Resource (www.SOCR.ucla.edu) provides portable online aids for probability and statistics education, technology-based instruction and statistical computing. We have developed a new Java-based infrastructure, SOCR Motion Charts, for discovery-based exploratory analysis of multivariate data. This interactive data visualization tool enables the visualization of high-dimensional longitudinal data. SOCR Motion Charts allows mapping of ordinal, nominal and quantitative variables onto time, 2D axes, size, colors, glyphs and appearance characteristics, which facilitates the interactive display of multidimensional data. We validated this new visualization paradigm using several publicly available multivariate datasets including Ice-Thickness, Housing Prices, Consumer Price Index, and California Ozone Data. SOCR Motion Charts is designed using object-oriented programming, implemented as a Java Web-applet and is available to the entire community on the web at www.socr.ucla.edu/SOCR_MotionCharts. It can be used as an instructional tool for rendering and interrogating high-dimensional data in the classroom, as well as a research tool for exploratory data analysis. PMID:21479108

Network structure of multivariate time series.

PubMed

Lacasa, Lucas; Nicosia, Vincenzo; Latora, Vito

2015-10-21

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

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…

The classification of gunshot residue using laser electrospray mass spectrometry and offline multivariate statistical analysis

USDA-ARS?s Scientific Manuscript database

Nonresonant laser vaporization combined with high-resolution electrospray time-of-flight mass spectrometry enables analysis of a casing after discharge of a firearm revealing organic signature molecules including methyl centralite (MC), diphenylamine (DPA), N-nitrosodiphenylamine (N-NO-DPA), 4-nitro...

Mapping Informative Clusters in a Hierarchial Framework of fMRI Multivariate Analysis

PubMed Central

Xu, Rui; Zhen, Zonglei; Liu, Jia

2010-01-01

Pattern recognition methods have become increasingly popular in fMRI data analysis, which are powerful in discriminating between multi-voxel patterns of brain activities associated with different mental states. However, when they are used in functional brain mapping, the location of discriminative voxels varies significantly, raising difficulties in interpreting the locus of the effect. Here we proposed a hierarchical framework of multivariate approach that maps informative clusters rather than voxels to achieve reliable functional brain mapping without compromising the discriminative power. In particular, we first searched for local homogeneous clusters that consisted of voxels with similar response profiles. Then, a multi-voxel classifier was built for each cluster to extract discriminative information from the multi-voxel patterns. Finally, through multivariate ranking, outputs from the classifiers were served as a multi-cluster pattern to identify informative clusters by examining interactions among clusters. Results from both simulated and real fMRI data demonstrated that this hierarchical approach showed better performance in the robustness of functional brain mapping than traditional voxel-based multivariate methods. In addition, the mapped clusters were highly overlapped for two perceptually equivalent object categories, further confirming the validity of our approach. In short, the hierarchical framework of multivariate approach is suitable for both pattern classification and brain mapping in fMRI studies. PMID:21152081

Rotorcraft flying qualities improvement using advanced control

NASA Technical Reports Server (NTRS)

Walker, D.; Postlethwaite, I.; Howitt, J.; Foster, N.

1993-01-01

We report on recent experience gained when a multivariable helicopter flight control law was tested on the Large Motion Simulator (LMS) at DRA Bedford. This was part of a study into the application of multivariable control theory to the design of full-authority flight control systems for high-performance helicopters. In this paper, we present some of the results that were obtained during the piloted simulation trial and from subsequent off-line simulation and analysis. The performance provided by the control law led to level 1 handling quality ratings for almost all of the mission task elements assessed, both during the real-time and off-line analysis.

Principal Angle Enrichment Analysis (PAEA): Dimensionally Reduced Multivariate Gene Set Enrichment Analysis Tool

PubMed Central

Clark, Neil R.; Szymkiewicz, Maciej; Wang, Zichen; Monteiro, Caroline D.; Jones, Matthew R.; Ma’ayan, Avi

2016-01-01

Gene set analysis of differential expression, which identifies collectively differentially expressed gene sets, has become an important tool for biology. The power of this approach lies in its reduction of the dimensionality of the statistical problem and its incorporation of biological interpretation by construction. Many approaches to gene set analysis have been proposed, but benchmarking their performance in the setting of real biological data is difficult due to the lack of a gold standard. In a previously published work we proposed a geometrical approach to differential expression which performed highly in benchmarking tests and compared well to the most popular methods of differential gene expression. As reported, this approach has a natural extension to gene set analysis which we call Principal Angle Enrichment Analysis (PAEA). PAEA employs dimensionality reduction and a multivariate approach for gene set enrichment analysis. However, the performance of this method has not been assessed nor its implementation as a web-based tool. Here we describe new benchmarking protocols for gene set analysis methods and find that PAEA performs highly. The PAEA method is implemented as a user-friendly web-based tool, which contains 70 gene set libraries and is freely available to the community. PMID:26848405

Principal Angle Enrichment Analysis (PAEA): Dimensionally Reduced Multivariate Gene Set Enrichment Analysis Tool.

PubMed

Clark, Neil R; Szymkiewicz, Maciej; Wang, Zichen; Monteiro, Caroline D; Jones, Matthew R; Ma'ayan, Avi

2015-11-01

Gene set analysis of differential expression, which identifies collectively differentially expressed gene sets, has become an important tool for biology. The power of this approach lies in its reduction of the dimensionality of the statistical problem and its incorporation of biological interpretation by construction. Many approaches to gene set analysis have been proposed, but benchmarking their performance in the setting of real biological data is difficult due to the lack of a gold standard. In a previously published work we proposed a geometrical approach to differential expression which performed highly in benchmarking tests and compared well to the most popular methods of differential gene expression. As reported, this approach has a natural extension to gene set analysis which we call Principal Angle Enrichment Analysis (PAEA). PAEA employs dimensionality reduction and a multivariate approach for gene set enrichment analysis. However, the performance of this method has not been assessed nor its implementation as a web-based tool. Here we describe new benchmarking protocols for gene set analysis methods and find that PAEA performs highly. The PAEA method is implemented as a user-friendly web-based tool, which contains 70 gene set libraries and is freely available to the community.

A simple prognostic model for overall survival in metastatic renal cell carcinoma.

PubMed

Assi, Hazem I; Patenaude, Francois; Toumishey, Ethan; Ross, Laura; Abdelsalam, Mahmoud; Reiman, Tony

2016-01-01

The primary purpose of this study was to develop a simpler prognostic model to predict overall survival for patients treated for metastatic renal cell carcinoma (mRCC) by examining variables shown in the literature to be associated with survival. We conducted a retrospective analysis of patients treated for mRCC at two Canadian centres. All patients who started first-line treatment were included in the analysis. A multivariate Cox proportional hazards regression model was constructed using a stepwise procedure. Patients were assigned to risk groups depending on how many of the three risk factors from the final multivariate model they had. There were three risk factors in the final multivariate model: hemoglobin, prior nephrectomy, and time from diagnosis to treatment. Patients in the high-risk group (two or three risk factors) had a median survival of 5.9 months, while those in the intermediate-risk group (one risk factor) had a median survival of 16.2 months, and those in the low-risk group (no risk factors) had a median survival of 50.6 months. In multivariate analysis, shorter survival times were associated with hemoglobin below the lower limit of normal, absence of prior nephrectomy, and initiation of treatment within one year of diagnosis.

A simple prognostic model for overall survival in metastatic renal cell carcinoma

PubMed Central

Assi, Hazem I.; Patenaude, Francois; Toumishey, Ethan; Ross, Laura; Abdelsalam, Mahmoud; Reiman, Tony

2016-01-01

Introduction: The primary purpose of this study was to develop a simpler prognostic model to predict overall survival for patients treated for metastatic renal cell carcinoma (mRCC) by examining variables shown in the literature to be associated with survival. Methods: We conducted a retrospective analysis of patients treated for mRCC at two Canadian centres. All patients who started first-line treatment were included in the analysis. A multivariate Cox proportional hazards regression model was constructed using a stepwise procedure. Patients were assigned to risk groups depending on how many of the three risk factors from the final multivariate model they had. Results: There were three risk factors in the final multivariate model: hemoglobin, prior nephrectomy, and time from diagnosis to treatment. Patients in the high-risk group (two or three risk factors) had a median survival of 5.9 months, while those in the intermediate-risk group (one risk factor) had a median survival of 16.2 months, and those in the low-risk group (no risk factors) had a median survival of 50.6 months. Conclusions: In multivariate analysis, shorter survival times were associated with hemoglobin below the lower limit of normal, absence of prior nephrectomy, and initiation of treatment within one year of diagnosis. PMID:27217858

Fast classification of hazelnut cultivars through portable infrared spectroscopy and chemometrics

NASA Astrophysics Data System (ADS)

Manfredi, Marcello; Robotti, Elisa; Quasso, Fabio; Mazzucco, Eleonora; Calabrese, Giorgio; Marengo, Emilio

2018-01-01

The authentication and traceability of hazelnuts is very important for both the consumer and the food industry, to safeguard the protected varieties and the food quality. This study investigates the use of a portable FTIR spectrometer coupled to multivariate statistical analysis for the classification of raw hazelnuts. The method discriminates hazelnuts from different origins/cultivars based on differences of the signal intensities of their IR spectra. The multivariate classification methods, namely principal component analysis (PCA) followed by linear discriminant analysis (LDA) and partial least square discriminant analysis (PLS-DA), with or without variable selection, allowed a very good discrimination among the groups, with PLS-DA coupled to variable selection providing the best results. Due to the fast analysis, high sensitivity, simplicity and no sample preparation, the proposed analytical methodology could be successfully used to verify the cultivar of hazelnuts, and the analysis can be performed quickly and directly on site.

Multivariate normative comparisons using an aggregated database

PubMed Central

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

2017-01-01

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

Application of multivariate statistical techniques in microbial ecology.

PubMed

Paliy, O; Shankar, V

2016-03-01

Recent advances in high-throughput methods of molecular analyses have led to an explosion of studies generating large-scale ecological data sets. In particular, noticeable effect has been attained in the field of microbial ecology, where new experimental approaches provided in-depth assessments of the composition, functions and dynamic changes of complex microbial communities. Because even a single high-throughput experiment produces large amount of data, powerful statistical techniques of multivariate analysis are well suited to analyse and interpret these data sets. Many different multivariate techniques are available, and often it is not clear which method should be applied to a particular data set. In this review, we describe and compare the most widely used multivariate statistical techniques including exploratory, interpretive and discriminatory procedures. We consider several important limitations and assumptions of these methods, and we present examples of how these approaches have been utilized in recent studies to provide insight into the ecology of the microbial world. Finally, we offer suggestions for the selection of appropriate methods based on the research question and data set structure. © 2016 John Wiley & Sons Ltd.

Hybrid approach combining chemometrics and likelihood ratio framework for reporting the evidential value of spectra.

PubMed

Martyna, Agnieszka; Zadora, Grzegorz; Neocleous, Tereza; Michalska, Aleksandra; Dean, Nema

2016-08-10

Many chemometric tools are invaluable and have proven effective in data mining and substantial dimensionality reduction of highly multivariate data. This becomes vital for interpreting various physicochemical data due to rapid development of advanced analytical techniques, delivering much information in a single measurement run. This concerns especially spectra, which are frequently used as the subject of comparative analysis in e.g. forensic sciences. In the presented study the microtraces collected from the scenarios of hit-and-run accidents were analysed. Plastic containers and automotive plastics (e.g. bumpers, headlamp lenses) were subjected to Fourier transform infrared spectrometry and car paints were analysed using Raman spectroscopy. In the forensic context analytical results must be interpreted and reported according to the standards of the interpretation schemes acknowledged in forensic sciences using the likelihood ratio approach. However, for proper construction of LR models for highly multivariate data, such as spectra, chemometric tools must be employed for substantial data compression. Conversion from classical feature representation to distance representation was proposed for revealing hidden data peculiarities and linear discriminant analysis was further applied for minimising the within-sample variability while maximising the between-sample variability. Both techniques enabled substantial reduction of data dimensionality. Univariate and multivariate likelihood ratio models were proposed for such data. It was shown that the combination of chemometric tools and the likelihood ratio approach is capable of solving the comparison problem of highly multivariate and correlated data after proper extraction of the most relevant features and variance information hidden in the data structure. Copyright © 2016 Elsevier B.V. All rights reserved.

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.

Viewpoints: Interactive Exploration of Large Multivariate Earth and Space Science Data Sets

NASA Astrophysics Data System (ADS)

Levit, C.; Gazis, P. R.

2006-05-01

Analysis and visualization of extremely large and complex data sets may be one of the most significant challenges facing earth and space science investigators in the forthcoming decades. While advances in hardware speed and storage technology have roughly kept up with (indeed, have driven) increases in database size, the same is not of our abilities to manage the complexity of these data. Current missions, instruments, and simulations produce so much data of such high dimensionality that they outstrip the capabilities of traditional visualization and analysis software. This problem can only be expected to get worse as data volumes increase by orders of magnitude in future missions and in ever-larger supercomputer simulations. For large multivariate data (more than 105 samples or records with more than 5 variables per sample) the interactive graphics response of most existing statistical analysis, machine learning, exploratory data analysis, and/or visualization tools such as Torch, MLC++, Matlab, S++/R, and IDL stutters, stalls, or stops working altogether. Fortunately, the graphics processing units (GPUs) built in to all professional desktop and laptop computers currently on the market are capable of transforming, filtering, and rendering hundreds of millions of points per second. We present a prototype open-source cross-platform application which leverages much of the power latent in the GPU to enable smooth interactive exploration and analysis of large high- dimensional data using a variety of classical and recent techniques. The targeted application is the interactive analysis of large, complex, multivariate data sets, with dimensionalities that may surpass 100 and sample sizes that may exceed 106-108.

Skype Synchronous Interaction Effectiveness in a Quantitative Management Science Course

ERIC Educational Resources Information Center

Strang, Kenneth David

2012-01-01

An experiment compared asynchronous versus synchronous instruction in an online quantitative course. Mann-Whitney U-tests, correlation, analysis of variance, t tests, and multivariate analysis of covariance (MANCOVA) were utilized to test the hypothesis that more high-quality online experiential learning interactions would increase grade.…

Independent Prognostic Factors for Acute Organophosphorus Pesticide Poisoning.

PubMed

Tang, Weidong; Ruan, Feng; Chen, Qi; Chen, Suping; Shao, Xuebo; Gao, Jianbo; Zhang, Mao

2016-07-01

Acute organophosphorus pesticide poisoning (AOPP) is becoming a significant problem and a potential cause of human mortality because of the abuse of organophosphate compounds. This study aims to determine the independent prognostic factors of AOPP by using multivariate logistic regression analysis. The clinical data for 71 subjects with AOPP admitted to our hospital were retrospectively analyzed. This information included the Acute Physiology and Chronic Health Evaluation II (APACHE II) scores, 6-h post-admission blood lactate levels, post-admission 6-h lactate clearance rates, admission blood cholinesterase levels, 6-h post-admission blood cholinesterase levels, cholinesterase activity, blood pH, and other factors. Univariate analysis and multivariate logistic regression analyses were conducted to identify all prognostic factors and independent prognostic factors, respectively. A receiver operating characteristic curve was plotted to analyze the testing power of independent prognostic factors. Twelve of 71 subjects died. Admission blood lactate levels, 6-h post-admission blood lactate levels, post-admission 6-h lactate clearance rates, blood pH, and APACHE II scores were identified as prognostic factors for AOPP according to the univariate analysis, whereas only 6-h post-admission blood lactate levels, post-admission 6-h lactate clearance rates, and blood pH were independent prognostic factors identified by multivariate logistic regression analysis. The receiver operating characteristic analysis suggested that post-admission 6-h lactate clearance rates were of moderate diagnostic value. High 6-h post-admission blood lactate levels, low blood pH, and low post-admission 6-h lactate clearance rates were independent prognostic factors identified by multivariate logistic regression analysis. Copyright © 2016 by Daedalus Enterprises.

A hybrid approach identifies metabolic signatures of high-producers for chinese hamster ovary clone selection and process optimization.

PubMed

Popp, Oliver; Müller, Dirk; Didzus, Katharina; Paul, Wolfgang; Lipsmeier, Florian; Kirchner, Florian; Niklas, Jens; Mauch, Klaus; Beaucamp, Nicola

2016-09-01

In-depth characterization of high-producer cell lines and bioprocesses is vital to ensure robust and consistent production of recombinant therapeutic proteins in high quantity and quality for clinical applications. This requires applying appropriate methods during bioprocess development to enable meaningful characterization of CHO clones and processes. Here, we present a novel hybrid approach for supporting comprehensive characterization of metabolic clone performance. The approach combines metabolite profiling with multivariate data analysis and fluxomics to enable a data-driven mechanistic analysis of key metabolic traits associated with desired cell phenotypes. We applied the methodology to quantify and compare metabolic performance in a set of 10 recombinant CHO-K1 producer clones and a host cell line. The comprehensive characterization enabled us to derive an extended set of clone performance criteria that not only captured growth and product formation, but also incorporated information on intracellular clone physiology and on metabolic changes during the process. These criteria served to establish a quantitative clone ranking and allowed us to identify metabolic differences between high-producing CHO-K1 clones yielding comparably high product titers. Through multivariate data analysis of the combined metabolite and flux data we uncovered common metabolic traits characteristic of high-producer clones in the screening setup. This included high intracellular rates of glutamine synthesis, low cysteine uptake, reduced excretion of aspartate and glutamate, and low intracellular degradation rates of branched-chain amino acids and of histidine. Finally, the above approach was integrated into a workflow that enables standardized high-content selection of CHO producer clones in a high-throughput fashion. In conclusion, the combination of quantitative metabolite profiling, multivariate data analysis, and mechanistic network model simulations can identify metabolic traits characteristic of high-performance clones and enables informed decisions on which clones provide a good match for a particular process platform. The proposed approach also provides a mechanistic link between observed clone phenotype, process setup, and feeding regimes, and thereby offers concrete starting points for subsequent process optimization. Biotechnol. Bioeng. 2016;113: 2005-2019. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

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

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.

Multivariate matching pursuit in optimal Gabor dictionaries: theory and software with interface for EEG/MEG via Svarog

PubMed Central

2013-01-01

Background Matching pursuit algorithm (MP), especially with recent multivariate extensions, offers unique advantages in analysis of EEG and MEG. Methods We propose a novel construction of an optimal Gabor dictionary, based upon the metrics introduced in this paper. We implement this construction in a freely available software for MP decomposition of multivariate time series, with a user friendly interface via the Svarog package (Signal Viewer, Analyzer and Recorder On GPL, http://braintech.pl/svarog), and provide a hands-on introduction to its application to EEG. Finally, we describe numerical and mathematical optimizations used in this implementation. Results Optimal Gabor dictionaries, based on the metric introduced in this paper, for the first time allowed for a priori assessment of maximum one-step error of the MP algorithm. Variants of multivariate MP, implemented in the accompanying software, are organized according to the mathematical properties of the algorithms, relevant in the light of EEG/MEG analysis. Some of these variants have been successfully applied to both multichannel and multitrial EEG and MEG in previous studies, improving preprocessing for EEG/MEG inverse solutions and parameterization of evoked potentials in single trials; we mention also ongoing work and possible novel applications. Conclusions Mathematical results presented in this paper improve our understanding of the basics of the MP algorithm. Simple introduction of its properties and advantages, together with the accompanying stable and user-friendly Open Source software package, pave the way for a widespread and reproducible analysis of multivariate EEG and MEG time series and novel applications, while retaining a high degree of compatibility with the traditional, visual analysis of EEG. PMID:24059247

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.

Factors Associated with Sexual Behavior among Adolescents: A Multivariate Analysis.

ERIC Educational Resources Information Center

Harvey, S. Marie; Spigner, Clarence

1995-01-01

A self-administered survey examining multiple factors associated with engaging in sexual intercourse was completed by 1,026 high school students in a classroom setting. Findings suggest that effective interventions to address teenage pregnancy need to utilize a multifaceted approach to the prevention of high-risk behaviors. (JPS)

A multivariate analysis of clinical and morphological prognostic factors in squamous cell carcinoma of the vulva.

PubMed

Smyczek-Gargya, B; Volz, B; Geppert, M; Dietl, J

1997-01-01

Clinical and histological data of 168 patients with squamous cell carcinoma of the vulva were analyzed with respect to survival. 151 patients underwent surgery, 12 patients were treated with primary radiation and in 5 patients no treatment was performed. Follow-up lasted from at least 2 up to 22 years' posttreatment. In univariate analysis, the following factors were highly significant: presurgery lymph node status, tumor infiltration beyond the vulva, tumor grading, histological inguinal lymph node status, pre- and postsurgery tumor stage, depth of invasion and tumor diameter. In the multivariate analysis (Cox regression), the most powerful factors were shown to be histological inguinal lymph node status, tumor diameter and tumor grading. The multivariate logistic regression analysis worked out as main prognostic factors for metastases of inguinal lymph nodes: presurgery inguinal lymph node status, tumor size, depth of invasion and tumor grading. Based on these results, tumor biology seems to be the decisive factor concerning recurrence and survival. Therefore, we suggest a more conservative treatment of vulvar carcinoma. Patients with confined carcinoma to the vulva, with a tumor diameter up to 3 cm and without clinical suspected lymph nodes, should be treated by wide excision/partial vulvectomy with ipsilateral lymphadenectomy.

Water quality analysis of the Rapur area, Andhra Pradesh, South India using multivariate techniques

NASA Astrophysics Data System (ADS)

Nagaraju, A.; Sreedhar, Y.; Thejaswi, A.; Sayadi, Mohammad Hossein

2017-10-01

The groundwater samples from Rapur area were collected from different sites to evaluate the major ion chemistry. The large number of data can lead to difficulties in the integration, interpretation, and representation of the results. Two multivariate statistical methods, hierarchical cluster analysis (HCA) and factor analysis (FA), were applied to evaluate their usefulness to classify and identify geochemical processes controlling groundwater geochemistry. Four statistically significant clusters were obtained from 30 sampling stations. This has resulted two important clusters viz., cluster 1 (pH, Si, CO3, Mg, SO4, Ca, K, HCO3, alkalinity, Na, Na + K, Cl, and hardness) and cluster 2 (EC and TDS) which are released to the study area from different sources. The application of different multivariate statistical techniques, such as principal component analysis (PCA), assists in the interpretation of complex data matrices for a better understanding of water quality of a study area. From PCA, it is clear that the first factor (factor 1), accounted for 36.2% of the total variance, was high positive loading in EC, Mg, Cl, TDS, and hardness. Based on the PCA scores, four significant cluster groups of sampling locations were detected on the basis of similarity of their water quality.

imDEV: a graphical user interface to R multivariate analysis tools in Microsoft Excel

PubMed Central

Grapov, Dmitry; Newman, John W.

2012-01-01

Summary: Interactive modules for Data Exploration and Visualization (imDEV) is a Microsoft Excel spreadsheet embedded application providing an integrated environment for the analysis of omics data through a user-friendly interface. Individual modules enables interactive and dynamic analyses of large data by interfacing R's multivariate statistics and highly customizable visualizations with the spreadsheet environment, aiding robust inferences and generating information-rich data visualizations. This tool provides access to multiple comparisons with false discovery correction, hierarchical clustering, principal and independent component analyses, partial least squares regression and discriminant analysis, through an intuitive interface for creating high-quality two- and a three-dimensional visualizations including scatter plot matrices, distribution plots, dendrograms, heat maps, biplots, trellis biplots and correlation networks. Availability and implementation: Freely available for download at http://sourceforge.net/projects/imdev/. Implemented in R and VBA and supported by Microsoft Excel (2003, 2007 and 2010). Contact: John.Newman@ars.usda.gov Supplementary Information: Installation instructions, tutorials and users manual are available at http://sourceforge.net/projects/imdev/. PMID:22815358

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

Peculiarities of data interpretation upon direct tissue analysis by Fourier transform ion cyclotron resonance mass spectrometry.

PubMed

Chagovets, Vtaliy; Kononikhin, Aleksey; Starodubtseva, Nataliia; Kostyukevich, Yury; Popov, Igor; Frankevich, Vladimir; Nikolaev, Eugene

2016-01-01

The importance of high-resolution mass spectrometry for the correct data interpretation of a direct tissue analysis is demonstrated with an example of its clinical application for an endometriosis study. Multivariate analysis of the data discovers lipid species differentially expressed in different tissues under investigation. High-resolution mass spectrometry allows unambiguous separation of peaks with close masses that correspond to proton and sodium adducts of phosphatidylcholines and to phosphatidylcholines differing in double bond number.

Multivariate Formation Pressure Prediction with Seismic-derived Petrophysical Properties from Prestack AVO inversion and Poststack Seismic Motion Inversion

NASA Astrophysics Data System (ADS)

Yu, H.; Gu, H.

2017-12-01

A novel multivariate seismic formation pressure prediction methodology is presented, which incorporates high-resolution seismic velocity data from prestack AVO inversion, and petrophysical data (porosity and shale volume) derived from poststack seismic motion inversion. In contrast to traditional seismic formation prediction methods, the proposed methodology is based on a multivariate pressure prediction model and utilizes a trace-by-trace multivariate regression analysis on seismic-derived petrophysical properties to calibrate model parameters in order to make accurate predictions with higher resolution in both vertical and lateral directions. With prestack time migration velocity as initial velocity model, an AVO inversion was first applied to prestack dataset to obtain high-resolution seismic velocity with higher frequency that is to be used as the velocity input for seismic pressure prediction, and the density dataset to calculate accurate Overburden Pressure (OBP). Seismic Motion Inversion (SMI) is an inversion technique based on Markov Chain Monte Carlo simulation. Both structural variability and similarity of seismic waveform are used to incorporate well log data to characterize the variability of the property to be obtained. In this research, porosity and shale volume are first interpreted on well logs, and then combined with poststack seismic data using SMI to build porosity and shale volume datasets for seismic pressure prediction. A multivariate effective stress model is used to convert velocity, porosity and shale volume datasets to effective stress. After a thorough study of the regional stratigraphic and sedimentary characteristics, a regional normally compacted interval model is built, and then the coefficients in the multivariate prediction model are determined in a trace-by-trace multivariate regression analysis on the petrophysical data. The coefficients are used to convert velocity, porosity and shale volume datasets to effective stress and then to calculate formation pressure with OBP. Application of the proposed methodology to a research area in East China Sea has proved that the method can bridge the gap between seismic and well log pressure prediction and give predicted pressure values close to pressure meassurements from well testing.

Obstructive urination problems after high-dose-rate brachytherapy boost treatment for prostate cancer are avoidable.

PubMed

Kragelj, Borut

2016-03-01

Aiming at improving treatment individualization in patients with prostate cancer treated with combination of external beam radiotherapy and high-dose-rate brachytherapy to boost the dose to prostate (HDRB-B), the objective was to evaluate factors that have potential impact on obstructive urination problems (OUP) after HDRB-B. In the follow-up study 88 patients consecutively treated with HDRB-B at the Institute of Oncology Ljubljana in the period 2006-2011 were included. The observed outcome was deterioration of OUP (DOUP) during the follow-up period longer than 1 year. Univariate and multivariate relationship analysis between DOUP and potential risk factors (treatment factors, patients' characteristics) was carried out by using binary logistic regression. ROC curve was constructed on predicted values and the area under the curve (AUC) calculated to assess the performance of the multivariate model. Analysis was carried out on 71 patients who completed 3 years of follow-up. DOUP was noted in 13/71 (18.3%) of them. The results of multivariate analysis showed statistically significant relationship between DOUP and anti-coagulation treatment (OR 4.86, 95% C.I. limits: 1.21-19.61, p = 0.026). Also minimal dose received by 90% of the urethra volume was close to statistical significance (OR = 1.23; 95% C.I. limits: 0.98-1.07, p = 0.099). The value of AUC was 0.755. The study emphasized the relationship between DOUP and anticoagulation treatment, and suggested the multivariate model with fair predictive performance. This model potentially enables a reduction of DOUP after HDRB-B. It supports the belief that further research should be focused on urethral sphincter as a critical structure for OUP.

Viewpoints: A New Computer Program for Interactive Exploration of Large Multivariate Space Science and Astrophysics Data.

NASA Astrophysics Data System (ADS)

Levit, Creon; Gazis, P.

2006-06-01

The graphics processing units (GPUs) built in to all professional desktop and laptop computers currently on the market are capable of transforming, filtering, and rendering hundreds of millions of points per second. We present a prototype open-source cross-platform (windows, linux, Apple OSX) application which leverages some of the power latent in the GPU to enable smooth interactive exploration and analysis of large high-dimensional data using a variety of classical and recent techniques. The targeted application area is the interactive analysis of complex, multivariate space science and astrophysics data sets, with dimensionalities that may surpass 100 and sample sizes that may exceed 10^6-10^8.

A Dynamic Intrusion Detection System Based on Multivariate Hotelling's T2 Statistics Approach for Network Environments

PubMed Central

Avalappampatty Sivasamy, Aneetha; Sundan, Bose

2015-01-01

The ever expanding communication requirements in today's world demand extensive and efficient network systems with equally efficient and reliable security features integrated for safe, confident, and secured communication and data transfer. Providing effective security protocols for any network environment, therefore, assumes paramount importance. Attempts are made continuously for designing more efficient and dynamic network intrusion detection models. In this work, an approach based on Hotelling's T2 method, a multivariate statistical analysis technique, has been employed for intrusion detection, especially in network environments. Components such as preprocessing, multivariate statistical analysis, and attack detection have been incorporated in developing the multivariate Hotelling's T2 statistical model and necessary profiles have been generated based on the T-square distance metrics. With a threshold range obtained using the central limit theorem, observed traffic profiles have been classified either as normal or attack types. Performance of the model, as evaluated through validation and testing using KDD Cup'99 dataset, has shown very high detection rates for all classes with low false alarm rates. Accuracy of the model presented in this work, in comparison with the existing models, has been found to be much better. PMID:26357668

A Dynamic Intrusion Detection System Based on Multivariate Hotelling's T2 Statistics Approach for Network Environments.

PubMed

Sivasamy, Aneetha Avalappampatty; Sundan, Bose

2015-01-01

The ever expanding communication requirements in today's world demand extensive and efficient network systems with equally efficient and reliable security features integrated for safe, confident, and secured communication and data transfer. Providing effective security protocols for any network environment, therefore, assumes paramount importance. Attempts are made continuously for designing more efficient and dynamic network intrusion detection models. In this work, an approach based on Hotelling's T(2) method, a multivariate statistical analysis technique, has been employed for intrusion detection, especially in network environments. Components such as preprocessing, multivariate statistical analysis, and attack detection have been incorporated in developing the multivariate Hotelling's T(2) statistical model and necessary profiles have been generated based on the T-square distance metrics. With a threshold range obtained using the central limit theorem, observed traffic profiles have been classified either as normal or attack types. Performance of the model, as evaluated through validation and testing using KDD Cup'99 dataset, has shown very high detection rates for all classes with low false alarm rates. Accuracy of the model presented in this work, in comparison with the existing models, has been found to be much better.

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

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.

Introducing Undergraduate Students to Metabolomics Using a NMR-Based Analysis of Coffee Beans

ERIC Educational Resources Information Center

Sandusky, Peter Olaf

2017-01-01

Metabolomics applies multivariate statistical analysis to sets of high-resolution spectra taken over a population of biologically derived samples. The objective is to distinguish subpopulations within the overall sample population, and possibly also to identify biomarkers. While metabolomics has become part of the standard analytical toolbox in…

In Quest of the Alanine R3 Radical: Multivariate EPR Spectral Analyses of X-Irradiated Alanine in the Solid State.

PubMed

Jåstad, Eirik O; Torheim, Turid; Villeneuve, Kathleen M; Kvaal, Knut; Hole, Eli O; Sagstuen, Einar; Malinen, Eirik; Futsaether, Cecilia M

2017-09-28

The amino acid l-α-alanine is the most commonly used material for solid-state electron paramagnetic resonance (EPR) dosimetry, due to the formation of highly stable radicals upon irradiation, with yields proportional to the radiation dose. Two major alanine radical components designated R1 and R2 have previously been uniquely characterized from EPR and electron-nuclear double resonance (ENDOR) studies as well as from quantum chemical calculations. There is also convincing experimental evidence of a third minor radical component R3, and a tentative radical structure has been suggested, even though no well-defined spectral signature has been observed experimentally. In the present study, temperature dependent EPR spectra of X-ray irradiated polycrystalline alanine were analyzed using five multivariate methods in further attempts to understand the composite nature of the alanine dosimeter EPR spectrum. Principal component analysis (PCA), maximum likelihood common factor analysis (MLCFA), independent component analysis (ICA), self-modeling mixture analysis (SMA), and multivariate curve resolution (MCR) were used to extract pure radical spectra and their fractional contributions from the experimental EPR spectra. All methods yielded spectral estimates resembling the established R1 spectrum. Furthermore, SMA and MCR consistently predicted both the established R2 spectrum and the shape of the R3 spectrum. The predicted shape of the R3 spectrum corresponded well with the proposed tentative spectrum derived from spectrum simulations. Thus, results from two independent multivariate data analysis techniques strongly support the previous evidence that three radicals are indeed present in irradiated alanine samples.

Is the prognostic significance of O6-methylguanine- DNA methyltransferase promoter methylation equally important in glioblastomas of patients from different continents? A systematic review with meta-analysis.

PubMed

Meng, Wei; Jiang, Yangyang; Ma, Jie

2017-01-01

O6-methylguanine-DNA methyltransferase (MGMT) is an independent predictor of therapeutic response and potential prognosis in patients with glioblastoma multiforme (GBM). However, its significance of clinical prognosis in different continents still needs to be explored. To explore the effects of MGMT promoter methylation on both progression-free survival (PFS) and overall survival (OS) among GBM patients from different continents, a systematic review of published studies was conducted. A total of 5103 patients from 53 studies were involved in the systematic review and the total percentage of MGMT promoter methylation was 45.53%. Of these studies, 16 studies performed univariate analyses and 17 performed multivariate analyses of MGMT promoter methylation on PFS. The pooled hazard ratio (HR) estimated for PFS was 0.55 (95% CI 0.50, 0.60) by univariate analysis and 0.43 (95% CI 0.38, 0.48) by multivariate analysis. The effect of MGMT promoter methylation on OS was explored in 30 studies by univariate analysis and in 30 studies by multivariate analysis. The combined HR was 0.48 (95% CI 0.44, 0.52) and 0.42 (95% CI 0.38, 0.45), respectively. In each subgroup divided by areas, the prognostic significance still remained highly significant. The proportion of methylation in each group was in inverse proportion to the corresponding HR in the univariate and multivariate analyses of PFS. However, from the perspective of OS, compared with data from Europe and the US, higher methylation rates in Asia did not bring better returns.

Patterns and Predictors of Language and Literacy Abilities 4-10 Years in the Longitudinal Study of Australian Children.

PubMed

Zubrick, Stephen R; Taylor, Catherine L; Christensen, Daniel

2015-01-01

Oral language is the foundation of literacy. Naturally, policies and practices to promote children's literacy begin in early childhood and have a strong focus on developing children's oral language, especially for children with known risk factors for low language ability. The underlying assumption is that children's progress along the oral to literate continuum is stable and predictable, such that low language ability foretells low literacy ability. This study investigated patterns and predictors of children's oral language and literacy abilities at 4, 6, 8 and 10 years. The study sample comprised 2,316 to 2,792 children from the first nationally representative Longitudinal Study of Australian Children (LSAC). Six developmental patterns were observed, a stable middle-high pattern, a stable low pattern, an improving pattern, a declining pattern, a fluctuating low pattern, and a fluctuating middle-high pattern. Most children (69%) fit a stable middle-high pattern. By contrast, less than 1% of children fit a stable low pattern. These results challenged the view that children's progress along the oral to literate continuum is stable and predictable. Multivariate logistic regression was used to investigate risks for low literacy ability at 10 years and sensitivity-specificity analysis was used to examine the predictive utility of the multivariate model. Predictors were modelled as risk variables with the lowest level of risk as the reference category. In the multivariate model, substantial risks for low literacy ability at 10 years, in order of descending magnitude, were: low school readiness, Aboriginal and/or Torres Strait Islander status and low language ability at 8 years. Moderate risks were high temperamental reactivity, low language ability at 4 years, and low language ability at 6 years. The following risk factors were not statistically significant in the multivariate model: Low maternal consistency, low family income, health care card, child not read to at home, maternal smoking, maternal education, family structure, temperamental persistence, and socio-economic area disadvantage. The results of the sensitivity-specificity analysis showed that a well-fitted multivariate model featuring risks of substantive magnitude did not do particularly well in predicting low literacy ability at 10 years.

Cartilage degradation biomarkers predict efficacy of a novel, highly selective matrix metalloproteinase 13 inhibitor in a dog model of osteoarthritis: confirmation by multivariate analysis that modulation of type II collagen and aggrecan degradation peptides parallels pathologic changes.

PubMed

Settle, Steven; Vickery, Lillian; Nemirovskiy, Olga; Vidmar, Tom; Bendele, Alison; Messing, Dean; Ruminski, Peter; Schnute, Mark; Sunyer, Teresa

2010-10-01

To demonstrate that the novel highly selective matrix metalloproteinase 13 (MMP-13) inhibitor PF152 reduces joint lesions in adult dogs with osteoarthritis (OA) and decreases biomarkers of cartilage degradation. The potency and selectivity of PF152 were evaluated in vitro using 16 MMPs, TACE, and ADAMTS-4 and ADAMTS-5, as well as ex vivo in human cartilage explants. In vivo effects were evaluated at 3 concentrations in mature beagles with partial medial meniscectomy. Gross and histologic changes in the femorotibial joints were evaluated using various measures of cartilage degeneration. Biomarkers of cartilage turnover were examined in serum, urine, or synovial fluid. Results were analyzed individually and in combination using multivariate analysis. The potent and selective MMP-13 inhibitor PF152 decreased human cartilage degradation ex vivo in a dose-dependent manner. PF152 treatment of dogs with OA reduced cartilage lesions and decreased biomarkers of type II collagen (type II collagen neoepitope) and aggrecan (peptides ending in ARGN or AGEG) degradation. The dose required for significant inhibition varied with the measure used, but multivariate analysis of 6 gross and histologic measures indicated that all doses differed significantly from vehicle but not from each other. Combined analysis of cartilage degradation markers showed similar results. This highly selective MMP-13 inhibitor exhibits chondroprotective effects in mature animals. Biomarkers of cartilage degradation, when evaluated in combination, parallel the joint structural changes induced by the MMP-13 inhibitor. These data support the potential therapeutic value of selective MMP-13 inhibitors and the use of a set of appropriate biomarkers to predict efficacy in OA clinical trials.

Discrimination of cultivation ages and cultivars of ginseng leaves using Fourier transform infrared spectroscopy combined with multivariate analysis

PubMed Central

Kwon, Yong-Kook; Ahn, Myung Suk; Park, Jong Suk; Liu, Jang Ryol; In, Dong Su; Min, Byung Whan; Kim, Suk Weon

2013-01-01

To determine whether Fourier transform (FT)-IR spectral analysis combined with multivariate analysis of whole-cell extracts from ginseng leaves can be applied as a high-throughput discrimination system of cultivation ages and cultivars, a total of total 480 leaf samples belonging to 12 categories corresponding to four different cultivars (Yunpung, Kumpung, Chunpung, and an open-pollinated variety) and three different cultivation ages (1 yr, 2 yr, and 3 yr) were subjected to FT-IR. The spectral data were analyzed by principal component analysis and partial least squares-discriminant analysis. A dendrogram based on hierarchical clustering analysis of the FT-IR spectral data on ginseng leaves showed that leaf samples were initially segregated into three groups in a cultivation age-dependent manner. Then, within the same cultivation age group, leaf samples were clustered into four subgroups in a cultivar-dependent manner. The overall prediction accuracy for discrimination of cultivars and cultivation ages was 94.8% in a cross-validation test. These results clearly show that the FT-IR spectra combined with multivariate analysis from ginseng leaves can be applied as an alternative tool for discriminating of ginseng cultivars and cultivation ages. Therefore, we suggest that this result could be used as a rapid and reliable F1 hybrid seed-screening tool for accelerating the conventional breeding of ginseng. PMID:24558311

Multivariate Analysis of Combined Fourier Transform Near-Infrared Spectrometry (FT-NIR) and Raman Datasets for Improved Discrimination of Drying Oils.

PubMed

Carlesi, Serena; Ricci, Marilena; Cucci, Costanza; La Nasa, Jacopo; Lofrumento, Cristiana; Picollo, Marcello; Becucci, Maurizio

2015-07-01

This work explores the application of chemometric techniques to the analysis of lipidic paint binders (i.e., drying oils) by means of Raman and near-infrared spectroscopy. These binders have been widely used by artists throughout history, both individually and in mixtures. We prepared various model samples of the pure binders (linseed, poppy seed, and walnut oils) obtained from different manufacturers. These model samples were left to dry and then characterized by Raman and reflectance near-infrared spectroscopy. Multivariate analysis was performed by applying principal component analysis (PCA) on the first derivative of the corresponding Raman spectra (1800-750 cm(-1)), near-infrared spectra (6000-3900 cm(-1)), and their combination to test whether spectral differences could enable samples to be distinguished on the basis of their composition. The vibrational bands we found most useful to discriminate between the different products we studied are the fundamental ν(C=C) stretching and methylenic stretching and bending combination bands. The results of the multivariate analysis demonstrated the potential of chemometric approaches for characterizing and identifying drying oils, and also for gaining a deeper insight into the aging process. Comparison with high-performance liquid chromatography data was conducted to check the PCA results.

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.

Linear regression analysis and its application to multivariate chromatographic calibration for the quantitative analysis of two-component mixtures.

PubMed

Dinç, Erdal; Ozdemir, Abdil

2005-01-01

Multivariate chromatographic calibration technique was developed for the quantitative analysis of binary mixtures enalapril maleate (EA) and hydrochlorothiazide (HCT) in tablets in the presence of losartan potassium (LST). The mathematical algorithm of multivariate chromatographic calibration technique is based on the use of the linear regression equations constructed using relationship between concentration and peak area at the five-wavelength set. The algorithm of this mathematical calibration model having a simple mathematical content was briefly described. This approach is a powerful mathematical tool for an optimum chromatographic multivariate calibration and elimination of fluctuations coming from instrumental and experimental conditions. This multivariate chromatographic calibration contains reduction of multivariate linear regression functions to univariate data set. The validation of model was carried out by analyzing various synthetic binary mixtures and using the standard addition technique. Developed calibration technique was applied to the analysis of the real pharmaceutical tablets containing EA and HCT. The obtained results were compared with those obtained by classical HPLC method. It was observed that the proposed multivariate chromatographic calibration gives better results than classical HPLC.

Psychosocial distress in patients with thyroid cancer.

PubMed

Buchmann, Luke; Ashby, Shaelene; Cannon, Richard B; Hunt, Jason P

2015-04-01

The purpose of this study is to evaluate levels of psychosocial distress in thyroid cancer patients. An analysis of factors contributing to levels of distress is included. Individual retrospective cohort study. Head and neck cancer clinic at the Huntsman Cancer Institute. A total of 118 newly diagnosed thyroid cancer patients were included in the study. Univariate and multivariate analyses evaluated levels of and factors contributing to distress. Almost half (43.3%) of patients had significant distress. Those with self-reported psychiatric history, use of antidepressant medication, and history of radiation treatment had higher levels of distress. On multivariate analysis, patient endorsement of emotional issues predicted a higher distress level. Thyroid cancer patients have high distress levels. Identification of thyroid cancer patients with high distress levels is important to offer additional support during cancer therapy. © American Academy of Otolaryngology—Head and Neck Surgery Foundation 2015.

High prevalence of suicide risk in people living with HIV: who is at higher risk?

PubMed

Passos, Susane Müller Klug; Souza, Luciano Dias de Mattos; Spessato, Bárbara Coiro

2014-01-01

A cross-sectional study was developed to evaluate suicide risk and associated factors in HIV/AIDS patients at a regional reference center for the treatment of HIV/AIDS in southern Brazil. We assessed 211 patients in regard to suicide risk, clinical and sociodemographic characteristics, drug use, depression, and anxiety. Suicide risk was assessed with Mini International Neuropsychiatric Interview, Module C. Multivariate analysis was performed using Poisson regression. Of the total sample, 34.1% were at risk of suicide. In the multivariate analysis, the following variables were independently associated with suicide risk: female gender; age up to 47 years; unemployment; indicative of anxiety; indicative of depression; and abuse or addiction on psychoactive substances. Suicide risk is high in this population. Psychosocial factors should be included in the physical and clinical evaluation, given their strong association with suicide risk.

Prognostic factors in prostate cancer patients treated by radical external beam radiotherapy.

PubMed

Garibaldi, Elisabetta; Gabriele, Domenico; Maggio, Angelo; Delmastro, Elena; Garibaldi, Monica; Russo, Filippo; Bresciani, Sara; Stasi, Michele; Gabriele, Pietro

2017-09-01

The aim of this paper was to analyze, retrospectively, in prostate cancer patients treated in our Centre with external beam radiotherapy, the prognostic factors and their impact on the outcome in terms of cancer-specific survival (CSS), biochemical disease-free survival (BDFS) and clinical disease-free survival (CDFS). From October 1999 and March 2012, 1080 patients were treated with radiotherapy at our Institution: 87% of them were classified as ≤cT2, 83% had a Gleason Score (GS) ≤7, their mean of iPSA was 18 ng/mL, and the rate of clinical positive nodes was 1%. The mean follow-up was 81 months. The statistically significant prognostic factors for all groups of patients at both, univariate and multivariate analysis, were the GS and the iPSA. In intermediate- and high- or very-high-risk patients at multivariate analysis other prognostic factors for CSS were positive nodes on computed tomography (CT) scan and rectal preparation during the treatment; for BDFS, the prognostic factors were patient risk classification, positive lymph nodes on CT scan and rectal/bladder preparation; for CDFS, the prognostic factors were the number of positive core on biopsy (P=0.003), positive lymph nodes on CT scan, and radiotherapy (RT) dose. In high/very-high risk patient group at multivariate analysis other prognostic factors for CSS were clinical/radiological stage and RT dose, for BDFS they were adjuvant hormone therapy, clinical/radiological stage, and RT dose >77.7 Gy, and for CDFS they were clinical/radiological stage and RT dose >77.7 Gy. The results of this study confirm the prognostic factors described in the recent literature, with the addition of rectal/bladder preparation, generally known for its effect on toxicity but not yet on outcome.

