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.

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.

Apparatus and system for multivariate spectral analysis

DOEpatents

Keenan, Michael R.; Kotula, Paul G.

2003-06-24

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

An Extension of Dominance Analysis to Canonical Correlation Analysis

ERIC Educational Resources Information Center

Huo, Yan; Budescu, David V.

2009-01-01

Dominance analysis (Budescu, 1993) offers a general framework for determination of relative importance of predictors in univariate and multivariate multiple regression models. This approach relies on pairwise comparisons of the contribution of predictors in all relevant subset models. In this article we extend dominance analysis to canonical…

Method of multivariate spectral analysis

DOEpatents

Keenan, Michael R.; Kotula, Paul G.

2004-01-06

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

Clinical management provided by board-certificated physiatrists in early rehabilitation is a significant determinant of functional improvement in acute stroke patients: a retrospective analysis of Japan rehabilitation database.

PubMed

Kinoshita, Shoji; Kakuda, Wataru; Momosaki, Ryo; Yamada, Naoki; Sugawara, Hidekazu; Watanabe, Shu; Abo, Masahiro

2015-05-01

Early rehabilitation for acute stroke patients is widely recommended. We tested the hypothesis that clinical outcome of stroke patients who receive early rehabilitation managed by board-certificated physiatrists (BCP) is generally better than that provided by other medical specialties. Data of stroke patients who underwent early rehabilitation in 19 acute hospitals between January 2005 and December 2013 were collected from the Japan Rehabilitation Database and analyzed retrospectively. Multivariate linear regression analysis using generalized estimating equations method was performed to assess the association between Functional Independence Measure (FIM) effectiveness and management provided by BCP in early rehabilitation. In addition, multivariate logistic regression analysis was also performed to assess the impact of management provided by BCP in acute phase on discharge destination. After setting the inclusion criteria, data of 3838 stroke patients were eligible for analysis. BCP provided early rehabilitation in 814 patients (21.2%). Both the duration of daily exercise time and the frequency of regular conferencing were significantly higher for patients managed by BCP than by other specialties. Although the mortality rate was not different, multivariate regression analysis showed that FIM effectiveness correlated significantly and positively with the management provided by BCP (coefficient, .35; 95% confidence interval [CI], .012-.059; P < .005). In addition, multivariate logistic analysis identified clinical management by BCP as a significant determinant of home discharge (odds ratio, 1.24; 95% CI, 1.08-1.44; P < .005). Our retrospective cohort study demonstrated that clinical management provided by BCP in early rehabilitation can lead to functional recovery of acute stroke. Copyright © 2015 National Stroke Association. Published by Elsevier Inc. All rights reserved.

Assessment of trace elements levels in patients with Type 2 diabetes using multivariate statistical analysis.

PubMed

Badran, M; Morsy, R; Soliman, H; Elnimr, T

2016-01-01

The trace elements metabolism has been reported to possess specific roles in the pathogenesis and progress of diabetes mellitus. Due to the continuous increase in the population of patients with Type 2 diabetes (T2D), this study aims to assess the levels and inter-relationships of fast blood glucose (FBG) and serum trace elements in Type 2 diabetic patients. This study was conducted on 40 Egyptian Type 2 diabetic patients and 36 healthy volunteers (Hospital of Tanta University, Tanta, Egypt). The blood serum was digested and then used to determine the levels of 24 trace elements using an inductive coupled plasma mass spectroscopy (ICP-MS). Multivariate statistical analysis depended on correlation coefficient, cluster analysis (CA) and principal component analysis (PCA), were used to analysis the data. The results exhibited significant changes in FBG and eight of trace elements, Zn, Cu, Se, Fe, Mn, Cr, Mg, and As, levels in the blood serum of Type 2 diabetic patients relative to those of healthy controls. The statistical analyses using multivariate statistical techniques were obvious in the reduction of the experimental variables, and grouping the trace elements in patients into three clusters. The application of PCA revealed a distinct difference in associations of trace elements and their clustering patterns in control and patients group in particular for Mg, Fe, Cu, and Zn that appeared to be the most crucial factors which related with Type 2 diabetes. Therefore, on the basis of this study, the contributors of trace elements content in Type 2 diabetic patients can be determine and specify with correlation relationship and multivariate statistical analysis, which confirm that the alteration of some essential trace metals may play a role in the development of diabetes mellitus. Copyright © 2015 Elsevier GmbH. All rights reserved.

Determinants of Adiponectin Levels in Patients with Chronic Systolic Heart Failure

PubMed Central

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

2010-01-01

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

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

PubMed

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

2013-04-10

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

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.

On-line evaluation of multiloop digital controller performance

NASA Technical Reports Server (NTRS)

Wieseman, Carol D.

1993-01-01

The purpose of this presentation is to inform the Guidance and Control community of capabilities which were developed by the Aeroservoelasticity Branch to evaluate the performance of multivariable control laws, on-line, during wind-tunnel testing. The capabilities are generic enough to be useful for all kinds of on-line analyses involving multivariable control in experimental testing. Consequently, it was decided to present this material at this workshop even though it has been presented elsewhere. Topics covered include: essential on-line analysis requirements; on-line analysis capabilities; on-line analysis software; frequency domain procedures; controller performance evaluation frequency-domain flutter suppression; and plant determination.

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.

Information extraction from multivariate images

NASA Technical Reports Server (NTRS)

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

1986-01-01

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

Application of multivariate analysis to investigate the trace element contamination in top soil of coal mining district in Jorong, South Kalimantan, Indonesia

NASA Astrophysics Data System (ADS)

Pujiwati, Arie; Nakamura, K.; Watanabe, N.; Komai, T.

2018-02-01

Multivariate analysis is applied to investigate geochemistry of several trace elements in top soils and their relation with the contamination source as the influence of coal mines in Jorong, South Kalimantan. Total concentration of Cd, V, Co, Ni, Cr, Zn, As, Pb, Sb, Cu and Ba was determined in 20 soil samples by the bulk analysis. Pearson correlation is applied to specify the linear correlation among the elements. Principal Component Analysis (PCA) and Cluster Analysis (CA) were applied to observe the classification of trace elements and contamination sources. The results suggest that contamination loading is contributed by Cr, Cu, Ni, Zn, As, and Pb. The elemental loading mostly affects the non-coal mining area, for instances the area near settlement and agricultural land use. Moreover, the contamination source is classified into the areas that are influenced by the coal mining activity, the agricultural types, and the river mixing zone. Multivariate analysis could elucidate the elemental loading and the contamination sources of trace elements in the vicinity of coal mine area.

Multivariate calibration on NIR data: development of a model for the rapid evaluation of ethanol content in bakery products.

PubMed

Bello, Alessandra; Bianchi, Federica; Careri, Maria; Giannetto, Marco; Mori, Giovanni; Musci, Marilena

2007-11-05

A new NIR method based on multivariate calibration for determination of ethanol in industrially packed wholemeal bread was developed and validated. GC-FID was used as reference method for the determination of actual ethanol concentration of different samples of wholemeal bread with proper content of added ethanol, ranging from 0 to 3.5% (w/w). Stepwise discriminant analysis was carried out on the NIR dataset, in order to reduce the number of original variables by selecting those that were able to discriminate between the samples of different ethanol concentrations. With the so selected variables a multivariate calibration model was then obtained by multiple linear regression. The prediction power of the linear model was optimized by a new "leave one out" method, so that the number of original variables resulted further reduced.

MULTIVARIATE ANALYSIS OF MACROINVERTEBRATE ASSEMBLAGES TO DETERMINE IMPACTS ON ROCKY MOUNTAIN STREAM ECOSYSTEMS

EPA Science Inventory

Using reduncancy (RDA) and canonical correlation analysis (CCA) we assessed relationships between chemical and physical characteristics and periphyton at 105 stream sites sampled by REMAP in the mineral belt of the southern Rockies ecoregion in Colorado. We contrasted results ob...

Optimal Multicomponent Analysis Using the Generalized Standard Addition Method.

ERIC Educational Resources Information Center

Raymond, Margaret; And Others

1983-01-01

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

The potential use of cuticular hydrocarbons and multivariate analysis to age empty puparial cases of Calliphora vicina and Lucilia sericata.

PubMed

Moore, Hannah E; Pechal, Jennifer L; Benbow, M Eric; Drijfhout, Falko P

2017-05-16

Cuticular hydrocarbons (CHC) have been successfully used in the field of forensic entomology for identifying and ageing forensically important blowfly species, primarily in the larval stages. However in older scenes where all other entomological evidence is no longer present, Calliphoridae puparial cases can often be all that remains and therefore being able to establish the age could give an indication of the PMI. This paper examined the CHCs present in the lipid wax layer of insects, to determine the age of the cases over a period of nine months. The two forensically important species examined were Calliphora vicina and Lucilia sericata. The hydrocarbons were chemically extracted and analysed using Gas Chromatography - Mass Spectrometry. Statistical analysis was then applied in the form of non-metric multidimensional scaling analysis (NMDS), permutational multivariate analysis of variance (PERMANOVA) and random forest models. This study was successful in determining age differences within the empty cases, which to date, has not been establish by any other technique.

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…

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.

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.

Estuarial fingerprinting through multidimensional fluorescence and multivariate analysis.

PubMed

Hall, Gregory J; Clow, Kerin E; Kenny, Jonathan E

2005-10-01

As part of a strategy for preventing the introduction of aquatic nuisance species (ANS) to U.S. estuaries, ballast water exchange (BWE) regulations have been imposed. Enforcing these regulations requires a reliable method for determining the port of origin of water in the ballast tanks of ships entering U.S. waters. This study shows that a three-dimensional fluorescence fingerprinting technique, excitation emission matrix (EEM) spectroscopy, holds great promise as a ballast water analysis tool. In our technique, EEMs are analyzed by multivariate classification and curve resolution methods, such as N-way partial least squares Regression-discriminant analysis (NPLS-DA) and parallel factor analysis (PARAFAC). We demonstrate that classification techniques can be used to discriminate among sampling sites less than 10 miles apart, encompassing Boston Harbor and two tributaries in the Mystic River Watershed. To our knowledge, this work is the first to use multivariate analysis to classify water as to location of origin. Furthermore, it is shown that curve resolution can show seasonal features within the multidimensional fluorescence data sets, which correlate with difficulty in classification.

A single determinant dominates the rate of yeast protein evolution.

PubMed

Drummond, D Allan; Raval, Alpan; Wilke, Claus O

2006-02-01

A gene's rate of sequence evolution is among the most fundamental evolutionary quantities in common use, but what determines evolutionary rates has remained unclear. Here, we carry out the first combined analysis of seven predictors (gene expression level, dispensability, protein abundance, codon adaptation index, gene length, number of protein-protein interactions, and the gene's centrality in the interaction network) previously reported to have independent influences on protein evolutionary rates. Strikingly, our analysis reveals a single dominant variable linked to the number of translation events which explains 40-fold more variation in evolutionary rate than any other, suggesting that protein evolutionary rate has a single major determinant among the seven predictors. The dominant variable explains nearly half the variation in the rate of synonymous and protein evolution. We show that the two most commonly used methods to disentangle the determinants of evolutionary rate, partial correlation analysis and ordinary multivariate regression, produce misleading or spurious results when applied to noisy biological data. We overcome these difficulties by employing principal component regression, a multivariate regression of evolutionary rate against the principal components of the predictor variables. Our results support the hypothesis that translational selection governs the rate of synonymous and protein sequence evolution in yeast.

Measures of dependence for multivariate Lévy distributions

NASA Astrophysics Data System (ADS)

Boland, J.; Hurd, T. R.; Pivato, M.; Seco, L.

2001-02-01

Recent statistical analysis of a number of financial databases is summarized. Increasing agreement is found that logarithmic equity returns show a certain type of asymptotic behavior of the largest events, namely that the probability density functions have power law tails with an exponent α≈3.0. This behavior does not vary much over different stock exchanges or over time, despite large variations in trading environments. The present paper proposes a class of multivariate distributions which generalizes the observed qualities of univariate time series. A new consequence of the proposed class is the "spectral measure" which completely characterizes the multivariate dependences of the extreme tails of the distribution. This measure on the unit sphere in M-dimensions, in principle completely general, can be determined empirically by looking at extreme events. If it can be observed and determined, it will prove to be of importance for scenario generation in portfolio risk management.

Defining critical habitats of threatened and endemic reef fishes with a multivariate approach.

PubMed

Purcell, Steven W; Clarke, K Robert; Rushworth, Kelvin; Dalton, Steven J

2014-12-01

Understanding critical habitats of threatened and endemic animals is essential for mitigating extinction risks, developing recovery plans, and siting reserves, but assessment methods are generally lacking. We evaluated critical habitats of 8 threatened or endemic fish species on coral and rocky reefs of subtropical eastern Australia, by measuring physical and substratum-type variables of habitats at fish sightings. We used nonmetric and metric multidimensional scaling (nMDS, mMDS), Analysis of similarities (ANOSIM), similarity percentages analysis (SIMPER), permutational analysis of multivariate dispersions (PERMDISP), and other multivariate tools to distinguish critical habitats. Niche breadth was widest for 2 endemic wrasses, and reef inclination was important for several species, often found in relatively deep microhabitats. Critical habitats of mainland reef species included small caves or habitat-forming hosts such as gorgonian corals and black coral trees. Hard corals appeared important for reef fishes at Lord Howe Island, and red algae for mainland reef fishes. A wide range of habitat variables are required to assess critical habitats owing to varied affinities of species to different habitat features. We advocate assessments of critical habitats matched to the spatial scale used by the animals and a combination of multivariate methods. Our multivariate approach furnishes a general template for assessing the critical habitats of species, understanding how these vary among species, and determining differences in the degree of habitat specificity. © 2014 Society for Conservation Biology.

An Interinstitutional Analysis of Faculty Teaching Load.

ERIC Educational Resources Information Center

Ahrens, Stephen W.

A two-year interinstitutional study among 15 cooperating universities was conducted to determine whether significant differences exist in teaching loads among the selected universities as measured by student credit hours produced by full-time equivalent faculty. The statistical model was a multivariate analysis of variance with fixed effects and…

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

PubMed Central

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

2003-01-01

A fully Bayesian analysis using Gibbs sampling and data augmentation in a multivariate model of Gaussian, right censored, and grouped Gaussian traits is described. The grouped Gaussian traits are either ordered categorical traits (with more than two categories) or binary traits, where the grouping is determined via thresholds on the underlying Gaussian scale, the liability scale. Allowances are made for unequal models, unknown covariance matrices and missing data. Having outlined the theory, strategies for implementation are reviewed. These include joint sampling of location parameters; efficient sampling from the fully conditional posterior distribution of augmented data, a multivariate truncated normal distribution; and sampling from the conditional inverse Wishart distribution, the fully conditional posterior distribution of the residual covariance matrix. Finally, a simulated dataset was analysed to illustrate the methodology. This paper concentrates on a model where residuals associated with liabilities of the binary traits are assumed to be independent. A Bayesian analysis using Gibbs sampling is outlined for the model where this assumption is relaxed. PMID:12633531

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.

Determination of equilibrium structures of bromothymol blue revealed by using quantum chemistry with an aid of multivariate analysis of electronic absorption spectra

NASA Astrophysics Data System (ADS)

Shimada, Toru; Hasegawa, Takeshi

2017-10-01

The pH dependent chemical structures of bromothymol blue (BTB), which have long been under controversy, are determined by employing a combined technique of multivariate analysis of electronic absorption spectra and quantum chemistry. Principle component analysis (PCA) of the pH dependent spectra apparently reveals that only two chemical species are adequate to fully account for the color changes, with which the spectral decomposition is readily performed by using augmented alternative least-squares (ALS) regression analysis. The quantity variation by the ALS analysis also reveals the practical acid dissociation constant, pKa‧. The determination of pKa‧ is performed for various ionic strengths, which reveals the thermodynamic acid constant (pKa = 7.5) and the number of charge on each chemical species; the yellow form is negatively charged species of - 1 and the blue form that of - 2. On this chemical information, the quantum chemical calculation is carried out to find that BTB molecules take the pure quinoid form in an acid solution and the quinoid-phenolate form in an alkaline solution. The time-dependent density functional theory (TD-DFT) calculations for the theoretically determined chemical structures account for the peak shift of the electronic spectra. In this manner, the structures of all the chemical species appeared in equilibrium have finally been confirmed.

Determination of equilibrium structures of bromothymol blue revealed by using quantum chemistry with an aid of multivariate analysis of electronic absorption spectra.

PubMed

Shimada, Toru; Hasegawa, Takeshi

2017-10-05

The pH dependent chemical structures of bromothymol blue (BTB), which have long been under controversy, are determined by employing a combined technique of multivariate analysis of electronic absorption spectra and quantum chemistry. Principle component analysis (PCA) of the pH dependent spectra apparently reveals that only two chemical species are adequate to fully account for the color changes, with which the spectral decomposition is readily performed by using augmented alternative least-squares (ALS) regression analysis. The quantity variation by the ALS analysis also reveals the practical acid dissociation constant, pK a '. The determination of pK a ' is performed for various ionic strengths, which reveals the thermodynamic acid constant (pK a =7.5) and the number of charge on each chemical species; the yellow form is negatively charged species of -1 and the blue form that of -2. On this chemical information, the quantum chemical calculation is carried out to find that BTB molecules take the pure quinoid form in an acid solution and the quinoid-phenolate form in an alkaline solution. The time-dependent density functional theory (TD-DFT) calculations for the theoretically determined chemical structures account for the peak shift of the electronic spectra. In this manner, the structures of all the chemical species appeared in equilibrium have finally been confirmed. Copyright © 2017 Elsevier B.V. All rights reserved.

[Prevalence and determinants of exclusive breastfeeding in the city of Serrana, São Paulo, Brazil].

PubMed

Queluz, Mariângela Carletti; Pereira, Maria José Bistafa; dos Santos, Claudia Benedita; Leite, Adriana Moraes; Ricco, Rubens Garcia

2012-06-01

The objective of this cross-sectional and quantitative study was to identify the prevalence and determinants of exclusive breastfeeding among infants less than six months of age in the city of Serrana, Sao Paulo, Brazil in 2009. A validated semi-structured questionnaire was administered to the guardians of the children less than six months of age who attended the second phase of a Brazilian vaccination campaign against polio. Univariate and multivariate analysis presented in odds ratios and confidence intervals was accomplished. Of the total of 275 infant participants, only 29.8% were exclusively breastfed. Univariate analysis revealed that mothers who work outside the home without maternity leave, mothers who did not work outside the home, adolescent mothers, and the use of pacifiers have a greater chance of interrupting exclusive breastfeeding. In the multivariate analysis, mothers who work outside the home without maternity leave are three times more likely to wean their children early. Results provide suggestions for the redirection and planning of interventions targeting breastfeeding.

Multielement analysis of Canadian wines by inductively coupled plasma mass spectrometry (ICP-MS) and multivariate statistics.

PubMed

Taylor, Vivien F; Longerich, Henry P; Greenough, John D

2003-02-12

Trace element fingerprints were deciphered for wines from Canada's two major wine-producing regions, the Okanagan Valley and the Niagara Peninsula, for the purpose of examining differences in wine element composition with region of origin and identifying elements important to determining provenance. Analysis by ICP-MS allowed simultaneous determination of 34 trace elements in wine (Li, Be, Mg, Al, P, Cl, Ca, Ti, V, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Br, Rb, Sr, Mo, Ag, Cd, Sb, I, Cs, Ba, La, Ce, Tl, Pb, Bi, Th, and U) at low levels of detection, and patterns in trace element concentrations were deciphered by multivariate statistical analysis. The two regions were discriminated with 100% accuracy using 10 of these elements. Differences in soil chemistry between the Niagara and Okanagan vineyards were evident, without a good correlation between soil and wine composition. The element Sr was found to be a good indicator of provenance and has been reported in fingerprinting studies of other regions.

Blackberry wines mineral and heavy metal content determination after dry ashing: multivariate data analysis as a tool for fruit wine quality control.

PubMed

Amidžić Klarić, Daniela; Klarić, Ilija; Mornar, Ana; Velić, Darko; Velić, Natalija

2015-08-01

This study brings out the data on the content of 21 mineral and heavy metal in 15 blackberry wines made of conventionally and organically grown blackberries. The objective of this study was to classify the blackberry wine samples based on their mineral composition and the applied cultivation method of the starting raw material by using chemometric analysis. The metal content of Croatian blackberry wine samples was determined by AAS after dry ashing. The comparison between an organic and conventional group of investigated blackberry wines showed statistically significant difference in concentrations of Si and Li, where the organic group contained higher concentrations of these compounds. According to multivariate data analysis, the model based on the original metal content data set finally included seven original variables (K, Fe, Mn, Cu, Ba, Cd and Cr) and gave a satisfactory separation of two applied cultivation methods of the starting raw material.

Multi-Fault Diagnosis of Rolling Bearings via Adaptive Projection Intrinsically Transformed Multivariate Empirical Mode Decomposition and High Order Singular Value Decomposition

PubMed Central

Lv, Yong; Song, Gangbing

2018-01-01

Rolling bearings are important components in rotary machinery systems. In the field of multi-fault diagnosis of rolling bearings, the vibration signal collected from single channels tends to miss some fault characteristic information. Using multiple sensors to collect signals at different locations on the machine to obtain multivariate signal can remedy this problem. The adverse effect of a power imbalance between the various channels is inevitable, and unfavorable for multivariate signal processing. As a useful, multivariate signal processing method, Adaptive-projection has intrinsically transformed multivariate empirical mode decomposition (APIT-MEMD), and exhibits better performance than MEMD by adopting adaptive projection strategy in order to alleviate power imbalances. The filter bank properties of APIT-MEMD are also adopted to enable more accurate and stable intrinsic mode functions (IMFs), and to ease mode mixing problems in multi-fault frequency extractions. By aligning IMF sets into a third order tensor, high order singular value decomposition (HOSVD) can be employed to estimate the fault number. The fault correlation factor (FCF) analysis is used to conduct correlation analysis, in order to determine effective IMFs; the characteristic frequencies of multi-faults can then be extracted. Numerical simulations and the application of multi-fault situation can demonstrate that the proposed method is promising in multi-fault diagnoses of multivariate rolling bearing signal. PMID:29659510

Multi-Fault Diagnosis of Rolling Bearings via Adaptive Projection Intrinsically Transformed Multivariate Empirical Mode Decomposition and High Order Singular Value Decomposition.

PubMed

Yuan, Rui; Lv, Yong; Song, Gangbing

2018-04-16

Rolling bearings are important components in rotary machinery systems. In the field of multi-fault diagnosis of rolling bearings, the vibration signal collected from single channels tends to miss some fault characteristic information. Using multiple sensors to collect signals at different locations on the machine to obtain multivariate signal can remedy this problem. The adverse effect of a power imbalance between the various channels is inevitable, and unfavorable for multivariate signal processing. As a useful, multivariate signal processing method, Adaptive-projection has intrinsically transformed multivariate empirical mode decomposition (APIT-MEMD), and exhibits better performance than MEMD by adopting adaptive projection strategy in order to alleviate power imbalances. The filter bank properties of APIT-MEMD are also adopted to enable more accurate and stable intrinsic mode functions (IMFs), and to ease mode mixing problems in multi-fault frequency extractions. By aligning IMF sets into a third order tensor, high order singular value decomposition (HOSVD) can be employed to estimate the fault number. The fault correlation factor (FCF) analysis is used to conduct correlation analysis, in order to determine effective IMFs; the characteristic frequencies of multi-faults can then be extracted. Numerical simulations and the application of multi-fault situation can demonstrate that the proposed method is promising in multi-fault diagnoses of multivariate rolling bearing signal.

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.

Determining venous thromboembolic risk assessment for patients with trauma: the Trauma Embolic Scoring System.

PubMed

Rogers, Frederick B; Shackford, Steven R; Horst, Michael A; Miller, Jo Ann; Wu, Daniel; Bradburn, Eric; Rogers, Amelia; Krasne, Margaret

2012-08-01

This study aimed to determine the relative "weight" of risk factors known to be associated with venous thromboembolism (VTE) for patients with trauma based on injuries and comorbidities. A retrospective review of 16,608 consecutive admissions to a trauma center was performed. Patients were separated into those who developed VTE (n = 141) versus those who did not (16,467). Univariate analysis was performed for each risk factor reported in the trauma literature. Risk factors that were shown to be significant (p < 0.05) by univariate analysis underwent multivariate analysis to develop odds ratios for VTE. The Trauma Embolic Scoring System (TESS) was derived from the multivariate coefficients. The resulting TESS was compared with a data set from the National Trauma Data Bank (2002-2006) to determine its ability to predict VTE. The multivariate analysis demonstrated that age, Injury Severity Score, obesity, ventilator use for more than 3 days, and lower-extremity trauma were significant predictors of VTE in our patient population. The TESS was from 0 to 14, with the best prediction for those patients with a score of more than 6 (sensitivity, 81.6%; specificity, 84%). Overall, the model had excellent discrimination in predicting VTE with a receiver operating characteristic curve of 0.89. The VTE rates for TESS in the National Trauma Data Bank data set were similar for all integers except for 3 and 4, in which the VTE rates were significantly higher (3, 0.2% vs. 0.6%; 4, 0.4% vs. 1.0%). The TESS provides an objective measure of classifying VTE risk for patients with trauma. The TESS could allow informed decision making regarding prophylaxis strategies in patients with trauma.

Tracking problem solving by multivariate pattern analysis and Hidden Markov Model algorithms.

PubMed

Anderson, John R

2012-03-01

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 involves using fMRI activity to track what students are doing as they solve a sequence of algebra problems. The methodology achieves considerable accuracy at determining both what problem-solving step the students are taking and whether they are performing that step correctly. The second "model discovery" application involves using statistical model evaluation to determine how many substates are involved in performing a step of algebraic problem solving. This research indicates that different steps involve different numbers of substates and these substates are associated with different fluency in algebra problem solving. Copyright © 2011 Elsevier Ltd. All rights reserved.

A Comparative Analysis of Student Motivation in Traditional Classroom and E-Learning Courses

ERIC Educational Resources Information Center

Rovai, Alfred; Ponton, Michael; Wighting, Mervyn; Baker, Jason

2007-01-01

Multivariate analysis of variance was used to determine if there were differences in seven measures of motivation between students enrolled in 12 e-learning and 12 traditional classroom university courses (N = 353). Study results provide evidence that e-learning students possess stronger intrinsic motivation than on-campus students who attend…

Exact Interval Estimation, Power Calculation, and Sample Size Determination in Normal Correlation Analysis

ERIC Educational Resources Information Center

Shieh, Gwowen

2006-01-01

This paper considers the problem of analysis of correlation coefficients from a multivariate normal population. A unified theorem is derived for the regression model with normally distributed explanatory variables and the general results are employed to provide useful expressions for the distributions of simple, multiple, and partial-multiple…

Multivariate Genetic Analysis of Learning and Early Reading Development

ERIC Educational Resources Information Center

Byrne, Brian; Wadsworth, Sally; Boehme, Kristi; Talk, Andrew C.; Coventry, William L.; Olson, Richard K.; Samuelsson, Stefan; Corley, Robin

2013-01-01

The genetic factor structure of a range of learning measures was explored in twin children, recruited in preschool and followed to Grade 2 ("N"?=?2,084). Measures of orthographic learning and word reading were included in the analyses to determine how these patterned with the learning processes. An exploratory factor analysis of the…

Regional magnetic resonance imaging measures for multivariate analysis in Alzheimer's disease and mild cognitive impairment.

PubMed

Westman, Eric; Aguilar, Carlos; Muehlboeck, J-Sebastian; Simmons, Andrew

2013-01-01

Automated structural magnetic resonance imaging (MRI) processing pipelines are gaining popularity for Alzheimer's disease (AD) research. They generate regional volumes, cortical thickness measures and other measures, which can be used as input for multivariate analysis. It is not clear which combination of measures and normalization approach are most useful for AD classification and to predict mild cognitive impairment (MCI) conversion. The current study includes MRI scans from 699 subjects [AD, MCI and controls (CTL)] from the Alzheimer's disease Neuroimaging Initiative (ADNI). The Freesurfer pipeline was used to generate regional volume, cortical thickness, gray matter volume, surface area, mean curvature, gaussian curvature, folding index and curvature index measures. 259 variables were used for orthogonal partial least square to latent structures (OPLS) multivariate analysis. Normalisation approaches were explored and the optimal combination of measures determined. Results indicate that cortical thickness measures should not be normalized, while volumes should probably be normalized by intracranial volume (ICV). Combining regional cortical thickness measures (not normalized) with cortical and subcortical volumes (normalized with ICV) using OPLS gave a prediction accuracy of 91.5 % when distinguishing AD versus CTL. This model prospectively predicted future decline from MCI to AD with 75.9 % of converters correctly classified. Normalization strategy did not have a significant effect on the accuracies of multivariate models containing multiple MRI measures for this large dataset. The appropriate choice of input for multivariate analysis in AD and MCI is of great importance. The results support the use of un-normalised cortical thickness measures and volumes normalised by ICV.

Estimation of failure criteria in multivariate sensory shelf life testing using survival analysis.

PubMed

Giménez, Ana; Gagliardi, Andrés; Ares, Gastón

2017-09-01

For most food products, shelf life is determined by changes in their sensory characteristics. A predetermined increase or decrease in the intensity of a sensory characteristic has frequently been used to signal that a product has reached the end of its shelf life. Considering all attributes change simultaneously, the concept of multivariate shelf life allows a single measurement of deterioration that takes into account all these sensory changes at a certain storage time. The aim of the present work was to apply survival analysis to estimate failure criteria in multivariate sensory shelf life testing using two case studies, hamburger buns and orange juice, by modelling the relationship between consumers' rejection of the product and the deterioration index estimated using PCA. In both studies, a panel of 13 trained assessors evaluated the samples using descriptive analysis whereas a panel of 100 consumers answered a "yes" or "no" question regarding intention to buy or consume the product. PC1 explained the great majority of the variance, indicating all sensory characteristics evolved similarly with storage time. Thus, PC1 could be regarded as index of sensory deterioration and a single failure criterion could be estimated through survival analysis for 25 and 50% consumers' rejection. The proposed approach based on multivariate shelf life testing may increase the accuracy of shelf life estimations. Copyright © 2017 Elsevier Ltd. All rights reserved.

Reexamining Sample Size Requirements for Multivariate, Abundance-Based Community Research: When Resources are Limited, the Research Does Not Have to Be.

PubMed

Forcino, Frank L; Leighton, Lindsey R; Twerdy, Pamela; Cahill, James F

2015-01-01

Community ecologists commonly perform multivariate techniques (e.g., ordination, cluster analysis) to assess patterns and gradients of taxonomic variation. A critical requirement for a meaningful statistical analysis is accurate information on the taxa found within an ecological sample. However, oversampling (too many individuals counted per sample) also comes at a cost, particularly for ecological systems in which identification and quantification is substantially more resource consuming than the field expedition itself. In such systems, an increasingly larger sample size will eventually result in diminishing returns in improving any pattern or gradient revealed by the data, but will also lead to continually increasing costs. Here, we examine 396 datasets: 44 previously published and 352 created datasets. Using meta-analytic and simulation-based approaches, the research within the present paper seeks (1) to determine minimal sample sizes required to produce robust multivariate statistical results when conducting abundance-based, community ecology research. Furthermore, we seek (2) to determine the dataset parameters (i.e., evenness, number of taxa, number of samples) that require larger sample sizes, regardless of resource availability. We found that in the 44 previously published and the 220 created datasets with randomly chosen abundances, a conservative estimate of a sample size of 58 produced the same multivariate results as all larger sample sizes. However, this minimal number varies as a function of evenness, where increased evenness resulted in increased minimal sample sizes. Sample sizes as small as 58 individuals are sufficient for a broad range of multivariate abundance-based research. In cases when resource availability is the limiting factor for conducting a project (e.g., small university, time to conduct the research project), statistically viable results can still be obtained with less of an investment.

Determinants of Budget Allocations to Academic Departments: A Case Study. ASHE 1987 Annual Meeting Paper.

ERIC Educational Resources Information Center

Winans, Glen T.

General fund budgetary determinants in 27 academic departments at the University of California Santa Barbara were studied for the period from 1977/78 through 1983/84. The focus was resource allocation and utilization within departments of the College of Letters and Science. The research design included a pooled multivariate regression analysis of…

Comparisons of Self-Determination among Students with Autism, Intellectual Disability, and Learning Disabilities: A Multivariate Analysis

ERIC Educational Resources Information Center

Chou, Yu-Chi; Wehmeyer, Michael L.; Palmer, Susan B.; Lee, Jaehoon

2017-01-01

This study examined differences in self-determination among students with autism spectrum disorders (ASD), students with intellectual disability (ID), and students with learning disabilities (LD). A total of 222 participants with an equal size group for each of the three disability categories were selected to participate in the comparison of total…

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

Determinants of children's use of and time spent in fast-food and full-service restaurants.

PubMed

McIntosh, Alex; Kubena, Karen S; Tolle, Glen; Dean, Wesley; Kim, Mi-Jeong; Jan, Jie-Sheng; Anding, Jenna

2011-01-01

Identify parental and children's determinants of children's use of and time spent in fast-food (FF) and full-service (FS) restaurants. Analysis of cross-sectional data. Parents were interviewed by phone; children were interviewed in their homes. Parents and children ages 9-11 or 13-15 from 312 families were obtained via random-digit dialing. Dependent variables were the use of and the time spent in FF and FS restaurants by children. Determinants included parental work schedules, parenting style, and family meal ritual perceptions. Logistic regression was used for multivariate analysis of use of restaurants. Least squares regression was used for multivariate analysis of time spent in restaurants. Significance set at P < .05. Factors related to use of and time spent in FF and FS restaurants included parental work schedules, fathers' use of such restaurants, and children's time spent in the family automobile. Parenting style, parental work, parental eating habits and perceptions of family meals, and children's other uses of their time influence children's use of and time spent in FF and FS restaurants. Copyright © 2011 Society for Nutrition Education. Published by Elsevier Inc. All rights reserved.

Ultraviolet spectroscopy combined with ultra-fast liquid chromatography and multivariate statistical analysis for quality assessment of wild Wolfiporia extensa from different geographical origins.

PubMed

Li, Yan; Zhang, Ji; Jin, Hang; Liu, Honggao; Wang, Yuanzhong

2016-08-05

A quality assessment system comprised of a tandem technique of ultraviolet (UV) spectroscopy and ultra-fast liquid chromatography (UFLC) aided by multivariate analysis was presented for the determination of geographic origin of Wolfiporia extensa collected from five regions in Yunnan Province of China. Characteristic UV spectroscopic fingerprints of samples were determined based on its methanol extract. UFLC was applied for the determination of pachymic acid (a biomarker) presented in individual test samples. The spectrum data matrix and the content of pachymic acid were integrated and analyzed by partial least squares discriminant analysis (PLS-DA) and hierarchical cluster analysis (HCA). The results showed that chemical properties of samples were clearly dominated by the epidermis and inner part as well as geographical origins. The relationships among samples obtained from these five regions have been also presented. Moreover, an interesting finding implied that geographical origins had much greater influence on the chemical properties of epidermis compared with that of the inner part. This study demonstrated that a rapid tool for accurate discrimination of W. extensa by UV spectroscopy and UFLC could be available for quality control of complicated medicinal mushrooms. Copyright © 2016 Elsevier B.V. All rights reserved.

Determinants of elevated healthcare utilization in patients with COPD.

PubMed

Simon-Tuval, Tzahit; Scharf, Steven M; Maimon, Nimrod; Bernhard-Scharf, Barbara J; Reuveni, Haim; Tarasiuk, Ariel

2011-01-13

Chronic obstructive pulmonary disease (COPD) imparts a substantial economic burden on western health systems. Our objective was to analyze the determinants of elevated healthcare utilization among patients with COPD in a single-payer health system. Three-hundred eighty-nine adults with COPD were matched 1:3 to controls by age, gender and area of residency. Total healthcare cost 5 years prior recruitment and presence of comorbidities were obtained from a computerized database. Health related quality of life (HRQoL) indices were obtained using validated questionnaires among a subsample of 177 patients. Healthcare utilization was 3.4-fold higher among COPD patients compared with controls (p < 0.001). The "most-costly" upper 25% of COPD patients (n = 98) consumed 63% of all costs. Multivariate analysis revealed that independent determinants of being in the "most costly" group were (OR; 95% CI): age-adjusted Charlson Comorbidity Index (1.09; 1.01-1.2), history of: myocardial infarct (2.87; 1.5-5.5), congestive heart failure (3.52; 1.9-6.4), mild liver disease (3.83; 1.3-11.2) and diabetes (2.02; 1.1-3.6). Bivariate analysis revealed that cost increased as HRQoL declined and severity of airflow obstruction increased but these were not independent determinants in a multivariate analysis. Comorbidity burden determines elevated utilization for COPD patients. Decision makers should prioritize scarce health care resources to a better care management of the "most costly" patients.

Assessing Principal Component Regression Prediction of Neurochemicals Detected with Fast-Scan Cyclic Voltammetry

PubMed Central

2011-01-01

Principal component regression is a multivariate data analysis approach routinely used to predict neurochemical concentrations from in vivo fast-scan cyclic voltammetry measurements. This mathematical procedure can rapidly be employed with present day computer programming languages. Here, we evaluate several methods that can be used to evaluate and improve multivariate concentration determination. The cyclic voltammetric representation of the calculated regression vector is shown to be a valuable tool in determining whether the calculated multivariate model is chemically appropriate. The use of Cook’s distance successfully identified outliers contained within in vivo fast-scan cyclic voltammetry training sets. This work also presents the first direct interpretation of a residual color plot and demonstrated the effect of peak shifts on predicted dopamine concentrations. Finally, separate analyses of smaller increments of a single continuous measurement could not be concatenated without substantial error in the predicted neurochemical concentrations due to electrode drift. Taken together, these tools allow for the construction of more robust multivariate calibration models and provide the first approach to assess the predictive ability of a procedure that is inherently impossible to validate because of the lack of in vivo standards. PMID:21966586

Assessing principal component regression prediction of neurochemicals detected with fast-scan cyclic voltammetry.

PubMed

Keithley, Richard B; Wightman, R Mark

2011-06-07

Principal component regression is a multivariate data analysis approach routinely used to predict neurochemical concentrations from in vivo fast-scan cyclic voltammetry measurements. This mathematical procedure can rapidly be employed with present day computer programming languages. Here, we evaluate several methods that can be used to evaluate and improve multivariate concentration determination. The cyclic voltammetric representation of the calculated regression vector is shown to be a valuable tool in determining whether the calculated multivariate model is chemically appropriate. The use of Cook's distance successfully identified outliers contained within in vivo fast-scan cyclic voltammetry training sets. This work also presents the first direct interpretation of a residual color plot and demonstrated the effect of peak shifts on predicted dopamine concentrations. Finally, separate analyses of smaller increments of a single continuous measurement could not be concatenated without substantial error in the predicted neurochemical concentrations due to electrode drift. Taken together, these tools allow for the construction of more robust multivariate calibration models and provide the first approach to assess the predictive ability of a procedure that is inherently impossible to validate because of the lack of in vivo standards.

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.

Are Canadian Adolescents Happy? A Gender-Based Analysis of a Nationally Representative Survey

ERIC Educational Resources Information Center

Weaver, Robert D.; Habibov, Nazim N.

2010-01-01

In this study, the authors analyzed data from a nationally representative survey of youth to study happiness amongst Canadian adolescents aged 12-17. Testing for differences in the level of happiness between female and male adolescents was conducted. Following this, multivariate analysis was employed to determine which factors were associated with…

Differentiation of benign and malignant ampullary obstruction by multi-row detector CT.

PubMed

Angthong, Wirana; Jiarakoop, Kran; Tangtiang, Kaan

2018-05-21

To determine useful CT parameters to differentiate ampullary carcinomas from benign ampullary obstruction. This study included 93 patients who underwent abdominal CT, 31 patients with ampullary carcinomas, and 62 patients with benign ampullary obstruction. Two radiologists independently evaluated CT parameters then reached consensus decisions. Statistically significant CT parameters were identified through univariate and multivariate analyses. In univariate analysis, the presence of ampullary mass, asymmetric, abrupt narrowing of distal common bile duct (CBD), dilated intrahepatic bile duct (IHD), dilated pancreatic duct (PD), peripancreatic lymphadenopathy, duodenal wall thickening, and delayed enhancement were more frequently in ampullary carcinomas observed (P < 0.05). Multivariate logistic regression analysis using significant CT parameters and clinical data from univariate analysis, and clinical symptom with jaundice (P = 0.005) was an independent predictor of ampullary carcinomas. For multivariate analysis using only significant CT parameters, abrupt narrowing of distal CBD was an independent predictor of ampullary carcinomas (P = 0.019). Among various CT criteria, abrupt narrowing of distal CBD and dilated IHD had highest sensitivity (77.4%) and highest accuracy (90.3%). The abrupt narrowing of distal CBD and dilated IHD is useful for differentiation of ampullary carcinomas from benign entity in patients without the presence of mass.