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…

Determination of rice syrup adulterant concentration in honey using three-dimensional fluorescence spectra and multivariate calibrations

NASA Astrophysics Data System (ADS)

Chen, Quansheng; Qi, Shuai; Li, Huanhuan; Han, Xiaoyan; Ouyang, Qin; Zhao, Jiewen

2014-10-01

To rapidly and efficiently detect the presence of adulterants in honey, three-dimensional fluorescence spectroscopy (3DFS) technique was employed with the help of multivariate calibration. The data of 3D fluorescence spectra were compressed using characteristic extraction and the principal component analysis (PCA). Then, partial least squares (PLS) and back propagation neural network (BP-ANN) algorithms were used for modeling. The model was optimized by cross validation, and its performance was evaluated according to root mean square error of prediction (RMSEP) and correlation coefficient (R) in prediction set. The results showed that BP-ANN model was superior to PLS models, and the optimum prediction results of the mixed group (sunflower ± longan ± buckwheat ± rape) model were achieved as follow: RMSEP = 0.0235 and R = 0.9787 in the prediction set. The study demonstrated that the 3D fluorescence spectroscopy technique combined with multivariate calibration has high potential in rapid, nondestructive, and accurate quantitative analysis of honey adulteration.

Multivariate fault isolation of batch processes via variable selection in partial least squares discriminant analysis.

PubMed

Yan, Zhengbing; Kuang, Te-Hui; Yao, Yuan

2017-09-01

In recent years, multivariate statistical monitoring of batch processes has become a popular research topic, wherein multivariate fault isolation is an important step aiming at the identification of the faulty variables contributing most to the detected process abnormality. Although contribution plots have been commonly used in statistical fault isolation, such methods suffer from the smearing effect between correlated variables. In particular, in batch process monitoring, the high autocorrelations and cross-correlations that exist in variable trajectories make the smearing effect unavoidable. To address such a problem, a variable selection-based fault isolation method is proposed in this research, which transforms the fault isolation problem into a variable selection problem in partial least squares discriminant analysis and solves it by calculating a sparse partial least squares model. As different from the traditional methods, the proposed method emphasizes the relative importance of each process variable. Such information may help process engineers in conducting root-cause diagnosis. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

The prognostic role of CD68 and FoxP3 expression in patients with primary central nervous system lymphoma.

PubMed

Cho, Hyunsoo; Kim, Se Hoon; Kim, Soo-Jeong; Chang, Jong Hee; Yang, Woo Ick; Suh, Chang-Ok; Cheong, June-Won; Kim, Yu Ri; Lee, Jung Yeon; Jang, Ji Eun; Kim, Yundeok; Min, Yoo Hong; Kim, Jin Seok

2017-07-01

The prognostic role of CD68 and FoxP3 in primary central nervous system lymphoma (PCNSL) has not been evaluated. Thus, we examined the prognostic significance of CD68 and FoxP3 expression in tumor samples of 76 newly diagnosed immunocompetent PCNSL patients. All patients were treated initially with high-dose methotrexate (HD-MTX)-based chemotherapy, and 16 (21.1%) patients received upfront autologous stem cell transplantation (ASCT) consolidation. High expression of CD68 (>55 cells/high-power field) or FoxP3 (>15 cells/high-power field) was observed in 10 patients, respectively. High CD68 expression was associated with inferior overall survival (OS) and progression-free survival (PFS) in multivariate analysis (P = 0.023 and P = 0.021, respectively). In addition, we performed subgroup analysis based on upfront ASCT. High CD68 expression was also associated with inferior OS and PFS in multivariate analysis (P = 0.013 and P < 0.001, respectively) among patients who did not receive upfront ASCT (n = 60), but not in patients who received upfront ASCT. The expression of FoxP3 was not significantly associated with survival. Therefore, we identified a prognostic significance of high CD68 expression in PCNSL, which suggests a need for further clinical trials and biological studies on the role of PCNSL tumor microenvironment.

Immediate versus delayed intramedullary nailing for open fractures of the tibial shaft: a multivariate analysis of factors affecting deep infection and fracture healing.

PubMed

Yokoyama, Kazuhiko; Itoman, Moritoshi; Uchino, Masataka; Fukushima, Kensuke; Nitta, Hiroshi; Kojima, Yoshiaki

2008-10-01

The purpose of this study was to evaluate contributing factors affecting deep infection and fracture healing of open tibia fractures treated with locked intramedullary nailing (IMN) by multivariate analysis. We examined 99 open tibial fractures (98 patients) treated with immediate or delayed locked IMN in static fashion from 1991 to 2002. Multivariate analyses following univariate analyses were derived to determine predictors of deep infection, nonunion, and healing time to union. The following predictive variables of deep infection were selected for analysis: age, sex, Gustilo type, fracture grade by AO type, fracture location, timing or method of IMN, reamed or unreamed nailing, debridement time (< or =6 h or >6 h), method of soft-tissue management, skin closure time (< or =1 week or >1 week), existence of polytrauma (ISS< 18 or ISS> or =18), existence of floating knee injury, and existence of superficial/pin site infection. The predictive variables of nonunion selected for analysis was the same as those for deep infection, with the addition of deep infection for exchange of pin site infection. The predictive variables of union time selected for analysis was the same as those for nonunion, excluding of location, debridement time, and existence of floating knee and superficial infection. Six (6.1%; type II Gustilo n=1, type IIIB Gustilo n=5) of the 99 open tibial fractures developed deep infections. Multivariate analysis revealed that timing or method of IMN, debridement time, method of soft-tissue management, and existence of superficial or pin site infection significantly correlated with the occurrence of deep infection (P< 0.0001). In the immediate nailing group alone, the deep infection rate in type IIIB + IIIC was significantly higher than those in type I + II and IIIA (P = 0.016). Nonunion occurred in 17 fractures (20.3%, 17/84). Multivariate analysis revealed that Gustilo type, skin closure time, and existence of deep infection significantly correlated with occurrence of nonunion (P < 0.05). Gustilo type and existence of deep infection were significantly correlated with healing time to union on multivariate analysis (r(2) = 0.263, P = 0.0001). Multivariate analyses for open tibial fractures treated with IMN showed that IMN after EF (especially in existence of pin site infection) was at high risk of deep infection, and that debridement within 6 h and appropriate soft-tissue managements were also important factor in preventing deep infections. These analyses postulated that both the Gustilo type and the existence of deep infection is related with fracture healing in open fractures treated with IMN. In addition, immediate IMN for type IIIB and IIIC is potentially risky, and canal reaming did not increase the risk of complication for open tibial fractures treated with IMN.

Factors Associated with Participation in Employment for High School Leavers with Autism

ERIC Educational Resources Information Center

Chiang, Hsu-Min; Cheung, Ying Kuen; Li, Huacheng; Tsai, Luke Y.

2013-01-01

This study aimed to identify the factors associated with participation in employment for high school leavers with autism. A secondary data analysis of the National Longitudinal Transition Study 2 (NLTS2) data was performed. Potential factors were assessed using a weighted multivariate logistic regression. This study found that annual household…

Comprehensive analysis of Polygoni Multiflori Radix of different geographical origins using ultra-high-performance liquid chromatography fingerprints and multivariate chemometric methods.

PubMed

Sun, Li-Li; Wang, Meng; Zhang, Hui-Jie; Liu, Ya-Nan; Ren, Xiao-Liang; Deng, Yan-Ru; Qi, Ai-Di

2018-01-01

Polygoni Multiflori Radix (PMR) is increasingly being used not just as a traditional herbal medicine but also as a popular functional food. In this study, multivariate chemometric methods and mass spectrometry were combined to analyze the ultra-high-performance liquid chromatograph (UPLC) fingerprints of PMR from six different geographical origins. A chemometric strategy based on multivariate curve resolution-alternating least squares (MCR-ALS) and three classification methods is proposed to analyze the UPLC fingerprints obtained. Common chromatographic problems, including the background contribution, baseline contribution, and peak overlap, were handled by the established MCR-ALS model. A total of 22 components were resolved. Moreover, relative species concentrations were obtained from the MCR-ALS model, which was used for multivariate classification analysis. Principal component analysis (PCA) and Ward's method have been applied to classify 72 PMR samples from six different geographical regions. The PCA score plot showed that the PMR samples fell into four clusters, which related to the geographical location and climate of the source areas. The results were then corroborated by Ward's method. In addition, according to the variance-weighted distance between cluster centers obtained from Ward's method, five components were identified as the most significant variables (chemical markers) for cluster discrimination. A counter-propagation artificial neural network has been applied to confirm and predict the effects of chemical markers on different samples. Finally, the five chemical markers were identified by UPLC-quadrupole time-of-flight mass spectrometer. Components 3, 12, 16, 18, and 19 were identified as 2,3,5,4'-tetrahydroxy-stilbene-2-O-β-d-glucoside, emodin-8-O-β-d-glucopyranoside, emodin-8-O-(6'-O-acetyl)-β-d-glucopyranoside, emodin, and physcion, respectively. In conclusion, the proposed method can be applied for the comprehensive analysis of natural samples. Copyright © 2016. Published by Elsevier B.V.

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

Forensic Discrimination of Latent Fingerprints Using Laser-Induced Breakdown Spectroscopy (LIBS) and Chemometric Approaches.

PubMed

Yang, Jun-Ho; Yoh, Jack J

2018-01-01

A novel technique is reported for separating overlapping latent fingerprints using chemometric approaches that combine laser-induced breakdown spectroscopy (LIBS) and multivariate analysis. The LIBS technique provides the capability of real time analysis and high frequency scanning as well as the data regarding the chemical composition of overlapping latent fingerprints. These spectra offer valuable information for the classification and reconstruction of overlapping latent fingerprints by implementing appropriate statistical multivariate analysis. The current study employs principal component analysis and partial least square methods for the classification of latent fingerprints from the LIBS spectra. This technique was successfully demonstrated through a classification study of four distinct latent fingerprints using classification methods such as soft independent modeling of class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA). The novel method yielded an accuracy of more than 85% and was proven to be sufficiently robust. Furthermore, through laser scanning analysis at a spatial interval of 125 µm, the overlapping fingerprints were reconstructed as separate two-dimensional forms.

Multivariate Classification of Original and Fake Perfumes by Ion Analysis and Ethanol Content.

PubMed

Gomes, Clêrton L; de Lima, Ari Clecius A; Loiola, Adonay R; da Silva, Abel B R; Cândido, Manuela C L; Nascimento, Ronaldo F

2016-07-01

The increased marketing of fake perfumes has encouraged us to investigate how to identify such products by their chemical characteristics and multivariate analysis. The aim of this study was to present an alternative approach to distinguish original from fake perfumes by means of the investigation of sodium, potassium, chloride ions, and ethanol contents by chemometric tools. For this, 50 perfumes were used (25 original and 25 counterfeit) for the analysis of ions (ion chromatography) and ethanol (gas chromatography). The results demonstrated that the fake perfume had low levels of ethanol and high levels of chloride compared to the original product. The data were treated by chemometric tools such as principal component analysis and linear discriminant analysis. This study proved that the analysis of ethanol is an effective method of distinguishing original from the fake products, and it may potentially be used to assist legal authorities in such cases. © 2016 American Academy of Forensic Sciences.

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

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

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

2008-01-01

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

Spatial assessment of air quality patterns in Malaysia using multivariate analysis

NASA Astrophysics Data System (ADS)

Dominick, Doreena; Juahir, Hafizan; Latif, Mohd Talib; Zain, Sharifuddin M.; Aris, Ahmad Zaharin

2012-12-01

This study aims to investigate possible sources of air pollutants and the spatial patterns within the eight selected Malaysian air monitoring stations based on a two-year database (2008-2009). The multivariate analysis was applied on the dataset. It incorporated Hierarchical Agglomerative Cluster Analysis (HACA) to access the spatial patterns, Principal Component Analysis (PCA) to determine the major sources of the air pollution and Multiple Linear Regression (MLR) to assess the percentage contribution of each air pollutant. The HACA results grouped the eight monitoring stations into three different clusters, based on the characteristics of the air pollutants and meteorological parameters. The PCA analysis showed that the major sources of air pollution were emissions from motor vehicles, aircraft, industries and areas of high population density. The MLR analysis demonstrated that the main pollutant contributing to variability in the Air Pollutant Index (API) at all stations was particulate matter with a diameter of less than 10 μm (PM10). Further MLR analysis showed that the main air pollutant influencing the high concentration of PM10 was carbon monoxide (CO). This was due to combustion processes, particularly originating from motor vehicles. Meteorological factors such as ambient temperature, wind speed and humidity were also noted to influence the concentration of PM10.

Assessment of changes of vector borne diseases with wetland characteristics using multivariate analysis.

PubMed

Sheela, A M; Sarun, S; Justus, J; Vineetha, P; Sheeja, R V

2015-04-01

Vector borne diseases are a threat to human health. Little attention has been paid to the prevention of these diseases. We attempted to identify the significant wetland characteristics associated with the spread of chikungunya, dengue fever and malaria in Kerala, a tropical region of South West India using multivariate analyses (hierarchical cluster analysis, factor analysis and multiple regression). High/medium turbid coastal lagoons and inland water-logged wetlands with aquatic vegetation have significant effect on the incidence of chikungunya while dengue influenced by high turbid coastal beaches and malaria by medium turbid coastal beaches. The high turbidity in water is due to the urban waste discharge namely sewage, sullage and garbage from the densely populated cities and towns. The large extent of wetland is low land area favours the occurrence of vector borne diseases. Hence the provision of pollution control measures at source including soil erosion control measures is vital. The identification of vulnerable zones favouring the vector borne diseases will help the authorities to control pollution especially from urban areas and prevent these vector borne diseases. Future research should cover land use cover changes, climatic factors, seasonal variations in weather and pollution factors favouring the occurrence of vector borne diseases.

An efficient approach to identify different chemical markers between fibrous root and rhizome of Anemarrhena asphodeloides by ultra high-performance liquid chromatography quadrupole time-of-flight tandem mass spectrometry with multivariate statistical analysis.

PubMed

Wang, Fang-Xu; Yuan, Jian-Chao; Kang, Li-Ping; Pang, Xu; Yan, Ren-Yi; Zhao, Yang; Zhang, Jie; Sun, Xin-Guang; Ma, Bai-Ping

2016-09-10

An ultra high-performance liquid chromatography quadrupole time-of-flight tandem mass spectrometry approach coupled with multivariate statistical analysis was established and applied to rapidly distinguish the chemical differences between fibrous root and rhizome of Anemarrhena asphodeloides. The datasets of tR-m/z pairs, ion intensity and sample code were processed by principal component analysis and orthogonal partial least squares discriminant analysis. Chemical markers could be identified based on their exact mass data, fragmentation characteristics, and retention times. And the new compounds among chemical markers could be isolated rapidly guided by the ultra high-performance liquid chromatography quadrupole time-of-flight tandem mass spectrometry and their definitive structures would be further elucidated by NMR spectra. Using this approach, twenty-four markers were identified on line including nine new saponins and five new steroidal saponins of them were obtained in pure form. The study validated this proposed approach as a suitable method for identification of the chemical differences between various medicinal parts in order to expand medicinal parts and increase the utilization rate of resources. Copyright © 2016 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…

Rapid and Simultaneous Prediction of Eight Diesel Quality Parameters through ATR-FTIR Analysis.

PubMed

Nespeca, Maurilio Gustavo; Hatanaka, Rafael Rodrigues; Flumignan, Danilo Luiz; de Oliveira, José Eduardo

2018-01-01

Quality assessment of diesel fuel is highly necessary for society, but the costs and time spent are very high while using standard methods. Therefore, this study aimed to develop an analytical method capable of simultaneously determining eight diesel quality parameters (density; flash point; total sulfur content; distillation temperatures at 10% (T10), 50% (T50), and 85% (T85) recovery; cetane index; and biodiesel content) through attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy and the multivariate regression method, partial least square (PLS). For this purpose, the quality parameters of 409 samples were determined using standard methods, and their spectra were acquired in ranges of 4000-650 cm -1 . The use of the multivariate filters, generalized least squares weighting (GLSW) and orthogonal signal correction (OSC), was evaluated to improve the signal-to-noise ratio of the models. Likewise, four variable selection approaches were tested: manual exclusion, forward interval PLS (FiPLS), backward interval PLS (BiPLS), and genetic algorithm (GA). The multivariate filters and variables selection algorithms generated more fitted and accurate PLS models. According to the validation, the FTIR/PLS models presented accuracy comparable to the reference methods and, therefore, the proposed method can be applied in the diesel routine monitoring to significantly reduce costs and analysis time.

Rapid and Simultaneous Prediction of Eight Diesel Quality Parameters through ATR-FTIR Analysis

PubMed Central

Hatanaka, Rafael Rodrigues; Flumignan, Danilo Luiz; de Oliveira, José Eduardo

2018-01-01

Quality assessment of diesel fuel is highly necessary for society, but the costs and time spent are very high while using standard methods. Therefore, this study aimed to develop an analytical method capable of simultaneously determining eight diesel quality parameters (density; flash point; total sulfur content; distillation temperatures at 10% (T10), 50% (T50), and 85% (T85) recovery; cetane index; and biodiesel content) through attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy and the multivariate regression method, partial least square (PLS). For this purpose, the quality parameters of 409 samples were determined using standard methods, and their spectra were acquired in ranges of 4000–650 cm−1. The use of the multivariate filters, generalized least squares weighting (GLSW) and orthogonal signal correction (OSC), was evaluated to improve the signal-to-noise ratio of the models. Likewise, four variable selection approaches were tested: manual exclusion, forward interval PLS (FiPLS), backward interval PLS (BiPLS), and genetic algorithm (GA). The multivariate filters and variables selection algorithms generated more fitted and accurate PLS models. According to the validation, the FTIR/PLS models presented accuracy comparable to the reference methods and, therefore, the proposed method can be applied in the diesel routine monitoring to significantly reduce costs and analysis time. PMID:29629209

Molecular monitoring of epithelial-to-mesenchymal transition in breast cancer cells by means of Raman spectroscopy.

PubMed

Marro, M; Nieva, C; Sanz-Pamplona, R; Sierra, A

2014-09-01

In breast cancer the presence of cells undergoing the epithelial-to-mesenchymal transition is indicative of metastasis progression. Since metabolic features of breast tumour cells are critical in cancer progression and drug resistance, we hypothesized that the lipid content of malignant cells might be a useful indirect measure of cancer progression. In this study Multivariate Curve Resolution was applied to cellular Raman spectra to assess the metabolic composition of breast cancer cells undergoing the epithelial to mesenchymal transition. Multivariate Curve Resolution analysis led to the conclusion that this transition affects the lipid profile of cells, increasing tryptophan but maintaining a low fatty acid content in comparison with highly metastatic cells. Supporting those results, a Partial Least Square-Discriminant analysis was performed to test the ability of Raman spectroscopy to discriminate the initial steps of epithelial to mesenchymal transition in breast cancer cells. We achieved a high level of sensitivity and specificity, 94% and 100%, respectively. In conclusion, Raman microspectroscopy coupled with Multivariate Curve Resolution enables deconvolution and tracking of the molecular content of cancer cells during a biochemical process, being a powerful, rapid, reagent-free and non-invasive tool for identifying metabolic features of breast cancer cell aggressiveness at first stages of malignancy. Copyright © 2014 Elsevier B.V. All rights reserved.

Identifying HIV associated neurocognitive disorder using large-scale Granger causality analysis on resting-state functional MRI

NASA Astrophysics Data System (ADS)

DSouza, Adora M.; Abidin, Anas Z.; Leistritz, Lutz; Wismüller, Axel

2017-02-01

We investigate the applicability of large-scale Granger Causality (lsGC) for extracting a measure of multivariate information flow between pairs of regional brain activities from resting-state functional MRI (fMRI) and test the effectiveness of these measures for predicting a disease state. Such pairwise multivariate measures of interaction provide high-dimensional representations of connectivity profiles for each subject and are used in a machine learning task to distinguish between healthy controls and individuals presenting with symptoms of HIV Associated Neurocognitive Disorder (HAND). Cognitive impairment in several domains can occur as a result of HIV infection of the central nervous system. The current paradigm for assessing such impairment is through neuropsychological testing. With fMRI data analysis, we aim at non-invasively capturing differences in brain connectivity patterns between healthy subjects and subjects presenting with symptoms of HAND. To classify the extracted interaction patterns among brain regions, we use a prototype-based learning algorithm called Generalized Matrix Learning Vector Quantization (GMLVQ). Our approach to characterize connectivity using lsGC followed by GMLVQ for subsequent classification yields good prediction results with an accuracy of 87% and an area under the ROC curve (AUC) of up to 0.90. We obtain a statistically significant improvement (p<0.01) over a conventional Granger causality approach (accuracy = 0.76, AUC = 0.74). High accuracy and AUC values using our multivariate method to connectivity analysis suggests that our approach is able to better capture changes in interaction patterns between different brain regions when compared to conventional Granger causality analysis known from the literature.

Effect of altered sensory conditions on multivariate descriptors of human postural sway

NASA Technical Reports Server (NTRS)

Kuo, A. D.; Speers, R. A.; Peterka, R. J.; Horak, F. B.; Peterson, B. W. (Principal Investigator)

1998-01-01

Multivariate descriptors of sway were used to test whether altered sensory conditions result not only in changes in amount of sway but also in postural coordination. Eigenvalues and directions of eigenvectors of the covariance of shnk and hip angles were used as a set of multivariate descriptors. These quantities were measured in 14 healthy adult subjects performing the Sensory Organization test, which disrupts visual and somatosensory information used for spatial orientation. Multivariate analysis of variance and discriminant analysis showed that resulting sway changes were at least bivariate in character, with visual and somatosensory conditions producing distinct changes in postural coordination. The most significant changes were found when somatosensory information was disrupted by sway-referencing of the support surface (P = 3.2 x 10(-10)). The resulting covariance measurements showed that subjects not only swayed more but also used increased hip motion analogous to the hip strategy. Disruption of vision, by either closing the eyes or sway-referencing the visual surround, also resulted in altered sway (P = 1.7 x 10(-10)), with proportionately more motion of the center of mass than with platform sway-referencing. As shown by discriminant analysis, an optimal univariate measure could explain at most 90% of the behavior due to altered sensory conditions. The remaining 10%, while smaller, are highly significant changes in posture control that depend on sensory conditions. The results imply that normal postural coordination of the trunk and legs requires both somatosensory and visual information and that each sensory modality makes a unique contribution to posture control. Descending postural commands are multivariate in nature, and the motion at each joint is affected uniquely by input from multiple sensors.

Multivariate meta-analysis of prognostic factor studies with multiple cut-points and/or methods of measurement.

PubMed

Riley, Richard D; Elia, Eleni G; Malin, Gemma; Hemming, Karla; Price, Malcolm P

2015-07-30

A prognostic factor is any measure that is associated with the risk of future health outcomes in those with existing disease. Often, the prognostic ability of a factor is evaluated in multiple studies. However, meta-analysis is difficult because primary studies often use different methods of measurement and/or different cut-points to dichotomise continuous factors into 'high' and 'low' groups; selective reporting is also common. We illustrate how multivariate random effects meta-analysis models can accommodate multiple prognostic effect estimates from the same study, relating to multiple cut-points and/or methods of measurement. The models account for within-study and between-study correlations, which utilises more information and reduces the impact of unreported cut-points and/or measurement methods in some studies. The applicability of the approach is improved with individual participant data and by assuming a functional relationship between prognostic effect and cut-point to reduce the number of unknown parameters. The models provide important inferential results for each cut-point and method of measurement, including the summary prognostic effect, the between-study variance and a 95% prediction interval for the prognostic effect in new populations. Two applications are presented. The first reveals that, in a multivariate meta-analysis using published results, the Apgar score is prognostic of neonatal mortality but effect sizes are smaller at most cut-points than previously thought. In the second, a multivariate meta-analysis of two methods of measurement provides weak evidence that microvessel density is prognostic of mortality in lung cancer, even when individual participant data are available so that a continuous prognostic trend is examined (rather than cut-points). © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Serum dehydroepiandrosterone sulphate, psychosocial factors and musculoskeletal pain in workers.

PubMed

Marinelli, A; Prodi, A; Pesel, G; Ronchese, F; Bovenzi, M; Negro, C; Larese Filon, F

2017-12-30

The serum level of dehydroepiandrosterone sulphate (DHEA-S) has been suggested as a biological marker of stress. To assess the association between serum DHEA-S, psychosocial factors and musculoskeletal (MS) pain in university workers. The study population included voluntary workers at the scientific departments of the University of Trieste (Italy) who underwent periodical health surveillance from January 2011 to June 2012. DHEA-S level was analysed in serum. The assessment tools included the General Health Questionnaire (GHQ) and a modified Nordic musculoskeletal symptoms questionnaire. The relation between DHEA-S, individual characteristics, pain perception and psychological factors was assessed by means of multivariable linear regression analysis. There were 189 study participants. The study population was characterized by high reward and low effort. Pain perception in the neck, shoulder, upper limbs, upper back and lower back was reported by 42, 32, 19, 29 and 43% of people, respectively. In multivariable regression analysis, gender, age and pain perception in the shoulder and upper limbs were significantly related to serum DHEA-S. Effort and overcommitment were related to shoulder and neck pain but not to DHEA-S. The GHQ score was associated with pain perception in different body sites and inversely to DHEA-S but significance was lost in multivariable regression analysis. DHEA-S was associated with age, gender and perception of MS pain, while effort-reward imbalance dimensions and GHQ score failed to reach the statistical significance in multivariable regression analysis. © The Author(s) 2017. Published by Oxford University Press on behalf of the Society of Occupational Medicine. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Multivariate functional response regression, with application to fluorescence spectroscopy in a cervical pre-cancer study.

PubMed

Zhu, Hongxiao; Morris, Jeffrey S; Wei, Fengrong; Cox, Dennis D

2017-07-01

Many scientific studies measure different types of high-dimensional signals or images from the same subject, producing multivariate functional data. These functional measurements carry different types of information about the scientific process, and a joint analysis that integrates information across them may provide new insights into the underlying mechanism for the phenomenon under study. Motivated by fluorescence spectroscopy data in a cervical pre-cancer study, a multivariate functional response regression model is proposed, which treats multivariate functional observations as responses and a common set of covariates as predictors. This novel modeling framework simultaneously accounts for correlations between functional variables and potential multi-level structures in data that are induced by experimental design. The model is fitted by performing a two-stage linear transformation-a basis expansion to each functional variable followed by principal component analysis for the concatenated basis coefficients. This transformation effectively reduces the intra-and inter-function correlations and facilitates fast and convenient calculation. A fully Bayesian approach is adopted to sample the model parameters in the transformed space, and posterior inference is performed after inverse-transforming the regression coefficients back to the original data domain. The proposed approach produces functional tests that flag local regions on the functional effects, while controlling the overall experiment-wise error rate or false discovery rate. It also enables functional discriminant analysis through posterior predictive calculation. Analysis of the fluorescence spectroscopy data reveals local regions with differential expressions across the pre-cancer and normal samples. These regions may serve as biomarkers for prognosis and disease assessment.

High serum uric acid concentration predicts poor survival in patients with breast cancer.

PubMed

Yue, Cai-Feng; Feng, Pin-Ning; Yao, Zhen-Rong; Yu, Xue-Gao; Lin, Wen-Bin; Qian, Yuan-Min; Guo, Yun-Miao; Li, Lai-Sheng; Liu, Min

2017-10-01

Uric acid is a product of purine metabolism. Recently, uric acid has gained much attraction in cancer. In this study, we aim to investigate the clinicopathological and prognostic significance of serum uric acid concentration in breast cancer patients. A total of 443 female patients with histopathologically diagnosed breast cancer were included. After a mean follow-up time of 56months, survival was analysed using the Kaplan-Meier method. To further evaluate the prognostic significance of uric acid concentrations, univariate and multivariate Cox regression analyses were applied. Of the clinicopathological parameters, uric acid concentration was associated with age, body mass index, ER status and PR status. Univariate analysis identified that patients with increased uric acid concentration had a significantly inferior overall survival (HR 2.13, 95% CI 1.15-3.94, p=0.016). In multivariate analysis, we found that high uric acid concentration is an independent prognostic factor predicting death, but insufficient to predict local relapse or distant metastasis. Kaplan-Meier analysis indicated that high uric acid concentration is related to the poor overall survival (p=0.013). High uric acid concentration predicts poor survival in patients with breast cancer, and might serve as a potential marker for appropriate management of breast cancer patients. Copyright © 2017 Elsevier B.V. All rights reserved.

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

PubMed

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

2017-02-09

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

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

PubMed Central

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

2017-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2017-02-01

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

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.

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.

Age, gender and tumour size predict work capacity after surgical treatment of vestibular schwannomas.

PubMed

Al-Shudifat, Abdul Rahman; Kahlon, Babar; Höglund, Peter; Soliman, Ahmed Y; Lindskog, Kristoffer; Siesjo, Peter

2014-01-01

The aim of the present study was to identify predictive factors for outcome after surgery of vestibular schwannomas. This is a retrospective study with partially collected prospective data of patients who were surgically treated for vestibular schwannomas at a single institution from 1979 to 2000. Patients with recurrent tumours, NF2 and those incapable of answering questionnaires were excluded from the study. The short form 36 (SF36) questionnaire and a specific questionnaire regarding neurological status, work status and independent life (IL) status were sent to all eligible patients. The questionnaires were sent to 430 eligible patients (out of 537) and 395 (93%) responded. Scores for work capacity (WC) and IL were compared with SF36 scores as outcome estimates. Patients were divided into two groups (<64, ≥64-years-old) in order to assess them for either WC or IL. Putative preoperative and postoperative predictive factors were tested in univariate and multivariable regression analysis for the outcome scores of WC, IL and SF36. In the group <64 years, age, gender and tumour diameter were independent predictive factors for postoperative WC in multivariate analysis. A high-risk group was identified in women with age >50 years and tumour diameter >25 mm. In patients ≥64, gender and tumour diameter were significant predictive factors for IL in univariate analysis. Perioperative and postoperative objective factors as length of surgery, blood loss and complications did not predict outcome in the multivariable analysis for any age group. Patients' assessment of change in balance function was the only neurological factor that showed significance both in univariate and multivariable analysis in both age cohorts. While SF36 scores were lower in surgically treated patients in relation to normograms for the general population, they did not correlate significantly to WC and IL. The SF36 questionnaire did not correlate to outcome measures as WC and IL in patients undergoing surgery for vestibular schwannomas. Women and patients above 50 years with larger tumours have a high risk for reduced WC after surgical treatment. These results question the validity of quality of life scores in assessment of outcome after surgery of benign skullbase lesions.

NIR and Py-mbms coupled with multivariate data analysis as a high-throughput biomass characterization technique: a review

PubMed Central

Xiao, Li; Wei, Hui; Himmel, Michael E.; Jameel, Hasan; Kelley, Stephen S.

2014-01-01

Optimizing the use of lignocellulosic biomass as the feedstock for renewable energy production is currently being developed globally. Biomass is a complex mixture of cellulose, hemicelluloses, lignins, extractives, and proteins; as well as inorganic salts. Cell wall compositional analysis for biomass characterization is laborious and time consuming. In order to characterize biomass fast and efficiently, several high through-put technologies have been successfully developed. Among them, near infrared spectroscopy (NIR) and pyrolysis-molecular beam mass spectrometry (Py-mbms) are complementary tools and capable of evaluating a large number of raw or modified biomass in a short period of time. NIR shows vibrations associated with specific chemical structures whereas Py-mbms depicts the full range of fragments from the decomposition of biomass. Both NIR vibrations and Py-mbms peaks are assigned to possible chemical functional groups and molecular structures. They provide complementary information of chemical insight of biomaterials. However, it is challenging to interpret the informative results because of the large amount of overlapping bands or decomposition fragments contained in the spectra. In order to improve the efficiency of data analysis, multivariate analysis tools have been adapted to define the significant correlations among data variables, so that the large number of bands/peaks could be replaced by a small number of reconstructed variables representing original variation. Reconstructed data variables are used for sample comparison (principal component analysis) and for building regression models (partial least square regression) between biomass chemical structures and properties of interests. In this review, the important biomass chemical structures measured by NIR and Py-mbms are summarized. The advantages and disadvantages of conventional data analysis methods and multivariate data analysis methods are introduced, compared and evaluated. This review aims to serve as a guide for choosing the most effective data analysis methods for NIR and Py-mbms characterization of biomass. PMID:25147552

Hierarchical multivariate covariance analysis of metabolic connectivity.

PubMed

Carbonell, Felix; Charil, Arnaud; Zijdenbos, Alex P; Evans, Alan C; Bedell, Barry J

2014-12-01

Conventional brain connectivity analysis is typically based on the assessment of interregional correlations. Given that correlation coefficients are derived from both covariance and variance, group differences in covariance may be obscured by differences in the variance terms. To facilitate a comprehensive assessment of connectivity, we propose a unified statistical framework that interrogates the individual terms of the correlation coefficient. We have evaluated the utility of this method for metabolic connectivity analysis using [18F]2-fluoro-2-deoxyglucose (FDG) positron emission tomography (PET) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. As an illustrative example of the utility of this approach, we examined metabolic connectivity in angular gyrus and precuneus seed regions of mild cognitive impairment (MCI) subjects with low and high β-amyloid burdens. This new multivariate method allowed us to identify alterations in the metabolic connectome, which would not have been detected using classic seed-based correlation analysis. Ultimately, this novel approach should be extensible to brain network analysis and broadly applicable to other imaging modalities, such as functional magnetic resonance imaging (MRI).

Identifying Pleiotropic Genes in Genome-Wide Association Studies for Multivariate Phenotypes with Mixed Measurement Scales

PubMed Central

Williams, L. Keoki; Buu, Anne

2017-01-01

We propose a multivariate genome-wide association test for mixed continuous, binary, and ordinal phenotypes. A latent response model is used to estimate the correlation between phenotypes with different measurement scales so that the empirical distribution of the Fisher’s combination statistic under the null hypothesis is estimated efficiently. The simulation study shows that our proposed correlation estimation methods have high levels of accuracy. More importantly, our approach conservatively estimates the variance of the test statistic so that the type I error rate is controlled. The simulation also shows that the proposed test maintains the power at the level very close to that of the ideal analysis based on known latent phenotypes while controlling the type I error. In contrast, conventional approaches–dichotomizing all observed phenotypes or treating them as continuous variables–could either reduce the power or employ a linear regression model unfit for the data. Furthermore, the statistical analysis on the database of the Study of Addiction: Genetics and Environment (SAGE) demonstrates that conducting a multivariate test on multiple phenotypes can increase the power of identifying markers that may not be, otherwise, chosen using marginal tests. The proposed method also offers a new approach to analyzing the Fagerström Test for Nicotine Dependence as multivariate phenotypes in genome-wide association studies. PMID:28081206

Combining ANOVA-PCA with POCHEMON to analyse micro-organism development in a polymicrobial environment.

PubMed

Geurts, Brigitte P; Neerincx, Anne H; Bertrand, Samuel; Leemans, Manja A A P; Postma, Geert J; Wolfender, Jean-Luc; Cristescu, Simona M; Buydens, Lutgarde M C; Jansen, Jeroen J

2017-04-22

Revealing the biochemistry associated to micro-organismal interspecies interactions is highly relevant for many purposes. Each pathogen has a characteristic metabolic fingerprint that allows identification based on their unique multivariate biochemistry. When pathogen species come into mutual contact, their co-culture will display a chemistry that may be attributed both to mixing of the characteristic chemistries of the mono-cultures and to competition between the pathogens. Therefore, investigating pathogen development in a polymicrobial environment requires dedicated chemometric methods to untangle and focus upon these sources of variation. The multivariate data analysis method Projected Orthogonalised Chemical Encounter Monitoring (POCHEMON) is dedicated to highlight metabolites characteristic for the interaction of two micro-organisms in co-culture. However, this approach is currently limited to a single time-point, while development of polymicrobial interactions may be highly dynamic. A well-known multivariate implementation of Analysis of Variance (ANOVA) uses Principal Component Analysis (ANOVA-PCA). This allows the overall dynamics to be separated from the pathogen-specific chemistry to analyse the contributions of both aspects separately. For this reason, we propose to integrate ANOVA-PCA with the POCHEMON approach to disentangle the pathogen dynamics and the specific biochemistry in interspecies interactions. Two complementary case studies show great potential for both liquid and gas chromatography - mass spectrometry to reveal novel information on chemistry specific to interspecies interaction during pathogen development. Copyright © 2017 The Author(s). Published by Elsevier B.V. All rights reserved.

Intrinsic Connectivity Provides the Baseline Framework for Variability in Motor Performance: A Multivariate Fusion Analysis of Low- and High-Frequency Resting-State Oscillations and Antisaccade Performance.

PubMed

Jamadar, Sharna D; Egan, Gary F; Calhoun, Vince D; Johnson, Beth; Fielding, Joanne

2016-07-01

Intrinsic brain activity provides the functional framework for the brain's full repertoire of behavioral responses; that is, a common mechanism underlies intrinsic and extrinsic neural activity, with extrinsic activity building upon the underlying baseline intrinsic activity. The generation of a motor movement in response to sensory stimulation is one of the most fundamental functions of the central nervous system. Since saccadic eye movements are among our most stereotyped motor responses, we hypothesized that individual variability in the ability to inhibit a prepotent saccade and make a voluntary antisaccade would be related to individual variability in intrinsic connectivity. Twenty-three individuals completed the antisaccade task and resting-state functional magnetic resonance imaging (fMRI). A multivariate analysis of covariance identified relationships between fMRI oscillations (0.01-0.2 Hz) of resting-state networks determined using high-dimensional independent component analysis and antisaccade performance (latency, error rate). Significant multivariate relationships between antisaccade latency and directional error rate were obtained in independent components across the entire brain. Some of the relationships were obtained in components that overlapped substantially with the task; however, many were obtained in components that showed little overlap with the task. The current results demonstrate that even in the absence of a task, spectral power in regions showing little overlap with task activity predicts an individual's performance on a saccade task.

Applications of High Resolution Laser Induced Breakdown Spectroscopy for Environmental and Biological Samples

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

Martin, Madhavi Z; Labbe, Nicole; Wagner, Rebekah J.

2013-01-01

This chapter details the application of LIBS in a number of environmental areas of research such as carbon sequestration and climate change. LIBS has also been shown to be useful in other high resolution environmental applications for example, elemental mapping and detection of metals in plant materials. LIBS has also been used in phytoremediation applications. Other biological research involves a detailed understanding of wood chemistry response to precipitation variations and also to forest fires. A cross-section of Mountain pine (pinceae Pinus pungen Lamb.) was scanned using a translational stage to determine the differences in the chemical features both before andmore » after a fire event. Consequently, by monitoring the elemental composition pattern of a tree and by looking for abrupt changes, one can reconstruct the disturbance history of a tree and a forest. Lastly we have shown that multivariate analysis of the LIBS data is necessary to standardize the analysis and correlate to other standard laboratory techniques. LIBS along with multivariate statistical analysis makes it a very powerful technology that can be transferred from laboratory to field applications with ease.« less

Patient Safety Incidents and Nursing Workload 1

PubMed Central

Carlesi, Katya Cuadros; Padilha, Kátia Grillo; Toffoletto, Maria Cecília; Henriquez-Roldán, Carlos; Juan, Monica Andrea Canales

2017-01-01

ABSTRACT Objective: to identify the relationship between the workload of the nursing team and the occurrence of patient safety incidents linked to nursing care in a public hospital in Chile. Method: quantitative, analytical, cross-sectional research through review of medical records. The estimation of workload in Intensive Care Units (ICUs) was performed using the Therapeutic Interventions Scoring System (TISS-28) and for the other services, we used the nurse/patient and nursing assistant/patient ratios. Descriptive univariate and multivariate analysis were performed. For the multivariate analysis we used principal component analysis and Pearson correlation. Results: 879 post-discharge clinical records and the workload of 85 nurses and 157 nursing assistants were analyzed. The overall incident rate was 71.1%. It was found a high positive correlation between variables workload (r = 0.9611 to r = 0.9919) and rate of falls (r = 0.8770). The medication error rates, mechanical containment incidents and self-removal of invasive devices were not correlated with the workload. Conclusions: the workload was high in all units except the intermediate care unit. Only the rate of falls was associated with the workload. PMID:28403334

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…

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

Transition from a multiport technique to a single-port technique for lung cancer surgery: is lymph node dissection inferior using the single-port technique?†.

PubMed

Liu, Chia-Chuan; Shih, Chih-Shiun; Pennarun, Nicolas; Cheng, Chih-Tao

2016-01-01

The feasibility and radicalism of lymph node dissection for lung cancer surgery by a single-port technique has frequently been challenged. We performed a retrospective cohort study to investigate this issue. Two chest surgeons initiated multiple-port thoracoscopic surgery in a 180-bed cancer centre in 2005 and shifted to a single-port technique gradually after 2010. Data, including demographic and clinical information, from 389 patients receiving multiport thoracoscopic lobectomy or segmentectomy and 149 consecutive patients undergoing either single-port lobectomy or segmentectomy for primary non-small-cell lung cancer were retrieved and entered for statistical analysis by multivariable linear regression models and Box-Cox transformed multivariable analysis. The mean number of total dissected lymph nodes in the lobectomy group was 28.5 ± 11.7 for the single-port group versus 25.2 ± 11.3 for the multiport group; the mean number of total dissected lymph nodes in the segmentectomy group was 19.5 ± 10.8 for the single-port group versus 17.9 ± 10.3 for the multiport group. In linear multivariable and after Box-Cox transformed multivariable analyses, the single-port approach was still associated with a higher total number of dissected lymph nodes. The total number of dissected lymph nodes for primary lung cancer surgery by single-port video-assisted thoracoscopic surgery (VATS) was higher than by multiport VATS in univariable, multivariable linear regression and Box-Cox transformed multivariable analyses. This study confirmed that highly effective lymph node dissection could be achieved through single-port VATS in our setting. © The Author 2015. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.

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.

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.

Time to antibiotics and outcomes in cancer patients with febrile neutropenia

PubMed Central

2014-01-01

Background Febrile neutropenia is an oncologic emergency. The timing of antibiotics administration in patients with febrile neutropenia may result in adverse outcomes. Our study aims to determine time-to- antibiotic administration in patients with febrile neutropenia, and its relationship with length of hospital stay, intensive care unit monitoring, and hospital mortality. Methods The study population was comprised of adult cancer patients with febrile neutropenia who were hospitalized, at a tertiary care hospital, between January 2010 and December 2011. Using Multination Association of Supportive Care in Cancer (MASCC) risk score, the study cohort was divided into high and low risk groups. A multivariate regression analysis was performed to assess relationship between time-to- antibiotic administration and various outcome variables. Results One hundred and five eligible patients with median age of 60 years (range: 18–89) and M:F of 43:62 were identified. Thirty-seven (35%) patients were in MASCC high risk group. Median time-to- antibiotic administration was 2.5 hrs (range: 0.03-50) and median length of hospital stay was 6 days (range: 1–57). In the multivariate analysis time-to- antibiotic administration (regression coefficient [RC]: 0.31 days [95% CI: 0.13-0.48]), known source of fever (RC: 4.1 days [95% CI: 0.76-7.5]), and MASCC high risk group (RC: 4 days [95% CI: 1.1-7.0]) were significantly correlated with longer hospital stay. Of 105 patients, 5 (4.7%) died & or required ICU monitoring. In multivariate analysis no variables significantly correlated with mortality or ICU monitoring. Conclusions Our study revealed that delay in antibiotics administration has been associated with a longer hospital stay. PMID:24716604

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.

Job insecurity and risk of diabetes: a meta-analysis of individual participant data.

PubMed

Ferrie, Jane E; Virtanen, Marianna; Jokela, Markus; Madsen, Ida E H; Heikkilä, Katriina; Alfredsson, Lars; Batty, G David; Bjorner, Jakob B; Borritz, Marianne; Burr, Hermann; Dragano, Nico; Elovainio, Marko; Fransson, Eleonor I; Knutsson, Anders; Koskenvuo, Markku; Koskinen, Aki; Kouvonen, Anne; Kumari, Meena; Nielsen, Martin L; Nordin, Maria; Oksanen, Tuula; Pahkin, Krista; Pejtersen, Jan H; Pentti, Jaana; Salo, Paula; Shipley, Martin J; Suominen, Sakari B; Tabák, Adam; Theorell, Töres; Väänänen, Ari; Vahtera, Jussi; Westerholm, Peter J M; Westerlund, Hugo; Rugulies, Reiner; Nyberg, Solja T; Kivimäki, Mika

2016-12-06

Job insecurity has been associated with certain health outcomes. We examined the role of job insecurity as a risk factor for incident diabetes. We used individual participant data from 8 cohort studies identified in 2 open-access data archives and 11 cohort studies participating in the Individual-Participant-Data Meta-analysis in Working Populations Consortium. We calculated study-specific estimates of the association between job insecurity reported at baseline and incident diabetes over the follow-up period. We pooled the estimates in a meta-analysis to produce a summary risk estimate. The 19 studies involved 140 825 participants from Australia, Europe and the United States, with a mean follow-up of 9.4 years and 3954 incident cases of diabetes. In the preliminary analysis adjusted for age and sex, high job insecurity was associated with an increased risk of incident diabetes compared with low job insecurity (adjusted odds ratio [OR] 1.19, 95% confidence interval [CI] 1.09-1.30). In the multivariable-adjusted analysis restricted to 15 studies with baseline data for all covariates (age, sex, socioeconomic status, obesity, physical activity, alcohol and smoking), the association was slightly attenuated (adjusted OR 1.12, 95% CI 1.01-1.24). Heterogeneity between the studies was low to moderate (age- and sex-adjusted model: I 2 = 24%, p = 0.2; multivariable-adjusted model: I 2 = 27%, p = 0.2). In the multivariable-adjusted analysis restricted to high-quality studies, in which the diabetes diagnosis was ascertained from electronic medical records or clinical examination, the association was similar to that in the main analysis (adjusted OR 1.19, 95% CI 1.04-1.35). Our findings suggest that self-reported job insecurity is associated with a modest increased risk of incident diabetes. Health care personnel should be aware of this association among workers reporting job insecurity. © 2016 Canadian Medical Association or its licensors.