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.

p Ka determinations of xanthene derivates in aqueous solutions by multivariate analysis applied to UV-Vis spectrophotometric data

NASA Astrophysics Data System (ADS)

Batistela, Vagner Roberto; Pellosi, Diogo Silva; de Souza, Franciane Dutra; da Costa, Willian Ferreira; de Oliveira Santin, Silvana Maria; de Souza, Vagner Roberto; Caetano, Wilker; de Oliveira, Hueder Paulo Moisés; Scarminio, Ieda Spacino; Hioka, Noboru

2011-09-01

Xanthenes form to an important class of dyes which are widely used. Most of them present three acid-base groups: two phenolic sites and one carboxylic site. Therefore, the p Ka determination and the attribution of each group to the corresponding p Ka value is a very important feature. Attempts to obtain reliable p Ka through the potentiometry titration and the electronic absorption spectrophotometry using the first and second orders derivative failed. Due to the close p Ka values allied to strong UV-Vis spectral overlap, multivariate analysis, a powerful chemometric method, is applied in this work. The determination was performed for eosin Y, erythrosin B, and bengal rose B, and also for other synthesized derivatives such as 2-(3,6-dihydroxy-9-acridinyl) benzoic acid, 2,4,5,7-tetranitrofluorescein, eosin methyl ester, and erythrosin methyl ester in water. These last two compounds (esters) permitted to attribute the p Ka of the phenolic group, which is not easily recognizable for some investigated dyes. Besides the p Ka determination, the chemometry allowed for estimating the electronic spectrum of some prevalent protolytic species and the substituents effects evaluation.

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

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.

Classification of adulterated honeys by multivariate analysis.

PubMed

Amiry, Saber; Esmaiili, Mohsen; Alizadeh, Mohammad

2017-06-01

In this research, honey samples were adulterated with date syrup (DS) and invert sugar syrup (IS) at three concentrations (7%, 15% and 30%). 102 adulterated samples were prepared in six batches with 17 replications for each batch. For each sample, 32 parameters including color indices, rheological, physical, and chemical parameters were determined. To classify the samples, based on type and concentrations of adulterant, a multivariate analysis was applied using principal component analysis (PCA) followed by a linear discriminant analysis (LDA). Then, 21 principal components (PCs) were selected in five sets. Approximately two-thirds were identified correctly using color indices (62.75%) or rheological properties (67.65%). A power discrimination was obtained using physical properties (97.06%), and the best separations were achieved using two sets of chemical properties (set 1: lactone, diastase activity, sucrose - 100%) (set 2: free acidity, HMF, ash - 95%). Copyright © 2016 Elsevier Ltd. All rights reserved.

Systematic wavelength selection for improved multivariate spectral analysis

DOEpatents

Thomas, Edward V.; Robinson, Mark R.; Haaland, David M.

1995-01-01

Methods and apparatus for determining in a biological material one or more unknown values of at least one known characteristic (e.g. the concentration of an analyte such as glucose in blood or the concentration of one or more blood gas parameters) with a model based on a set of samples with known values of the known characteristics and a multivariate algorithm using several wavelength subsets. The method includes selecting multiple wavelength subsets, from the electromagnetic spectral region appropriate for determining the known characteristic, for use by an algorithm wherein the selection of wavelength subsets improves the model's fitness of the determination for the unknown values of the known characteristic. The selection process utilizes multivariate search methods that select both predictive and synergistic wavelengths within the range of wavelengths utilized. The fitness of the wavelength subsets is determined by the fitness function F=.function.(cost, performance). The method includes the steps of: (1) using one or more applications of a genetic algorithm to produce one or more count spectra, with multiple count spectra then combined to produce a combined count spectrum; (2) smoothing the count spectrum; (3) selecting a threshold count from a count spectrum to select these wavelength subsets which optimize the fitness function; and (4) eliminating a portion of the selected wavelength subsets. The determination of the unknown values can be made: (1) noninvasively and in vivo; (2) invasively and in vivo; or (3) in vitro.

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

Are prostatic calculi independent predictive factors of lower urinary tract symptoms?

PubMed

Park, Sung-Woo; Nam, Jong-Kil; Lee, Sang-Don; Chung, Moon-Kee

2010-03-01

We determined the correlation between prostatic calculi and lower urinary tract symptoms (LUTS), as well as the predisposing factors of prostatic calculi. Of the 1 527 patients who presented at our clinic for LUTS, 802 underwent complete evaluations, including transrectal ultrasonography, voided bladder-3 specimen and international prostatic symptoms score (IPSS). A total of 335 patients with prostatic calculi and 467 patients without prostatic calculi were divided into calculi and no calculi groups, respectively. Predictive factors of severe LUTS and prostatic calculi were determined using uni/multivariate analysis. The overall IPSS score was 15.7 +/- 9.2 and 14.1 +/- 9.2 in the calculi and no calculi group, respectively (P = 0.013). The maximum flow rate was 12.1 +/- 6.9 and 14.2 +/- 8.2 mL s(-1) in the calculi and no calculi group, respectively (P = 0.003). On univariate analysis for predicting factors of severe LUTS, differences on age (P = 0.042), prostatic calculi (P = 0.048) and prostatitis (P = 0.018) were statistically significant. However, on multivariate analysis, no factor was significant. On multivariate analysis for predisposing factors of prostatic calculi, differences on age (P < 0.001) and prostate volume (P = 0.001) were significant. To our knowledge, patients who have prostatic calculi complain of more severe LUTS. However, prostatic calculi are not an independent predictive factor of severe LUTS. Therefore, men with prostatic calculi have more severe LUTS not only because of prostatic calculi but also because of age and other factors. In addition, old age and large prostate volume are independent predisposing factors for prostatic calculi.

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.

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.

Pretreatment health behaviors predict survival among patients with head and neck squamous cell carcinoma.

PubMed

Duffy, Sonia A; Ronis, David L; McLean, Scott; Fowler, Karen E; Gruber, Stephen B; Wolf, Gregory T; Terrell, Jeffrey E

2009-04-20

Our prior work has shown that the health behaviors of head and neck cancer patients are interrelated and are associated with quality of life; however, other than smoking, the relationship between health behaviors and survival is unclear. A prospective cohort study was conducted to determine the relationship between five pretreatment health behaviors (smoking, alcohol, diet, physical activity, and sleep) and all-cause survival among 504 head and neck cancer patients. Smoking status was the strongest predictor of survival, with both current smokers (hazard ratio [HR] = 2.4; 95% CI, 1.3 to 4.4) and former smokers (HR = 2.0; 95% CI, 1.2 to 3.5) showing significant associations with poor survival. Problem drinking was associated with survival in the univariate analysis (HR = 1.4; 95% CI, 1.0 to 2.0) but lost significance when controlling for other factors. Low fruit intake was negatively associated with survival in the univariate analysis only (HR = 1.6; 95% CI, 1.1 to 2.1), whereas vegetable intake was not significant in either univariate or multivariate analyses. Although physical activity was associated with survival in the univariate analysis (HR = 0.95; 95% CI, 0.93 to 0.97), it was not significant in the multivariate model. Sleep was not significantly associated with survival in either univariate or multivariate analysis. Control variables that were also independently associated with survival in the multivariate analysis were age, education, tumor site, cancer stage, and surgical treatment. Variation in selected pretreatment health behaviors (eg, smoking, fruit intake, and physical activity) in this population is associated with variation in survival.

Regression analysis for LED color detection of visual-MIMO system

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

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

PubMed Central

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

2014-01-01

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

A standards-based method for compositional analysis by energy dispersive X-ray spectrometry using multivariate statistical analysis: application to multicomponent alloys.

PubMed

Rathi, Monika; Ahrenkiel, S P; Carapella, J J; Wanlass, M W

2013-02-01

Given an unknown multicomponent alloy, and a set of standard compounds or alloys of known composition, can one improve upon popular standards-based methods for energy dispersive X-ray (EDX) spectrometry to quantify the elemental composition of the unknown specimen? A method is presented here for determining elemental composition of alloys using transmission electron microscopy-based EDX with appropriate standards. The method begins with a discrete set of related reference standards of known composition, applies multivariate statistical analysis to those spectra, and evaluates the compositions with a linear matrix algebra method to relate the spectra to elemental composition. By using associated standards, only limited assumptions about the physical origins of the EDX spectra are needed. Spectral absorption corrections can be performed by providing an estimate of the foil thickness of one or more reference standards. The technique was applied to III-V multicomponent alloy thin films: composition and foil thickness were determined for various III-V alloys. The results were then validated by comparing with X-ray diffraction and photoluminescence analysis, demonstrating accuracy of approximately 1% in atomic fraction.

Multivariate analysis to determine the factors affecting the attitudes toward organ donation of healthcare assistants in Spanish and Mexican healthcare centers.

PubMed

Ríos, A; López-Navas, A; Ayala-García, M A; Sebastián, M; Febrero, B; Ramírez, E J; Muñoz, G; Palacios, G; Rodríguez, J S; Martínez, M A; Nieto, A; Martínez-Alarcón, L; Ramis, G; Ramírez, P; Parrilla, P

2012-01-01

Healthcare assistants are an important group of workers who can influence public opinion. Their attitudes toward organ donation may influence public awareness of healthcare matters; negative attitudes toward donation and transplantation could have a negative impact on public attitudes. Our objective was analyze the attitudes of healthcare assistants, in Spanish and Mexican healthcare centers toward organ donation and determine factors affecting them using a multivariate analysis. As part of the "International Collaborative Donor Project," 32 primary care centers and 4 hospitals were selected in Spain and 5 hospitals in Mexico. A randomized sample of healthcare assistants was stratified according to healthcare services. Attitudes were evaluated using a validated questionnaire of the psychosocial aspects of donation, which was self-completed anonymously by the respondent. Statistical analysis used the chi-square test, Student t test, and logistic regression analysis. Of 532 respondents, 66% in favored donation and 34% were against it or undecided. Upon multivariate analysis, the following variables had the most weight: 1) country of origin (Mexicans were more in favor than Spanish; odds ratio [OR]) = 1.964; P = .014); 2) a partner with a favorable attitude (OR = 2.597; P = .013); 3) not being concerned about possible bodily mutilation after donation (OR = 2.631; P = .006); 4) preference for options apart from burial for handling the body after death (OR = 4.694; P < .001) and 5) accepting an autopsy if one was needed (OR = 3.584; P < .001). The attitudes of healthcare assistants toward organ donation varied considerably according to the respondent's country of origin. The psycho-social profile of a person with a positive attitude to donation was similar to that described within the general public. Copyright © 2012 Elsevier Inc. All rights reserved.

Classification of Ilex species based on metabolomic fingerprinting using nuclear magnetic resonance and multivariate data analysis.

PubMed

Choi, Young Hae; Sertic, Sarah; Kim, Hye Kyong; Wilson, Erica G; Michopoulos, Filippos; Lefeber, Alfons W M; Erkelens, Cornelis; Prat Kricun, Sergio D; Verpoorte, Robert

2005-02-23

The metabolomic analysis of 11 Ilex species, I. argentina, I. brasiliensis, I. brevicuspis, I. dumosavar. dumosa, I. dumosa var. guaranina, I. integerrima, I. microdonta, I. paraguariensis var. paraguariensis, I. pseudobuxus, I. taubertiana, and I. theezans, was carried out by NMR spectroscopy and multivariate data analysis. The analysis using principal component analysis and classification of the (1)H NMR spectra showed a clear discrimination of those samples based on the metabolites present in the organic and aqueous fractions. The major metabolites that contribute to the discrimination are arbutin, caffeine, phenylpropanoids, and theobromine. Among those metabolites, arbutin, which has not been reported yet as a constituent of Ilex species, was found to be a biomarker for I. argentina,I. brasiliensis, I. brevicuspis, I. integerrima, I. microdonta, I. pseudobuxus, I. taubertiana, and I. theezans. This reliable method based on the determination of a large number of metabolites makes the chemotaxonomical analysis of Ilex species possible.

Rapid determination of chemical composition and classification of bamboo fractions using visible-near infrared spectroscopy coupled with multivariate data analysis.

PubMed

Yang, Zhong; Li, Kang; Zhang, Maomao; Xin, Donglin; Zhang, Junhua

2016-01-01

During conversion of bamboo into biofuels and chemicals, it is necessary to efficiently predict the chemical composition and digestibility of biomass. However, traditional methods for determination of lignocellulosic biomass composition are expensive and time consuming. In this work, a novel and fast method for quantitative and qualitative analysis of chemical composition and enzymatic digestibilities of juvenile bamboo and mature bamboo fractions (bamboo green, bamboo timber, bamboo yellow, bamboo node, and bamboo branch) using visible-near infrared spectra was evaluated. The developed partial least squares models yielded coefficients of determination in calibration of 0.88, 0.94, and 0.96, for cellulose, xylan, and lignin of bamboo fractions in raw spectra, respectively. After visible-near infrared spectra being pretreated, the corresponding coefficients of determination in calibration yielded by the developed partial least squares models are 0.994, 0.990, and 0.996, respectively. The score plots of principal component analysis of mature bamboo, juvenile bamboo, and different fractions of mature bamboo were obviously distinguished in raw spectra. Based on partial least squares discriminant analysis, the classification accuracies of mature bamboo, juvenile bamboo, and different fractions of bamboo (bamboo green, bamboo timber, bamboo yellow, and bamboo branch) all reached 100 %. In addition, high accuracies of evaluation of the enzymatic digestibilities of bamboo fractions after pretreatment with aqueous ammonia were also observed. The results showed the potential of visible-near infrared spectroscopy in combination with multivariate analysis in efficiently analyzing the chemical composition and hydrolysabilities of lignocellulosic biomass, such as bamboo fractions.

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.

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.

Jaundice: an important, poorly recognized risk factor for diminished survival in patients with adenocarcinoma of the head of the pancreas

PubMed Central

Strasberg, Steven M; Gao, Feng; Sanford, Dominic; Linehan, David C; Hawkins, William G; Fields, Ryan; Carpenter, Danielle H; Brunt, Elizabeth M; Phillips, Carolyn

2014-01-01

Objectives: Jaundice impairs cellular immunity, an important defence against the dissemination of cancer. Jaundice is a common mode of presentation in pancreatic head adenocarcinoma. The purpose of this study was to determine whether there is an association between preoperative jaundice and survival in patients who have undergone resection of such tumours. Methods: Thirty possible survival risk factors were evaluated in a database of over 400 resected patients. Univariate analysis was used to determine odds ratio for death. All factors for which a P-value of <0.30 was obtained were entered into a multivariate analysis using the Cox model with backward selection. Results: Preoperative jaundice, age, positive node status, poor differentiation and lymphatic invasion were significant indicators of poor outcome in multivariate analysis. Absence of jaundice was a highly favourable prognostic factor. Interaction emerged between jaundice and nodal status. The benefit conferred by the absence of jaundice was restricted to patients in whom negative node status was present. Five-year overall survival in this group was 66%. Jaundiced patients who underwent preoperative stenting had a survival advantage. Conclusions: Preoperative jaundice is a negative risk factor in adenocarcinoma of the pancreas. Additional studies are required to determine the exact mechanism for this effect. PMID:23600768

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

PubMed

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

2014-11-01

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

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…

Predictors of obesity in Michigan Operating Engineers.

PubMed

Duffy, Sonia A; Cohen, Kathleen A; Choi, Seung Hee; McCullagh, Marjorie C; Noonan, Devon

2012-06-01

Blue collar workers are at risk for obesity. Little is known about obesity in Operating Engineers, a group of blue collar workers, who operate heavy earth-moving equipment in road building and construction. Therefore, 498 Operating Engineers in Michigan were recruited to participate in a cross-sectional survey to determine variables related to obesity in this group. Bivariate and multivariate analyses were conducted to determine personal, psychological, and behavioral factors predicting obesity. Approximately 45% of the Operating Engineers screened positive for obesity, and another 40% were overweight. Multivariate analysis revealed that younger age, male sex, higher numbers of self-reported co-morbidities, not smoking, and low physical activity levels were significantly associated with obesity among Operating Engineers. Operating Engineers are significantly at risk for obesity, and workplace interventions are needed to address this problem.

A Combined Approach of Infrared Spectroscopy and Multivariate Analysis for the Simultaneous Determination of Sugars and Fructans in Strawberry Juices During Storage.

PubMed

Cassani, Lucía; Santos, Mauricio; Gerbino, Esteban; Del Rosario Moreira, María; Gómez-Zavaglia, Andrea

2018-03-01

In this work, a Fourier transform mid-infrared spectroscopy (FTIR)-based method was developed for simultaneously quantifying simple sugars and exogenously added fructooligosaccharides (FOS) in strawberry juices preserved for up to 14 d using nonthermal techniques (geraniol and vanillin+ultrasound). The main spectral differences were observed in the 1200 to 900 cm -1 region. The presence of FOS was identified by the typical bands at 1134, 1034, and 935 cm -1 . During storage, a significant decrease of sucrose was concomitant to an increase of glucose and fructose in juices stored without any previous preservation treatment, as determined by high-performance liquid chromatography (HPLC). A principal component analysis was performed on the FTIR spectra corresponding to the different treatments. The groups observed explained more than 94% of the variance and were related to changes in the carbohydrate composition during storage. Then, different partial least square models (PLS) were defined to determine the concentrations of glucose, sucrose, fructose, and those of exogenously added FOS with degrees of polymerization within 3 and 5. The carbohydrates' concentrations determined by HPLC were used as reference method. The models were validated with independent sets of data. The mean of predicted values fitted nicely those obtained by HPLC (correlation and R 2  > 0.97), thus supporting the use of the PLS models to monitor the quality of strawberry juices in unknown samples. In conclusion, FTIR spectroscopy appears as an adequate analytical tool to quick assess whether juice formulations meet specifications in terms of authenticity, contamination and/or deterioration. FTIR spectroscopy provided a method potentially transferable to the food industry when associated with the multivariate analysis. The robust 21 PLS models defined in this work provided reliable tools for the rapid monitoring of juices' authenticity and/or deterioration. In this regard, FTIR associated to multivariate analysis enabled the determination of different sugars in a single measurement without the need of pure sugars as standards. This experimental simplicity supports the use of FTIR at the production line, and also contributes to save time in determining carbohydrates' composition and stability, in an environmentally friendly way. © 2017 Institute of Food Technologists®.

Stability indicating methods for the analysis of cefprozil in the presence of its alkaline induced degradation product

NASA Astrophysics Data System (ADS)

Attia, Khalid A. M.; Nassar, Mohammed W. I.; El-Zeiny, Mohamed B.; Serag, Ahmed

2016-04-01

Three simple, specific, accurate and precise spectrophotometric methods were developed for the determination of cefprozil (CZ) in the presence of its alkaline induced degradation product (DCZ). The first method was the bivariate method, while the two other multivariate methods were partial least squares (PLS) and spectral residual augmented classical least squares (SRACLS). The multivariate methods were applied with and without variable selection procedure (genetic algorithm GA). These methods were tested by analyzing laboratory prepared mixtures of the above drug with its alkaline induced degradation product and they were applied to its commercial pharmaceutical products.

TU-FG-201-05: Varian MPC as a Statistical Process Control Tool

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

Carver, A; Rowbottom, C

Purpose: Quality assurance in radiotherapy requires the measurement of various machine parameters to ensure they remain within permitted values over time. In Truebeam release 2.0 the Machine Performance Check (MPC) was released allowing beam output and machine axis movements to be assessed in a single test. We aim to evaluate the Varian Machine Performance Check (MPC) as a tool for Statistical Process Control (SPC). Methods: Varian’s MPC tool was used on three Truebeam and one EDGE linac for a period of approximately one year. MPC was commissioned against independent systems. After this period the data were reviewed to determine whethermore » or not the MPC was useful as a process control tool. Analyses on individual tests were analysed using Shewhart control plots, using Matlab for analysis. Principal component analysis was used to determine if a multivariate model was of any benefit in analysing the data. Results: Control charts were found to be useful to detect beam output changes, worn T-nuts and jaw calibration issues. Upper and lower control limits were defined at the 95% level. Multivariate SPC was performed using Principal Component Analysis. We found little evidence of clustering beyond that which might be naively expected such as beam uniformity and beam output. Whilst this makes multivariate analysis of little use it suggests that each test is giving independent information. Conclusion: The variety of independent parameters tested in MPC makes it a sensitive tool for routine machine QA. We have determined that using control charts in our QA programme would rapidly detect changes in machine performance. The use of control charts allows large quantities of tests to be performed on all linacs without visual inspection of all results. The use of control limits alerts users when data are inconsistent with previous measurements before they become out of specification. A. Carver has received a speaker’s honorarium from Varian.« less

Combining microwave resonance technology to multivariate data analysis as a novel PAT tool to improve process understanding in fluid bed granulation.

PubMed

Lourenço, Vera; Herdling, Thorsten; Reich, Gabriele; Menezes, José C; Lochmann, Dirk

2011-08-01

A set of 192 fluid bed granulation batches at industrial scale were in-line monitored using microwave resonance technology (MRT) to determine moisture, temperature and density of the granules. Multivariate data analysis techniques such as multiway partial least squares (PLS), multiway principal component analysis (PCA) and multivariate batch control charts were applied onto collected batch data sets. The combination of all these techniques, along with off-line particle size measurements, led to significantly increased process understanding. A seasonality effect could be put into evidence that impacted further processing through its influence on the final granule size. Moreover, it was demonstrated by means of a PLS that a relation between the particle size and the MRT measurements can be quantitatively defined, highlighting a potential ability of the MRT sensor to predict information about the final granule size. This study has contributed to improve a fluid bed granulation process, and the process knowledge obtained shows that the product quality can be built in process design, following Quality by Design (QbD) and Process Analytical Technology (PAT) principles. Copyright © 2011. Published by Elsevier B.V.

Chromatography methods and chemometrics for determination of milk fat adulterants

NASA Astrophysics Data System (ADS)

Trbović, D.; Petronijević, R.; Đorđević, V.

2017-09-01

Milk and milk-based products are among the leading food categories according to reported cases of food adulteration. Although many authentication problems exist in all areas of the food industry, adequate control methods are required to evaluate the authenticity of milk and milk products in the dairy industry. Moreover, gas chromatography (GC) analysis of triacylglycerols (TAGs) or fatty acid (FA) profiles of milk fat (MF) in combination with multivariate statistical data processing have been used to detect adulterations of milk and dairy products with foreign fats. The adulteration of milk and butter is a major issue for the dairy industry. The major adulterants of MF are vegetable oils (soybean, sunflower, groundnut, coconut, palm and peanut oil) and animal fat (cow tallow and pork lard). Multivariate analysis enables adulterated MF to be distinguished from authentic MF, while taking into account many analytical factors. Various multivariate analysis methods have been proposed to quantitatively detect levels of adulterant non-MFs, with multiple linear regression (MLR) seemingly the most suitable. There is a need for increased use of chemometric data analyses to detect adulterated MF in foods and for their expanded use in routine quality assurance testing.

United States Marine Corps Basic Reconnaissance Course: Predictors of Success

DTIC Science & Technology

2017-03-01

PAGE INTENTIONALLY LEFT BLANK 81 VI. CONCLUSIONS AND RECOMMENDATIONS A. CONCLUSIONS The objective of my research is to provide quantitative ...percent over the last three years, illustrating there is room for improvement. This study conducts a quantitative and qualitative analysis of the...criteria used to select candidates for the BRC. The research uses multi-variate logistic regression models and survival analysis to determine to what

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

Laser ablation molecular isotopic spectroscopy (LAMIS) towards the determination of multivariate LODs via PLS calibration model of 10B and 11B Boric acid mixtures

NASA Astrophysics Data System (ADS)

Harris, C. D.; Profeta, Luisa T. M.; Akpovo, Codjo A.; Johnson, Lewis; Stowe, Ashley C.

2017-05-01

A calibration model was created to illustrate the detection capabilities of laser ablation molecular isotopic spectroscopy (LAMIS) discrimination in isotopic analysis. The sample set contained boric acid pellets that varied in isotopic concentrations of 10B and 11B. Each sample set was interrogated with a Q-switched Nd:YAG ablation laser operating at 532 nm. A minimum of four band heads of the β system B2∑ -> Χ2∑transitions were identified and verified with previous literature on BO molecular emission lines. Isotopic shifts were observed in the spectra for each transition and used as the predictors in the calibration model. The spectra along with their respective 10/11B isotopic ratios were analyzed using Partial Least Squares Regression (PLSR). An IUPAC novel approach for determining a multivariate Limit of Detection (LOD) interval was used to predict the detection of the desired isotopic ratios. The predicted multivariate LOD is dependent on the variation of the instrumental signal and other composites in the calibration model space.

The result of adjuvant chemotherapy for localized pT3 upper urinary tract carcinoma in a multi-institutional study.

PubMed

Kawashima, Atsunari; Nakai, Yasutomo; Nakayama, Masashi; Ujike, Takeshi; Tanigawa, Go; Ono, Yutaka; Kamoto, Akihito; Takada, Tsuyosi; Yamaguchi, Yuichiro; Takayama, Hitoshi; Nishimura, Kazuo; Nonomura, Norio; Tsujimura, Akira

2012-10-01

To determine through the analysis of our multi-institutional database whether postoperative adjuvant chemotherapy for upper urinary tract carcinoma with localized invasive upper urinary tract carcinoma (UUTC) is beneficial. A study population of 93 patients with pT3N0/xM0 UUTC was eligible for this study. Clinical features evaluated were sex, tumor location, adjuvant chemotherapy status, tumor pathology (histology, grade, infiltrating growth, lymphovascular invasion (LVI)), and cause of death. Cancer-specific survival (CSS) was estimated by Kaplan-Meier method. Prognostic factors related to CSS were analyzed by Cox proportional hazards regression model for multivariate analysis. In pT3 patients, overall 5-year CSS rate was 68.4% and median CSS time was 31 months (range 3-114 months). In the adjuvant chemotherapy group, 5-year CSS rate was 80.8%, whereas 5-year CSS rate was 64.4% in the non-adjuvant chemotherapy group. By multivariate analysis, adjuvant chemotherapy status was significantly associated with CSS (P = 0.008) were sex, tumor grade, tumor histology, and LVI presence. This study, although it was retrospective study, revealed that adjuvant chemotherapy after RNU may be beneficial in pT3N0/X patients by multivariate analysis. Prospective studies evaluating adjuvant therapy regimens for UTTC are required.

Natural history definition and a suggested clinical approach to Buerger's disease: a case-control study with survival analysis.

PubMed

Fazeli, Bahare; Ravari, Hassan; Assadi, Reza

2012-08-01

The aim of this study was first to describe the natural history of Buerger's disease (BD) and then to discuss a clinical approach to this disease based on multivariate analysis. One hundred eight patients who corresponded with Shionoya's criteria were selected from 2000 to 2007 for this study. Major amputation was considered the ultimate adverse event. Survival analyses were performed by Kaplan-Meier curves. Independent variables including gender, duration of smoking, number of cigarettes smoked per day, minor amputation events and type of treatments, were determined by multivariate Cox regression analysis. The recorded data demonstrated that BD may present in four forms, including relapsing-remitting (75%), secondary progressive (4.6%), primary progressive (14.2%) and benign BD (6.2%). Most of the amputations occurred due to relapses within the six years after diagnosis of BD. In multivariate analysis, duration of smoking of more than 20 years had a significant relationship with further major amputation among patients with BD. Smoking cessation programs with experienced psychotherapists are strongly recommended for those areas in which Buerger's disease is common. Patients who have smoked for more than 20 years should be encouraged to quit smoking, but should also be recommended for more advanced treatment for limb salvage.

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

PubMed

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

2016-05-15

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

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.

Sampling effort affects multivariate comparisons of stream assemblages

USGS Publications Warehouse

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

2002-01-01

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

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.

Trend Detection and Bivariate Frequency Analysis for Nonstrationary Rainfall Data

NASA Astrophysics Data System (ADS)

Joo, K.; Kim, H.; Shin, J. Y.; Heo, J. H.

2017-12-01

Multivariate frequency analysis has been developing for hydro-meteorological data such as rainfall, flood, and drought. Particularly, the copula has been used as a useful tool for multivariate probability model which has no limitation on deciding marginal distributions. The time-series rainfall data can be characterized to rainfall event by inter-event time definition (IETD) and each rainfall event has a rainfall depth and rainfall duration. In addition, nonstationarity in rainfall event has been studied recently due to climate change and trend detection of rainfall event is important to determine the data has nonstationarity or not. With the rainfall depth and duration of a rainfall event, trend detection and nonstationary bivariate frequency analysis has performed in this study. 62 stations from Korea Meteorological Association (KMA) over 30 years of hourly recorded data used in this study and the suitability of nonstationary copula for rainfall event has examined by the goodness-of-fit test.

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

PubMed

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

2016-03-01

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

Analysis of multivariate longitudinal kidney function outcomes using generalized linear mixed models.

PubMed

Jaffa, Miran A; Gebregziabher, Mulugeta; Jaffa, Ayad A

2015-06-14

Renal transplant patients are mandated to have continuous assessment of their kidney function over time to monitor disease progression determined by changes in blood urea nitrogen (BUN), serum creatinine (Cr), and estimated glomerular filtration rate (eGFR). Multivariate analysis of these outcomes that aims at identifying the differential factors that affect disease progression is of great clinical significance. Thus our study aims at demonstrating the application of different joint modeling approaches with random coefficients on a cohort of renal transplant patients and presenting a comparison of their performance through a pseudo-simulation study. The objective of this comparison is to identify the model with best performance and to determine whether accuracy compensates for complexity in the different multivariate joint models. We propose a novel application of multivariate Generalized Linear Mixed Models (mGLMM) to analyze multiple longitudinal kidney function outcomes collected over 3 years on a cohort of 110 renal transplantation patients. The correlated outcomes BUN, Cr, and eGFR and the effect of various covariates such patient's gender, age and race on these markers was determined holistically using different mGLMMs. The performance of the various mGLMMs that encompass shared random intercept (SHRI), shared random intercept and slope (SHRIS), separate random intercept (SPRI) and separate random intercept and slope (SPRIS) was assessed to identify the one that has the best fit and most accurate estimates. A bootstrap pseudo-simulation study was conducted to gauge the tradeoff between the complexity and accuracy of the models. Accuracy was determined using two measures; the mean of the differences between the estimates of the bootstrapped datasets and the true beta obtained from the application of each model on the renal dataset, and the mean of the square of these differences. The results showed that SPRI provided most accurate estimates and did not exhibit any computational or convergence problem. Higher accuracy was demonstrated when the level of complexity increased from shared random coefficient models to the separate random coefficient alternatives with SPRI showing to have the best fit and most accurate estimates.

Kidney transplantation from deceased donors with elevated serum creatinine.

PubMed

Gallinat, Anja; Leerhoff, Sabine; Paul, Andreas; Molmenti, Ernesto P; Schulze, Maren; Witzke, Oliver; Sotiropoulos, Georgios C

2016-12-01

Elevated donor serum creatinine has been associated with inferior graft survival in kidney transplantation (KT). The aim of this study was to evaluate the impact of elevated donor serum creatinine on short and long-term outcomes and to determine possible ways to optimize the use of these organs. All kidney transplants from 01-2000 to 12-2012 with donor creatinine ≥ 2 mg/dl were considered. Risk factors for delayed graft function (DGF) were explored with uni- and multivariate regression analyses. Donor and recipient data were analyzed with uni- and multivariate cox proportional hazard analyses. Graft and patient survival were calculated using the Kaplan-Meier method. Seventy-eight patients were considered. Median recipient age and waiting time on dialysis were 53 years and 5.1 years, respectively. After a median follow-up of 6.2 years, 63 patients are alive. 1, 3, and 5-year graft and patient survival rates were 92, 89, and 89 % and 96, 93, and 89 %, respectively. Serum creatinine level at procurement and recipient's dialysis time prior to KT were predictors of DGF in multivariate analysis (p = 0.0164 and p = 0.0101, respectively). Charlson comorbidity score retained statistical significance by multivariate regression analysis for graft survival (p = 0.0321). Recipient age (p = 0.0035) was predictive of patient survival by multivariate analysis. Satisfactory long-term kidney transplant outcomes in the setting of elevated donor serum creatinine ≥2 mg/dl can be achieved when donor creatinine is <3.5 mg/dl, and the recipient has low comorbidities, is under 56 years of age, and remains in dialysis prior to KT for <6.8 years.

Cultural Correlates of Parent-Nonparent Stereotypes: A Multivariate Analysis.

ERIC Educational Resources Information Center

Bigner, Jerry J.; And Others

1981-01-01

This study determines cultural meanings of parent v nonparent roles using the following variables: one's generational group, place of residence, and family size. Generational differences appear to be the overwhelming influence on parental roles. The decision not to be a parent is an emerging role in contemporary culture. (CT)

Competent Counseling for Middle Eastern American Clients: Implications for Trainees

ERIC Educational Resources Information Center

Soheilian, Sepideh S.; Inman, Arpana G.

2015-01-01

The authors used a factorial multivariate analysis of variance (MANOVA) to determine whether counselor trainees' group differences on measures of multicultural competence, empathy, and multicultural counseling self-efficacy (CSE) when working with Middle Eastern American (MEA) clients were moderated by trainee race. Two hundred and fifty-six…

A nonparametric clustering technique which estimates the number of clusters

NASA Technical Reports Server (NTRS)

Ramey, D. B.

1983-01-01

In applications of cluster analysis, one usually needs to determine the number of clusters, K, and the assignment of observations to each cluster. A clustering technique based on recursive application of a multivariate test of bimodality which automatically estimates both K and the cluster assignments is presented.

Pretreatment Health Behaviors Predict Survival Among Patients With Head and Neck Squamous Cell Carcinoma

PubMed Central

Duffy, Sonia A.; Ronis, David L.; McLean, Scott; Fowler, Karen E.; Gruber, Stephen B.; Wolf, Gregory T.; Terrell, Jeffrey E.

2009-01-01

Purpose Our prior work has shown that the health behaviors of head and neck cancer patients are interrelated and are associated with quality of life; however, other than smoking, the relationship between health behaviors and survival is unclear. Patients and Methods A prospective cohort study was conducted to determine the relationship between five pretreatment health behaviors (smoking, alcohol, diet, physical activity, and sleep) and all-cause survival among 504 head and neck cancer patients. Results Smoking status was the strongest predictor of survival, with both current smokers (hazard ratio [HR] = 2.4; 95% CI, 1.3 to 4.4) and former smokers (HR = 2.0; 95% CI, 1.2 to 3.5) showing significant associations with poor survival. Problem drinking was associated with survival in the univariate analysis (HR = 1.4; 95% CI, 1.0 to 2.0) but lost significance when controlling for other factors. Low fruit intake was negatively associated with survival in the univariate analysis only (HR = 1.6; 95% CI, 1.1 to 2.1), whereas vegetable intake was not significant in either univariate or multivariate analyses. Although physical activity was associated with survival in the univariate analysis (HR = 0.95; 95% CI, 0.93 to 0.97), it was not significant in the multivariate model. Sleep was not significantly associated with survival in either univariate or multivariate analysis. Control variables that were also independently associated with survival in the multivariate analysis were age, education, tumor site, cancer stage, and surgical treatment. Conclusion Variation in selected pretreatment health behaviors (eg, smoking, fruit intake, and physical activity) in this population is associated with variation in survival. PMID:19289626

Chemometrics resolution and quantification power evaluation: Application on pharmaceutical quaternary mixture of Paracetamol, Guaifenesin, Phenylephrine and p-aminophenol

NASA Astrophysics Data System (ADS)

Yehia, Ali M.; Mohamed, Heba M.

2016-01-01

Three advanced chemmometric-assisted spectrophotometric methods namely; Concentration Residuals Augmented Classical Least Squares (CRACLS), Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) and Principal Component Analysis-Artificial Neural Networks (PCA-ANN) were developed, validated and benchmarked to PLS calibration; to resolve the severely overlapped spectra and simultaneously determine; Paracetamol (PAR), Guaifenesin (GUA) and Phenylephrine (PHE) in their ternary mixture and in presence of p-aminophenol (AP) the main degradation product and synthesis impurity of Paracetamol. The analytical performance of the proposed methods was described by percentage recoveries, root mean square error of calibration and standard error of prediction. The four multivariate calibration methods could be directly used without any preliminary separation step and successfully applied for pharmaceutical formulation analysis, showing no excipients' interference.

Are prostatic calculi independent predictive factors of lower urinary tract symptoms?

PubMed Central

Park, Sung-Woo; Nam, Jong-Kil; Lee, Sang-Don; Chung, Moon-Kee

2010-01-01

We determined the correlation between prostatic calculi and lower urinary tract symptoms (LUTS), as well as the predisposing factors of prostatic calculi. Of the 1 527 patients who presented at our clinic for LUTS, 802 underwent complete evaluations, including transrectal ultrasonography, voided bladder-3 specimen and international prostatic symptoms score (IPSS). A total of 335 patients with prostatic calculi and 467 patients without prostatic calculi were divided into calculi and no calculi groups, respectively. Predictive factors of severe LUTS and prostatic calculi were determined using uni/multivariate analysis. The overall IPSS score was 15.7 ± 9.2 and 14.1 ± 9.2 in the calculi and no calculi group, respectively (P = 0.013). The maximum flow rate was 12.1 ± 6.9 and 14.2 ± 8.2 mL s−1 in the calculi and no calculi group, respectively (P = 0.003). On univariate analysis for predicting factors of severe LUTS, differences on age (P = 0.042), prostatic calculi (P = 0.048) and prostatitis (P = 0.018) were statistically significant. However, on multivariate analysis, no factor was significant. On multivariate analysis for predisposing factors of prostatic calculi, differences on age (P < 0.001) and prostate volume (P = 0.001) were significant. To our knowledge, patients who have prostatic calculi complain of more severe LUTS. However, prostatic calculi are not an independent predictive factor of severe LUTS. Therefore, men with prostatic calculi have more severe LUTS not only because of prostatic calculi but also because of age and other factors. In addition, old age and large prostate volume are independent predisposing factors for prostatic calculi. PMID:19966831

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…

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

Determinants of quality of life of patients with heart failure and iron deficiency treated with ferric carboxymaltose: FAIR-HF sub-analysis.

PubMed

Gutzwiller, Florian S; Pfeil, Alena M; Comin-Colet, Josep; Ponikowski, Piotr; Filippatos, Gerasimos; Mori, Claudio; Braunhofer, Peter G; Szucs, Thomas D; Schwenkglenks, Matthias; Anker, Stefan D

2013-10-09

Heart failure (HF) is a burden to patients and health care systems. The objectives of HF treatment are to improve health related quality of life (HRQoL) and reduce mortality and morbidity. We aimed to evaluate determinants of health-related quality of life (HRQoL) in patients with iron deficiency and HF treated with intravenous (i.v.) iron substitution or placebo. A randomised, double-blind, placebo-controlled trial (n = 459) in iron-deficient chronic heart failure (CHF) patients with or without anaemia studied clinical and HRQoL benefits of i.v. iron substitution using ferric carboxymaltose (FCM) over a 24-week trial period. Multivariate analysis was carried out with various clinical variables as independent variables and HRQoL measures as dependent variables. Mean change from baseline of European Quality of Life - 5 Dimensions (EQ-5D) (value set-based) utilities (on a 0 to 100 scale) at week 24 was 8.91 (i.v. iron) and 0.68 (placebo; p < 0.01). In a multivariate analysis excluding baseline HRQoL, a higher exercise tolerance and i.v. iron substitution positively influenced HRQoL, whereas impaired renal function and a history of stroke had a negative effect. The level of HRQoL was also influenced by country of residence. When baseline HRQoL was factored in, the multivariate model remained stable. In this study, i.v. iron substitution, exercise tolerance, stroke, country of residence and renal function influenced measures of HRQoL in patients with heart failure and iron deficiency. © 2013.

Searching for New Biomarkers and the Use of Multivariate Analysis in Gastric Cancer Diagnostics.

PubMed

Kucera, Radek; Smid, David; Topolcan, Ondrej; Karlikova, Marie; Fiala, Ondrej; Slouka, David; Skalicky, Tomas; Treska, Vladislav; Kulda, Vlastimil; Simanek, Vaclav; Safanda, Martin; Pesta, Martin

2016-04-01

The first aim of this study was to search for new biomarkers to be used in gastric cancer diagnostics. The second aim was to verify the findings presented in literature on a sample of the local population and investigate the risk of gastric cancer in that population using a multivariant statistical analysis. We assessed a group of 36 patients with gastric cancer and 69 healthy individuals. We determined carcinoembryonic antigen, cancer antigen 19-9, cancer antigen 72-4, matrix metalloproteinases (-1, -2, -7, -8 and -9), osteoprotegerin, osteopontin, prothrombin induced by vitamin K absence-II, pepsinogen I, pepsinogen II, gastrin and Helicobacter pylori for each sample. The multivariate stepwise logistic regression identified the following biomarkers as the best gastric cancer predictors: CEA, CA72-4, pepsinogen I, Helicobacter pylori presence and MMP7. CEA and CA72-4 remain the best markers for gastric cancer diagnostics. We suggest a mathematical model for the assessment of risk of gastric cancer. Copyright© 2016 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved.

Effects of Flavor and Texture on the Sensory Perception of Gouda-Type Cheese Varieties during Ripening Using Multivariate Analysis.

PubMed

Shiota, Makoto; Iwasawa, Ai; Suzuki-Iwashima, Ai; Iida, Fumiko

2015-12-01

The impact of flavor composition, texture, and other factors on desirability of different commercial sources of Gouda-type cheese using multivariate analyses on the basis of sensory and instrumental analyses were investigated. Volatile aroma compounds were measured using headspace solid-phase microextraction gas chromatography/mass spectrometry (GC/MS) and steam distillation extraction (SDE)-GC/MS, and fatty acid composition, low-molecular-weight compounds, including amino acids, and organic acids, as well pH, texture, and color were measured to determine their relationship with sensory perception. Orthogonal partial least squares-discriminant analysis (OPLS-DA) was performed to discriminate between 2 different ripening periods in 7 sample sets, revealing that ethanol, ethyl acetate, hexanoic acid, and octanoic acid increased with increasing sensory attribute scores for sweetness, fruity, and sulfurous. A partial least squares (PLS) regression model was constructed to predict the desirability of cheese using these parameters. We showed that texture and buttery flavors are important factors affecting the desirability of Gouda-type cheeses for Japanese consumers using these multivariate analyses. © 2015 Institute of Food Technologists®

An evaluation of the use of near infrared (NIR) spectroscopy to identify water and oil-borne preservatives

Treesearch

Chi-Leung So; Stan T. Lebow; Leslie H. Groom; Todd F. Shupe

2003-01-01

In this research we experimented with a new and rapid way of analyzing wood. Near Infrared (NIR)spectroscopy together with multivariate analysis is becoming a widely used technique in the field of forest products especially for property determination and is already firmly established in the pulp and paper industry. This method is ideal for the chemical analysis of wood...

The discrimination of honey origin using melissopalynology and Raman spectroscopy techniques coupled with multivariate analysis.

PubMed

Corvucci, Francesca; Nobili, Lara; Melucci, Dora; Grillenzoni, Francesca-Vittoria

2015-02-15

Honey traceability to food quality is required by consumers and food control institutions. Melissopalynologists traditionally use percentages of nectariferous pollens to discriminate the botanical origin and the entire pollen spectrum (presence/absence, type and quantities and association of some pollen types) to determinate the geographical origin of honeys. To improve melissopalynological routine analysis, principal components analysis (PCA) was used. A remarkable and innovative result was that the most significant pollens for the traditional discrimination of the botanical and geographical origin of honeys were the same as those individuated with the chemometric model. The reliability of assignments of samples to honey classes was estimated through explained variance (85%). This confirms that the chemometric model properly describes the melissopalynological data. With the aim to improve honey discrimination, FT-microRaman spectrography and multivariate analysis were also applied. Well performing PCA models and good agreement with known classes were achieved. Encouraging results were obtained for botanical discrimination. Copyright © 2014 Elsevier Ltd. All rights reserved.

Multivariate Analysis of Electron Detachment Dissociation and Infrared Multiphoton Dissociation Mass Spectra of Heparan Sulfate Tetrasaccharides Differing Only in Hexuronic acid Stereochemistry

NASA Astrophysics Data System (ADS)

Oh, Han Bin; Leach, Franklin E.; Arungundram, Sailaja; Al-Mafraji, Kanar; Venot, Andre; Boons, Geert-Jan; Amster, I. Jonathan

2011-03-01

The structural characterization of glycosaminoglycan (GAG) carbohydrates by mass spectrometry has been a long-standing analytical challenge due to the inherent heterogeneity of these biomolecules, specifically polydispersity, variability in sulfation, and hexuronic acid stereochemistry. Recent advances in tandem mass spectrometry methods employing threshold and electron-based ion activation have resulted in the ability to determine the location of the labile sulfate modification as well as assign the stereochemistry of hexuronic acid residues. To facilitate the analysis of complex electron detachment dissociation (EDD) spectra, principal component analysis (PCA) is employed to differentiate the hexuronic acid stereochemistry of four synthetic GAG epimers whose EDD spectra are nearly identical upon visual inspection. For comparison, PCA is also applied to infrared multiphoton dissociation spectra (IRMPD) of the examined epimers. To assess the applicability of multivariate methods in GAG mixture analysis, PCA is utilized to identify the relative content of two epimers in a binary mixture.

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.

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

Simultaneous Determination of Metamizole, Thiamin and Pyridoxin Using UV-Spectroscopy in Combination with Multivariate Calibration

PubMed Central

Chotimah, Chusnul; Sudjadi; Riyanto, Sugeng; Rohman, Abdul

2015-01-01

Purpose: Analysis of drugs in multicomponent system officially is carried out using chromatographic technique, however, this technique is too laborious and involving sophisticated instrument. Therefore, UV-VIS spectrophotometry coupled with multivariate calibration of partial least square (PLS) for quantitative analysis of metamizole, thiamin and pyridoxin is developed in the presence of cyanocobalamine without any separation step. Methods: The calibration and validation samples are prepared. The calibration model is prepared by developing a series of sample mixture consisting these drugs in certain proportion. Cross validation of calibration sample using leave one out technique is used to identify the smaller set of components that provide the greatest predictive ability. The evaluation of calibration model was based on the coefficient of determination (R2) and root mean square error of calibration (RMSEC). Results: The results showed that the coefficient of determination (R2) for the relationship between actual values and predicted values for all studied drugs was higher than 0.99 indicating good accuracy. The RMSEC values obtained were relatively low, indicating good precision. The accuracy and presision results of developed method showed no significant difference compared to those obtained by official method of HPLC. Conclusion: The developed method (UV-VIS spectrophotometry in combination with PLS) was succesfully used for analysis of metamizole, thiamin and pyridoxin in tablet dosage form. PMID:26819934

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.

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…

Impact of an Adlerian Based Pretrial Diversion Program: Self Concept and Dissociation

ERIC Educational Resources Information Center

Norvell, Jeanell J.

2010-01-01

Clients' self concepts and dissociative experiences were examined to determine the impact of an Adlerian based pretrial diversion program. Clients completing the program displayed a significant change in self concepts and dissociative experiences. A repeated measures multivariate analysis of variance indicated a 35% change, made up of the…

The application of nirvana to silvicultural studies

Treesearch

Chi-Leung So; Thomas Elder; Leslie Groom; John S. Kush; Jennifer Myszewski; Todd Shupe

2006-01-01

Previous results from this laboratory have shown that near infrared (NIR) spectroscopy, coupled with multivariate analysis, can be a powerful tool for the prediction of wood quality. While wood quality measurements are of utility, their determination can be both time and labor intensive, thus limiting their use where large sample sizes are concerned. This paper will...

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.

Comparative artificial neural network and partial least squares models for analysis of Metronidazole, Diloxanide, Spiramycin and Cliquinol in pharmaceutical preparations.

PubMed

Elkhoudary, Mahmoud M; Abdel Salam, Randa A; Hadad, Ghada M

2014-09-15

Metronidazole (MNZ) is a widely used antibacterial and amoebicide drug. Therefore, it is important to develop a rapid and specific analytical method for the determination of MNZ in mixture with Spiramycin (SPY), Diloxanide (DIX) and Cliquinol (CLQ) in pharmaceutical preparations. This work describes simple, sensitive and reliable six multivariate calibration methods, namely linear and nonlinear artificial neural networks preceded by genetic algorithm (GA-ANN) and principle component analysis (PCA-ANN) as well as partial least squares (PLS) either alone or preceded by genetic algorithm (GA-PLS) for UV spectrophotometric determination of MNZ, SPY, DIX and CLQ in pharmaceutical preparations with no interference of pharmaceutical additives. The results manifest the problem of nonlinearity and how models like ANN can handle it. Analytical performance of these methods was statistically validated with respect to linearity, accuracy, precision and specificity. The developed methods indicate the ability of the previously mentioned multivariate calibration models to handle and solve UV spectra of the four components' mixtures using easy and widely used UV spectrophotometer. Copyright © 2014 Elsevier B.V. All rights reserved.

Watch what happens: using a web-based multimedia platform to enhance intraoperative learning and development of clinical reasoning.

PubMed

Fingeret, Abbey L; Martinez, Rebecca H; Hsieh, Christine; Downey, Peter; Nowygrod, Roman

2016-02-01

We aim to determine whether observed operations or internet-based video review predict improved performance in the surgery clerkship. A retrospective review of students' usage of surgical videos, observed operations, evaluations, and examination scores were used to construct an exploratory principal component analysis. Multivariate regression was used to determine factors predictive of clerkship performance. Case log data for 231 students revealed a median of 25 observed cases. Students accessed the web-based video platform a median of 15 times. Principal component analysis yielded 4 factors contributing 74% of the variability with a Kaiser-Meyer-Olkin coefficient of .83. Multivariate regression predicted shelf score (P < .0001), internal clinical skills examination score (P < .0001), subjective evaluations (P < .001), and video website utilization (P < .001) but not observed cases to be significantly associated with overall performance. Utilization of a web-based operative video platform during a surgical clerkship is an independently associated with improved clinical reasoning, fund of knowledge, and overall evaluation. Thus, this modality can serve as a useful adjunct to live observation. Copyright © 2016 Elsevier Inc. All rights reserved.

Comparative artificial neural network and partial least squares models for analysis of Metronidazole, Diloxanide, Spiramycin and Cliquinol in pharmaceutical preparations

NASA Astrophysics Data System (ADS)

Elkhoudary, Mahmoud M.; Abdel Salam, Randa A.; Hadad, Ghada M.

2014-09-01

Metronidazole (MNZ) is a widely used antibacterial and amoebicide drug. Therefore, it is important to develop a rapid and specific analytical method for the determination of MNZ in mixture with Spiramycin (SPY), Diloxanide (DIX) and Cliquinol (CLQ) in pharmaceutical preparations. This work describes simple, sensitive and reliable six multivariate calibration methods, namely linear and nonlinear artificial neural networks preceded by genetic algorithm (GA-ANN) and principle component analysis (PCA-ANN) as well as partial least squares (PLS) either alone or preceded by genetic algorithm (GA-PLS) for UV spectrophotometric determination of MNZ, SPY, DIX and CLQ in pharmaceutical preparations with no interference of pharmaceutical additives. The results manifest the problem of nonlinearity and how models like ANN can handle it. Analytical performance of these methods was statistically validated with respect to linearity, accuracy, precision and specificity. The developed methods indicate the ability of the previously mentioned multivariate calibration models to handle and solve UV spectra of the four components’ mixtures using easy and widely used UV spectrophotometer.

Multivariate analysis and extraction of parameters in resistive RAMs using the Quantum Point Contact model

NASA Astrophysics Data System (ADS)

Roldán, J. B.; Miranda, E.; González-Cordero, G.; García-Fernández, P.; Romero-Zaliz, R.; González-Rodelas, P.; Aguilera, A. M.; González, M. B.; Jiménez-Molinos, F.

2018-01-01

A multivariate analysis of the parameters that characterize the reset process in Resistive Random Access Memory (RRAM) has been performed. The different correlations obtained can help to shed light on the current components that contribute in the Low Resistance State (LRS) of the technology considered. In addition, a screening method for the Quantum Point Contact (QPC) current component is presented. For this purpose, the second derivative of the current has been obtained using a novel numerical method which allows determining the QPC model parameters. Once the procedure is completed, a whole Resistive Switching (RS) series of thousands of curves is studied by means of a genetic algorithm. The extracted QPC parameter distributions are characterized in depth to get information about the filamentary pathways associated with LRS in the low voltage conduction regime.

Chemometrics resolution and quantification power evaluation: Application on pharmaceutical quaternary mixture of Paracetamol, Guaifenesin, Phenylephrine and p-aminophenol.

PubMed

Yehia, Ali M; Mohamed, Heba M

2016-01-05

Three advanced chemmometric-assisted spectrophotometric methods namely; Concentration Residuals Augmented Classical Least Squares (CRACLS), Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) and Principal Component Analysis-Artificial Neural Networks (PCA-ANN) were developed, validated and benchmarked to PLS calibration; to resolve the severely overlapped spectra and simultaneously determine; Paracetamol (PAR), Guaifenesin (GUA) and Phenylephrine (PHE) in their ternary mixture and in presence of p-aminophenol (AP) the main degradation product and synthesis impurity of Paracetamol. The analytical performance of the proposed methods was described by percentage recoveries, root mean square error of calibration and standard error of prediction. The four multivariate calibration methods could be directly used without any preliminary separation step and successfully applied for pharmaceutical formulation analysis, showing no excipients' interference. Copyright © 2015 Elsevier B.V. All rights reserved.

Multivariate analysis of nystatin and metronidazole in a semi-solid matrix by means of diffuse reflectance NIR spectroscopy and PLS regression.

PubMed

Baratieri, Sabrina C; Barbosa, Juliana M; Freitas, Matheus P; Martins, José A

2006-01-23

A multivariate method of analysis of nystatin and metronidazole in a semi-solid matrix, based on diffuse reflectance NIR measurements and partial least squares regression, is reported. The product, a vaginal cream used in the antifungal and antibacterial treatment, is usually, quantitatively analyzed through microbiological tests (nystatin) and HPLC technique (metronidazole), according to pharmacopeial procedures. However, near infrared spectroscopy has demonstrated to be a valuable tool for content determination, given the rapidity and scope of the method. In the present study, it was successfully applied in the prediction of nystatin (even in low concentrations, ca. 0.3-0.4%, w/w, which is around 100,000 IU/5g) and metronidazole contents, as demonstrated by some figures of merit, namely linearity, precision (mean and repeatability) and accuracy.

Simultaneous grouping and ranking with combination of SOM and TOPSIS for selection of preferable analytical procedure for furan determination in food.

PubMed

Jędrkiewicz, Renata; Tsakovski, Stefan; Lavenu, Aurore; Namieśnik, Jacek; Tobiszewski, Marek

2018-02-01

Novel methodology for grouping and ranking with application of self-organizing maps and multicriteria decision analysis is presented. The dataset consists of 22 objects that are analytical procedures applied to furan determination in food samples. They are described by 10 variables, referred to their analytical performance, environmental and economic aspects. Multivariate statistics analysis allows to limit the amount of input data for ranking analysis. Assessment results show that the most beneficial procedures are based on microextraction techniques with GC-MS final determination. It is presented how the information obtained from both tools complement each other. The applicability of combination of grouping and ranking is also discussed. Copyright © 2017 Elsevier B.V. All rights reserved.

Multivariate η-μ fading distribution with arbitrary correlation model

NASA Astrophysics Data System (ADS)

Ghareeb, Ibrahim; Atiani, Amani

2018-03-01

An extensive analysis for the multivariate ? distribution with arbitrary correlation is presented, where novel analytical expressions for the multivariate probability density function, cumulative distribution function and moment generating function (MGF) of arbitrarily correlated and not necessarily identically distributed ? power random variables are derived. Also, this paper provides exact-form expression for the MGF of the instantaneous signal-to-noise ratio at the combiner output in a diversity reception system with maximal-ratio combining and post-detection equal-gain combining operating in slow frequency nonselective arbitrarily correlated not necessarily identically distributed ?-fading channels. The average bit error probability of differentially detected quadrature phase shift keying signals with post-detection diversity reception system over arbitrarily correlated and not necessarily identical fading parameters ?-fading channels is determined by using the MGF-based approach. The effect of fading correlation between diversity branches, fading severity parameters and diversity level is studied.

Prognostic implications of adhesion molecule expression in colorectal cancer.

PubMed

Seo, Kyung-Jin; Kim, Maru; Kim, Jeana

2015-01-01

Research on the expression of adhesion molecules, E-cadherin (ECAD), CD24, CD44 and osteopontin (OPN) in colorectal cancer (CRC) has been limited, even though CRC is one of the leading causes of cancer-related deaths. This study was conducted to evaluate the expression of adhesion molecules in CRC and to determine their relationships with clinicopathologic variables, and the prognostic significance. The expression of ECAD, CD24, CD44 and OPN was examined in 174 stage II and III CRC specimens by immunohistochemistry of TMA. Negative ECAD expression was significantly correlated with advanced nodal stage and poor tumor differentiation. Multivariate analysis showed that both negative expression of ECAD and positive expression of CD24 were independent prognostic factors for disease-free survival (DFS) in CRC patients (P<0.001, relative risk [RR] = 5.596, 95% CI = 2.712-11.549; P = 0.038, RR = 3.768, 95% CI = 1.077-13.185, respectively). However, for overall survival (OS), only ECAD negativity showed statistically significant results in multivariate analysis (P<0.001, RR = 4.819, 95% CI = 2.515-9.234). Positive expression of CD24 was associated with poor OS in univariate analysis but was of no prognostic value in multivariate analysis. In conclusion, our study suggests that among these four adhesion molecules, ECAD and CD24 expression can be considered independent prognostic factors. The role of CD44 and OPN may need further evaluation.

Prognostic implications of adhesion molecule expression in colorectal cancer

PubMed Central

Seo, Kyung-Jin; Kim, Maru; Kim, Jeana

2015-01-01

Research on the expression of adhesion molecules, E-cadherin (ECAD), CD24, CD44 and osteopontin (OPN) in colorectal cancer (CRC) has been limited, even though CRC is one of the leading causes of cancer-related deaths. This study was conducted to evaluate the expression of adhesion molecules in CRC and to determine their relationships with clinicopathologic variables, and the prognostic significance. The expression of ECAD, CD24, CD44 and OPN was examined in 174 stage II and III CRC specimens by immunohistochemistry of TMA. Negative ECAD expression was significantly correlated with advanced nodal stage and poor tumor differentiation. Multivariate analysis showed that both negative expression of ECAD and positive expression of CD24 were independent prognostic factors for disease-free survival (DFS) in CRC patients (P<0.001, relative risk [RR] = 5.596, 95% CI = 2.712-11.549; P = 0.038, RR = 3.768, 95% CI = 1.077-13.185, respectively). However, for overall survival (OS), only ECAD negativity showed statistically significant results in multivariate analysis (P<0.001, RR = 4.819, 95% CI = 2.515-9.234). Positive expression of CD24 was associated with poor OS in univariate analysis but was of no prognostic value in multivariate analysis. In conclusion, our study suggests that among these four adhesion molecules, ECAD and CD24 expression can be considered independent prognostic factors. The role of CD44 and OPN may need further evaluation. PMID:26097606

Challenging a dogma: five-year survival does not equal cure in all colorectal cancer patients.

PubMed

Abdel-Rahman, Omar

2018-02-01

The current study tried to evaluate the factors affecting 10- to 20- years' survival among long term survivors (>5 years) of colorectal cancer (CRC). Surveillance, Epidemiology and End Results (SEER) database (1988-2008) was queried through SEER*Stat program.Univariate probability of overall and cancer-specific survival was determined and the difference between groups was examined. Multivariate analysis for factors affecting overall and cancer-specific survival was also conducted. Among node positive patients (Dukes C), 34% of the deaths beyond 5 years can be attributed to CRC; while among M1 patients, 63% of the deaths beyond 5 years can be attributed to CRC. The following factors were predictors of better overall survival in multivariate analysis: younger age, white race (versus black race), female gender, Right colon location (versus rectal location), earlier stage and surgery (P <0.0001 for all parameters). Similarly, the following factors were predictors of better cancer-specific survival in multivariate analysis: younger age, white race (versus black race), female gender, Right colon location (versus left colon and rectal locations), earlier stage and surgery (P <0.0001 for all parameters). Among node positive long-term CRC survivors, more than one third of all deaths can be attributed to CRC.

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

Identification of offal adulteration in beef by laser induced breakdown spectroscopy (LIBS).

PubMed

Velioglu, Hasan Murat; Sezer, Banu; Bilge, Gonca; Baytur, Süleyman Efe; Boyaci, Ismail Hakki

2018-04-01

Minced meat is the major ingredient in sausages, beef burgers, and similar products; and thus it is the main product subjected to adulteration with meat offal. Determination of this kind of meat adulteration is crucial due to religious, economic and ethical concerns. The aim of the present study is to discriminate the beef meat and offal samples by using laser induced breakdown spectroscopy (LIBS). To this end, LIBS and multivariate data analysis were used to discriminate pure beef and offal samples qualitatively and to determine the offal mixture adulteration quantitatively. In this analysis, meat samples were frozen and LIBS analysis were performed. The results indicate that by using principal component analysis (PCA), discrimination of pure offal and offal mixture adulterated beef samples can be achieved successfully. Besides, adulteration ratio can be determined using partial least square analysis method (PLS) with 0.947 coefficient of determination (R 2 ) and 3.8% of limit of detection (LOD) values for offal mixture adulterated beef samples. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

Leptospira Exposure and Gardeners: A Case-Control Seroprevalence Study

PubMed Central

Alvarado-Esquivel, Cosme; Hernandez-Tinoco, Jesus; Sanchez-Anguiano, Luis Francisco; Ramos-Nevarez, Agar; Cerrillo-Soto, Sandra Margarita; Guido-Arreola, Carlos Alberto

2016-01-01

Background Leptospira can be found in soil. However, it is unclear whether occupational exposure to soil may represent a risk for Leptospira infection in humans. Therefore, we sought to determine the association of Leptospira IgG seroprevalence with the occupation of gardener, and to determine the epidemiological characteristics of gardeners associated with Leptospira exposure. Methods We performed a case-control study in 168 gardeners and 168 age- and gender-matched control subjects without gardening occupation in Durango City, Mexico. The seroprevalence of anti-Leptospira IgG antibodies in cases and controls was determined using an enzyme immunoassay. Bivariate and multivariate analyses were used to assess the association of Leptospira exposure and the characteristics of the gardeners. Results Anti-Leptospira IgG antibodies were found in 10 (6%) of 168 gardeners and in 15 (8.9%) of 168 control subjects (odds ratio (OR): 0.64; 95% confidence interval (CI): 0.28 - 1.48; P = 0.40). Multivariate analysis showed that Leptospira seropositivity was positively associated with female gender (OR: 5.82; 95% CI: 1.11 - 30.46; P = 0.03), and negatively associated with eating while working (OR: 0.21; 95% CI: 0.05 - 0.87; P = 0.03). In addition, multivariate analysis showed that high anti-Leptospira levels were associated with consumption of boar meat (OR: 28.00; 95% CI: 1.20 - 648.80; P = 0.03). Conclusions This is the first case-control study of Leptospira exposure in gardeners. Results do not support an association of Leptospira exposure with the occupation of gardener. However, further studies to confirm the lack of this association are needed. The potential role of consumption of boar meat in Leptospira infection deserves further investigation. PMID:26668679

Hydration status measured by BCM: A potential modifiable risk factor for peritonitis in patients on peritoneal dialysis.

PubMed

Santhakumaran, Territa; Samad, Nasreen; Fan, Stanley L

2016-05-01

Peritoneal dialysis peritonitis and fluid overhydration (OH) are frequent problems in peritoneal dialysis. The latter can cause gut wall oedema or be associated with malnutrition. Both may lead to increased peritonitis risk. We wished to determine if OH is an independent risk factor for peritonitis (caused by enteric organisms). Retrospectively study of patients with >2 bioimpedance assessments (Body Composition Monitor). We compared peritonitis rates of patients with above or below the median time-averaged hydration parameter (OH/extracellular water, OH/ECW). Multivariate analysis was performed to determine independent risk factors for peritonitis by enteric organism. We studied 580 patients. Peritonitis was experienced by 28% patients (followed up for an average of 17 months). The overall peritonitis rate was 1:34 patient months. Patients with low OH/ECW values had significantly lower rates of peritonitis from enteric organisms than overhydrated patients (incident rate ratio 1.53, 95% confidence interval 1.38-1.70, P < 0.001). Hydration remained an independent predictor of peritonitis from enteric organisms when multivariate model included demographic parameters (odds ratio for a 1% increment of OH/ECW was 1.05; 95% confidence interval 1.01-1.10, P < 0.02). However, including biochemical parameters of malnutrition reduced the predictive power of overhydration. We found an association between overhydration and increased rates of peritonitis. While this may partly be due to the high co-morbidity of patients (advanced age and diabetes), on multivariate analysis, only inclusion of nutritional parameters reduced this association. It remains to be determined if overhydration will prove to be a modifiable risk factor for peritonitis or whether malnutrition will prove to be more important. © 2015 Asian Pacific Society of Nephrology.

Quantification of proportions of different water sources in a mining operation.

PubMed

Scheiber, Laura; Ayora, Carlos; Vázquez-Suñé, Enric

2018-04-01

The water drained in mining operations (galleries, shafts, open pits) usually comes from different sources. Evaluating the contribution of these sources is very often necessary for water management. To determine mixing ratios, a conventional mass balance is often used. However, the presence of more than two sources creates uncertainties in mass balance applications. Moreover, the composition of the end-members is not commonly known with certainty and/or can vary in space and time. In this paper, we propose a powerful tool for solving such problems and managing groundwater in mining sites based on multivariate statistical analysis. This approach was applied to the Cobre Las Cruces mining complex, the largest copper mine in Europe. There, the open pit water is a mixture of three end-members: runoff (RO), basal Miocene (Mb) and Paleozoic (PZ) groundwater. The volume of water drained from the Miocene base aquifer must be determined and compensated via artificial recharging to comply with current regulations. Through multivariate statistical analysis of samples from a regional field campaign, the compositions of PZ and Mb end-members were firstly estimated, and then used for mixing calculations at the open pit scale. The runoff end-member was directly determined from samples collected in interception trenches inside the open pit. The application of multivariate statistical methods allowed the estimation of mixing ratios for the hydrological years 2014-2015 and 2015-2016. Open pit water proportions have changed from 15% to 7%, 41% to 36%, and 44% to 57% for runoff, Mb and PZ end-members, respectively. An independent estimation of runoff based on the curve method yielded comparable results. Copyright © 2017 Elsevier B.V. All rights reserved.

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.

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

PubMed

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

2016-05-01

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

Analysis of Lard in Lipstick Formulation Using FTIR Spectroscopy and Multivariate Calibration: A Comparison of Three Extraction Methods.

PubMed

Waskitho, Dri; Lukitaningsih, Endang; Sudjadi; Rohman, Abdul

2016-01-01

Analysis of lard extracted from lipstick formulation containing castor oil has been performed using FTIR spectroscopic method combined with multivariate calibration. Three different extraction methods were compared, namely saponification method followed by liquid/liquid extraction with hexane/dichlorometane/ethanol/water, saponification method followed by liquid/liquid extraction with dichloromethane/ethanol/water, and Bligh & Dyer method using chloroform/methanol/water as extracting solvent. Qualitative and quantitative analysis of lard were performed using principle component (PCA) and partial least square (PLS) analysis, respectively. The results showed that, in all samples prepared by the three extraction methods, PCA was capable of identifying lard at wavelength region of 1200-800 cm -1 with the best result was obtained by Bligh & Dyer method. Furthermore, PLS analysis at the same wavelength region used for qualification showed that Bligh and Dyer was the most suitable extraction method with the highest determination coefficient (R 2 ) and the lowest root mean square error of calibration (RMSEC) as well as root mean square error of prediction (RMSEP) values.

Multivariate Tensor-based Morphometry on Surfaces: Application to Mapping Ventricular Abnormalities in HIV/AIDS

PubMed Central

Wang, Yalin; Zhang, Jie; Gutman, Boris; Chan, Tony F.; Becker, James T.; Aizenstein, Howard J.; Lopez, Oscar L.; Tamburo, Robert J.; Toga, Arthur W.; Thompson, Paul M.

2010-01-01

Here we developed a new method, called multivariate tensor-based surface morphometry (TBM), and applied it to study lateral ventricular surface differences associated with HIV/AIDS. Using concepts from differential geometry and the theory of differential forms, we created mathematical structures known as holomorphic one-forms, to obtain an efficient and accurate conformal parameterization of the lateral ventricular surfaces in the brain. The new meshing approach also provides a natural way to register anatomical surfaces across subjects, and improves on prior methods as it handles surfaces that branch and join at complex 3D junctions. To analyze anatomical differences, we computed new statistics from the Riemannian surface metrics - these retain multivariate information on local surface geometry. We applied this framework to analyze lateral ventricular surface morphometry in 3D MRI data from 11 subjects with HIV/AIDS and 8 healthy controls. Our method detected a 3D profile of surface abnormalities even in this small sample. Multivariate statistics on the local tensors gave better effect sizes for detecting group differences, relative to other TBM-based methods including analysis of the Jacobian determinant, the largest and smallest eigenvalues of the surface metric, and the pair of eigenvalues of the Jacobian matrix. The resulting analysis pipeline may improve the power of surface-based morphometry studies of the brain. PMID:19900560

[Factors associated with the timely treatment of women with breast cancer supported by a non-governmental organization in Bogotá, Colombia].

PubMed

Sánchez, Guillermo; Niño, Carlos Gustavo; Estupiñán, Carolina

2015-01-01

The prognosis for a woman with breast cancer is related to the time that elapses before diagnosis and integral treatment. Colombian women face barriers that determine effective access to the health system. To establish the determinants of timely treatment for breast cancer in a group of women supported by a non-governmental organization in Bogotá. An observational analytical study was carried out on 136 women with breast cancer supported by the non-governmental organization. The cut-off point for timely treatment was defined as 90 days, calculated as the time between the appearance of symptoms and the initiation of treatment. Predictors of timely treatment were explored by means of multivariate analysis. Although 96% of the women had health insurance only 26.4% received timely treatment, and 36 of them reported being denied medical services. Of these women, 23% took legal action to gain access to their healthcare rights. Significant associations were established by multivariate analysis for timely treatment among women belonging to socioeconomic strata IV and V (OR=3.39), as well as those with higher education (OR=2.72). According to the international literature, the prognosis for women with breast cancer improves when they are able to access opportune treatment. In this group of women socioeconomic factors appeared to determine effective access to treatment, revealing the existence of inequalities that may be socially determined.

Seroepidemiology of cytomegalovirus infection in pregnant women in Durango City, Mexico.

PubMed

Alvarado-Esquivel, Cosme; Hernández-Tinoco, Jesús; Sánchez-Anguiano, Luis Francisco; Ramos-Nevárez, Agar; Cerrillo-Soto, Sandra Margarita; Estrada-Martínez, Sergio; Martínez-Ramírez, Lucio; Pérez-Álamos, Alma Rosa; Guido-Arreola, Carlos Alberto

2014-09-05

Cytomegalovirus causes congenital infections all around the world. The seroepidemiology of cytomegalovirus infection in pregnant women in Mexico is largely unknown. We sought to determine the seroprevalence of cytomegalovirus infection in pregnant women in Durango City, Mexico; and to determine seroprevalence association with socio-demographic, clinical and behavioral characteristics of pregnant women. Through a cross-sectional study design, 343 pregnant women were examined for anti-cytomegalovirus IgG and IgM antibodies in Durango City, Mexico. We used a standardized questionnaire to obtain the general characteristics of the pregnant women. Multivariate analysis was performed to determine the association of cytomegalovirus infection with the characteristics of the pregnant women. Anti-CMV IgG and IgM antibodies were detected in 225 (65.6%) and in none of the 343 pregnant women studied, respectively. Multivariate analysis showed that CMV exposure was associated with increasing age (OR = 1.67; 95% CI: 1.01-2.76; P = 0.04). Other women characteristics including socioeconomic status, education, blood transfusion, transplantation, sexual promiscuity and number of previous pregnancies or deliveries did not show an association with CMV exposure. This is the first seroepidemiology study of CMV infection in pregnant women in Mexico. A number of known factors associated with CMV infection were not associated with CMV exposure in the women studied. Further studies to determine routes of CMV infection in pregnant women in Mexico are needed.

The Impact of Work Ability on Work Motivation and Health: A Longitudinal Study Based on Older Employees.

PubMed

Feißel, Annemarie; Swart, Enno; March, Stefanie

2018-05-01

Work participation is determined by work motivation and work ability with health as a significant component. Within the lidA-study, we explore the impact of work ability on work motivation and health with consideration of further influencing factors. Four thousand one hundred nine older employees were interviewed two times (t0 = 2011, t1 = 2014). Two multivariate analyses were performed regarding the influence of work ability on work motivation (Model 1) and health (Model 2). Within the multivariate analysis, of all the influencing factors, work ability has the strongest effect on work motivation (F = 37.761) and health (F = 76.402). It appears as a decisive determinant for both dimensions. Regarding the results, it is useful to focus on the work ability of older employees in order to maintain and boost their work motivation and health.

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.

The association between physical activity and social isolation in community-dwelling older adults.

PubMed

Robins, Lauren M; Hill, Keith D; Finch, Caroline F; Clemson, Lindy; Haines, Terry

2018-02-01

Social isolation is an increasing concern in older community-dwelling adults. There is growing need to determine effective interventions addressing social isolation. This study aimed to determine whether a relationship exists between physical activity (recreational and/or household-based) and social isolation. An examination was conducted for whether group- or home-based falls prevention exercise was associated with social isolation. Cross-sectional analysis of telephone survey data was used to investigate relationships between physical activity, health, age, gender, living arrangements, ethnicity and participation in group- or home-based falls prevention exercise on social isolation. Univariable and multivariable ordered logistic regression analyses were conducted. Factors found to be significantly associated with reduced social isolation in multivariable analysis included living with a partner/spouse, reporting better general health, higher levels of household-based physical activity (OR = 1.03, CI = 1.01-1.05) and feeling less downhearted/depressed. Being more socially isolated was associated with symptoms of depression and a diagnosis of congestive heart failure (pseudo R 2 = 0.104). Findings suggest that household-based physical activity is related to social isolation in community-dwelling older adults. Further research is required to determine the nature of this relationship and to investigate the impact of group physical activity interventions on social isolation.

Quality assessment of pharmaceutical tablet samples using Fourier transform near infrared spectroscopy and multivariate analysis

NASA Astrophysics Data System (ADS)

Kandpal, Lalit Mohan; Tewari, Jagdish; Gopinathan, Nishanth; Stolee, Jessica; Strong, Rick; Boulas, Pierre; Cho, Byoung-Kwan

2017-09-01

Determination of the content uniformity, assessed by the amount of an active pharmaceutical ingredient (API), and hardness of pharmaceutical materials is important for achieving a high-quality formulation and to ensure the intended therapeutic effects of the end-product. In this work, Fourier transform near infrared (FT-NIR) spectroscopy was used to determine the content uniformity and hardness of a pharmaceutical mini-tablet and standard tablet samples. Tablet samples were scanned using an FT-NIR instrument and tablet spectra were collected at wavelengths of 1000-2500 nm. Furthermore, multivariate analysis was applied to extract the relationship between the FT-NIR spectra and the measured parameters. The results of FT-NIR spectroscopy for API and hardness prediction were as precise as the reference high-performance liquid chromatography and mechanical hardness tests. For the prediction of mini-tablet API content, the highest coefficient of determination for the prediction (R2p) was found to be 0.99 with a standard error of prediction (SEP) of 0.72 mg. Moreover, the standard tablet hardness measurement had a R2p value of 0.91 with an SEP of 0.25 kg. These results suggest that FT-NIR spectroscopy is an alternative and accurate nondestructive measurement tool for the detection of the chemical and physical properties of pharmaceutical samples.

Determinants of graft survival in pediatric and adolescent live donor kidney transplant recipients: a single center experience.

PubMed

El-Husseini, Amr A; Foda, Mohamed A; Shokeir, Ahmed A; Shehab El-Din, Ahmed B; Sobh, Mohamed A; Ghoneim, Mohamed A

2005-12-01

To study the independent determinants of graft survival among pediatric and adolescent live donor kidney transplant recipients. Between March 1976 and March 2004, 1600 live donor kidney transplants were carried out in our center. Of them 284 were 20 yr old or younger (mean age 13.1 yr, ranging from 5 to 20 yr). Evaluation of the possible variables that may affect graft survival were carried out using univariate and multivariate analyses. Studied factors included age, gender, relation between donor and recipient, original kidney disease, ABO blood group, pretransplant blood transfusion, human leukocyte antigen (HLA) matching, pretransplant dialysis, height standard deviation score (SDS), pretransplant hypertension, cold ischemia time, number of renal arteries, ureteral anastomosis, time to diuresis, time of transplantation, occurrence of acute tubular necrosis (ATN), primary and secondary immunosuppression, total dose of steroids in the first 3 months, development of acute rejection and post-transplant hypertension. Using univariate analysis, the significant predictors for graft survival were HLA matching, type of primary urinary recontinuity, time to diuresis, ATN, acute rejection and post-transplant hypertension. The multivariate analysis restricted the significance to acute rejection and post-transplant hypertension. The independent determinants of graft survival in live-donor pediatric and adolescent renal transplant recipients are acute rejection and post-transplant hypertension.

Discriminant analysis of cardiovascular and respiratory variables for classification of road cyclists by specialty.

PubMed

Nikolić, Biljana; Martinović, Jelena; Matić, Milan; Stefanović, Đorđe

2018-05-29

Different variables determine the performance of cyclists, which brings up the question how these parameters may help in their classification by specialty. The aim of the study was to determine differences in cardiorespiratory parameters of male cyclists according to their specialty, flat rider (N=21), hill rider (N=35) and sprinter (N=20) and obtain the multivariate model for further cyclists classification by specialties, based on selected variables. Seventeen variables were measured at submaximal and maximum load on the cycle ergometer Cosmed E 400HK (Cosmed, Rome, Italy) (initial 100W with 25W increase, 90-100 rpm). Multivariate discriminant analysis was used to determine which variables group cyclists within their specialty, and to predict which variables can direct cyclists to a particular specialty. Among nine variables that statistically contribute to the discriminant power of the model, achieved power on the anaerobic threshold and the produced CO2 had the biggest impact. The obtained discriminatory model correctly classified 91.43% of flat riders, 85.71% of hill riders, while sprinters were classified completely correct (100%), i.e. 92.10% of examinees were correctly classified, which point out the strength of the discriminatory model. Respiratory indicators mostly contribute to the discriminant power of the model, which may significantly contribute to training practice and laboratory tests in future.

Application of Multivariable Analysis and FTIR-ATR Spectroscopy to the Prediction of Properties in Campeche Honey

PubMed Central

Pat, Lucio; Ali, Bassam; Guerrero, Armando; Córdova, Atl V.; Garduza, José P.

2016-01-01

Attenuated total reflectance-Fourier transform infrared spectrometry and chemometrics model was used for determination of physicochemical properties (pH, redox potential, free acidity, electrical conductivity, moisture, total soluble solids (TSS), ash, and HMF) in honey samples. The reference values of 189 honey samples of different botanical origin were determined using Association Official Analytical Chemists, (AOAC), 1990; Codex Alimentarius, 2001, International Honey Commission, 2002, methods. Multivariate calibration models were built using partial least squares (PLS) for the measurands studied. The developed models were validated using cross-validation and external validation; several statistical parameters were obtained to determine the robustness of the calibration models: (PCs) optimum number of components principal, (SECV) standard error of cross-validation, (R 2 cal) coefficient of determination of cross-validation, (SEP) standard error of validation, and (R 2 val) coefficient of determination for external validation and coefficient of variation (CV). The prediction accuracy for pH, redox potential, electrical conductivity, moisture, TSS, and ash was good, while for free acidity and HMF it was poor. The results demonstrate that attenuated total reflectance-Fourier transform infrared spectrometry is a valuable, rapid, and nondestructive tool for the quantification of physicochemical properties of honey. PMID:28070445

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.

Risk Assessment of Hepatocellular Carcinoma in Patients with Hepatitis C in China and the USA.

PubMed

Parikh, Neehar D; Fu, Sherry; Rao, Huiying; Yang, Ming; Li, Yumeng; Powell, Corey; Wu, Elizabeth; Lin, Andy; Xing, Baocai; Wei, Lai; Lok, Anna S F

2017-11-01

Hepatitis C (HCV) infection is an increasingly common cause of hepatocellular carcinoma (HCC) in China. We aimed to determine differences in demographic and behavioral profiles associated with HCC in HCV+ patients in China and the USA. Consecutive HCV+ patients were recruited from centers in China and the USA. Clinical data and lifestyle profiles were obtained through standardized questionnaires. Multivariable analysis was conducted to determine factors associated with HCC diagnosis within groups. We included 41 HCC patients from China and 71 from the USA, and 931 non-HCC patients in China and 859 in China. Chinese patients with HCC were significantly younger, less likely to be male and to be obese than US patients with HCC (all p < 0.001). Chinese patients with HCC had a significantly lower rate of cirrhosis diagnosis (36.6 vs. 78.9%, p < 0.001); however, they also had a higher rate of hepatitis B core antibody positivity (63.4 vs. 36.8%, p = 0.007). In a multivariable analysis of the entire Chinese cohort, age > 55, male sex, the presence of diabetes, and time from maximum weight were associated with HCC, while tea consumption was associated with a decreased HCC risk (OR 0.37, 95% CI 0.16-0.88). In the US cohort, age > 55, male sex, and cirrhosis were associated with HCC on multivariable analysis. With the aging Chinese population and increasing rates of diabetes, there will likely be continued increase in the incidence of HCV-related HCC in China. The protective effect of tea consumption on HCC development deserves further validation.