Patterns and Predictors of Language and Literacy Abilities 4-10 Years in the Longitudinal Study of Australian Children

PubMed Central

Zubrick, Stephen R.; Taylor, Catherine L.; Christensen, Daniel

2015-01-01

Aims Oral language is the foundation of literacy. Naturally, policies and practices to promote children’s literacy begin in early childhood and have a strong focus on developing children’s oral language, especially for children with known risk factors for low language ability. The underlying assumption is that children’s progress along the oral to literate continuum is stable and predictable, such that low language ability foretells low literacy ability. This study investigated patterns and predictors of children’s oral language and literacy abilities at 4, 6, 8 and 10 years. The study sample comprised 2,316 to 2,792 children from the first nationally representative Longitudinal Study of Australian Children (LSAC). Six developmental patterns were observed, a stable middle-high pattern, a stable low pattern, an improving pattern, a declining pattern, a fluctuating low pattern, and a fluctuating middle-high pattern. Most children (69%) fit a stable middle-high pattern. By contrast, less than 1% of children fit a stable low pattern. These results challenged the view that children’s progress along the oral to literate continuum is stable and predictable. Findings Multivariate logistic regression was used to investigate risks for low literacy ability at 10 years and sensitivity-specificity analysis was used to examine the predictive utility of the multivariate model. Predictors were modelled as risk variables with the lowest level of risk as the reference category. In the multivariate model, substantial risks for low literacy ability at 10 years, in order of descending magnitude, were: low school readiness, Aboriginal and/or Torres Strait Islander status and low language ability at 8 years. Moderate risks were high temperamental reactivity, low language ability at 4 years, and low language ability at 6 years. The following risk factors were not statistically significant in the multivariate model: Low maternal consistency, low family income, health care card, child not read to at home, maternal smoking, maternal education, family structure, temperamental persistence, and socio-economic area disadvantage. The results of the sensitivity-specificity analysis showed that a well-fitted multivariate model featuring risks of substantive magnitude did not do particularly well in predicting low literacy ability at 10 years. PMID:26352436

Multivariate analysis of heavy metal contents in soils, sediments and water in the region of Meknes (central Morocco).

PubMed

Tahri, M; Benyaïch, F; Bounakhla, M; Bilal, E; Gruffat, J J; Moutte, J; Garcia, D

2005-03-01

Concentrations of Al, Fe, Cr, Cu, Ni, Pb and Zn in soils, sediments and water samples collected along the Oued Boufekrane river (Meknes, central Morocco) were determined. In soils, a homogeneous distribution of metal concentrations was observed throughout the study area except for Pb, which presents high enrichment at sites located at the vicinity of a main highway. In sediments, high enrichment, with respect to upstream sites, were observed downstream of the city of Meknes for Al, Cr, Fe and Ni and inside the city for Cu, Zn and Pb. In water samples, the metal contents showed to correlate with their homologues in sediments suggesting that the metal contents in water and sediments have identical origins. Descriptive statistics and multivariate analysis (principal factor method, PFM) were used to assist the interpretation of elemental data. This allowed the determination of the correlations between the metals and the identification of three main factor loadings controlling the metal variability in soils and sediments.

Boosting Higgs pair production in the [Formula: see text] final state with multivariate techniques.

PubMed

Behr, J Katharina; Bortoletto, Daniela; Frost, James A; Hartland, Nathan P; Issever, Cigdem; Rojo, Juan

2016-01-01

The measurement of Higgs pair production will be a cornerstone of the LHC program in the coming years. Double Higgs production provides a crucial window upon the mechanism of electroweak symmetry breaking and has a unique sensitivity to the Higgs trilinear coupling. We study the feasibility of a measurement of Higgs pair production in the [Formula: see text] final state at the LHC. Our analysis is based on a combination of traditional cut-based methods with state-of-the-art multivariate techniques. We account for all relevant backgrounds, including the contributions from light and charm jet mis-identification, which are ultimately comparable in size to the irreducible 4 b QCD background. We demonstrate the robustness of our analysis strategy in a high pileup environment. For an integrated luminosity of [Formula: see text] ab[Formula: see text], a signal significance of [Formula: see text] is obtained, indicating that the [Formula: see text] final state alone could allow for the observation of double Higgs production at the High Luminosity LHC.

Hyponatremia in Guillain-Barré Syndrome.

PubMed

Rumalla, Kavelin; Reddy, Adithi Y; Letchuman, Vijay; Mittal, Manoj K

2017-06-01

To evaluate incidence, risk factors, and in-hospital outcomes associated with hyponatremia in patients hospitalized for Guillain-Barré Syndrome (GBS). We identified adult patients with GBS in the Nationwide Inpatient Sample (2002-2011). Univariate and multivariable analyses were used. Among 54,778 patients hospitalized for GBS, the incidence of hyponatremia was 11.8% (compared with 4.0% in non-GBS patients) and increased from 6.9% in 2002 to 13.5% in 2011 (P < 0.0001). Risk factors associated with hyponatremia in multivariable analysis included advanced age, deficiency anemia, alcohol abuse, hypertension, and intravenous immunoglobulin (all P < 0.0001). Hyponatremia was associated with prolonged length of stay (16.07 vs. 10.41, days), increased costs (54,001 vs. 34,125, $USD), and mortality (20.5% vs. 11.6%) (all P < 0.0001). In multivariable analysis, hyponatremia was independently associated with adverse discharge disposition (odds ratio: 2.07, 95% confidence interval, 1.91-2.25, P < 0.0001). Hyponatremia is prevalent in GBS and is detrimental to patient-centered outcomes and health care costs. Sodium levels should be carefully monitored in high-risk patients.

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.

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…

Preoperative prognostic value of dynamic contrast-enhanced MRI-derived contrast transfer coefficient and plasma volume in patients with cerebral gliomas.

PubMed

Nguyen, T B; Cron, G O; Mercier, J F; Foottit, C; Torres, C H; Chakraborty, S; Woulfe, J; Jansen, G H; Caudrelier, J M; Sinclair, J; Hogan, M J; Thornhill, R E; Cameron, I G

2015-01-01

The prognostic value of dynamic contrast-enhanced MR imaging-derived plasma volume obtained in tumor and the contrast transfer coefficient has not been well-established in patients with gliomas. We determined whether plasma volume and contrast transfer coefficient in tumor correlated with survival in patients with gliomas in addition to other factors such as age, type of surgery, preoperative Karnofsky score, contrast enhancement, and histopathologic grade. This prospective study included 46 patients with a new pathologically confirmed diagnosis of glioma. The contrast transfer coefficient and plasma volume obtained in tumor maps were calculated directly from the signal-intensity curve without T1 measurements, and values were obtained from multiple small ROIs placed within tumors. Survival curve analysis was performed by dichotomizing patients into groups of high and low contrast transfer coefficient and plasma volume. Univariate analysis was performed by using dynamic contrast-enhanced parameters and clinical factors. Factors that were significant on univariate analysis were entered into multivariate analysis. For all patients with gliomas, survival was worse for groups of patients with high contrast transfer coefficient and plasma volume obtained in tumor (P < .05). In subgroups of high- and low-grade gliomas, survival was worse for groups of patients with high contrast transfer coefficient and plasma volume obtained in tumor (P < .05). Univariate analysis showed that factors associated with lower survival were age older than 50 years, low Karnofsky score, biopsy-only versus resection, marked contrast enhancement versus no/mild enhancement, high contrast transfer coefficient, and high plasma volume obtained in tumor (P < .05). In multivariate analysis, a low Karnofsky score, biopsy versus resection in combination with marked contrast enhancement, and a high contrast transfer coefficient were associated with lower survival rates (P < .05). In patients with glioma, those with a high contrast transfer coefficient have lower survival than those with low parameters. © 2015 by American Journal of Neuroradiology.

Cytokine activation is predictive of mortality in Zambian patients with AIDS-related diarrhoea.

PubMed

Zulu, Isaac; Hassan, Ghaniah; Njobvu R N, Lungowe; Dhaliwal, Winnie; Sianongo, Sandie; Kelly, Paul

2008-11-13

Mortality in Zambian AIDS patients is high, especially in patients with diarrhoea, and there is still unacceptably high mortality in Zambian patients just starting anti-retroviral therapy. We set out to determine if high concentrations of serum cytokines correlate with mortality. Serum samples from 30 healthy controls (HIV seropositive and seronegative) and 50 patients with diarrhoea (20 of whom died within 6 weeks) were analysed. Concentrations of tumour necrosis factor receptor p55 (TNFR p55), macrophage migration inhibitory factor (MIF), interleukin (IL)-6, IL-12, interferon (IFN)-gamma and C-reactive protein (CRP) were measured by ELISA, and correlated with mortality after 6 weeks follow-up. Apart from IL-12, concentrations of all cytokines, TNFR p55 and CRP increased with worsening severity of disease, showing highly statistically significant trends. In a multivariable analysis high TNFR p55, IFN-gamma, CRP and low CD4 count (CD4 count <100) were predictive of mortality. Although nutritional status (assessed by body mass index, BMI) was predictive in univariate analysis, it was not an independent predictor in multivariate analysis. High serum concentrations of TNFR p55, IFN-gamma, CRP and low CD4 count correlated with disease severity and short-term mortality in HIV-infected Zambian adults with diarrhoea. These factors were better predictors of survival than BMI. Understanding the cause of TNFR p55, IFN-gamma and CRP elevation may be useful in development of interventions to reduce mortality in AIDS patients with chronic diarrhoea in Africa.

Identification of complex metabolic states in critically injured patients using bioinformatic cluster analysis.

PubMed

Cohen, Mitchell J; Grossman, Adam D; Morabito, Diane; Knudson, M Margaret; Butte, Atul J; Manley, Geoffrey T

2010-01-01

Advances in technology have made extensive monitoring of patient physiology the standard of care in intensive care units (ICUs). While many systems exist to compile these data, there has been no systematic multivariate analysis and categorization across patient physiological data. The sheer volume and complexity of these data make pattern recognition or identification of patient state difficult. Hierarchical cluster analysis allows visualization of high dimensional data and enables pattern recognition and identification of physiologic patient states. We hypothesized that processing of multivariate data using hierarchical clustering techniques would allow identification of otherwise hidden patient physiologic patterns that would be predictive of outcome. Multivariate physiologic and ventilator data were collected continuously using a multimodal bioinformatics system in the surgical ICU at San Francisco General Hospital. These data were incorporated with non-continuous data and stored on a server in the ICU. A hierarchical clustering algorithm grouped each minute of data into 1 of 10 clusters. Clusters were correlated with outcome measures including incidence of infection, multiple organ failure (MOF), and mortality. We identified 10 clusters, which we defined as distinct patient states. While patients transitioned between states, they spent significant amounts of time in each. Clusters were enriched for our outcome measures: 2 of the 10 states were enriched for infection, 6 of 10 were enriched for MOF, and 3 of 10 were enriched for death. Further analysis of correlations between pairs of variables within each cluster reveals significant differences in physiology between clusters. Here we show for the first time the feasibility of clustering physiological measurements to identify clinically relevant patient states after trauma. These results demonstrate that hierarchical clustering techniques can be useful for visualizing complex multivariate data and may provide new insights for the care of critically injured patients.

Univariate and multivariate analysis of tannin-impregnated wood species using vibrational spectroscopy.

PubMed

Schnabel, Thomas; Musso, Maurizio; Tondi, Gianluca

2014-01-01

Vibrational spectroscopy is one of the most powerful tools in polymer science. Three main techniques--Fourier transform infrared spectroscopy (FT-IR), FT-Raman spectroscopy, and FT near-infrared (NIR) spectroscopy--can also be applied to wood science. Here, these three techniques were used to investigate the chemical modification occurring in wood after impregnation with tannin-hexamine preservatives. These spectroscopic techniques have the capacity to detect the externally added tannin. FT-IR has very strong sensitivity to the aromatic peak at around 1610 cm(-1) in the tannin-treated samples, whereas FT-Raman reflects the peak at around 1600 cm(-1) for the externally added tannin. This high efficacy in distinguishing chemical features was demonstrated in univariate analysis and confirmed via cluster analysis. Conversely, the results of the NIR measurements show noticeable sensitivity for small differences. For this technique, multivariate analysis is required and with this chemometric tool, it is also possible to predict the concentration of tannin on the surface.

Sugar and acid content of Citrus prediction modeling using FT-IR fingerprinting in combination with multivariate statistical analysis.

PubMed

Song, Seung Yeob; Lee, Young Koung; Kim, In-Jung

2016-01-01

A high-throughput screening system for Citrus lines were established with higher sugar and acid contents using Fourier transform infrared (FT-IR) spectroscopy in combination with multivariate analysis. FT-IR spectra confirmed typical spectral differences between the frequency regions of 950-1100 cm(-1), 1300-1500 cm(-1), and 1500-1700 cm(-1). Principal component analysis (PCA) and subsequent partial least square-discriminant analysis (PLS-DA) were able to discriminate five Citrus lines into three separate clusters corresponding to their taxonomic relationships. The quantitative predictive modeling of sugar and acid contents from Citrus fruits was established using partial least square regression algorithms from FT-IR spectra. The regression coefficients (R(2)) between predicted values and estimated sugar and acid content values were 0.99. These results demonstrate that by using FT-IR spectra and applying quantitative prediction modeling to Citrus sugar and acid contents, excellent Citrus lines can be early detected with greater accuracy. Copyright © 2015 Elsevier Ltd. All rights reserved.

Influence analysis for high-dimensional time series with an application to epileptic seizure onset zone detection

PubMed Central

Flamm, Christoph; Graef, Andreas; Pirker, Susanne; Baumgartner, Christoph; Deistler, Manfred

2013-01-01

Granger causality is a useful concept for studying causal relations in networks. However, numerical problems occur when applying the corresponding methodology to high-dimensional time series showing co-movement, e.g. EEG recordings or economic data. In order to deal with these shortcomings, we propose a novel method for the causal analysis of such multivariate time series based on Granger causality and factor models. We present the theoretical background, successfully assess our methodology with the help of simulated data and show a potential application in EEG analysis of epileptic seizures. PMID:23354014

Risk factors for superficial surgical site infection after elective rectal cancer resection: a multivariate analysis of 8880 patients from the American College of Surgeons National Surgical Quality Improvement Program database.

PubMed

Sutton, Elie; Miyagaki, Hiromichi; Bellini, Geoffrey; Shantha Kumara, H M C; Yan, Xiaohong; Howe, Brett; Feigel, Amanda; Whelan, Richard L

2017-01-01

Superficial surgical site infection (sSSI) is one of the most common complications after colorectal resection. The goal of this study was to determine the comorbidities and operative characteristics that place patients at risk for sSSI in patients who underwent rectal cancer resection. The American College of Surgeons National Surgical Quality Improvement Program database was queried (via diagnosis and Current Procedural Terminology codes) for patients with rectal cancer who underwent elective resection between 2005 and 2012. Patients for whom data concerning 27 demographic factors, comorbidities, and operative characteristics were available were eligible. A univariate and multivariate analysis was performed to identify possible risk factors for sSSI. A total of 8880 patients met the entry criteria and were included. sSSIs were diagnosed in 861 (9.7%) patients. Univariate analysis found 14 patients statistically significant risk factors for sSSI. Multivariate analysis revealed the following risk factors: male gender, body mass index (BMI) >30, current smoking, history of chronic obstructive pulmonary disease (COPD), American Society of Anesthesiologists III/IV, abdominoperineal resection (APR), stoma formation, open surgery (versus laparoscopic), and operative time >217 min. The greatest difference in sSSI rates was noted in patients with COPD (18.9 versus 9.5%). Of note, 54.2% of sSSIs was noted after hospital discharge. With regard to the timing of presentation, univariate analysis revealed a statistically significant delay in sSSI presentation in patients with the following factors and/or characteristics: BMI <30, previous radiation therapy (RT), APR, minimally invasive surgery, and stoma formation. Multivariate analysis suggested that only laparoscopic surgery (versus open) and preoperative RT were risk factors for delay. Rectal cancer resections are associated with a high incidence of sSSIs, over half of which are noted after discharge. Nine patient and operative characteristics, including smoking, BMI, COPD, APR, and open surgery were found to be significant risk factors for SSI on multivariate analysis. Furthermore, sSSI presentation in patients who had laparoscopic surgery and those who had preoperative RT is significantly delayed for unclear reasons. Copyright © 2016 Elsevier Inc. All rights reserved.

Quantitative fibrosis parameters highly predict esophageal-gastro varices in primary biliary cirrhosis.

PubMed

Wu, Q-M; Zhao, X-Y; You, H

2016-01-01

Esophageal-gastro Varices (EGV) may develop in any histological stages of primary biliary cirrhosis (PBC). We aim to establish and validate quantitative fibrosis (qFibrosis) parameters in portal, septal and fibrillar areas as ideal predictors of EGV in PBC patients. PBC patients with liver biopsy, esophagogastroscopy and Second Harmonic Generation (SHG)/Two-photon Excited Fluorescence (TPEF) microscopy images were retrospectively enrolled in this study. qFibrosis parameters in portal, septal and fibrillar areas were acquired by computer-assisted SHG/TPEF imaging system. Independent predictor was identified using multivariate logistic regression analysis. PBC patients with liver biopsy, esophagogastroscopy and Second Harmonic Generation (SHG)/Two-photon Excited Fluorescence (TPEF) microscopy images were retrospectively enrolled in this study. qFibrosis parameters in portal, septal and fibrillar areas were acquired by computer-assisted SHG/TPEF imaging system. Independent predictor was identified using multivariate logistic regression analysis. Among the forty-nine PBC patients with qFibrosis images, twenty-nine PBC patients with both esophagogastroscopy data and qFibrosis data were selected out for EGV prognosis analysis and 44.8% (13/29) of them had EGV. The qFibrosis parameters of collagen percentage and number of crosslink in fibrillar area, short/long/thin strings number and length/width of the strings in septa area were associated with EGV (p < 0.05). Multivariate logistic analysis showed that the collagen percentage in fibrillar area ≥ 3.6% was an independent factor to predict EGV (odds ratio 6.9; 95% confidence interval 1.6-27.4). The area under receiver operating characteristic (ROC), diagnostic sensitivity and specificity was 0.9, 100% and 75% respectively. Collagen percentage in Collagen percentage in the fibrillar area as an independent predictor can highly predict EGV in PBC patients.

Dynamic Multivariate Accelerated Corrosion Test Protocol

DTIC Science & Technology

2014-10-01

atmospheric, accelerated, AA2024-T3, AA6061-T6, AA7075-T3, 1010 steel, AgCl, rare earth conversion coat, magnesium rich primer, polyurethane , Eyring, Monte...morphology and elemental analysis by scanning electron microscopy-energy dispersive spectroscopy (SEM-EDS) and electrochemical determinations of...in the FT-IR analysis; degradation of the components of the high performance polyurethane coatings exposed in the UV/ozone chamber were more

A Multilevel Multivariate Analysis of Academic Performances in College Based on NCAA Student-Athletes

ERIC Educational Resources Information Center

McArdle, John J.; Paskus, Thomas S.; Boker, Steven M.

2013-01-01

This is an application of contemporary multilevel regression modeling to the prediction of academic performances of 1st-year college students. At a first level of analysis, the data come from N greater than 16,000 students who were college freshman in 1994-1995 and who were also participants in high-level college athletics. At a second level of…

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.

Multivariate analysis of mixed contaminants (PAHs and heavy metals) at manufactured gas plant site soils.

PubMed

Thavamani, Palanisami; Megharaj, Mallavarapu; Naidu, Ravi

2012-06-01

Principal component analysis (PCA) was used to provide an overview of the distribution pattern of polycyclic aromatic hydrocarbons (PAHs) and heavy metals in former manufactured gas plant (MGP) site soils. PCA is the powerful multivariate method to identify the patterns in data and expressing their similarities and differences. Ten PAHs (naphthalene, acenapthylene, acenaphthene, fluorene, phenanthrene, anthracene, fluoranthene, pyrene, chrysene, benzo[a]pyrene) and four toxic heavy metals - lead (Pb), cadmium (Cd), chromium (Cr) and zinc (Zn) - were detected in the site soils. PAH contamination was contributed equally by both low and high molecular weight PAHs. PCA was performed using the varimax rotation method in SPSS, 17.0. Two principal components accounting for 91.7% of the total variance was retained using scree test. Principle component 1 (PC1) substantially explained the dominance of PAH contamination in the MGP site soils. All PAHs, except anthracene, were positively correlated in PC1. There was a common thread in high molecular weight PAHs loadings, where the loadings were inversely proportional to the hydrophobicity and molecular weight of individual PAHs. Anthracene, which was less correlated with other individual PAHs, deviated well from the origin which can be ascribed to its lower toxicity and different origin than its isomer phenanthrene. Among the four major heavy metals studied in MGP sites, Pb, Cd and Cr were negatively correlated in PC1 but showed strong positive correlation in principle component 2 (PC2). Although metals may not have originated directly from gaswork processes, the correlation between PAHs and metals suggests that the materials used in these sites may have contributed to high concentrations of Pb, Cd, Cr and Zn. Thus, multivariate analysis helped to identify the sources of PAHs, heavy metals and their association in MGP site, and thereby better characterise the site risk, which would not be possible if one uses chemical analysis alone.

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

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.

Network Analysis of Associations between Serum Interferon Alpha Activity, Autoantibodies, and Clinical Features in Systemic Lupus Erythematosus

PubMed Central

Weckerle, Corinna E.; Franek, Beverly S.; Kelly, Jennifer A.; Kumabe, Marissa; Mikolaitis, Rachel A.; Green, Stephanie L.; Utset, Tammy O.; Jolly, Meenakshi; James, Judith A.; Harley, John B.; Niewold, Timothy B.

2010-01-01

Background Interferon-alpha (IFN-α) is a primary pathogenic factor in systemic lupus erythematosus (SLE), and high IFN-α levels may be associated with particular clinical manifestations. The prevalence of individual clinical and serologic features differs significantly by ancestry. We used multivariate and network analyses to detect associations between clinical and serologic disease manifestations and serum IFN-α activity in a large diverse SLE cohort. Methods 1089 SLE patients were studied (387 African-American, 186 Hispanic-American, and 516 European-American). Presence or absence of ACR clinical criteria for SLE, autoantibodies, and serum IFN-α activity data were analyzed in univariate and multivariate models. Iterative multivariate logistic regression was performed in each background separately to establish the network of associations between variables that were independently significant following Bonferroni correction. Results In all ancestral backgrounds, high IFN-α activity was associated with anti-Ro and anti-dsDNA antibodies (p-values 4.6×10−18 and 2.9 × 10−16 respectively). Younger age, non-European ancestry, and anti-RNP were also independently associated with increased serum IFN-α activity (p≤6.7×10−4). We found 14 unique associations between variables in network analysis, and only 7 of these associations were shared by more than one ancestral background. Associations between clinical criteria were different in different ancestral backgrounds, while autoantibody-IFN-α relationships were similar across backgrounds. IFN-α activity and autoantibodies were not associated with ACR clinical features in multivariate models. Conclusions Serum IFN-α activity was strongly and consistently associated with autoantibodies, and not independently associated with clinical features in SLE. IFN-α may be more relevant to humoral tolerance and initial pathogenesis than later clinical disease manifestations. PMID:21162028

Unbiased metabolite profiling by liquid chromatography-quadrupole time-of-flight mass spectrometry and multivariate data analysis for herbal authentication: classification of seven Lonicera species flower buds.

PubMed

Gao, Wen; Yang, Hua; Qi, Lian-Wen; Liu, E-Hu; Ren, Mei-Ting; Yan, Yu-Ting; Chen, Jun; Li, Ping

2012-07-06

Plant-based medicines become increasingly popular over the world. Authentication of herbal raw materials is important to ensure their safety and efficacy. Some herbs belonging to closely related species but differing in medicinal properties are difficult to be identified because of similar morphological and microscopic characteristics. Chromatographic fingerprinting is an alternative method to distinguish them. Existing approaches do not allow a comprehensive analysis for herbal authentication. We have now developed a strategy consisting of (1) full metabolic profiling of herbal medicines by rapid resolution liquid chromatography (RRLC) combined with quadrupole time-of-flight mass spectrometry (QTOF MS), (2) global analysis of non-targeted compounds by molecular feature extraction algorithm, (3) multivariate statistical analysis for classification and prediction, and (4) marker compounds characterization. This approach has provided a fast and unbiased comparative multivariate analysis of the metabolite composition of 33-batch samples covering seven Lonicera species. Individual metabolic profiles are performed at the level of molecular fragments without prior structural assignment. In the entire set, the obtained classifier for seven Lonicera species flower buds showed good prediction performance and a total of 82 statistically different components were rapidly obtained by the strategy. The elemental compositions of discriminative metabolites were characterized by the accurate mass measurement of the pseudomolecular ions and their chemical types were assigned by the MS/MS spectra. The high-resolution, comprehensive and unbiased strategy for metabolite data analysis presented here is powerful and opens the new direction of authentication in herbal analysis. Copyright © 2012 Elsevier B.V. All rights reserved.

CD147 as a novel biomarker for predicting the prognosis and clinicopathological features of bladder cancer: a meta-analysis

PubMed Central

Li, Hongru; Xu, Yadong; Li, Hui

2017-01-01

Objective To assess the prognostic and clinicopathological characteristics of CD147 in human bladder cancer. Methods Studies on CD147 expression in bladder cancer were retrieved from PubMed, EMBASE, the Cochrane Library, Web of Science, China National Knowledge Infrastructure, and the WanFang databases. Outcomes were pooled with meta-analyzing softwares RevMan 5.3 and STATA 14.0. Results Twenty-four studies with 25 datasets demonstrated that CD147 expression was higher in bladder cancer than in non-cancer tissues (OR=43.64, P<0.00001). Moreover, this increase was associated with more advanced clinical stages (OR=73.89, P<0.0001), deeper invasion (OR=3.22, P<0.00001), lower histological differentiation (OR=4.54, P=0.0005), poorer overall survival (univariate analysis, HR=2.63, P<0.00001; multivariate analysis, HR=1.86, P=0.00036), disease specific survival (univariate analysis, HR=1.65, P=0.002), disease recurrence-free survival (univariate analysis, HR=2.78, P=0.001; multivariate analysis, HR=5.51, P=0.017), rate of recurrence (OR=1.91, P=0.0006), invasive depth (pT2∼T4 vs. pTa∼T1; OR=3.22, P<0.00001), and histological differentiation (low versus moderate-to-high; OR=4.54, P=0.0005). No difference was found among disease specific survival in multivariate analysis (P=0.067), lymph node metastasis (P=0.12), and sex (P=0.15). Conclusion CD147 could be a biomarker for early diagnosis, treatment, and prognosis of bladder cancer. PMID:28977970

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.

SurvMicro: assessment of miRNA-based prognostic signatures for cancer clinical outcomes by multivariate survival analysis.

PubMed

Aguirre-Gamboa, Raul; Trevino, Victor

2014-06-01

MicroRNAs (miRNAs) play a key role in post-transcriptional regulation of mRNA levels. Their function in cancer has been studied by high-throughput methods generating valuable sources of public information. Thus, miRNA signatures predicting cancer clinical outcomes are emerging. An important step to propose miRNA-based biomarkers before clinical validation is their evaluation in independent cohorts. Although it can be carried out using public data, such task is time-consuming and requires a specialized analysis. Therefore, to aid and simplify the evaluation of prognostic miRNA signatures in cancer, we developed SurvMicro, a free and easy-to-use web tool that assesses miRNA signatures from publicly available miRNA profiles using multivariate survival analysis. SurvMicro is composed of a wide and updated database of >40 cohorts in different tissues and a web tool where survival analysis can be done in minutes. We presented evaluations to portray the straightforward functionality of SurvMicro in liver and lung cancer. To our knowledge, SurvMicro is the only bioinformatic tool that aids the evaluation of multivariate prognostic miRNA signatures in cancer. SurvMicro and its tutorial are freely available at http://bioinformatica.mty.itesm.mx/SurvMicro. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

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

Independent Prognostic Value of Serum Markers in Diffuse Large B-Cell Lymphoma in the Era of the NCCN-IPI.

PubMed

Melchardt, Thomas; Troppan, Katharina; Weiss, Lukas; Hufnagl, Clemens; Neureiter, Daniel; Tränkenschuh, Wolfgang; Schlick, Konstantin; Huemer, Florian; Deutsch, Alexander; Neumeister, Peter; Greil, Richard; Pichler, Martin; Egle, Alexander

2015-12-01

Several serum parameters have been evaluated for adding prognostic value to clinical scoring systems in diffuse large B-cell lymphoma (DLBCL), but none of the reports used multivariate testing of more than one parameter at a time. The goal of this study was to validate widely available serum parameters for their independent prognostic impact in the era of the National Comprehensive Cancer Network-International Prognostic Index (NCCN-IPI) score to determine which were the most useful. This retrospective bicenter analysis includes 515 unselected patients with DLBCL who were treated with rituximab and anthracycline-based chemoimmunotherapy between 2004 and January 2014. Anemia, high C-reactive protein, and high bilirubin levels had an independent prognostic value for survival in multivariate analyses in addition to the NCCN-IPI, whereas neutrophil-to-lymphocyte ratio, high gamma-glutamyl transferase levels, and platelets-to-lymphocyte ratio did not. In our cohort, we describe the most promising markers to improve the NCCN-IPI. Anemia and high C-reactive protein levels retain their power in multivariate testing even in the era of the NCCN-IPI. The negative role of high bilirubin levels may be associated as a marker of liver function. Further studies are warranted to incorporate these markers into prognostic models and define their role opposite novel molecular markers. Copyright © 2015 by the National Comprehensive Cancer Network.

Resolving Identification Issues of Saraca asoca from Its Adulterant and Commercial Samples Using Phytochemical Markers

PubMed Central

Hegde, Satisha; Hegde, Harsha Vasudev; Jalalpure, Sunil Satyappa; Peram, Malleswara Rao; Pai, Sandeep Ramachandra; Roy, Subarna

2017-01-01

Saraca asoca (Roxb.) De Wilde (Ashoka) is a highly valued endangered medicinal tree species from Western Ghats of India. Besides treating cardiac and circulatory problems, S. asoca provides immense relief in gynecological disorders. Higher price and demand, in contrast to the smaller population size of the plant, have motivated adulteration with other plants such as Polyalthia longifolia (Sonnerat) Thwaites. The fundamental concerns in quality control of S. asoca arise due to its part of medicinal value (Bark) and the chemical composition. Phytochemical fingerprinting with proper selection of analytical markers is a promising method in addressing quality control issues. In the present study, high-performance liquid chromatography of phenolic compounds (gallic acid, catechin, and epicatechin) coupled to multivariate analysis was used. Five samples each of S. asoca, P. longifolia from two localities alongside five commercial market samples showed evidence of adulteration. Subsequently, multivariate hierarchical cluster analysis and principal component analysis was established to discriminate the adulterants of S. asoca. The proposed method ascertains identification of S. asoca from its putative adulterant P. longifolia and commercial market samples. The data generated may also serve as baseline data to form a quality standard for pharmacopoeias. SUMMARY Simultaneous quantification of gallic acid, catechin, epicatechin from Saraca asoca by high-performance liquid chromatographyDetection of S. asoca from adulterant and commercial samplesUse of analytical method along with a statistical tool for addressing quality issues. Abbreviations used: HPLC: High Performance Liquid Chromatography; RP-HPLC: Reverse Phase High Performance Liquid Chromatography; CAT: Catechin; EPI: Epicatechin; GA: Gallic acid; PCA: Principal Component Analysis. PMID:28808391

The Multi-Isotope Process (MIP) Monitor Project: FY13 Final Report

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

Meier, David E.; Coble, Jamie B.; Jordan, David V.

The Multi-Isotope Process (MIP) Monitor provides an efficient approach to monitoring the process conditions in reprocessing facilities in support of the goal of “… (minimization of) the risks of nuclear proliferation and terrorism.” The MIP Monitor measures the distribution of the radioactive isotopes in product and waste streams of a nuclear reprocessing facility. These isotopes are monitored online by gamma spectrometry and compared, in near-real-time, to spectral patterns representing “normal” process conditions using multivariate analysis and pattern recognition algorithms. The combination of multivariate analysis and gamma spectroscopy allows us to detect small changes in the gamma spectrum, which may indicatemore » changes in process conditions. By targeting multiple gamma-emitting indicator isotopes, the MIP Monitor approach is compatible with the use of small, portable, relatively high-resolution gamma detectors that may be easily deployed throughout an existing facility. The automated multivariate analysis can provide a level of data obscurity, giving a built-in information barrier to protect sensitive or proprietary operational data. Proof-of-concept simulations and experiments have been performed in previous years to demonstrate the validity of this tool in a laboratory setting for systems representing aqueous reprocessing facilities. However, pyroprocessing is emerging as an alternative to aqueous reprocessing techniques.« less

Redo surgery risk in patients with cardiac prosthetic valve dysfunction

PubMed Central

Maciejewski, Marek; Piestrzeniewicz, Katarzyna; Bielecka-Dąbrowa, Agata; Piechowiak, Monika; Jaszewski, Ryszard

2011-01-01

Introduction The aim of the study was to analyse the risk factors of early and late mortality in patients undergoing the first reoperation for prosthetic valve dysfunction. Material and methods A retrospective observational study was performed in 194 consecutive patients (M = 75, F = 119; mean age 53.2 ±11 years) with a mechanical prosthetic valve (n = 103 cases; 53%) or bioprosthesis (91; 47%). Univariate and multivariate Cox statistical analysis was performed to determine risk factors of early and late mortality. Results The overall early mortality was 18.6%: 31.4% in patients with symptoms of NYHA functional class III-IV and 3.4% in pts in NYHA class I-II. Multivariate analysis identified symptoms of NYHA class III-IV and endocarditis as independent predictors of early mortality. The overall late mortality (> 30 days) was 8.2% (0.62% year/patient). Multivariate analysis identified age at the time of reoperation as a strong independent predictor of late mortality. Conclusions Reoperation in patients with prosthetic valves, performed urgently, especially in patients with symptoms of NYHA class III-IV or in the case of endocarditis, bears a high mortality rate. Risk of planned reoperation, mostly in patients with symptoms of NYHA class I-II, does not differ from the risk of the first operation. PMID:22291767

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.

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.

Population differences in the postcrania of modern South Africans and the implications for ancestry estimation.

PubMed

Liebenberg, Leandi; L'Abbé, Ericka N; Stull, Kyra E

2015-12-01

The cranium is widely recognized as the most important skeletal element to use when evaluating population differences and estimating ancestry. However, the cranium is not always intact or available for analysis, which emphasizes the need for postcranial alternatives. The purpose of this study was to quantify postcraniometric differences among South Africans that can be used to estimate ancestry. Thirty-nine standard measurements from 11 postcranial bones were collected from 360 modern black, white and coloured South Africans; the sex and ancestry distribution were equal. Group differences were explored with analysis of variance (ANOVA) and Tukey's honestly significant difference (HSD) test. Linear and flexible discriminant analysis (LDA and FDA, respectively) were conducted with bone models as well as numerous multivariate subsets to identify the model and method that yielded the highest correct classifications. Leave-one-out (LDA) and k-fold (k=10; FDA) cross-validation with equal priors were used for all models. ANOVA and Tukey's HSD results reveal statistically significant differences between at least two of the three groups for the majority of the variables, with varying degrees of group overlap. Bone models, which consisted of all measurements per bone, resulted in low accuracies that ranged from 46% to 63% (LDA) and 41% to 66% (FDA). In contrast, the multivariate subsets, which consisted of different variable combinations from all elements, achieved accuracies as high as 85% (LDA) and 87% (FDA). Thus, when using a multivariate approach, the postcranial skeleton can distinguish among three modern South African groups with high accuracy. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Groundwater quality in Ghaziabad district, Uttar Pradesh, India: Multivariate and health risk assessment.

PubMed

Chabukdhara, Mayuri; Gupta, Sanjay Kumar; Kotecha, Yatharth; Nema, Arvind K

2017-07-01

This study aimed to assess the quality of groundwater and potential health risk due to ingestion of heavy metals in the peri-urban and urban-industrial clusters of Ghaziabad district, Uttar Pradesh, India. Furthermore, the study aimed to evaluate heavy metals sources and their pollution level using multivariate analysis and fuzzy comprehensive assessment (FCA), respectively. Multivariate analysis using principle component analysis (PCA) showed mixed origin for Pb, Cd, Zn, Fe, and Ni, natural source for Cu and Mn and anthropogenic source for Cr. Among all the metals, Pb, Cd, Fe and Ni were above the safe limits of Bureau of Indian Standards (BIS) and World Health Organization (WHO) except Ni. Health risk in terms of hazard quotient (HQ) showed that the HQ values for children were higher than the safe level (HQ = 1) for Pb (2.4) and Cd (2.1) in pre-monsoon while in post-monsoon the value exceeded only for Pb (HQ = 1.23). The health risks of heavy metals for the adults were well within safe limits. The finding of this study indicates potential health risks to the children due to chronic exposure to contaminated groundwater in the region. Based on FCA, groundwater pollution could be categorized as quite high in the peri-urban region, and absolutely high in the urban region of Ghaziabad district. This study showed that different approaches are required for the integrated assessment of the groundwater pollution, and provides a scientific basis for the strategic future planning and comprehensive management. Copyright © 2017 Elsevier Ltd. All rights reserved.

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 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.

A survey of variable selection methods in two Chinese epidemiology journals

PubMed Central

2010-01-01

Background Although much has been written on developing better procedures for variable selection, there is little research on how it is practiced in actual studies. This review surveys the variable selection methods reported in two high-ranking Chinese epidemiology journals. Methods Articles published in 2004, 2006, and 2008 in the Chinese Journal of Epidemiology and the Chinese Journal of Preventive Medicine were reviewed. Five categories of methods were identified whereby variables were selected using: A - bivariate analyses; B - multivariable analysis; e.g. stepwise or individual significance testing of model coefficients; C - first bivariate analyses, followed by multivariable analysis; D - bivariate analyses or multivariable analysis; and E - other criteria like prior knowledge or personal judgment. Results Among the 287 articles that reported using variable selection methods, 6%, 26%, 30%, 21%, and 17% were in categories A through E, respectively. One hundred sixty-three studies selected variables using bivariate analyses, 80% (130/163) via multiple significance testing at the 5% alpha-level. Of the 219 multivariable analyses, 97 (44%) used stepwise procedures, 89 (41%) tested individual regression coefficients, but 33 (15%) did not mention how variables were selected. Sixty percent (58/97) of the stepwise routines also did not specify the algorithm and/or significance levels. Conclusions The variable selection methods reported in the two journals were limited in variety, and details were often missing. Many studies still relied on problematic techniques like stepwise procedures and/or multiple testing of bivariate associations at the 0.05 alpha-level. These deficiencies should be rectified to safeguard the scientific validity of articles published in Chinese epidemiology journals. PMID:20920252

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.

Evaluation of factors associated with severe and frequent back pain in high school athletes.

PubMed

Noll, Matias; Silveira, Erika Aparecida; Avelar, Ivan Silveira de

2017-01-01

Several studies have shown that half of all young athletes experience back pain (BP). However, high intensity and frequency of BP may be harmful, and the factors associated with BP severity have not been investigated in detail. Here, we investigated the factors associated with a high intensity and high frequency of BP in high school athletes. We included 251 athletes (173 boys and 78 girls [14-20 years old]) in this cross-sectional study. The dependent variables were a high frequency and high intensity of BP, and the independent variables were demographic, socioeconomic, psychosocial, hereditary, anthropometric, behavioural, and postural factors and the level of exercise. The effect measure is presented as prevalence ratio (PR) with 95% confidence interval (CI). Of 251 athletes, 104 reported BP; thus, only these athletes were included in the present analysis. Results of multivariable analysis showed an association between high BP intensity and time spent using a computer (PR: 1.15, CI: 1.01-1.33), posture while writing (PR: 1.41, CI: 1.27-1.58), and posture while using a computer (PR: 1.39, CI: 1.26-1.54). Multivariable analysis also revealed an association of high BP frequency with studying in bed (PR: 1.19, CI: 1.01-1.40) and the method of carrying a backpack (PR: 1.19, CI: 1.01-1.40). In conclusion, we found that behavioural and postural factors are associated with a high intensity and frequency of BP. To the best of our knowledge, this study is the first to compare different intensities and frequencies of BP, and our results may help physicians and coaches to better understand BP in high school athletes.

Evaluation of factors associated with severe and frequent back pain in high school athletes

PubMed Central

Noll, Matias; Silveira, Erika Aparecida; de Avelar, Ivan Silveira

2017-01-01

Several studies have shown that half of all young athletes experience back pain (BP). However, high intensity and frequency of BP may be harmful, and the factors associated with BP severity have not been investigated in detail. Here, we investigated the factors associated with a high intensity and high frequency of BP in high school athletes. We included 251 athletes (173 boys and 78 girls [14–20 years old]) in this cross-sectional study. The dependent variables were a high frequency and high intensity of BP, and the independent variables were demographic, socioeconomic, psychosocial, hereditary, anthropometric, behavioural, and postural factors and the level of exercise. The effect measure is presented as prevalence ratio (PR) with 95% confidence interval (CI). Of 251 athletes, 104 reported BP; thus, only these athletes were included in the present analysis. Results of multivariable analysis showed an association between high BP intensity and time spent using a computer (PR: 1.15, CI: 1.01–1.33), posture while writing (PR: 1.41, CI: 1.27–1.58), and posture while using a computer (PR: 1.39, CI: 1.26–1.54). Multivariable analysis also revealed an association of high BP frequency with studying in bed (PR: 1.19, CI: 1.01–1.40) and the method of carrying a backpack (PR: 1.19, CI: 1.01–1.40). In conclusion, we found that behavioural and postural factors are associated with a high intensity and frequency of BP. To the best of our knowledge, this study is the first to compare different intensities and frequencies of BP, and our results may help physicians and coaches to better understand BP in high school athletes. PMID:28222141

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…

Multisite-multivariable sensitivity analysis of distributed watershed models: enhancing the perceptions from computationally frugal methods

USDA-ARS?s Scientific Manuscript database

This paper assesses the impact of different likelihood functions in identifying sensitive parameters of the highly parameterized, spatially distributed Soil and Water Assessment Tool (SWAT) watershed model for multiple variables at multiple sites. The global one-factor-at-a-time (OAT) method of Morr...

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.

Hierarchical multivariate covariance analysis of metabolic connectivity

PubMed Central

Carbonell, Felix; Charil, Arnaud; Zijdenbos, Alex P; Evans, Alan C; Bedell, Barry J

2014-01-01

Conventional brain connectivity analysis is typically based on the assessment of interregional correlations. Given that correlation coefficients are derived from both covariance and variance, group differences in covariance may be obscured by differences in the variance terms. To facilitate a comprehensive assessment of connectivity, we propose a unified statistical framework that interrogates the individual terms of the correlation coefficient. We have evaluated the utility of this method for metabolic connectivity analysis using [18F]2-fluoro-2-deoxyglucose (FDG) positron emission tomography (PET) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. As an illustrative example of the utility of this approach, we examined metabolic connectivity in angular gyrus and precuneus seed regions of mild cognitive impairment (MCI) subjects with low and high β-amyloid burdens. This new multivariate method allowed us to identify alterations in the metabolic connectome, which would not have been detected using classic seed-based correlation analysis. Ultimately, this novel approach should be extensible to brain network analysis and broadly applicable to other imaging modalities, such as functional magnetic resonance imaging (MRI). PMID:25294129

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.

Multivariate analysis of subsurface radiometric data in Rongsohkham area, East Khasi Hills district, Meghalaya (India): implication on uranium exploration.

PubMed

Kukreti, B M; Pandey, Pradeep; Singh, R V

2012-08-01

Non-coring based exploratory drilling was under taken in the sedimentary environment of Rangsohkham block, East Khasi Hills district to examine the eastern extension of existing uranium resources located at Domiasiat and Wakhyn in the Mahadek basin of Meghalaya (India). Although radiometric survey and radiometric analysis of surface grab/channel samples in the block indicate high uranium content but the gamma ray logging results of exploratory boreholes in the block, did not obtain the expected results. To understand this abrupt discontinuity between the two sets of data (surface and subsurface) multivariate statistical analysis of primordial radioactive elements (K(40), U(238) and Th(232)) was performed using the concept of representative subsurface samples, drawn from the randomly selected 11 boreholes of this block. The study was performed to a high confidence level (99%), and results are discussed for assessing the U and Th behavior in the block. Results not only confirm the continuation of three distinct geological formations in the area but also the uranium bearing potential in the Mahadek sandstone of the eastern part of Mahadek Basin. Copyright © 2012 Elsevier Ltd. All rights reserved.

Investigating the Moisture Content of Polyamide 6 by Raman-Microscopy and Multivariate Data Analysis

NASA Astrophysics Data System (ADS)

Lechner, Tobias; Noack, Kristina; Thöne, Manuel; Amend, Philipp; Schmidt, Michael; Will, Stefan

Thermal malleability of thermoplastics results in a high product diversity in various industry sectors. However, industrial applications require a constant and high component quality. Hence, material processing such as laser welding has to consider that, e.g., the moisture content of thermoplastics influences the mechanical properties such as the tensile strength. Moreover, water evaporates during laser welding and can form pores and defects. Thus, there is a large need for non-invasive material inspection before processing. To that end, we developed a methodology based on Raman-microscopy and multivariate data analysis (MVD) to determine the moisture content of polyamide (MCP). Further, the impact of the MCP on the mechanical properties was verified. For samples with a defined variation of the MCP, xyz-Raman-scans were carried out and analysed using MVD. For reference purposes, the samples were weighted and tensile tests were performed. An evaluation by means of partial least squares regression analysis (PLSR) resulted in a prediction of the MCP with a correlation coefficient >98%. Consequently, Raman-microscopy shows large potential for developing new techniques for inspection and quality control of plastics before processing. Dedicated to Professor Alfred Leipertz on the occasion of his 70th birthday.

The effect of heavy metal contamination on the bacterial community structure at Jiaozhou Bay, China.

PubMed

Yao, Xie-Feng; Zhang, Jiu-Ming; Tian, Li; Guo, Jian-Hua

In this study, determination of heavy metal parameters and microbiological characterization of marine sediments obtained from two heavily polluted sites and one low-grade contaminated reference station at Jiaozhou Bay in China were carried out. The microbial communities found in the sampled marine sediments were studied using PCR-DGGE (denaturing gradient gel electrophoresis) fingerprinting profiles in combination with multivariate analysis. Clustering analysis of DGGE and matrix of heavy metals displayed similar occurrence patterns. On this basis, 17 samples were classified into two clusters depending on the presence or absence of the high level contamination. Moreover, the cluster of highly contaminated samples was further classified into two sub-groups based on the stations of their origin. These results showed that the composition of the bacterial community is strongly influenced by heavy metal variables present in the sediments found in the Jiaozhou Bay. This study also suggested that metagenomic techniques such as PCR-DGGE fingerprinting in combination with multivariate analysis is an efficient method to examine the effect of metal contamination on the bacterial community structure. Copyright © 2016 Sociedade Brasileira de Microbiologia. Published by Elsevier Editora Ltda. All rights reserved.

Method for factor analysis of GC/MS data

DOEpatents

Van Benthem, Mark H; Kotula, Paul G; Keenan, Michael R

2012-09-11

The method of the present invention provides a fast, robust, and automated multivariate statistical analysis of gas chromatography/mass spectroscopy (GC/MS) data sets. The method can involve systematic elimination of undesired, saturated peak masses to yield data that follow a linear, additive model. The cleaned data can then be subjected to a combination of PCA and orthogonal factor rotation followed by refinement with MCR-ALS to yield highly interpretable results.

Effect of membrane flux and dialyzer biocompatibility on survival in end-stage diabetic nephropathy.