Searching for forcing signatures in decadal patterns of shoreline change

NASA Astrophysics Data System (ADS)

Burningham, H.; French, J.

2016-12-01

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

Unmet Dental Needs and Barriers to Dental Care among Children with Autism Spectrum Disorders

ERIC Educational Resources Information Center

Lai, Bien; Milano, Michael; Roberts, Michael W.; Hooper, Stephen R.

2012-01-01

Mail-in pilot-tested questionnaires were sent to a stratified random sample of 1,500 families from the North Carolina Autism Registry. Multivariate logistic regression analysis was used to determine the significance of unmet dental needs and other predictors. Of 568 surveys returned (Response Rate = 38%), 555 were complete and usable. Sixty-five…

Communication and Other Critical Predictors of Orientation to Change: A Multivariate Analysis.

ERIC Educational Resources Information Center

Edeani, David O.

A study was conducted to examine the contributions to the determination of individuals' orientation to change made by such factors as the individuals' demographic, social structural, personality, and cognitive characteristics. Data were collected in a field sample of 159 adult residents of New Athens, Illinois, a town of 2,000 inhabitants near…

Studying the Relationship between Children's Self-Control and Academic Achievement: An Application of Second-Order Growth Curve Model Analysis.

ERIC Educational Resources Information Center

Kim, Sooyeon; Murry, Velma McBride; Brody, Gene H.

The functional relationships between developmental change in children's self-control and academic achievement were examined using longitudinal family data. Multivariate latent growth models (LGM) were specified to determine whether the rate of growth in academic achievement changes as a function of developmental change in self-control. Data came…

An Analysis of Secondary Teachers' Reasoning with Participatory Sensing Data

ERIC Educational Resources Information Center

Gould, Robert; Bargagliotti, Anna; Johnson, Terri

2017-01-01

Participatory sensing is a data collection method in which communities of people collect and share data to investigate large-scale processes. These data have many features often associated with the big data paradigm: they are rich and multivariate, include non-numeric data, and are collected as determined by an algorithm rather than by traditional…

Determining the Number of Component Clusters in the Standard Multivariate Normal Mixture Model Using Model-Selection Criteria.

DTIC Science & Technology

1983-06-16

has been advocated by Gnanadesikan and 𔃾ilk (1969), and others in the literature. This suggests that, if we use the formal signficance test type...American Statistical Asso., 62, 1159-1178. Gnanadesikan , R., and Wilk, M..B. (1969). Data Analytic Methods in Multi- variate Statistical Analysis. In

A Multivariate Analysis of Personality, Values and Expectations as Correlates of Career Aspirations of Final Year Medical Students

ERIC Educational Resources Information Center

Rogers, Mary E.; Searle, Judy; Creed, Peter A.; Ng, Shu-Kay

2010-01-01

This study reports on the career intentions of 179 final year medical students who completed an online survey that included measures of personality, values, professional and lifestyle expectations, and well-being. Logistic regression analyses identified the determinants of preferred medical specialty, practice location and hours of work.…

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…

Traditional foods and 25(OH)D concentrations in a subarctic First Nations community.

PubMed

Mansuri, Sudaba; Badawi, Alaa; Kayaniyil, Sheena; Cole, David E; Harris, Stewart B; Mamakeesick, Mary; Wolever, Thomas; Gittelsohn, Joel; Maguire, Jonathon L; Connelly, Philip W; Zinman, Bernard; Hanley, Anthony J

2016-01-01

Sub-optimal vitamin D status is common worldwide and the condition may be associated with increased risk for various chronic diseases. In particular, low vitamin D status is highly prevalent in indigenous communities in Canada, although limited data are available on the determinants of serum 25-hydroxyvitamin D (25(OH)D) concentrations in this population. The relationship between traditional food consumption and vitamin D status has not been well documented. To investigate the determinants of serum 25(OH)D status in a First Nations community in Ontario, Canada, with a focus on the role of traditional food consumption and activities. A cross-sectional analysis was conducted within the Sandy Lake Health and Diabetes Project (2003-2005). A total of 445 participants (>12 years of age) were assessed for serum 25(OH)D status, anthropometric and lifestyle variables, including traditional and non-traditional dietary practices and activities. Diet patterns were identified using factor analysis, and multivariate linear regression analysis was used to analyse the determinants of 25(OH)D concentrations. Mean serum 25(OH)D concentrations were 22.1 nmol/L (16.9, 29.9 nmol/L) in men and 20.5 nmol/L (16.0, 27.3 nmol/L) in women. Multivariate determinants of higher serum 25(OH)D included higher consumption of traditional and healthier market foods, higher wild fish consumption, male gender, spring/summer season of blood collection and more frequent physical activity. Significant negative determinants included hours of TV/day, higher BMI and higher consumption of unhealthy market foods. Traditional food consumption contributed independently to higher 25(OH)D concentrations in a First Nations community with a high prevalence of sub-optimal vitamin D status.

Determinants of brain-derived neurotrophic factor (BDNF) in umbilical cord and maternal serum.

PubMed

Flöck, A; Weber, S K; Ferrari, N; Fietz, C; Graf, C; Fimmers, R; Gembruch, U; Merz, W M

2016-01-01

Brain-derived neurotrophic factor (BDNF) plays a fundamental role in brain development; additionally, it is involved in various aspects of cerebral function, including neurodegenerative and psychiatric diseases. Involvement of BDNF in parturition has not been investigated. The aim of our study was to analyze determinants of umbilical cord BDNF (UC-BDNF) concentrations of healthy, term newborns and their respective mothers. This cross-sectional prospective study was performed at a tertiary referral center. Maternal venous blood samples were taken on admission to labor ward; newborn venous blood samples were drawn from the umbilical cord (UC), before delivery of the placenta. Analysis was performed with a commercially available immunoassay. Univariate analyses and stepwise multivariate regression models were applied. 120 patients were recruited. UC-BDNF levels were lower than maternal serum concentrations (median 641 ng/mL, IQR 506 vs. median 780 ng/mL, IQR 602). Correlation between UC- and maternal BDNF was low (R=0.251, p=0.01). In univariate analysis, mode of delivery (MoD), gestational age (GA), body mass index at delivery, and gestational diabetes were determinants of UC-BDNF (MoD and smoking for maternal BDNF, respectively). Stepwise multivariate regression analysis revealed a model with MoD and GA as determinants for UC-BDNF (MoD for maternal BDNF). MoD and GA at delivery are determinants of circulating BDNF in the mother and newborn. We hypothesize that BDNF, like other neuroendocrine factors, is involved in the neuroendocrine cascade of delivery. Timing and mode of delivery may exert BDNF-induced effects on the cerebral function of newborns and their mothers. Copyright © 2015 Elsevier Ltd. All rights reserved.

[Application of multivariate analysis to the serum mineral and trace element content on differentiation of healthy subjects].

PubMed

Rodríguez Rodríguez, E; Henríquez Sánchez, P; López Blanco, F; Díaz Romero, C; Serra Majem, L

2004-01-01

Serum concentrations of Na, K, Ca, Mg, Fe, Cu, Zn, Se, Mn and P were determined in apparently health individuals representing of the population of the Canary Islands. Multivariate analysis was applied on the data matrix in order to differentiate the individuals according several criteria such as gender, age, island and province of residence, smoking and drinking habits and physical exercise. 395 serum samples (187 men and 208 women) were analyzed mean age of 38.4 +/- 20.0 years. Individuals data about age, gender, weight, height, alcohol consumption, smoking habits and physical exercise were recorded using standardized questionnaires. The determination of minerals was carried out by flame emission spectrometry (Na and K) and atomic absorption spectrometry with flame air/acetylene (Ca, Mg, Fe, Cu, Zn), hybride generation (Se) and graphite furnace (Mn). The P was determined by a colorimetric method. The sex and age of individuals influenced on the serum concentrations of some minerals, Cu and Fe, and P and Se, respectively. The island of residence influenced the mean concentrations of the most the minerals analysed. The smoking and drinking habits do not seem to influence the mean contents of the minerals in an important manner. Physical exercise had significant influence on the P, Cu and Mn concentrations in serum. The water for consumption influenced on the serum concentrations of the electrolytes and Ca and Mg, but it did not affect the concentrations of the trace elements. Applying discriminant analysis the individuals lower 18 years were reasonably well differentiated (89% of the individuals correctly classified) from the rest of individuals. A tendency for differentiation of individuals according to the island of residence was also observed. A low differentiation of the individuals according to the sex, province or island or residence and habits or life style was observed after application of multivariate analysis techniques. However, the adults were reasonably differentiated from the children and adolescent, and the inhabitants of Lanzarote and La Palma tend to separate from the rest of the individuals of their province.

Multivariate analysis: A statistical approach for computations

NASA Astrophysics Data System (ADS)

Michu, Sachin; Kaushik, Vandana

2014-10-01

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

Multivariate Cluster Analysis.

ERIC Educational Resources Information Center

McRae, Douglas J.

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

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.

Insurance Coverage for Rehabilitation Therapies and Association with Social Participation Outcomes among Low-Income Children.

PubMed

Mirza, Mansha; Kim, Yoonsang

2016-01-01

(1) To profile children's health insurance coverage rates for specific rehabilitation therapies; (2) to determine whether coverage for rehabilitation therapies is associated with social participation outcomes after adjusting for child and household characteristics; (3) to assess whether rehabilitation insurance differentially affects social participation of children with and without disabilities. We conducted a cross-sectional analysis of secondary survey data on 756 children (ages 3-17) from 370 households living in low-income neighborhoods in a Midwestern U.S. city. Multivariate mixed effects logistic regression models were estimated. Significantly higher proportions of children with disabilities had coverage for physical therapy, occupational therapy, and speech and language pathology, yet gaps in coverage were noted. Multivariate analysis indicated that rehabilitation insurance coverage was significantly associated with social participation (OR = 1.67, 95% CI: 1.013-2.75). This trend was sustained in subgroup analysis. Findings support the need for comprehensive coverage of all essential services under children's health insurance programs.

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

PubMed

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

2012-01-11

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

Influence of the site of origin on the outcome of squamous cell carcinoma of the maxilla-oral versus sinus.

PubMed

Bobinskas, A M; Wiesenfeld, D; Chandu, A

2014-02-01

The maxilla may be affected by squamous cell carcinoma (SCC) from both oral and sinus sites. We sought to determine whether the site of origin of the maxillary tumour, oral as compared to sinus, influences survival. Univariate Kaplan-Meier and multivariate Cox proportional hazard models analysis of 58 patients with SCC involving the maxilla, treated with curative intent, was conducted. The overall 5-year disease-free survival for the group was 41.7%. Five-year disease-free survival for oral subsite SCC was 56.8%, while for sinus subsite was only 21.6%. Univariate analysis found SCC of sinus origin to be associated with a poorer prognosis, however this was not confirmed on multivariate analysis. T-stage and positive margins were found to be the only independent risk factors. For SCC of the maxilla, sinus origin of the tumour per se does not confer a poorer prognosis; however, as a result of the complex anatomy of the midface, these tumours can present at an advanced stage, while surgical control of the disease can be more difficult, especially posteriorly. Tumour size and positive margins were the determinants of a poor prognosis in this group of patients with maxillary SCC. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.

National reimbursement listing determinants of new cancer drugs: a retrospective analysis of 58 cancer treatment appraisals in 2007-2016 in South Korea.

PubMed

Kim, Eun-Sook; Kim, Jung-Ae; Lee, Eui-Kyung

2017-08-01

Since the positive-list system was introduced, concerns have been raised over restricting access to new cancer drugs in Korea. Policy changes in the decision-making process, such as risk-sharing agreement and the waiver of pharmacoeconomic data submission, were implemented to improve access to oncology medicines, and other factors are also involved in the reimbursement for cancer drugs. The aim of this study is to investigate the reimbursement listing determinants of new cancer drugs in Korea. All cancer treatment appraisals of Health Insurance Review and Assessment during 2007-2016 were analyzed based on 13 independent variables (comparative effectiveness, cost-effectiveness, drug-price comparison, oncology-specific policy, and innovation such as new mode of action). Univariate and multivariate logistic analyses were conducted. Of 58 analyzed submissions, 40% were listed in the national reimbursement formulary. In univariate analysis, four variables were related to listing: comparative effectiveness, drug-price comparison, new mode of action, and risk-sharing agreement. In multivariate logistic analysis, three variables significantly increased the likelihood of listing: clinical improvement, below alternative's price, and risk-sharing arrangement. Cancer drug's listing increased from 17% to 47% after risk-sharing agreement implementation. Clinical improvement, cost-effectiveness, and RSA application are critical to successful national reimbursement listing.

Exploring laser-induced breakdown spectroscopy for nuclear materials analysis and in-situ applications

NASA Astrophysics Data System (ADS)

Martin, Madhavi Z.; Allman, Steve; Brice, Deanne J.; Martin, Rodger C.; Andre, Nicolas O.

2012-08-01

Laser-induced breakdown spectroscopy (LIBS) has been used to determine the limits of detection of strontium (Sr) and cesium (Cs), common nuclear fission products. Additionally, detection limits were determined for cerium (Ce), often used as a surrogate for radioactive plutonium in laboratory studies. Results were obtained using a laboratory instrument with a Nd:YAG laser at fundamental wavelength of 1064 nm, frequency doubled to 532 nm with energy of 50 mJ/pulse. The data was compared for different concentrations of Sr and Ce dispersed in a CaCO3 (white) and carbon (black) matrix. We have addressed the sampling errors, limits of detection, reproducibility, and accuracy of measurements as they relate to multivariate analysis in pellets that were doped with the different elements at various concentrations. These results demonstrate that LIBS technique is inherently well suited for in situ analysis of nuclear materials in hot cells. Three key advantages are evident: (1) small samples (mg) can be evaluated; (2) nuclear materials can be analyzed with minimal sample preparation; and (3) samples can be remotely analyzed very rapidly (ms-seconds). Our studies also show that the methods can be made quantitative. Very robust multivariate models have been used to provide quantitative measurement and statistical evaluation of complex materials derived from our previous research on wood and soil samples.

Experimental analysis of multivariate female choice in gray treefrogs (Hyla versicolor): evidence for directional and stabilizing selection.

PubMed

Gerhardt, H Carl; Brooks, Robert

2009-10-01

Even simple biological signals vary in several measurable dimensions. Understanding their evolution requires, therefore, a multivariate understanding of selection, including how different properties interact to determine the effectiveness of the signal. We combined experimental manipulation with multivariate selection analysis to assess female mate choice on the simple trilled calls of male gray treefrogs. We independently and randomly varied five behaviorally relevant acoustic properties in 154 synthetic calls. We compared response times of each of 154 females to one of these calls with its response to a standard call that had mean values of the five properties. We found directional and quadratic selection on two properties indicative of the amount of signaling, pulse number, and call rate. Canonical rotation of the fitness surface showed that these properties, along with pulse rate, contributed heavily to a major axis of stabilizing selection, a result consistent with univariate studies showing diminishing effects of increasing pulse number well beyond the mean. Spectral properties contributed to a second major axis of stabilizing selection. The single major axis of disruptive selection suggested that a combination of two temporal and two spectral properties with values differing from the mean should be especially attractive.

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.

Time-varying correlations in global real estate markets: A multivariate GARCH with spatial effects approach

NASA Astrophysics Data System (ADS)

Gu, Huaying; Liu, Zhixue; Weng, Yingliang

2017-04-01

The present study applies the multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) with spatial effects approach for the analysis of the time-varying conditional correlations and contagion effects among global real estate markets. A distinguishing feature of the proposed model is that it can simultaneously capture the spatial interactions and the dynamic conditional correlations compared with the traditional MGARCH models. Results reveal that the estimated dynamic conditional correlations have exhibited significant increases during the global financial crisis from 2007 to 2009, thereby suggesting contagion effects among global real estate markets. The analysis further indicates that the returns of the regional real estate markets that are in close geographic and economic proximities exhibit strong co-movement. In addition, evidence of significantly positive leverage effects in global real estate markets is also determined. The findings have significant implications on global portfolio diversification opportunities and risk management practices.

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

PubMed

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

2016-03-23

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

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

NASA Astrophysics Data System (ADS)

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

2016-03-01

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

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

Liver Transplantation for Fulminant Hepatic Failure

PubMed Central

Farmer, Douglas G.; Anselmo, Dean M.; Ghobrial, R. Mark; Yersiz, Hasan; McDiarmid, Suzanne V.; Cao, Carlos; Weaver, Michael; Figueroa, Jesus; Khan, Khurram; Vargas, Jorge; Saab, Sammy; Han, Steven; Durazo, Francisco; Goldstein, Leonard; Holt, Curtis; Busuttil, Ronald W.

2003-01-01

Objective To analyze outcomes after liver transplantation (LT) in patients with fulminant hepatic failure (FHF) with emphasis on pretransplant variables that can potentially help predict posttransplant outcome. Summary Background Data FHF is a formidable clinical problem associated with a high mortality rate. While LT is the treatment of choice for irreversible FHF, few investigations have examined pretransplant variables that can potentially predict outcome after LT. Methods A retrospective review was undertaken of all patients undergoing LT for FHF at a single transplant center. The median follow-up was 41 months. Thirty-five variables were analyzed by univariate and multivariate analysis to determine their impact on patient and graft survival. Results Two hundred four patients (60% female, median age 20.2 years) required urgent LT for FHF. Before LT, the majority of patients were comatose (76%), on hemodialysis (16%), and ICU-bound. The 1- and 5-year survival rates were 73% and 67% (patient) and 63% and 57% (graft). The primary cause of patient death was sepsis, and the primary cause of graft failure was primary graft nonfunction. Univariate analysis of pre-LT variables revealed that 19 variables predicted survival. From these results, multivariate analysis determined that the serum creatinine was the single most important prognosticator of patient survival. Conclusions This study, representing one of the largest published series on LT for FHF, demonstrates a long-term survival of nearly 70% and develops a clinically applicable and readily measurable set of pretransplant factors that determine posttransplant outcome. PMID:12724633

On the interpretation of weight vectors of linear models in multivariate neuroimaging.

PubMed

Haufe, Stefan; Meinecke, Frank; Görgen, Kai; Dähne, Sven; Haynes, John-Dylan; Blankertz, Benjamin; Bießmann, Felix

2014-02-15

The increase in spatiotemporal resolution of neuroimaging devices is accompanied by a trend towards more powerful multivariate analysis methods. Often it is desired to interpret the outcome of these methods with respect to the cognitive processes under study. Here we discuss which methods allow for such interpretations, and provide guidelines for choosing an appropriate analysis for a given experimental goal: For a surgeon who needs to decide where to remove brain tissue it is most important to determine the origin of cognitive functions and associated neural processes. In contrast, when communicating with paralyzed or comatose patients via brain-computer interfaces, it is most important to accurately extract the neural processes specific to a certain mental state. These equally important but complementary objectives require different analysis methods. Determining the origin of neural processes in time or space from the parameters of a data-driven model requires what we call a forward model of the data; such a model explains how the measured data was generated from the neural sources. Examples are general linear models (GLMs). Methods for the extraction of neural information from data can be considered as backward models, as they attempt to reverse the data generating process. Examples are multivariate classifiers. Here we demonstrate that the parameters of forward models are neurophysiologically interpretable in the sense that significant nonzero weights are only observed at channels the activity of which is related to the brain process under study. In contrast, the interpretation of backward model parameters can lead to wrong conclusions regarding the spatial or temporal origin of the neural signals of interest, since significant nonzero weights may also be observed at channels the activity of which is statistically independent of the brain process under study. As a remedy for the linear case, we propose a procedure for transforming backward models into forward models. This procedure enables the neurophysiological interpretation of the parameters of linear backward models. We hope that this work raises awareness for an often encountered problem and provides a theoretical basis for conducting better interpretable multivariate neuroimaging analyses. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.

Polytopic vector analysis in igneous petrology: Application to lunar petrogenesis

NASA Technical Reports Server (NTRS)

Shervais, John W.; Ehrlich, R.

1993-01-01

Lunar samples represent a heterogeneous assemblage of rocks with complex inter-relationships that are difficult to decipher using standard petrogenetic approaches. These inter-relationships reflect several distinct petrogenetic trends as well as thermomechanical mixing of distinct components. Additional complications arise from the unequal quality of chemical analyses and from the fact that many samples (e.g., breccia clasts) are too small to be representative of the system from which they derived. Polytopic vector analysis (PVA) is a multi-variate procedure used as a tool for exploratory data analysis. PVA allows the analyst to classify samples and clarifies relationships among heterogenous samples with complex petrogenetic histories. It differs from orthogonal factor analysis in that it uses non-orthogonal multivariate sample vectors to extract sample endmember compositions. The output from a Q-mode (sample based) factor analysis is the initial step in PVA. The Q-mode analysis, using criteria established by Miesch and Klovan and Miesch, is used to determine the number of endmembers in the data system. The second step involves determination of endmembers and mixing proportions with all output expressed in the same geochemical variable as the input. The composition of endmembers is derived by analysis of the variability of the data set. Endmembers need not be present in the data set, nor is it necessary for their composition to be known a priori. A set of any endmembers defines a 'polytope' or classification figure (triangle for a three component system, tetrahedron for a four component system, a 'five-tope' in four dimensions for five component system, et cetera).

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

PubMed

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

2016-05-17

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

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…

Interaction of Soil Heavy Metal Pollution with Industrialisation and the Landscape Pattern in Taiyuan City, China

PubMed Central

Liu, Yong; Su, Chao; Zhang, Hong; Li, Xiaoting; Pei, Jingfei

2014-01-01

Many studies indicated that industrialization and urbanization caused serious soil heavy metal pollution from industrialized age. However, fewer previous studies have conducted a combined analysis of the landscape pattern, urbanization, industrialization, and heavy metal pollution. This paper was aimed at exploring the relationships of heavy metals in the soil (Pb, Cu, Ni, As, Cd, Cr, Hg, and Zn) with landscape pattern, industrialisation, urbanisation in Taiyuan city using multivariate analysis. The multivariate analysis included correlation analysis, analysis of variance (ANOVA), independent-sample T test, and principal component analysis (PCA). Geographic information system (GIS) was also applied to determine the spatial distribution of the heavy metals. The spatial distribution maps showed that the heavy metal pollution of the soil was more serious in the centre of the study area. The results of the multivariate analysis indicated that the correlations among heavy metals were significant, and industrialisation could significantly affect the concentrations of some heavy metals. Landscape diversity showed a significant negative correlation with the heavy metal concentrations. The PCA showed that a two-factor model for heavy metal pollution, industrialisation, and the landscape pattern could effectively demonstrate the relationships between these variables. The model explained 86.71% of the total variance of the data. Moreover, the first factor was mainly loaded with the comprehensive pollution index (P), and the second factor was primarily loaded with landscape diversity and dominance (H and D). An ordination of 80 samples could show the pollution pattern of all the samples. The results revealed that local industrialisation caused heavy metal pollution of the soil, but such pollution could respond negatively to the landscape pattern. The results of the study could provide a basis for agricultural, suburban, and urban planning. PMID:25251460

Interaction of soil heavy metal pollution with industrialisation and the landscape pattern in Taiyuan city, China.

PubMed

Liu, Yong; Su, Chao; Zhang, Hong; Li, Xiaoting; Pei, Jingfei

2014-01-01

Many studies indicated that industrialization and urbanization caused serious soil heavy metal pollution from industrialized age. However, fewer previous studies have conducted a combined analysis of the landscape pattern, urbanization, industrialization, and heavy metal pollution. This paper was aimed at exploring the relationships of heavy metals in the soil (Pb, Cu, Ni, As, Cd, Cr, Hg, and Zn) with landscape pattern, industrialisation, urbanisation in Taiyuan city using multivariate analysis. The multivariate analysis included correlation analysis, analysis of variance (ANOVA), independent-sample T test, and principal component analysis (PCA). Geographic information system (GIS) was also applied to determine the spatial distribution of the heavy metals. The spatial distribution maps showed that the heavy metal pollution of the soil was more serious in the centre of the study area. The results of the multivariate analysis indicated that the correlations among heavy metals were significant, and industrialisation could significantly affect the concentrations of some heavy metals. Landscape diversity showed a significant negative correlation with the heavy metal concentrations. The PCA showed that a two-factor model for heavy metal pollution, industrialisation, and the landscape pattern could effectively demonstrate the relationships between these variables. The model explained 86.71% of the total variance of the data. Moreover, the first factor was mainly loaded with the comprehensive pollution index (P), and the second factor was primarily loaded with landscape diversity and dominance (H and D). An ordination of 80 samples could show the pollution pattern of all the samples. The results revealed that local industrialisation caused heavy metal pollution of the soil, but such pollution could respond negatively to the landscape pattern. The results of the study could provide a basis for agricultural, suburban, and urban planning.

Impact of liver volume and liver function on posthepatectomy liver failure after portal vein embolization- A multivariable cohort analysis.

PubMed

Alizai, Patrick H; Haelsig, Annabel; Bruners, Philipp; Ulmer, Florian; Klink, Christian D; Dejong, Cornelis H C; Neumann, Ulf P; Schmeding, Maximilian

2018-01-01

Liver failure remains a life-threatening complication after liver resection, and is difficult to predict preoperatively. This retrospective cohort study evaluated different preoperative factors in regard to their impact on posthepatectomy liver failure (PHLF) after extended liver resection and previous portal vein embolization (PVE). Patient characteristics, liver function and liver volumes of patients undergoing PVE and subsequent liver resection were analyzed. Liver function was determined by the LiMAx test (enzymatic capacity of cytochrome P450 1A2). Factors associated with the primary end point PHLF (according to ISGLS definition) were identified through multivariable analysis. Secondary end points were 30-day mortality and morbidity. 95 patients received PVE, of which 64 patients underwent major liver resection. PHLF occurred in 7 patients (11%). Calculated postoperative liver function was significantly lower in patients with PHLF than in patients without PHLF (67 vs. 109 μg/kg/h; p = 0.01). Other factors associated with PHLF by univariable analysis were age, future liver remnant, MELD score, ASA score, renal insufficiency and heart insufficiency. By multivariable analysis, future liver remnant was the only factor significantly associated with PHLF (p = 0.03). Mortality and morbidity rates were 4.7% and 29.7% respectively. Future liver remnant is the only preoperative factor with a significant impact on PHLF. Assessment of preoperative liver function may additionally help identify patients at risk for PHLF.

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.

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

NASA Astrophysics Data System (ADS)

zhang, L.

2011-12-01

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

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

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

Ladd-Lively, Jennifer L

2014-01-01

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

Histologic prognosticators in feline osteosarcoma: a comparison with phenotypically similar canine osteosarcoma.

PubMed

Dimopoulou, Maria; Kirpensteijn, Jolle; Moens, Hester; Kik, Marja

2008-07-01

To investigate the histologic characteristics of feline osteosarcoma (OS) and compare the histologic data with phenotypically comparable canine OS. The effects of histologic and clinical variables on survival statistics were evaluated. Retrospective study. Cats (n=62) and dogs (22). Medical records of 62 cats with OS were reviewed for clinically relevant data. Clinical outcome was obtained by telephone interview. Histologic characteristics of OS were classified using a standardized grading system. Histologic characteristics in 22 feline skeletal OS were compared with 22 canine skeletal OS of identical location and subtype. Prognostic variables for clinical outcome were determined using multivariate analysis. Feline OS was characterized by moderate to abundant cellular pleomorphism, low mitotic index, small to moderate amounts of matrix, high cellularity, and a moderate amount of necrosis. There was no significant difference between histologic variables in feline and canine OS. Histologic grade, surgery, and mitotic index significantly influenced clinical outcome as determined by multivariate analysis. Tumor invasion into vessels was not identified as a significant prognosticator. Feline and canine skeletal OS have similar histologic but different prognostic characteristics. Prognosis for cats with OS is related to histologic grade and mitotic index of the tumor.

Independent risk factors of morbidity in penetrating colon injuries.

PubMed

Girgin, Sadullah; Gedik, Ercan; Uysal, Ersin; Taçyildiz, Ibrahim Halil

2009-05-01

The present study explored the factors effective on colon-related morbidity in patients with penetrating injury of the colon. The medical records of 196 patients were reviewed for variables including age, gender, factor of trauma, time between injury and operation, shock, duration of operation, Penetrating Abdominal Trauma Index (PATI), Injury Severity Score (ISS), site of colon injury, Colon Injury Score, fecal contamination, number of associated intra- and extraabdominal organ injuries, units of transfused blood within the first 24 hours, and type of surgery. In order to determine the independent risk factors, multivariate logistic regression analysis was performed. Gunshot wounds, interval between injury and operation > or =6 hours, shock, duration of the operation > or =6 hours, PATI > or =25, ISS > or =20, Colon Injury Score > or = grade 3, major fecal contamination, number of associated intraabdominal organ injuries >2, number of associated extraabdominal organ injuries >2, multiple blood transfusions, and diversion were significantly associated with morbidity. Multivariate logistic regression analysis showed diversion and transfusion of > or =4 units in the first 24 hours as independent risk factors affecting colon-related morbidity. Diversion and transfusion of > or =4 units in the first 24 hours were determined to be independent risk factors for colon-related morbidity.

Malnutrition: prevalence and risk factors among the children younger than five years in a semi-urban area of Abidjan.

PubMed

Sackou Kouakou, J G; Aka, B S; Hounsa, A E; Attia, R; Wilson, R; Ake, O; Oga, S; Houenou, Y; Kouadio, L

2016-08-01

In Côte d'Ivoire, the prevalence of malnutrition among children younger than 5 years exceeded 5% in 2011 and was thus considered serious. This overall prevalence may nonetheless mask differences and specificities between regions and municipalities. This study sought to determine the prevalence and risk factors of malnutrition among children in this age group in a semi-urban area of Abidjan. This exhaustive, descriptive, cross-sectional survey took place from May 6 to July 31, 2010. The children's nutritional status was determined according to the WHO criteria. Univariate and multivariate analysis of factors associated with malnutrition (social and demographic characteristics, immunization status, children's eating practices, and household characteristics) were studied. We visited 668 households and recruited 809 children. The prevalence of malnutrition was 22.5%. Multivariate analysis showed that the introduction of porridge after 6 months halved the risk of malnutrition. Risk tripled for children whose father's occupation did not guarantee a regular income. Among the factors highlighted by this study, dietary practices seem the most amenable to corrective action. For example, the adoption of outreach programs by the Maternal and Child Protection services could improve nutritional practices in households.

Simultaneous determination of Nifuroxazide and Drotaverine hydrochloride in pharmaceutical preparations by bivariate and multivariate spectral analysis

NASA Astrophysics Data System (ADS)

Metwally, Fadia H.

2008-02-01

The quantitative predictive abilities of the new and simple bivariate spectrophotometric method are compared with the results obtained by the use of multivariate calibration methods [the classical least squares (CLS), principle component regression (PCR) and partial least squares (PLS)], using the information contained in the absorption spectra of the appropriate solutions. Mixtures of the two drugs Nifuroxazide (NIF) and Drotaverine hydrochloride (DRO) were resolved by application of the bivariate method. The different chemometric approaches were applied also with previous optimization of the calibration matrix, as they are useful in simultaneous inclusion of many spectral wavelengths. The results found by application of the bivariate, CLS, PCR and PLS methods for the simultaneous determinations of mixtures of both components containing 2-12 μg ml -1 of NIF and 2-8 μg ml -1 of DRO are reported. Both approaches were satisfactorily applied to the simultaneous determination of NIF and DRO in pure form and in pharmaceutical formulation. The results were in accordance with those given by the EVA Pharma reference spectrophotometric method.

Elemental analysis of soils using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) and laser-induced breakdown spectroscopy (LIBS) with multivariate discrimination: tape mounting as an alternative to pellets for small forensic transfer specimens.

PubMed

Jantzi, Sarah C; Almirall, José R

2014-01-01

Elemental analysis of soil is a useful application of both laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) and laser-induced breakdown spectroscopy (LIBS) in geological, agricultural, environmental, archeological, planetary, and forensic sciences. In forensic science, the question to be answered is often whether soil specimens found on objects (e.g., shoes, tires, or tools) originated from the crime scene or other location of interest. Elemental analysis of the soil from the object and the locations of interest results in a characteristic elemental profile of each specimen, consisting of the amount of each element present. Because multiple elements are measured, multivariate statistics can be used to compare the elemental profiles in order to determine whether the specimen from the object is similar to one of the locations of interest. Previous work involved milling and pressing 0.5 g of soil into pellets before analysis using LA-ICP-MS and LIBS. However, forensic examiners prefer techniques that require smaller samples, are less time consuming, and are less destructive, allowing for future analysis by other techniques. An alternative sample introduction method was developed to meet these needs while still providing quantitative results suitable for multivariate comparisons. The tape-mounting method involved deposition of a thin layer of soil onto double-sided adhesive tape. A comparison of tape-mounting and pellet method performance is reported for both LA-ICP-MS and LIBS. Calibration standards and reference materials, prepared using the tape method, were analyzed by LA-ICP-MS and LIBS. As with the pellet method, linear calibration curves were achieved with the tape method, as well as good precision and low bias. Soil specimens from Miami-Dade County were prepared by both the pellet and tape methods and analyzed by LA-ICP-MS and LIBS. Principal components analysis and linear discriminant analysis were applied to the multivariate data. Results from both the tape method and the pellet method were nearly identical, with clear groupings and correct classification rates of >94%.

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

Multivariate Analysis and Machine Learning in Cerebral Palsy Research

PubMed Central

Zhang, Jing

2017-01-01

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

Multivariate Analysis and Machine Learning in Cerebral Palsy Research.

PubMed

Zhang, Jing

2017-01-01

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

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.

Determinants of Paramedic Response Readiness for CBRNE Threats

PubMed Central

Jones, Alison; Smith, George; Nelson, Jenny; Agho, Kingsley; Taylor, Melanie; Raphael, Beverley

2010-01-01

Paramedics play a pivotal role in the response to major emergencies. Recent evidence indicates that their confidence and willingness to respond to chemical, biological, radiological, nuclear, and explosives-related (CBRNE) incidents differs from that relating to their “routine” emergency work. To further investigate the factors underpinning their readiness to respond to CBRNE incidents, paramedics in New South Wales (NSW), Australia, were asked to complete a validated online survey instrument. Univariate and multivariate analyses were performed to examine associated factors determining readiness. The sample of 663 respondents was weighted to reflect the NSW paramedic population as a whole. The univariate analysis indicated that gender, length of service, deployment concern, perceived personal resilience, CBRNE training, and incident experience were significantly associated with perceived CBRNE response readiness. In the initial multivariate analysis, significantly higher response readiness was associated with male gender, university education, and greater length of service (10-15 years). In the final multivariate model, the combined effect of training/incident experience negated the significant effects observed in the initial model and, importantly, showed that those with recent training reported higher readiness, irrespective of incident experience. Those with lower concern regarding CBRNE deployment and those with higher personal resilience were significantly more likely to report higher readiness (Adjusted Relative Risk [ARR] = 0.91, 95% CI: 0.84-0.99; ARR = 1.40, 95% CI: 1.11-1.72, respectively). These findings will assist emergency medical planners in recognizing occupational and dispositional factors associated with enhanced CBRNE readiness and highlight the important role of training in redressing potential readiness differences associated with these factors. PMID:20569060

Factors associated with seasonal influenza vaccination in pregnant women.

PubMed

Henninger, Michelle L; Irving, Stephanie A; Thompson, Mark; Avalos, Lyndsay Ammon; Ball, Sarah W; Shifflett, Pat; Naleway, Allison L

2015-05-01

This observational study followed a cohort of pregnant women during the 2010-2011 influenza season to determine factors associated with vaccination. Participants were 1105 pregnant women who completed a survey assessing health beliefs related to vaccination upon enrollment and were then followed to determine vaccination status by the end of the 2010-2011 influenza season. We conducted univariate and multivariate analyses to explore factors associated with vaccination status and a factor analysis of survey items to identify health beliefs associated with vaccination. Sixty-three percent (n=701) of the participants were vaccinated. In the univariate analyses, multiple factors were associated with vaccination status, including maternal age, race, marital status, educational level, and gravidity. Factor analysis identified two health belief factors associated with vaccination: participant's positive views (factor 1) and negative views (factor 2) of influenza vaccination. In a multivariate logistic regression model, factor 1 was associated with increased likelihood of vaccination (adjusted odds ratio [aOR]=2.18; 95% confidence interval [CI]=1.72-2.78), whereas factor 2 was associated with decreased likelihood of vaccination (aOR=0.36; 95% CI=0.28-0.46). After controlling for the two health belief factors in multivariate analyses, demographic factors significant in univariate analyses were no longer significant. Women who received a provider recommendation were about three times more likely to be vaccinated (aOR=3.14; 95% CI=1.99-4.96). Pregnant women's health beliefs about vaccination appear to be more important than demographic and maternal factors previously associated with vaccination status. Provider recommendation remains one of the most critical factors influencing vaccination during pregnancy.

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.

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…

Risk factors for lower extremity injuries among half marathon and marathon runners of the Lage Landen Marathon Eindhoven 2012: A prospective cohort study in the Netherlands.

PubMed

van Poppel, D; de Koning, J; Verhagen, A P; Scholten-Peeters, G G M

2016-02-01

To determine risk factors for running injuries during the Lage Landen Marathon Eindhoven 2012. Prospective cohort study. Population-based study. This study included 943 runners. Running injuries after the Lage Landen Marathon. Sociodemographic and training-related factors as well as lifestyle factors were considered as potential risk factors and assessed in a questionnaire 1 month before the running event. The association between potential risk factors and injuries was determined, per running distance separately, using univariate and multivariate logistic regression analysis. In total, 154 respondents sustained a running injury. Among the marathon runners, in the univariate model, body mass index ≥ 26 kg/m(2), ≤ 5 years of running experience, and often performing interval training, were significantly associated with running injuries, whereas in the multivariate model only ≤ 5 years of running experience and not performing interval training on a regular basis were significantly associated with running injuries. Among marathon runners, no multivariate model could be created because of the low number of injuries and participants. This study indicates that interval training on a regular basis may be recommended to marathon runners to reduce the risk of injury. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

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…

[Study on the status of institutional delivery and its determinants in rural Guangxi autonomous region].

PubMed

Li, Jian; Chen, Li-Li; Chen, Shu-Zhen; Cen, Ming-Yang; Zhao, Nai-Qing; Qian, Xu

2008-03-01

To understand the situation of institutional delivery of rural pregnant women in Guangxi Autonomous Region in the period of 1998 - 2003 and to identify the determinants on institutional delivery utilization. Using Andersen's behavioral model as analytical framework and Guangxi databank of the 3rd National Health Service Survey as data source, we described the status of institutional delivery with the rural women having had live birth history in the period of 1998 - 2003 as subjects, while and the univariate analysis and multivariate logistic analysis were done to identify determinants of institutional delivery utilization. Among a total number of 407 women with live birth history, 39.80 percent of them delivered at the health-care facilities. The rate of institutional delivery increased annually in 1998 - 2003 (P< 0.0001). The proportion of delivery in township health centers increased and the proportion of home delivery decreased by year (P< 0.0001). Results from both univariate and multivariate analysis showed that parity, education background of women, type of drinking water, time needed to get to the nearest healthcare facilities by the most convenient traffic,frequency of prenatal checkup, together with whether or not being advocated on institutional delivery etc. were determinants of delivery utilization. The OR value were 1.749 for multipara, 1.995 for those going to the nearest healthcare facilities by the most convenient traffic in less than 10 minutes, 3.011 for those drinking tap water, 5.435 for those with the education of high school, 29.149 for those with over 5 times in terms of frequency of prenatal checkup and 37.822 for those being advocated on institutional delivery. Socio-economic situation, status of maternal health care and parity made main contribution to institutional delivery and skilled birth attendance for women in rural Guangxi.

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

PubMed

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

2015-03-01

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

Laser-Induced Breakdown Spectroscopy (LIBS) Measurement of Uranium in Molten Salt.

PubMed

Williams, Ammon; Phongikaroon, Supathorn

2018-01-01

In this current study, the molten salt aerosol-laser-induced breakdown spectroscopy (LIBS) system was used to measure the uranium (U) content in a ternary UCl 3 -LiCl-KCl salt to investigate and assess a near real-time analytical approach for material safeguards and accountability. Experiments were conducted using five different U concentrations to determine the analytical figures of merit for the system with respect to U. In the analysis, three U lines were used to develop univariate calibration curves at the 367.01 nm, 385.96 nm, and 387.10 nm lines. The 367.01 nm line had the lowest limit of detection (LOD) of 0.065 wt% U. The 385.96 nm line had the best root mean square error of cross-validation (RMSECV) of 0.20 wt% U. In addition to the univariate calibration approach, a multivariate partial least squares (PLS) model was developed to further analyze the data. Using partial least squares (PLS) modeling, an RMSECV of 0.085 wt% U was determined. The RMSECV from the multivariate approach was significantly better than the univariate case and the PLS model is recommended for future LIBS analysis. Overall, the aerosol-LIBS system performed well in monitoring the U concentration and it is expected that the system could be used to quantitatively determine the U compositions within the normal operational concentrations of U in pyroprocessing molten salts.