PubMed

Götz, Angela K; Böger, Carsten A; Popal, Massoud; Banas, Bernhard; Krämer, Bernhard K

2008-01-01

We examined the effects of dialyzer membrane flux and biocompatibility on mortality in diabetic dialysis patients. We enrolled 402 prevalent chronic hemodialysis patients from 30 centers in Germany in 1999 for a prospective observational study until 2003. We compared 2 groups in post hoc analysis: high-flux (HF, n = 166) versus low-flux (LF, n = 236) membrane, and high biocompatibility (HB, n = 300) versus low biocompatibility (LB, n = 102). All-cause mortality (ACM) was the primary endpoint. Death causes were the secondary endpoints. Multivariate Cox regression analysis showed no significant difference in risk for ACM with respect to flux (hazard ratio, HR, 0.79; p = 0.08; ACM 63% in HF vs. 70% in LF dialysis) and biocompatibility level (HR 1.00; p = 0.98; ACM 67% for HB vs. 66% for LB). The multivariate analysis of different causes of death did not reveal any outcome differences dependent on flux and biocompatibility level apart from a slightly better cumulative survival regarding the death cause 'infectious' in our HF dialysis group (HR 0.48; p = 0.07, Kaplan-Meier analysis p = 0.03). Our data indicate that mortality of hemodialysis patients with type-2 diabetic nephropathy is influenced neither by dialyzer flux level nor by biocompatibility. Copyright 2008 S. Karger AG, Basel.

Quantitative investigation of inappropriate regression model construction and the importance of medical statistics experts in observational medical research: a cross-sectional study.

PubMed

Nojima, Masanori; Tokunaga, Mutsumi; Nagamura, Fumitaka

2018-05-05

To investigate under what circumstances inappropriate use of 'multivariate analysis' is likely to occur and to identify the population that needs more support with medical statistics. The frequency of inappropriate regression model construction in multivariate analysis and related factors were investigated in observational medical research publications. The inappropriate algorithm of using only variables that were significant in univariate analysis was estimated to occur at 6.4% (95% CI 4.8% to 8.5%). This was observed in 1.1% of the publications with a medical statistics expert (hereinafter 'expert') as the first author, 3.5% if an expert was included as coauthor and in 12.2% if experts were not involved. In the publications where the number of cases was 50 or less and the study did not include experts, inappropriate algorithm usage was observed with a high proportion of 20.2%. The OR of the involvement of experts for this outcome was 0.28 (95% CI 0.15 to 0.53). A further, nation-level, analysis showed that the involvement of experts and the implementation of unfavourable multivariate analysis are associated at the nation-level analysis (R=-0.652). Based on the results of this study, the benefit of participation of medical statistics experts is obvious. Experts should be involved for proper confounding adjustment and interpretation of statistical models. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

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.

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.

Predict value of adiponectin for coronary atherosclerosis plaques according to computed tomography angiography in an asymptomatic population.

PubMed

Gan, Lu; Yang, Li; Yan, Guangtao

2018-05-25

The association between serum adiponectin levels and coronary atherosclerosis plaque characteristics in asymptomatic populations is unclear. To examine the predictive value of serum adiponectin levels for coronary high risk plaques as detected by computed tomography angiography (CTA). This was a cross-sectional study. All patients were divided into high risk plaque group and non high risk plaque group. The FRS was calculated for each patient. CTA was performed for each patient. Adiponectin levels were measured by flow fluorescence immunmicrobead assay (FFIA). Receiver-operating characteristic (ROC) curves and multivariate analysis was used to determine the predictive value of adiponectin for high risk plaques. The high risk plaque group showed lower adiponectin levels than non high risk plaque group (median, 7.27 vs. 8.51 μg/ml, P = 0.003). The multivariate analysis showed that age (OR = 2.62, 95%CI: 1.51-4.56, P = 0.001), hyperlipidemia (OR = 1.89, 95%CI: 1.07-3.36, P = 0.029), high-density lipoprotein cholesterol (HDL-C) (OR = 0.46, 95%CI: 0.24-0.87, P = 0.02), the ratio of total cholesterol to high-density lipoproteincholesterol (TC/HDL-C) (OR = 0.69, 95%CI: 0.50-0.94, P = 0.02), apolipoprotein B (apoB) (OR = 3.08, 95%CI: 1.50-6.32, P = 0.002), and adiponectin (OR = 0.37, 95%CI: 0.19-0.74, P = 0.005) were independently associated with the presence of high risk plaques. AUC of the multivariate model for high-risk plaques was 0.728 (95%CI: 0.627-0.783). Sensitivity was 74.9%, specificity was 60.2%, the positive predictive value was 65.3%, and the negative predictive value was 70.6%. Decreased adiponectin levels were associated with the presence of high-risk plaques in asymptomatic populations at low to intermediate FRS. Adiponectin can play an important role in plaque screening before coronary CTA. Copyright © 2018 Elsevier Inc. All rights reserved.

Multivariate meta-analysis with an increasing number of parameters

PubMed Central

Boca, Simina M.; Pfeiffer, Ruth M.; Sampson, Joshua N.

2017-01-01

Summary Meta-analysis can average estimates of multiple parameters, such as a treatment’s effect on multiple outcomes, across studies. Univariate meta-analysis (UVMA) considers each parameter individually, while multivariate meta-analysis (MVMA) considers the parameters jointly and accounts for the correlation between their estimates. The performance of MVMA and UVMA has been extensively compared in scenarios with two parameters. Our objective is to compare the performance of MVMA and UVMA as the number of parameters, p, increases. Specifically, we show that (i) for fixed-effect meta-analysis, the benefit from using MVMA can substantially increase as p increases; (ii) for random effects meta-analysis, the benefit from MVMA can increase as p increases, but the potential improvement is modest in the presence of high between-study variability and the actual improvement is further reduced by the need to estimate an increasingly large between study covariance matrix; and (iii) when there is little to no between study variability, the loss of efficiency due to choosing random effects MVMA over fixed-effect MVMA increases as p increases. We demonstrate these three features through theory, simulation, and a meta-analysis of risk factors for Non-Hodgkin Lymphoma. PMID:28195655

Cross-sectional study of anal intraepithelial lesions in women with cervical neoplasia without HIV.

PubMed

Heráclio, Sandra A; de Souza, Alex S R; de Souza, Paulo R E; Katz, Leila; Lima Junior, Sergio F; Amorim, Melania M R

2018-02-01

To evaluate the prevalence of anal intraepithelial lesions and associated risk factors in women with cervical neoplasia. The present cross-sectional study enrolled patients with intraepithelial or invasive cervical neoplasia who had been referred to the lower genital tract pathology outpatient department of the Instituto de Medicina Integral Prof. Fernando Figueira, Recife, Brazil, between December 1, 2008, and December 31, 2009; patients with HIV infections were excluded. All participants underwent anal cytology and high-resolution anoscopy; sociodemographic and clinical risk factors were identified using multivariate analysis. There were 324 patients included and 37 (11.4%) had anal intraepithelial neoplasia. Factors associated with anal intraepithelial neoplasia in the multivariate analysis were being older than 35 years of age (P=0.002), having completed no more than 4 years of education (P=0.012), anomalous anal cytology (P=0.003), and anomalous high-resolution anoscopy findings (P<0.001); subclinical HPV lesions on vulvoscopy (P=0.057) were not associated with anal intraepithelial neoplasia. The prevalence of anal intraepithelial neoplasia was high among patients with cervical neoplasia who did not have HIV, particularly patients older than 35 years. © 2017 International Federation of Gynecology and Obstetrics.

A flexible model for multivariate interval-censored survival times with complex correlation structure.

PubMed

Falcaro, Milena; Pickles, Andrew

2007-02-10

We focus on the analysis of multivariate survival times with highly structured interdependency and subject to interval censoring. Such data are common in developmental genetics and genetic epidemiology. We propose a flexible mixed probit model that deals naturally with complex but uninformative censoring. The recorded ages of onset are treated as possibly censored ordinal outcomes with the interval censoring mechanism seen as arising from a coarsened measurement of a continuous variable observed as falling between subject-specific thresholds. This bypasses the requirement for the failure times to be observed as falling into non-overlapping intervals. The assumption of a normal age-of-onset distribution of the standard probit model is relaxed by embedding within it a multivariate Box-Cox transformation whose parameters are jointly estimated with the other parameters of the model. Complex decompositions of the underlying multivariate normal covariance matrix of the transformed ages of onset become possible. The new methodology is here applied to a multivariate study of the ages of first use of tobacco and first consumption of alcohol without parental permission in twins. The proposed model allows estimation of the genetic and environmental effects that are shared by both of these risk behaviours as well as those that are specific. 2006 John Wiley & Sons, Ltd.

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.

Multivariate Meta-Analysis of Brain-Mass Correlations in Eutherian Mammals

PubMed Central

Steinhausen, Charlene; Zehl, Lyuba; Haas-Rioth, Michaela; Morcinek, Kerstin; Walkowiak, Wolfgang; Huggenberger, Stefan

2016-01-01

The general assumption that brain size differences are an adequate proxy for subtler differences in brain organization turned neurobiologists toward the question why some groups of mammals such as primates, elephants, and whales have such remarkably large brains. In this meta-analysis, an extensive sample of eutherian mammals (115 species distributed in 14 orders) provided data about several different biological traits and measures of brain size such as absolute brain mass (AB), relative brain mass (RB; quotient from AB and body mass), and encephalization quotient (EQ). These data were analyzed by established multivariate statistics without taking specific phylogenetic information into account. Species with high AB tend to (1) feed on protein-rich nutrition, (2) have a long lifespan, (3) delayed sexual maturity, and (4) long and rare pregnancies with small litter sizes. Animals with high RB usually have (1) a short life span, (2) reach sexual maturity early, and (3) have short and frequent gestations. Moreover, males of species with high RB also have few potential sexual partners. In contrast, animals with high EQs have (1) a high number of potential sexual partners, (2) delayed sexual maturity, and (3) rare gestations with small litter sizes. Based on these correlations, we conclude that Eutheria with either high AB or high EQ occupy positions at the top of the network of food chains (high trophic levels). Eutheria of low trophic levels can develop a high RB only if they have small body masses. PMID:27746724

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.

Intake of Fiber and Nuts during Adolescence and Incidence of Proliferative Benign Breast Disease

PubMed Central

Su, Xuefen; Tamimi, Rulla M.; Collins, Laura C.; Baer, Heather J.; Cho, Eunyoung; Sampson, Laura; Willett, Walter C.; Schnitt, Stuart J.; Connolly, James L.; Rosner, Bernard A.; Colditz, Graham A.

2011-01-01

Objective We examined the association between adolescent fiber intake and proliferative BBD, a marker of increased breast cancer risk, in the Nurses’ Health Study II. Methods Among 29,480 women who completed a high school diet questionnaire in 1998, 682 proliferative BBD cases were identified and confirmed by centralized pathology review between 1991 and 2001. Multivariate-adjusted Cox proportional hazards regression was used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). Results Women in the highest quintile of adolescent fiber intake had a 25% lower risk of proliferative BBD (multivariate HR (95% CI): 0.75 (0.59, 0.96), p-trend = 0.01) than women in the lowest quintile. High school intake of nuts and apples was also related to significantly reduced BBD risk. Women consuming ≥2 servings of nuts/week had a 36% lower risk (multivariate HR (95% CI): 0.64 (0.48, 0.85), p-trend < 0.01) than women consuming <1 serving/month. Results were essentially the same when the analysis was restricted to prospective cases (n = 142) diagnosed after return of the high school diet questionnaire. Conclusions These findings support the hypothesis that dietary intake of fiber and nuts during adolescence influence subsequent risk of breast disease and may suggest a viable means for breast cancer prevention. PMID:20229245

Cytokine activation is predictive of mortality in Zambian patients with AIDS-related diarrhoea

PubMed Central

Zulu, Isaac; Hassan, Ghaniah; Njobvu RN, Lungowe; Dhaliwal, Winnie; Sianongo, Sandie; Kelly, Paul

2008-01-01

Background Mortality in Zambian AIDS patients is high, especially in patients with diarrhoea, and there is still unacceptably high mortality in Zambian patients just starting anti-retroviral therapy. We set out to determine if high concentrations of serum cytokines correlate with mortality. Methods Serum samples from 30 healthy controls (HIV seropositive and seronegative) and 50 patients with diarrhoea (20 of whom died within 6 weeks) were analysed. Concentrations of tumour necrosis factor receptor p55 (TNFR p55), macrophage migration inhibitory factor (MIF), interleukin (IL)-6, IL-12, interferon (IFN)-γ and C-reactive protein (CRP) were measured by ELISA, and correlated with mortality after 6 weeks follow-up. Results Apart from IL-12, concentrations of all cytokines, TNFR p55 and CRP increased with worsening severity of disease, showing highly statistically significant trends. In a multivariable analysis high TNFR p55, IFN-γ, CRP and low CD4 count (CD4 count <100) were predictive of mortality. Although nutritional status (assessed by body mass index, BMI) was predictive in univariate analysis, it was not an independent predictor in multivariate analysis. Conclusion High serum concentrations of TNFR p55, IFN-γ, CRP and low CD4 count correlated with disease severity and short-term mortality in HIV-infected Zambian adults with diarrhoea. These factors were better predictors of survival than BMI. Understanding the cause of TNFR p55, IFN-γ and CRP elevation may be useful in development of interventions to reduce mortality in AIDS patients with chronic diarrhoea in Africa. PMID:19014537

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

Textural analysis of pre-therapeutic [18F]-FET-PET and its correlation with tumor grade and patient survival in high-grade gliomas.

PubMed

Pyka, Thomas; Gempt, Jens; Hiob, Daniela; Ringel, Florian; Schlegel, Jürgen; Bette, Stefanie; Wester, Hans-Jürgen; Meyer, Bernhard; Förster, Stefan

2016-01-01

Amino acid positron emission tomography (PET) with [18F]-fluoroethyl-L-tyrosine (FET) is well established in the diagnostic work-up of malignant brain tumors. Analysis of FET-PET data using tumor-to-background ratios (TBR) has been shown to be highly valuable for the detection of viable hypermetabolic brain tumor tissue; however, it has not proven equally useful for tumor grading. Recently, textural features in 18-fluorodeoxyglucose-PET have been proposed as a method to quantify the heterogeneity of glucose metabolism in a variety of tumor entities. Herein we evaluate whether textural FET-PET features are of utility for grading and prognostication in patients with high-grade gliomas. One hundred thirteen patients (70 men, 43 women) with histologically proven high-grade gliomas were included in this retrospective study. All patients received static FET-PET scans prior to first-line therapy. TBR (max and mean), volumetric parameters and textural parameters based on gray-level neighborhood difference matrices were derived from static FET-PET images. Receiver operating characteristic (ROC) and discriminant function analyses were used to assess the value for tumor grading. Kaplan-Meier curves and univariate and multivariate Cox regression were employed for analysis of progression-free and overall survival. All FET-PET textural parameters showed the ability to differentiate between World Health Organization (WHO) grade III and IV tumors (p < 0.001; AUC 0.775). Further improvement in discriminatory power was possible through a combination of texture and metabolic tumor volume, classifying 85 % of tumors correctly (AUC 0.830). TBR and volumetric parameters alone were correlated with tumor grade, but showed lower AUC values (0.644 and 0.710, respectively). Furthermore, a correlation of FET-PET texture but not TBR was shown with patient PFS and OS, proving significant in multivariate analysis as well. Volumetric parameters were predictive for OS, but this correlation did not hold in multivariate analysis. Determination of uptake heterogeneity in pre-therapeutic FET-PET using textural features proved valuable for the (sub-)grading of high-grade glioma as well as prediction of tumor progression and patient survival, and showed improved performance compared to standard parameters such as TBR and tumor volume. Our results underscore the importance of intratumoral heterogeneity in the biology of high-grade glial cell tumors and may contribute to individual therapy planning in the future, although they must be confirmed in prospective studies before incorporation into clinical routine.

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.

MIDAS, prototype Multivariate Interactive Digital Analysis System, phase 1. Volume 1: System description

NASA Technical Reports Server (NTRS)

Kriegler, F. J.

1974-01-01

The MIDAS System is described as a third-generation fast multispectral recognition system able to keep pace with the large quantity and high rates of data acquisition from present and projected sensors. A principal objective of the MIDAS program is to provide a system well interfaced with the human operator and thus to obtain large overall reductions in turnaround time and significant gains in throughput. The hardware and software are described. The system contains a mini-computer to control the various high-speed processing elements in the data path, and a classifier which implements an all-digital prototype multivariate-Gaussian maximum likelihood decision algorithm operating at 200,000 pixels/sec. Sufficient hardware was developed to perform signature extraction from computer-compatible tapes, compute classifier coefficients, control the classifier operation, and diagnose operation.

Control-group feature normalization for multivariate pattern analysis of structural MRI data using the support vector machine.

PubMed

Linn, Kristin A; Gaonkar, Bilwaj; Satterthwaite, Theodore D; Doshi, Jimit; Davatzikos, Christos; Shinohara, Russell T

2016-05-15

Normalization of feature vector values is a common practice in machine learning. Generally, each feature value is standardized to the unit hypercube or by normalizing to zero mean and unit variance. Classification decisions based on support vector machines (SVMs) or by other methods are sensitive to the specific normalization used on the features. In the context of multivariate pattern analysis using neuroimaging data, standardization effectively up- and down-weights features based on their individual variability. Since the standard approach uses the entire data set to guide the normalization, it utilizes the total variability of these features. This total variation is inevitably dependent on the amount of marginal separation between groups. Thus, such a normalization may attenuate the separability of the data in high dimensional space. In this work we propose an alternate approach that uses an estimate of the control-group standard deviation to normalize features before training. We study our proposed approach in the context of group classification using structural MRI data. We show that control-based normalization leads to better reproducibility of estimated multivariate disease patterns and improves the classifier performance in many cases. Copyright © 2016 Elsevier Inc. All rights reserved.

Phylogenetic Factor Analysis.

PubMed

Tolkoff, Max R; Alfaro, Michael E; Baele, Guy; Lemey, Philippe; Suchard, Marc A

2018-05-01

Phylogenetic comparative methods explore the relationships between quantitative traits adjusting for shared evolutionary history. This adjustment often occurs through a Brownian diffusion process along the branches of the phylogeny that generates model residuals or the traits themselves. For high-dimensional traits, inferring all pair-wise correlations within the multivariate diffusion is limiting. To circumvent this problem, we propose phylogenetic factor analysis (PFA) that assumes a small unknown number of independent evolutionary factors arise along the phylogeny and these factors generate clusters of dependent traits. Set in a Bayesian framework, PFA provides measures of uncertainty on the factor number and groupings, combines both continuous and discrete traits, integrates over missing measurements and incorporates phylogenetic uncertainty with the help of molecular sequences. We develop Gibbs samplers based on dynamic programming to estimate the PFA posterior distribution, over 3-fold faster than for multivariate diffusion and a further order-of-magnitude more efficiently in the presence of latent traits. We further propose a novel marginal likelihood estimator for previously impractical models with discrete data and find that PFA also provides a better fit than multivariate diffusion in evolutionary questions in columbine flower development, placental reproduction transitions and triggerfish fin morphometry.

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

Association of tumor growth on nude mice and poor clinical outcome in soft tissue sarcoma patients.

PubMed

Budach, W; Budach, V

2001-09-01

Permanent growth in nude mice (PGNM) may be associated with poor clinical outcome. We tested this hypothesis in a group of soft tissue sarcoma (STS) patients. Small chunks from fresh tumor biopsies of 81 patients with STS were transplanted subcutaneously into NMRI-nu/nu nude mice. Tumor cell lines exhibiting growth in nude mice for more than three tumor passages were considered as permanently established. Clinical outcome of all patients was monitored with a median follow-up of 38 months. 39/81 (48%) STSs exhibited PGNM. High grade, high S-phase proportion, and aneuploidy were significant predictors of PGNM. Overall survival (OS) at 3 years was 21% (+7% standard error of median) for STS patients with PGNM and 53% (+/-8%) for patients without PGNM (P<0.01). Considering only patients without distant metastasis at the time of biopsy (n = 49), 3-year-OS was 25% (+/-10%) and 71% (+/-9%) for STS with PGNM and without PGNM, respectively (P<0.01). In the univariate analysis, PGNM, aneuploidy high S-phase proportion, tumor location at the trunk, high tumor grade, and non-liposarcoma histology were associated with reduced survival time. In the multivariate analysis, aneuploidy and tumor location at the trunk were the only independent predictors of overall survival. Permanent growth of STS on nude mice is associated with poor clinical outcome in the univariate analysis, but is not an independent predictor of survival in the multivariate analysis due to a strong co-correlation to other known adverse prognostic factors.

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.

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.

Cement Leakage in Percutaneous Vertebral Augmentation for Osteoporotic Vertebral Compression Fractures: Analysis of Risk Factors.

PubMed

Xie, Weixing; Jin, Daxiang; Ma, Hui; Ding, Jinyong; Xu, Jixi; Zhang, Shuncong; Liang, De

2016-05-01

The risk factors for cement leakage were retrospectively reviewed in 192 patients who underwent percutaneous vertebral augmentation (PVA). To discuss the factors related to the cement leakage in PVA procedure for the treatment of osteoporotic vertebral compression fractures. PVA is widely applied for the treatment of osteoporotic vertebral fractures. Cement leakage is a major complication of this procedure. The risk factors for cement leakage were controversial. A retrospective review of 192 patients who underwent PVA was conducted. The following data were recorded: age, sex, bone density, number of fractured vertebrae before surgery, number of treated vertebrae, severity of the treated vertebrae, operative approach, volume of injected bone cement, preoperative vertebral compression ratio, preoperative local kyphosis angle, intraosseous clefts, preoperative vertebral cortical bone defect, and ratio and type of cement leakage. To study the correlation between each factor and cement leakage ratio, bivariate regression analysis was employed to perform univariate analysis, whereas multivariate linear regression analysis was employed to perform multivariate analysis. The study included 192 patients (282 treated vertebrae), and cement leakage occurred in 100 vertebrae (35.46%). The vertebrae with preoperative cortical bone defects generally exhibited higher cement leakage ratio, and the leakage is typically type C. Vertebrae with intact cortical bones before the procedure tend to experience type S leakage. Univariate analysis showed that patient age, bone density, number of fractured vertebrae before surgery, and vertebral cortical bone were associated with cement leakage ratio (P<0.05). Multivariate analysis showed that the main factors influencing bone cement leakage are bone density and vertebral cortical bone defect, with standardized partial regression coefficients of -0.085 and 0.144, respectively. High bone density and vertebral cortical bone defect are independent risk factors associated with bone cement leakage.

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

PubMed

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

2018-03-01

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

Plasma metabolic profiling analysis of nephrotoxicity induced by acyclovir using metabonomics coupled with multivariate data analysis.

PubMed

Zhang, Xiuxiu; Li, Yubo; Zhou, Huifang; Fan, Simiao; Zhang, Zhenzhu; Wang, Lei; Zhang, Yanjun

2014-08-01

Acyclovir (ACV) is an antiviral agent. However, its use is limited by adverse side effect, particularly by its nephrotoxicity. Metabonomics technology can provide essential information on the metabolic profiles of biofluids and organs upon drug administration. Therefore, in this study, mass spectrometry-based metabonomics coupled with multivariate data analysis was used to identify the plasma metabolites and metabolic pathways related to nephrotoxicity caused by intraperitoneal injection of low (50mg/kg) and high (100mg/kg) doses of acyclovir. Sixteen biomarkers were identified by metabonomics and nephrotoxicity results revealed the dose-dependent effect of acyclovir on kidney tissues. The present study showed that the top four metabolic pathways interrupted by acyclovir included the metabolisms of arachidonic acid, tryptophan, arginine and proline, and glycerophospholipid. This research proves the established metabonomic approach can provide information on changes in metabolites and metabolic pathways, which can be applied to in-depth research on the mechanism of acyclovir-induced kidney injury. Copyright © 2014 Elsevier B.V. All rights reserved.

A cross-species socio-emotional behaviour development revealed by a multivariate analysis.

PubMed

Koshiba, Mamiko; Senoo, Aya; Mimura, Koki; Shirakawa, Yuka; Karino, Genta; Obara, Saya; Ozawa, Shinpei; Sekihara, Hitomi; Fukushima, Yuta; Ueda, Toyotoshi; Kishino, Hirohisa; Tanaka, Toshihisa; Ishibashi, Hidetoshi; Yamanouchi, Hideo; Yui, Kunio; Nakamura, Shun

2013-01-01

Recent progress in affective neuroscience and social neurobiology has been propelled by neuro-imaging technology and epigenetic approach in neurobiology of animal behaviour. However, quantitative measurements of socio-emotional development remains lacking, though sensory-motor development has been extensively studied in terms of digitised imaging analysis. Here, we developed a method for socio-emotional behaviour measurement that is based on the video recordings under well-defined social context using animal models with variously social sensory interaction during development. The behaviour features digitized from the video recordings were visualised in a multivariate statistic space using principal component analysis. The clustering of the behaviour parameters suggested the existence of species- and stage-specific as well as cross-species behaviour modules. These modules were used to characterise the behaviour of children with or without autism spectrum disorders (ASDs). We found that socio-emotional behaviour is highly dependent on social context and the cross-species behaviour modules may predict neurobiological basis of ASDs.

Study of archaeological coins of different dynasties using libs coupled with multivariate analysis

NASA Astrophysics Data System (ADS)

Awasthi, Shikha; Kumar, Rohit; Rai, G. K.; Rai, A. K.

2016-04-01

Laser Induced Breakdown Spectroscopy (LIBS) is an atomic emission spectroscopic technique having unique capability of an in-situ monitoring tool for detection and quantification of elements present in different artifacts. Archaeological coins collected form G.R. Sharma Memorial Museum; University of Allahabad, India has been analyzed using LIBS technique. These coins were obtained from excavation of Kausambi, Uttar Pradesh, India. LIBS system assembled in the laboratory (laser Nd:YAG 532 nm, 4 ns pulse width FWHM with Ocean Optics LIBS 2000+ spectrometer) is employed for spectral acquisition. The spectral lines of Ag, Cu, Ca, Sn, Si, Fe and Mg are identified in the LIBS spectra of different coins. LIBS along with Multivariate Analysis play an effective role for classification and contribution of spectral lines in different coins. The discrimination between five coins with Archaeological interest has been carried out using Principal Component Analysis (PCA). The results show the potential relevancy of the methodology used in the elemental identification and classification of artifacts with high accuracy and robustness.

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.

Identification of Enterococcus, Streptococcus, and Staphylococcus by Multivariate Analysis of Proton Magnetic Resonance Spectroscopic Data from Plate Cultures

PubMed Central

Bourne, Roger; Himmelreich, Uwe; Sharma, Ansuiya; Mountford, Carolyn; Sorrell, Tania

2001-01-01

A new fingerprinting technique with the potential for rapid identification of bacteria was developed by combining proton magnetic resonance spectroscopy (1H MRS) with multivariate statistical analysis. This resulted in an objective identification strategy for common clinical isolates belonging to the bacterial species Staphylococcus aureus, Staphylococcus epidermidis, Enterococcus faecalis, Streptococcus pneumoniae, Streptococcus pyogenes, Streptococcus agalactiae, and the Streptococcus milleri group. Duplicate cultures of 104 different isolates were examined one or more times using 1H MRS. A total of 312 cultures were examined. An optimized classifier was developed using a bootstrapping process and a seven-group linear discriminant analysis to provide objective classification of the spectra. Identification of isolates was based on consistent high-probability classification of spectra from duplicate cultures and achieved 92% agreement with conventional methods of identification. Fewer than 1% of isolates were identified incorrectly. Identification of the remaining 7% of isolates was defined as indeterminate. PMID:11474013

Implications of Supermarket Access, Neighborhood Walkability, and Poverty Rates for Diabetes Risk in an Employee Population

PubMed Central

Herrick, Cynthia J.; Yount, Byron W.; Eyler, Amy A.

2016-01-01

Objective Diabetes is a growing public health problem, and the environment in which people live and work may affect diabetes risk. The goal of this study was to examine the association between multiple aspects of environment and diabetes risk in an employee population. Design This was a retrospective cross-sectional analysis. Home environment variables were derived using employee zip code. Descriptive statistics were run on all individual and zip code level variables, stratified by diabetes risk and worksite. A multivariable logistic regression analysis was then conducted to determine the strongest associations with diabetes risk. Setting Data was collected from employee health fairs in a Midwestern health system 2009–2012. Subjects The dataset contains 25,227 unique individuals across four years of data. From this group, using an individual’s first entry into the database, 15,522 individuals had complete data for analysis. Results The prevalence of high diabetes risk in this population was 2.3%. There was significant variability in individual and zip code level variables across worksites. From the multivariable analysis, living in a zip code with higher percent poverty and higher walk score was positively associated with high diabetes risk, while living in a zip code with higher supermarket density was associated with a reduction in high diabetes risk. Conclusions Our study underscores the important relationship between poverty, home neighborhood environment, and diabetes risk, even in a relatively healthy employed population, and suggests a role for the employer in promoting health. PMID:26638995

Implications of supermarket access, neighbourhood walkability and poverty rates for diabetes risk in an employee population.

PubMed

Herrick, Cynthia J; Yount, Byron W; Eyler, Amy A

2016-08-01

Diabetes is a growing public health problem, and the environment in which people live and work may affect diabetes risk. The goal of the present study was to examine the association between multiple aspects of environment and diabetes risk in an employee population. This was a retrospective cross-sectional analysis. Home environment variables were derived using employees' zip code. Descriptive statistics were run on all individual- and zip-code-level variables, stratified by diabetes risk and worksite. A multivariable logistic regression analysis was then conducted to determine the strongest associations with diabetes risk. Data were collected from employee health fairs in a Midwestern health system, 2009-2012. The data set contains 25 227 unique individuals across four years of data. From this group, using an individual's first entry into the database, 15 522 individuals had complete data for analysis. The prevalence of high diabetes risk in this population was 2·3 %. There was significant variability in individual- and zip-code-level variables across worksites. From the multivariable analysis, living in a zip code with higher percentage of poverty and higher walk score was positively associated with high diabetes risk, while living in a zip code with higher supermarket density was associated with a reduction in high diabetes risk. Our study underscores the important relationship between poverty, home neighbourhood environment and diabetes risk, even in a relatively healthy employed population, and suggests a role for the employer in promoting health.

Treatment results and prognostic factors of pediatric neuroblastoma: a retrospective study.

PubMed

El-Sayed, Mohamed I; Ali, Amany M; Sayed, Heba A; Zaky, Eman M

2010-12-24

We conducted a retrospective analysis to investigate treatment results and prognostic factors of pediatric neuroblastoma patients. This retrospective study was carried out analyzing the medical records of patients with the pathological diagnosis of neuroblastoma seen at South Egypt Cancer Institute, Assiut University during the period from January 2001 and January 2010. After induction chemotherapy, response according to international neuoblastoma response criteria was assessed. Radiotherapy to patients with residual primary tumor was applied. Overall and event free survival (OAS and EFS) rates were estimated using Graphed prism program. The Log-rank test was used to examine differences in OAS and EFS rates. Cox-regression multivariate analysis was done to determine the independent prognostic factors affecting survival rates. Fifty three cases were analyzed. The median follow-up duration was 32 months and ranged from 2 to 84 months. The 3-year OAS and EFS rates were 39.4% and 29.3% respectively. Poor prognostic factors included age >1 year of age, N-MYC amplification, and high risk group. The majority of patients (68%) presented in high risk group, where treatment outcome was poor, as only 21% of patients survived for 3 year. Multivariate analysis confirmed only the association between survival and risk group. However, in univariate analysis, local radiation therapy resulted in significant survival improvement. Therefore, radiotherapy should be given to patients with residual tumor evident after induction chemotherapy and surgery. Future attempts to improve OAS in high risk group patients with aggressive chemotherapy and bone marrow transplantation should be considered.

Classification of the medicinal plants of the genus Atractylodes using high-performance liquid chromatography with diode array and tandem mass spectrometry detection combined with multivariate statistical analysis.

PubMed

Cho, Hyun-Deok; Kim, Unyong; Suh, Joon Hyuk; Eom, Han Young; Kim, Junghyun; Lee, Seul Gi; Choi, Yong Seok; Han, Sang Beom

2016-04-01

Analytical methods using high-performance liquid chromatography with diode array and tandem mass spectrometry detection were developed for the discrimination of the rhizomes of four Atractylodes medicinal plants: A. japonica, A. macrocephala, A. chinensis, and A. lancea. A quantitative study was performed, selecting five bioactive components, including atractylenolide I, II, III, eudesma-4(14),7(11)-dien-8-one and atractylodin, on twenty-six Atractylodes samples of various origins. Sample extraction was optimized to sonication with 80% methanol for 40 min at room temperature. High-performance liquid chromatography with diode array detection was established using a C18 column with a water/acetonitrile gradient system at a flow rate of 1.0 mL/min, and the detection wavelength was set at 236 nm. Liquid chromatography with tandem mass spectrometry was applied to certify the reliability of the quantitative results. The developed methods were validated by ensuring specificity, linearity, limit of quantification, accuracy, precision, recovery, robustness, and stability. Results showed that cangzhu contained higher amounts of atractylenolide I and atractylodin than baizhu, and especially atractylodin contents showed the greatest variation between baizhu and cangzhu. Multivariate statistical analysis, such as principal component analysis and hierarchical cluster analysis, were also employed for further classification of the Atractylodes plants. The established method was suitable for quality control of the Atractylodes plants. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Determinants of Anabolic-Androgenic Steroid Risk Perceptions in Youth Populations: A Multivariate Analysis

ERIC Educational Resources Information Center

Denham, Bryan E.

2009-01-01

Grounded conceptually in social cognitive theory, this research examines how personal, behavioral, and environmental factors are associated with risk perceptions of anabolic-androgenic steroids. Ordinal logistic regression and logit log-linear models applied to data gathered from high-school seniors (N = 2,160) in the 2005 Monitoring the Future…

The Hazard of Graduation: Analysis of Three Multivariate Statistics Used to Study Multi-Institutional Attendance

ERIC Educational Resources Information Center

Muehlberg, Jessica Marie

2013-01-01

Adelman (2006) observed that a large quantity of research on retention is "institution-specific or use institutional characteristics as independent variables" (p. 81). However, he observed that over 60% of the students he studied attended multiple institutions making the calculation of institutional effects highly problematic. He argued…

Income and Education in Turkey: A Multivariate Analysis

ERIC Educational Resources Information Center

Sari, Ramazan; Soytas, Ugur

2006-01-01

Although the role of education in an economy is emphasized in theoretical studies, empirical literature finds mixed results for the relationship between growth and education. We examine the relationship between Gross Domestic Product (GDP) and enrollments in primary, secondary, and high schools, as well as universities in Turkey for 1937-1996, in…

Use of the Analysis of the Volatile Faecal Metabolome in Screening for Colorectal Cancer

PubMed Central

2015-01-01

Diagnosis of colorectal cancer is an invasive and expensive colonoscopy, which is usually carried out after a positive screening test. Unfortunately, existing screening tests lack specificity and sensitivity, hence many unnecessary colonoscopies are performed. Here we report on a potential new screening test for colorectal cancer based on the analysis of volatile organic compounds (VOCs) in the headspace of faecal samples. Faecal samples were obtained from subjects who had a positive faecal occult blood sample (FOBT). Subjects subsequently had colonoscopies performed to classify them into low risk (non-cancer) and high risk (colorectal cancer) groups. Volatile organic compounds were analysed by selected ion flow tube mass spectrometry (SIFT-MS) and then data were analysed using both univariate and multivariate statistical methods. Ions most likely from hydrogen sulphide, dimethyl sulphide and dimethyl disulphide are statistically significantly higher in samples from high risk rather than low risk subjects. Results using multivariate methods show that the test gives a correct classification of 75% with 78% specificity and 72% sensitivity on FOBT positive samples, offering a potentially effective alternative to FOBT. PMID:26086914

Prolonged Instability Prior to a Regime Shift | Science ...

EPA Pesticide Factsheets

Regime shifts are generally defined as the point of ‘abrupt’ change in the state of a system. However, a seemingly abrupt transition can be the product of a system reorganization that has been ongoing much longer than is evident in statistical analysis of a single component of the system. Using both univariate and multivariate statistical methods, we tested a long-term high-resolution paleoecological dataset with a known change in species assemblage for a regime shift. Analysis of this dataset with Fisher Information and multivariate time series modeling showed that there was a∼2000 year period of instability prior to the regime shift. This period of instability and the subsequent regime shift coincide with regional climate change, indicating that the system is undergoing extrinsic forcing. Paleoecological records offer a unique opportunity to test tools for the detection of thresholds and stable-states, and thus to examine the long-term stability of ecosystems over periods of multiple millennia. This manuscript explores various methods of assessing the transition between alternative states in an ecological system described by a long-term high-resolution paleoecological dataset.

Variety identification of brown sugar using short-wave near infrared spectroscopy and multivariate calibration

NASA Astrophysics Data System (ADS)

Yang, Haiqing; Wu, Di; He, Yong

2007-11-01

Near-infrared spectroscopy (NIRS) with the characteristics of high speed, non-destructiveness, high precision and reliable detection data, etc. is a pollution-free, rapid, quantitative and qualitative analysis method. A new approach for variety discrimination of brown sugars using short-wave NIR spectroscopy (800-1050nm) was developed in this work. The relationship between the absorbance spectra and brown sugar varieties was established. The spectral data were compressed by the principal component analysis (PCA). The resulting features can be visualized in principal component (PC) space, which can lead to discovery of structures correlative with the different class of spectral samples. It appears to provide a reasonable variety clustering of brown sugars. The 2-D PCs plot obtained using the first two PCs can be used for the pattern recognition. Least-squares support vector machines (LS-SVM) was applied to solve the multivariate calibration problems in a relatively fast way. The work has shown that short-wave NIR spectroscopy technique is available for the brand identification of brown sugar, and LS-SVM has the better identification ability than PLS when the calibration set is small.

The Irvine, Beatties, and Bresnahan (IBB) Forelimb Recovery Scale: An Assessment of Reliability and Validity

PubMed Central

Irvine, Karen-Amanda; Ferguson, Adam R.; Mitchell, Kathleen D.; Beattie, Stephanie B.; Lin, Amity; Stuck, Ellen D.; Huie, J. Russell; Nielson, Jessica L.; Talbott, Jason F.; Inoue, Tomoo; Beattie, Michael S.; Bresnahan, Jacqueline C.

2014-01-01

The IBB scale is a recently developed forelimb scale for the assessment of fine control of the forelimb and digits after cervical spinal cord injury [SCI; (1)]. The present paper describes the assessment of inter-rater reliability and face, concurrent and construct validity of this scale following SCI. It demonstrates that the IBB is a reliable and valid scale that is sensitive to severity of SCI and to recovery over time. In addition, the IBB correlates with other outcome measures and is highly predictive of biological measures of tissue pathology. Multivariate analysis using principal component analysis (PCA) demonstrates that the IBB is highly predictive of the syndromic outcome after SCI (2), and is among the best predictors of bio-behavioral function, based on strong construct validity. Altogether, the data suggest that the IBB, especially in concert with other measures, is a reliable and valid tool for assessing neurological deficits in fine motor control of the distal forelimb, and represents a powerful addition to multivariate outcome batteries aimed at documenting recovery of function after cervical SCI in rats. PMID:25071704

Multivariate analysis of correlation between electrophysiological and hemodynamic responses during cognitive processing

PubMed Central

Kujala, Jan; Sudre, Gustavo; Vartiainen, Johanna; Liljeström, Mia; Mitchell, Tom; Salmelin, Riitta

2014-01-01

Animal and human studies have frequently shown that in primary sensory and motor regions the BOLD signal correlates positively with high-frequency and negatively with low-frequency neuronal activity. However, recent evidence suggests that this relationship may also vary across cortical areas. Detailed knowledge of the possible spectral diversity between electrophysiological and hemodynamic responses across the human cortex would be essential for neural-level interpretation of fMRI data and for informative multimodal combination of electromagnetic and hemodynamic imaging data, especially in cognitive tasks. We applied multivariate partial least squares correlation analysis to MEG–fMRI data recorded in a reading paradigm to determine the correlation patterns between the data types, at once, across the cortex. Our results revealed heterogeneous patterns of high-frequency correlation between MEG and fMRI responses, with marked dissociation between lower and higher order cortical regions. The low-frequency range showed substantial variance, with negative and positive correlations manifesting at different frequencies across cortical regions. These findings demonstrate the complexity of the neurophysiological counterparts of hemodynamic fluctuations in cognitive processing. PMID:24518260

HPLC-TOF-MS and HPLC-MS/MS combined with multivariate analysis for the characterization and discrimination of phenolic profiles in nonfumigated and sulfur-fumigated rhubarb.

PubMed

Yan, Yan; Zhang, Qianqian; Feng, Fang

2016-07-01

Sulfur fumigation has recently been used during the postharvest handling of rhubarb to reduce the drying duration and control pests. However, a few reports question the effect of sulfur fumigation on the bioactive components of rhubarb, which is crucial for the quality evaluation of the herbal medicine. The bottleneck limiting the study comes from the complex compounds that exist in herb samples with diverse structural features, wide concentration range and the difficulty to obtain all the reference standards. In this study, an integrated strategy based on the highly effective separation and analysis by liquid chromatography coupled with diode-array detection and time-of-flight/triple-quadruple tandem mass spectrometry combined with multivariate analysis was established. 68 phenolic compounds that exist in nonfumigated and sulfur-fumigated herb samples of rhubarb were tentatively assigned based on their retention behavior, UV spectra, accurate molecular weight, and mass spectral fragments. Qualitative and semiquantitative comparison revealed a serious reduction of the majority of phenolic compounds in sulfur-fumigated rhubarb. Furthermore, multivariate analysis was applied to holistically discriminate nonfumigated from sulfur-fumigated rhubarb and explore the characteristic chemical markers. The established approach was specific and rapid for characterizing and screening sulfur-fumigated rhubarb among commercial samples and could be applied for the quality assessment of other sulfur-fumigated herbs. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Anthropometric profile of combat athletes via multivariate analysis.

PubMed

Burdukiewicz, Anna; Pietraszewska, Jadwiga; Stachoń, Aleksandra; Andrzejewska, Justyna

2017-11-07

Athletic success is a complex phenotype influenced by multiple factors, from sport-specific skills to anthropometric characteristics. Considering the latter, the literature has repeatedly indicated that athletes possess distinct physical characteristics depending on the practiced discipline. The aim of the present study was to apply univariate and multivariate methods to assess a wide range of morphometric and somatotypic characteristics in male combat athletes. Biometric data were obtained from 206 male university-level practitioners of judo, jiu-jitsu, karate, kickboxing, taekwondo, and wrestling. Measures included height- and length-based variables, breadths, circumferences, and skinfolds. Body proportions and somatotype, using Sheldon's method of somatotopy as modified by Heath and Carter, were then determined. Body fat percentage was assessed by bioelectrical impedance analysis using tetrapolar hand-to-foot electrodes. Data were subjected to a wide array of statistical analysis. The results show between-group differences in the magnitudes of the analyzed characteristics. While mesomorphy was the dominant component of each group somatotype, enhanced ectomorphy was observed in those disciplines that require a high level of agility. Principal component analysis reduced the multivariate dimensionality of the data to three components (characterizing body size, height-based measures, and the anthropometric structure of the upper extremities) that explained the majority of data variance. The development of a sport-specific anthropometric profile via height- and mass-based and morphometric and somatotypic variables can aid in the design of training protocols and the identification of athlete markers as well as serve as a diagnostic criterion in predicting combat athlete performance.

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...

Root Cause Analysis of Quality Defects Using HPLC-MS Fingerprint Knowledgebase for Batch-to-batch Quality Control of Herbal Drugs.

PubMed

Yan, Binjun; Fang, Zhonghua; Shen, Lijuan; Qu, Haibin

2015-01-01

The batch-to-batch quality consistency of herbal drugs has always been an important issue. To propose a methodology for batch-to-batch quality control based on HPLC-MS fingerprints and process knowledgebase. The extraction process of Compound E-jiao Oral Liquid was taken as a case study. After establishing the HPLC-MS fingerprint analysis method, the fingerprints of the extract solutions produced under normal and abnormal operation conditions were obtained. Multivariate statistical models were built for fault detection and a discriminant analysis model was built using the probabilistic discriminant partial-least-squares method for fault diagnosis. Based on multivariate statistical analysis, process knowledge was acquired and the cause-effect relationship between process deviations and quality defects was revealed. The quality defects were detected successfully by multivariate statistical control charts and the type of process deviations were diagnosed correctly by discriminant analysis. This work has demonstrated the benefits of combining HPLC-MS fingerprints, process knowledge and multivariate analysis for the quality control of herbal drugs. Copyright © 2015 John Wiley & Sons, Ltd.

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 ...

Association of the Aspartate Aminotransferase to Alanine Aminotransferase Ratio with BNP Level and Cardiovascular Mortality in the General Population: The Yamagata Study 10-Year Follow-Up

PubMed Central

Yokoyama, Miyuki; Otaki, Yoichiro; Takahashi, Hiroki; Arimoto, Takanori; Shishido, Tetsuro; Miyamoto, Takuya; Konta, Tsuneo; Shibata, Yoko; Daimon, Makoto; Kayama, Takamasa; Kubota, Isao

2016-01-01

Background. Early identification of high risk subjects for cardiovascular disease in health check-up is still unmet medical need. Cardiovascular disease is characterized by the superior increase in aspartate aminotransferase (AST) to alanine aminotransferase (ALT). However, the association of AST/ALT ratio with brain natriuretic peptide (BNP) levels and cardiovascular mortality remains unclear in the general population. Methods and Results. This longitudinal cohort study included 3,494 Japanese subjects who participated in a community-based health check-up, with a 10-year follow-up. The AST/ALT ratio increased with increasing BNP levels. And multivariate logistic analysis showed that the AST/ALT ratio was significantly associated with a high BNP (≥100 pg/mL). There were 250 all-cause deaths including 79 cardiovascular deaths. Multivariate Cox proportional hazard regression analysis revealed that a high AST/ALT ratio (>90 percentile) was an independent predictor of all-cause and cardiovascular mortality after adjustment for confounding factors. Kaplan-Meier analysis demonstrated that cardiovascular mortality was higher in subjects with a high AST/ALT ratio than in those without. Conclusions. The AST/ALT ratio was associated with an increase in BNP and was predictive of cardiovascular mortality in a general population. Measuring the AST/ALT ratio during routine health check-ups may be a simple and cost-effective marker for cardiovascular mortality. PMID:27872510

Assessment of craniometric traits in South Indian dry skulls for sex determination.