Magnitude and Determinants of the Ratio between Prevalence of Low Vision and Blindness in Rapid Assessment of Avoidable Blindness Surveys.

PubMed

Kaphle, Dinesh; Lewallen, Susan

2017-10-01

To determine the magnitude and determinants of the ratio between prevalence of low vision and prevalence of blindness in rapid assessment of avoidable blindness (RAAB) surveys globally. Standard RAAB reports were downloaded from the repository or requested from principal investigators. Potential predictor variables included prevalence of uncorrected refractive error (URE) as well as gross domestic product (GDP) per capita, health expenditure per capita of the country across World Bank regions. Univariate and multivariate linear regression were used to investigate the correlation between potential predictor variables and the ratio. The results of 94 surveys from 43 countries showed that the ratio ranged from 1.35 in Mozambique to 11.03 in India with a median value of 3.90 (Interquartile range 3.06;5.38). Univariate regression analysis showed that prevalence of URE (p = 0.04), logarithm of GDP per capita (p = 0.01) and logarithm of health expenditure per capita (p = 0.03) were significantly associated with the higher ratio. However, only prevalence of URE was found to be significant in multivariate regression analysis (p = 0.03). There is a wide variation in the ratio of the prevalence of low vision to the prevalence of blindness. Eye care service utilization indicators such as the prevalence of URE may explain some of the variation across the regions.

[Multivariate analysis of factors influencing the effect of radiosynovectomy].

PubMed

Farahati, J; Schulz, G; Wendler, J; Körber, C; Geling, M; Kenn, W; Schmeider, P; Reidemeister, C; Reiners, Chr

2002-04-01

In this prospective study, the time to remission after Radiosynovectomy (RSV) was analyzed and the influence of age, sex, underlying disease, type of joint, and duration of illness on the success rate of RSV was determined. A total number of 57 patients with rheumatoid arthritis (n = 33) and arthrosis (n = 21) with a total number of 130 treated joints (36 knee, 66 small and 28 medium-size joints) were monitored using visual analogue scales (VAS) from one week before RSV up to four to six months after RSV. The patients had to answer 3 times daily for pain intensity of the treated joint. The time until remission was determined according to the Kaplan-Meier survivorship function. The influence of the prognosis parameters on outcome of RSV was determined by multivariate discriminant analysis. After six months, the probability of pain relief of more than 20% amounted to 78% and was significantly dependent on the age of the patient (p = 0.02) and the duration of illness (p = 0.05), however not on sex (p = 0.17), underlying disease (p = 0.23), and type of joint (p = 0.69). Irrespective of sex, type of joint and underlying disease, a measurable pain relief can be achieved with RSV in 78% of the patients with synovitis, whereby effectiveness is decreasing with increasing age and progress of illness.

Time-Domain Nuclear Magnetic Resonance (TD-NMR) and Chemometrics for Determination of Fat Content in Commercial Products of Milk Powder.

PubMed

Nascimento, Paloma Andrade Martins; Barsanelli, Paulo Lopes; Rebellato, Ana Paula; Pallone, Juliana Azevedo Lima; Colnago, Luiz Alberto; Pereira, Fabíola Manhas Verbi

2017-03-01

This study shows the use of time-domain (TD)-NMR transverse relaxation (T2) data and chemometrics in the nondestructive determination of fat content for powdered food samples such as commercial dried milk products. Most proposed NMR spectroscopy methods for measuring fat content correlate free induction decay or echo intensities with the sample's mass. The need for the sample's mass limits the analytical frequency of NMR determination, because weighing the samples is an additional step in this procedure. Therefore, the method proposed here is based on a multivariate model of T2 decay, measured with Carr-Purcell-Meiboom-Gill pulse sequence and reference values of fat content. The TD-NMR spectroscopy method shows high correlation (r = 0.95) with the lipid content, determined by the standard extraction method of Bligh and Dyer. For comparison, fat content determination was also performed using a multivariate model with near-IR (NIR) spectroscopy, which is also a nondestructive method. The advantages of the proposed TD-NMR method are that it (1) minimizes toxic residue generation, (2) performs measurements with high analytical frequency (a few seconds per analysis), and (3) does not require sample preparation (such as pelleting, needed for NIR spectroscopy analyses) or weighing the samples.

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.

Bivariate versus multivariate smart spectrophotometric calibration methods for the simultaneous determination of a quaternary mixture of mosapride, pantoprazole and their degradation products.

PubMed

Hegazy, M A; Yehia, A M; Moustafa, A A

2013-05-01

The ability of bivariate and multivariate spectrophotometric methods was demonstrated in the resolution of a quaternary mixture of mosapride, pantoprazole and their degradation products. The bivariate calibrations include bivariate spectrophotometric method (BSM) and H-point standard addition method (HPSAM), which were able to determine the two drugs, simultaneously, but not in the presence of their degradation products, the results showed that simultaneous determinations could be performed in the concentration ranges of 5.0-50.0 microg/ml for mosapride and 10.0-40.0 microg/ml for pantoprazole by bivariate spectrophotometric method and in the concentration ranges of 5.0-45.0 microg/ml for both drugs by H-point standard addition method. Moreover, the applied multivariate calibration methods were able for the determination of mosapride, pantoprazole and their degradation products using concentration residuals augmented classical least squares (CRACLS) and partial least squares (PLS). The proposed multivariate methods were applied to 17 synthetic samples in the concentration ranges of 3.0-12.0 microg/ml mosapride, 8.0-32.0 microg/ml pantoprazole, 1.5-6.0 microg/ml mosapride degradation products and 2.0-8.0 microg/ml pantoprazole degradation products. The proposed bivariate and multivariate calibration methods were successfully applied to the determination of mosapride and pantoprazole in their pharmaceutical preparations.

Preoperative Detection of Sarcopenic Obesity Helps to Predict the Occurrence of Gastric Leak After Sleeve Gastrectomy.

PubMed

Gaillard, Martin; Tranchart, Hadrien; Maitre, Sophie; Perlemuter, Gabriel; Lainas, Panagiotis; Dagher, Ibrahim

2018-03-02

Sleeve gastrectomy (SG) has become the primary procedure for many bariatric teams and staple-line leak represents its most feared complication. Sarcopenic obesity combines the risks of obesity and depleted lean mass leading possibly to an inferior surgical outcome after abdominal surgery. The aim of this study was to evaluate the existence of a potential link between radiologically determined sarcopenic obesity and staple-line leak risk after SG. A retrospective analysis of a prospective database was performed in consecutive patients undergoing SG as primary procedure. Total psoas muscles (TPA) and total visible muscles (TMA) areas were measured on a preoperative computed tomography (CT). Sarcopenia was defined as lowest tertile of skeletal muscular mass indexes (muscular areas over square of height) in each gender (using TPA or TMA). Multivariate analysis was performed to determine preoperative risk factors for staple-line leak. During the study period, 205 patients were included in the analysis. Median BMI was 40.8 kg/m 2 (34.2-49.6), and 9 patients (4.4%) presented a gastric leak. The sex-specific cut-offs for skeletal muscular mass index according to TPA were 8.2 cm 2 /m 2 for men and 6.08 cm 2 /m 2 for women. After multivariate analysis, preoperative weight (OR = 1043) and sarcopenia (TPA) (OR = 5204) were independent predictive factors for gastric leak. The present series suggests that CT scan-determined sarcopenic obesity is associated with increased risk of gastric leak after SG. This preoperatively radiological examination would be a useful clinical tool to tailor patient management according to gastric leak risk.

Determinants of Unintentional Leaks During CPAP Treatment in OSA.

PubMed

Lebret, Marius; Arnol, Nathalie; Martinot, Jean-Benoît; Lambert, Loïc; Tamisier, Renaud; Pepin, Jean-Louis; Borel, Jean-Christian

2018-04-01

Unintentional leakage from the mouth or around the mask may lead to cessation of CPAP treatment; however, the causes of unintentional leaks are poorly understood. The objectives of this study were (1) to identify determining factors of unintentional leakage and (2) to determine the effect of the type of mask (nasal/oronasal) used on unintentional leakage. Seventy-four polysomnograms from patients with OSA syndrome treated with auto-CPAP were analyzed (23 women; 56 ± 13 years; BMI, 32.9 kg/m 2 (range, 29.0-38.0 kg/m 2 ). Polysomnographic recordings were obtained under auto-CPAP, and mandibular behavior was measured with a magnetic sensor. After sleep and respiratory scoring, polysomnographic signals were computed as mean values over nonoverlapping 10-s intervals. The presence/absence of unintentional leakage was dichotomized for each 10-s interval (yes/no). Univariate and multivariate conditional regression models estimated the risk of unintentional leaks during an interval "T" based on the explanatory variables from the previous interval "T-1." A sensitivity analysis for the type of mask was then conducted. The univariate analysis showed that mandibular lowering (mouth opening), a high level of CPAP, body position (other than supine), and rapid eye movement (REM) sleep increased the risk of unintentional leaks and microarousal decreased it. In the multivariate analysis, the same variables remained independently associated with an increased risk of unintentional leakage. The sensitivity analysis showed that oronasal masks reduced the risk of unintentional leaks in cases of mouth opening and REM sleep. Mouth opening, CPAP level, sleep position, and REM sleep independently contribute to unintentional leakage. These results provide a strong rationale for the definition of phenotypes and the individual management of leaks during CPAP treatment. Copyright © 2017 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.

[Liver volume, intrahepatic fat and body weight in the course of a lifestyle interventional study: Analysis with quantitative MR-based methods].

PubMed

Bongers, M N; Stefan, N; Fritsche, A; Häring, H-U; Nikolaou, K; Schick, F; Machann, J

2015-04-01

The aim of this study was to investigate potential associations between changes in liver volume, the amount of intrahepatic lipids (IHL) and body weight during lifestyle interventions. In a prospective study 150 patients with an increased risk for developing type 2 diabetes mellitus were included who followed a caloric restriction diet for 6 months. In the retrospective analysis 18 women and 9 men (age range 22-71 years) with an average body mass index (BMI) of 32 kg/m(2) were enrolled. The liver volume was determined at the beginning and after 6 months by three-dimensional magnetic resonance imaging (3D-MRI, echo gradient, opposed-phase) and IHLs were quantified by volume-selective MR spectroscopy in single voxel stimulated echo acquisition mode (STEAM). Univariable and multivariable correlation analyses between changes of liver volume (Δliver volume), intrahepatic lipids (ΔIHL) and body weight (ΔBW) were performed. Univariable correlation analysis in the whole study cohort showed associations between ΔIHL and ΔBW (r = 0.69; p < 0.0001), ΔIHL and Δliver volume (r = 0.66; p = 0.0002) as well as ΔBW and Δliver volume (r = 0.5; p = 0.0073). Multivariable correlation analysis revealed that changes of liver volume are primarily determined by changes in IHL independent of changes in body weight (β = 0.0272; 95% CI: 0.0155-0.034; p < 0.0001). Changes of liver volume during lifestyle interventions are independent of changes of body weight primarily determined by changes of IHL. These results show the reversibility of augmented liver volume in steatosis if it is possible to reduce IHLs during lifestyle interventions.

Endometrial Carcinomas with POLE Exonuclease Domain Mutations Have a Favorable Prognosis.

PubMed

McConechy, Melissa K; Talhouk, Aline; Leung, Samuel; Chiu, Derek; Yang, Winnie; Senz, Janine; Reha-Krantz, Linda J; Lee, Cheng-Han; Huntsman, David G; Gilks, C Blake; McAlpine, Jessica N

2016-06-15

The aim of this study was to confirm the prognostic significance of POLE exonuclease domain mutations (EDM) in endometrial carcinoma patients. In addition, the effect of treatment on POLE-mutated tumors was assessed. A retrospective patient cohort of 496 endometrial carcinoma patients was identified for targeted sequencing of the POLE exonuclease domain, yielding 406 evaluable tumors. Univariable and multivariable analyses were performed to determine the effect of POLE mutation status on progression-free survival (PFS), disease-specific survival (DSS), and overall survival (OS). Combining results from eight studies in a meta-analysis, we computed pooled HR for PFS, DSS, and OS. POLE EDMs were identified in 39 of 406 (9.6%) endometrial carcinomas. Women with POLE-mutated endometrial carcinomas were younger, with stage I (92%) tumors, grade 3 (62%), endometrioid histology (82%), and frequent (49%) lymphovascular invasion. In univariable analysis, POLE-mutated endometrial carcinomas had significantly improved outcomes compared with patients with no EDMs for PFS, DSS, and OS. In multivariable analysis, POLE EDMs were only significantly associated with improved PFS. The effect of adjuvant treatment on POLE-mutated cases could not be determined conclusively; however, both treated and untreated patients with POLE EDMs had good outcomes. Meta-analysis revealed an association between POLE EDMs and improved PFS and DSS with pooled HRs 0.34 [95% confidence interval (CI), 0.15-0.73] and 0.35 (95% CI, 0.13-0.92), respectively. POLE EDMs are prognostic markers associated with excellent outcomes for endometrial carcinoma patients. Further investigation is needed to conclusively determine if treatment is necessary for this group of women. Clin Cancer Res; 22(12); 2865-73. ©2016 AACR. ©2016 American Association for Cancer Research.

Significance of serum CA125 and TPS antigen levels for determination of overall survival after three chemotherapy courses in ovarian cancer patients during long-term follow-up.

PubMed

van Dalen, A; Favier, J; Hallensleben, E; Burges, A; Stieber, P; de Bruijn, H W A; Fink, D; Ferrero, A; McGing, P; Harlozinska, A; Kainz, Ch; Markowska, J; Molina, R; Sturgeon, C; Bowman, A; Einarsson, R; Goike, H

2009-01-01

To evaluate the prognostic significance for overall survival rate for the marker combination TPS and CA125 in ovarian cancer patients after three chemotherapy courses during long-term clinical follow-up. The overall survival of 212 (out of 213) ovarian cancer patients (FIGO Stages I-IV) was analyzed in a prospective multicenter study during a 10-year clinical follow-up by univariate and multivariate analysis. In patients with ovarian cancer FIGO Stage I (34 patients) or FIGO Stage II (30 patients) disease, the univariate and multivariate analysis of the 10-year overall survival data showed that CA125 and TPS serum levels were not independent prognostic factors. In the FIGO Stage III group (112 patients), the 10-year overall survival was 15.2%; while in the FIGO Stage IV group (36 patients) a 10-year overall survival of 5.6% was seen. Here, the tumor markers CA125 and TPS levels were significant prognostic factors in both univariate and multivariate analysis (p < 0.0001). In a combined FIGO Stage III + FIGO Stage IV group (60 patients with optimal debulking surgery), multivariate analysis demonstrated that CA125 and TPS levels were independent prognostic factors. For patients in this combined FIGO Stage III + IV group having both markers below respective discrimination level, 35.3% survived for more than ten years, as opposed to patients having one marker above the discrimination level where the 10-year survival was reduced to 10% of the patients. For patients showing both markers above the respective discrimination level, none of the patients survived for the 10-year follow-up time. In FIGO III and IV ovarian cancer patients, only patients with CA 125 and TPS markers below the discrimination level after three chemotherapy courses indicated a favorable prognosis. Patients with an elevated level of CA 125 or TPS or both markers after three chemotherapy courses showed unfavorable prognosis.

Brain regions with abnormal network properties in severe epilepsy of Lennox-Gastaut phenotype: Multivariate analysis of task-free fMRI.

PubMed

Pedersen, Mangor; Curwood, Evan K; Archer, John S; Abbott, David F; Jackson, Graeme D

2015-11-01

Lennox-Gastaut syndrome, and the similar but less tightly defined Lennox-Gastaut phenotype, describe patients with severe epilepsy, generalized epileptic discharges, and variable intellectual disability. Our previous functional neuroimaging studies suggest that abnormal diffuse association network activity underlies the epileptic discharges of this clinical phenotype. Herein we use a data-driven multivariate approach to determine the spatial changes in local and global networks of patients with severe epilepsy of the Lennox-Gastaut phenotype. We studied 9 adult patients and 14 controls. In 20 min of task-free blood oxygen level-dependent functional magnetic resonance imaging data, two metrics of functional connectivity were studied: Regional homogeneity or local connectivity, a measure of concordance between each voxel to a focal cluster of adjacent voxels; and eigenvector centrality, a global connectivity estimate designed to detect important neural hubs. Multivariate pattern analysis of these data in a machine-learning framework was used to identify spatial features that classified disease subjects. Multivariate pattern analysis was 95.7% accurate in classifying subjects for both local and global connectivity measures (22/23 subjects correctly classified). Maximal discriminating features were the following: increased local connectivity in frontoinsular and intraparietal areas; increased global connectivity in posterior association areas; decreased local connectivity in sensory (visual and auditory) and medial frontal cortices; and decreased global connectivity in the cingulate cortex, striatum, hippocampus, and pons. Using a data-driven analysis method in task-free functional magnetic resonance imaging, we show increased connectivity in critical areas of association cortex and decreased connectivity in primary cortex. This supports previous findings of a critical role for these association cortical regions as a final common pathway in generating the Lennox-Gastaut phenotype. Abnormal function of these areas is likely to be important in explaining the intellectual problems characteristic of this disorder. Wiley Periodicals, Inc. © 2015 International League Against Epilepsy.

Risk factors of hepatitis B virus infection among blood donors in Duhok city, Kurdistan Region, Iraq.

PubMed

R Hussein, Nawfal

2018-01-01

Hepatitis B virus (HBV) infection is a public health problem. The lack of information about the seroprevalence and risk factors is an obstacle for preventive public health plans to reduce the burden of viral hepatitis. Therefore, this study was conducted in Iraq, where no studies had been performed to determine the prevalence and risk factors of HBV infection. Blood samples were collected form 438 blood donors attending blood bank in Duhok city. Serum samples were tested for HBV core-antibodies (HBcAb) and HBV surface-antigen (HBsAg) by ELISA. Various risk factors were recorded and multivariate analysis was performed. 5/438 (1.14%) of the subjects were HBsAg positive (HBsAg and HBcAb positive) and 36/438 (8.2%) were HBcAb positive. Hence, 41 cases were exposed to HBV and data analysis was based on that. Univariate analysis showed that there were significant associations between history of illegitimate sexual contact, history of alcohol or history of dental surgeries and HBV exposure (p<0.05 for all). Then, multivariate analysis was conducted to find HBV exposure predictive factors. It was found that history of dental surgery was a predictive factor for exposure to the virus (P=0.03, OR: 2.397). This study suggested that the history of dental surgery was predictive for HBV transmission in Duhok city. Further population-based study is needed to determine HBV risk factors in the society and public health plan based on that should be considered.

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.

Social Determinants of Discharge Outcomes in Older People Admitted to a Geriatric Medicine Ward.

PubMed

Hawker, M; Romero-Ortuno, R

2016-01-01

The factors determining hospital discharge outcomes in older people are complex. This retrospective study was carried out in an in-patient geriatric ward over a month in 2015 and aimed to explore if self-reported feeling of loneliness and clinical frailty contribute to longer hospital stays or higher rates of readmission to hospital after discharge in the older population. Twenty-two men and twenty-five women (mean age 85.1 years) were assessed. There was a significant multivariate association between both self-reported loneliness (p=0.021) and the Clinical Frailty Scale (p=0.010) with length of stay, after adjusting for age, dementia and living alone. In multivariate analysis, patients who lived alone were more likely to be readmitted to hospital within 30 days (p=0.036). Loneliness, living alone and clinical frailty were associated with adverse discharge outcomes. Lower thresholds for referral to voluntary organisations and for psychosocial interventions in patients who report loneliness or live alone may be beneficial.

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.

Sexual Partnership Types as Determinant of HIV Risk in South African MSM: An Event-Level Cluster Analysis

PubMed Central

Sandfort, Theo; Yi, Huso; Knox, Justin; Reddy, Vasu

2012-01-01

While individual determinants of HIV risk among MSM have been widely studied, there is limited understanding of how relational characteristics determine sexual risk. Based on data collected among 300 South African men who have sex with men (MSM) and using cluster analysis, this study developed a typology of four partnership types: the “Race-Economic Similar,” “Age-Race-Economic Discordant,” “Non-regular Neighbourhood,” and “Familiar” partnership types. Support for the meaningfulness of these types was found through associations of these partnership types with participant characteristics and characteristics of the last anal sex event. Furthermore, in a multivariate analysis, only partnership type independently predicted whether the last anal sex event was unprotected. Findings of the study illustrate the importance of taking into account the relational context in understanding unprotected sexual practices and present ways to target intervention efforts as well as identify relationship specific determinants of unprotected sex. PMID:22956229

Comparative assessment of essential and heavy metals in fruits from different geographical origins.

PubMed

Grembecka, Małgorzata; Szefer, Piotr

2013-11-01

The aim of this investigation was to estimate and compare essential and heavy metals contents in 98 commercially available fresh fruits from different geographic regions using multivariate techniques. The concentrations of 12 elements (calcium, magnesium, potassium, sodium, phophorus, cobalt (Co), manganese, iron, chromium (Cr), nickel (Ni), zinc and copper) were determined using flame atomic absorption spectrometry with deuterium-background correction. Phosphorus was determined in the form of phosphomolybdate by a spectrophotometric method. Reliability of the procedure was checked by analysis of the certified reference materials tea (NCS DC 73351), cabbage (IAEA-359) and spinach leaves (NIST-1570). Recoveries of the elements analysed varied between 85.5 and 103%, and precisions for the reference materials were 0.13-6.08%. Based on recommended dietary allowance and adequate intake estimated for essential elements, it was concluded that accessory fruits such as pineapples, raspberries and strawberries supply organism with the highest amounts of bioelements. Although accessory fruits were also found to be the greatest source of Ni among all the analysed fruits, in all the fruits Ni was more abundant than Cr and Co. Significant correlation coefficients (p < 0.001, p < 0.01 and p < 0.05) were found between concentrations of some metals in fresh fruits. Application of ANOVA Kruskal-Wallis test and multivariate techniques such as factor analysis and cluster analysis enabled us to differentiate particular botanical families and types of fruits.

Cerebrovascular risk factors for patients with cerebral watershed infarction: A case-control study based on computed tomography angiography in a population from Southwest China.

PubMed

Dong, Mei-Xue; Hu, Ling; Huang, Yuan-Jun; Xu, Xiao-Min; Liu, Yang; Wei, You-Dong

2017-07-01

To determine cerebrovascular risk factors for patients with cerebral watershed infarction (CWI) from Southwest China.Patients suffering from acute ischemic stroke were categorized into internal CWI (I-CWI), external CWI (E-CWI), or non-CWI (patients without CWI) groups. Clinical data were collected and degrees of steno-occlusion of all cerebral arteries were scored. Arteries associated with the circle of Willis were also assessed. Data were compared using Pearson chi-squared tests for categorical data and 1-way analysis of variance with Bonferroni post hoc tests for continuous data, as appropriate. Multivariate binary logistic regression analysis was performed to determine independent cerebrovascular risk factors for CWI.Compared with non-CWI, I-CWI had higher degrees of steno-occlusion of the ipsilateral middle cerebral artery, ipsilateral carotid artery, and contralateral middle cerebral artery. E-CWI showed no significant differences. All the 3 arteries were independent cerebrovascular risk factors for I-CWI confirmed by multivariate binary logistic regression analysis. I-CWI had higher degrees of steno-occlusion of the ipsilateral middle cerebral artery compared with E-CWI. No significant differences were found among arteries associated with the circle of Willis.The ipsilateral middle cerebral artery, carotid artery, and contralateral middle cerebral artery were independent cerebrovascular risk factors for I-CWI. No cerebrovascular risk factor was identified for E-CWI.

Association between breastfeeding duration, non-nutritive sucking habits and dental arch dimensions in deciduous dentition: a cross-sectional study.

PubMed

Agarwal, Shiv Shankar; Nehra, Karan; Sharma, Mohit; Jayan, Balakrishna; Poonia, Anish; Bhattal, Hiteshwar

2014-10-31

This cross-sectional retrospective study was conducted to determine association between breastfeeding duration, non-nutritive sucking habits, dental arch transverse diameters, posterior crossbite and anterior open bite in deciduous dentition. 415 children (228 males and 187 females), 4 to 6 years old, from a mixed Indian population were clinically examined. Based on written questionnaire answered by parents, children were divided into two groups: group 1 (breastfed for <6 months (n = 158)) and group 2 (breastfed for ≥6 months (n = 257)). The associations were analysed using chi-square test (P < 0.05 taken as statistically significant). Odds ratio (OR) was calculated to determine the strength of associations tested. Multivariate logistic regression analysis was done for obtaining independent predictors of posterior crossbite and maxillary and mandibular IMD (Inter-molar distance) and ICD (Inter-canine distance). Non-nutritive sucking (NNS) was present in 15.18% children (20.3% in group 1 as compared to 12.1% in group 2 (P = 0.024)). The average ICD and IMD in maxilla and average IMD in mandible were significantly higher among group 2 as compared to group 1 (P < 0.01). In mandible, average ICD did not differ significantly between the two groups (P = 0.342). The distribution of anterior open bite did not differ significantly between the two groups (P = 0.865). The distribution of posterior crossbite was significantly different between the two groups (P = 0.001). OR assessment (OR = 1.852) revealed that group 1 had almost twofold higher prevalence of NNS habits than group 2. Multivariate logistic regression analysis revealed that the first group had independently fourfold increased risk of developing crossbite compared to the second group (OR = 4.3). Multivariate linear regression analysis also revealed that age and breastfeeding duration were the most significant determinants of ICD and IMD. An increased prevalence of NNS in the first group suggests that NNS is a dominant variable in the association between breastfeeding duration and reduced intra-arch transverse diameters which leads to increased prevalence of posterior crossbites as seen in our study. Mandibular inter-canine width is however unaffected due to a lowered tongue posture seen in these children.

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

PubMed

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

2016-01-01

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

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

PubMed

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

2016-07-01

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

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

PubMed Central

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

2016-01-01

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

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

PubMed

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

2016-12-01

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

Sarcopenia predicts 1-year mortality in elderly patients undergoing curative gastrectomy for gastric cancer: a prospective study.

PubMed

Huang, Dong-Dong; Chen, Xiao-Xi; Chen, Xi-Yi; Wang, Su-Lin; Shen, Xian; Chen, Xiao-Lei; Yu, Zhen; Zhuang, Cheng-Le

2016-11-01

One-year mortality is vital for elderly oncologic patients undergoing surgery. Recent studies have demonstrated that sarcopenia can predict outcomes after major abdominal surgeries, but the association of sarcopenia and 1-year mortality has never been investigated in a prospective study. We conducted a prospective study of elderly patients (≥65 years) who underwent curative gastrectomy for gastric cancer from July 2014 to July 2015. Sarcopenia was determined by the measurements of muscle mass, handgrip strength, and gait speed. Univariate and multivariate analyses were used to identify the risk factors associated with 1-year mortality. A total of 173 patients were included, in which 52 (30.1 %) patients were identified as having sarcopenia. Twenty-four (13.9 %) patients died within 1 year of surgery. Multivariate analysis showed that sarcopenia was an independent risk factor for 1-year mortality. Area under the receiver operating characteristic curve demonstrated an increased predictive power for 1-year mortality with the inclusion of sarcopenia, from 0.835 to 0.868. Solely low muscle mass was not predictive of 1-year mortality in the multivariate analysis. Sarcopenia is predictive of 1-year mortality in elderly patients undergoing gastric cancer surgery. The measurement of muscle function is important for sarcopenia as a preoperative assessment tool.

LGE Provides Incremental Prognostic Information Over Serum Biomarkers in AL Cardiac Amyloidosis.

PubMed

Boynton, Samuel J; Geske, Jeffrey B; Dispenzieri, Angela; Syed, Imran S; Hanson, Theodore J; Grogan, Martha; Araoz, Philip A

2016-06-01

This study sought to determine the prognostic value of cardiac magnetic resonance (CMR) late gadolinium enhancement (LGE) in amyloid light chain (AL) cardiac amyloidosis. Cardiac involvement is the major determinant of mortality in AL amyloidosis. CMR LGE is a marker of amyloid infiltration of the myocardium. The purpose of this study was to evaluate retrospectively the prognostic value of CMR LGE for determining all-cause mortality in AL amyloidosis and to compare the prognostic power with the biomarker stage. Seventy-six patients with histologically proven AL amyloidosis underwent CMR LGE imaging. LGE was categorized as global, focal patchy, or none. Global LGE was considered present if it was visualized on LGE images or if the myocardium nulled before the blood pool on a cine multiple inversion time (TI) sequence. CMR morphologic and functional evaluation, echocardiographic diastolic evaluation, and cardiac biomarker staging were also performed. Subjects' charts were reviewed for all-cause mortality. Cox proportional hazards analysis was used to evaluate survival in univariate and multivariate analysis. There were 40 deaths, and the median study follow-up period was 34.4 months. Global LGE was associated with all-cause mortality in univariate analysis (hazard ratio = 2.93; p < 0.001). In multivariate modeling with biomarker stage, global LGE remained prognostic (hazard ratio = 2.43; p = 0.01). Diffuse LGE provides incremental prognosis over cardiac biomarker stage in patients with AL cardiac amyloidosis. Copyright © 2016 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

Sensor failure and multivariable control for airbreathing propulsion systems. Ph.D. Thesis - Dec. 1979 Final Report

NASA Technical Reports Server (NTRS)

Behbehani, K.

1980-01-01

A new sensor/actuator failure analysis technique for turbofan jet engines was developed. Three phases of failure analysis, namely detection, isolation, and accommodation are considered. Failure detection and isolation techniques are developed by utilizing the concept of Generalized Likelihood Ratio (GLR) tests. These techniques are applicable to both time varying and time invariant systems. Three GLR detectors are developed for: (1) hard-over sensor failure; (2) hard-over actuator failure; and (3) brief disturbances in the actuators. The probability distribution of the GLR detectors and the detectability of sensor/actuator failures are established. Failure type is determined by the maximum of the GLR detectors. Failure accommodation is accomplished by extending the Multivariable Nyquest Array (MNA) control design techniques to nonsquare system designs. The performance and effectiveness of the failure analysis technique are studied by applying the technique to a turbofan jet engine, namely the Quiet Clean Short Haul Experimental Engine (QCSEE). Single and multiple sensor/actuator failures in the QCSEE are simulated and analyzed and the effects of model degradation are studied.

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

NASA Astrophysics Data System (ADS)

Hu, Chongqing; Li, Aihua; Zhao, Xingyang

2011-02-01

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

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

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…

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.

Race and Older Mothers’ Differentiation: A Sequential Quantitative and Qualitative Analysis

PubMed Central

Sechrist, Jori; Suitor, J. Jill; Riffin, Catherine; Taylor-Watson, Kadari; Pillemer, Karl

2011-01-01

The goal of this paper is to demonstrate a process by which qualitative and quantitative approaches are combined to reveal patterns in the data that are unlikely to be detected and confirmed by either method alone. Specifically, we take a sequential approach to combining qualitative and quantitative data to explore race differences in how mothers differentiate among their adult children. We began with a standard multivariate analysis examining race differences in mothers’ differentiation among their adult children regarding emotional closeness and confiding. Finding no race differences in this analysis, we conducted an in-depth comparison of the Black and White mothers’ narratives to determine whether there were underlying patterns that we had been unable to detect in our first analysis. Using this method, we found that Black mothers were substantially more likely than White mothers to emphasize interpersonal relationships within the family when describing differences among their children. In our final step, we developed a measure of familism based on the qualitative data and conducted a multivariate analysis to confirm the patterns revealed by the in-depth comparison of the mother’s narratives. We conclude that using such a sequential mixed methods approach to data analysis has the potential to shed new light on complex family relations. PMID:21967639

Application of two tests of multivariate discordancy to fisheries data sets

USGS Publications Warehouse

Stapanian, M.A.; Kocovsky, P.M.; Garner, F.C.

2008-01-01

The generalized (Mahalanobis) distance and multivariate kurtosis are two powerful tests of multivariate discordancies (outliers). Unlike the generalized distance test, the multivariate kurtosis test has not been applied as a test of discordancy to fisheries data heretofore. We applied both tests, along with published algorithms for identifying suspected causal variable(s) of discordant observations, to two fisheries data sets from Lake Erie: total length, mass, and age from 1,234 burbot, Lota lota; and 22 combinations of unique subsets of 10 morphometrics taken from 119 yellow perch, Perca flavescens. For the burbot data set, the generalized distance test identified six discordant observations and the multivariate kurtosis test identified 24 discordant observations. In contrast with the multivariate tests, the univariate generalized distance test identified no discordancies when applied separately to each variable. Removing discordancies had a substantial effect on length-versus-mass regression equations. For 500-mm burbot, the percent difference in estimated mass after removing discordancies in our study was greater than the percent difference in masses estimated for burbot of the same length in lakes that differed substantially in productivity. The number of discordant yellow perch detected ranged from 0 to 2 with the multivariate generalized distance test and from 6 to 11 with the multivariate kurtosis test. With the kurtosis test, 108 yellow perch (90.7%) were identified as discordant in zero to two combinations, and five (4.2%) were identified as discordant in either all or 21 of the 22 combinations. The relationship among the variables included in each combination determined which variables were identified as causal. The generalized distance test identified between zero and six discordancies when applied separately to each variable. Removing the discordancies found in at least one-half of the combinations (k=5) had a marked effect on a principal components analysis. In particular, the percent of the total variation explained by second and third principal components, which explain shape, increased by 52 and 44% respectively when the discordancies were removed. Multivariate applications of the tests have numerous ecological advantages over univariate applications, including improved management of fish stocks and interpretation of multivariate morphometric data. ?? 2007 Springer Science+Business Media B.V.

Microbial composition analyses by 16S rRNA sequencing: A proof of concept approach to provenance determination of archaeological ochre.

PubMed

Lenehan, Claire E; Tobe, Shanan S; Smith, Renee J; Popelka-Filcoff, Rachel S

2017-01-01

Many archaeological science studies use the concept of "provenance", where the origins of cultural material can be determined through physical or chemical properties that relate back to the origins of the material. Recent studies using DNA profiling of bacteria have been used for the forensic determination of soils, towards determination of geographic origin. This manuscript presents a novel approach to the provenance of archaeological minerals and related materials through the use of 16S rRNA sequencing analysis of microbial DNA. Through the microbial DNA characterization from ochre and multivariate statistics, we have demonstrated the clear discrimination between four distinct Australian cultural ochre sites.

The impact of lungs from diabetic donors on lung transplant recipients†.

PubMed

Ambur, Vishnu; Taghavi, Sharven; Jayarajan, Senthil; Kadakia, Sagar; Zhao, Huaqing; Gomez-Abraham, Jesus; Toyoda, Yoshiya

2017-02-01

We attempted to determine if transplants of lungs from diabetic donors (DDs) is associated with increased mortality of recipients in the modern era of the lung allocation score (LAS). The United Network for Organ Sharing (UNOS) database was queried for all adult lung transplant recipients from 2006 to 2014. Patients receiving a lung from a DD were compared to those receiving a transplant from a non-DD. Multivariate Cox regression analysis using variables associated with mortality was used to examine survival. A total of 13 159 adult lung transplants were performed between January 2006 and June 2014: 4278 (32.5%) were single-lung transplants (SLT) and 8881 (67.5%) were double-lung transplants (DLT). The log-rank test demonstrated a lower median survival in the DD group (5.6 vs 5.0 years, P = 0.003). We performed additional analysis by dividing this initial cohort into two cohorts by transplant type. On multivariate analysis, receiving an SLT from a DD was associated with increased mortality (HR 1.28, 95% CI 1.07–1.54, P = 0.011). Interestingly, multivariate analysis demonstrated no difference in mortality rates for patients receiving a DLT from a DD (HR 1.12, 95% CI 0.97–1.30, P = 0.14). DLT with DDs can be performed safely without increased mortality, but SLT using DDs results in worse survival and post-transplant outcomes. Preference should be given to DLT when using lungs from donors with diabetes. © The Author 2016. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.

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

PubMed

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

2016-12-01

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 variables. However, this univariate approach decreases power, increases the risk for Type 1 error, and contradicts the rationale for conducting multivariate tests in the first place. The purpose of this study was to provide a user-friendly primer on conducting descriptive discriminant analysis (DDA), which is a post-hoc strategy to MANOVA that takes into account the complex relationships among multiple dependent variables. A real-world example using the Statistical Package for the Social Sciences syntax and data from 1,095 middle school students on their body composition and body image are provided to explain and interpret the results from DDA. While univariate post hocs increased the risk for Type 1 error to 76%, the DDA identified which dependent variables contributed to group differences and which groups were different from each other. For example, students in the very lean and Healthy Fitness Zone categories for body mass index experienced less pressure to lose weight, more satisfaction with their body, and higher physical self-concept than the Needs Improvement Zone groups. However, perceived pressure to gain weight did not contribute to group differences because it was a suppressor variable. Researchers are encouraged to use DDA when investigating group differences on multiple correlated dependent variables to determine which variables contributed to group differences.

Differential use of fresh water environments by wintering waterfowl of coastal Texas

USGS Publications Warehouse

White, D.H.; James, D.

1978-01-01

A comparative study of the environmental relationships among 14 species of wintering waterfowl was conducted at the Welder Wildlife Foundation, San Patricia County, near Sinton, Texas during the fall and early winter of 1973. Measurements of 20 environmental factors (social, vegetational, physical, and chemical) were subjected to multivariate statistical methods to determine certain niche characteristics and environmental relationships of waterfowl wintering in the aquatic community.....Each waterfowl species occupied a unique realized niche by responding to distinct combinations of environmental factors identified by principal component analysis. One percent confidence ellipses circumscribing the mean scores plotted for the first and second principal components gave an indication of relative niche width for each species. The waterfowl environments were significantly different interspecifically and water depth at feeding site and % emergent vegetation were most important in the separation. This was shown by subjecting the transformed data to multivariate analysis of variance with an associated step-down procedure. The species were distributed along a community cline extending from shallow water with abundant emergent vegetation to open deep water with little emergent vegetation of any kind. Four waterfowl subgroups were significantly separated along the cline, as indicated by one-way analysis of variance with Duncan?s multiple range test. Clumping of the bird species toward the middle of the available habitat hyperspace was shown in a plot of the principal component scores for the random samples and individual species.....Naturally occurring relationships among waterfowl were clarified using principal comcomponent analysis and related multivariate procedures. These techniques may prove useful in wetland management for particular groups of waterfowl based on habitat preferences.

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.

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.

Factors Influencing Cecal Intubation Time during Retrograde Approach Single-Balloon Enteroscopy

PubMed Central

Chen, Peng-Jen; Shih, Yu-Lueng; Huang, Hsin-Hung; Hsieh, Tsai-Yuan

2014-01-01

Background and Aim. The predisposing factors for prolonged cecal intubation time (CIT) during colonoscopy have been well identified. However, the factors influencing CIT during retrograde SBE have not been addressed. The aim of this study was to determine the factors influencing CIT during retrograde SBE. Methods. We investigated patients who underwent retrograde SBE at a medical center from January 2011 to March 2014. The medical charts and SBE reports were reviewed. The patients' characteristics and procedure-associated data were recorded. These data were analyzed with univariate analysis as well as multivariate logistic regression analysis to identify the possible predisposing factors. Results. We enrolled 66 patients into this study. The median CIT was 17.4 minutes. With univariate analysis, there was no statistical difference in age, sex, BMI, or history of abdominal surgery, except for bowel preparation (P = 0.021). Multivariate logistic regression analysis showed that inadequate bowel preparation (odds ratio 30.2, 95% confidence interval 4.63–196.54; P < 0.001) was the independent predisposing factors for prolonged CIT during retrograde SBE. Conclusions. For experienced endoscopist, inadequate bowel preparation was the independent predisposing factor for prolonged CIT during retrograde SBE. PMID:25505904

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.

Prevalence and risk factors for scrub typhus in South India.

PubMed

Trowbridge, Paul; P, Divya; Premkumar, Prasanna S; Varghese, George M

2017-05-01

To determine the prevalence and risk factors of scrub typhus in Tamil Nadu, South India. We performed a clustered seroprevalence study of the areas around Vellore. All participants completed a risk factor survey, with seropositive and seronegative participants acting as cases and controls, respectively, in a risk factor analysis. After univariate analysis, variables found to be significant underwent multivariate analysis. Of 721 people participating in this study, 31.8% tested seropositive. By univariate analysis, after accounting for clustering, having a house that was clustered with other houses, having a fewer rooms in a house, having fewer people living in a household, defecating outside, female sex, age >60 years, shorter height, lower weight, smaller body mass index and smaller mid-upper arm circumference were found to be significantly associated with seropositivity. After multivariate regression modelling, living in a house clustered with other houses, female sex and age >60 years were significantly associated with scrub typhus exposure. Overall, scrub typhus is much more common than previously thought. Previously described individual environmental and habitual risk factors seem to have less importance in South India, perhaps because of the overall scrub typhus-conducive nature of the environment in this region. © 2017 John Wiley & Sons Ltd.

Multivariate Autoregressive Modeling and Granger Causality Analysis of Multiple Spike Trains

PubMed Central

Krumin, Michael; Shoham, Shy

2010-01-01

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

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

PubMed

Jackson, Daniel; Riley, Richard D

2014-02-20

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

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

NASA Astrophysics Data System (ADS)

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

2010-01-01

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

Statistical Development of Flood Frequency and Magnitude Equations for the Cosumnes and Mokelumne River Drainage Basins, Sierra Nevada, California

NASA Astrophysics Data System (ADS)

Burns, R. G.; Meyer, R. W.; Cornwell, K.

2003-12-01

In-basin statistical relations allow for development of regional flood frequency and magnitude equations in the Cosumnes River and Mokelumne River drainage basins. Current equations were derived from data collected through 1975, and do not reflect newer data with some significant flooding. Physical basin characteristics (area, mean basin elevation, slope of longest reach, and mean annual precipitation) were correlated against predicted flood discharges for each of the 5, 10, 25, 50, 100, 200, and 500-year recurrence intervals in a multivariate analysis. Predicted maximum instantaneous flood discharges were determined using the PEAKFQ program with default settings, for 24 stream gages within the study area presumed not affected by flow management practices. For numerical comparisons, GIS-based methods using Spatial Analyst and the Arc Hydro Tools extension were applied to derive physical basin characteristics as predictor variables from a 30m digital elevation model (DEM) and a mean annual precipitation raster (PRISM). In a bivariate analysis, examination of Pearson correlation coefficients, F-statistic, and t & p thresholds show good correlation between area and flood discharges. Similar analyses show poor correlation for mean basin elevation, slope and precipitation, with flood discharge. Bivariate analysis suggests slope may not be an appropriate predictor term for use in the multivariate analysis. Precipitation and elevation correlate very well, demonstrating possible orographic effects. From the multivariate analysis, less than 6% of the variability in the correlation is not explained for flood recurrences up to 25 years. Longer term predictions up to 500 years accrue greater uncertainty with as much as 15% of the variability in the correlation left unexplained.

Multivariate analysis of the immune response to a vaccine as an alternative to the repetition of animal challenge studies for vaccines with demonstrated efficacy.

PubMed

Chapat, Ludivine; Hilaire, Florence; Bouvet, Jérome; Pialot, Daniel; Philippe-Reversat, Corinne; Guiot, Anne-Laure; Remolue, Lydie; Lechenet, Jacques; Andreoni, Christine; Poulet, Hervé; Day, Michael J; De Luca, Karelle; Cariou, Carine; Cupillard, Lionel

2017-07-01

The assessment of vaccine combinations, or the evaluation of the impact of minor modifications of one component in well-established vaccines, requires animal challenges in the absence of previously validated correlates of protection. As an alternative, we propose conducting a multivariate analysis of the specific immune response to the vaccine. This approach is consistent with the principles of the 3Rs (Refinement, Reduction and Replacement) and avoids repeating efficacy studies based on infectious challenges in vivo. To validate this approach, a set of nine immunological parameters was selected in order to characterize B and T lymphocyte responses against canine rabies virus and to evaluate the compatibility between two canine vaccines, an inactivated rabies vaccine (RABISIN ® ) and a combined vaccine (EURICAN ® DAPPi-Lmulti) injected at two different sites in the same animals. The analysis was focused on the magnitude and quality of the immune response. The multi-dimensional picture given by this 'immune fingerprint' was used to assess the impact of the concomitant injection of the combined vaccine on the immunogenicity of the rabies vaccine. A principal component analysis fully discriminated the control group from the groups vaccinated with RABISIN ® alone or RABISIN ® +EURICAN ® DAPPi-Lmulti and confirmed the compatibility between the rabies vaccines. This study suggests that determining the immune fingerprint, combined with a multivariate statistical analysis, is a promising approach to characterizing the immunogenicity of a vaccine with an established record of efficacy. It may also avoid the need to repeat efficacy studies involving challenge infection in case of minor modifications of the vaccine or for compatibility studies. Copyright © 2017 Elsevier B.V. All rights reserved.

An integrated analysis for determining the geographical origin of medicinal herbs using ICP-AES/ICP-MS and (1)H NMR analysis.

PubMed

Kwon, Yong-Kook; Bong, Yeon-Sik; Lee, Kwang-Sik; Hwang, Geum-Sook

2014-10-15

ICP-MS and (1)H NMR are commonly used to determine the geographical origin of food and crops. In this study, data from multielemental analysis performed by ICP-AES/ICP-MS and metabolomic data obtained from (1)H NMR were integrated to improve the reliability of determining the geographical origin of medicinal herbs. Astragalus membranaceus and Paeonia albiflora with different origins in Korea and China were analysed by (1)H NMR and ICP-AES/ICP-MS, and an integrated multivariate analysis was performed to characterise the differences between their origins. Four classification methods were applied: linear discriminant analysis (LDA), k-nearest neighbour classification (KNN), support vector machines (SVM), and partial least squares-discriminant analysis (PLS-DA). Results were compared using leave-one-out cross-validation and external validation. The integration of multielemental and metabolomic data was more suitable for determining geographical origin than the use of each individual data set alone. The integration of the two analytical techniques allowed diverse environmental factors such as climate and geology, to be considered. Our study suggests that an appropriate integration of different types of analytical data is useful for determining the geographical origin of food and crops with a high degree of reliability. Copyright © 2014 Elsevier Ltd. All rights reserved.

Analysis of risk factors for central venous port failure in cancer patients

PubMed Central

Hsieh, Ching-Chuan; Weng, Hsu-Huei; Huang, Wen-Shih; Wang, Wen-Ke; Kao, Chiung-Lun; Lu, Ming-Shian; Wang, Chia-Siu

2009-01-01

AIM: To analyze the risk factors for central port failure in cancer patients administered chemotherapy, using univariate and multivariate analyses. METHODS: A total of 1348 totally implantable venous access devices (TIVADs) were implanted into 1280 cancer patients in this cohort study. A Cox proportional hazard model was applied to analyze risk factors for failure of TIVADs. Log-rank test was used to compare actuarial survival rates. Infection, thrombosis, and surgical complication rates (χ2 test or Fisher’s exact test) were compared in relation to the risk factors. RESULTS: Increasing age, male gender and open-ended catheter use were significant risk factors reducing survival of TIVADs as determined by univariate and multivariate analyses. Hematogenous malignancy decreased the survival time of TIVADs; this reduction was not statistically significant by univariate analysis [hazard ratio (HR) = 1.336, 95% CI: 0.966-1.849, P = 0.080)]. However, it became a significant risk factor by multivariate analysis (HR = 1.499, 95% CI: 1.079-2.083, P = 0.016) when correlated with variables of age, sex and catheter type. Close-ended (Groshong) catheters had a lower thrombosis rate than open-ended catheters (2.5% vs 5%, P = 0.015). Hematogenous malignancy had higher infection rates than solid malignancy (10.5% vs 2.5%, P < 0.001). CONCLUSION: Increasing age, male gender, open-ended catheters and hematogenous malignancy were risk factors for TIVAD failure. Close-ended catheters had lower thrombosis rates and hematogenous malignancy had higher infection rates. PMID:19787834

Survival in Patients with Advanced Non-cystic Fibrosis Bronchiectasis Versus Cystic Fibrosis on the Waitlist for Lung Transplantation.

PubMed

Hayes, Don; Kopp, Benjamin T; Tobias, Joseph D; Woodley, Frederick W; Mansour, Heidi M; Tumin, Dmitry; Kirkby, Stephen E

2015-12-01

Survival in non-cystic fibrosis (CF) bronchiectasis is not well studied. The United Network for Organ Sharing database was queried from 1987 to 2013 to compare survival in adult patients with non-CF bronchiectasis to patients with CF listed for lung transplantation (LTx). Each subject was tracked from waitlist entry date until death or censoring to determine survival differences between the two groups. Of 2112 listed lung transplant candidates with bronchiectasis (180 non-CF, 1932 CF), 1617 were used for univariate Cox and Kaplan-Meier survival function analysis, 1173 for multivariate Cox models, and 182 for matched-pairs analysis based on propensity scores. Compared to CF, patients with non-CF bronchiectasis had a significantly lower mortality by univariate Cox analysis (HR 0.565; 95 % CI 0.424, 0.754; p < 0.001). Adjusting for potential confounders, multivariate Cox models identified a significant reduction in risk for death associated with non-CF bronchiectasis who were lung transplant candidates (HR 0.684; 95 % CI 0.475, 0.985; p = 0.041). Results were consistent in multivariate models adjusting for pulmonary hypertension and forced expiratory volume in one second. Non-CF bronchiectasis with advanced lung disease was associated with significantly lower mortality hazard compared to CF bronchiectasis on the waitlist for LTx. Separate referral and listing criteria for LTx in non-CF and CF populations should be considered.

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.

Microwave-Assisted Extraction of Phenolic Compounds from Almond Skin Byproducts (Prunus amygdalus): A Multivariate Analysis Approach.

PubMed

Valdés, Arantzazu; Vidal, Lorena; Beltrán, Ana; Canals, Antonio; Garrigós, María Carmen

2015-06-10

A microwave-assisted extraction (MAE) procedure to isolate phenolic compounds from almond skin byproducts was optimized. A three-level, three-factor Box-Behnken design was used to evaluate the effect of almond skin weight, microwave power, and irradiation time on total phenolic content (TPC) and antioxidant activity (DPPH). Almond skin weight was the most important parameter in the studied responses. The best extraction was achieved using 4 g, 60 s, 100 W, and 60 mL of 70% (v/v) ethanol. TPC, antioxidant activity (DPPH, FRAP), and chemical composition (HPLC-DAD-ESI-MS/MS) were determined by using the optimized method from seven different almond cultivars. Successful discrimination was obtained for all cultivars by using multivariate linear discriminant analysis (LDA), suggesting the influence of cultivar type on polyphenol content and antioxidant activity. The results show the potential of almond skin as a natural source of phenolics and the effectiveness of MAE for the reutilization of these byproducts.

Detecting subtle hydrochemical anomalies with multivariate statistics: an example from homogeneous groundwaters in the Great Artesian Basin, Australia

NASA Astrophysics Data System (ADS)

O'Shea, Bethany; Jankowski, Jerzy

2006-12-01

The major ion composition of Great Artesian Basin groundwater in the lower Namoi River valley is relatively homogeneous in chemical composition. Traditional graphical techniques have been combined with multivariate statistical methods to determine whether subtle differences in the chemical composition of these waters can be delineated. Hierarchical cluster analysis and principal components analysis were successful in delineating minor variations within the groundwaters of the study area that were not visually identified in the graphical techniques applied. Hydrochemical interpretation allowed geochemical processes to be identified in each statistically defined water type and illustrated how these groundwaters differ from one another. Three main geochemical processes were identified in the groundwaters: ion exchange, precipitation, and mixing between waters from different sources. Both statistical methods delineated an anomalous sample suspected of being influenced by magmatic CO2 input. The use of statistical methods to complement traditional graphical techniques for waters appearing homogeneous is emphasized for all investigations of this type. Copyright

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

PubMed Central

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

2016-01-01

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

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.

Vitamin-A deficiency and its determinants among preschool children: a community based cross-sectional study in Ethiopia.

PubMed

Tariku, Amare; Fekadu, Abel; Ferede, Ayanaw Tsega; Mekonnen Abebe, Solomon; Adane, Akilew Awoke

2016-06-24

Vitamin A deficiency is the leading cause of preventable visual impairments in children. It is also an underlying cause for nearly one-fourth of global child mortality associated with measles, diarrhea, and malaria. The limited literature available in Ethiopia shows severe public health significance of vitamin-A deficiency. Hence the aim of the current study was to assess the prevalence and factors determining vitamin-A deficiency among preschool children in Dembia District, northwest Ethiopia. A community-based cross-sectional study was conducted among preschool children of Dembia District from January to February, 2015. A multi-stage sampling, followed by a systematic sampling technique was employed to select study participants. A structured interviewer-administered questionnaire was used to collect data. Using a binary logistic regression model, multivariable analysis was fitted to identify the associated factors of vitamin-A deficiency. The adjusted odds ratio (AOR) with a 95 % confidence interval was computed to assess the strength of the association, and variables with a p value of <0.05 in multivariable analysis were considered as statistically significant. Six hundred eighty-one preschool children were included in the study, giving a response rate of 96.5 %. The overall prevalence of xerophthalmia was 8.6 %. The result of the multivariable analysis revealed that nonattendance at the antenatal care clinic [AOR 2.65,95 % CI (1.39,5.07)], being male [AOR 1.81, 95 % CI (1.01,3.24)], and in the age group of 49-59 months [AOR 3.00, 95 % CI (1.49,6.02)] were significantly associated with vitamin-A deficiency. Vitamin-A deficiency is a severe public health problem in the study area. Further strengthening antenatal care utilization and giving emphasis to preschool children will help to mitigate vitamin-A deficiency in the study area.

[Prevalence and factors associated with renal disease among patients with newly diagnoses of HIV in Brazzaville, Republic of Congo].

PubMed

Ekat, M H; Courpotin, C; Diafouka, M; Akolbout, M; Mahambou-Nsonde, D; Bitsindou, P R; Nzounza, P; Simon, B

2013-05-01

The aim of this study was to determine the prevalence of kidney disease in patients newly diagnosed as HIV-positive in Brazzaville and to identify the associated risk factors. Descriptive and analytical study of patients diagnosed with HIV infection at the Ambulatory Treatment Center in Brazzaville, Republic of Congo, from January 1, 2009, through December 31, 2010. Estimated glomerular filtration rate (eGFR) was assessed with the Modification of Diet in Renal Disease equation (MDRD-GFR), and kidney disease was defined by an eGFR less than 60 mL/min/1.73 m(2). We conducted a univariate and then a multivariate logistic regression analysis to determine the factors associated with kidney disease in this population. The study included 562 patients newly identified as HIV-infected, 66.13% of whom were women. Their median age was 38.84 years interquartile range (IQR): 33.18-46.23) and their median body mass index (BMI) 20.31 kg/m(2) (IQR: 17.97-22.89). Their median CD4 count was 192 cells/mm(3) (IQR: 81-350), and 70.8% were at WHO stage III/IV. Finally, the median MDRD-GFR was 95.59 (IQR: 78.76-114.92) mL/min/1.73 m(2) and 8.5% had a GFR less than 60 mL/min/1.73 m(2), that is, moderate impairment of kidney function. The only factor associated with kidney disease in the multivariate analysis was a BMI less than 18.5 kg/m(2) (adjusted odds ratio: 2.54, 95% confidence interval: 1.25-5.15, p = 0.01). The prevalence of kidney disease in patients newly diagnosed with HIV in Brazzaville is relatively high. The only factor associated with it in the multivariate analysis was a BMI less than 18.5 kg/m(2).

Integrated Data Visualization and Virtual Reality Tool

NASA Technical Reports Server (NTRS)

Dryer, David A.

1998-01-01

The Integrated Data Visualization and Virtual Reality Tool (IDVVRT) Phase II effort was for the design and development of an innovative Data Visualization Environment Tool (DVET) for NASA engineers and scientists, enabling them to visualize complex multidimensional and multivariate data in a virtual environment. The objectives of the project were to: (1) demonstrate the transfer and manipulation of standard engineering data in a virtual world; (2) demonstrate the effects of design and changes using finite element analysis tools; and (3) determine the training and engineering design and analysis effectiveness of the visualization system.

Neuropsychological Testing Predicts Cerebrospinal Fluid Aβ in Mild Cognitive Impairment (MCI)

PubMed Central

Kandel, Benjamin M.; Avants, Brian B.; Gee, James C.; Arnold, Steven E.; Wolk, David A.

2015-01-01

Background Psychometric tests predict conversion of Mild Cognitive Impairment (MCI) to probable Alzheimer's Disease (AD). Because the definition of clinical AD relies on those same psychometric tests, the ability of these tests to identify underlying AD pathology remains unclear. Objective To determine the degree to which psychometric testing predicts molecular evidence of AD amyloid pathology, as indicated by CSF Aβ1–42, in patients with MCI, as compared to neuroimaging biomarkers. Methods We identified 408 MCI subjects with CSF Aβ levels, psychometric test data, FDG-PET scans, and acceptable volumetric MR scans from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). We used psychometric tests and imaging biomarkers in univariate and multivariate models to predict Aβ status. Results The 30-minute delayed recall score of the Rey Auditory Verbal Learning Test (AVLT) was the best predictor of Aβ status among the psychometric tests, achieving an AUC of 0.67±0.02 and odds ratio of 2.5±0.4. FDG-PET was the best imaging-based biomarker (AUC 0.67±0.03, OR 3.2±1.2), followed by hippocampal volume (AUC 0.64±0.02,,OR 2.4±0.3). A multivariate analysis based on the psychometric tests improved on the univariate predictors, achieving an AUC of 0.68±0.03 (OR 3.38±1.2). Adding imaging biomarkers to the multivariate analysis did not improve the AUC. Conclusion Psychometric tests perform as well as imaging biomarkers to predict presence of molecular markers of AD pathology in MCI patients and should be considered in the determination of the likelihood that MCI is due to AD. PMID:25881908

Single-Isocenter Multiple-Target Stereotactic Radiosurgery: Risk of Compromised Coverage

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

Roper, Justin, E-mail: justin.roper@emory.edu; Department of Biostatistics and Bioinformatics, Winship Cancer Institute of Emory University, Atlanta, Georgia; Chanyavanich, Vorakarn

2015-11-01

Purpose: To determine the dosimetric effects of rotational errors on target coverage using volumetric modulated arc therapy (VMAT) for multitarget stereotactic radiosurgery (SRS). Methods and Materials: This retrospective study included 50 SRS cases, each with 2 intracranial planning target volumes (PTVs). Both PTVs were planned for simultaneous treatment to 21 Gy using a single-isocenter, noncoplanar VMAT SRS technique. Rotational errors of 0.5°, 1.0°, and 2.0° were simulated about all axes. The dose to 95% of the PTV (D95) and the volume covered by 95% of the prescribed dose (V95) were evaluated using multivariate analysis to determine how PTV coverage was relatedmore » to PTV volume, PTV separation, and rotational error. Results: At 0.5° rotational error, D95 values and V95 coverage rates were ≥95% in all cases. For rotational errors of 1.0°, 7% of targets had D95 and V95 values <95%. Coverage worsened substantially when the rotational error increased to 2.0°: D95 and V95 values were >95% for only 63% of the targets. Multivariate analysis showed that PTV volume and distance to isocenter were strong predictors of target coverage. Conclusions: The effects of rotational errors on target coverage were studied across a broad range of SRS cases. In general, the risk of compromised coverage increased with decreasing target volume, increasing rotational error and increasing distance between targets. Multivariate regression models from this study may be used to quantify the dosimetric effects of rotational errors on target coverage given patient-specific input parameters of PTV volume and distance to isocenter.« less

Predicting need for additional CT scan in children with a non-diagnostic ultrasound for appendicitis in the emergency department.

PubMed

Nishizawa, Takuya; Maeda, Shigenobu; Goldman, Ran D; Hayashi, Hiroyuki

2018-01-01

This study aimed to determine which children with suspected appendicitis should be considered for a computerized tomography (CT) scan after a non-diagnostic ultrasound (US) in the Emergency Department (ED). We retrospectively reviewed patients 0-18year old, who presented to the ED with complaints of abdominal pain, during 2011-2015 and while in the hospital had both US and CT. We recorded demographic and clinical data and outcomes, and used univariate and multivariate methods for comparing patients who did and didn't have appendicitis on CT after non-diagnostic US. Multivariate analysis was performed using logistic regression to determine what variables were independently associated with appendicitis. A total of 328 patients were enrolled, 257 with non-diagnostic US (CT: 82 had appendicitis, 175 no-appendicitis). Younger children and those who reported vomiting or had right lower abdominal quadrant (RLQ) tenderness, peritoneal signs or White Blood Cell (WBC) count >10,000 in mm 3 were more likely to have appendicitis on CT. RLQ tenderness (Odds Ratio: 2.84, 95%CI: 1.07-7.53), peritoneal signs (Odds Ratio: 11.37, 95%CI: 5.08-25.47) and WBC count >10,000 in mm 3 (Odds Ratio: 21.88, 95%CI: 7.95-60.21) remained significant after multivariate analysis. Considering CT with 2 or 3 of these predictors would have resulted in sensitivity of 94%, specificity of 67%, positive predictive value of 57% and negative predictive value of 96% for appendicitis. Ordering CT should be considered after non-diagnostic US for appendicitis only when children meet at least 2 predictors of RLQ tenderness, peritoneal signs and WBC>10,000 in mm 3 . Copyright © 2017 Elsevier Inc. All rights reserved.

Use of chemometrics to compare NIR and HPLC for the simultaneous determination of drug levels in fixed-dose combination tablets employed in tuberculosis treatment.

PubMed

Teixeira, Kelly Sivocy Sampaio; da Cruz Fonseca, Said Gonçalves; de Moura, Luís Carlos Brigido; de Moura, Mario Luís Ribeiro; Borges, Márcia Herminia Pinheiro; Barbosa, Euzébio Guimaraes; De Lima E Moura, Túlio Flávio Accioly

2018-02-05

The World Health Organization recommends that TB treatment be administered using combination therapy. The methodologies for quantifying simultaneously associated drugs are highly complex, being costly, extremely time consuming and producing chemical residues harmful to the environment. The need to seek alternative techniques that minimize these drawbacks is widely discussed in the pharmaceutical industry. Therefore, the objective of this study was to develop and validate a multivariate calibration model in association with the near infrared spectroscopy technique (NIR) for the simultaneous determination of rifampicin, isoniazid, pyrazinamide and ethambutol. These models allow the quality control of these medicines to be optimized using simple, fast, low-cost techniques that produce no chemical waste. In the NIR - PLS method, spectra readings were acquired in the 10,000-4000cm -1 range using an infrared spectrophotometer (IRPrestige - 21 - Shimadzu) with a resolution of 4cm -1 , 20 sweeps, under controlled temperature and humidity. For construction of the model, the central composite experimental design was employed on the program Statistica 13 (StatSoft Inc.). All spectra were treated by computational tools for multivariate analysis using partial least squares regression (PLS) on the software program Pirouette 3.11 (Infometrix, Inc.). Variable selections were performed by the QSAR modeling program. The models developed by NIR in association with multivariate analysis provided good prediction of the APIs for the external samples and were therefore validated. For the tablets, however, the slightly different quantitative compositions of excipients compared to the mixtures prepared for building the models led to results that were not statistically similar, despite having prediction errors considered acceptable in the literature. Copyright © 2017 Elsevier B.V. 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 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.

Quality of in-hospital stroke care according to evidence-based performance measures: results from the first audit of stroke, Catalonia, Spain.

PubMed

Abilleira, Sònia; Gallofré, Miquel; Ribera, Aida; Sánchez, Emília; Tresserras, Ricard

2009-04-01

Evidence-based standards are used worldwide to determine quality of care. We assessed quality of in-hospital stroke care in all acute-care hospitals in Catalonia by determining adherence to 13 evidence-based performance measures (PMs) of process of care. Data on PMs were collected by retrospective review of medical records of consecutive stroke admissions (January to June, 2005). Compliance with PMs was calculated according to 3 hospital levels determined by their annual stroke case-load (level 1, <150 admissions/yr; level 2, 150 to 350; and level 3, >350). We defined sampling weights that represented each patient's inverse probability of inclusion in the study sample. Sampling weights were applied to produce estimates of compliance. Factors that predicted good/bad compliance were determined by multivariate weighted logistic regression models. An external monitoring of 10% of cases recruited at each hospital was undertaken, after random selection, to assess quality of data. We analyzed data from 1791 stroke cases (17% of all stroke admissions). Global interobserver agreement was 0.7. Eight PMs achieved compliances >or=75%, 4 of which were more than 90%, and the remaining showed adherences

Determination of Diethyl Phthalate and Polyhexamethylene Guanidine in Surrogate Alcohol from Russia

PubMed Central

Monakhova, Yulia B.; Kuballa, Thomas; Leitz, Jenny; Lachenmeier, Dirk W.

2011-01-01

Analytical methods based on spectroscopic techniques were developed and validated for the determination of diethyl phthalate (DEP) and polyhexamethylene guanidine (PHMG), which may occur in unrecorded alcohol. Analysis for PHMG was based on UV-VIS spectrophotometry after derivatization with Eosin Y and 1H NMR spectroscopy of the DMSO extract. Analysis of DEP was performed with direct UV-VIS and 1H NMR methods. Multivariate curve resolution and spectra computation methods were used to confirm the presence of PHMG and DEP in the investigated beverages. Of 22 analysed alcohol samples, two contained DEP or PHMG. 1H NMR analysis also revealed the presence of signals of hawthorn extract in three medicinal alcohols used as surrogate alcohol. The simple and cheap UV-VIS methods can be used for rapid screening of surrogate alcohol samples for impurities, while 1H NMR is recommended for specific confirmatory analysis if required. PMID:21647285

Determination of diethyl phthalate and polyhexamethylene guanidine in surrogate alcohol from Russia.

PubMed

Monakhova, Yulia B; Kuballa, Thomas; Leitz, Jenny; Lachenmeier, Dirk W

2011-01-01

Analytical methods based on spectroscopic techniques were developed and validated for the determination of diethyl phthalate (DEP) and polyhexamethylene guanidine (PHMG), which may occur in unrecorded alcohol. Analysis for PHMG was based on UV-VIS spectrophotometry after derivatization with Eosin Y and (1)H NMR spectroscopy of the DMSO extract. Analysis of DEP was performed with direct UV-VIS and (1)H NMR methods. Multivariate curve resolution and spectra computation methods were used to confirm the presence of PHMG and DEP in the investigated beverages. Of 22 analysed alcohol samples, two contained DEP or PHMG. (1)H NMR analysis also revealed the presence of signals of hawthorn extract in three medicinal alcohols used as surrogate alcohol. The simple and cheap UV-VIS methods can be used for rapid screening of surrogate alcohol samples for impurities, while (1)H NMR is recommended for specific confirmatory analysis if required.

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…

Continuation of measurement of hydrologic soil-cover complex with airborne scatterometers. [Texas

NASA Technical Reports Server (NTRS)

Blanchard, B. J.; Nieber, J. L.; Blanchard, A. J. (Principal Investigator)

1979-01-01

The author has identified the following significant results. Analysis of radar scatterometry data obtained over five flight lines in Texas by NASA C-130 aircraft demonstrated that multivariant radar data can be used to distinguish difference in land use, and hence be an indicator of surface runoff characteristics. The capability of using microwave sensors to detect flood inundation of timbered land was also determined.

Biologic and social determinants of sequelae and long-term survival of pediatric HIV in Romania.

PubMed

Kozinetz, Claudia A; Matusa, Rodica; Hacker, Carl S

2006-08-01

The aim of the study is to investigate the effect of social context and clinical factors on survival in a cohort of 333 children to identify issues useful in the treatment and care of human immunodeficiency virus (HIV)-infected youth in developing countries. A prospective cohort study design was used, and data were gathered at baseline and 1-year follow-up. The study cohort consisted of children given a diagnosis of HIV between 1995 and 1999 and receiving medical care in Constanta, Romania. Data were examined by means of multivariate Cox regression analysis models. The majority of the cohort were in the moderate (41%) or severe (40%) stages of HIV at baseline. Multivariate analysis indicated that social-context factors were the most significant determinants of HIV survival. The hazard for death for those with mothers or fathers with a higher level of education was approximately one quarter (relative hazard, 0.3-0.4; confidence interval, 0.1-1.0) that for a parent with a lower level of education. Subjects with employed mothers were four times more likely to survive than subjects with unemployed mothers. Results suggest that recognition of social-context risk factors for HIV disease progression and survival is important in developing countries, as it is in developed countries.

Simultaneous determination of potassium guaiacolsulfonate, guaifenesin, diphenhydramine HCl and carbetapentane citrate in syrups by using HPLC-DAD coupled with partial least squares multivariate calibration.

PubMed

Dönmez, Ozlem Aksu; Aşçi, Bürge; Bozdoğan, Abdürrezzak; Sungur, Sidika

2011-02-15

A simple and rapid analytical procedure was proposed for the determination of chromatographic peaks by means of partial least squares multivariate calibration (PLS) of high-performance liquid chromatography with diode array detection (HPLC-DAD). The method is exemplified with analysis of quaternary mixtures of potassium guaiacolsulfonate (PG), guaifenesin (GU), diphenhydramine HCI (DP) and carbetapentane citrate (CP) in syrup preparations. In this method, the area does not need to be directly measured and predictions are more accurate. Though the chromatographic and spectral peaks of the analytes were heavily overlapped and interferents coeluted with the compounds studied, good recoveries of analytes could be obtained with HPLC-DAD coupled with PLS calibration. This method was tested by analyzing the synthetic mixture of PG, GU, DP and CP. As a comparison method, a classsical HPLC method was used. The proposed methods were applied to syrups samples containing four drugs and the obtained results were statistically compared with each other. Finally, the main advantage of HPLC-PLS method over the classical HPLC method tried to emphasized as the using of simple mobile phase, shorter analysis time and no use of internal standard and gradient elution. Copyright © 2010 Elsevier B.V. All rights reserved.

Prevalence of helminth infestation during pregnancy and its association with maternal anemia and low birth weight.

PubMed

Aderoba, Adeniyi K; Iribhogbe, Oseihie I; Olagbuji, Biodun N; Olokor, Oghenefegor E; Ojide, Chiedozie K; Ande, Adedapo B

2015-06-01

To determine the prevalence of helminth infestation during pregnancy and the associated risks of adverse maternal and infant outcomes. A cross-sectional study of women with a singleton pregnancy of at least 34 weeks was conducted at a teaching hospital in Benin City, Nigeria, between April 1 and September 30, 2010. Socioeconomic and clinical data were obtained. Stool samples were used to determine helminth infection. Birth weight was recorded at delivery. Multivariable analysis was used to assess the link between helminth infestation and maternal and perinatal outcomes. Among 178 women, 31 (17.4%) had a helminth infestation (15 [8.4%] had ascariasis, 8 [4.5%] trichuriasis, and 25 [14.0%] hookworm infestation). Multivariate analysis found that helminth infestations was associated with maternal anemia (adjusted odds ratio 12.4; 95% confidence interval 4.2-36.3) and low birth weight (adjusted odds ratio 6.8; 95% confidence interval 2.1-21.9). Approximately one in five women had a helminth infestation in the third trimester of pregnancy. Maternal helminth infestation significantly increased the risks of maternal anemia and low birth weight, indicating that routine administration of anthelminthic drugs during early pregnancy might improve perinatal outcomes. Copyright © 2015 International Federation of Gynecology and Obstetrics. Published by Elsevier Ireland Ltd. All rights reserved.

Understanding the relationship between access to care and facility-based delivery through analysis of the 2008 Ghana Demographic Health Survey.

PubMed

Moyer, Cheryl A; McLaren, Zoë M; Adanu, Richard M; Lantz, Paula M

2013-09-01

To determine the types of access to care most strongly associated with facility-based delivery among women in Ghana. Data relating to the "5 As of Access" framework were extracted from the 2008 Ghana Demographic Health Survey and analyzed using multivariate logistic regression. In all, 55.5% of a weighted sample of 1102 women delivered in a healthcare facility, whereas 45.5% delivered at home. Affordability was the strongest access factor associated with delivery location, with health insurance coverage tripling the odds of facility delivery. Availability, accessibility (except urban residence), acceptability, and social access variables were not significant factors in the final models. Social access variables, including needing permission to seek healthcare and not being involved in decisions regarding healthcare, were associated with a reduced likelihood of facility-based delivery when examined individually. Multivariate analysis suggested that these variables reflected maternal literacy, health insurance coverage, and household wealth, all of which attenuated the effects of social access. Affordability was an important determinant of facility delivery in Ghana-even among women with health insurance-but social access variables had a mediating role. Copyright © 2013 International Federation of Gynecology and Obstetrics. Published by Elsevier Ireland Ltd. All rights reserved.

Determinants of outcomes in patients with simple gastroschisis.

PubMed

Youssef, Fouad; Laberge, Jean-Martin; Puligandla, Pramod; Emil, Sherif

2017-05-01

We analyzed the determinants of outcomes in simple gastroschisis (GS) not complicated by intestinal atresia, perforation, or necrosis. All simple GS patients enrolled in a national prospective registry from 2005 to 2013 were studied. Patients below the median for total parenteral nutrition (TPN) duration (26days) and hospital stay (34days) were compared to those above. Univariate and multivariate logistic and linear regression analyses were employed using maternal, patient, postnatal, and treatment variables. Of 700 patients with simple GS, representing 76.8% of all GS patients, 690 (98.6%) survived. TPN was used in 352 (51.6%) and 330 (48.4%) patients for ≤26 and >26days, respectively. Hospital stay for 356 (51.9%) and 330 (48.1%) infants was ≤34 and >34days, respectively. Univariate analysis revealed significant differences in several patient, treatment, and postnatal factors. On multivariate analysis, prenatal sonographic bowel dilation, older age at closure, necrotizing enterocolitis, longer mechanical ventilation, and central-line associated blood stream infection (CLABSI) were independently associated with longer TPN duration and hospital stay, with CLABSI being the strongest predictor. Prenatal bowel dilation is associated with increased morbidity in simple GS. CLABSI is the strongest predictor of outcomes. Bowel matting is not an independent risk factor. 2c. Copyright © 2017 Elsevier Inc. All rights reserved.

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.

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.

An analysis of fracture trace patterns in areas of flat-lying sedimentary rocks for the detection of buried geologic structure. [Kansas and Texas

NASA Technical Reports Server (NTRS)

Podwysocki, M. H.

1974-01-01

Two study areas in a cratonic platform underlain by flat-lying sedimentary rocks were analyzed to determine if a quantitative relationship exists between fracture trace patterns and their frequency distributions and subsurface structural closures which might contain petroleum. Fracture trace lengths and frequency (number of fracture traces per unit area) were analyzed by trend surface analysis and length frequency distributions also were compared to a standard Gaussian distribution. Composite rose diagrams of fracture traces were analyzed using a multivariate analysis method which grouped or clustered the rose diagrams and their respective areas on the basis of the behavior of the rays of the rose diagram. Analysis indicates that the lengths of fracture traces are log-normally distributed according to the mapping technique used. Fracture trace frequency appeared higher on the flanks of active structures and lower around passive reef structures. Fracture trace log-mean lengths were shorter over several types of structures, perhaps due to increased fracturing and subsequent erosion. Analysis of rose diagrams using a multivariate technique indicated lithology as the primary control for the lower grouping levels. Groupings at higher levels indicated that areas overlying active structures may be isolated from their neighbors by this technique while passive structures showed no differences which could be isolated.

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

Spatio-temporal variability of hydro-chemical characteristics of coastal waters of Gulf of Mannar Marine Biosphere Reserve (GoMMBR), South India

NASA Astrophysics Data System (ADS)

Kathiravan, K.; Natesan, Usha; Vishnunath, R.

2017-03-01

The intention of this study was to appraise the spatial and temporal variations in the physico-chemical parameters of coastal waters of Rameswaram Island, Gulf of Mannar Marine Biosphere Reserve, south India, using multivariate statistical techniques, such as cluster analysis, factor analysis and principal component analysis. Spatio-temporal variations among the physico-chemical parameters are observed in the coastal waters of Gulf of Mannar, especially during northeast and post monsoon seasons. It is inferred that the high loadings of pH, temperature, suspended particulate matter, salinity, dissolved oxygen, biochemical oxygen demand, chlorophyll a, nutrient species of nitrogen and phosphorus strongly determine the discrimination of coastal water quality. Results highlight the important role of monsoonal variations to determine the coastal water quality around Rameswaram Island.

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.

Determinants of distance walked during the six-minute walk test in patients undergoing cardiac surgery at hospital discharge

PubMed Central

2014-01-01

Introduction The aim of this study was to identify the determinants of distance walked in six-minute walk test (6MWD) in patients undergoing cardiac surgery at hospital discharge. Methods The assessment was performed preoperatively and at discharge. Data from patient records were collected and measurement of the Functional Independence Measure (FIM) and the Nottingham Health Profile (NHP) were performed. The six-minute walk test (6MWT) was performed at discharge. Patients undergoing elective cardiac surgery, coronary artery bypass grafting or valve replacement were eligible. Patients older than 75 years who presented arrhythmia during the protocol, with psychiatric disorders, muscular or neurological disorders were excluded from the study. Results Sixty patients (44.26% male, mean age 51.53 ± 13 years) were assessed. In multivariate analysis the following variables were selected: type of surgery (P = 0.001), duration of cardiopulmonary bypass (CPB) (P = 0.001), Functional Independence Measure - FIM (0.004) and body mass index - BMI (0.007) with r = 0.91 and r2 = 0.83 with P < 0.001. The equation derived from multivariate analysis: 6MWD = Surgery (89.42) + CPB (1.60) + MIF (2.79 ) - BMI (7.53) - 127.90. Conclusion In this study, the determinants of 6MWD in patients undergoing cardiac surgery were: the type of surgery, CPB time, functional capacity and body mass index. PMID:24885130

Prevalence and risk factors for Staphylococcus aureus and methicillin-resistant Staphylococcus aureus nasal carriage inpatients in a tertiary care hospital's chest clinic in Turkey.

PubMed

Oguzkaya-Artan, M; Artan, C; Baykan, Z

2016-01-01

We aimed to determine the prevalence and associated risk factors for nasal methicillin-sensitive and methicillin-resistant Staphylococcus aureus (MSSA/MRSA) carriage among patients admitted to a chest clinic of a tertiary care hospital in this study. Nasal samples were taken from anterior nares were cultured in CHROMagar S. aureus plates, MRSA was determined by disc diffusion method (cefoxitin 30 μg) according to the Clinical and Laboratory Standards Institute guidelines and CHROMagar MRSA plates. A questionnaire was applied to determine the demographic characteristics of the participants and risk factors for carriage. Fisher's exact test, univariate and multivariate logistic regression analysis were used. A P < 0.05 indicated a statistically significant difference. This is a cross-sectional study covering all the patients (n = 431) admitted to Kayseri Training and Research Hospital's Chest Clinic from January 1st to 31st 2014. Of all these patients 55 (12.8%) were nasal S. aureus carriers. MRSA positivity was in five among these patients. In multivariate analysis, being under 65 years of age (odds ratio [OR], 1.9; 95% confidence interval [CI], 1.0-3.3), and having prosthesis (OR, 4.8; 95% CI, 1.6-13.9) were found as risk factors for MSSA colonization. The prevalence of nasal carriage of MSSA was low in our study population. The only risk factors playing role in carriage were found as being under the age of 65 and having prosthesis.

Comparative statistical analysis of chrome and vegetable tanning effluents and their effects on related soil.

PubMed

Tariq, Saadia R; Shah, Munir H; Shaheen, Nazia

2009-09-30

Two tanning units of Pakistan, namely, Kasur and Mian Channun were investigated with respect to the tanning processes (chrome and vegetable, respectively) and the effects of the tanning agents on the quality of soil in vicinity of tanneries were evaluated. The effluent and soil samples from 16 tanneries each of Kasur and Mian Channun were collected. The levels of selected metals (Na, K, Ca, Mg, Fe, Cr, Mn, Co, Cd, Ni, Pb and Zn) were determined by using flame atomic absorption spectrophotometer under optimum analytical conditions. The data thus obtained were subjected to univariate and multivariate statistical analyses. Most of the metals exhibited considerably higher concentrations in the effluents and soils of Kasur compared with those of Mian Channun. It was observed that the soil of Kasur was highly contaminated by Na, K, Ca and Mg emanating from various processes of leather manufacture. Furthermore, the levels of Cr were also present at much enhanced levels than its background concentration due to the adoption of chrome tanning. The levels of Cr determined in soil samples collected from the vicinity of Mian Channun tanneries were almost comparable to the background levels. The soil of this city was found to have contaminated only by the metals originating from pre-tanning processes. The apportionment of selected metals in the effluent and soil samples was determined by a multivariate cluster analysis, which revealed significant differences in chrome and vegetable tanning processes.

Response to rocuronium and its determinants in patients with myasthenia gravis: A case-control study.