PubMed

Ramamoorthy, Balakrishnan; Pai, Mangala M; Prabhu, Latha V; Muralimanju, B V; Rai, Rajalakshmi

2016-01-01

The skeleton plays an important role in sex determination in forensic anthropology. The skull bone is considered as the second best after the pelvic bone in sex determination due to its better retention of morphological features. Different populations have varying skeletal characteristics, making population specific analysis for sex determination essential. Hence the objective of this investigation is to obtain the accuracy of sex determination using cranial parameters of adult skulls to the highest percentage in South Indian population and to provide a baseline data for sex determination in South India. Seventy adult preserved human skulls were taken and based on the morphological traits were classified into 43 male skulls and 27 female skulls. A total of 26 craniometric parameters were studied. The data were analyzed by using the SPSS discriminant function. The analysis of stepwise, multivariate, and univariate discriminant function gave an accuracy of 77.1%, 85.7%, and 72.9% respectively. Multivariate direct discriminant function analysis classified skull bones into male and female with highest levels of accuracy. Using stepwise discriminant function analysis, the most dimorphic variable to determine sex of the skull, was biauricular breadth followed by weight. Subjecting the best dimorphic variables to univariate discriminant analysis, high levels of accuracy of sexual dimorphism was obtained. Percentage classification of high accuracies were obtained in this study indicating high level of sexual dimorphism in the crania, setting specific discriminant equations for the gender determination in South Indian people. Copyright © 2015 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

[Multivariate analysis of the association between consumption of fried food and gastric cancer and precancerous lesions].

PubMed

Guo, L W; Liu, S Z; Zhang, M; Chen, Q; Zhang, S K; Sun, X B

2018-02-06

Objective: To investigate the effect of fried food intake on the pathogenesis of gastric cancer and precancerous lesions. Methods: From 2005 to 2013, the residents aged 40-69 years from 11 counties/cities where cancer screening of upper gastrointestinal cancer were conducted in rural areas of Henan province as the subjects (82 367 cases). The information such as demography and lifestyle was collected. The residents were screened with endoscopic examination. The biopsy sampleswere diagnosed pathologically, according to pathological diagnosis criteria, the subjects with high risk were divided into the groups with different pathological degrees. The multivariate ordinal logistic regression analysis was used to analyze the relationship between the frequency of fried food intake and gastric cancer and precancerous lesions. Results: The study coverd 46 425 males and 35 942 females, with a age of (53.46±8.07)years. The study collected 6 707 cases of normal stomach, 2 325 cases of low grade intraepithelial neoplasia, 226 cases of high grade intraepithelial neoplasia and 331 cases of gastric cancer. Multivariate logistic regression analysis showed that, compared with those whoeat fried food less than one time per week, fried foods intake (<2 times/week: OR= 1.89, 95 %CI: 1.57-2.28; ≥ 2 times/week: OR= 1.91, 95 %CI: 1.66-2.20) were a risk factor for gastric cancer and precancerous lesions after adjustment for age, sex, marital status, educational level, body mass index (BMI), smoking and drinking status. Conclusion: The intake of fried food is a risk factor for gastric cancer and precancerous lesions. Therefore, reducing the intake of fried food can prevent the occurrence of gastric carcinoma and precancerous lesions.

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

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.

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.

Prolonged instability prior to a regime shift

USGS Publications Warehouse

Spanbauer, Trisha; Allen, Craig R.; Angeler, David G.; Eason, Tarsha; Fritz, Sherilyn C.; Garmestani, Ahjond S.; Nash, Kirsty L.; Stone, Jeffery R.

2014-01-01

Regime shifts are generally defined as the point of ‘abrupt’ change in the state of a system. However, a seemingly abrupt transition can be the product of a system reorganization that has been ongoing much longer than is evident in statistical analysis of a single component of the system. Using both univariate and multivariate statistical methods, we tested a long-term high-resolution paleoecological dataset with a known change in species assemblage for a regime shift. Analysis of this dataset with Fisher Information and multivariate time series modeling showed that there was a∼2000 year period of instability prior to the regime shift. This period of instability and the subsequent regime shift coincide with regional climate change, indicating that the system is undergoing extrinsic forcing. Paleoecological records offer a unique opportunity to test tools for the detection of thresholds and stable-states, and thus to examine the long-term stability of ecosystems over periods of multiple millennia.

Multivariate curve resolution using a combination of mid-infrared and near-infrared spectra for the analysis of isothermal epoxy curing reaction

NASA Astrophysics Data System (ADS)

Yamasaki, Hideki; Morita, Shigeaki

2018-05-01

Multivariate curve resolution (MCR) was applied to a hetero-spectrally combined dataset consisting of mid-infrared (MIR) and near-infrared (NIR) spectra collected during the isothermal curing reaction of an epoxy resin. An epoxy monomer, bisphenol A diglycidyl ether (BADGE), and a hardening agent, 4,4‧-diaminodiphenyl methane (DDM), were used for the reaction. The fundamental modes of the Nsbnd H and Osbnd H stretches were highly overlapped in the MIR region, while their first overtones could be independently identified in the NIR region. The concentration profiles obtained by MCR using the hetero-spectral combination showed good agreement with the results of calculations based on the Beer-Lambert law and the mass balance. The band assignments and absorption sites estimated by the analysis also showed good agreement with the results using two-dimensional (2D) hetero-correlation spectroscopy.

Multivariate curve resolution using a combination of mid-infrared and near-infrared spectra for the analysis of isothermal epoxy curing reaction.

PubMed

Yamasaki, Hideki; Morita, Shigeaki

2018-05-15

Multivariate curve resolution (MCR) was applied to a hetero-spectrally combined dataset consisting of mid-infrared (MIR) and near-infrared (NIR) spectra collected during the isothermal curing reaction of an epoxy resin. An epoxy monomer, bisphenol A diglycidyl ether (BADGE), and a hardening agent, 4,4'-diaminodiphenyl methane (DDM), were used for the reaction. The fundamental modes of the NH and OH stretches were highly overlapped in the MIR region, while their first overtones could be independently identified in the NIR region. The concentration profiles obtained by MCR using the hetero-spectral combination showed good agreement with the results of calculations based on the Beer-Lambert law and the mass balance. The band assignments and absorption sites estimated by the analysis also showed good agreement with the results using two-dimensional (2D) hetero-correlation spectroscopy. Copyright © 2017 Elsevier B.V. All rights reserved.

Multivariate analysis of the influences of oceanic and meteorological processes on suspended particulate matter distributions in Mississippi coastal waters

NASA Astrophysics Data System (ADS)

O'Brien, S. J.; Fitzpatrick, P. J.; Dzwonkowski, B.; Dykstra, S. L.; Wallace, D. J.; Church, I.; Wiggert, J. D.

2016-02-01

The Mississippi Sound is influenced by a high volume of sediment discharge from the Biloxi River, Mobile Bay via Pas aux Herons, Pascagoula River, Pearl River, Wolf River, and Lake Pontchartrain through the Rigolets. The river discharge, variable wind speed, wind direction and tides have a significant impact on the turbidity and transport of sediments in the Sound. Level 1 Moderate Resolution Imaging Spectroradiometer (MODIS) data is processed to extract the remote sensing reflectance at the wavelength of 645 nm and binned into an 8-day composite at a resolution of 500 m. The study uses a regional ocean color algorithm to compute suspended particulate matter (SPM) concentration based on these 8-day composite images. Multivariate analysis is applied between the SPM and time series of tides, wind, turbidity and river discharge measured at federal and academic institutions' stations and moorings. The multivariate analysis also includes in situ measurements of suspended sediment concentration and advective exchanges through the Mississippi Sound's tidal inlets between the coastal shelf and the nearshore estuarine waters. Mechanisms underlying the observed spatiotemporal distribution of SPM, including material exchange between the Sound and adjacent shelf waters, will be explored. The results of this study will contribute to current understanding of exchange mechanisms and pathways with the Mississippi Bight via the Mississippi Sound's tidal inlets.

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

NASA Astrophysics Data System (ADS)

Daftedar Abdelhadi, Raghda Mohamed

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

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...

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.

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.

Small Renal Masses in Close Proximity to the Collecting System and Renal Sinus Are Enriched for Malignancy and High Fuhrman Grade and Should Be Considered for Early Intervention.

PubMed

Correa, Andres F; Toussi, Amir; Amin, Milon; Hrebinko, Ronald L; Gayed, Bishoy A; Parwani, Anil V; Maranchie, Jodi K

2018-02-05

Recent reports show a correlation between renal tumor radiographic characteristics and pathologic features. We hypothesize that a more central location within the relatively hypoxic renal medulla might confer a more aggressive tumor phenotype. To test this, radiographic tumor characteristics were compared with tumor grade and histology. We retrospectively reviewed renal masses <4 cm in diameter that underwent resection between 2008 and 2013. Tumor location was recorded using standard R.E.N.A.L. Nephrometry Score. Multivariate logistic regression was performed to compare independent anatomic features with incidence of malignancy and high nuclear grade. A total of 334 renal tumors had information available for analysis. Univariate analysis showed that increasing endophycity and proximity to the collecting system (<4 mm) were predictors of malignancy and high-grade features. In multivariate analysis, proximity to the collecting system <4 mm remained the as the only anatomical variable predictive of malignancy (odds ratio [OR], 3.58; 95% confidence interval [CI], 1.06-12.05; P = .04) and high nuclear grade (OR, 2.81; 95% CI, 1.44-5.51; P = .003). Malignancy and high tumor grade occur with much greater frequency when tumors are located deep in the kidney, in close proximity to the collecting system and renal sinus. Ninety-six percent of small renal masses in this region were cancers and nearly half were Fuhrman Grade 3 or 4, suggesting that these small centrally located tumors should be targeted for early intervention. Copyright © 2018 Elsevier Inc. All rights reserved.

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…

Integrative Exploratory Analysis of Two or More Genomic Datasets.

PubMed

Meng, Chen; Culhane, Aedin

2016-01-01

Exploratory analysis is an essential step in the analysis of high throughput data. Multivariate approaches such as correspondence analysis (CA), principal component analysis, and multidimensional scaling are widely used in the exploratory analysis of single dataset. Modern biological studies often assay multiple types of biological molecules (e.g., mRNA, protein, phosphoproteins) on a same set of biological samples, thereby creating multiple different types of omics data or multiassay data. Integrative exploratory analysis of these multiple omics data is required to leverage the potential of multiple omics studies. In this chapter, we describe the application of co-inertia analysis (CIA; for analyzing two datasets) and multiple co-inertia analysis (MCIA; for three or more datasets) to address this problem. These methods are powerful yet simple multivariate approaches that represent samples using a lower number of variables, allowing a more easily identification of the correlated structure in and between multiple high dimensional datasets. Graphical representations can be employed to this purpose. In addition, the methods simultaneously project samples and variables (genes, proteins) onto the same lower dimensional space, so the most variant variables from each dataset can be selected and associated with samples, which can be further used to facilitate biological interpretation and pathway analysis. We applied CIA to explore the concordance between mRNA and protein expression in a panel of 60 tumor cell lines from the National Cancer Institute. In the same 60 cell lines, we used MCIA to perform a cross-platform comparison of mRNA gene expression profiles obtained on four different microarray platforms. Last, as an example of integrative analysis of multiassay or multi-omics data we analyzed transcriptomic, proteomic, and phosphoproteomic data from pluripotent (iPS) and embryonic stem (ES) cell lines.

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

Multiple Hypothesis Testing for Experimental Gingivitis Based on Wilcoxon Signed Rank Statistics

PubMed Central

Preisser, John S.; Sen, Pranab K.; Offenbacher, Steven

2011-01-01

Dental research often involves repeated multivariate outcomes on a small number of subjects for which there is interest in identifying outcomes that exhibit change in their levels over time as well as to characterize the nature of that change. In particular, periodontal research often involves the analysis of molecular mediators of inflammation for which multivariate parametric methods are highly sensitive to outliers and deviations from Gaussian assumptions. In such settings, nonparametric methods may be favored over parametric ones. Additionally, there is a need for statistical methods that control an overall error rate for multiple hypothesis testing. We review univariate and multivariate nonparametric hypothesis tests and apply them to longitudinal data to assess changes over time in 31 biomarkers measured from the gingival crevicular fluid in 22 subjects whereby gingivitis was induced by temporarily withholding tooth brushing. To identify biomarkers that can be induced to change, multivariate Wilcoxon signed rank tests for a set of four summary measures based upon area under the curve are applied for each biomarker and compared to their univariate counterparts. Multiple hypothesis testing methods with choice of control of the false discovery rate or strong control of the family-wise error rate are examined. PMID:21984957

Predictive model for falling in Parkinson disease patients.

PubMed

Custodio, Nilton; Lira, David; Herrera-Perez, Eder; Montesinos, Rosa; Castro-Suarez, Sheila; Cuenca-Alfaro, Jose; Cortijo, Patricia

2016-12-01

Falls are a common complication of advancing Parkinson's disease (PD). Although numerous risk factors are known, reliable predictors of future falls are still lacking. The aim of this study was to develop a multivariate model to predict falling in PD patients. Prospective cohort with forty-nine PD patients. The area under the receiver-operating characteristic curve (AUC) was calculated to evaluate predictive performance of the purposed multivariate model. The median of PD duration and UPDRS-III score in the cohort was 6 years and 24 points, respectively. Falls occurred in 18 PD patients (30%). Predictive factors for falling identified by univariate analysis were age, PD duration, physical activity, and scores of UPDRS motor, FOG, ACE, IFS, PFAQ and GDS ( p -value < 0.001), as well as fear of falling score ( p -value = 0.04). The final multivariate model (PD duration, FOG, ACE, and physical activity) showed an AUC = 0.9282 (correctly classified = 89.83%; sensitivity = 92.68%; specificity = 83.33%). This study showed that our multivariate model have a high performance to predict falling in a sample of PD patients.

A novel multi-variant epitope ensemble vaccine against avian leukosis virus subgroup J.

PubMed

Wang, Xiaoyu; Zhou, Defang; Wang, Guihua; Huang, Libo; Zheng, Qiankun; Li, Chengui; Cheng, Ziqiang

2017-12-04

The hypervariable antigenicity and immunosuppressive features of avian leukosis virus subgroup J (ALV-J) has led to great challenges to develop effective vaccines. Epitope vaccine will be a perspective trend. Previously, we identified a variant antigenic neutralizing epitope in hypervariable region 1 (hr1) of ALV-J, N-LRDFIA/E/TKWKS/GDDL/HLIRPYVNQS-C. BLAST analysis showed that the mutation of A, E, T and H in this epitope cover 79% of all ALV-J strains. Base on this data, we designed a multi-variant epitope ensemble vaccine comprising the four mutation variants linked with glycine and serine. The recombinant multi-variant epitope gene was expressed in Escherichia coli BL21. The expressed protein of the variant multi-variant epitope gene can react with positive sera and monoclonal antibodies of ALV-J, while cannot react with ALV-J negative sera. The multi-variant epitope vaccine that conjugated Freund's adjuvant complete/incomplete showed high immunogenicity that reached the titer of 1:64,000 at 42 days post immunization and maintained the immune period for at least 126 days in SPF chickens. Further, we demonstrated that the antibody induced by the variant multi-variant ensemble epitope vaccine recognized and neutralized different ALV-J strains (NX0101, TA1, WS1, BZ1224 and BZ4). Protection experiment that was evaluated by clinical symptom, viral shedding, weight gain, gross and histopathology showed 100% chickens that inoculated the multi-epitope vaccine were well protected against ALV-J challenge. The result shows a promising multi-variant epitope ensemble vaccine against hypervariable viruses in animals. Copyright © 2017 Elsevier Ltd. All rights reserved.

Multivariable Techniques for High-Speed Research Flight Control Systems

NASA Technical Reports Server (NTRS)

Newman, Brett A.

1999-01-01

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

Blood neutrophil-lymphocyte ratio predicts survival in locally advanced cancer stomach treated with neoadjuvant chemotherapy FOLFOX 4.

PubMed

el Aziz, Lamiss Mohamed Abd

2014-12-01

Accurate predictors of survival for patients with advanced gastric cancer treated with neoadjuvant chemotherapy are currently lacking. In this study, we aimed to evaluate the prognostic significance of the neutrophil-lymphocyte ratio (NLR) in patients with stage III-IV gastric cancer who received neoadjuvant chemotherapy FOLFOX 4 as neoadjuvant chemotherapy. We enrolled 70 patients with stage III-IV cancer stomach in this study. Patients received FOLFOX 4 as neoadjuvant chemotherapy. Blood sample was collected before chemotherapy. The NLR was divided into two groups: high (>3) and low (≤ 3). Univariate analysis on progression-free survival (PFS) and overall survival (OS) was performed using the Kaplan-Meier and log-rank tests, and multivariate analysis was conducted using the Cox proportional hazards regression model. The toxicity was evaluated according to National Cancer Institute Common Toxicity Criteria. The univariate analysis showed that PFS and OS were both worse for patients with high NLR than for those with low NLR before chemotherapy (median PFS 28 and 44 months, respectively, P = 0.001; median OS 30 and 48 months, P = 0.001). Multivariate analysis showed that NLRs before chemotherapy were independent prognostic factors of OS but not for progression-free survival. NLR may serve as a potential biomarker for survival prognosis in patients with stage III-IV gastric cancer receiving neoadjuvant chemotherapy. The FOLFOX 4 demonstrated an acceptable toxicity.

The influence of different classification standards of age groups on prognosis in high-grade hemispheric glioma patients.

PubMed

Chen, Jian-Wu; Zhou, Chang-Fu; Lin, Zhi-Xiong

2015-09-15

Although age is thought to correlate with the prognosis of glioma patients, the most appropriate age-group classification standard to evaluate prognosis had not been fully studied. This study aimed to investigate the influence of age-group classification standards on the prognosis of patients with high-grade hemispheric glioma (HGG). This retrospective study of 125 HGG patients used three different classification standards of age-groups (≤ 50 and >50 years old, ≤ 60 and >60 years old, ≤ 45 and 45-65 and ≥ 65 years old) to evaluate the impact of age on prognosis. The primary end-point was overall survival (OS). The Kaplan-Meier method was applied for univariate analysis and Cox proportional hazards model for multivariate analysis. Univariate analysis showed a significant correlation between OS and all three classification standards of age-groups as well as between OS and pathological grade, gender, location of glioma, and regular chemotherapy and radiotherapy treatment. Multivariate analysis showed that the only independent predictors of OS were classification standard of age-groups ≤ 50 and > 50 years old, pathological grade and regular chemotherapy. In summary, the most appropriate classification standard of age-groups as an independent prognostic factor was ≤ 50 and > 50 years old. Pathological grade and chemotherapy were also independent predictors of OS in post-operative HGG patients. Copyright © 2015. Published by Elsevier B.V.

Assessment of heavy metals contamination in sediments from three adjacent regions of the Yellow River using metal chemical fractions and multivariate analysis techniques.

PubMed

Ma, Xiaoling; Zuo, Hang; Tian, Mengjing; Zhang, Liyang; Meng, Jia; Zhou, Xuening; Min, Na; Chang, Xinyuan; Liu, Ying

2016-02-01

Metal chemical fractions obtained by optimized BCR three-stage extraction procedure and multivariate analysis techniques were exploited for assessing 7 heavy metals (Cr, Pb, Cd, Co, Cu, Zn and Ni) in sediments from Gansu province, Ningxia and Inner Mongolia Autonomous Regions of the Yellow River in Northern China. The results indicated that higher susceptibility and bioavailability of Cr and Cd with a strong anthropogenic source were due to their higher availability in the exchangeable fraction. A portion of Pb, Cd, Co, Zn, and Ni in reducible fraction may be due to the fact that they can form stable complexes with Fe and Mn oxides. Substantial amount of Pb, Co, Ni and Cu was observed as oxidizable fraction because of their strong affinity to the organic matters so that they can complex with humic substances in sediments. The high geo-accumulation indexes (I(geo)) for Cr and Cd showed their higher environmental risk to the aquatic biota. Principal component analysis (PCA) revealed that high toxic Cr and Cd in polluted sites (Cd in S10, S11 and Cr in S13) may be contributed to anthropogenic sources, it was consistent with the results of dual hierarchical clustering analysis (DHCA), which could give more details about contributing sources. Copyright © 2015 Elsevier Ltd. All rights reserved.

MIDAS, prototype Multivariate Interactive Digital Analysis System, phase 1. Volume 3: Wiring diagrams

NASA Technical Reports Server (NTRS)

Kriegler, F. J.; Christenson, D.; Gordon, M.; Kistler, R.; Lampert, S.; Marshall, R.; Mclaughlin, R.

1974-01-01

The Midas System is a third-generation, fast, multispectral recognition system able to keep pace with the large quantity and high rates of data acquisition from present and projected sensors. A principal objective of the MIDAS Program is to provide a system well interfaced with the human operator and thus to obtain large overall reductions in turn-around time and significant gains in throughput. The hardware and software generated in Phase I of the overall program are described. The system contains a mini-computer to control the various high-speed processing elements in the data path and a classifier which implements an all-digital prototype multivariate-Gaussian maximum likelihood decision algorithm operating at 2 x 100,000 pixels/sec. Sufficient hardware was developed to perform signature extraction from computer-compatible tapes, compute classifier coefficients, control the classifier operation, and diagnose operation. The MIDAS construction and wiring diagrams are given.

MIDAS, prototype Multivariate Interactive Digital Analysis System, Phase 1. Volume 2: Diagnostic system

NASA Technical Reports Server (NTRS)

Kriegler, F. J.; Christenson, D.; Gordon, M.; Kistler, R.; Lampert, S.; Marshall, R.; Mclaughlin, R.

1974-01-01

The MIDAS System is a third-generation, fast, multispectral recognition system able to keep pace with the large quantity and high rates of data acquisition from present and projected sensors. A principal objective of the MIDAS Program is to provide a system well interfaced with the human operator and thus to obtain large overall reductions in turn-around time and significant gains in throughout. The hardware and software generated in Phase I of the over-all program are described. The system contains a mini-computer to control the various high-speed processing elements in the data path and a classifier which implements an all-digital prototype multivariate-Gaussian maximum likelihood decision algorithm operating 2 x 105 pixels/sec. Sufficient hardware was developed to perform signature extraction from computer-compatible tapes, compute classifier coefficients, control the classifier operation, and diagnose operation. Diagnostic programs used to test MIDAS' operations are presented.

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.

The Association between Electronic Bullying and School Absenteeism among High School Students in the United States

ERIC Educational Resources Information Center

Grinshteyn, Erin; Yang, Y. T.

2017-01-01

Background: We examined the relationship between exposure to electronic bullying and absenteeism as a result of being afraid. Methods: This multivariate, multinomial regression analysis of the 2013 Youth Risk Behavior Survey data assessed the association between experiencing electronic bullying in the past year and how often students were absent…

A non-targeted UHPLC-UV methid with classical and multi-variate data analysis to detect adulteration of skim milk powder with foreign proteins

USDA-ARS?s Scientific Manuscript database

Ultra-High performance liquid chromatography (UHPLC) with single wavelength (215 nm) detection was used to obtain chromatographic profiles of authentic skim milk powder (SMP) and synthetic mixtures of SMP with variable amounts of soy (SPI), pea (PPI), brown rice (BRP), and hydrolyzed wheat protein (...

"Generalist Genes" and Mathematics in 7-Year-Old Twins

ERIC Educational Resources Information Center

Kovas, Y.; Harlaar, N.; Petrill, S. A.; Plomin, R.

2005-01-01

Mathematics performance at 7 years as assessed by teachers using UK national curriculum criteria has been found to be highly heritable. For almost 3000 pairs of 7-year-old same-sex twins, we used multivariate genetic analysis to investigate the extent to which these genetic effects on mathematics performance overlap with genetic effects on reading…

Language and Cognitive Predictors of Text Comprehension: Evidence from Multivariate Analysis

ERIC Educational Resources Information Center

Kim, Young-Suk

2015-01-01

Using data from children in South Korea (N = 145, M[subscript age] = 6.08), it was determined how low-level language and cognitive skills (vocabulary, syntactic knowledge, and working memory) and high-level cognitive skills (comprehension monitoring and theory of mind [ToM]) are related to listening comprehension and whether listening…

Prognostic value of stromal decorin expression in patients with breast cancer: a meta-analysis.

PubMed

Li, Shuang-Jiang; Chen, Da-Li; Zhang, Wen-Biao; Shen, Cheng; Che, Guo-Wei

2015-11-01

Numbers of studies have investigated the biological functions of decorin (DCN) in oncogenesis, tumor progression, angiogenesis and metastasis. Although many of them aim to highlight the prognostic value of stromal DCN expression in breast cancer, some controversial results still exist and a consensus has not been reached until now. Therefore, our meta-analysis aims to determine the prognostic significance of stromal DCN expression in breast cancer patients. PubMed, EMBASE, the Web of Science and China National Knowledge Infrastructure (CNKI) databases were searched for full-text literatures met out inclusion criteria. We applied the hazard ratio (HR) with 95% confidence interval (CI) as the appropriate summarized statistics. Q-test and I(2) statistic were employed to estimate the level of heterogeneity across the included studies. Sensitivity analysis was conducted to further identify the possible origins of heterogeneity. The publication bias was detected by Begg's test and Egger's test. There were three English literatures (involving 6 studies) included into our meta-analysis. On the one hand, both the summarized outcomes based on univariate analysis (HR: 0.513; 95% CI: 0.406-0.648; P<0.001) and multivariate analysis (HR: 0.544; 95% CI: 0.388-0.763; P<0.001) indicated that stromal DCN expression could promise the high cancer-specific survival (CSS) of breast cancer patients. On the other hand, both the summarized outcomes based on univariate analysis (HR: 0.504; 95% CI: 0.389-0.651; P<0.001) and multivariate analysis (HR: 0.568; 95% CI: 0.400-0.806; P=0.002) also indicated that stromal DCN expression was positively associated with high disease-free survival (DFS) of breast cancer patients. No significant heterogeneity or publication bias was observed within this meta-analysis. The present evidences indicate that high stromal DCN expression can significantly predict the good prognosis in patients with breast cancer. The discoveries from our meta-analysis have better be confirmed in the updated review pooling more relevant investigations in the future.

Correlation between ultrafiltration rate and phase angle measured by BIA in chronic kidney disease patients on regular hemodialysis

NASA Astrophysics Data System (ADS)

Nasution, B. R.; Lubis, A. R.

2018-03-01

Chronic Kidney Disease (CKD) patients with regular hemodialysis have high rates of morbidity and mortality that may be related to the hemodynamic effects of rapid UFR and low PhA value. In this study, we investigated whether high UFR is associated with a low value of PhA thus indirectly affect the risk of morbidity and mortality. UFR and Bioelectrical Impedance Analysis (BIA) examination on 92 subjects were recorded shortly after HD and analyzed by using Pearson correlation test. Multivariate analysis was also conducted to identify several factors that can affect the value of Phase angle. The number of HD regular CKD patients with PhA<4 based on the division of the UFR (cc/kg/h) <10, 10-13, ≥ 13, respectively were3, 10 and 6, whereas patients with ≥ 4 PhA <10, 10-13, ≥ 13respectively were 60, 11, and 2. The results showed a significant relationship between UFR with PhA. In CKD patients with regular HD, UFR has aninverse relationship with the value of PhA. After multivariate analysis, the UFR and the etiology of HD are still significantly affect the value of PhA. UFR optimal value in patients with CKD with regular HD is <10 cc/kg/h.

Decompressive craniectomy in severe traumatic brain injury: prognostic factors and complications

PubMed Central

Grille, Pedro; Tommasino, Nicolas

2015-01-01

Objective To analyze the clinical characteristics, complications and factors associated with the prognosis of severe traumatic brain injury among patients who undergo a decompressive craniectomy. Methods Retrospective study of patients seen in an intensive care unit with severe traumatic brain injury in whom a decompressive craniectomy was performed between the years 2003 and 2012. Patients were followed until their discharge from the intensive care unit. Their clinical-tomographic characteristics, complications, and factors associated with prognosis (univariate and multivariate analysis) were analyzed. Results A total of 64 patients were studied. Primary and lateral decompressive craniectomies were performed for the majority of patients. A high incidence of complications was found (78% neurological and 52% nonneurological). A total of 42 patients (66%) presented poor outcomes, and 22 (34%) had good neurological outcomes. Of the patients who survived, 61% had good neurological outcomes. In the univariate analysis, the factors significantly associated with poor neurological outcome were postdecompressive craniectomy intracranial hypertension, greater severity and worse neurological state at admission. In the multivariate analysis, only postcraniectomy intracranial hypertension was significantly associated with a poor outcome. Conclusion This study involved a very severe and difficult to manage group of patients with high morbimortality. Intracranial hypertension was a main factor of poor outcome in this population. PMID:26340150

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.

Combine bivariate statistics analysis and multivariate statistics analysis to assess landslide susceptibility in Chen-Yu-Lan watershed, Nantou, Taiwan.

NASA Astrophysics Data System (ADS)

Ngan Nguyen, Thi To; Liu, Cheng-Chien

2013-04-01

How landslides occurred and which factors triggered and sped up landslide occurrences were usually asked by researchers in the past decades. Many investigations carried out in many places in the world to finding out methods that predict and prevent damages from landslides phenomena. Chen-Yu-Lan River watershed is reputed as a 'hot pot' of landslide researches in Taiwan by its complicated geological structures with the significant tectonic fault systems and steeply mountainous terrain. Beside annual high precipitation concentration and the abrupt slopes, some natural disaster, as typhoons (Sinlaku-2008, Kalmaegi-2008, and Marakot-2009) and earthquake (Chi-Chi earthquake-1999) are also the triggered factors cause landslides with serious damages in this place. This research expresses the quantitative approaches to generate landslide susceptible map for Chen-Yu-Lan watershed, a mountainous area in the central Taiwan. Landslide inventories data, which were detected from the Formosat-2 imageries for eight years from 2004 to 2011, were applied to carry out landslide susceptibility mapping. Bivariate statistics analysis and multivariate statistics analysis would be applied to calculate susceptible index of landslides. The weights of parameters were computed based on landslide data for eight years from 2004 to 2011. To validate effective levels of factors to landslide occurrences, this method built some multivariate algorithms and compared these results with real landslide occurrences. Besides this method, the historical data of landslides were also used to assess and classify landslide susceptibility levels. From long-term landslide data, relation between landslide susceptibility levels and landslide repetition was assigned. The results demonstrated differently effective levels of potential factors, such as, slope gradient, drainage density, lithology and land use to landslide phenomena. The results also showed logical relationship between weights and characteristics of factors' classes. Depending on these results be able to help planning managers localize the high risk areas of landslide or safely areas by building and human activities.

Value of dual contrast liver MRI at 3.0 T in differentiating well-differentiated hepatocellular carcinomas from dysplastic nodules: preliminary results of multivariate analysis.

PubMed

Yoon, Min A; Kim, Se Hyung; Park, Hee Sun; Lee, Dong Ho; Lee, Jae Young; Han, Joon Koo; Choi, Byung Ihn

2009-10-01

To assess the diagnostic value of dual contrast magnetic resonance imaging (DC-MRI) in the differentiation of well-differentiated hepatocellular carcinomas (WD-HCCs) from dysplastic nodules (DNs) and to determine the significant MRI predictors using univariate and multivariate analyses. Thirty-two WD-HCCs and 33 DNs in 28 patients who underwent liver transplantation with available histopathology as a gold standard were enrolled in this study. All patients underwent DC-MRI using superparamagnetic iron oxide (SPIO) and gadolinium (Gd) agents on a 3 T MRI unit. For all patients, precontrast T1- and T2-weighted (T2W) images as well as post-SPIO T2- and T2*W images were obtained. Then, for dynamic MRI, arterial (AP), portal, and equilibrium images were also obtained. Two radiologists reviewed the MR images for analyzing signal intensity on the all MR sequences in consensus. On AP images, the degree of enhancement was subjectively categorized into 4 groups: no, minimal, moderate, and strong enhancement. For quantitative analysis, relative arterial enhancement ratio was calculated by averaging 3 regions of interest values of each nodule on pre-Gd T1W and AP images. Each variable was initially evaluated using univariate analyses to assess statistically significant MRI findings differentiating HCCs and DNs, then with multivariate logistic regression analysis to find the most predictable MRI findings. Twenty WD-HCCs showed iso- or high SI on precontrast T2W images, whereas 23 DNs showed low SI (P = 0.003). Most DNs showed low SI on post-SPIO T2W (30/33) and T2*W (25/33) images, whereas HCCs tended to show heterogeneous high or high SI (16/32 and 19/32) (P < 0.012). On post-SPIO and pre-Gd T1W GRE images, 28 WD-HCCs showed iso- or high SI, whereas 24 DNs showed low SI (P < 0.001). On AP images, 20 HCCs revealed more than minimal degree of enhancement, whereas 32 DNs did not show any enhancement (P < 0.001). Mean relative arterial enhancement ratio of HCCs (39.4%) was also significantly larger than that of DNs (15.6%) (P = 0.001). On portal images, WD-HCCs tended to show iso- or high SI (n = 21), whereas DNs showed low SI (n = 29) (P < 0.001). Multivariate analysis revealed that a subjective degree of enhancement on AP images and SI on post-SPIO and pre-Gd T1W GRE images were the 2 variables that independently differentiated WD-HCCs from DNs. The use of DC-MRI is helpful in the differentiation of WD HCCs and DNs. More specifically, a subjective degree of enhancement on AP images and SI on post-SPIO and pre-Gd T1W GRE images are the 2 variables that independently differentiate WD-HCCs from DNs.

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

Elemental analysis of tissue pellets for the differentiation of epidermal lesion and normal skin by laser-induced breakdown spectroscopy

PubMed Central

Moon, Youngmin; Han, Jung Hyun; Shin, Sungho; Kim, Yong-Chul; Jeong, Sungho

2016-01-01

By laser induced breakdown spectroscopy (LIBS) analysis of epidermal lesion and dermis tissue pellets of hairless mouse, it is shown that Ca intensity in the epidermal lesion is higher than that in dermis, whereas Na and K intensities have an opposite tendency. It is demonstrated that epidermal lesion and normal dermis can be differentiated with high selectivity either by univariate or multivariate analysis of LIBS spectra with an intensity ratio difference by factor of 8 or classification accuracy over 0.995, respectively. PMID:27231610

A detailed comparison of analysis processes for MCC-IMS data in disease classification—Automated methods can replace manual peak annotations

PubMed Central

Horsch, Salome; Kopczynski, Dominik; Kuthe, Elias; Baumbach, Jörg Ingo; Rahmann, Sven

2017-01-01

Motivation Disease classification from molecular measurements typically requires an analysis pipeline from raw noisy measurements to final classification results. Multi capillary column—ion mobility spectrometry (MCC-IMS) is a promising technology for the detection of volatile organic compounds in the air of exhaled breath. From raw measurements, the peak regions representing the compounds have to be identified, quantified, and clustered across different experiments. Currently, several steps of this analysis process require manual intervention of human experts. Our goal is to identify a fully automatic pipeline that yields competitive disease classification results compared to an established but subjective and tedious semi-manual process. Method We combine a large number of modern methods for peak detection, peak clustering, and multivariate classification into analysis pipelines for raw MCC-IMS data. We evaluate all combinations on three different real datasets in an unbiased cross-validation setting. We determine which specific algorithmic combinations lead to high AUC values in disease classifications across the different medical application scenarios. Results The best fully automated analysis process achieves even better classification results than the established manual process. The best algorithms for the three analysis steps are (i) SGLTR (Savitzky-Golay Laplace-operator filter thresholding regions) and LM (Local Maxima) for automated peak identification, (ii) EM clustering (Expectation Maximization) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) for the clustering step and (iii) RF (Random Forest) for multivariate classification. Thus, automated methods can replace the manual steps in the analysis process to enable an unbiased high throughput use of the technology. PMID:28910313

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…

Multivariate meta-analysis with an increasing number of parameters.

PubMed

Boca, Simina M; Pfeiffer, Ruth M; Sampson, Joshua N

2017-05-01

Meta-analysis can average estimates of multiple parameters, such as a treatment's effect on multiple outcomes, across studies. Univariate meta-analysis (UVMA) considers each parameter individually, while multivariate meta-analysis (MVMA) considers the parameters jointly and accounts for the correlation between their estimates. The performance of MVMA and UVMA has been extensively compared in scenarios with two parameters. Our objective is to compare the performance of MVMA and UVMA as the number of parameters, p, increases. Specifically, we show that (i) for fixed-effect (FE) meta-analysis, the benefit from using MVMA can substantially increase as p increases; (ii) for random effects (RE) meta-analysis, the benefit from MVMA can increase as p increases, but the potential improvement is modest in the presence of high between-study variability and the actual improvement is further reduced by the need to estimate an increasingly large between study covariance matrix; and (iii) when there is little to no between-study variability, the loss of efficiency due to choosing RE MVMA over FE MVMA increases as p increases. We demonstrate these three features through theory, simulation, and a meta-analysis of risk factors for non-Hodgkin lymphoma. © Published 2017. This article is a U.S. Government work and is in the public domain in the USA.

Low enhancement on multiphase contrast-enhanced CT images: an independent predictor of the presence of high tumor grade of clear cell renal cell carcinoma.

PubMed

Zhu, Ye-Hua; Wang, Xun; Zhang, Jin; Chen, Yong-Hui; Kong, Wen; Huang, Yi-Ran

2014-09-01

The purpose of this study was to assess the relation between tumor enhancement on multiphase contrast-enhanced CT images and Fuhrman grade of clear cell renal cell carcinoma. A single-institution retrospective review was conducted on the records of 255 patients who underwent radical or partial nephrectomy and received a histologic diagnosis of clear cell renal cell carcinoma. Two radiologists recorded the radiographic features of each patient, including the attenuation value of the lesion, lesion size, calcification within the lesion, cystic versus solid appearance, and margin regularity. Parameters representing the extent of tumor enhancement were defined and calculated. The association between tumor enhancement and Fuhrman grade was analyzed, and multivariate analysis was performed to find independent predictors of high tumor grade. Significant differences existed in tumor enhancement among different Fuhrman grades (p < 0.001). High-grade tumors had significantly lower enhancement (p < 0.001). The enhancement parameter had a sensitivity of 0.84 and specificity of 0.93 in prediction of high tumor grade. In the multivariate analysis, more advanced age, irregular margin, and low tumor enhancement were the three independent predictors of high tumor grade. Tumor enhancement of clear cell renal cell carcinoma on multiphase contrast-enhanced CT images is associated with Fuhrman grade. Low tumor enhancement in the corticomedullary phase is an independent predictor of high tumor grade. This system may be helpful in clinical decision making about the care of patients treated by nonsurgical approaches.

Evaluation of High Ipsilateral Subventricular Zone Radiation Therapy Dose in Glioblastoma: A Pooled Analysis

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

Lee, Percy, E-mail: percylee@mednet.ucla.edu; UCLA Jonsson Comprehensive Cancer Center, Los Angeles, California; Eppinga, Wietse

Purpose: Cancer stem cells (CSCs) may play a role in the recurrence of glioblastoma. They are believed to originate from neural stem cells in the subventricular zone (SVZ). Because of their radioresistance, we hypothesized that high doses of radiation (>59.4 Gy) to the SVZ are necessary to control CSCs and improve progression-free survival (PFS) or overall survival (OS) in glioblastoma. Methods and Materials: 173 patients with glioblastoma pooled from 2 academic centers were treated with resection followed by chemoradiation therapy. The SVZ was segmented on computed tomography to calculate radiation doses delivered to the presumptive CSC niches. The relationships betweenmore » high SVZ doses and PFS and OS were examined using Cox proportional hazards models. Five covariates were included to estimate their impact on PFS or OS: ipsilateral and contralateral SVZ doses, clinical target volume dose, age, and extent of resection. Results: Median PFS and OS were 10.4 and 19.6 months for the cohort. The mean ipsilateral SVZ, contralateral SVZ, and clinical target volume doses were 49.2, 35.2, and 60.1 Gy, respectively. Twenty-one patients who received high ipsilateral SVZ dose (>59.4 Gy) had significantly longer median PFS (12.6 vs 9.9 months, P=.042) and longer OS (25.8 vs 19.2 months, P=.173). On multivariate analysis, high radiation therapy doses to ipsilateral SVZ remained a statistically significant independent predictor of improved PFS but not of OS. The extent of surgery affected both PFS and OS on multivariate analysis. Conclusion: High radiation therapy doses to ipsilateral CSC niches are associated with improved PFS in glioblastoma.« less

High-risk sexual behavior among people living with HIV/AIDS attending tertiary care hospitals in district of Northern India

PubMed Central

Shukla, Mukesh; Agarwal, Monica; Singh, Jai Vir; Tripathi, Anil Kumar; Srivastava, Anand Kumar; Singh, Vijay Kumar

2016-01-01

Context: Prevention with a positive approach has been advocated as one of the main strategies to diminish the new instances of HIV and the target are those who are engaged in high-risk sexual behavior. Therefore, understanding the risky behaviors of the HIV-infected individual is important. Aims: This study aimed to assess the prevalence and the predictors of high-risk sexual behavior among people living with HIV/AIDS (PLHA). Settings and Design: A hospital-based cross-sectional study was conducted at antiretroviral therapy centers of two tertiary care hospitals in Lucknow. Materials and Methods: A total of 322 HIV-positive patients were interviewed about their sexual behaviors during last 3 months using a pretested questionnaire. Statistical Analysis Used: Probability (p) was calculated to test for statistical significance at 5% level of significance. Association between risk factors and high-risk sexual behavior was determined using bivariate analysis followed by multivariate logistic regression. Results: Prevalence of high-risk sexual behavior was 24.5%. Of these patients, multiple sexual partners were reported by 67.3% whereas about 46.9% were engaged in unprotected sex. Multivariate logistic regression analysis revealed that high-risk sexual behavior was significantly associated with nonsupporting attitude of spouse (odds ratio [OR]: 18; 95% confidence interval [CI]: 1.4–225.5; P = 0.02) and alcohol consumption (OR: 9.3; 95% CI: 2.4–35.4; P = 0.001). Conclusions: Specific intervention addressing alcohol consumption and encouragement of spouse and family support should be integrated in the routine HIV/AIDS care and treatment apart from HIV transmission and prevention knowledge. PMID:27190412

Decoy receptor 3 is a prognostic factor in renal cell cancer.

PubMed

Macher-Goeppinger, Stephan; Aulmann, Sebastian; Wagener, Nina; Funke, Benjamin; Tagscherer, Katrin E; Haferkamp, Axel; Hohenfellner, Markus; Kim, Sunghee; Autschbach, Frank; Schirmacher, Peter; Roth, Wilfried

2008-10-01

Decoy receptor 3 (DcR3) is a soluble protein that binds to and inactivates the death ligand CD95L. Here, we studied a possible association between DcR3 expression and prognosis in patients with renal cell carcinomas (RCCs). A tissue microarray containing RCC tumor tissue samples and corresponding normal tissue samples was generated. Decoy receptor 3 expression in tumors of 560 patients was examined by immunohistochemistry. The effect of DcR3 expression on disease-specific survival and progression-free survival was assessed using univariate analysis and multivariate Cox regression analysis. Decoy receptor 3 serum levels were determined by ELISA. High DcR3 expression was associated with high-grade (P = .005) and high-stage (P = .048) RCCs. The incidence of distant metastasis (P = .03) and lymph node metastasis (P = .002) was significantly higher in the group with high DcR3 expression. Decoy receptor 3 expression correlated negatively with disease-specific survival (P < .001) and progression-free survival (P < .001) in univariate analyses. A multivariate Cox regression analysis retained DcR3 expression as an independent prognostic factor that outperformed the Karnofsky performance status. In patients with high-stage RCCs expressing DcR3, the 2-year survival probability was 25%, whereas in patients with DcR3-negative tumors, the survival probability was 65% (P < .001). Moreover, DcR3 serum levels were significantly higher in patients with high-stage localized disease (P = .007) and metastatic disease (P = .001). DcR3 expression is an independent prognostic factor of RCC progression and mortality. Therefore, the assessment of DcR3 expression levels offers valuable prognostic information that could be used to select patients for adjuvant therapy studies.

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.

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

Using a Grocery List Is Associated With a Healthier Diet and Lower BMI Among Very High-Risk Adults.

PubMed

Dubowitz, Tamara; Cohen, Deborah A; Huang, Christina Y; Beckman, Robin A; Collins, Rebecca L

2015-01-01

Examine whether use of a grocery list is associated with healthier diet and weight among food desert residents. Cross-sectional analysis of in-person interview data from randomly selected household food shoppers in 2 low-income, primarily African American urban neighborhoods in Pittsburgh, PA with limited access to healthy foods. Multivariate ordinary least-square regressions conducted among 1,372 participants and controlling for sociodemographic factors and other potential confounding variables indicated that although most of the sample (78%) was overweight or obese, consistently using a list was associated with lower body mass index (based on measured height and weight) (adjusted multivariant coefficient = 0.095) and higher dietary quality (based on the Healthy Eating Index-2005) (adjusted multivariant coefficient = 0.103) (P < .05). Shopping with a list may be a useful tool for low-income individuals to improve diet or decrease body mass index. Copyright © 2015 Society for Nutrition Education and Behavior. Published by Elsevier Inc. All rights reserved.

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

PubMed

Davatzikos, Christos

2016-10-01

The past 20 years have seen a mushrooming growth of the field of computational neuroanatomy. Much of this work has been enabled by the development and refinement of powerful, high-dimensional image warping methods, which have enabled detailed brain parcellation, voxel-based morphometric analyses, and multivariate pattern analyses using machine learning approaches. The evolution of these 3 types of analyses over the years has overcome many challenges. We present the evolution of our work in these 3 directions, which largely follows the evolution of this field. We discuss the progression from single-atlas, single-registration brain parcellation work to current ensemble-based parcellation; from relatively basic mass-univariate t-tests to optimized regional pattern analyses combining deformations and residuals; and from basic application of support vector machines to generative-discriminative formulations of multivariate pattern analyses, and to methods dealing with heterogeneity of neuroanatomical patterns. We conclude with discussion of some of the future directions and challenges. Copyright © 2016. Published by Elsevier B.V.

Hierarchical Bayesian spatial models for predicting multiple forest variables using waveform LiDAR, hyperspectral imagery, and large inventory datasets

USGS Publications Warehouse

Finley, Andrew O.; Banerjee, Sudipto; Cook, Bruce D.; Bradford, John B.

2013-01-01

In this paper we detail a multivariate spatial regression model that couples LiDAR, hyperspectral and forest inventory data to predict forest outcome variables at a high spatial resolution. The proposed model is used to analyze forest inventory data collected on the US Forest Service Penobscot Experimental Forest (PEF), ME, USA. In addition to helping meet the regression model's assumptions, results from the PEF analysis suggest that the addition of multivariate spatial random effects improves model fit and predictive ability, compared with two commonly applied modeling approaches. This improvement results from explicitly modeling the covariation among forest outcome variables and spatial dependence among observations through the random effects. Direct application of such multivariate models to even moderately large datasets is often computationally infeasible because of cubic order matrix algorithms involved in estimation. We apply a spatial dimension reduction technique to help overcome this computational hurdle without sacrificing richness in modeling.

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

PubMed Central

Davatzikos, Christos

2017-01-01

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

Comparing lagged linear correlation, lagged regression, Granger causality, and vector autoregression for uncovering associations in EHR data.

PubMed

Levine, Matthew E; Albers, David J; Hripcsak, George

2016-01-01

Time series analysis methods have been shown to reveal clinical and biological associations in data collected in the electronic health record. We wish to develop reliable high-throughput methods for identifying adverse drug effects that are easy to implement and produce readily interpretable results. To move toward this goal, we used univariate and multivariate lagged regression models to investigate associations between twenty pairs of drug orders and laboratory measurements. Multivariate lagged regression models exhibited higher sensitivity and specificity than univariate lagged regression in the 20 examples, and incorporating autoregressive terms for labs and drugs produced more robust signals in cases of known associations among the 20 example pairings. Moreover, including inpatient admission terms in the model attenuated the signals for some cases of unlikely associations, demonstrating how multivariate lagged regression models' explicit handling of context-based variables can provide a simple way to probe for health-care processes that confound analyses of EHR data.

The Statistical Consulting Center for Astronomy (SCCA)

NASA Technical Reports Server (NTRS)

Akritas, Michael

2001-01-01

The process by which raw astronomical data acquisition is transformed into scientifically meaningful results and interpretation typically involves many statistical steps. Traditional astronomy limits itself to a narrow range of old and familiar statistical methods: means and standard deviations; least-squares methods like chi(sup 2) minimization; and simple nonparametric procedures such as the Kolmogorov-Smirnov tests. These tools are often inadequate for the complex problems and datasets under investigations, and recent years have witnessed an increased usage of maximum-likelihood, survival analysis, multivariate analysis, wavelet and advanced time-series methods. The Statistical Consulting Center for Astronomy (SCCA) assisted astronomers with the use of sophisticated tools, and to match these tools with specific problems. The SCCA operated with two professors of statistics and a professor of astronomy working together. Questions were received by e-mail, and were discussed in detail with the questioner. Summaries of those questions and answers leading to new approaches were posted on the Web (www.state.psu.edu/ mga/SCCA). In addition to serving individual astronomers, the SCCA established a Web site for general use that provides hypertext links to selected on-line public-domain statistical software and services. The StatCodes site (www.astro.psu.edu/statcodes) provides over 200 links in the areas of: Bayesian statistics; censored and truncated data; correlation and regression, density estimation and smoothing, general statistics packages and information; image analysis; interactive Web tools; multivariate analysis; multivariate clustering and classification; nonparametric analysis; software written by astronomers; spatial statistics; statistical distributions; time series analysis; and visualization tools. StatCodes has received a remarkable high and constant hit rate of 250 hits/week (over 10,000/year) since its inception in mid-1997. It is of interest to scientists both within and outside of astronomy. The most popular sections are multivariate techniques, image analysis, and time series analysis. Hundreds of copies of the ASURV, SLOPES and CENS-TAU codes developed by SCCA scientists were also downloaded from the StatCodes site. In addition to formal SCCA duties, SCCA scientists continued a variety of related activities in astrostatistics, including refereeing of statistically oriented papers submitted to the Astrophysical Journal, talks in meetings including Feigelson's talk to science journalists entitled "The reemergence of astrostatistics" at the American Association for the Advancement of Science meeting, and published papers of astrostatistical content.

A high plasma: red blood cell transfusion ratio during liver transplantation is associated with decreased blood utilization.

PubMed

Pagano, M B; Metcalf, R A; Hess, J R; Reyes, J; Perkins, J D; Montenovo, M I

2018-04-01

During massive transfusion, the volume ratio of administered plasma (PL Vol) to red blood cell (RBC Vol) appears to be associated with reduced blood utilization and improved survival. The aim of this study was to evaluate the optimal component ratio in the setting of liver transplantation. This is a retrospective chart review of patients who underwent liver transplantation and received at least 500 ml of red blood cells from January 2013 through December 2015. Kernel smoothing analysis determined the proper component ratios to evaluate were a ≥0·85:1 ratio (high) to a ≤0·85:1 ratio (low). Two groups, plasma volume to RBC volume (PL Vol/RBC Vol) and plasma contained in the platelet units added to the plasma calculation [PL + PLT (platelet)] Vol/RBC Vol, were used to evaluate the component ratios. A total of 188 patients were included in the analysis. In the PL Vol/RBC Vol evaluation, a low ratio revealed that 1238 ml (977-1653 ml) (P < 0·0001) and 1178 ml (747-1178) (P < 0·0001) of RBC were used in excess compared to the high ratio, in the univariable and multivariable analysis, respectively. In the PL +PLT Vol/RBC Vol evaluation, a low ratio used 734 ml (193-1275) (P = 0·008) and 886 ml (431-1340) (P < 0·0001) of RBC in excess when compared to high ratio in the univariable and multivariable analysis, respectively. In patients undergoing liver transplantation, the transfusion of plasma to RBC ratio ≥0·85 was associated with decreased need of RBC transfusions. © 2018 International Society of Blood Transfusion.

Quality of care received and patient-reported regret in prostate cancer: Analysis of a population-based prospective cohort.

PubMed

Holmes, Jordan A; Bensen, Jeannette T; Mohler, James L; Song, Lixin; Mishel, Merle H; Chen, Ronald C

2017-01-01

Meeting quality of care standards in oncology is recognized as important by physicians, professional organizations, and payers. Data from a population-based cohort of patients with prostate cancer were used to examine whether receipt of care was consistent with published consensus metrics and whether receiving high-quality care was associated with less patient-reported treatment decisional regret. Patients with incident prostate cancer were enrolled in collaboration with the North Carolina Central Cancer Registry, with an oversampling of minority patients. Medical record abstraction was used to determine whether participants received high-quality care based on 5 standards: 1) discussion of all treatment options; 2) complete workup (prostate-specific antigen, Gleason grade, and clinical stage); 3) low-risk participants did not undergo a bone scan; 4) high-risk participants treated with radiotherapy (RT) received androgen deprivation therapy; and 5) participants treated with RT received conformal or intensity-modulated RT. Treatment decisional regret was assessed using a validated instrument. A total of 804 participants were analyzed. Overall, 66% of African American and 73% of white participants received care that met all standards (P = .03); this racial difference was confirmed by multivariable analysis. Care that included "discussion of all treatment options" was found to be associated with less patient-reported regret on univariable analysis (P = .03) and multivariable analysis (odds ratio, 0.59; 95% confidence interval, 0.37-0.95). The majority of participants received high-quality care, but racial disparity existed. Participants who discussed all treatment options appeared to have less treatment decisional regret. To the authors' knowledge, this is the first study to demonstrate an association between a quality of care metric and patient-reported outcome. Cancer 2017;138-143. © 2016 American Cancer Society. © 2016 American Cancer Society.

Long non-coding RNA PVT1 as a novel potential biomarker for predicting the prognosis of colorectal cancer.

PubMed

Fan, Heng; Zhu, Jian-Hua; Yao, Xue-Qing

2018-05-01

Long non-coding RNA (lncRNA) plays a very important role in the occurrence and development of various tumors, and is a potential biomarker for cancer diagnosis and prognosis. The purpose of this study was to investigate the relationship between the expression of lncRNA plasmacytoma variant translocation 1 (PVT1) and the prognostic significance in patients with colorectal cancer. The expression of PVT1 was measured by real-time quantitative reverse transcription-polymerase chain reaction (qRT-PCR) in cancerous and adjacent tissues of 210 colorectal cancer patients. The disease-free survival and overall survival of colorectal cancer patients were evaluated by Kaplan-Meier analysis, and univariate and multivariate analysis were performed by Cox proportional-hazards model. Our results revealed that PVT1 expression in cancer tissues of colorectal cancer was significantly higher than that of adjacent tissues ( P<0.001). High PVT1 expression was increased by 51.4% (108/210), which was significantly correlated with the tumor differentiation, the depth of invasion, the stage of tumor, node, metastasis (TNM), and lymphatic metastasis. The Kaplan-Meier analysis showed that high PVT1 expression resulted in a shorter disease-free survival (Log-rank test P<0.001) and overall survival (Log-rank test P<0.001) compared with the low PVT1 expression group in colorectal cancer patients, whether at TNM I/II stage or at TNM III/IV stage. A multivariate Cox regression analysis demonstrated that high PVT1 expression was an independent predictor of poor prognosis in colorectal cancer patients. Our results suggest that high PVT1 expression might be a potential biomarker for assessing tumor recurrence and prognosis in colorectal cancer patients.

[Neutrophil to lymphocyte ratio in peripheral blood: a novel independent prognostic factor in patients with head and neck squamous cell carcinoma].

PubMed

Wu, F; Wu, L L; Zhu, L X

2017-01-23

Objective: To investigate whether neutrophil to lymphocyte ratio (NLR) in peripheral blood can be an independent prognostic factor in patients with head and neck squamous cell carcinoma (HNSCC). Methods: Clinical data of 97 HNSCC patients who received surgical treatment in our department between January 2008 and January 2012 were analyzed retrospectively. The 97 patients were divided into low NLR group (NLR≤5, n =69) and high NLR group (NLR>5, n =28) according to the NLR in preoperative peripheral blood. The relationships of NLR and clinicopathological features were analyzed. Kaplan-Meier method was used for univariate survival analysis and Cox proportional hazard model for multivariate survival analysis. Results: The clinical stages were significantly different between high NLR group and low NLR group ( P <0.05), however, the age, gender, location, lymph node metastasis, smoking and alcohol of the two groups showed no significant differences ( P > 0.05 of all). Univariate survival analysis showed that smoking, lymph node metastasis, clinical stage and NLR value were risk factors for 3-year overall survival (OS) rate and relapse-free survival (RFS) rate of HNSCC patients ( P <0.05). The OS rate of high NLR and low NLR groups was 42.9% and 91.3%, and the RFS rate was 44.2% and 80.1%, respectively, with a statistically significant difference ( P <0.05 for both). Cox multivariate survival analysis showed that clinical stage and NLR were independent factors for prognostic evaluation of HNSCC patients ( P <0.05 for both). Conclusions: NLR level is significantly associated with clinical stage of HNSCC. High NLR is an independent prognostic rick factor and plays an important role in prognostic evaluation of HNSCC patients.

Multivariate Modeling of Proteins Related to Trapezius Myalgia, a Comparative Study of Female Cleaners with or without Pain

PubMed Central

Hadrevi, Jenny; Ghafouri, Bijar; Larsson, Britt; Gerdle, Björn; Hellström, Fredrik

2013-01-01

The prevalence of chronic trapezius myalgia is high in women with high exposure to awkward working positions, repetitive movements and movements with high precision demands. The mechanisms behind chronic trapezius myalgia are not fully understood. The purpose of this study was to explore the differences in protein content between healthy and myalgic trapezius muscle using proteomics. Muscle biopsies from 12 female cleaners with work-related trapezius myalgia and 12 pain free female cleaners were obtained from the descending part of the trapezius. Proteins were separated with two-dimensional differential gel electrophoresis (2D-DIGE) and selected proteins were identified with mass spectrometry. In order to discriminate the two groups, quantified proteins were fitted to a multivariate analysis: partial least square discriminate analysis. The model separated 28 unique proteins which were related to glycolysis, the tricaboxylic acid cycle, to the contractile apparatus, the cytoskeleton and to acute response proteins. The results suggest altered metabolism, a higher abundance of proteins related to inflammation in myalgic cleaners compared to healthy, and a possible alteration of the contractile apparatus. This explorative proteomic screening of proteins related to chronic pain in the trapezius muscle provides new important aspects of the pathophysiology behind chronic trapezius myalgia. PMID:24023854

Multivariate modeling of proteins related to trapezius myalgia, a comparative study of female cleaners with or without pain.

PubMed

Hadrevi, Jenny; Ghafouri, Bijar; Larsson, Britt; Gerdle, Björn; Hellström, Fredrik

2013-01-01

The prevalence of chronic trapezius myalgia is high in women with high exposure to awkward working positions, repetitive movements and movements with high precision demands. The mechanisms behind chronic trapezius myalgia are not fully understood. The purpose of this study was to explore the differences in protein content between healthy and myalgic trapezius muscle using proteomics. Muscle biopsies from 12 female cleaners with work-related trapezius myalgia and 12 pain free female cleaners were obtained from the descending part of the trapezius. Proteins were separated with two-dimensional differential gel electrophoresis (2D-DIGE) and selected proteins were identified with mass spectrometry. In order to discriminate the two groups, quantified proteins were fitted to a multivariate analysis: partial least square discriminate analysis. The model separated 28 unique proteins which were related to glycolysis, the tricaboxylic acid cycle, to the contractile apparatus, the cytoskeleton and to acute response proteins. The results suggest altered metabolism, a higher abundance of proteins related to inflammation in myalgic cleaners compared to healthy, and a possible alteration of the contractile apparatus. This explorative proteomic screening of proteins related to chronic pain in the trapezius muscle provides new important aspects of the pathophysiology behind chronic trapezius myalgia.

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…

Exploring Pattern of Socialisation Conditions and Human Development by Nonlinear Multivariate Analysis.

ERIC Educational Resources Information Center

Grundmann, Matthias

Following the assumptions of ecological socialization research, adequate analysis of socialization conditions must take into account the multilevel and multivariate structure of social factors that impact on human development. This statement implies that complex models of family configurations or of socialization factors are needed to explain the…

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 ...

Resemblance profiles as clustering decision criteria: Estimating statistical power, error, and correspondence for a hypothesis test for multivariate structure.

PubMed

Kilborn, Joshua P; Jones, David L; Peebles, Ernst B; Naar, David F

2017-04-01

Clustering data continues to be a highly active area of data analysis, and resemblance profiles are being incorporated into ecological methodologies as a hypothesis testing-based approach to clustering multivariate data. However, these new clustering techniques have not been rigorously tested to determine the performance variability based on the algorithm's assumptions or any underlying data structures. Here, we use simulation studies to estimate the statistical error rates for the hypothesis test for multivariate structure based on dissimilarity profiles (DISPROF). We concurrently tested a widely used algorithm that employs the unweighted pair group method with arithmetic mean (UPGMA) to estimate the proficiency of clustering with DISPROF as a decision criterion. We simulated unstructured multivariate data from different probability distributions with increasing numbers of objects and descriptors, and grouped data with increasing overlap, overdispersion for ecological data, and correlation among descriptors within groups. Using simulated data, we measured the resolution and correspondence of clustering solutions achieved by DISPROF with UPGMA against the reference grouping partitions used to simulate the structured test datasets. Our results highlight the dynamic interactions between dataset dimensionality, group overlap, and the properties of the descriptors within a group (i.e., overdispersion or correlation structure) that are relevant to resemblance profiles as a clustering criterion for multivariate data. These methods are particularly useful for multivariate ecological datasets that benefit from distance-based statistical analyses. We propose guidelines for using DISPROF as a clustering decision tool that will help future users avoid potential pitfalls during the application of methods and the interpretation of results.

Treatment results and prognostic factors of pediatric neuroblastoma: a retrospective study

PubMed Central

2010-01-01

Background We conducted a retrospective analysis to investigate treatment results and prognostic factors of pediatric neuroblastoma patients. Methods This retrospective study was carried out analyzing the medical records of patients with the pathological diagnosis of neuroblastoma seen at South Egypt Cancer Institute, Assiut University during the period from January 2001 and January 2010. After induction chemotherapy, response according to international neuoblastoma response criteria was assessed. Radiotherapy to patients with residual primary tumor was applied. Overall and event free survival (OAS and EFS) rates were estimated using Graphed prism program. The Log-rank test was used to examine differences in OAS and EFS rates. Cox-regression multivariate analysis was done to determine the independent prognostic factors affecting survival rates. Results Fifty three cases were analyzed. The median follow-up duration was 32 months and ranged from 2 to 84 months. The 3-year OAS and EFS rates were 39.4% and 29.3% respectively. Poor prognostic factors included age >1 year of age, N-MYC amplification, and high risk group. The majority of patients (68%) presented in high risk group, where treatment outcome was poor, as only 21% of patients survived for 3 year. Conclusion Multivariate analysis confirmed only the association between survival and risk group. However, in univariate analysis, local radiation therapy resulted in significant survival improvement. Therefore, radiotherapy should be given to patients with residual tumor evident after induction chemotherapy and surgery. Future attempts to improve OAS in high risk group patients with aggressive chemotherapy and bone marrow transplantation should be considered. PMID:21182799

Staging research of human lung cancer tissues by high-resolution magic angle spinning proton nuclear magnetic resonance spectroscopy (HRMAS 1 H NMR) and multivariate data analysis.

PubMed

Chen, Wenxue; Lu, Shaohua; Wang, Guifang; Chen, Fener; Bai, Chunxue

2017-10-01

High-resolution magic-angle spinning proton nuclear magnetic resonance (HRMAS 1 H NMR) spectroscopy technique was employed to analyze the metabonomic characterizations of lung cancer tissues in hope to identify potential diagnostic biomarkers for malignancy detection and staging research of lung tissues. HRMAS 1 H NMR spectroscopy technique can rapidly provide important information for accurate diagnosis and staging of cancer tissues owing to its noninvasive nature and limited requirement for the samples, and thus has been acknowledged as an excellent tool to investigate tissue metabolism and provide a more realistic insight into the metabonomics of tissues when combined with multivariate data analysis (MVDA) such as component analysis and orthogonal partial least squares-discriminant analysis in particular. HRMAS 1 H NMR spectra displayed the metabonomic differences of 32 lung cancer tissues at the different stages from 32 patients. The significant changes (P < 0.05) of some important metabolites such as lipids, aspartate and choline-containing compounds in cancer tissues at the different stages had been identified. Furthermore, the combination of HRMAS 1 H NMR spectroscopy and MVDA might potentially and precisely provided for a high sensitivity, specificity, prediction accuracy in the positive identification of the staging for the cancer tissues in contrast with the pathological data in clinic. This study highlighted the potential of metabonomics in clinical settings so that the techniques might be further exploited for the diagnosis and staging prediction of lung cancer in future. © 2016 John Wiley & Sons Australia, Ltd.

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.

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

PubMed

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

2015-11-01

Centralized and federated models for sharing data in research networks currently exist. To build multivariate data analysis for centralized networks, transfer of patient-level data to a central computation resource is necessary. The authors implemented distributed multivariate models for federated networks in which patient-level data is kept at each site and data exchange policies are managed in a study-centric manner. The objective was to implement infrastructure that supports the functionality of some existing research networks (e.g., cohort discovery, workflow management, and estimation of multivariate analytic models on centralized data) while adding additional important new features, such as algorithms for distributed iterative multivariate models, a graphical interface for multivariate model specification, synchronous and asynchronous response to network queries, investigator-initiated studies, and study-based control of staff, protocols, and data sharing policies. Based on the requirements gathered from statisticians, administrators, and investigators from multiple institutions, the authors developed infrastructure and tools to support multisite comparative effectiveness studies using web services for multivariate statistical estimation in the SCANNER federated network. The authors implemented massively parallel (map-reduce) computation methods and a new policy management system to enable each study initiated by network participants to define the ways in which data may be processed, managed, queried, and shared. The authors illustrated the use of these systems among institutions with highly different policies and operating under different state laws. Federated research networks need not limit distributed query functionality to count queries, cohort discovery, or independently estimated analytic models. Multivariate analyses can be efficiently and securely conducted without patient-level data transport, allowing institutions with strict local data storage requirements to participate in sophisticated analyses based on federated research networks. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.

Effect of Components in Water on the Extraction of Herbal Medicine
—Advanced Approach Using Multivariate Analysis—

NASA Astrophysics Data System (ADS)

Kanzaki, Yasushi

Many kinds of water products have been offered commercially suggesting some strange efficacy beyond our scientific knowledge even now at which various advanced scientific and technological research have been highly promoted. However, it seems quite obvious that such a strange efficacy must be nonexistent. If such efficacy were really existing, it must be solved by some suitable scientific procedure. In this study, the extraction of paeoniflorin from paeoniae radix was examined by varying the kind of extracting water. Then, the result was analyzed using multivariate analysis where the effect on the extraction was assumed to be ascribed to the ionic species dissolved in each water examined. The dissolved species were analyzed by chemical and instrumental analyses. According to the multivariate analysis, the amount of extracted paeoniflorin (Y) was presented by the following regression equation. The result shows that pH, [Ca2+], and [HCO3 -] were significant parameters and the combination of Ca2+ and HCO3 - affected negatively on the extraction of paeoniflorin.
Y=28.11-0.71 pH-0.0034[Ca2+]-0.93[HCO3 -]
where [Ca2+] is the concentration of calcium ion and [HCO3 -] is that of bicarbonate ion.

Strategies to optimize monitoring schemes of recreational waters from Salta, Argentina: a multivariate approach

PubMed Central

Gutiérrez-Cacciabue, Dolores; Teich, Ingrid; Poma, Hugo Ramiro; Cruz, Mercedes Cecilia; Balzarini, Mónica; Rajal, Verónica Beatriz

2014-01-01

Several recreational surface waters in Salta, Argentina, were selected to assess their quality. Seventy percent of the measurements exceeded at least one of the limits established by international legislation becoming unsuitable for their use. To interpret results of complex data, multivariate techniques were applied. Arenales River, due to the variability observed in the data, was divided in two: upstream and downstream representing low and high pollution sites, respectively; and Cluster Analysis supported that differentiation. Arenales River downstream and Campo Alegre Reservoir were the most different environments and Vaqueros and La Caldera Rivers were the most similar. Canonical Correlation Analysis allowed exploration of correlations between physicochemical and microbiological variables except in both parts of Arenales River, and Principal Component Analysis allowed finding relationships among the 9 measured variables in all aquatic environments. Variable’s loadings showed that Arenales River downstream was impacted by industrial and domestic activities, Arenales River upstream was affected by agricultural activities, Campo Alegre Reservoir was disturbed by anthropogenic and ecological effects, and La Caldera and Vaqueros Rivers were influenced by recreational activities. Discriminant Analysis allowed identification of subgroup of variables responsible for seasonal and spatial variations. Enterococcus, dissolved oxygen, conductivity, E. coli, pH, and fecal coliforms are sufficient to spatially describe the quality of the aquatic environments. Regarding seasonal variations, dissolved oxygen, conductivity, fecal coliforms, and pH can be used to describe water quality during dry season, while dissolved oxygen, conductivity, total coliforms, E. coli, and Enterococcus during wet season. Thus, the use of multivariate techniques allowed optimizing monitoring tasks and minimizing costs involved. PMID:25190636

Metabolomic Fingerprinting of Romaneschi Globe Artichokes by NMR Spectroscopy and Multivariate Data Analysis.

PubMed

de Falco, Bruna; Incerti, Guido; Pepe, Rosa; Amato, Mariana; Lanzotti, Virginia

2016-09-01

Globe artichoke (Cynara cardunculus L. var. scolymus L. Fiori) and cardoon (Cynara cardunculus L. var. altilis DC) are sources of nutraceuticals and bioactive compounds. To apply a NMR metabolomic fingerprinting approach to Cynara cardunculus heads to obtain simultaneous identification and quantitation of the major classes of organic compounds. The edible part of 14 Globe artichoke populations, belonging to the Romaneschi varietal group, were extracted to obtain apolar and polar organic extracts. The analysis was also extended to one species of cultivated cardoon for comparison. The (1) H-NMR of the extracts allowed simultaneous identification of the bioactive metabolites whose quantitation have been obtained by spectral integration followed by principal component analysis (PCA). Apolar organic extracts were mainly based on highly unsaturated long chain lipids. Polar organic extracts contained organic acids, amino acids, sugars (mainly inulin), caffeoyl derivatives (mainly cynarin), flavonoids, and terpenes. The level of nutraceuticals was found to be highest in the Italian landraces Bianco di Pertosa zia E and Natalina while cardoon showed the lowest content of all metabolites thus confirming the genetic distance between artichokes and cardoon. Metabolomic approach coupling NMR spectroscopy with multivariate data analysis allowed for a detailed metabolite profile of artichoke and cardoon varieties to be obtained. Relevant differences in the relative content of the metabolites were observed for the species analysed. This work is the first application of (1) H-NMR with multivariate statistics to provide a metabolomic fingerprinting of Cynara scolymus. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Prognostic importance of DNA ploidy in non-endometrioid, high-risk endometrial carcinomas.

PubMed

Sorbe, Bengt

2016-03-01

The present study investigated the predictive and prognostic impact of DNA ploidy together with other well-known prognostic factors in a series of non-endometrioid, high-risk endometrial carcinomas. From a complete consecutive series of 4,543 endometrial carcinomas of International Federation of Gynecology and Obstetrics (FIGO) stages I-IV, 94 serous carcinomas, 48 clear cell carcinomas and 231 carcinosarcomas were selected as a non-endometrioid, high-risk group for further studies regarding prognosis. The impact of DNA ploidy, as assessed by flow cytometry, was of particular focus. The age of the patients, FIGO stage, depth of myometrial infiltration and tumor expression of p53 were also included in the analyses (univariate and multivariate). In the complete series of cases, the recurrence rate was 37%, and the 5-year overall survival rate was 39% with no difference between the three histological subtypes. The primary cure rate (78%) was also similar for all tumor types studied. DNA ploidy was a significant predictive factor (on univariate analysis) for primary tumor cure rate, and a prognostic factor for survival rate (on univariate and multivariate analyses). The predictive and prognostic impact of DNA ploidy was higher in carcinosarcomas than in serous and clear cell carcinomas. In the majority of multivariate analyses, FIGO stage and depth of myometrial infiltration were the most important predictive (tumor recurrence) and prognostic (survival rate) factors. DNA ploidy status is a less important predictive and prognostic factor in non-endometrioid, high-risk endometrial carcinomas than in the common endometrioid carcinomas, in which FIGO and nuclear grade also are highly significant and important factors.

Extended-release niacin therapy and risk of ischemic stroke in patients with cardiovascular disease: the Atherothrombosis Intervention in Metabolic Syndrome with low HDL/High Triglycerides: Impact on Global Health Outcome (AIM-HIGH) trial.

PubMed

Teo, Koon K; Goldstein, Larry B; Chaitman, Bernard R; Grant, Shannon; Weintraub, William S; Anderson, David C; Sila, Cathy A; Cruz-Flores, Salvador; Padley, Robert J; Kostuk, William J; Boden, William E

2013-10-01

In Atherothrombosis Intervention in Metabolic Syndrome with low HDL/High Triglycerides: Impact on Global Health Outcomes (AIM-HIGH) trial, addition of extended-release niacin (ERN) to simvastatin in participants with established cardiovascular disease, low high-density lipoprotein cholesterol, and high triglycerides had no incremental benefit, despite increases in high-density lipoprotein cholesterol. Preliminary analysis based on incomplete end point adjudication suggested increased ischemic stroke risk among participants randomized to ERN. This final analysis was conducted after complete AIM-HIGH event ascertainment to further explore potential relationship between niacin therapy and ischemic stroke risk. There was no group difference in trial primary composite end point at a mean 36-month follow-up among 3414 patients (85% men; mean age, 64±9 years) randomized to simvastatin plus ERN (1500-2000 mg/d) versus simvastatin plus matching placebo. In the intention-to-treat analysis, there were 50 fatal or nonfatal ischemic strokes: 18 (1.06%) in placebo arm versus 32 (1.86%) in ERN arm (hazard ratio [HR], 1.78 [95% confidence interval {CI}, 1.00-3.17; P=0.050). Multivariate analysis showed independent associations between ischemic stroke risk and >65 years of age (HR, 3.58; 95% CI, 1.82-7.05; P=0.0002), history of stroke/transient ischemic attack/carotid disease (HR, 2.18; 95% CI, 1.23-3.88; P=0.0079), elevated baseline Lp(a) (HR, 2.80; 95% CI, 1.25-6.27 comparing the middle with the lowest tertile; HR, 2.31; 95% CI, 1.002-5.30 comparing the highest with the lowest tertile; overall P=0.042) but a nonsignificant association with ERN (HR, 1.74; 95% CI, 0.97-3.11; P=0.063). Although there were numerically more ischemic strokes with addition of ERN to simvastatin that reached nominal significance, the number was small, and multivariable analysis accounting for known risk factors did not support a significant association between niacin and ischemic stroke risk. http://www.clinicaltrials.gov. Unique identifier: NCT00120289.

Metabolic phenotyping of urine for discriminating alcohol-dependent from social drinkers and alcohol-naive subjects.

PubMed

Mostafa, Hamza; Amin, Arwa M; Teh, Chin-Hoe; Murugaiyah, Vikneswaran; Arif, Nor Hayati; Ibrahim, Baharudin

2016-12-01

Alcohol-dependence (AD) is a ravaging public health and social problem. AD diagnosis depends on questionnaires and some biomarkers, which lack specificity and sensitivity, however, often leading to less precise diagnosis, as well as delaying treatment. This represents a great burden, not only on AD individuals but also on their families. Metabolomics using nuclear magnetic resonance spectroscopy (NMR) can provide novel techniques for the identification of novel biomarkers of AD. These putative biomarkers can facilitate early diagnosis of AD. To identify novel biomarkers able to discriminate between alcohol-dependent, non-AD alcohol drinkers and controls using metabolomics. Urine samples were collected from 30 alcohol-dependent persons who did not yet start AD treatment, 54 social drinkers and 60 controls, who were then analysed using NMR. Data analysis was done using multivariate analysis including principal component analysis (PCA) and orthogonal partial least square-discriminate analysis (OPLS-DA), followed by univariate and multivariate logistic regression to develop the discriminatory model. The reproducibility was done using intraclass correlation coefficient (ICC). The OPLS-DA revealed significant discrimination between AD and other groups with sensitivity 86.21%, specificity 97.25% and accuracy 94.93%. Six biomarkers were significantly associated with AD in the multivariate logistic regression model. These biomarkers were cis-aconitic acid, citric acid, alanine, lactic acid, 1,2-propanediol and 2-hydroxyisovaleric acid. The reproducibility of all biomarkers was excellent (0.81-1.0). This study revealed that metabolomics analysis of urine using NMR identified AD novel biomarkers which can discriminate AD from social drinkers and controls with high accuracy. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

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

High performance computing enabling exhaustive analysis of higher order single nucleotide polymorphism interaction in Genome Wide Association Studies.

PubMed

Goudey, Benjamin; Abedini, Mani; Hopper, John L; Inouye, Michael; Makalic, Enes; Schmidt, Daniel F; Wagner, John; Zhou, Zeyu; Zobel, Justin; Reumann, Matthias

2015-01-01

Genome-wide association studies (GWAS) are a common approach for systematic discovery of single nucleotide polymorphisms (SNPs) which are associated with a given disease. Univariate analysis approaches commonly employed may miss important SNP associations that only appear through multivariate analysis in complex diseases. However, multivariate SNP analysis is currently limited by its inherent computational complexity. In this work, we present a computational framework that harnesses supercomputers. Based on our results, we estimate a three-way interaction analysis on 1.1 million SNP GWAS data requiring over 5.8 years on the full "Avoca" IBM Blue Gene/Q installation at the Victorian Life Sciences Computation Initiative. This is hundreds of times faster than estimates for other CPU based methods and four times faster than runtimes estimated for GPU methods, indicating how the improvement in the level of hardware applied to interaction analysis may alter the types of analysis that can be performed. Furthermore, the same analysis would take under 3 months on the currently largest IBM Blue Gene/Q supercomputer "Sequoia" at the Lawrence Livermore National Laboratory assuming linear scaling is maintained as our results suggest. Given that the implementation used in this study can be further optimised, this runtime means it is becoming feasible to carry out exhaustive analysis of higher order interaction studies on large modern GWAS.

HbA1c is significantly associated with arterial stiffness but not with carotid atherosclerosis in a community-based population without type 2 diabetes: The Dong-gu study.

PubMed

Lee, Young-Hoon; Shin, Min-Ho; Choi, Jin-Su; Rhee, Jung-Ae; Nam, Hae-Sung; Jeong, Seul-Ki; Park, Kyeong-Soo; Ryu, So-Yeon; Choi, Seong-Woo; Kim, Bok-Hee; Oh, Gyung-Jae; Kweon, Sun-Seog

2016-04-01

We examined the associations between HbA1c levels and various atherosclerotic vascular parameters among adults without diabetes from the general population. A total of 6500 community-dwelling adults, who were free of type 2 diabetes and ≥50 years of age, were included. High-resolution B-mode ultrasound was used to evaluate carotid artery structure, including intima-media thickness (IMT), plaque, and luminal diameter. Brachial-ankle pulse wave velocity (baPWV), which is a useful indicator of systemic arterial stiffness, was determined using an automatic waveform analysis device. No significant associations were observed between HbA1c, carotid IMT, plaque, or luminal diameter in a fully adjusted model. However, the odds ratio (95% confidence interval) for high baPWV (defined as the highest quartile) increased by 1.43 (1.19-1.71) per 1% HbA1c increase after adjusting for conventional risk factors in a multivariate logistic regression analysis. In addition, HbA1c was independently associated with baPWV in a multivariate linear regression analysis. High-normal HbA1c level was independently associated with arterial stiffness, but not with carotid atherosclerotic parameters, in the general population without diabetes. Our results suggest that the functional atherosclerotic process may already be accelerated according to HbA1c level, even at a level below the diagnostic threshold for diabetes. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Occurrence and multivariate exploratory analysis of the natural radioactivity anomaly in the south coastal region of Kenya

NASA Astrophysics Data System (ADS)

Kaniu, M. I.; Angeyo, K. H.; Darby, I. G.

2018-05-01

Characterized by a variety of rock formations, namely alkaline, igneous and sedimentary that contain significant deposits of monazite and pyrochlore ores, the south coastal region of Kenya may be regarded as highly heterogeneous with regard to its geochemistry, mineralogy as well as geological morphology. The region is one of the several alkaline carbonatite complexes of Kenya that are associated with high natural background radiation and therefore radioactivity anomaly. However, this high background radiation (HBR) anomaly has hardly been systematically assessed and delineated with regard to the spatial, geological, geochemical as well as anthropogenic variability and co-dependencies. We conducted wide-ranging in-situ gamma-ray spectrometric measurements in this area. The goal of the study was to assess the radiation exposure as well as determine the underlying natural radioactivity levels in the region. In this paper we report the occurrence, exploratory analysis and modeling to assess the multivariate geo-dependence and spatial variability of the radioactivity and associated radiation exposure. Unsupervised principal component analysis and ternary plots were utilized in the study. It was observed that areas which exhibit HBR anomalies are located along the south coast paved road and in the Mrima-Kiruku complex. These areas showed a trend towards enhanced levels of 232Th and 238U and low 40K. The spatial variability of the radioactivity anomaly was found to be mainly constrained by anthropogenic activities, underlying geology and geochemical processes in the terrestrial environment.

Application of multivariate statistical analysis in the pollution and health risk of traffic-related heavy metals.

PubMed

Ebqa'ai, Mohammad; Ibrahim, Bashar

2017-12-01

This study aims to analyse the heavy metal pollutants in Jeddah, the second largest city in the Gulf Cooperation Council with a population exceeding 3.5 million, and many vehicles. Ninety-eight street dust samples were collected seasonally from the six major roads as well as the Jeddah Beach, and subsequently digested using modified Leeds Public Analyst method. The heavy metals (Fe, Zn, Mn, Cu, Cd, and Pb) were extracted from the ash using methyl isobutyl ketone as solvent extraction and eventually analysed by atomic absorption spectroscopy. Multivariate statistical techniques, principal component analysis (PCA), and hierarchical cluster analysis were applied to these data. Heavy metal concentrations were ranked according to the following descending order: Fe > Zn > Mn > Cu > Pb > Cd. In order to study the pollution and health risk from these heavy metals as well as estimating their effect on the environment, pollution indices, integrated pollution index, enrichment factor, daily dose average, hazard quotient, and hazard index were all analysed. The PCA showed high levels of Zn, Fe, and Cd in Al Kurnish road, while these elements were consistently detected on King Abdulaziz and Al Madina roads. The study indicates that high levels of Zn and Pb pollution were recorded for major roads in Jeddah. Six out of seven roads had high pollution indices. This study is the first step towards further investigations into current health problems in Jeddah, such as anaemia and asthma.

Retrospective Evaluation Reveals That Long-term Androgen Deprivation Therapy Improves Cause-Specific and Overall Survival in the Setting of Dose-Escalated Radiation for High-Risk Prostate Cancer

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

Feng, Felix Y., E-mail: ffeng@med.umich.edu; Department of Radiation Oncology, Veterans Affairs Medical Center, Ann Arbor, Michigan; Blas, Kevin

2013-05-01

Purpose: To evaluate the role of androgen deprivation therapy (ADT) and duration for high-risk prostate cancer patients treated with dose-escalated radiation therapy (RT). Methods and Materials: A retrospective analysis of high-risk prostate cancer patients treated with dose-escalated RT (minimum 75 Gy) with or without ADT was performed. The relationship between ADT use and duration with biochemical failure (BF), metastatic failure (MF), prostate cancer-specific mortality (PCSM), non-prostate cancer death (NPCD), and overall survival (OS) was assessed as a function of pretreatment characteristics, comorbid medical illness, and treatment using Fine and Gray's cumulative incidence methodology. Results: The median follow-up time was 64more » months. In men with National Comprehensive Cancer Network defined high-risk prostate cancer treated with dose-escalated RT, on univariate analysis, both metastasis (P<.0001; hazard ratio 0.34; 95% confidence interval 0.18-0.67; cumulative incidence at 60 months 13% vs 35%) and PCSM (P=.015; hazard ratio 0.41; 95% confidence interval 0.2-1.0; cumulative incidence at 60 months 6% vs 11%) were improved with the use of ADT. On multivariate analysis for all high-risk patients, Gleason score was the strongest negative prognostic factor, and long-term ADT (LTAD) improved MF (P=.002), PCSM (P=.034), and OS (P=.001). In men with prostate cancer and Gleason scores 8 to 10, on multivariate analysis after adjustment for other risk features, there was a duration-dependent improvement in BF, metastasis, PCSM, and OS, all favoring LTAD in comparison with STAD or RT alone. Conclusion: For men with high-risk prostate cancer treated with dose-escalated EBRT, this retrospective study suggests that the combination of LTAD and RT provided a significant improvement in clinical outcome, which was especially true for those with Gleason scores of 8 to 10.« less

Central sleep apnea detection from ECG-derived respiratory signals. Application of multivariate recurrence plot analysis.

PubMed

Maier, C; Dickhaus, H

2010-01-01

This study examines the suitability of recurrence plot analysis for the problem of central sleep apnea (CSA) detection and delineation from ECG-derived respiratory (EDR) signals. A parameter describing the average length of vertical line structures in recurrence plots is calculated at a time resolution of 1 s as 'instantaneous trapping time'. Threshold comparison of this parameter is used to detect ongoing CSA. In data from 26 patients (duration 208 h) we assessed sensitivity for detection of CSA and mixed apnea (MSA) events by comparing the results obtained from 8-channel Holter ECGs to the annotations (860 CSA, 480 MSA) of simultaneously registered polysomnograms. Multivariate combination of the EDR from different ECG leads improved the detection accuracy significantly. When all eight leads were considered, an average instantaneous vertical line length above 5 correctly identified 1126 of the 1340 events (sensitivity 84%) with a total number of 1881 positive detections. We conclude that recurrence plot analysis is a promising tool for detection and delineation of CSA epochs from EDR signals with high time resolution. Moreover, the approach is likewise applicable to directly measured respiratory signals.

Screening and analysis of aconitum alkaloids and their metabolites in rat urine after oral administration of aconite roots extract using LC-TOFMS-based metabolomics.

PubMed

Tan, Guangguo; Lou, Ziyang; Jing, Jing; Li, Wuhong; Zhu, Zhenyu; Zhao, Liang; Zhang, Guoqing; Chai, Yifeng

2011-12-01

Aconite roots are popularly used in herbal medicines in China. Many cases of accidental and intentional intoxication with this plant have been reported; some of these are fatal because the toxicity of aconitum is very high. It is thus important to detect and identify aconitum alkaloids in biofluids. In this work, an improved method employing LC-TOFMS with multivariate data analysis was developed for screening and analysis of major aconitum alkaloids and their metabolites in rat urine following oral administration of aconite roots extract. Thirty-four signals highlighted by multivariate statistical analyses including 24 parent components and 10 metabolites were screened out and further identified by adjustment of the fragmentor voltage to produce structure-relevant fragment ions. It is helpful for studying aconite roots in toxicology, pharmacology and forensic medicine. This work also confirmed that the metabolomic approach provides effective tools for screening multiple absorbed and metabolic components of Chinese herbal medicines in vivo. Copyright © 2011 John Wiley & Sons, Ltd.

Detection and discrimination of microorganisms on various substrates with quantum cascade laser spectroscopy

NASA Astrophysics Data System (ADS)

Padilla-Jiménez, Amira C.; Ortiz-Rivera, William; Rios-Velazquez, Carlos; Vazquez-Ayala, Iris; Hernández-Rivera, Samuel P.

2014-06-01

Investigations focusing on devising rapid and accurate methods for developing signatures for microorganisms that could be used as biological warfare agents' detection, identification, and discrimination have recently increased significantly. Quantum cascade laser (QCL)-based spectroscopic systems have revolutionized many areas of defense and security including this area of research. In this contribution, infrared spectroscopy detection based on QCL was used to obtain the mid-infrared (MIR) spectral signatures of Bacillus thuringiensis, Escherichia coli, and Staphylococcus epidermidis. These bacteria were used as microorganisms that simulate biothreats (biosimulants) very truthfully. The experiments were conducted in reflection mode with biosimulants deposited on various substrates including cardboard, glass, travel bags, wood, and stainless steel. Chemometrics multivariate statistical routines, such as principal component analysis regression and partial least squares coupled to discriminant analysis, were used to analyze the MIR spectra. Overall, the investigated infrared vibrational techniques were useful for detecting target microorganisms on the studied substrates, and the multivariate data analysis techniques proved to be very efficient for classifying the bacteria and discriminating them in the presence of highly IR-interfering media.

NCA-LDAS land analysis: Development and performance of a multisensory, multivariate land data assimilation for the National Climate Assessment

NASA Astrophysics Data System (ADS)

Kumar, S.; Jasinski, M. F.; Mocko, D. M.; Rodell, M.; Borak, J.; Li, B.; Beaudoing, H. K.; Peters-Lidard, C. D.

2017-12-01

This presentation will describe one of the first successful examples of multisensor, multivariate land data assimilation, encompassing a large suite of soil moisture, snow depth, snow cover and irrigation intensity environmental data records (EDRs) from Scanning Multi-channel Microwave Radiometer (SMMR), the Special Sensor Microwave Imager (SSM/I), the Advanced Scatterometer (ASCAT), the Moderate-Resolution Imaging Spectroradiometer (MODIS), the Advanced Microwave Scanning Radiometer (AMSR-E and AMSR2), the Soil Moisture Ocean Salinity (SMOS) mission and the Soil Moisture Active Passive (SMAP) mission. The analysis is performed using the NASA Land Information System (LIS) as an enabling tool for the U.S. National Climate Assessment (NCA). The performance of NCA Land Data Assimilation System (NCA-LDAS) is evaluated by comparing to a number of hydrological reference data products. Results indicate that multivariate assimilation provides systematic improvements in simulated soil moisture and snow depth, with marginal effects on the accuracy of simulated streamflow and ET. An important conclusion is that across all evaluated variables, assimilation of data from increasingly more modern sensors (e.g. SMOS, SMAP, AMSR2, ASCAT) produces more skillful results than assimilation of data from older sensors (e.g. SMMR, SSM/I, AMSR-E). The evaluation also indicates high skill of NCA-LDAS when compared with other land analysis products. Further, drought indicators based on NCA-LDAS output suggest a trend of longer and more severe droughts over parts of Western U.S. during 1979-2015, particularly in the Southwestern U.S.

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.

The factors associated to psychosocial stress among general practitioners in Lithuania. Cross-sectional study.

PubMed

Vanagas, Giedrius; Bihari-Axelsson, Susanna

2005-06-10

There are number of studies showing that general practice is one of the most stressful workplace among health care workers. Since Baltic States regained independence in 1990, the reform of the health care system took place in which new role and more responsibilities were allocated to general practitioners' in Lithuania. This study aimed to explore the psychosocial stress level among Lithuanian general practitioner's and examine the relationship between psychosocial stress and work characteristics. The cross-sectional study of 300 Lithuanian General practitioners. Psychosocial stress was investigated with a questionnaire based on the Reeder scale. Job demands were investigated with the R. Karasek scale. The analysis included descriptive statistics; interrelationship analysis between characteristics and multivariate logistic regression to estimate odds ratios for each of the independent variables in the model. Response rate 66% (N = 197). Our study highlighted highest prevalence of psychosocial stress among widowed, single and female general practitioners. Lowest prevalence of psychosocial stress was among males and older age general practitioners. Psychosocial stress occurs when job demands are high and job decision latitude is low (chi2 = 18,9; p < 0,01). The multivariate analysis shows that high job demands (OR 4,128; CI 2,102-8,104; p < 0,001), patient load more than 18 patients per day (OR 5,863; CI 1,549-22,188; p < 0,01) and young age of GP's (OR 6,874; CI 1,292-36,582; p < 0,05) can be assigned as significant predictors for psychosocial stress. One half of respondents suffering from work related psychosocial stress. High psychological workload demands combined with low decision latitude has the greatest impact to stress caseness among GP's. High job demands, high patient load and young age of GP's can be assigned as significant predictors of psychosocial stress among GP's.

Multivariate geomorphic analysis of forest streams: Implications for assessment of land use impacts on channel condition

Treesearch

Richard. D. Wood-Smith; John M. Buffington

1996-01-01

Multivariate statistical analyses of geomorphic variables from 23 forest stream reaches in southeast Alaska result in successful discrimination between pristine streams and those disturbed by land management, specifically timber harvesting and associated road building. Results of discriminant function analysis indicate that a three-variable model discriminates 10...

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.

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.

A Review of Multivariate Methods for Multimodal Fusion of Brain Imaging Data

PubMed Central

Adali, Tülay; Yu, Qingbao; Calhoun, Vince D.

2011-01-01

The development of various neuroimaging techniques is rapidly improving the measurements of brain function/structure. However, despite improvements in individual modalities, it is becoming increasingly clear that the most effective research approaches will utilize multi-modal fusion, which takes advantage of the fact that each modality provides a limited view of the brain. The goal of multimodal fusion is to capitalize on the strength of each modality in a joint analysis, rather than a separate analysis of each. This is a more complicated endeavor that must be approached more carefully and efficient methods should be developed to draw generalized and valid conclusions from high dimensional data with a limited number of subjects. Numerous research efforts have been reported in the field based on various statistical approaches, e.g. independent component analysis (ICA), canonical correlation analysis (CCA) and partial least squares (PLS). In this review paper, we survey a number of multivariate methods appearing in previous reports, which are performed with or without prior information and may have utility for identifying potential brain illness biomarkers. We also discuss the possible strengths and limitations of each method, and review their applications to brain imaging data. PMID:22108139

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.