PubMed

Fujimoto, Masafumi; Terasaki, Shuhei; Nishi, Masaaki; Yamamoto, Tatsuo

2015-10-01

Several previous studies using univariate analysis have suggested that the pre-anaesthetic train-of-four (TOF) ratio, concentration of anti-acetylcholine receptor (AChR) antibodies and the presence of preoperative generalised muscular involvement are determinants of an increased response to neuromuscular blocking agents (NMBAs) in patients with myasthenia gravis. However, the determinants of the response of patients with myasthenia gravis to rocuronium, which is expected to be used more frequently since the advent of sugammadex, have not been studied. To clarify whether previously suggested determinants of the response to other intermediate-acting NMBAs would also affect the response to rocuronium and to reveal the determinants of the increased response to rocuronium in individual patients with myasthenia gravis using multivariate analysis. Case control study. Kumamoto University Hospital, November 2010 to September 2013. Thirty-eight patients with myasthenia gravis having surgery using a total intravenous anaesthetic technique were investigated. After induction of general anaesthesia, the 95% effective dose (ED95) of rocuronium was calculated using cumulative dose-finding methods. Neuromuscular function was monitored by acceleromyographic assessment of TOF responses of the adductor pollicis muscle to ulnar nerve stimulation. Patients were then divided into the increased response (ED95 <0.15 mg kg, n = 13) and non-increased response groups (ED95 ≥0.15 mg kg, n = 25). Demographic data, TOF ratio before rocuronium injection (baseline TOF ratio), concentration of anti-AChR antibodies, Osserman classification and treatment for myasthenia gravis in the two groups were compared. Stepwise logistic regression identified baseline TOF ratio and age of onset of myasthenia gravis as determinants of the increased response to rocuronium in patients with myasthenia gravis [odds ratios (95% confidence interval) of 0.87 (0.77 to 0.98; P = 0.02) and 0.92 (0.86 to 0.99; P = 0.03), respectively]. Multivariate analysis identified baseline TOF ratio and age of disease onset as determinants of the increased response to rocuronium in patients with myasthenia gravis. Registered with UMIN Clinical Trials Registry, identifier: UMIN000006766.

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.

Visual classification of very fine-grained sediments: Evaluation through univariate and multivariate statistics

USGS Publications Warehouse

Hohn, M. Ed; Nuhfer, E.B.; Vinopal, R.J.; Klanderman, D.S.

1980-01-01

Classifying very fine-grained rocks through fabric elements provides information about depositional environments, but is subject to the biases of visual taxonomy. To evaluate the statistical significance of an empirical classification of very fine-grained rocks, samples from Devonian shales in four cored wells in West Virginia and Virginia were measured for 15 variables: quartz, illite, pyrite and expandable clays determined by X-ray diffraction; total sulfur, organic content, inorganic carbon, matrix density, bulk density, porosity, silt, as well as density, sonic travel time, resistivity, and ??-ray response measured from well logs. The four lithologic types comprised: (1) sharply banded shale, (2) thinly laminated shale, (3) lenticularly laminated shale, and (4) nonbanded shale. Univariate and multivariate analyses of variance showed that the lithologic classification reflects significant differences for the variables measured, difference that can be detected independently of stratigraphic effects. Little-known statistical methods found useful in this work included: the multivariate analysis of variance with more than one effect, simultaneous plotting of samples and variables on canonical variates, and the use of parametric ANOVA and MANOVA on ranked data. ?? 1980 Plenum Publishing Corporation.

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

PubMed

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

2017-03-01

The present article describes integration of environmental monitoring and discharge data and interpretation using multivariate statistics, principal component analysis (PCA), and partial least squares (PLS) regression. The monitoring was carried out at the Peregrino oil field off the coast of Brazil. One sensor platform and 3 sediment traps were placed on the seabed. The sensors measured current speed and direction, turbidity, temperature, and conductivity. The sediment trap samples were used to determine suspended particulate matter that was characterized with respect to a number of chemical parameters (26 alkanes, 16 PAHs, N, C, calcium carbonate, and Ba). Data on discharges of drill cuttings and water-based drilling fluid were provided on a daily basis. The monitoring was carried out during 7 campaigns from June 2010 to October 2012, each lasting 2 to 3 months due to the capacity of the sediment traps. The data from the campaigns were preprocessed, combined, and interpreted using multivariate statistics. No systematic difference could be observed between campaigns or traps despite the fact that the first campaign was carried out before drilling, and 1 of 3 sediment traps was located in an area not expected to be influenced by the discharges. There was a strong covariation between suspended particulate matter and total N and organic C suggesting that the majority of the sediment samples had a natural and biogenic origin. Furthermore, the multivariate regression showed no correlation between discharges of drill cuttings and sediment trap or turbidity data taking current speed and direction into consideration. Because of this lack of correlation with discharges from the drilling location, a more detailed evaluation of chemical indicators providing information about origin was carried out in addition to numerical modeling of dispersion and deposition. The chemical indicators and the modeling of dispersion and deposition support the conclusions from the multivariate statistics. Integr Environ Assess Manag 2017;13:387-395. © 2016 SETAC. © 2016 SETAC.

Study of the questionnaire of the Polytechnic University of Valencia (UPV) teaching staff, using students opinion survey. Statistical treatment

NASA Astrophysics Data System (ADS)

Martinez Gomez, Monica

Quality improvement of university institutions represents the most important challenge in the next years, and the potential tool to achieve it is based on the institutional evaluation in general, and specially the evaluation of the teaching performance. The opinion questionnaire from the students is the most generalised tool used to evaluate the teaching performance at Spanish universities. The general objective of this thesis is to develop a statistical methodology suitable to extract, analyse and interpret the information contained in the Questionnaire of Teaching Evaluation from Student Opinion (CEDA) of the UPV, aimed at optimising its practical use. The study is centred in the application of different multivariate techniques and has been structured in three parts: (1) Evaluation of the reliability, validity and dimensionality of the tool. The multivariate method used for this purpose is the Factorial Analysis. (2) Determination of the capacity of the questionnaire to identify different profiles of lecturers based on the quality perceived by students. This target is conducted with different multivariate classification techniques: hierarchical cluster analysis, non-hierarchical and two-stage analysis. Moreover, those items that best discriminate among the teaching typologies obtained are identified in the questionnaire. (3) Identification of the teaching typologies according to different descriptive characteristics referent to the subject and lecturer, with the use of decision trees. Once identified these typologies, a new discriminant analysis is conducted aimed at identifying those items that best characterise each typology. Finally, a study is carried out with the classification method SIMCA (Soft Independent Modelling of Class Analogy) in order to determine the discriminant loading of every item among the identified teaching typologies, allowing the identification of those that best distinguish the different classes obtained. With the combined use of the proposed techniques, it is expected to optimise the use of CEDA as a measuring tool and an indicator of the teaching quality at the university, that would allow the introduction of actions for the continuous improvement in the teaching processes of the UPV.

Impact of age on outcome after colorectal cancer surgery in the elderly - a developing country perspective.

PubMed

Khan, Muhammad Rizwan; Bari, Hassaan; Zafar, Syed Nabeel; Raza, Syed Ahsan

2011-08-17

Colorectal cancer (CRC) is a major source of morbidity and mortality in the elderly population and surgery is often the only definitive management option. The suitability of surgical candidates based on age alone has traditionally been a source of controversy. Surgical resection may be considered detrimental in the elderly solely on the basis of advanced age. Based on recent evidence suggesting that age alone is not a predictor of outcomes, Western societies are increasingly performing definitive procedures on the elderly. Such evidence is not available from our region. We aimed to determine whether age has an independent effect on complications after surgery for colorectal cancer in our population. A retrospective review of all patients who underwent surgery for pathologically confirmed colorectal cancer at Aga Khan University Hospital, Karachi between January 1999 and December 2008 was conducted. Using a cut-off of 70 years, patients were divided into two groups. Patient demographics, tumor characteristics and postoperative complications and 30-day mortality were compared. Multivariate logistic regression analysis was performed with clinically relevant variables to determine whether age had an independent and significant association with the outcome. A total of 271 files were reviewed, of which 56 belonged to elderly patients (≥ 70 years). The gender ratio was equal in both groups. Elderly patients had a significantly higher comorbidity status, Charlson score and American society of anesthesiologists (ASA) class (all p < 0.001). Upon multivariate analysis, factors associated with more complications were ASA status (95% CI = 1.30-6.25), preoperative perforation (95% CI = 1.94-48.0) and rectal tumors (95% CI = 1.21-5.34). Old age was significantly associated with systemic complications upon univariate analysis (p = 0.05), however, this association vanished upon multivariate analysis (p = 0.36). Older patients have more co-morbid conditions and higher ASA scores, but increasing age itself is not independently associated with complications after surgery for CRC. Therefore patient selection should focus on the clinical status and ASA class of the patient rather than age.

Instrumental Neutron Activation Analysis and Multivariate Statistics for Pottery Provenance

NASA Astrophysics Data System (ADS)

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

2004-06-01

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

A Study of Effects of MultiCollinearity in the Multivariable Analysis

PubMed Central

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

2015-01-01

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

A Study of Effects of MultiCollinearity in the Multivariable Analysis.

PubMed

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

2014-10-01

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

Feasibility of Image-Guided Transthoracic Core Needle Biopsy in the BATTLE Lung Trial

PubMed Central

Tam, Alda L.; Kim, Edward S.; Lee, J. Jack; Ensor, Joe E.; Hicks, Marshall E.; Tang, Ximing; Blumenschein, George R.; Alden, Christine M.; Erasmus, Jeremy J.; Tsao, Anne; Lippman, Scott M.; Hong, Waun K.; Wistuba, Ignacio I.; Gupta, Sanjay

2013-01-01

Purpose As therapy for non-small cell lung cancer (NSCLC) patients becomes more personalized, additional tissue in the form of core needle biopsies (CNBs) for biomarker analysis is increasingly required for determining appropriate treatment and for enrollment into clinical trials. We report our experience with small-caliber percutaneous transthoracic (PT) CNBs for the evaluation of multiple molecular biomarkers in BATTLE (Biomarker-integrated Approaches of Targeted Therapy for Lung Cancer Elimination), a personalized, targeted therapy NSCLC clinical trial. Methods The medical records of patients who underwent PTCNB for consideration of enrollment in BATTLE, were reviewed for diagnostic yield of 11 predetermined molecular markers, and procedural complications. Univariate and multivariate analyses of factors related to patient and lesion characteristics were performed to determine possible influences on diagnostic yield. Results One hundred and seventy PTCNBs were performed using 20-gauge biopsy needles in 151 NSCLC patients screened for the trial. 82.9% of the biopsy specimens were found to have adequate tumor tissue for analysis of the required biomarkers. On multivariate analysis, metastatic lesions were 5.4 times more likely to yield diagnostic tissue as compared to primary tumors (p = 0.0079). Pneumothorax and chest tube insertion rates were 15.3% and 9.4%, respectively. Conclusions Image-guided 20-gauge PTCNB is safe and provides adequate tissue for analysis of multiple biomarkers in the majority of patients being considered for enrollment into a personalized, targeted therapy NSCLC clinical trial. Metastatic lesions are more likely to yield diagnostic tissue as compared to primary tumors. PMID:23442309

Risk factors of hepatitis B virus infection among blood donors in Duhok city, Kurdistan Region, Iraq

PubMed Central

R Hussein, Nawfal

2018-01-01

Background: Hepatitis B virus (HBV) infection is a public health problem. The lack of information about the seroprevalence and risk factors is an obstacle for preventive public health plans to reduce the burden of viral hepatitis. Therefore, this study was conducted in Iraq, where no studies had been performed to determine the prevalence and risk factors of HBV infection. Methods: Blood samples were collected form 438 blood donors attending blood bank in Duhok city. Serum samples were tested for HBV core-antibodies (HBcAb) and HBV surface-antigen (HBsAg) by ELISA. Various risk factors were recorded and multivariate analysis was performed. Results: 5/438 (1.14%) of the subjects were HBsAg positive (HBsAg and HBcAb positive) and 36/438 (8.2%) were HBcAb positive. Hence, 41 cases were exposed to HBV and data analysis was based on that. Univariate analysis showed that there were significant associations between history of illegitimate sexual contact, history of alcohol or history of dental surgeries and HBV exposure (p<0.05 for all). Then, multivariate analysis was conducted to find HBV exposure predictive factors. It was found that history of dental surgery was a predictive factor for exposure to the virus (P=0.03, OR: 2.397). Conclusions: This study suggested that the history of dental surgery was predictive for HBV transmission in Duhok city. Further population-based study is needed to determine HBV risk factors in the society and public health plan based on that should be considered. PMID:29387315

Arterial compression of the retro-olivary sulcus of the medulla in essential hypertension: a multivariate analysis.

PubMed

Coffee, Robert E; Nicholas, Joyce S; Egan, Brent M; Rumboldt, Zoran; D'Agostino, Sabino; Patel, Sunil J

2005-11-01

Pulsatile arterial compression (AC) of the ventrolateral medulla (VLM) has been postulated to cause neurogenically mediated essential hypertension (EHTN). We aimed to establish whether the association between AC of specifically the retro-olivary sulcus (ROS) of the VLM and EHTN was significant, while controlling for other risks associated with EHTN. Case-control study. Posterior fossa magnetic resonance imaging scans of 131 subjects, including 58 subjects with EHTN and 73 normotensives, were reviewed to determine the presence of AC in the ROS. The history of other risk factors for EHTN was obtained by reviewing medical records. Multivariate logistic regression analysis of these data shows a significant association between AC in the ROS (right and/or left) and EHTN [odds ratio (OR) = 3.03, 95% confidence interval (CI) = 1.30, 7.06]. This analysis was done controlling for other known EHTN risk factors such as age, race, sex, diabetes, and obesity. A secondary analysis also controlling for these variables shows that AC of both the right and left ROS are independently associated with EHTN (right AC: OR = 5.04, 95% CI = 1.33, 19.17; left AC: OR = 3.39, 95% CI = 1.20, 9.60). In this retrospective study of subjects with EHTN and normotensive controls that had undergone magnetic resonance imaging of the posterior fossa, AC of the ROS on either side of the medulla is a significant independent risk factor in EHTN. Further studies are required to determine whether this is true for the general population of patients with neurogenically mediated EHTN.

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.

Development and validation of a Partial Least Squares-Discriminant Analysis (PLS-DA) model based on the determination of ethyl glucuronide (EtG) and fatty acid ethyl esters (FAEEs) in hair for the diagnosis of chronic alcohol abuse.

PubMed

Alladio, E; Giacomelli, L; Biosa, G; Corcia, D Di; Gerace, E; Salomone, A; Vincenti, M

2018-01-01

The chronic intake of an excessive amount of alcohol is currently ascertained by determining the concentration of direct alcohol metabolites in the hair samples of the alleged abusers, including ethyl glucuronide (EtG) and, less frequently, fatty acid ethyl esters (FAEEs). Indirect blood biomarkers of alcohol abuse are still determined to support hair EtG results and diagnose a consequent liver impairment. In the present study, the supporting role of hair FAEEs is compared with indirect blood biomarkers with respect to the contexts in which hair EtG interpretation is uncertain. Receiver Operating Characteristics (ROC) curves and multivariate Principal Component Analysis (PCA) demonstrated much stronger correlation of EtG results with FAEEs than with any single indirect biomarker or their combinations. Partial Least Squares Discriminant Analysis (PLS-DA) models based on hair EtG and FAEEs were developed to maximize the biomarkers information content on a multivariate background. The final PLS-DA model yielded 100% correct classification on a training/evaluation dataset of 155 subjects, including both chronic alcohol abusers and social drinkers. Then, the PLS-DA model was validated on an external dataset of 81 individual providing optimal discrimination ability between chronic alcohol abusers and social drinkers, in terms of specificity and sensitivity. The PLS-DA scores obtained for each subject, with respect to the PLS-DA model threshold that separates the probabilistic distributions for the two classes, furnished a likelihood ratio value, which in turn conveys the strength of the experimental data support to the classification decision, within a Bayesian logic. Typical boundary real cases from daily work are discussed, too. Copyright © 2017 Elsevier B.V. All rights reserved.

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

Assessing heavy metal sources in sugarcane Brazilian soils: an approach using multivariate analysis.

PubMed

da Silva, Fernando Bruno Vieira; do Nascimento, Clístenes Williams Araújo; Araújo, Paula Renata Muniz; da Silva, Luiz Henrique Vieira; da Silva, Roberto Felipe

2016-08-01

Brazil is the world's largest sugarcane producer and soils in the northeastern part of the country have been cultivated with the crop for over 450 years. However, so far, there has been no study on the status of heavy metal accumulation in these long-history cultivated soils. To fill the gap, we collect soil samples from 60 sugarcane fields in order to determine the contents of Cd, Cr, Cu, Ni, Pb, and Zn. We used multivariate analysis to distinguish between natural and anthropogenic sources of these metals in soils. Analytical determinations were performed in ICP-OES after microwave acid solution digestion. Mean concentrations of Cd, Cr, Cu, Ni, Pb, and Zn were 1.9, 18.8, 6.4, 4.9, 11.2, and 16.2 mg kg(-1), respectively. The principal component one was associated with lithogenic origin and comprised the metals Cr, Cu, Ni, and Zn. Cluster analysis confirmed that 68 % of the evaluated sites have soil heavy metal concentrations close to the natural background. The Cd concentration (principal component two) was clearly associated with anthropogenic sources with P fertilization being the most likely source of Cd to soils. On the other hand, the third component (Pb concentration) indicates a mixed origin for this metal (natural and anthropogenic); hence, Pb concentrations are probably related not only to the soil parent material but also to industrial emissions and urbanization in the vicinity of the agricultural areas.

Disparities in the surgical treatment of colorectal liver metastases.

PubMed

Munene, Gitonga; Parker, Robyn D; Shaheen, Abdel Aziz; Myers, Robert P; Quan, May Lynn; Ball, Chad G; Dixon, Elijah

2013-01-01

Hepatectomy is an accepted standard of care for patients with resectable colorectal liver metastases (CLM). Given that it is unclear whether disparities exist between different patient populations, a population-based analysis was performed to analyze this issue with regards to resection rates and surgical mortality in patients with CLM. Using the Nationwide Inpatient Sample, characteristics and outcomes of adult patients with a diagnosis of colorectal cancer and colorectal metastases that subsequently underwent a liver resection during the years 1993-2007 were identified. Multivariate analysis was used to determine the effects of demographic and clinical covariables on resection rates and in-hospital mortality. Incident colorectal and liver metastases were identified in 138,565 patients; 3,528 patients (2.6%) underwent subsequent resection. African American and Hispanic race were associated with lower resection rates compared to Caucasian patients (adjusted OR 0.61 (0.52 - 0.71) and 0.81 (0.68 - 0.96) respectively). Medicaid insurance was associated with decreased resection rates compared to private insurance (AOR 0.47 (0.40 - 0.56)). The overall inpatient mortality rate was 3.1%. Multivariate analysis determined that mortality rate was correlated to both insurance status and geographic region. The national resection rate is significantly lower than has been reported by most case series. Race and insurance status appear to be correlated to the likelihood of surgical resection. In-hospital mortality is equivalent to the rates reported elsewhere, but is correlated to insurance status and region.

The FIB-4 score predicts postoperative short-term outcomes of hepatocellular carcinoma fulfilling the milan criteria.

PubMed

Dong, J; Xu, X-h; Ke, M-y; Xiang, J-x; Liu, W-y; Liu, X-m; Wang, B; Zhang, X-f; Lv, Y

2016-05-01

The fibrosis score 4 (FIB-4) score is a useful tool to determine the degree of hepatic fibrosis. Liver fibrosis and cirrhosis are well-known predictors of postoperative complications after hepatectomy. This study examined the impact of FIB-4 on postoperative short-term outcomes of patients with hepatocellular carcinoma (HCC). Three hundred and fifty patients undergoing hepatectomy for HCC between 2008 and 2013 were enrolled. The receiver operating characteristic (ROC) curve analysis was performed to determine the cutoff value of the FIB-4. Univariate and multivariate analysis was performed to identify the risk factors. The correlation of the preoperative FIB-4 value with clinicopathological parameters was examined. Postoperative complications were observed in 202 (57.7%) patients. The optimal cutoff value of the FIB-4 was set at 2.88 and 3.85 for postoperative complications and intraoperative blood loss respectively. It was also an independent prognostic factor for postoperative complications (hazard ratio [HR], 1.202; 95% CI, 1.076-1.344; P = 0.001) and intraoperative blood loss (HR, 1.196; 95% CI, 1.091-1.343; P < 0.001) by multivariate analysis. The FIB-4 was significantly correlated with age, liver function, coagulation function, blood loss, intraoperative blood transfusion (all P < 0.05). Preoperative FIB-4 is a useful index to predict postoperative outcomes in patients with HCC. The FIB-4 should be assessed routinely for hepatocellular carcinoma patients. Copyright © 2016 Elsevier Ltd. All rights reserved.

Liver transplantation of hepatitis B surface antigen positive donors to hepatitis B core antibody recipients: analysis of 27 patients.

PubMed

Krishnamoorthi, R; Manickam, P; Cappell, M S

2014-06-01

Shortage of donor livers is the major limiting factor for liver transplantation (LT). While livers from patients with past infection of Hepatitis-B (HBcAb+) are commonly used as donors, scant data exists on outcomes following transplantation of HBsAg+ donor livers. The impact of donor HBsAg positivity on recipient survival is currently analyzed. Post hoc analysis of all adults undergoing LT from October 1987-September 2010 registered in United Network for Organ Sharing/Organ Procurement and Transplantation Network, a concurrent, limited access database of all American LT recipients. Only recipients who were HBcAb+ were analyzed. LTs with missing donor or recipient serologic parameters for Hepatitis-B were excluded. Significant predictors of survival were determined by univariate analysis. Cox proportional hazards model was used to determine independent risk predictors in the multivariate analysis. The population consisted of 13,329 LT recipients. The mean age of donors and recipients were 40±16 years and 52±9 years respectively. The mean follow-up was 3.7 years. Study population included 27 recipients transplanted with HBsAg+ grafts, of whom 7 (28%) died. Outcomes were adjusted for donor age, recipient age, donor gender, recipient gender, type of LT, MELD score, HCV status, previous LT, and cold ischemic time. On multivariate analysis, LT recipient outcomes were not significantly different for HBsAg+ donors versus donors without prior hepatitis B infection (HR: 1.14, 95% CI: 0.93-1.39, P=0.17). Kaplan-Meier curves revealed no significant survival difference between the two groups. These results suggest that donor HBsAg positivity did not affect overall survival of LT recipients. These findings could potentially expand the pool of liver donors.

Tumour location within the breast: Does tumour site have prognostic ability?

PubMed

Rummel, Seth; Hueman, Matthew T; Costantino, Nick; Shriver, Craig D; Ellsworth, Rachel E

2015-01-01

Tumour location within the breast varies with the highest frequency in the upper outer quadrant (UOQ) and lowest frequency in the lower inner quadrant (LIQ). Whether tumour location is prognostic is unclear. To determine whether tumour location is prognostic, associations between tumour site and clinicopathological characteristics were evaluated. All patients enrolled in the Clinical Breast Care Project whose tumour site-UOQ, upper inner quadrant (UIQ), central, LIQ, lower outer quadrant (LOQ)-was determined by a single, dedicated breast pathologist were included in this study. Patients with multicentric disease (n = 122) or tumours spanning multiple quadrants (n = 381) were excluded from further analysis. Clinicopathological characteristics were analysed using chi-square tests for univariate analysis with multivariate analysis performed using principal components analysis (PCA) and multiple logistic regression. Significance was defined as P < 0.05. Of the 980 patients with defined tumour location, 30 had bilateral disease. Tumour location in the UOQ (51.5%) was significantly higher than in the UIQ (15.6%), LOQ (14.2%), central (10.6%), or LIQ (8.1%). Tumours in the central quadrant were significantly more likely to have higher tumour stage (P = 0.003) and size (P < 0.001), metastatic lymph nodes (P < 0.001), and mortality (P = 0.011). After multivariate analysis, only tumour size and lymph node status remained significantly associated with survival. Evaluation of tumour location as a prognostic factor revealed that although tumours in the central region are associated with less favourable outcome, these associations are not independent of location but rather driven by larger tumour size. Tumours in the central region are more difficult to detect mammographically, resulting in larger tumour size at diagnosis and thus less favourable prognosis. Together, these data demonstrate that tumour location is not an independent prognostic factor.

Rank estimation and the multivariate analysis of in vivo fast-scan cyclic voltammetric data

PubMed Central

Keithley, Richard B.; Carelli, Regina M.; Wightman, R. Mark

2010-01-01

Principal component regression has been used in the past to separate current contributions from different neuromodulators measured with in vivo fast-scan cyclic voltammetry. Traditionally, a percent cumulative variance approach has been used to determine the rank of the training set voltammetric matrix during model development, however this approach suffers from several disadvantages including the use of arbitrary percentages and the requirement of extreme precision of training sets. Here we propose that Malinowski’s F-test, a method based on a statistical analysis of the variance contained within the training set, can be used to improve factor selection for the analysis of in vivo fast-scan cyclic voltammetric data. These two methods of rank estimation were compared at all steps in the calibration protocol including the number of principal components retained, overall noise levels, model validation as determined using a residual analysis procedure, and predicted concentration information. By analyzing 119 training sets from two different laboratories amassed over several years, we were able to gain insight into the heterogeneity of in vivo fast-scan cyclic voltammetric data and study how differences in factor selection propagate throughout the entire principal component regression analysis procedure. Visualizing cyclic voltammetric representations of the data contained in the retained and discarded principal components showed that using Malinowski’s F-test for rank estimation of in vivo training sets allowed for noise to be more accurately removed. Malinowski’s F-test also improved the robustness of our criterion for judging multivariate model validity, even though signal-to-noise ratios of the data varied. In addition, pH change was the majority noise carrier of in vivo training sets while dopamine prediction was more sensitive to noise. PMID:20527815

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

Socio-economic Correlates of Malnutrition among Married Women in Bangladesh.

PubMed

Mostafa Kamal, S M; Md Aynul, Islam

2010-12-01

This paper examines the prevalence and socio-economic correlates of malnutrition among ever married non-pregnant women of reproductive age of Bangladesh using a nationally representative weighted sample of 10,145. Body mass index was used to measure nutritional status. Both bivariate and multivariate statistical analyses were employed to assess the relationship between socio-economic characteristics and women's nutritional status. Overall, 28.5% of the women were found to be underweight. The fixed effect multivariate binary logistic regression analysis yielded significantly increased risk of underweight for the young, currently working, non-Muslim, rural residents, widowed, divorced or separated women. Significant wide variations of malnourishment prevailed in the administrative regions of the country. Wealth index and women's education were the most important determinants of underweight. The multivariate logistic regression analysis revealed that the risk of being underweight was almost seven times higher (OR=6.76, 95% CI=5.20-8.80) among women with no formal education as compared to those with higher education and the likelihood of underweight was significantly (p<0.001) 5.2 times (OR=5.23, 95% CI=4.51-6.07) in the poorest as compared to their richest counterparts. Poverty alleviation programmes should be strengthened targeting the poor. Effective policies, information and health education programmes for women are required to ensure adequate access to health services and for them to understand the components of a healthy diet.

Measuring center of pressure signals to quantify human balance using multivariate multiscale entropy by designing a force platform.

PubMed

Huang, Cheng-Wei; Sue, Pei-Der; Abbod, Maysam F; Jiang, Bernard C; Shieh, Jiann-Shing

2013-08-08

To assess the improvement of human body balance, a low cost and portable measuring device of center of pressure (COP), known as center of pressure and complexity monitoring system (CPCMS), has been developed for data logging and analysis. In order to prove that the system can estimate the different magnitude of different sways in comparison with the commercial Advanced Mechanical Technology Incorporation (AMTI) system, four sway tests have been developed (i.e., eyes open, eyes closed, eyes open with water pad, and eyes closed with water pad) to produce different sway displacements. Firstly, static and dynamic tests were conducted to investigate the feasibility of the system. Then, correlation tests of the CPCMS and AMTI systems have been compared with four sway tests. The results are within the acceptable range. Furthermore, multivariate empirical mode decomposition (MEMD) and enhanced multivariate multiscale entropy (MMSE) analysis methods have been used to analyze COP data reported by the CPCMS and compare it with the AMTI system. The improvements of the CPCMS are 35% to 70% (open eyes test) and 60% to 70% (eyes closed test) with and without water pad. The AMTI system has shown an improvement of 40% to 80% (open eyes test) and 65% to 75% (closed eyes test). The results indicate that the CPCMS system can achieve similar results to the commercial product so it can determine the balance.

Measuring Center of Pressure Signals to Quantify Human Balance Using Multivariate Multiscale Entropy by Designing a Force Platform

PubMed Central

Huang, Cheng-Wei; Sue, Pei-Der; Abbod, Maysam F.; Jiang, Bernard C.; Shieh, Jiann-Shing

2013-01-01

To assess the improvement of human body balance, a low cost and portable measuring device of center of pressure (COP), known as center of pressure and complexity monitoring system (CPCMS), has been developed for data logging and analysis. In order to prove that the system can estimate the different magnitude of different sways in comparison with the commercial Advanced Mechanical Technology Incorporation (AMTI) system, four sway tests have been developed (i.e., eyes open, eyes closed, eyes open with water pad, and eyes closed with water pad) to produce different sway displacements. Firstly, static and dynamic tests were conducted to investigate the feasibility of the system. Then, correlation tests of the CPCMS and AMTI systems have been compared with four sway tests. The results are within the acceptable range. Furthermore, multivariate empirical mode decomposition (MEMD) and enhanced multivariate multiscale entropy (MMSE) analysis methods have been used to analyze COP data reported by the CPCMS and compare it with the AMTI system. The improvements of the CPCMS are 35% to 70% (open eyes test) and 60% to 70% (eyes closed test) with and without water pad. The AMTI system has shown an improvement of 40% to 80% (open eyes test) and 65% to 75% (closed eyes test). The results indicate that the CPCMS system can achieve similar results to the commercial product so it can determine the balance. PMID:23966184

Two time-series analyses of the impact of antibiotic consumption and alcohol-based hand disinfection on the incidences of nosocomial methicillin-resistant Staphylococcus aureus infection and Clostridium difficile infection.

PubMed

Kaier, Klaus; Hagist, Christian; Frank, Uwe; Conrad, Andreas; Meyer, Elisabeth

2009-04-01

To determine the impact of antibiotic consumption and alcohol-based hand disinfection on the incidences of nosocomial methicillin-resistant Staphylococcus aureus (MRSA) infection and Clostridium difficile infection (CDI). Two multivariate time-series analyses were performed that used as dependent variables the monthly incidences of nosocomial MRSA infection and CDI at the Freiburg University Medical Center during the period January 2003 through October 2007. The volume of alcohol-based hand rub solution used per month was quantified in liters per 1,000 patient-days. Antibiotic consumption was calculated in terms of the number of defined daily doses per 1,000 patient-days per month. The use of alcohol-based hand rub was found to have a significant impact on the incidence of nosocomial MRSA infection (P< .001). The multivariate analysis (R2=0.66) showed that a higher volume of use of alcohol-based hand rub was associated with a lower incidence of nosocomial MRSA infection. Conversely, a higher level of consumption of selected antimicrobial agents was associated with a higher incidence of nosocomial MRSA infection. This analysis showed this relationship was the same for the use of second-generation cephalosporins (P= .023), third-generation cephalosporins (P= .05), fluoroquinolones (P= .01), and lincosamides (P= .05). The multivariate analysis (R2=0.55) showed that a higher level of consumption of third-generation cephalosporins (P= .008), fluoroquinolones (P= .084), and/or macrolides (P= .007) was associated with a higher incidence of CDI. A correlation with use of alcohol-based hand rub was not detected. In 2 multivariate time-series analyses, we were able to show the impact of hand hygiene and antibiotic use on the incidence of nosocomial MRSA infection, but we found no association between hand hygiene and incidence of CDI.

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.

Non-destructive analysis of the conformational differences among feedstock sources and their corresponding co-products from bioethanol production with molecular spectroscopy.

PubMed

Gamage, I H; Jonker, A; Zhang, X; Yu, P

2014-01-24

The objective of this study was to determine the possibility of using molecular spectroscopy with multivariate technique as a fast method to detect the source effects among original feedstock sources of wheat and their corresponding co-products, wheat DDGS, from bioethanol production. Different sources of the bioethanol feedstock and their corresponding bioethanol co-products, three samples per source, were collected from the same newly-built bioethanol plant with current bioethanol processing technology. Multivariate molecular spectral analyses were carried out using agglomerative hierarchical cluster analysis (AHCA) and principal component analysis (PCA). The molecular spectral data of different feedstock sources and their corresponding co-products were compared at four different regions of ca. 1800-1725 cm(-1) (carbonyl CO ester, mainly related to lipid structure conformation), ca. 1725-1482 cm(-1) (amide I and amide II region mainly related to protein structure conformation), ca. 1482-1180 cm(-1) (mainly associated with structural carbohydrate) and ca. 1180-800 cm(-1) (mainly related to carbohydrates) in complex plant-based system. The results showed that the molecular spectroscopy with multivariate technique could reveal the structural differences among the bioethanol feedstock sources and among their corresponding co-products. The AHCA and PCA analyses were able to distinguish the molecular structure differences associated with chemical functional groups among the different sources of the feedstock and their corresponding co-products. The molecular spectral differences indicated the differences in functional, biomolecular and biopolymer groups which were confirmed by wet chemical analysis. These biomolecular and biopolymer structural differences were associated with chemical and nutrient profiles and nutrient utilization and availability. Molecular spectral analyses had the potential to identify molecular structure difference among bioethanol feedstock sources and their corresponding co-products. Copyright © 2013 Elsevier B.V. All rights reserved.

Positron emission tomography–computed tomography predictors of progression after DA-R-EPOCH for PMBCL

PubMed Central

Ng, Andrea K.; Dabaja, Bouthaina S.; Milgrom, Sarah A.; Gunther, Jillian R.; Fuller, C. David; Smith, Grace L.; Abou Yehia, Zeinab; Qiao, Wei; Wogan, Christine F.; Akhtari, Mani; Mawlawi, Osama; Medeiros, L. Jeffrey; Chuang, Hubert H.; Martin-Doyle, William; Armand, Philippe; LaCasce, Ann S.; Oki, Yasuhiro; Fanale, Michelle; Westin, Jason; Neelapu, Sattva; Nastoupil, Loretta

2018-01-01

Dose-adjusted rituximab plus etoposide, prednisone, vincristine, cyclophosphamide, and doxorubicin (DA-R-EPOCH) has produced good outcomes in primary mediastinal B-cell lymphoma (PMBCL), but predictors of resistance to this treatment are unclear. We investigated whether [18F]fluorodeoxyglucose positron emission tomography–computed tomography (PET-CT) findings could identify patients with PMBCL who would not respond completely to DA-R-EPOCH. We performed a retrospective analysis of 65 patients with newly diagnosed stage I to IV PMBCL treated at 2 tertiary cancer centers who had PET-CT scans available before and after frontline therapy with DA-R-EPOCH. Pretreatment variables assessed included metabolic tumor volume (MTV) and total lesion glycolysis (TLG). Optimal cutoff points for progression-free survival (PFS) were determined by a machine learning approach. Univariate and multivariable models were constructed to assess associations between radiographic variables and PFS. At a median follow-up of 36.6 months (95% confidence interval, 28.1-45.1), 2-year PFS and overall survival rates for the 65 patients were 81.4% and 98.4%, respectively. Machine learning–derived thresholds for baseline MTV and TLG were associated with inferior PFS (elevated MTV: hazard ratio [HR], 11.5; P = .019; elevated TLG: HR, 8.99; P = .005); other pretreatment clinical factors, including International Prognostic Index and bulky (>10 cm) disease, were not. On multivariable analysis, only TLG retained statistical significance (P = .049). Univariate analysis of posttreatment variables revealed that residual CT tumor volume, maximum standardized uptake value, and Deauville score were associated with PFS; a Deauville score of 5 remained significant on multivariable analysis (P = .006). A model combining baseline TLG and end-of-therapy Deauville score identified patients at increased risk of progression. PMID:29895624

Confocal Raman microscopy and multivariate statistical analysis for determination of different penetration abilities of caffeine and propylene glycol applied simultaneously in a mixture on porcine skin ex vivo.

PubMed

Mujica Ascencio, Saul; Choe, ChunSik; Meinke, Martina C; Müller, Rainer H; Maksimov, George V; Wigger-Alberti, Walter; Lademann, Juergen; Darvin, Maxim E

2016-07-01

Propylene glycol is one of the known substances added in cosmetic formulations as a penetration enhancer. Recently, nanocrystals have been employed also to increase the skin penetration of active components. Caffeine is a component with many applications and its penetration into the epidermis is controversially discussed in the literature. In the present study, the penetration ability of two components - caffeine nanocrystals and propylene glycol, applied topically on porcine ear skin in the form of a gel, was investigated ex vivo using two confocal Raman microscopes operated at different excitation wavelengths (785nm and 633nm). Several depth profiles were acquired in the fingerprint region and different spectral ranges, i.e., 526-600cm(-1) and 810-880cm(-1) were chosen for independent analysis of caffeine and propylene glycol penetration into the skin, respectively. Multivariate statistical methods such as principal component analysis (PCA) and linear discriminant analysis (LDA) combined with Student's t-test were employed to calculate the maximum penetration depths of each substance (caffeine and propylene glycol). The results show that propylene glycol penetrates significantly deeper than caffeine (20.7-22.0μm versus 12.3-13.0μm) without any penetration enhancement effect on caffeine. The results confirm that different substances, even if applied onto the skin as a mixture, can penetrate differently. The penetration depths of caffeine and propylene glycol obtained using two different confocal Raman microscopes are comparable showing that both types of microscopes are well suited for such investigations and that multivariate statistical PCA-LDA methods combined with Student's t-test are very useful for analyzing the penetration of different substances into the skin. Copyright © 2016 Elsevier B.V. All rights reserved.

Factors influencing pre-operative urinary calcium excretion in primary hyperparathyroidism.

PubMed

Kaderli, Reto M; Riss, Philipp; Geroldinger, Angelika; Selberherr, Andreas; Scheuba, Christian; Niederle, Bruno

2017-07-01

Normal or elevated 24-hour urinary calcium (Ca) excretion is a diagnostic marker in primary hyperparathyroidism (PHPT). It is used to distinguish familial hypocalciuric hypercalcaemia (FHH) from PHPT by calculating the Ca/creatinine clearance ratio (CCCR). The variance of CCCR in patients with PHPT is considerable. The aim of this study was to analyse the parameters affecting CCCR in patients with PHPT. The data were collected prospectively. Patients with sporadic PHPT undergoing successful surgery were included in a retrospective analysis. The analysis covered 381 patients with pre-operative workup 2 days before removal of a solitary parathyroid adenoma. The impact of serum Ca and 25-hydroxyvitamin D3 (25-OH D3) on CCCR. The coefficient of determination (R 2 ) in the multivariable model for CCCR consisting of age, Ca, 25-OH D3, 1,25-dihydroxyvitamin D3 (1,25-(OH)2 D3), testosterone (separately for males and females), intact parathyroid hormone (iPTH) and osteocalcin was 25.8%. The only significant parameters in the multivariable analysis were 1,25-(OH)2 D3 and osteocalcin with a drop in R 2 of 15.4% (P<.001) and 2.4% (P=.006), respectively. Bone mineral densities at the lumbar spine, distal radius and left femoral neck were not associated with CCCR (r=-.08, r=-.10 and r=-0.09). In multivariable analysis, 1,25-(OH)2 D3 and osteocalcin were the only factors correlating with CCCR. Vitamin D3 replacement may therefore impair the diagnostic value of CCCR and increase the importance of close monitoring of urinary Ca excretion during treatment. © 2017 John Wiley & Sons Ltd.

Non-destructive analysis of the conformational differences among feedstock sources and their corresponding co-products from bioethanol production with molecular spectroscopy

NASA Astrophysics Data System (ADS)

Gamage, I. H.; Jonker, A.; Zhang, X.; Yu, P.

2014-01-01

The objective of this study was to determine the possibility of using molecular spectroscopy with multivariate technique as a fast method to detect the source effects among original feedstock sources of wheat and their corresponding co-products, wheat DDGS, from bioethanol production. Different sources of the bioethanol feedstock and their corresponding bioethanol co-products, three samples per source, were collected from the same newly-built bioethanol plant with current bioethanol processing technology. Multivariate molecular spectral analyses were carried out using agglomerative hierarchical cluster analysis (AHCA) and principal component analysis (PCA). The molecular spectral data of different feedstock sources and their corresponding co-products were compared at four different regions of ca. 1800-1725 cm-1 (carbonyl Cdbnd O ester, mainly related to lipid structure conformation), ca. 1725-1482 cm-1 (amide I and amide II region mainly related to protein structure conformation), ca. 1482-1180 cm-1 (mainly associated with structural carbohydrate) and ca. 1180-800 cm-1 (mainly related to carbohydrates) in complex plant-based system. The results showed that the molecular spectroscopy with multivariate technique could reveal the structural differences among the bioethanol feedstock sources and among their corresponding co-products. The AHCA and PCA analyses were able to distinguish the molecular structure differences associated with chemical functional groups among the different sources of the feedstock and their corresponding co-products. The molecular spectral differences indicated the differences in functional, biomolecular and biopolymer groups which were confirmed by wet chemical analysis. These biomolecular and biopolymer structural differences were associated with chemical and nutrient profiles and nutrient utilization and availability. Molecular spectral analyses had the potential to identify molecular structure difference among bioethanol feedstock sources and their corresponding co-products.

Positron emission tomography-computed tomography predictors of progression after DA-R-EPOCH for PMBCL.