Attention deficit hyperactivity disorder in cocaine-dependent adults: a psychiatric comorbidity analysis.

PubMed

Daigre, Constanza; Roncero, Carlos; Grau-López, Lara; Martínez-Luna, Nieves; Prat, Gemma; Valero, Sergi; Tejedor, Rosa; Ramos-Quiroga, Josep A; Casas, Miguel

2013-01-01

Attention deficit hyperactivity disorder (ADHD) is highly prevalent among drug abusers. We studied the psychiatric comorbidity and characteristics of cocaine use in relation to the presence of ADHD among patients with cocaine dependence. A total of 200 cocaine-dependent patients attending an Outpatient Drug Clinic participated in the study. A systematic evaluation of ADHD (CAADID-II), the severity of addiction (EuropASI) and other axes I and II psychiatric disorders was made (SCID-I and SCID-II). A descriptive, bivariate, and multivariate analysis of the data was performed. In the multivariate analysis, the identified risk factors for the development of ADHD were a history of behavioral disorder in childhood (OR: 3.04), a lifetime history of cannabis dependence in the course of life (OR: 2.68), and age at the start of treatment (OR: 1.08). The bivariate analysis showed ADHD to be associated with other factors such as male gender, age at start of cocaine use and dependence, the amount of cocaine consumed weekly, increased occupational alteration, alcohol consumption, general psychological discomfort, depressive disorder, and antisocial personality disorder. We conclude that ADHD is associated with increased psychiatric comorbidity and greater severity of addiction. Copyright © American Academy of Addiction Psychiatry.

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.

A diagnostic analysis of the VVP single-doppler retrieval technique

NASA Technical Reports Server (NTRS)

Boccippio, Dennis J.

1995-01-01

A diagnostic analysis of the VVP (volume velocity processing) retrieval method is presented, with emphasis on understanding the technique as a linear, multivariate regression. Similarities and differences to the velocity-azimuth display and extended velocity-azimuth display retrieval techniques are discussed, using this framework. Conventional regression diagnostics are then employed to quantitatively determine situations in which the VVP technique is likely to fail. An algorithm for preparation and analysis of a robust VVP retrieval is developed and applied to synthetic and actual datasets with high temporal and spatial resolution. A fundamental (but quantifiable) limitation to some forms of VVP analysis is inadequate sampling dispersion in the n space of the multivariate regression, manifest as a collinearity between the basis functions of some fitted parameters. Such collinearity may be present either in the definition of these basis functions or in their realization in a given sampling configuration. This nonorthogonality may cause numerical instability, variance inflation (decrease in robustness), and increased sensitivity to bias from neglected wind components. It is shown that these effects prevent the application of VVP to small azimuthal sectors of data. The behavior of the VVP regression is further diagnosed over a wide range of sampling constraints, and reasonable sector limits are established.

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.

Multivariate statistical data analysis methods for detecting baroclinic wave interactions in the thermally driven rotating annulus

NASA Astrophysics Data System (ADS)

von Larcher, Thomas; Harlander, Uwe; Alexandrov, Kiril; Wang, Yongtai

2010-05-01

Experiments on baroclinic wave instabilities in a rotating cylindrical gap have been long performed, e.g., to unhide regular waves of different zonal wave number, to better understand the transition to the quasi-chaotic regime, and to reveal the underlying dynamical processes of complex wave flows. We present the application of appropriate multivariate data analysis methods on time series data sets acquired by the use of non-intrusive measurement techniques of a quite different nature. While the high accurate Laser-Doppler-Velocimetry (LDV ) is used for measurements of the radial velocity component at equidistant azimuthal positions, a high sensitive thermographic camera measures the surface temperature field. The measurements are performed at particular parameter points, where our former studies show that kinds of complex wave patterns occur [1, 2]. Obviously, the temperature data set has much more information content as the velocity data set due to the particular measurement techniques. Both sets of time series data are analyzed by using multivariate statistical techniques. While the LDV data sets are studied by applying the Multi-Channel Singular Spectrum Analysis (M - SSA), the temperature data sets are analyzed by applying the Empirical Orthogonal Functions (EOF ). Our goal is (a) to verify the results yielded with the analysis of the velocity data and (b) to compare the data analysis methods. Therefor, the temperature data are processed in a way to become comparable to the LDV data, i.e. reducing the size of the data set in such a manner that the temperature measurements would imaginary be performed at equidistant azimuthal positions only. This approach initially results in a great loss of information. But applying the M - SSA to the reduced temperature data sets enable us to compare the methods. [1] Th. von Larcher and C. Egbers, Experiments on transitions of baroclinic waves in a differentially heated rotating annulus, Nonlinear Processes in Geophysics, 2005, 12, 1033-1041, NPG Print: ISSN 1023-5809, NPG Online: ISSN 1607-7946 [2] U. Harlander, Th. von Larcher, Y. Wang and C. Egbers, PIV- and LDV-measurements of baroclinic wave interactions in a thermally driven rotating annulus, Experiments in Fluids, 2009, DOI: 10.1007/s00348-009-0792-5

High Center Volume Does Not Mitigate Risk Associated with Using High Donor Risk Organs in Liver Transplantation.

PubMed

Beal, Eliza W; Black, Sylvester M; Mumtaz, Khalid; Hayes, Don; El-Hinnawi, Ashraf; Washburn, Kenneth; Tumin, Dmitry

2017-09-01

High-risk donor allografts increase access to liver transplant, but potentially reduce patient and graft survival. It is unclear whether the risk associated with using marginal donor livers is mitigated by increasing center experience. The United Network for Organ Sharing registry was queried for adult first-time liver transplant recipients between 2/2002 and 12/2015. High donor risk was defined as donor risk index >1.9, and 1-year patient and graft survival were compared according to donor risk index in small and large centers. Multivariable Cox regression estimated the hazard ratio (HR) associated with using high-risk donor organs, according to a continuous measure of annual center volume. The analysis included 51,770 patients. In 67 small and 67 large centers, high donor risk index predicted increased mortality (p = 0.001). In multivariable analysis, high-donor risk index allografts predicted greater mortality hazard at centers performing 20 liver transplants per year (HR 1.35; 95% CI 1.22, 1.49; p < 0.001) and, similarly, at centers performing 70 per year (HR 1.35; 95% CI 1.26, 1.43; p < 0.001). The interaction between high donor risk index and center volume was not statistically significant (p = 0.747), confirming that the risk associated with using marginal donor livers was comparable between smaller and larger centers. Results were consistent when examining graft loss. At both small and large centers, high-risk donor allografts were associated with reduced patient and graft survival after liver transplant. Specific strategies to mitigate the risk of liver transplant involving high-risk donors are needed, in addition to accumulation of center expertise.

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.

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.

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.

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.

Risk factors for low receptive vocabulary abilities in the preschool and early school years in the longitudinal study of Australian children.

PubMed

Christensen, Daniel; Zubrick, Stephen R; Lawrence, David; Mitrou, Francis; Taylor, Catherine L

2014-01-01

Receptive vocabulary development is a component of the human language system that emerges in the first year of life and is characterised by onward expansion throughout life. Beginning in infancy, children's receptive vocabulary knowledge builds the foundation for oral language and reading skills. The foundations for success at school are built early, hence the public health policy focus on reducing developmental inequalities before children start formal school. The underlying assumption is that children's development is stable, and therefore predictable, over time. This study investigated this assumption in relation to children's receptive vocabulary ability. We investigated the extent to which low receptive vocabulary ability at 4 years was associated with low receptive vocabulary ability at 8 years, and the predictive utility of a multivariate model that included child, maternal and family risk factors measured at 4 years. The study sample comprised 3,847 children from the first nationally representative Longitudinal Study of Australian Children (LSAC). Multivariate logistic regression was used to investigate risks for low receptive vocabulary ability from 4-8 years and sensitivity-specificity analysis was used to examine the predictive utility of the multivariate model. In the multivariate model, substantial risk factors for receptive vocabulary delay from 4-8 years, in order of descending magnitude, were low receptive vocabulary ability at 4 years, low maternal education, and low school readiness. Moderate risk factors, in order of descending magnitude, were low maternal parenting consistency, socio-economic area disadvantage, low temperamental persistence, and NESB status. The following risk factors were not significant: One or more siblings, low family income, not reading to the child, high maternal work hours, and Aboriginal or Torres Strait Islander ethnicity. The results of the sensitivity-specificity analysis showed that a well-fitted multivariate model featuring risks of substantive magnitude does not do particularly well in predicting low receptive vocabulary ability from 4-8 years.

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

Quantitative transmission electron microscopy analysis of multi-variant grains in present L1{sub 0}-FePt based heat assisted magnetic recording media

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

Ho, Hoan, E-mail: hoan.ho@wdc.com; Department of Materials Science and Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213; Zhu, Jingxi, E-mail: jingxiz@andrew.cmu.edu

2014-11-21

We present a study on atomic ordering within individual grains in granular L1{sub 0}-FePt thin films using transmission electron microscopy techniques. The film, used as a medium for heat assisted magnetic recording, consists of a single layer of FePt grains separated by non-magnetic grain boundaries and is grown on an MgO underlayer. Using convergent-beam techniques, diffraction patterns of individual grains are obtained for a large number of crystallites. The study found that although the majority of grains are ordered in the perpendicular direction, more than 15% of them are multi-variant, or of in-plane c-axis orientation, or disordered fcc. It wasmore » also found that these multi-variant and in-plane grains have always grown across MgO grain boundaries separating two or more MgO grains of the underlayer. The in-plane ordered portion within a multi-variant L1{sub 0}-FePt grain always lacks atomic coherence with the MgO directly underneath it, whereas, the perpendicularly ordered portion is always coherent with the underlying MgO grain. Since the existence of multi-variant and in-plane ordered grains are severely detrimental to high density data storage capability, the understanding of their formation mechanism obtained here should make a significant impact on the future development of hard disk drive technology.« less

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.

Multivariate moment closure techniques for stochastic kinetic models

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

Lakatos, Eszter, E-mail: e.lakatos13@imperial.ac.uk; Ale, Angelique; Kirk, Paul D. W.

2015-09-07

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

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.

A high proportion of cells carrying trisomy 12 is associated with a worse outcome in patients with chronic lymphocytic leukemia.

PubMed

González-Gascón Y Marín, Isabel; Hernández-Sánchez, María; Rodríguez-Vicente, Ana-Eugenia; Sanzo, Carmen; Aventín, Anna; Puiggros, Anna; Collado, Rosa; Heras, Cecilia; Muñoz, Carolina; Delgado, Julio; Ortega, Margarita; González, María-Teresa; Marugán, Isabel; de la Fuente, Ignacio; Recio, Isabel; Bosch, Francesc; Espinet, Blanca; González, Marcos; Hernández-Rivas, Jesús-María; Hernández, José-Ángel

2016-06-01

The prognosis of chronic lymphocytic leukemia (CLL) patients displaying trisomy 12 (+12) remains unclear. In this study, we analyzed the influence of the proportion of cells with +12, and other clinical and biologic factors, in time to first therapy (TTFT) and overall survival (OS), in 289 patients diagnosed with CLL carrying +12. Median OS was 129 months. One hundred seventy-four patients (60.2%) presented +12 in <60% of cells. TTFT and OS for this subgroup were longer than for the subgroup with +12 in ≥60% of cells, with a median TTFT of 49 months (CI95%, 39-58) vs 30 months (CI95%, 22-38) (P = 0.001); and a median OS of 159 months (CI95%, 119-182), vs 96 months (CI95%, 58-134) (P = 0.015). Other factors associated with a shorter TTFT were: Binet stage, B symptoms, lymphadenopathy, splenomegaly, high lymphocyte count, 11q-, high β2 microglobulin, and high LDH. In the multivariate analysis, clinical stage, +12 in ≥60% of cells, high lymphocyte count, B symptoms, and 11q- in addition, resulted of significance in predicting shorter TTFT. Significant variables for OS were: Binet stage, lymphadenopathy, splenomegaly, high LDH, high β2 microglobulin, 11q-, and CD38. In the multivariate analysis, only Binet stage, 11q-, and high β2microglobulin significantly predicted shorter OS. CLL with +12 entails a heterogeneous group with intermediate prognosis. However, a high proportion of cells carrying +12 separates a subgroup of patients with poor outcome. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

Label-free Chemical Imaging of Fungal Spore Walls by Raman Microscopy and Multivariate Curve Resolution Analysis

PubMed Central

Noothalapati, Hemanth; Sasaki, Takahiro; Kaino, Tomohiro; Kawamukai, Makoto; Ando, Masahiro; Hamaguchi, Hiro-o; Yamamoto, Tatsuyuki

2016-01-01

Fungal cell walls are medically important since they represent a drug target site for antifungal medication. So far there is no method to directly visualize structurally similar cell wall components such as α-glucan, β-glucan and mannan with high specificity, especially in a label-free manner. In this study, we have developed a Raman spectroscopy based molecular imaging method and combined multivariate curve resolution analysis to enable detection and visualization of multiple polysaccharide components simultaneously at the single cell level. Our results show that vegetative cell and ascus walls are made up of both α- and β-glucans while spore wall is exclusively made of α-glucan. Co-localization studies reveal the absence of mannans in ascus wall but are distributed primarily in spores. Such detailed picture is believed to further enhance our understanding of the dynamic spore wall architecture, eventually leading to advancements in drug discovery and development in the near future. PMID:27278218

PGI chicory (Cichorium intybus L.) traceability by means of HRMAS-NMR spectroscopy: a preliminary study.

PubMed

Ritota, Mena; Casciani, Lorena; Valentini, Massimiliano

2013-05-01

Analytical traceability of PGI and PDO foods (Protected Geographical Indication and Protected Denomination Origin respectively) is one of the most challenging tasks of current applied research. Here we proposed a metabolomic approach based on the combination of (1)H high-resolution magic angle spinning-nuclear magnetic resonance (HRMAS-NMR) spectroscopy with multivariate analysis, i.e. PLS-DA, as a reliable tool for the traceability of Italian PGI chicories (Cichorium intybus L.), i.e. Radicchio Rosso di Treviso and Radicchio Variegato di Castelfranco, also known as red and red-spotted, respectively. The metabolic profile was gained by means of HRMAS-NMR, and multivariate data analysis allowed us to build statistical models capable of providing clear discrimination among the two varieties and classification according to the geographical origin. Based on Variable Importance in Projection values, the molecular markers for classifying the different types of red chicories analysed were found accounting for both the cultivar and the place of origin. © 2012 Society of Chemical Industry.

Predictors of in-hospital mortality amongst octogenarians undergoing emergency general surgery: a retrospective cohort study.

PubMed

Wilson, Iain; Paul Barrett, Michael; Sinha, Ashish; Chan, Shirley

2014-11-01

Elderly patients are often judged to be fit for emergency surgery based on age alone. This study identified risk factors predictive of in-hospital mortality amongst octogenarians undergoing emergency general surgery. A retrospective review of octogenarians undergoing emergency general surgery over 3 years was performed. Parametric survival analysis using Cox multivariate regression model was used to identify risk factors predictive of in-hospital mortality. Hazard ratios (HR) and corresponding 95% confidence interval were calculated. Seventy-three patients with a median age of 84 years were identified. Twenty-eight (38%) patients died post-operatively. Multivariate analysis identified ASA grade (ASA 5 HR 23.4 95% CI 2.38-230, p = 0.007) and chronic obstructive pulmonary disease (COPD) (HR 3.35 95% CI 1.15-9.69, p = 0.026) to be the only significant predictors of in-hospital mortality. Identification of high risk surgical patients should be based on physiological fitness for surgery rather than chronological age. Crown Copyright © 2014. Published by Elsevier Ltd. All rights reserved.

Effect of environment and genotype on commercial maize hybrids using LC/MS-based metabolomics.

PubMed

Baniasadi, Hamid; Vlahakis, Chris; Hazebroek, Jan; Zhong, Cathy; Asiago, Vincent

2014-02-12

We recently applied gas chromatography coupled to time-of-flight mass spectrometry (GC/TOF-MS) and multivariate statistical analysis to measure biological variation of many metabolites due to environment and genotype in forage and grain samples collected from 50 genetically diverse nongenetically modified (non-GM) DuPont Pioneer commercial maize hybrids grown at six North American locations. In the present study, the metabolome coverage was extended using a core subset of these grain and forage samples employing ultra high pressure liquid chromatography (uHPLC) mass spectrometry (LC/MS). A total of 286 and 857 metabolites were detected in grain and forage samples, respectively, using LC/MS. Multivariate statistical analysis was utilized to compare and correlate the metabolite profiles. Environment had a greater effect on the metabolome than genetic background. The results of this study support and extend previously published insights into the environmental and genetic associated perturbations to the metabolome that are not associated with transgenic modification.

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

Profiling and multivariate statistical analysis of Panax ginseng based on ultra-high-performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry.

PubMed

Wu, Wei; Sun, Le; Zhang, Zhe; Guo, Yingying; Liu, Shuying

2015-03-25

An ultra-high-performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry (UHPLC-Q-TOF-MS) method was developed for the detection and structural analysis of ginsenosides in white ginseng and related processed products (red ginseng). Original neutral, malonyl, and chemically transformed ginsenosides were identified in white and red ginseng samples. The aglycone types of ginsenosides were determined by MS/MS as PPD (m/z 459), PPT (m/z 475), C-24, -25 hydrated-PPD or PPT (m/z 477 or m/z 493), and Δ20(21)-or Δ20(22)-dehydrated-PPD or PPT (m/z 441 or m/z 457). Following the structural determination, the UHPLC-Q-TOF-MS-based chemical profiling coupled with multivariate statistical analysis method was applied for global analysis of white and processed ginseng samples. The chemical markers present between the processed products red ginseng and white ginseng could be assigned. Process-mediated chemical changes were recognized as the hydrolysis of ginsenosides with large molecular weight, chemical transformations of ginsenosides, changes in malonyl-ginsenosides, and generation of 20-(R)-ginsenoside enantiomers. The relative contents of compounds classified as PPD, PPT, malonyl, and transformed ginsenosides were calculated based on peak areas in ginseng before and after processing. This study provides possibility to monitor multiple components for the quality control and global evaluation of ginseng products during processing. Copyright © 2014 Elsevier B.V. All rights reserved.

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.

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...

Propensity score analysis of recurrence for neutrophil-to-lymphocyte ratio in colorectal cancer.

PubMed

Balde, Alpha I; Fang, Suzhen; He, Linyun; Cai, Zhai; Han, Shuai; Wang, Weiwei; Li, Zhou; Kang, Liang

2017-11-01

The perioperative serum neutrophil-to-lymphocyte ratio (NLR) has been proposed to predict adverse prognosis in colorectal cancer (CRC). However, its interpretation remains unclear. The present study aimed to clarify the prognostic value of NLR in predicting survival among CRC patients. A single-centre, retrospective, propensity score-matched study of adenocarcinoma patients who underwent D3 lymphadenectomy via laparoscopic or open surgery between 2010 and 2016 was conducted. A cutoff of 3.5 was used based on the receiver operating characteristic curve. To overcome selection biases, we performed a 1:1 match using six covariates. The high-preoperative NLR group had a higher recurrence rate than the low group (P < 0.001). Univariate analysis showed that increased NLR (P < 0.001), N1 (P = 0.016), and N2 (P < 0.001) were associated with worse recurrence-free survival (RFS). Multivariate analysis showed that N2 (hazard ratio [HR], 2.492; P = 0.008) was an adverse prognostic factor for RFS. Univariate analysis for overall survival (OS) revealed that high perioperative NLR (P = 0.001), N1 (P = 0.01), N2 (P < 0.001), and distant metastasis (P < 0.001) were adverse prognostic factors. Subsequent multivariate analysis showed that M1 (HR, 3.973; P < 0.001) and N2 (HR, 2.381; P = 0.013) were highly adverse factors for OS. Clinical assessments performed during a 21.14 (±16.20)-mo follow-up revealed that OS (P = 0.001) and RFS (P < 0.001) were worse in the high-perioperative group than in the low group between the matched groups. An elevated preoperative NLR is a strong predictor of worse RFS and OS in CRC patients. Copyright © 2017 Elsevier Inc. All rights reserved.

Investigation of cell wall composition related to stem lodging resistance in wheat (Triticum aestivum L.) by FTIR spectroscopy.

PubMed

Wang, Jian; Zhu, Jinmao; Huang, RuZhu; Yang, YuSheng

2012-07-01

We explored the rapid qualitative analysis of wheat cultivars with good lodging resistances by Fourier transform infrared resonance (FTIR) spectroscopy and multivariate statistical analysis. FTIR imaging showing that wheat stem cell walls were mainly composed of cellulose, pectin, protein, and lignin. Principal components analysis (PCA) was used to eliminate multicollinearity among multiple peak absorptions. PCA revealed the developmental internodes of wheat stems could be distributed from low to high along the load of the second principal component, which was consistent with the corresponding bands of cellulose in the FTIR spectra of the cell walls. Furthermore, four distinct stem populations could also be identified by spectral features related to their corresponding mechanical properties via PCA and cluster analysis. Histochemical staining of four types of wheat stems with various abilities to resist lodging revealed that cellulose contributed more than lignin to the ability to resist lodging. These results strongly suggested that the main cell wall component responsible for these differences was cellulose. Therefore, the combination of multivariate analysis and FTIR could rapidly screen wheat cultivars with good lodging resistance. Furthermore, the application of these methods to a much wider range of cultivars of unknown mechanical properties promises to be of interest.

Classification of edible oils by employing 31P and 1H NMR spectroscopy in combination with multivariate statistical analysis. A proposal for the detection of seed oil adulteration in virgin olive oils.

PubMed

Vigli, Georgia; Philippidis, Angelos; Spyros, Apostolos; Dais, Photis

2003-09-10

A combination of (1)H NMR and (31)P NMR spectroscopy and multivariate statistical analysis was used to classify 192 samples from 13 types of vegetable oils, namely, hazelnut, sunflower, corn, soybean, sesame, walnut, rapeseed, almond, palm, groundnut, safflower, coconut, and virgin olive oils from various regions of Greece. 1,2-Diglycerides, 1,3-diglycerides, the ratio of 1,2-diglycerides to total diglycerides, acidity, iodine value, and fatty acid composition determined upon analysis of the respective (1)H NMR and (31)P NMR spectra were selected as variables to establish a classification/prediction model by employing discriminant analysis. This model, obtained from the training set of 128 samples, resulted in a significant discrimination among the different classes of oils, whereas 100% of correct validated assignments for 64 samples were obtained. Different artificial mixtures of olive-hazelnut, olive-corn, olive-sunflower, and olive-soybean oils were prepared and analyzed by (1)H NMR and (31)P NMR spectroscopy. Subsequent discriminant analysis of the data allowed detection of adulteration as low as 5% w/w, provided that fresh virgin olive oil samples were used, as reflected by their high 1,2-diglycerides to total diglycerides ratio (D > or = 0.90).

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 and discrimination of raw and vinegar-baked Bupleuri radix based on UHPLC-Q-TOF-MS coupled with multivariate statistical analysis.

PubMed

Lei, Tianli; Chen, Shifeng; Wang, Kai; Zhang, Dandan; Dong, Lin; Lv, Chongning; Wang, Jing; Lu, Jincai

2018-02-01

Bupleuri Radix is a commonly used herb in clinic, and raw and vinegar-baked Bupleuri Radix are both documented in the Pharmacopoeia of People's Republic of China. According to the theories of traditional Chinese medicine, Bupleuri Radix possesses different therapeutic effects before and after processing. However, the chemical mechanism of this processing is still unknown. In this study, ultra-high-performance liquid chromatography with quadruple time-of-flight mass spectrometry coupled with multivariate statistical analysis including principal component analysis and orthogonal partial least square-discriminant analysis was developed to holistically compare the difference between raw and vinegar-baked Bupleuri Radix for the first time. As a result, 50 peaks in raw and processed Bupleuri Radix were detected, respectively, and a total of 49 peak chemical compounds were identified. Saikosaponin a, saikosaponin d, saikosaponin b 3 , saikosaponin e, saikosaponin c, saikosaponin b 2 , saikosaponin b 1 , 4''-O-acetyl-saikosaponin d, hyperoside and 3',4'-dimethoxy quercetin were explored as potential markers of raw and vinegar-baked Bupleuri Radix. This study has been successfully applied for global analysis of raw and vinegar-processed samples. Furthermore, the underlying hepatoprotective mechanism of Bupleuri Radix was predicted, which was related to the changes of chemical profiling. Copyright © 2017 John Wiley & Sons, Ltd.

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

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.

Prognostic factors in patients with malignant pleural effusion: Is it possible to predict mortality in patients with good performance status?

PubMed

Abrao, Fernando Conrado; Peixoto, Renata D'Alpino; de Abreu, Igor Renato Louro Bruno; Janini, Maria Cláudia; Viana, Geisa Garcia; de Oliveira, Mariana Campello; Younes, Riad Naim

2016-04-01

The aim of this study was to identify predictors of mortality only in patients with malignant pleural effusion (MPE) showing good performance status which required pleural palliative procedures. All patients with MPE submitted to pleural palliative procedure were enrolled in a prospective study between 2013 and 2014. Patients with Eastern cooperative oncology group (ECOG) score zero, one, and two were considered with good performance status. The possible prognostic factors were tested for significance using the log-rank test (Kaplan-Meier method) and those with significance on univariate analysis were entered into a multivariable Cox model. A total of 64 patients were included in the analysis. Median follow-up time for surviving patients was 263 days. Median survival for the entire cohort was not reached yet. In the multivariate analysis, gastrointestinal primary site (P = 0.006), low albumin concentration in the pleural fluid (P = 0.017), and high serum NLR (P = 0.007) were associated with mortality. In our cohort of ECOG 0-2 patients with MPE submitted to pleural palliative procedures, gastrointestinal malignancy compared to other sites, low pleural fluid albumin and high NLR were significantly associated with mortality. The identification of these prognostic factors may assist the choice of the optimal palliative technique. J. Surg. Oncol. 2016;113:570-574. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Elevated red cell distribution width contributes to a poor prognosis in patients with esophageal carcinoma.

PubMed

Wan, Guo-Xing; Chen, Ping; Cai, Xiao-Jun; Li, Lin-Jun; Yu, Xiong-Jie; Pan, Dong-Feng; Wang, Xian-He; Wang, Xuan-Bin; Cao, Feng-Jun

2016-01-15

The red cell distribution width (RDW) has also been reported to reliably reflect the inflammation and nutrition status and predict the prognosis across several types of cancer, however, the prognostic value of RDW in esophageal carcinoma has seldom been studied. A retrospective study was performed to assess the prognostic value of RDW in patients with esophageal carcinoma by the Kaplan-Meier analysis and multivariate Cox regression proportional hazard model. All enrolled patients were divided into high RDW group (≧15%) and low RDW group (<15%) according to the detected RDW values. Clinical and laboratory data from a total of 179 patients with esophageal carcinoma were retrieved. With a median follow-up of 21months, the high RDW group exhibited a shorter disease-free survival (DFS) (p<0.001) and an unfavorable overall survival (OS) (p<0.001) in the univariate analysis. The multivariate analysis revealed that elevated RDW at diagnosis was an independent prognostic factor for shorter PFS (p=0.043, HR=1.907, 95% CI=1.020-3.565) and poor OS (p=0.042, HR=1.895, 95% CI=1.023-3.508) after adjustment with other cancer-related prognostic factors. The present study suggests that elevated preoperative RDW(≧15%) at the diagnosis may independently predict poorer disease-free and overall survival among patients with esophageal carcinoma. Copyright © 2015 Elsevier B.V. All rights reserved.

Pre-treatment inflammatory indexes as predictors of survival and cetuximab efficacy in metastatic colorectal cancer patients with wild-type RAS.

PubMed

Yang, Jing; Guo, Xinli; Wang, Manni; Ma, Xuelei; Ye, Xiaoyang; Lin, Panpan

2017-12-07

This study aims at evaluating the prognostic significance of neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), and systemic immune-inflammation indexes (SII) in metastatic colorectal cancer (mCRC) patients treated with cetuximab. Ninety-five patients receiving cetuximab for mCRC were categorized into the high or low NLR, PLR, LMR, and SII groups based on their median index values. Univariate and multivariate survival analysis were performed to identify the indexes' correlation with progression-free survival (PFS) and overall survival (OS). In the univariate analysis, ECOG performance status, neutrphil counts, lymphocyte counts, monocyte counts, NLR, PLR, and LDH were associated with survival. Multivariate analysis showed that ECOG performance status of 0 (hazard ratio [HR] 3.608, p < 0.001; HR 5.030, p < 0.001, respectively), high absolute neutrophil counts (HR 2.837, p < 0.001; HR 1.922, p = 0.026, respectively), low lymphocyte counts (HR 0.352, p < 0.001; HR 0.440, p = 0.001, respectively), elevated NLR (HR 3.837, p < 0.001; HR 2.467, p = 0.006) were independent predictors of shorter PFS and OS. In conclusion, pre-treatment inflammatory indexes, especially NLR were potential biomarkers to predict the survival of mCRC patients with cetuximab therapy.

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.

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

Big-data reflection high energy electron diffraction analysis for understanding epitaxial film growth processes.

PubMed

Vasudevan, Rama K; Tselev, Alexander; Baddorf, Arthur P; Kalinin, Sergei V

2014-10-28

Reflection high energy electron diffraction (RHEED) has by now become a standard tool for in situ monitoring of film growth by pulsed laser deposition and molecular beam epitaxy. Yet despite the widespread adoption and wealth of information in RHEED images, most applications are limited to observing intensity oscillations of the specular spot, and much additional information on growth is discarded. With ease of data acquisition and increased computation speeds, statistical methods to rapidly mine the data set are now feasible. Here, we develop such an approach to the analysis of the fundamental growth processes through multivariate statistical analysis of a RHEED image sequence. This approach is illustrated for growth of La(x)Ca(1-x)MnO(3) films grown on etched (001) SrTiO(3) substrates, but is universal. The multivariate methods including principal component analysis and k-means clustering provide insight into the relevant behaviors, the timing and nature of a disordered to ordered growth change, and highlight statistically significant patterns. Fourier analysis yields the harmonic components of the signal and allows separation of the relevant components and baselines, isolating the asymmetric nature of the step density function and the transmission spots from the imperfect layer-by-layer (LBL) growth. These studies show the promise of big data approaches to obtaining more insight into film properties during and after epitaxial film growth. Furthermore, these studies open the pathway to use forward prediction methods to potentially allow significantly more control over growth process and hence final film quality.

Histological changes associated with neoadjuvant chemotherapy are predictive of nodal metastases in high-risk prostate cancer patients

PubMed Central

O’Brien, Catherine; True, Lawrence D.; Higano, Celestia S.; Rademacher, Brooks L. S.; Garzotto, Mark; Beer, Tomasz M.

2011-01-01

Clinical trials are evaluating the effect of neoadjuvant chemotherapy on men with high risk prostate cancer. Little is known about the clinical significance of post-chemotherapy tumor histopathology. We assessed the prognostic and predictive value of histological features (intraductal carcinoma, vacuolated cell morphology, inconspicuous glands, cribriform architecture, and inconspicuous cancer cells) observed in 50 high-risk prostate cancers treated with pre-prostatectomy docetaxel and mitoxantrone. At a median follow-up of 65 months, the overall relapse-free survival (RFS) at 2 and 5 years was 65% and 49%, respectively. In univariate analyses (using Kaplan-Meier method and log-rank tests) intraductal (p=0.001) and cribriform (p=0.014) histologies were associated with shorter RFS. In multivariate analyses, using Cox’s proportional hazards regression, baseline PSA (p=0.004), lymph node metastases (p<0.001), and cribriform histology (p=0.007) were associated with shorter RFS. In multivariable logistic regression analysis, only intraductal pattern (p=0.007) predicted lymph node metastases. Intraductal and cribriform histologies apparently predict post-chemotherapy outcome. PMID:20231619

Bibliography on Cold Regions Science and Technology. Volume 41. Part 2

DTIC Science & Technology

1987-12-01

Aletschgletscher [1984, p.9-25, eng, 41-622 Aleksandrov, B.M. Multivariate regression analysis of the process of frozen peat dehydration [1986. p.15-19...freezing of high- way bridge decks [1977. 5p., eng] 41-4604 Britton, K.B. Low temperature effects on sorption. hydrolysis ...snowy season in 1986 at Sapporo [1986. p.17-23. jpn) 41-3503 Ishikawa, S. Experimental decomposition of

Negative Affect, Delinquency, and Alcohol Use among Rural and Urban African-American Adolescents: A Brief Report

ERIC Educational Resources Information Center

Taylor, Matthew J.; Merritt, Stephanie M.; Austin, Chammie C.

2013-01-01

A model of negative affect and alcohol use was replicated on a sample of African-American high school students. Participants (N = 5,086) were randomly selected from a previously collected data set and consisted of 2,253 males and 2,833 females residing in both rural and urban locations. Multivariate analysis of covariance and structural equation…

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.

Exploring Radiotherapy Targeting Strategy and Dose: A Pooled Analysis of Cooperative Group Trials of Combined Modality Therapy for Stage III Non-Small Cell Lung Cancer.

PubMed

Schild, Steven E; Fan, Wen; Stinchcombe, Thomas E; Vokes, Everett E; Ramalingam, Suresh S; Bradley, Jeffrey D; Kelly, Karen; Pang, Herbert H; Wang, Xiaofei

2018-04-21

Concurrent chemoradiotherapy(CRT) is standard therapy for locally-advanced non-small-cell lung cancer(LA-NSCLC)patients. This study was performed to examine thoracic radiotherapy(TRT) parameters and their impact on patient survival. We collected Individual patient data(IPD) from 3600LA-NSCLC patients participating in 16 cooperative group trials of concurrent CRT. The primary TRT parameters examined included field design strategy(elective nodal irradiation(ENI) compared to involved field TRT(IF-TRT)), total dose, and biologically effective dose(BED). Hazard ratios(HRs) for overall survival were calculated with univariable and multivariable Cox models. TRT doses ranged from 60 to 74 Gy with most treatments administered once-daily. ENI was associated with poorer survival than IF-TRT(univariable HR,1.37;95%CI,1.24-1.51,p<0.0001;multivariable HR,1.31;95%CI,1.08-1.59,p=0.002). The median survival of the IF and ENI patients were 24 and 16 months, respectively. Patients were divided into 3 dose groups: low total dose(60 Gy), medium total dose(>60Gy-66Gy) and high total dose(>66Gy-74 Gy). With reference to the low dose group, the multivariable HR's were 1.08 for the medium dose group(95%CI=0.93-1.25) and 1.12 for the high dose group(CI=0.97-1.30).The univariate p=0.054 and multivariable p=0.17. BED was grouped as follows: low(<55.5Gy 10 ), medium(=55.5 Gy 10) , or high(>55.5 Gy 10 ). With reference to the low BED group, the HR was 1.00(95%CI=0.85-1.18) for the medium BED group and 1.10(95%CI=0.93-1.31) for the high BED group. The univariable p=0.076 and multivariable p=0.16. For LA-NSCLC patients treated with concurrent CRT, IF-TRT was associated with significantly better survival than ENI-TRT. TRT total and BED dose levels were not significantly associated with patient survival. Future progress will require research focusing on better systemic therapy and TRT. Copyright © 2018. Published by Elsevier Inc.

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.

Influence of microclimatic ammonia levels on productive performance of different broilers' breeds estimated with univariate and multivariate approaches.

PubMed

Soliman, Essam S; Moawed, Sherif A; Hassan, Rania A

2017-08-01

Birds litter contains unutilized nitrogen in the form of uric acid that is converted into ammonia; a fact that does not only affect poultry performance but also has a negative effect on people's health around the farm and contributes in the environmental degradation. The influence of microclimatic ammonia emissions on Ross and Hubbard broilers reared in different housing systems at two consecutive seasons (fall and winter) was evaluated using a discriminant function analysis to differentiate between Ross and Hubbard breeds. A total number of 400 air samples were collected and analyzed for ammonia levels during the experimental period. Data were analyzed using univariate and multivariate statistical methods. Ammonia levels were significantly higher (p< 0.01) in the Ross compared to the Hubbard breed farm, although no significant differences (p>0.05) were found between the two farms in body weight, body weight gain, feed intake, feed conversion ratio, and performance index (PI) of broilers. Body weight; weight gain and PI had increased values (p< 0.01) during fall compared to winter irrespective of broiler breed. Ammonia emissions were positively (although weekly) correlated with the ambient relative humidity (r=0.383; p< 0.01), but not with the ambient temperature (r=-0.045; p>0.05). Test of significance of discriminant function analysis did not show a classification based on the studied traits suggesting that they cannot been used as predictor variables. The percentage of correct classification was 52% and it was improved after deletion of highly correlated traits to 57%. The study revealed that broiler's growth was negatively affected by increased microclimatic ammonia concentrations and recommended the analysis of broilers' growth performance parameters data using multivariate discriminant function analysis.

Influence of microclimatic ammonia levels on productive performance of different broilers’ breeds estimated with univariate and multivariate approaches

PubMed Central

Soliman, Essam S.; Moawed, Sherif A.; Hassan, Rania A.

2017-01-01

Background and Aim: Birds litter contains unutilized nitrogen in the form of uric acid that is converted into ammonia; a fact that does not only affect poultry performance but also has a negative effect on people’s health around the farm and contributes in the environmental degradation. The influence of microclimatic ammonia emissions on Ross and Hubbard broilers reared in different housing systems at two consecutive seasons (fall and winter) was evaluated using a discriminant function analysis to differentiate between Ross and Hubbard breeds. Materials and Methods: A total number of 400 air samples were collected and analyzed for ammonia levels during the experimental period. Data were analyzed using univariate and multivariate statistical methods. Results: Ammonia levels were significantly higher (p< 0.01) in the Ross compared to the Hubbard breed farm, although no significant differences (p>0.05) were found between the two farms in body weight, body weight gain, feed intake, feed conversion ratio, and performance index (PI) of broilers. Body weight; weight gain and PI had increased values (p< 0.01) during fall compared to winter irrespective of broiler breed. Ammonia emissions were positively (although weekly) correlated with the ambient relative humidity (r=0.383; p< 0.01), but not with the ambient temperature (r=−0.045; p>0.05). Test of significance of discriminant function analysis did not show a classification based on the studied traits suggesting that they cannot been used as predictor variables. The percentage of correct classification was 52% and it was improved after deletion of highly correlated traits to 57%. Conclusion: The study revealed that broiler’s growth was negatively affected by increased microclimatic ammonia concentrations and recommended the analysis of broilers’ growth performance parameters data using multivariate discriminant function analysis. PMID:28919677

Transforming growth factor-β and toll-like receptor-4 polymorphisms are not associated with fibrosis in haemochromatosis

PubMed Central

Wood, Marnie J; Powell, Lawrie W; Dixon, Jeannette L; Subramaniam, V Nathan; Ramm, Grant A

2013-01-01

AIM: To investigate the role of genetic polymorphisms in the progression of hepatic fibrosis in hereditary haemochromatosis. METHODS: A cohort of 245 well-characterised C282Y homozygous patients with haemochromatosis was studied, with all subjects having liver biopsy data and DNA available for testing. This study assessed the association of eight single nucleotide polymorphisms (SNPs) in a total of six genes including toll-like receptor 4 (TLR4), transforming growth factor-beta (TGF-β), oxoguanine DNA glycosylase, monocyte chemoattractant protein 1, chemokine C-C motif receptor 2 and interleukin-10 with liver disease severity. Genotyping was performed using high resolution melt analysis and sequencing. The results were analysed in relation to the stage of hepatic fibrosis in multivariate analysis incorporating other cofactors including alcohol consumption and hepatic iron concentration. RESULTS: There were significant associations between the cofactors of male gender (P = 0.0001), increasing age (P = 0.006), alcohol consumption (P = 0.0001), steatosis (P = 0.03), hepatic iron concentration (P < 0.0001) and the presence of hepatic fibrosis. Of the candidate gene polymorphisms studied, none showed a significant association with hepatic fibrosis in univariate or multivariate analysis incorporating cofactors. We also specifically studied patients with hepatic iron loading above threshold levels for cirrhosis and compared the genetic polymorphisms between those with no fibrosis vs cirrhosis however there was no significant effect from any of the candidate genes studied. Importantly, in this large, well characterised cohort of patients there was no association between SNPs for TGF-β or TLR4 and the presence of fibrosis, cirrhosis or increasing fibrosis stage in multivariate analysis. CONCLUSION: In our large, well characterised group of haemochromatosis subjects we did not demonstrate any relationship between candidate gene polymorphisms and hepatic fibrosis or cirrhosis. PMID:24409064

Transforming growth factor-β and toll-like receptor-4 polymorphisms are not associated with fibrosis in haemochromatosis.

PubMed

Wood, Marnie J; Powell, Lawrie W; Dixon, Jeannette L; Subramaniam, V Nathan; Ramm, Grant A

2013-12-28

To investigate the role of genetic polymorphisms in the progression of hepatic fibrosis in hereditary haemochromatosis. A cohort of 245 well-characterised C282Y homozygous patients with haemochromatosis was studied, with all subjects having liver biopsy data and DNA available for testing. This study assessed the association of eight single nucleotide polymorphisms (SNPs) in a total of six genes including toll-like receptor 4 (TLR4), transforming growth factor-beta (TGF-β), oxoguanine DNA glycosylase, monocyte chemoattractant protein 1, chemokine C-C motif receptor 2 and interleukin-10 with liver disease severity. Genotyping was performed using high resolution melt analysis and sequencing. The results were analysed in relation to the stage of hepatic fibrosis in multivariate analysis incorporating other cofactors including alcohol consumption and hepatic iron concentration. There were significant associations between the cofactors of male gender (P = 0.0001), increasing age (P = 0.006), alcohol consumption (P = 0.0001), steatosis (P = 0.03), hepatic iron concentration (P < 0.0001) and the presence of hepatic fibrosis. Of the candidate gene polymorphisms studied, none showed a significant association with hepatic fibrosis in univariate or multivariate analysis incorporating cofactors. We also specifically studied patients with hepatic iron loading above threshold levels for cirrhosis and compared the genetic polymorphisms between those with no fibrosis vs cirrhosis however there was no significant effect from any of the candidate genes studied. Importantly, in this large, well characterised cohort of patients there was no association between SNPs for TGF-β or TLR4 and the presence of fibrosis, cirrhosis or increasing fibrosis stage in multivariate analysis. In our large, well characterised group of haemochromatosis subjects we did not demonstrate any relationship between candidate gene polymorphisms and hepatic fibrosis or cirrhosis.

Detection of cervical lesions by multivariate analysis of diffuse reflectance spectra: a clinical study.

PubMed

Prabitha, Vasumathi Gopala; Suchetha, Sambasivan; Jayanthi, Jayaraj Lalitha; Baiju, Kamalasanan Vijayakumary; Rema, Prabhakaran; Anuraj, Koyippurath; Mathews, Anita; Sebastian, Paul; Subhash, Narayanan

2016-01-01

Diffuse reflectance (DR) spectroscopy is a non-invasive, real-time, and cost-effective tool for early detection of malignant changes in squamous epithelial tissues. The present study aims to evaluate the diagnostic power of diffuse reflectance spectroscopy for non-invasive discrimination of cervical lesions in vivo. A clinical trial was carried out on 48 sites in 34 patients by recording DR spectra using a point-monitoring device with white light illumination. The acquired data were analyzed and classified using multivariate statistical analysis based on principal component analysis (PCA) and linear discriminant analysis (LDA). Diagnostic accuracies were validated using random number generators. The receiver operating characteristic (ROC) curves were plotted for evaluating the discriminating power of the proposed statistical technique. An algorithm was developed and used to classify non-diseased (normal) from diseased sites (abnormal) with a sensitivity of 72 % and specificity of 87 %. While low-grade squamous intraepithelial lesion (LSIL) could be discriminated from normal with a sensitivity of 56 % and specificity of 80 %, and high-grade squamous intraepithelial lesion (HSIL) from normal with a sensitivity of 89 % and specificity of 97 %, LSIL could be discriminated from HSIL with 100 % sensitivity and specificity. The areas under the ROC curves were 0.993 (95 % confidence interval (CI) 0.0 to 1) and 1 (95 % CI 1) for the discrimination of HSIL from normal and HSIL from LSIL, respectively. The results of the study show that DR spectroscopy could be used along with multivariate analytical techniques as a non-invasive technique to monitor cervical disease status in real time.

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

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)

Reported Nonadherence to Immunosuppressive Medication in Young Adults After Heart Transplantation: A Retrospective Analysis of a National Registry.

PubMed

Tumin, Dmitry; McConnell, Patrick I; Galantowicz, Mark; Tobias, Joseph D; Hayes, Don

2017-02-01

Young adult heart transplantation (HTx) recipients experience high mortality risk attributed to increased nonadherence to immunosuppressive medication in this age window. This study sought to test whether a high-risk age window in HTx recipients persisted in the absence of reported nonadherence. Heart transplantation recipients aged 2 to 40 years, transplanted between October 1999 and January 2007, were identified in the United Network for Organ Sharing database. Multivariable survival analysis was used to estimate influences of age at transplantation and attained posttransplant age on mortality hazard among patients stratified by center report of nonadherence to immunosuppression that compromised recovery. Three thousand eighty-one HTx recipients were included, with univariate analysis demonstrating peak hazards of mortality and reported nonadherence among 567 patients transplanted between ages 17 and 24 years. Multivariable analysis adjusting for reported nonadherence demonstrated lower mortality among patients transplanted at younger (hazards ratio, 0.813; 95% confidence interval, 0.663-0.997; P = 0.047) or older (hazards ratio, 0.835; 95% confidence interval, 0.701-0.994; P = 0.042) ages. Peak mortality hazard at ages 17 to 24 years was confirmed in the subgroup of patients with no nonadherence reported during follow-up. This result was replicated using attained age after HTx as the time metric, with younger and older ages predicting improved survival in the absence of reported nonadherence. Late adolescence and young adulthood coincide with greater mortality hazard and greater chances of nonadherence to immunosuppressive medication after HTx, but the elevation of mortality hazard in this age range persists in the absence of reported nonadherence. Other causes of the high-risk age window for post-HTx mortality should be demonstrated to identify opportunities for intervention.

CTNNB1 (beta-catenin) mutation identifies low grade, early stage endometrial cancer patients at increased risk of recurrence.

PubMed

Kurnit, Katherine C; Kim, Grace N; Fellman, Bryan M; Urbauer, Diana L; Mills, Gordon B; Zhang, Wei; Broaddus, Russell R

2017-07-01

Although the majority of low grade, early stage endometrial cancer patients will have good survival outcomes with surgery alone, those patients who do recur tend to do poorly. Optimal identification of the subset of patients who are at high risk of recurrence and would benefit from adjuvant treatment has been difficult. The purpose of this study was to evaluate the impact of somatic tumor mutation on survival outcomes in this patient population. For this study, low grade was defined as endometrioid FIGO grades 1 or 2, while early stage was defined as endometrioid stages I or II (disease confined to the uterus). Next-generation sequencing was performed using panels comprised of 46-200 genes. Recurrence-free and overall survival was compared across gene mutational status in both univariate and multivariate analyses. In all, 342 patients were identified, 245 of which had endometrioid histology. For grades 1-2, stages I-II endometrioid endometrial cancer patients, age (HR 1.07, 95% CI 1.03-1.10), CTNNB1 mutation (HR 5.97, 95% CI 2.69-13.21), and TP53 mutation (HR 4.07, 95% CI 1.57-10.54) were associated with worse recurrence-free survival on multivariate analysis. When considering endometrioid tumors of all grades and stages, CTNNB1 mutant tumors were associated with significantly higher rates of grades 1-2 disease, lower rates of deep myometrial invasion, and lower rates of lymphatic/vascular space invasion. When both TP53 and CTNNB1 mutations were considered, presence of either TP53 mutation or CTNNB1 mutation remained a statistically significant predictor of recurrence-free survival on multivariate analysis and was associated with a more precise confidence interval (HR 4.69, 95% CI 2.38-9.24). Thus, mutational analysis of a 2 gene panel of CTNNB1 and TP53 can help to identify a subset of low grade, early stage endometrial cancer patients who are at high risk of recurrence.

Risk Factors for Urinary Tract Infections in Cardiac Surgical Patients

PubMed Central

Gillen, Jacob R.; Isbell, James M.; Michaels, Alex D.; Lau, Christine L.

2015-01-01

Abstract Background: Risk factors for catheter-associated urinary tract infections (CAUTIs) in patients undergoing non-cardiac surgical procedures have been well documented. However, the variables associated with CAUTIs in the cardiac surgical population have not been clearly defined. Therefore, the purpose of this study was to investigate risk factors associated with CAUTIs in patients undergoing cardiac procedures. Methods: All patients undergoing cardiac surgery at a single institution from 2006 through 2012 (4,883 patients) were reviewed. Patients with U.S. Centers for Disease Control (CDC) criteria for CAUTI were identified from the hospital's Quality Assessment database. Pre-operative, operative, and post-operative patient factors were evaluated. Univariate and multivariable analyses were used to identify significant correlations between perioperative characteristics and CAUTIs. Results: There were 55 (1.1%) documented CAUTIs in the study population. On univariate analysis, older age, female gender, diabetes mellitus, cardiogenic shock, urgent or emergent operation, packed red blood cell (PRBC) units transfused, and intensive care unit length of stay (ICU LOS) were all significantly associated with CAUTI [p<0.05]. On multivariable logistic regression, older age, female gender, diabetes mellitus, and ICU LOS remained significantly associated with CAUTI. Additionally, there was a significant association between CAUTI and 30-d mortality on univariate analysis. However, when controlling for common predictors of operative mortality on multivariable analysis, CAUTI was no longer associated with mortality. Conclusions: There are several identifiable risk factors for CAUTI in patients undergoing cardiac procedures. CAUTI is not independently associated with increased mortality, but it does serve as a marker of sicker patients more likely to die from other comorbidities or complications. Therefore, awareness of the high-risk nature of these patients should lead to increased diligence and may help to improve peri-operative outcomes. Recognizing patients at high risk for CAUTI may lead to improved measures to decrease CAUTI rates within this population. PMID:26115336

Lesion stiffness measured by shear-wave elastography: Preoperative predictor of the histologic underestimation of US-guided core needle breast biopsy.

PubMed

Park, Ah Young; Son, Eun Ju; Kim, Jeong-Ah; Han, Kyunghwa; Youk, Ji Hyun

2015-12-01

To determine whether lesion stiffness measured by shear-wave elastography (SWE) can be used to predict the histologic underestimation of ultrasound (US)-guided 14-gauge core needle biopsy (CNB) for breast masses. This retrospective study enrolled 99 breast masses from 93 patients, including 40 high-risk lesions and 59 ductal carcinoma in situ (DCIS), which were diagnosed by US-guided 14-gauge CNB. SWE was performed for all breast masses to measure quantitative elasticity values before US-guided CNB. To identify the preoperative factors associated with histologic underestimation, patients' age, symptoms, lesion size, B-mode US findings, and quantitative SWE parameters were compared according to the histologic upgrade after surgery using the chi-square test, Fisher's exact test, or independent t-test. The independent factors for predicting histologic upgrade were evaluated using multivariate logistic regression analysis. The underestimation rate was 28.3% (28/99) in total, 25.0% (10/40) in high-risk lesions, and 30.5% (18/59) in DCIS. All elasticity values of the upgrade group were significantly higher than those of the non-upgrade group (P<0.001). On multivariate analysis, the mean (Odds ratio [OR]=1.021, P=0.001), maximum (OR=1.015, P=0.008), and minimum (OR=1.028, P=0.001) elasticity values were independently associated with histologic underestimation. The patients' age, lesion size, and final assessment category on US of the upgrade group were higher than those of the non-upgrade group (P=0.046 for age; P=0.021 for lesion size; P=0.030 for US category), but these were not independent predictors of histologic underestimation on multivariate analysis. Breast lesion stiffness quantitatively measured by SWE could be helpful to predict the underestimation of malignancy in US-guided 14-gauge CNB. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Risk Factors for Urinary Tract Infections in Cardiac Surgical Patients.

PubMed

Gillen, Jacob R; Isbell, James M; Michaels, Alex D; Lau, Christine L; Sawyer, Robert G

2015-10-01

Risk factors for catheter-associated urinary tract infections (CAUTIs) in patients undergoing non-cardiac surgical procedures have been well documented. However, the variables associated with CAUTIs in the cardiac surgical population have not been clearly defined. Therefore, the purpose of this study was to investigate risk factors associated with CAUTIs in patients undergoing cardiac procedures. All patients undergoing cardiac surgery at a single institution from 2006 through 2012 (4,883 patients) were reviewed. Patients with U.S. Centers for Disease Control (CDC) criteria for CAUTI were identified from the hospital's Quality Assessment database. Pre-operative, operative, and post-operative patient factors were evaluated. Univariate and multivariable analyses were used to identify significant correlations between perioperative characteristics and CAUTIs. There were 55 (1.1%) documented CAUTIs in the study population. On univariate analysis, older age, female gender, diabetes mellitus, cardiogenic shock, urgent or emergent operation, packed red blood cell (PRBC) units transfused, and intensive care unit length of stay (ICU LOS) were all significantly associated with CAUTI [p<0.05]. On multivariable logistic regression, older age, female gender, diabetes mellitus, and ICU LOS remained significantly associated with CAUTI. Additionally, there was a significant association between CAUTI and 30-d mortality on univariate analysis. However, when controlling for common predictors of operative mortality on multivariable analysis, CAUTI was no longer associated with mortality. There are several identifiable risk factors for CAUTI in patients undergoing cardiac procedures. CAUTI is not independently associated with increased mortality, but it does serve as a marker of sicker patients more likely to die from other comorbidities or complications. Therefore, awareness of the high-risk nature of these patients should lead to increased diligence and may help to improve peri-operative outcomes. Recognizing patients at high risk for CAUTI may lead to improved measures to decrease CAUTI rates within this population.

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.

Comparison of family-planning service quality reported by adolescents and young adult women in Mexico.

PubMed

Darney, Blair G; Saavedra-Avendano, Biani; Sosa-Rubi, Sandra G; Lozano, Rafael; Rodriguez, Maria I

2016-07-01

Associations between age and patient-reported quality of family planning services were examined among young women in Mexico. A repeated cross-sectional analysis of survey data collected in 2006, 2009, and 2014 was performed. Data from women aged 15-29years who had not undergone sterilization and were currently using a modern contraceptive method were included. The primary outcome was high-quality care, defined as positive responses to all five quality items regarding contraceptive services included in the survey. Multivariable logistic regression and marginal probabilities were used to compare adolescents and women aged 20-29years. The responses of respondents using different contraceptive methods were compared. Data were included from 15 835 individuals. The multivariable analysis demonstrated lower odds of reporting high-quality care among women aged 15-19years (odds ratio 0.73; 95% confidence interval 0.60-0.88) and 20-24years (odds ratio 0.85; 95% confidence interval 0.75-0.96) compared with women aged 25-29years. Adolescents using hormonal and long-acting reversible contraception had significantly lower odds of reporting high-quality care compared with women aged 25-29. Adolescents in Mexico reported a lower quality of family planning services compared with young adult women. Continued research and policies are needed to improve the quality of contraceptive services. Copyright © 2016 International Federation of Gynecology and Obstetrics. Published by Elsevier Ireland Ltd. All rights reserved.

Prediction of decannulation, oral intake recovery, overall survival and lung metastasis following oral malignant tumor resection and reconstruction

PubMed Central

Suzuki, Hidenori; Hyodo, Ikuo; Hasegawa, Yasuhisa

2018-01-01

The present study investigated whether tongue base and mandibular bone defects were associated with the rate of decannulation and oral intake recovery, and survival time, including overall and lung metastasis-free survival time, in patients that underwent oral malignant tumor (OMT) resection with reconstruction. A total of 105 patients that underwent OMT resection with laryngeal preservation and reconstruction were recruited. The extent of defects was classified according to Urken's classification. The rates of decannulation and oral intake recovery were assessed with the Kaplan-Meier method. It was identified that 4–5 section segmental mandibulectomy (SM) and total glossectomy (TG) were significantly associated with a lower rate of decannulation and oral intake recovery by univariate and multivariate analysis using a Cox's proportional model. Patients in the high risk group (4–5 sections or TG) were significantly less likely to achieve decannulation and unaided oral intake. Patients in the high risk group exhibited a significantly shorter overall and lung metastasis-free survival time. Following multivariate analysis adjusted for the clinical stage (IV/I–III), past history of or postoperative radiotherapy (yes/no) and age (per year), the high risk group was associated with a significantly rate of decannulation and unaided oral intake. In conclusion, TG or wide SM is a prognostic parameter for functional and survival outcomes, including lung metastasis, in OMT. PMID:29434993

Impact of national income and inequality on sugar and caries relationship.

PubMed

Masood, M; Masood, Y; Newton, T

2012-01-01

The aim of this study was to examine the impact that national income and income inequality in high and low income countries have on the relationship between dental caries and sugar consumption. An ecological study design was used in this study of 73 countries. The mean decayed, missing, or filled permanent teeth (DMFT) for 12-year-old children were obtained from the WHO Oral Health Country/Area Profile Programme. United Nations Food and Agricultural Organization data were used for per capita sugar consumption. Gross national incomes per capita based on purchasing power parity and the Gini coefficient were obtained from World Bank data. Bivariate and multivariate linear regression analysis was performed to estimate the associations between mean DMFT and per capita sugar consumption in different income and income inequality countries. Bivariate and multivariate regression analysis showed that countries with a high national income and low income inequality have a strong negative association between sugar consumption and caries (B = -2.80, R2 = 0.17), whereas countries with a low income and high income inequality have a strong positive relationship between DMFT and per capita sugar consumption (B = -0.89, R2 = 0.20). The relationship between per capita consumption of sugar and dental caries is modified by the absolute level of income of the country, but not by the level of income inequality within a country. Copyright © 2012 S. Karger AG, Basel.

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

Ecological prediction with nonlinear multivariate time-frequency functional data models

USGS Publications Warehouse

Yang, Wen-Hsi; Wikle, Christopher K.; Holan, Scott H.; Wildhaber, Mark L.

2013-01-01

Time-frequency analysis has become a fundamental component of many scientific inquiries. Due to improvements in technology, the amount of high-frequency signals that are collected for ecological and other scientific processes is increasing at a dramatic rate. In order to facilitate the use of these data in ecological prediction, we introduce a class of nonlinear multivariate time-frequency functional models that can identify important features of each signal as well as the interaction of signals corresponding to the response variable of interest. Our methodology is of independent interest and utilizes stochastic search variable selection to improve model selection and performs model averaging to enhance prediction. We illustrate the effectiveness of our approach through simulation and by application to predicting spawning success of shovelnose sturgeon in the Lower Missouri River.

Toward high value sensing: monolayer-protected metal nanoparticles in multivariable gas and vapor sensors.

PubMed

Potyrailo, Radislav A

2017-08-29

For detection of gases and vapors in complex backgrounds, "classic" analytical instruments are an unavoidable alternative to existing sensors. Recently a new generation of sensors, known as multivariable sensors, emerged with a fundamentally different perspective for sensing to eliminate limitations of existing sensors. In multivariable sensors, a sensing material is designed to have diverse responses to different gases and vapors and is coupled to a multivariable transducer that provides independent outputs to recognize these diverse responses. Data analytics tools provide rejection of interferences and multi-analyte quantitation. This review critically analyses advances of multivariable sensors based on ligand-functionalized metal nanoparticles also known as monolayer-protected nanoparticles (MPNs). These MPN sensing materials distinctively stand out from other sensing materials for multivariable sensors due to their diversity of gas- and vapor-response mechanisms as provided by organic and biological ligands, applicability of these sensing materials for broad classes of gas-phase compounds such as condensable vapors and non-condensable gases, and for several principles of signal transduction in multivariable sensors that result in non-resonant and resonant electrical sensors as well as material- and structure-based photonic sensors. Such features should allow MPN multivariable sensors to be an attractive high value addition to existing analytical instrumentation.

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.

Sensitive analytical method for simultaneous analysis of some vasoconstrictors with highly overlapped analytical signals

NASA Astrophysics Data System (ADS)

Nikolić, G. S.; Žerajić, S.; Cakić, M.

2011-10-01

Multivariate calibration method is a powerful mathematical tool that can be applied in analytical chemistry when the analytical signals are highly overlapped. The method with regression by partial least squares is proposed for the simultaneous spectrophotometric determination of adrenergic vasoconstrictors in decongestive solution containing two active components: phenyleprine hydrochloride and trimazoline hydrochloride. These sympathomimetic agents are that frequently associated in pharmaceutical formulations against the common cold. The proposed method, which is, simple and rapid, offers the advantages of sensitivity and wide range of determinations without the need for extraction of the vasoconstrictors. In order to minimize the optimal factors necessary to obtain the calibration matrix by multivariate calibration, different parameters were evaluated. The adequate selection of the spectral regions proved to be important on the number of factors. In order to simultaneously quantify both hydrochlorides among excipients, the spectral region between 250 and 290 nm was selected. A recovery for the vasoconstrictor was 98-101%. The developed method was applied to assay of two decongestive pharmaceutical preparations.

Centralized PI control for high dimensional multivariable systems based on equivalent transfer function.

PubMed

Luan, Xiaoli; Chen, Qiang; Liu, Fei

2014-09-01

This article presents a new scheme to design full matrix controller for high dimensional multivariable processes based on equivalent transfer function (ETF). Differing from existing ETF method, the proposed ETF is derived directly by exploiting the relationship between the equivalent closed-loop transfer function and the inverse of open-loop transfer function. Based on the obtained ETF, the full matrix controller is designed utilizing the existing PI tuning rules. The new proposed ETF model can more accurately represent the original processes. Furthermore, the full matrix centralized controller design method proposed in this paper is applicable to high dimensional multivariable systems with satisfactory performance. Comparison with other multivariable controllers shows that the designed ETF based controller is superior with respect to design-complexity and obtained performance. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

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

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

Ensembles of radial basis function networks for spectroscopic detection of cervical precancer

NASA Technical Reports Server (NTRS)

Tumer, K.; Ramanujam, N.; Ghosh, J.; Richards-Kortum, R.

1998-01-01

The mortality related to cervical cancer can be substantially reduced through early detection and treatment. However, current detection techniques, such as Pap smear and colposcopy, fail to achieve a concurrently high sensitivity and specificity. In vivo fluorescence spectroscopy is a technique which quickly, noninvasively and quantitatively probes the biochemical and morphological changes that occur in precancerous tissue. A multivariate statistical algorithm was used to extract clinically useful information from tissue spectra acquired from 361 cervical sites from 95 patients at 337-, 380-, and 460-nm excitation wavelengths. The multivariate statistical analysis was also employed to reduce the number of fluorescence excitation-emission wavelength pairs required to discriminate healthy tissue samples from precancerous tissue samples. The use of connectionist methods such as multilayered perceptrons, radial basis function (RBF) networks, and ensembles of such networks was investigated. RBF ensemble algorithms based on fluorescence spectra potentially provide automated and near real-time implementation of precancer detection in the hands of nonexperts. The results are more reliable, direct, and accurate than those achieved by either human experts or multivariate statistical algorithms.

Additive genetic variation and evolvability of a multivariate trait can be increased by epistatic gene action.

PubMed

Griswold, Cortland K

2015-12-21

Epistatic gene action occurs when mutations or alleles interact to produce a phenotype. Theoretically and empirically it is of interest to know whether gene interactions can facilitate the evolution of diversity. In this paper, we explore how epistatic gene action affects the additive genetic component or heritable component of multivariate trait variation, as well as how epistatic gene action affects the evolvability of multivariate traits. The analysis involves a sexually reproducing and recombining population. Our results indicate that under stabilizing selection conditions a population with a mixed additive and epistatic genetic architecture can have greater multivariate additive genetic variation and evolvability than a population with a purely additive genetic architecture. That greater multivariate additive genetic variation can occur with epistasis is in contrast to previous theory that indicated univariate additive genetic variation is decreased with epistasis under stabilizing selection conditions. In a multivariate setting, epistasis leads to less relative covariance among individuals in their genotypic, as well as their breeding values, which facilitates the maintenance of additive genetic variation and increases a population׳s evolvability. Our analysis involves linking the combinatorial nature of epistatic genetic effects to the ancestral graph structure of a population to provide insight into the consequences of epistasis on multivariate trait variation and evolution. Copyright © 2015 Elsevier Ltd. All rights reserved.

Stature and jumping height are required in female volleyball, but motor coordination is a key factor for future elite success.

PubMed

Pion, Johan A; Fransen, Job; Deprez, Dieter N; Segers, Veerle I; Vaeyens, Roel; Philippaerts, Renaat M; Lenoir, Matthieu

2015-06-01

It was hypothesized that differences in anthropometry, physical performance, and motor coordination would be found between Belgian elite and sub-elite level female volleyball players using a retrospective analysis of test results gathered over a 5-year period. The test sample in this study consisted of 21 young female volleyball players (15.3 ± 1.5 years) who were selected to train at the Flemish Top Sports Academy for Volleyball in 2008. All players (elite, n = 13; sub-elite, n = 8) were included in the same talent development program, and the elite-level athletes were of a high to very high performance levels according to European competition level in 2013. Five multivariate analyses of variance were used. There was no significant effect of playing level on measures of anthropometry (F = 0.455, p = 0.718, (Equation is included in full-text article.)= 0.07), flexibility (F = 1.861, p = 0.188, (Equation is included in full-text article.)= 0.19), strength (F = 1.218, p = 0.355, (Equation is included in full-text article.)= 0.32); and speed and agility (F = 1.176, p = 0.350, (Equation is included in full-text article.)= 0.18). Multivariate analyses of variance revealed significant multivariate effects between playing levels for motor coordination (F = 3.470, p = 0.036, (Equation is included in full-text article.)= 0.59). A Mann-Whitney U test and a sequential discriminant analysis confirmed these results. Previous research revealed that stature and jump height are prerequisites for talent identification in female volleyball. In addition, the results show that motor coordination is an important factor in determining inclusion into the elite level in female volleyball.

Vegetation characteristics important to common songbirds in east Texas

USGS Publications Warehouse

Conner, Richard N.; Dickson, James G.; Locke, Brian A.; Segelquist, Charles A.

1983-01-01

Multivariate studies of breeding bird communities have used principal component analysis (PCA) or several-group (three or more groups) discriminant function analysis (DFA) to ordinate bird species on vegetational continua (Cody 1968, James 1971, Whitmore 1975). In community studies, high resolution of habitat requirements for individual species is not always possible with either PCA or several-group DFA. When habitat characteristics of several species are examined with a DFA the resultant axes optimally discriminate among all species simultaneously. Hence, the characteristics assigned to a particular species reflect in part the presence of other species in the analyses. A better resolution of each species' habitat requirements may be obtained from a two-group DFA, wherein habitats selected by a species are discriminated from all other available habitats. Analyses using two-group DFAs to compare habitat used by a species with habitat unused by the same species have the potential to provide an optimal frame of reference from which to examine habitat variables (Martinka 1972, Conner and Adkisson 1976, Whitmore 1981). Mathematically (DFA) it is possible to maximally separate two groups of multivariate observations with a single axis (Harner and whitmore 1977). A line drawn in three or n-dimensional space can easily be positioned to intersect two multivariate means (centroids). If three or more centroids for species are analyzed simultaneously, a single line can no longer intersect all centroids unless a perfectly linear relationship exists for the species being examined. The probability of such an occurrence is extremely low. Thus, a high degree of resolution can be realized when a two-group DFA is used to determine habitat parameters important to individual species. We have used two-group DFA to identify vegetation variable important to 12 common species of songbirds in East Texas.

Quantifying uncertainty in high-resolution coupled hydrodynamic-ecosystem models

NASA Astrophysics Data System (ADS)

Allen, J. I.; Somerfield, P. J.; Gilbert, F. J.

2007-01-01

Marine ecosystem models are becoming increasingly complex and sophisticated, and are being used to estimate the effects of future changes in the earth system with a view to informing important policy decisions. Despite their potential importance, far too little attention has been, and is generally, paid to model errors and the extent to which model outputs actually relate to real-world processes. With the increasing complexity of the models themselves comes an increasing complexity among model results. If we are to develop useful modelling tools for the marine environment we need to be able to understand and quantify the uncertainties inherent in the simulations. Analysing errors within highly multivariate model outputs, and relating them to even more complex and multivariate observational data, are not trivial tasks. Here we describe the application of a series of techniques, including a 2-stage self-organising map (SOM), non-parametric multivariate analysis, and error statistics, to a complex spatio-temporal model run for the period 1988-1989 in the Southern North Sea, coinciding with the North Sea Project which collected a wealth of observational data. We use model output, large spatio-temporally resolved data sets and a combination of methodologies (SOM, MDS, uncertainty metrics) to simplify the problem and to provide tractable information on model performance. The use of a SOM as a clustering tool allows us to simplify the dimensions of the problem while the use of MDS on independent data grouped according to the SOM classification allows us to validate the SOM. The combination of classification and uncertainty metrics allows us to pinpoint the variables and associated processes which require attention in each region. We recommend the use of this combination of techniques for simplifying complex comparisons of model outputs with real data, and analysis of error distributions.

Energy Drink and Alcohol mixed Energy Drink use among high school adolescents: Association with risk taking behavior, social characteristics.

PubMed

Scalese, Marco; Denoth, Francesca; Siciliano, Valeria; Bastiani, Luca; Cotichini, Rodolfo; Cutilli, Arianna; Molinaro, Sabrina

2017-09-01

The aims of the study were to: a) examine the prevalence of energy drink (ED) and alcohol mixed with energy drink (AmED) consumption; b) investigate the relationships between ED and AmED with alcohol, binge drinking and drugs accounting for at risk behaviors among a representative sample of Italian adolescents. A representative sample of 30,588 Italian high school students, aged 15-19years, was studied. Binary and multivariate logistic regression analyses were performed to determine the independent association of the potential predictors' characteristics with the ED and AmED drinking during the last year. Respectively 41.4% and 23.2% of respondents reported drinking EDs and AmEDs in the last year. Multivariate analysis revealed that consumption of EDs and AmEDs during the last year were significantly associated with daily smoking, binge drinking, use of cannabis and other psychotropic drugs. Among life habits and risky behaviors the following were positively associated: going out with friends for fun, participating in sports, experiencing physical fights/accidents or injury, engaging in sexual intercourse without protection and being involved in accidents while driving. This study demonstrates the popularity of ED and AmED consumption among the Italian school population aged 15-19years old: 4 out of 10 students consumed EDs in the last year and 2 out of 10 AmED. Multivariate analysis highlighted the association with illicit drug consumption and harming behaviors, confirming that consumption of EDs and AmEDs is a compelling issue especially during adolescence, as it can effect health as well as risk taking behaviors. Copyright © 2017 Elsevier Ltd. All rights reserved.

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…

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…

{sup 18}Fluorodeoxyglucose PET Is Prognostic of Progression-Free and Overall Survival in Locally Advanced Pancreas Cancer Treated With Stereotactic Radiotherapy

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

Schellenberg, Devin; Quon, Andy; Minn, A. Yuriko

2010-08-01

Purpose: This study analyzed the prognostic value of positron emission tomography (PET) for locally advanced pancreas cancer patients undergoing stereotactic body radiotherapy (SBRT). Patients and Methods: Fifty-five previously untreated, unresectable pancreas cancer patients received a single fraction of 25-Gy SBRT sequentially with gemcitabine-based chemotherapy. On the preradiation PET-CT, the tumor was contoured and the maximum standardized uptake value (SUVmax) and metabolic tumor burden (MTB) were calculated using an in-house software application. High-SUVmax and low-SUVmax subgroups were created by categorizing patients above or below the median SUVmax. The analysis was repeated to form high-MTB and low-MTB subgroups as well as clinicallymore » relevant subgroups with SUVmax values of <5, 5-10, or >10. Multivariate analysis analyzing SUVmax, MTB, age, chemotherapy cycles, and pretreatment carbohydrate antigen (CA)19-9 was performed. Results: For the entire population, median survival was 12.7 months. Median survival was 9.8 vs.15.3 months for the high- and low- SUVmax subgroups (p <0.01). Similarly, median survival was 10.1 vs. 18.0 months for the high MTB and low MTB subgroups (p <0.01). When clinical SUVmax cutoffs were used, median survival was 6.4 months in those with SUVmax >10, 9.5 months with SUVmax 5.0-10.0, and 17.7 months in those with SUVmax <5 (p <0.01). On multivariate analysis, clinical SUVmax was an independent predictor for overall survival (p = 0.03) and progression-free survival (p = 0.03). Conclusion: PET scan parameters can predict for length of survival in locally advanced pancreas cancer patients.« less

Current clinical presentation and treatment of localized prostate cancer in the United States.

PubMed

Mahmood, Usama; Levy, Lawrence B; Nguyen, Paul L; Lee, Andrew K; Kuban, Deborah A; Hoffman, Karen E

2014-12-01

SEER recently released patient Gleason scores at biopsy/transurethral resection of the prostate. For the first time this permits accurate assessment of prostate cancer presentation and treatment according to clinical factors at diagnosis. We used the SEER database to identify men diagnosed with localized prostate cancer in 2010 who were assigned NCCN(®) risk based on clinical factors. We identified sociodemographic factors associated with high risk disease and analyzed the impact of these factors along with NCCN risk on local treatment. Of the 42,403 men identified disease was high, intermediate and low risk in 38%, 40% and 22%, respectively. On multivariate analysis patients who were older, nonwhite, unmarried or living in a county with a higher poverty rate were more likely to be diagnosed with high risk disease (each p <0.05). Of the 38,634 men in whom prostate cancer was the first malignancy 23% underwent no local treatment, 40% were treated with prostatectomy, 36% received radiation therapy and 1% underwent local tumor destruction, predominantly cryotherapy. On multivariate analysis patients who were older, black, unmarried or living in a county with a higher poverty rate, or who had low risk disease were less likely to receive local treatment (each p <0.05). Our analysis provides information on the current clinical presentation and treatment of localized prostate cancer in the United States. Nonwhite and older men living in a county with a higher poverty rate were more likely to be diagnosed with high risk disease and less likely to receive local treatment. Copyright © 2014 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Prevalence of difficult venous access and associated risk factors in highly complex hospitalised patients.

PubMed

Armenteros-Yeguas, Victoria; Gárate-Echenique, Lucía; Tomás-López, Maria Aranzazu; Cristóbal-Domínguez, Estíbaliz; Moreno-de Gusmão, Breno; Miranda-Serrano, Erika; Moraza-Dulanto, Maria Inmaculada

2017-12-01

To estimate the prevalence of difficult venous access in complex patients with multimorbidity and to identify associated risk factors. In highly complex patients, factors like ageing, the need for frequent use of irritant medication and multiple venous catheterisations to complete treatment could contribute to exhaustion of venous access. A cross-sectional study was conducted. 'Highly complex' patients (n = 135) were recruited from March 2013-November 2013. The main study variable was the prevalence of difficult venous access, assessed using one of the following criteria: (1) a history of difficulties obtaining venous access based on more than two attempts to insert an intravenous line and (2) no visible or palpable veins. Other factors potentially associated with the risk of difficult access were also measured (age, gender and chronic illnesses). Univariate analysis was performed for each potential risk factor. Factors with p < 0·2 were then included in multivariable logistic regression analysis. Odds ratios were also calculated. The prevalence of difficult venous access was 59·3%. The univariate logistic regression analysis indicated that gender, a history of vascular access complications and osteoarticular disease were significantly associated with difficult venous access. The multivariable logistic regression showed that only gender was an independent risk factor and the odds ratios was 2·85. The prevalence of difficult venous access is high in this population. Gender (female) is the only independent risk factor associated with this. Previous history of several attempts at catheter insertion is an important criterion in the assessment of difficult venous access. The prevalence of difficult venous access in complex patients is 59·3%. Significant risk factors include being female and a history of complications related to vascular access. © 2017 John Wiley & Sons Ltd.

Survival outcomes of radical prostatectomy and external beam radiotherapy in clinically localized high-risk prostate cancer: a population-based, propensity score matched study

PubMed Central

Gu, Xiaobin; Gao, Xianshu; Cui, Ming; Xie, Mu; Ma, Mingwei; Qin, Shangbin; Li, Xiaoying; Qi, Xin; Bai, Yun; Wang, Dian

2018-01-01

Objective This study was aimed to compare survival outcomes in high-risk prostate cancer (PCa) patients receiving external beam radiotherapy (EBRT) or radical prostatectomy (RP). Materials and methods The Surveillance, Epidemiology, and End Results (SEER) database was used to identify PCa patients with high-risk features who received RP alone or EBRT alone from 2004 to 2008. Propensity-score matching (PSM) was performed. Kaplan–Meier survival analysis was used to compare cancer-specific survival (CSS) and overall survival (OS). Multivariate Cox regression analysis was used to identify independent prognostic factors. Results A total of 24,293 patients were identified, 14,460 patients receiving RP and 9833 patients receiving EBRT. Through PSM, 3828 patients were identified in each group. The mean CSS was 128.6 and 126.7 months for RP and EBRT groups, respectively (P<0.001). The subgroup analyses showed that CSS of the RP group was better than that of the EBRT group for patients aged <65 years (P<0.001), White race (P<0.001), and married status (P<0.001). However, there was no significant difference in CSS for patients aged ≥65 years, Black race, other race, and unmarried status. Similar trends were observed for OS. Multivariate analysis showed that EBRT treatment modality, T3–T4 stage, Gleason score 8–10, and prostate-specific antigen >20 ng/mL were significant risk factors for both CSS and OS. Conclusion This study suggested that survival outcomes might be better with RP than EBRT in high-risk PCa patients aged <65 years; however, RP and EBRT provided equivalent survival outcomes in older patients, which argues for primary radiotherapy in this older cohort.

Quantifying team cooperation through intrinsic multi-scale measures: respiratory and cardiac synchronization in choir singers and surgical teams.

PubMed

Hemakom, Apit; Powezka, Katarzyna; Goverdovsky, Valentin; Jaffer, Usman; Mandic, Danilo P

2017-12-01

A highly localized data-association measure, termed intrinsic synchrosqueezing transform (ISC), is proposed for the analysis of coupled nonlinear and non-stationary multivariate signals. This is achieved based on a combination of noise-assisted multivariate empirical mode decomposition and short-time Fourier transform-based univariate and multivariate synchrosqueezing transforms. It is shown that the ISC outperforms six other combinations of algorithms in estimating degrees of synchrony in synthetic linear and nonlinear bivariate signals. Its advantage is further illustrated in the precise identification of the synchronized respiratory and heart rate variability frequencies among a subset of bass singers of a professional choir, where it distinctly exhibits better performance than the continuous wavelet transform-based ISC. We also introduce an extension to the intrinsic phase synchrony (IPS) measure, referred to as nested intrinsic phase synchrony (N-IPS), for the empirical quantification of physically meaningful and straightforward-to-interpret trends in phase synchrony. The N-IPS is employed to reveal physically meaningful variations in the levels of cooperation in choir singing and performing a surgical procedure. Both the proposed techniques successfully reveal degrees of synchronization of the physiological signals in two different aspects: (i) precise localization of synchrony in time and frequency (ISC), and (ii) large-scale analysis for the empirical quantification of physically meaningful trends in synchrony (N-IPS).

Comparison of DNA aneuploidy, chromosome 1 abnormalities, MYCN amplification and CD44 expression as prognostic factors in neuroblastoma.

PubMed

Christiansen, H; Sahin, K; Berthold, F; Hero, B; Terpe, H J; Lampert, F

1995-01-01

A comparison of the prognostic impact of five molecular variables in a large series was made, including tests of their nonrandom association and multivariate analysis. Molecular data were available for 377 patients and MYCN amplification, cytogenetic chromosome 1p deletion, loss of chromosome 1p heterozygosity, DNA ploidy and CD44 expression were investigated. Their interdependence and influence on event-free survival was tested uni- and multivariately using Pearson's chi 2-test, Kaplan-Meier estimates, log rank tests and the Cox's regression model. MYCN amplification was present in 18% (58/322) of cases and predicted poorer prognosis in localised (P < 0.001), metastatic (P = 0.002) and even 4S (P = 0.040) disease. CD44 expression was found in 86% (127/148) of cases, and was a marker for favourable outcome in patients with neuroblastoma stages 1-3 (P = 0.003) and 4 (P = 0.017). Chromosome 1p deletion was cytogenetically detected in 51% (28/55), and indicated reduced event-free survival in localised neuroblastoma (P = 0.020). DNA ploidy and loss of heterozygosity on chromosome 1p were of less prognostic value. Most factors of prognostic significance were associated with each other. By multivariate analysis, MYCN was selected as the only relevant factor. Risk estimation of high discriminating power is, therefore, possible for patients with localised and metastatic neuroblastoma using stage and MYCN.

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.

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.

Impact of Small Body Weight on Tenofovir-Associated Renal Dysfunction in HIV-Infected Patients: A Retrospective Cohort Study of Japanese Patients

PubMed Central

Nishijima, Takeshi; Komatsu, Hirokazu; Gatanaga, Hiroyuki; Aoki, Takahiro; Watanabe, Koji; Kinai, Ei; Honda, Haruhito; Tanuma, Junko; Yazaki, Hirohisa; Tsukada, Kunihisa; Honda, Miwako; Teruya, Katsuji; Kikuchi, Yoshimi; Oka, Shinichi

2011-01-01

Background Treatment with tenofovir is sometimes associated with renal dysfunction. Limited information is available on this side effect in patients with small body weight, although the use of tenofovir will spread rapidly in Asia and Africa, where patients are likely to be of smaller body weight. Methods In a single-center cohort, Japanese patients with HIV infection who started tenofovir-containing antiretroviral therapy were retrospectively analyzed. The incidence of tenofovir-associated renal dysfunction, defined as more than 25% decrement of estimated glomerular filtration rate (eGFR) from the baseline, was determined. The effects of small body weight and body mass index (BMI) on tenofovir-associated renal dysfunction, respectively, were estimated in univariate and multivariate Cox hazards models as the primary exposure. Other possible risk factors were evaluated by univariate analysis and those found significant were entered into the multivariate analysis. Results The median weight of 495 patients was 63 kg. Tenofovir-related renal dysfunction occurred in 97 (19.6%) patients (incidence: 10.5 per 100 person-years). Univariate analysis showed that the incidence of tenofovir-related renal dysfunction was significantly associated with smaller body weight and BMI, respectively (per 5 kg decrement, HR = 1.23; 95% CI, 1.10–1.37; p<0.001)(per 1 kg/m2 decrement, HR = 1.14; 95% CI, 1.05–1.23; p = 0.001). Old age, high baseline eGFR, low serum creatinine, low CD4 count, high HIV viral load, concurrent nephrotoxic drugs, hepatitis C infection, and current smoking were also associated with tenofovir-related renal dysfunction. Multivariate analysis identified small body weight as a significant risk (adjusted HR = 1.13; 95% CI, 1.01–1.27; p = 0.039), while small BMI had marginal significance (adjusted HR = 1.07; 95% CI 1.00–1.16; p = 0.058). Conclusion The incidence of tenofovir-associated renal dysfunction in Japanese patients was high. Small body weight was identified as an independent risk factor for tenofovir-associated renal dysfunction. Close monitoring of renal function is advocated for patients with small body weight treated with tenofovir. PMID:21799928

Linear models of coregionalization for multivariate lattice data: Order-dependent and order-free cMCARs.

PubMed

MacNab, Ying C

2016-08-01

This paper concerns with multivariate conditional autoregressive models defined by linear combination of independent or correlated underlying spatial processes. Known as linear models of coregionalization, the method offers a systematic and unified approach for formulating multivariate extensions to a broad range of univariate conditional autoregressive models. The resulting multivariate spatial models represent classes of coregionalized multivariate conditional autoregressive models that enable flexible modelling of multivariate spatial interactions, yielding coregionalization models with symmetric or asymmetric cross-covariances of different spatial variation and smoothness. In the context of multivariate disease mapping, for example, they facilitate borrowing strength both over space and cross variables, allowing for more flexible multivariate spatial smoothing. Specifically, we present a broadened coregionalization framework to include order-dependent, order-free, and order-robust multivariate models; a new class of order-free coregionalized multivariate conditional autoregressives is introduced. We tackle computational challenges and present solutions that are integral for Bayesian analysis of these models. We also discuss two ways of computing deviance information criterion for comparison among competing hierarchical models with or without unidentifiable prior parameters. The models and related methodology are developed in the broad context of modelling multivariate data on spatial lattice and illustrated in the context of multivariate disease mapping. The coregionalization framework and related methods also present a general approach for building spatially structured cross-covariance functions for multivariate geostatistics. © The Author(s) 2016.

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.

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.

Monitoring of pistachio (Pistacia Vera) ripening by high field nuclear magnetic resonance spectroscopy.

PubMed

Sciubba, Fabio; Avanzato, Damiano; Vaccaro, Angela; Capuani, Giorgio; Spagnoli, Mariangela; Di Cocco, Maria Enrica; Tzareva, Irina Nikolova; Delfini, Maurizio

2017-04-01

The metabolic profiling of pistachio (Pistacia vera) aqueous extracts from two different cultivars, namely 'Bianca' and 'Gloria', was monitored over the months from May to September employing high field NMR spectroscopy. A large number of water-soluble metabolites were assigned by means of 1D and 2D NMR experiments. The change in the metabolic profiles monitored over time allowed the pistachio development to be investigated. Specific temporal trends of amino acids, sugars, organic acids and other metabolites were observed and analysed by multivariate Partial Least Squares (PLS) analysis. Statistical analysis showed that while in the period from May to September there were few differences between the two cultivars, the ripening rate was different.

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.

Factors Associated With Burnout Among US Hospital Clinical Pharmacy Practitioners: Results of a Nationwide Pilot Survey.

PubMed

Jones, G Morgan; Roe, Neil A; Louden, Les; Tubbs, Crystal R

2017-12-01

Background: In health care, burnout has been defined as a psychological process whereby human service professionals attempting to positively impact the lives of others become overwhelmed and frustrated by unforeseen job stressors. Burnout among various physician groups who primarily practice in the hospital setting has been extensively studied; however, no evidence exists regarding burnout among hospital clinical pharmacists. Objective: The aim of this study was to characterize the level of and identify factors independently associated with burnout among clinical pharmacists practicing in an inpatient hospital setting within the United States. Methods: We conducted a prospective, cross-sectional pilot study utilizing an online, Qualtrics survey. Univariate analysis related to burnout was conducted, with multivariable logistic regression analysis used to identify factors independently associated with the burnout. Results: A total of 974 responses were analyzed (11.4% response rate). The majority were females who had practiced pharmacy for a median of 8 years. The burnout rate was high (61.2%) and largely driven by high emotional exhaustion. On multivariable analysis, we identified several subjective factors as being predictors of burnout, including inadequate administrative and teaching time, uncertainty of health care reform, too many nonclinical duties, difficult pharmacist colleagues, and feeling that contributions are underappreciated. Conclusions: The burnout rate of hospital clinical pharmacy providers was very high in this pilot survey. However, the overall response rate was low at 11.4%. The negative effects of burnout require further study and intervention to determine the influence of burnout on the lives of clinical pharmacists and on other health care-related outcomes.

Prognostic value of transformer 2β expression in prostate cancer.

PubMed

Diao, Yan; Wu, Dong; Dai, Zhijun; Kang, Huafeng; Wang, Ziming; Wang, Xijing

2015-01-01

Deregulation of transformer 2β (Tra2β) has been implicated in several cancers. However, the role of Tra2β expression in prostate cancer (PCa) is unclear. Therefore, this study was to investigate the expression of Tra2β in PCa and evaluated its association with clinicopathological variables and prognosis. Thirty paired fresh PCa samples were analyzed for Tra2β expression by Western blot analysis. Immunohistochemistry (IHC) assay was performed in 160 PCa samples after radical prostatectomy and adjacent non-cancerous tissues. Tra2β protein expression was divided into high expression group and low expression group by IHC. We also investigated the association of Tra2β expression with clinical and pathologic parameters. Kaplan-Meier plots and Cox proportional hazards regression model were used to analyze the association between Tra2β protein expression and prognosis of PCa patients. Our results showed that Tra2β was significantly upregulated in PCa tissues by western blot and IHC. Our data indicated that high expression of Tra2β was significantly associated with lymph node metastasis (P=0.002), clinical stage (P=0.015), preoperative prostate-specific antigen (P=0.003), Gleason score (P=0.001), and biochemical recurrence (P=0.021). High Tra2β expression was a significant predictor of poor biochemical recurrence free survival and overall survival both in univariate and multivariate analysis. We show that Tra2β was significantly upregulated in PCa patients after radical prostatectomy, and multivariate analysis confirmed Tra2β as an independent prognostic factor.

Groundwater source contamination mechanisms: Physicochemical profile clustering, risk factor analysis and multivariate modelling

NASA Astrophysics Data System (ADS)

Hynds, Paul; Misstear, Bruce D.; Gill, Laurence W.; Murphy, Heather M.

2014-04-01

An integrated domestic well sampling and "susceptibility assessment" programme was undertaken in the Republic of Ireland from April 2008 to November 2010. Overall, 211 domestic wells were sampled, assessed and collated with local climate data. Based upon groundwater physicochemical profile, three clusters have been identified and characterised by source type (borehole or hand-dug well) and local geological setting. Statistical analysis indicates that cluster membership is significantly associated with the prevalence of bacteria (p = 0.001), with mean Escherichia coli presence within clusters ranging from 15.4% (Cluster-1) to 47.6% (Cluster-3). Bivariate risk factor analysis shows that on-site septic tank presence was the only risk factor significantly associated (p < 0.05) with bacterial presence within all clusters. Point agriculture adjacency was significantly associated with both borehole-related clusters. Well design criteria were associated with hand-dug wells and boreholes in areas characterised by high permeability subsoils, while local geological setting was significant for hand-dug wells and boreholes in areas dominated by low/moderate permeability subsoils. Multivariate susceptibility models were developed for all clusters, with predictive accuracies of 84% (Cluster-1) to 91% (Cluster-2) achieved. Septic tank setback was a common variable within all multivariate models, while agricultural sources were also significant, albeit to a lesser degree. Furthermore, well liner clearance was a significant factor in all models, indicating that direct surface ingress is a significant well contamination mechanism. Identification and elucidation of cluster-specific contamination mechanisms may be used to develop improved overall risk management and wellhead protection strategies, while also informing future remediation and maintenance efforts.

[Multivariate ordinal logistic regression analysis on the association between consumption of fried food and both esophageal cancer and precancerous lesions].

PubMed

Guo, L W; Liu, S Z; Zhang, M; Chen, Q; Zhang, S K; Sun, X B

2017-12-10

Objective: To investigate the effect of fried food intake on the pathogenesis of esophageal cancer and precancerous lesions. Methods: From 2005 to 2013, all the residents aged 40-69 years from 11 counties (cities) where cancer screening of upper gastrointestinal cancer had been conducted in rural areas of Henan province, were recruited as the subjects of study. Information on demography and lifestyle was collected. The residents under study were screened with iodine staining endoscopic examination and biopsy samples were diagnosed pathologically, under standardized criteria. Subjects with high risk were divided into the groups based on their different pathological degrees. Multivariate ordinal logistic regression analysis was used to analyze the relationship between the frequency of fried food intake and esophageal cancer and precancerous lesions. Results: A total number of 8 792 cases with normal esophagus, 3 680 with mild hyperplasia, 972 with moderate hyperplasia, 413 with severe hyperplasia carcinoma in situ, and 336 cases of esophageal cancer were recruited. Results from multivariate logistic regression analysis showed that, when compared with those who did not eat fried food, the intake of fried food (<2 times/week: OR =1.60, 95% CI : 1.40-1.83; ≥2 times/week: OR =2.58, 95% CI : 1.98-3.37) appeared a risk factor for both esophageal cancer or precancerous lesions after adjustment for age, sex, marital status, educational level, body mass index, smoking and alcohol intake. Conclusion: The intake of fried food appeared a risk factor for both esophageal cancer and precancerous lesions.

Revealing hidden spectral information of chlorine and sulfur in data of a mobile Laser-induced Breakdown Spectroscopy system using chemometrics

NASA Astrophysics Data System (ADS)

Gottlieb, C.; Millar, S.; Günther, T.; Wilsch, G.

2017-06-01

For the damage assessment of reinforced concrete structures the quantified ingress profiles of harmful species like chlorides, sulfates and alkali need to be determined. In order to provide on-site analysis of concrete a fast and reliable method is necessary. Low transition probabilities as well as the high ionization energies for chlorine and sulfur in the near-infrared range makes the detection of Cl I and S I in low concentrations a difficult task. For the on-site analysis a mobile LIBS-system (λ = 1064 nm, Epulse ≤ 3 mJ, τ = 1.5 ns) with an automated scanner has been developed at BAM. Weak chlorine and sulfur signal intensities do not allow classical univariate analysis for process data derived from the mobile system. In order to improve the analytical performance multivariate analysis like PLS-R will be presented in this work. A comparison to standard univariate analysis will be carried out and results covering important parameters like detection and quantification limits (LOD, LOQ) as well as processing variances will be discussed (Allegrini and Olivieri, 2014 [1]; Ostra et al., 2008 [2]). It will be shown that for the first time a low cost mobile system is capable of providing reproducible chlorine and sulfur analysis on concrete by using a low sensitive system in combination with multivariate evaluation.