PubMed

Pinnix, Chelsea C; Ng, Andrea K; Dabaja, Bouthaina S; Milgrom, Sarah A; Gunther, Jillian R; Fuller, C David; Smith, Grace L; Abou Yehia, Zeinab; Qiao, Wei; Wogan, Christine F; Akhtari, Mani; Mawlawi, Osama; Medeiros, L Jeffrey; Chuang, Hubert H; Martin-Doyle, William; Armand, Philippe; LaCasce, Ann S; Oki, Yasuhiro; Fanale, Michelle; Westin, Jason; Neelapu, Sattva; Nastoupil, Loretta

2018-06-12

Dose-adjusted rituximab plus etoposide, prednisone, vincristine, cyclophosphamide, and doxorubicin (DA-R-EPOCH) has produced good outcomes in primary mediastinal B-cell lymphoma (PMBCL), but predictors of resistance to this treatment are unclear. We investigated whether [ 18 F]fluorodeoxyglucose positron emission tomography-computed tomography (PET-CT) findings could identify patients with PMBCL who would not respond completely to DA-R-EPOCH. We performed a retrospective analysis of 65 patients with newly diagnosed stage I to IV PMBCL treated at 2 tertiary cancer centers who had PET-CT scans available before and after frontline therapy with DA-R-EPOCH. Pretreatment variables assessed included metabolic tumor volume (MTV) and total lesion glycolysis (TLG). Optimal cutoff points for progression-free survival (PFS) were determined by a machine learning approach. Univariate and multivariable models were constructed to assess associations between radiographic variables and PFS. At a median follow-up of 36.6 months (95% confidence interval, 28.1-45.1), 2-year PFS and overall survival rates for the 65 patients were 81.4% and 98.4%, respectively. Machine learning-derived thresholds for baseline MTV and TLG were associated with inferior PFS (elevated MTV: hazard ratio [HR], 11.5; P = .019; elevated TLG: HR, 8.99; P = .005); other pretreatment clinical factors, including International Prognostic Index and bulky (>10 cm) disease, were not. On multivariable analysis, only TLG retained statistical significance ( P = .049). Univariate analysis of posttreatment variables revealed that residual CT tumor volume, maximum standardized uptake value, and Deauville score were associated with PFS; a Deauville score of 5 remained significant on multivariable analysis ( P = .006). A model combining baseline TLG and end-of-therapy Deauville score identified patients at increased risk of progression. © 2018 by The American Society of Hematology.

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

ERIC Educational Resources Information Center

Marascuilo, Leonard A.; Loban, Walter

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

Design of multivariable feedback control systems via spectral assignment. [as applied to aircraft flight control

NASA Technical Reports Server (NTRS)

Liberty, S. R.; Mielke, R. R.; Tung, L. J.

1981-01-01

Applied research in the area of spectral assignment in multivariable systems is reported. A frequency domain technique for determining the set of all stabilizing controllers for a single feedback loop multivariable system is described. It is shown that decoupling and tracking are achievable using this procedure. The technique is illustrated with a simple example.

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

PubMed

Pourahmadi, Mohsen; Noorbaloochi, Siamak

2016-04-01

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

Prenatal Sonographic Predictors of Neonatal Coarctation of the Aorta.

PubMed

Anuwutnavin, Sanitra; Satou, Gary; Chang, Ruey-Kang; DeVore, Greggory R; Abuel, Ashley; Sklansky, Mark

2016-11-01

To identify practical prenatal sonographic markers for the postnatal diagnosis of coarctation of the aorta. We reviewed the fetal echocardiograms and postnatal outcomes of fetal cases of suspected coarctation of the aorta seen at a single institution between 2010 and 2014. True- and false-positive cases were compared. Logistic regression analysis was used to determine echocardiographic predictors of coarctation of the aorta. Optimal cutoffs for these markers and a multivariable threshold scoring system were derived to discriminate fetuses with coarctation of the aorta from those without coarctation of the aorta. Among 35 patients with prenatal suspicion of coarctation of the aorta, the diagnosis was confirmed postnatally in 9 neonates (25.7% true-positive rate). Significant predictors identified from multivariate analysis were as follows: Z score for the ascending aorta diameter of -2 or less (P = < .001), Z score for the mitral valve annulus of -2 or less (P= .033), Zscore for the transverse aortic arch diameter of -2 or less (P= .028), and abnormal aortic valve morphologic features (P= .026). Among all variables studied, the ascending aortic Z score had the highest sensitivity (78%) and specificity (92%) for detection of coarctation of the aorta. A multivariable threshold scoring system identified fetuses with coarctation of the aorta with still greater sensitivity (89%) and only mildly decreased specificity (88%). The finding of a diminutive ascending aorta represents a powerful and practical prenatal predictor of neonatal coarctation of the aorta. A multivariable scoring system, including dimensions of the ascending and transverse aortas, mitral valve annulus, and morphologic features of the aortic valve, provides excellent sensitivity and specificity. The use of these practical sonographic markers may improve prenatal detection of coarctation of the aorta. © 2016 by the American Institute of Ultrasound in Medicine.

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.

Spatial characterization of dissolved trace elements and heavy metals in the upper Han River (China) using multivariate statistical techniques.

PubMed

Li, Siyue; Zhang, Quanfa

2010-04-15

A data matrix (4032 observations), obtained during a 2-year monitoring period (2005-2006) from 42 sites in the upper Han River is subjected to various multivariate statistical techniques including cluster analysis, principal component analysis (PCA), factor analysis (FA), correlation analysis and analysis of variance to determine the spatial characterization of dissolved trace elements and heavy metals. Our results indicate that waters in the upper Han River are primarily polluted by Al, As, Cd, Pb, Sb and Se, and the potential pollutants include Ba, Cr, Hg, Mn and Ni. Spatial distribution of trace metals indicates the polluted sections mainly concentrate in the Danjiang, Danjiangkou Reservoir catchment and Hanzhong Plain, and the most contaminated river is in the Hanzhong Plain. Q-model clustering depends on geographical location of sampling sites and groups the 42 sampling sites into four clusters, i.e., Danjiang, Danjiangkou Reservoir region (lower catchment), upper catchment and one river in headwaters pertaining to water quality. The headwaters, Danjiang and lower catchment, and upper catchment correspond to very high polluted, moderate polluted and relatively low polluted regions, respectively. Additionally, PCA/FA and correlation analysis demonstrates that Al, Cd, Mn, Ni, Fe, Si and Sr are controlled by natural sources, whereas the other metals appear to be primarily controlled by anthropogenic origins though geogenic source contributing to them. 2009 Elsevier B.V. All rights reserved.

Comparative Analysis of the Volatile Components of Agrimonia eupatoria from Leaves and Roots by Gas Chromatography-Mass Spectrometry and Multivariate Curve Resolution

PubMed Central

Feng, Xiao-Liang; He, Yun-biao; Liang, Yi-Zeng; Wang, Yu-Lin; Huang, Lan-Fang; Xie, Jian-Wei

2013-01-01

Gas chromatography-mass spectrometry and multivariate curve resolution were applied to the differential analysis of the volatile components in Agrimonia eupatoria specimens from different plant parts. After extracted with water distillation method, the volatile components in Agrimonia eupatoria from leaves and roots were detected by GC-MS. Then the qualitative and quantitative analysis of the volatile components in the main root of Agrimonia eupatoria was completed with the help of subwindow factor analysis resolving two-dimensional original data into mass spectra and chromatograms. 68 of 87 separated constituents in the total ion chromatogram of the volatile components were identified and quantified, accounting for about 87.03% of the total content. Then, the common peaks in leaf were extracted with orthogonal projection resolution method. Among the components determined, there were 52 components coexisting in the studied samples although the relative content of each component showed difference to some extent. The results showed a fair consistency in their GC-MS fingerprint. It was the first time to apply orthogonal projection method to compare different plant parts of Agrimonia eupatoria, and it reduced the burden of qualitative analysis as well as the subjectivity. The obtained results proved the combined approach powerful for the analysis of complex Agrimonia eupatoria samples. The developed method can be used to further study and quality control of Agrimonia eupatoria. PMID:24286016

Comparative Analysis of the Volatile Components of Agrimonia eupatoria from Leaves and Roots by Gas Chromatography-Mass Spectrometry and Multivariate Curve Resolution.

PubMed

Feng, Xiao-Liang; He, Yun-Biao; Liang, Yi-Zeng; Wang, Yu-Lin; Huang, Lan-Fang; Xie, Jian-Wei

2013-01-01

Gas chromatography-mass spectrometry and multivariate curve resolution were applied to the differential analysis of the volatile components in Agrimonia eupatoria specimens from different plant parts. After extracted with water distillation method, the volatile components in Agrimonia eupatoria from leaves and roots were detected by GC-MS. Then the qualitative and quantitative analysis of the volatile components in the main root of Agrimonia eupatoria was completed with the help of subwindow factor analysis resolving two-dimensional original data into mass spectra and chromatograms. 68 of 87 separated constituents in the total ion chromatogram of the volatile components were identified and quantified, accounting for about 87.03% of the total content. Then, the common peaks in leaf were extracted with orthogonal projection resolution method. Among the components determined, there were 52 components coexisting in the studied samples although the relative content of each component showed difference to some extent. The results showed a fair consistency in their GC-MS fingerprint. It was the first time to apply orthogonal projection method to compare different plant parts of Agrimonia eupatoria, and it reduced the burden of qualitative analysis as well as the subjectivity. The obtained results proved the combined approach powerful for the analysis of complex Agrimonia eupatoria samples. The developed method can be used to further study and quality control of Agrimonia eupatoria.

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.

Non-invasive glucose monitor

NASA Technical Reports Server (NTRS)

Lambert, James L. (Inventor); Borchert, Mark S. (Inventor)

2001-01-01

A non-invasive method for determining blood level of an analyte of interest, such as glucose, comprises: generating an excitation laser beam (e.g., at a wavelength of 700 to 900 nanometers); focusing the excitation laser beam into the anterior chamber of an eye of the subject so that aqueous humor in the anterior chamber is illuminated; detecting (preferably confocally detecting) a Raman spectrum from the illuminated aqueous humor; and then determining the blood glucose level (or the level of another analyte of interest) for the subject from the Raman spectrum. Preferably, the detecting step is followed by the step of subtracting a confounding fluorescence spectrum from the Raman spectrum to produce a difference spectrum; and determining the blood level of the analyte of interest for the subject from that difference spectrum, preferably using linear or nonlinear multivariate analysis such as partial least squares analysis. Apparatus for carrying out the foregoing method is also disclosed.

Comparative study of the efficiency of computed univariate and multivariate methods for the estimation of the binary mixture of clotrimazole and dexamethasone using two different spectral regions

NASA Astrophysics Data System (ADS)

Fayez, Yasmin Mohammed; Tawakkol, Shereen Mostafa; Fahmy, Nesma Mahmoud; Lotfy, Hayam Mahmoud; Shehata, Mostafa Abdel-Aty

2018-04-01

Three methods of analysis are conducted that need computational procedures by the Matlab® software. The first is the univariate mean centering method which eliminates the interfering signal of the one component at a selected wave length leaving the amplitude measured to represent the component of interest only. The other two multivariate methods named PLS and PCR depend on a large number of variables that lead to extraction of the maximum amount of information required to determine the component of interest in the presence of the other. Good accurate and precise results are obtained from the three methods for determining clotrimazole in the linearity range 1-12 μg/mL and 75-550 μg/mL with dexamethasone acetate 2-20 μg/mL in synthetic mixtures and pharmaceutical formulation using two different spectral regions 205-240 nm and 233-278 nm. The results obtained are compared statistically to each other and to the official methods.

Multivariate analysis: greater insights into complex systems

USDA-ARS?s Scientific Manuscript database

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

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

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

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

Thermal decomposition of poly dimethyl siloxane compounds, Sylgard{reg_sign} 184 and 186, were examined using thermal desorption coupled gas chromatography-mass spectrometry (TD/GC-MS) and multivariate analysis. This work describes a method of producing multiway data using a stepped thermal desorption. The technique involves sequentially heating a sample of the material of interest with subsequent analysis in a commercial GC/MS system. The decomposition chromatograms were analyzed using multivariate analysis tools including principal component analysis (PCA), factor rotation employing the varimax criterion, and multivariate curve resolution. The results of the analysis show seven components related to offgassing of various fractions of siloxanes that varymore » as a function of temperature. Thermal desorption coupled with gas chromatography-mass spectrometry (TD/GC-MS) is a powerful analytical technique for analyzing chemical mixtures. It has great potential in numerous analytic areas including materials analysis, sports medicine, in the detection of designer drugs; and biological research for metabolomics. Data analysis is complicated, far from automated and can result in high false positive or false negative rates. We have demonstrated a step-wise TD/GC-MS technique that removes more volatile compounds from a sample before extracting the less volatile compounds. This creates an additional dimension of separation before the GC column, while simultaneously generating three-way data. Sandia's proven multivariate analysis methods, when applied to these data, have several advantages over current commercial options. It also has demonstrated potential for success in finding and enabling identification of trace compounds. Several challenges remain, however, including understanding the sources of noise in the data, outlier detection, improving the data pretreatment and analysis methods, developing a software tool for ease of use by the chemist, and demonstrating our belief that this multivariate analysis will enable superior differentiation capabilities. In addition, noise and system artifacts challenge the analysis of GC-MS data collected on lower cost equipment, ubiquitous in commercial laboratories. This research has the potential to affect many areas of analytical chemistry including materials analysis, medical testing, and environmental surveillance. It could also provide a method to measure adsorption parameters for chemical interactions on various surfaces by measuring desorption as a function of temperature for mixtures. We have presented results of a novel method for examining offgas products of a common PDMS material. Our method involves utilizing a stepped TD/GC-MS data acquisition scheme that may be almost totally automated, coupled with multivariate analysis schemes. This method of data generation and analysis can be applied to a number of materials aging and thermal degradation studies.« less

Multivariable PID controller design tuning using bat algorithm for activated sludge process

NASA Astrophysics Data System (ADS)

Atikah Nor’Azlan, Nur; Asmiza Selamat, Nur; Mat Yahya, Nafrizuan

2018-04-01

The designing of a multivariable PID control for multi input multi output is being concerned with this project by applying four multivariable PID control tuning which is Davison, Penttinen-Koivo, Maciejowski and Proposed Combined method. The determination of this study is to investigate the performance of selected optimization technique to tune the parameter of MPID controller. The selected optimization technique is Bat Algorithm (BA). All the MPID-BA tuning result will be compared and analyzed. Later, the best MPID-BA will be chosen in order to determine which techniques are better based on the system performances in terms of transient response.

MANCOVA for one way classification with homogeneity of regression coefficient vectors

NASA Astrophysics Data System (ADS)

Mokesh Rayalu, G.; Ravisankar, J.; Mythili, G. Y.

2017-11-01

The MANOVA and MANCOVA are the extensions of the univariate ANOVA and ANCOVA techniques to multidimensional or vector valued observations. The assumption of a Gaussian distribution has been replaced with the Multivariate Gaussian distribution for the vectors data and residual term variables in the statistical models of these techniques. The objective of MANCOVA is to determine if there are statistically reliable mean differences that can be demonstrated between groups later modifying the newly created variable. When randomization assignment of samples or subjects to groups is not possible, multivariate analysis of covariance (MANCOVA) provides statistical matching of groups by adjusting dependent variables as if all subjects scored the same on the covariates. In this research article, an extension has been made to the MANCOVA technique with more number of covariates and homogeneity of regression coefficient vectors is also tested.

Evaluation and simplification of the occupational slip, trip and fall risk-assessment test

PubMed Central

NAKAMURA, Takehiro; OYAMA, Ichiro; FUJINO, Yoshihisa; KUBO, Tatsuhiko; KADOWAKI, Koji; KUNIMOTO, Masamizu; ODOI, Haruka; TABATA, Hidetoshi; MATSUDA, Shinya

2016-01-01

Objective: The purpose of this investigation is to evaluate the efficacy of the occupational slip, trip and fall (STF) risk assessment test developed by the Japan Industrial Safety and Health Association (JISHA). We further intended to simplify the test to improve efficiency. Methods: A previous cohort study was performed using 540 employees aged ≥50 years who took the JISHA’s STF risk assessment test. We conducted multivariate analysis using these previous results as baseline values and answers to questionnaire items or score on physical fitness tests as variables. The screening efficiency of each model was evaluated based on the obtained receiver operating characteristic (ROC) curve. Results: The area under the ROC obtained in multivariate analysis was 0.79 when using all items. Six of the 25 questionnaire items were selected for stepwise analysis, giving an area under the ROC curve of 0.77. Conclusion: Based on the results of follow-up performed one year after the initial examination, we successfully determined the usefulness of the STF risk assessment test. Administering a questionnaire alone is sufficient for screening subjects at risk of STF during the subsequent one-year period. PMID:27021057

Prognostic factors in patients with advanced cholangiocarcinoma: Role of surgery, chemotherapy and body mass index

PubMed Central

Farhat, Mirna H; Shamseddine, Ali I; Tawil, Ayman N; Berjawi, Ghina; Sidani, Charif; Shamseddeen, Wael; Barada, Kassem A

2008-01-01

AIM: To study the factors that may affect survival of cholangiocarcinoma in Lebanon. METHODS: A retrospective review of the medical records of 55 patients diagnosed with cholangio-carcinoma at the American University of Beirut between 1990 and 2005 was conducted. Univariate and multivariate analyses were performed to determine the impact of surgery, chemotherapy, body mass index, bilirubin level and other factors on survival. RESULTS: The median survival of all patients was 8.57 mo (0.03-105.2). Univariate analysis showed that low bilirubin level (< 10 mg/dL), radical surgery and chemotherapy administration were significantly associated with better survival (P = 0.012, 0.038 and 0.038, respectively). In subgroup analysis on patients who had no surgery, chemotherapy administration prolonged median survival significantly (17.0 mo vs 3.5 mo, P = 0.001). Multivariate analysis identified only low bilirubin level < 10 mg/dL and chemotherapy administration as independent predictors associated with better survival (P < 0.05). CONCLUSION: Our data show that palliative and postoperative chemotherapy as well as a bilirubin level < 10 mg/dL are independent predictors of a significant increase in survival in patients with cholangiocarcinoma. PMID:18506930

The Prognostic Role of Cancer Stem Cell Markers for Long-term Outcome After Resection of Colonic Liver Metastases.

PubMed

Spelt, Lidewij; Sasor, Agata; Ansari, Daniel; Hilmersson, Katarzyna Said; Andersson, Roland

2018-01-01

To assess the expression of cancer stem cell (CSC) markers CD44, CD133 and CD24 in colon cancer liver metastases and analyse their predictive value for overall survival (OS) and disease-free survival (DFS) after liver resection. Patients operated on for colon cancer liver metastases were included. CSC marker expression was determined through immunohistochemistry analysis. OS and DFS were compared between marker-positive and marker-negative patients. Multivariate analysis was performed to select predictive variables for OS and DFS. CD133-positive patients had a worse DFS than CD133-negative patients, with a median DFS of 12 and 25 months (p=0.051). Multivariate analysis selected CD133 expression as a significant predictor for DFS. CD44 and CD24 were not found to predict OS or DFS. CD133 expression in colonic liver metastases is a negative prognostic factor for DFS after liver resection. In the future, CD133 could be used as a biomarker for risk stratification, and possibly for developing novel targeted therapy. Copyright© 2018, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

Personality traits in the differentiation of major depressive disorder and bipolar disorder during a depressive episode.

PubMed

Araujo, Jaciana Marlova Gonçalves; dos Passos, Miguel Bezerra; Molina, Mariane Lopez; da Silva, Ricardo Azevedo; Souza, Luciano Dias de Mattos

2016-02-28

The aim of this study was to determine the differences in personality traits between individuals with Major Depressive Disorder (MDD) and Bipolar Disorder (BD) during a depressive episode, when it can be hard to differentiate them. Data on personality traits (NEO-FFI), mental disorders (Mini International Neuropsychiatric Interview Plus) and socioeconomic variables were collected from 245 respondents who were in a depressive episode. Individuals with MDD (183) and BD (62) diagnosis were compared concerning personality traits, clinical aspects and socioeconomic variables through bivariate analyses (chi-square and ANOVA) and multivariate analysis (logistic regression). There were no differences in the prevalence of the disorders between socioeconomic and clinical variables. As for the personality traits, only the difference in Agreeableness was statistically significant. Considering the control of suicide risk, gender and anxiety comorbidity in the multivariate analysis, the only variable that remained associated was Agreeableness, with an increase in MDD cases. The brief version of the NEO inventories (NEO-FFI) does not allow for the analysis of personality facets. During a depressive episode, high levels of Agreeableness can indicate that MDD is a more likely diagnosis than BD. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Complex codon usage pattern and compositional features of retroviruses.

PubMed

RoyChoudhury, Sourav; Mukherjee, Debaprasad

2013-01-01

Retroviruses infect a wide range of organisms including humans. Among them, HIV-1, which causes AIDS, has now become a major threat for world health. Some of these viruses are also potential gene transfer vectors. In this study, the patterns of synonymous codon usage in retroviruses have been studied through multivariate statistical methods on ORFs sequences from the available 56 retroviruses. The principal determinant for evolution of the codon usage pattern in retroviruses seemed to be the compositional constraints, while selection for translation of the viral genes plays a secondary role. This was further supported by multivariate analysis on relative synonymous codon usage. Thus, it seems that mutational bias might have dominated role over translational selection in shaping the codon usage of retroviruses. Codon adaptation index was used to identify translationally optimal codons among genes from retroviruses. The comparative analysis of the preferred and optimal codons among different retroviral groups revealed that four codons GAA, AAA, AGA, and GGA were significantly more frequent in most of the retroviral genes inspite of some differences. Cluster analysis also revealed that phylogenetically related groups of retroviruses have probably evolved their codon usage in a concerted manner under the influence of their nucleotide composition.

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.

Impact of loneliness and depression on mortality: results from the Longitudinal Ageing Study Amsterdam.

PubMed

Holwerda, Tjalling J; van Tilburg, Theo G; Deeg, Dorly J H; Schutter, Natasja; Van, Rien; Dekker, Jack; Stek, Max L; Beekman, Aartjan T F; Schoevers, Robert A

2016-08-01

Loneliness is highly prevalent among older people, has serious health consequences and is an important predictor of mortality. Loneliness and depression may unfavourably interact with each other over time but data on this topic are scarce. To determine whether loneliness is associated with excess mortality after 19 years of follow-up and whether the joint effect with depression confers further excess mortality. Different aspects of loneliness were measured with the De Jong Gierveld scale and depression with the Centre for Epidemiologic Studies Depression Scale in a cohort of 2878 people aged 55-85 with 19 years of follow-up. Excess mortality hypotheses were tested with Kaplan-Meier and Cox proportional hazard analyses controlling for potential confounders. At follow-up loneliness and depression were associated with excess mortality in older men and women in bivariate analysis but not in multivariate analysis. In multivariate analysis, severe depression was associated with excess mortality in men who were lonely but not in women. Loneliness and depression are important predictors of early death in older adults. Severe depression has a strong association with excess mortality in older men who were lonely, indicating a lethal combination in this group. © The Royal College of Psychiatrists 2016.

Variation of Water Quality Parameters with Siltation Depth for River Ichamati Along International Border with Bangladesh Using Multivariate Statistical Techniques

NASA Astrophysics Data System (ADS)

Roy, P. K.; Pal, S.; Banerjee, G.; Biswas Roy, M.; Ray, D.; Majumder, A.

2014-12-01

River is considered as one of the main sources of freshwater all over the world. Hence analysis and maintenance of this water resource is globally considered a matter of major concern. This paper deals with the assessment of surface water quality of the Ichamati river using multivariate statistical techniques. Eight distinct surface water quality observation stations were located and samples were collected. For the samples collected statistical techniques were applied to the physico-chemical parameters and depth of siltation. In this paper cluster analysis is done to determine the relations between surface water quality and siltation depth of river Ichamati. Multiple regressions and mathematical equation modeling have been done to characterize surface water quality of Ichamati river on the basis of physico-chemical parameters. It was found that surface water quality of the downstream river was different from the water quality of the upstream. The analysis of the water quality parameters of the Ichamati river clearly indicate high pollution load on the river water which can be accounted to agricultural discharge, tidal effect and soil erosion. The results further reveal that with the increase in depth of siltation, water quality degraded.

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.

Surgical Treatment of Recurrent Endometrial Cancer: Time for a Paradigm Shift.

PubMed

Papadia, Andrea; Bellati, Filippo; Ditto, Antonino; Bogani, Giorgio; Gasparri, Maria Luisa; Di Donato, Violante; Martinelli, Fabio; Lorusso, Domenica; Benedetti-Panici, Pierluigi; Raspagliesi, Francesco

2015-12-01

Although surgery represents the cornerstone treatment of endometrial cancer at initial diagnosis, scarce data are available in recurrent setting. The purpose of this study was to review the outcome of surgery in these patients. Medical records of all patients undergoing surgery for recurrent endometrial cancer at NCI Milano between January 2003 and January 2014 were reviewed. Survival was determined from the time of surgery for recurrence to last follow-up. Survival was estimated using Kaplan-Meier methods. Differences in survival were analyzed using the log-rank test. The Fisher's exact test was used to compare optimal versus suboptimal cytoreduction against possible predictive factors. Sixty-four patients were identified. Median age was 66 years. Recurrences were multiple in 38 % of the cases. Optimal cytoreduction was achieved in 65.6 %. Median OR time was 165 min, median postoperative hemoglobin drop was 2.4 g/dl, and median length hospital stay was 5.5 days. Eleven patients developed postoperative complications, but only four required surgical management. Estimated 5-year progression-free survival (PFS) was 42 and 19 % in optimally and suboptimally cytoreduced patients, respectively. At multivariate analysis, only residual disease was associated with PFS. Estimated 5-year overall survival (OS) was 60 and 30 % in optimally and suboptimally cytoreduced patients, respectively. At multivariate analysis, residual disease and histotype were associated with OS. At multivariate analysis, only performance status was associated with optimal cytoreduction. Secondary cytoreduction in endometrial cancer is associated with long PFS and OS. The only factors associated with improved long-term outcome are the absence of residual disease at the end of surgical resection and histotype.

Influence of Pelvicaliceal Anatomy on Stone Clearance After Flexible Ureteroscopy and Holmium Laser Lithotripsy for Large Renal Stones.

PubMed

Inoue, Takaaki; Murota, Takashi; Okada, Shinsuke; Hamamoto, Shuzo; Muguruma, Kouei; Kinoshita, Hidefumi; Matsuda, Tadashi

2015-09-01

This study was performed to evaluate the impact of pelvicaliceal anatomy on stone clearance in patients with remnant fragments in the lower pole after flexible ureteroscopy and holmium laser lithotripsy (fURSL) for renal stones >15 mm. This retrospective study included 67 patients with radiopaque residual fragments (>2 mm) in the lower pole after fURSL for large renal stones (>15 mm). The preoperative infundibular length (IL), infundibular width (IW), infundibulopelvic angle (IPA), and caliceal pelvic height (CPH) were measured using intravenous urography. Multivariate analysis was performed to determine whether any of these measurements affected stone clearance. Of the 67 patients, 55 (82.1%) were stone free (SF) 3 months after fURSL. The anatomic factors significantly favorable for an SF status were a short IL, broad IW, wide IPA, and low CPH. On multivariate analysis, the IPA had a significant influence on an SF status after fURSL (p=0.010). An IPA <30° was a negative risk factor (p=0.019). Postoperative complications occurred in nine patients (13.4%), including Clavien grade I complications in two patients (2.9%), grade II in six patients (8.9%), and grade IIIa in one patient (1.8%). Almost all complications were minor. An IPA <30° is the only negative risk factor for stone clearance after fURSL for large renal stones according to our multivariate analysis. Additional studies are required to further evaluate the characteristics of the pelvicaliceal anatomy influencing stone clearance.

Brain natriuretic peptide predicts functional outcome in ischemic stroke

PubMed Central

Rost, Natalia S; Biffi, Alessandro; Cloonan, Lisa; Chorba, John; Kelly, Peter; Greer, David; Ellinor, Patrick; Furie, Karen L

2011-01-01

Background Elevated serum levels of brain natriuretic peptide (BNP) have been associated with cardioembolic (CE) stroke and increased post-stroke mortality. We sought to determine whether BNP levels were associated with functional outcome after ischemic stroke. Methods We measured BNP in consecutive patients aged ≥18 years admitted to our Stroke Unit between 2002–2005. BNP quintiles were used for analysis. Stroke subtypes were assigned using TOAST criteria. Outcomes were measured as 6-month modified Rankin Scale score (“good outcome” = 0–2 vs. “poor”) as well as mortality. Multivariate logistic regression was used to assess association between the quintiles of BNP and outcomes. Predictive performance of BNP as compared to clinical model alone was assessed by comparing ROC curves. Results Of 569 ischemic stroke patients, 46% were female; mean age was 67.9 ± 15 years. In age- and gender-adjusted analysis, elevated BNP was associated with lower ejection fraction (p<0.0001) and left atrial dilatation (p<0.001). In multivariate analysis, elevated BNP decreased the odds of good functional outcome (OR 0.64, 95%CI 0.41–0.98) and increased the odds of death (OR 1.75, 95%CI 1.36–2.24) in these patients. Addition of BNP to multivariate models increased their predictive performance for functional outcome (p=0.013) and mortality (p<0.03) after CE stroke. Conclusions Serum BNP levels are strongly associated with CE stroke and functional outcome at 6 months after ischemic stroke. Inclusion of BNP improved prediction of mortality in patients with CE stroke. PMID:22116811

Laser-induced breakdown spectroscopy in industrial and security applications

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

Bol'shakov, Alexander A.; Yoo, Jong H.; Liu Chunyi

2010-05-01

Laser-induced breakdown spectroscopy (LIBS) offers rapid, localized chemical analysis of solid or liquid materials with high spatial resolution in lateral and depth profiling, without the need for sample preparation. Principal component analysis and partial least squares algorithms were applied to identify a variety of complex organic and inorganic samples. This work illustrates how LIBS analyzers can answer a multitude of real-world needs for rapid analysis, such as determination of lead in paint and children's toys, analysis of electronic and solder materials, quality control of fiberglass panels, discrimination of coffee beans from different vendors, and identification of generic versus brand-name drugs.more » Lateral and depth profiling was performed on children's toys and paint layers. Traditional one-element calibration or multivariate chemometric procedures were applied for elemental quantification, from single laser shot determination of metal traces at {approx}10 {mu}g/g to determination of halogens at 90 {mu}g/g using 50-shot spectral accumulation. The effectiveness of LIBS for security applications was demonstrated in the field by testing the 50-m standoff LIBS rasterizing detector.« less

Causes of death in long-term lung cancer survivors: a SEER database analysis.

PubMed

Abdel-Rahman, Omar

2017-07-01

Long-term (>5 years) lung cancer survivors represent a small but distinct subgroup of lung cancer patients and information about the causes of death of this subgroup is scarce. The Surveillance, Epidemiology and End Results (SEER) database (1988-2008) was utilized to determine the causes of death of long-term survivors of lung cancer. Survival analysis was conducted using Kaplan-Meier analysis and multivariate analysis was conducted using a Cox proportional hazard model. Clinicopathological characteristics and survival outcomes were assessed for the whole cohort. A total of 78,701 lung cancer patients with >5 years survival were identified. This cohort included 54,488 patients surviving 5-10 years and 24,213 patients surviving >10 years. Among patients surviving 5-10 years, 21.8% were dead because of primary lung cancer, 10.2% were dead because of other cancers, 6.8% were dead because of cardiac disease and 5.3% were dead because of non-malignant pulmonary disease. Among patients surviving >10 years, 12% were dead because of primary lung cancer, 6% were dead because of other cancers, 6.9% were dead because of cardiac disease and 5.6% were dead because of non-malignant pulmonary disease. On multivariate analysis, factors associated with longer cardiac-disease-specific survival in multivariate analysis include younger age at diagnosis (p < .0001), white race (vs. African American race) (p = .005), female gender (p < .0001), right-sided disease (p = .003), adenocarcinoma (vs. large cell or small cell carcinoma), histology and receiving local treatment by surgery rather than radiotherapy (p < .0001). The probability of death from primary lung cancer is still significant among other causes of death even 20 years after diagnosis of lung cancer. Moreover, cardiac as well as non-malignant pulmonary causes contribute a considerable proportion of deaths in long-term lung cancer survivors.

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.

Accuracy of remotely sensed data: Sampling and analysis procedures

NASA Technical Reports Server (NTRS)

Congalton, R. G.; Oderwald, R. G.; Mead, R. A.

1982-01-01

A review and update of the discrete multivariate analysis techniques used for accuracy assessment is given. A listing of the computer program written to implement these techniques is given. New work on evaluating accuracy assessment using Monte Carlo simulation with different sampling schemes is given. The results of matrices from the mapping effort of the San Juan National Forest is given. A method for estimating the sample size requirements for implementing the accuracy assessment procedures is given. A proposed method for determining the reliability of change detection between two maps of the same area produced at different times is given.

Can texture analysis of tooth microwear detect within guild niche partitioning in extinct species?

NASA Astrophysics Data System (ADS)

Purnell, Mark; Nedza, Christopher; Rychlik, Leszek

2017-04-01

Recent work shows that tooth microwear analysis can be applied further back in time and deeper into the phylogenetic history of vertebrate clades than previously thought (e.g. niche partitioning in early Jurassic insectivorous mammals; Gill et al., 2014, Nature). Furthermore, quantitative approaches to analysis based on parameterization of surface roughness are increasing the robustness and repeatability of this widely used dietary proxy. Discriminating between taxa within dietary guilds has the potential to significantly increase our ability to determine resource use and partitioning in fossil vertebrates, but how sensitive is the technique? To address this question we analysed tooth microwear texture in sympatric populations of shrew species (Neomys fodiens, Neomys anomalus, Sorex araneus, Sorex minutus) from BiaŁ owieza Forest, Poland. These populations are known to exhibit varying degrees of niche partitioning (Churchfield & Rychlik, 2006, J. Zool.) with greatest overlap between the Neomys species. Sorex araneus also exhibits some niche overlap with N. anomalus, while S. minutus is the most specialised. Multivariate analysis based only on tooth microwear textures recovers the same pattern of niche partitioning. Our results also suggest that tooth textures track seasonal differences in diet. Projecting data from fossils into the multivariate dietary space defined using microwear from extant taxa demonstrates that the technique is capable of subtle dietary discrimination in extinct insectivores.

Multivariate statistical analysis of heavy metal concentration in soils of Yelagiri Hills, Tamilnadu, India--spectroscopical approach.

PubMed

Chandrasekaran, A; Ravisankar, R; Harikrishnan, N; Satapathy, K K; Prasad, M V R; Kanagasabapathy, K V

2015-02-25

Anthropogenic activities increase the accumulation of heavy metals in the soil environment. Soil pollution significantly reduces environmental quality and affects the human health. In the present study soil samples were collected at different locations of Yelagiri Hills, Tamilnadu, India for heavy metal analysis. The samples were analyzed for twelve selected heavy metals (Mg, Al, K, Ca, Ti, Fe, V, Cr, Mn, Co, Ni and Zn) using energy dispersive X-ray fluorescence (EDXRF) spectroscopy. Heavy metals concentration in soil were investigated using enrichment factor (EF), geo-accumulation index (Igeo), contamination factor (CF) and pollution load index (PLI) to determine metal accumulation, distribution and its pollution status. Heavy metal toxicity risk was assessed using soil quality guidelines (SQGs) given by target and intervention values of Dutch soil standards. The concentration of Ni, Co, Zn, Cr, Mn, Fe, Ti, K, Al, Mg were mainly controlled by natural sources. Multivariate statistical methods such as correlation matrix, principal component analysis and cluster analysis were applied for the identification of heavy metal sources (anthropogenic/natural origin). Geo-statistical methods such as kirging identified hot spots of metal contamination in road areas influenced mainly by presence of natural rocks. Copyright © 2014 Elsevier B.V. All rights reserved.

Factors Controlling Sediment Load in The Central Anatolia Region of Turkey: Ankara River Basin.

PubMed

Duru, Umit; Wohl, Ellen; Ahmadi, Mehdi

2017-05-01

Better understanding of the factors controlling sediment load at a catchment scale can facilitate estimation of soil erosion and sediment transport rates. The research summarized here enhances understanding of correlations between potential control variables on suspended sediment loads. The Soil and Water Assessment Tool was used to simulate flow and sediment at the Ankara River basin. Multivariable regression analysis and principal component analysis were then performed between sediment load and controlling variables. The physical variables were either directly derived from a Digital Elevation Model or from field maps or computed using established equations. Mean observed sediment rate is 6697 ton/year and mean sediment yield is 21 ton/y/km² from the gage. Soil and Water Assessment Tool satisfactorily simulated observed sediment load with Nash-Sutcliffe efficiency, relative error, and coefficient of determination (R²) values of 0.81, -1.55, and 0.93, respectively in the catchment. Therefore, parameter values from the physically based model were applied to the multivariable regression analysis as well as principal component analysis. The results indicate that stream flow, drainage area, and channel width explain most of the variability in sediment load among the catchments. The implications of the results, efficient siltation management practices in the catchment should be performed to stream flow, drainage area, and channel width.

Factors Controlling Sediment Load in The Central Anatolia Region of Turkey: Ankara River Basin

NASA Astrophysics Data System (ADS)

Duru, Umit; Wohl, Ellen; Ahmadi, Mehdi

2017-05-01

Better understanding of the factors controlling sediment load at a catchment scale can facilitate estimation of soil erosion and sediment transport rates. The research summarized here enhances understanding of correlations between potential control variables on suspended sediment loads. The Soil and Water Assessment Tool was used to simulate flow and sediment at the Ankara River basin. Multivariable regression analysis and principal component analysis were then performed between sediment load and controlling variables. The physical variables were either directly derived from a Digital Elevation Model or from field maps or computed using established equations. Mean observed sediment rate is 6697 ton/year and mean sediment yield is 21 ton/y/km² from the gage. Soil and Water Assessment Tool satisfactorily simulated observed sediment load with Nash-Sutcliffe efficiency, relative error, and coefficient of determination ( R²) values of 0.81, -1.55, and 0.93, respectively in the catchment. Therefore, parameter values from the physically based model were applied to the multivariable regression analysis as well as principal component analysis. The results indicate that stream flow, drainage area, and channel width explain most of the variability in sediment load among the catchments. The implications of the results, efficient siltation management practices in the catchment should be performed to stream flow, drainage area, and channel width.

Role of Surgical Services in Profitability of Hospitals in California: An Analysis of Office of Statewide Health Planning and Development Annual Financial Data.

PubMed

Moazzez, Ashkan; de Virgilio, Christian

2016-10-01

With constant changes in health-care laws and payment methods, profitability, and financial sustainability of hospitals are of utmost importance. The purpose of this study is to determine the relationship between surgical services and hospital profitability. The Office of Statewide Health Planning and Development annual financial databases for the years 2009 to 2011 were used for this study. The hospitals' characteristics and income statement elements were extracted for statistical analysis using bivariate and multivariate linear regression. A total of 989 financial records of 339 hospitals were included. On bivariate analysis, the number of inpatient and ambulatory operating rooms (ORs), the number of cases done both as inpatient and outpatient in each OR, and the average minutes used in inpatient ORs were significantly related with the net income of the hospital. On multivariate regression analysis, when controlling for hospitals' payer mix and the study year, only the number of inpatient cases done in the inpatient ORs (β = 832, P = 0.037), and the number of ambulatory ORs (β = 1,485, 466, P = 0.001) were significantly related with the net income of the hospital. These findings suggest that hospitals can maximize their profitability by diverting and allocating outpatient surgeries to ambulatory ORs, to allow for more inpatient surgeries.

Construction of inorganic elemental fingerprint and multivariate statistical analysis of marine traditional Chinese medicine Meretricis concha from Rushan Bay

NASA Astrophysics Data System (ADS)

Wu, Xia; Zheng, Kang; Zhao, Fengjia; Zheng, Yongjun; Li, Yantuan

2014-08-01

Meretricis concha is a kind of marine traditional Chinese medicine (TCM), and has been commonly used for the treatment of asthma and scald burns. In order to investigate the relationship between the inorganic elemental fingerprint and the geographical origin identification of Meretricis concha, the elemental contents of M. concha from five sampling points in Rushan Bay have been determined by means of inductively coupled plasma optical emission spectrometry (ICP-OES). Based on the contents of 14 inorganic elements (Al, As, Cd, Co, Cr, Cu, Fe, Hg, Mn, Mo, Ni, Pb, Se, and Zn), the inorganic elemental fingerprint which well reflects the elemental characteristics was constructed. All the data from the five sampling points were discriminated with accuracy through hierarchical cluster analysis (HCA) and principle component analysis (PCA), indicating that a four-factor model which could explain approximately 80% of the detection data was established, and the elements Al, As, Cd, Cu, Ni and Pb could be viewed as the characteristic elements. This investigation suggests that the inorganic elemental fingerprint combined with multivariate statistical analysis is a promising method for verifying the geographical origin of M. concha, and this strategy should be valuable for the authenticity discrimination of some marine TCM.

Multivariate Analysis as a Method for Evaluating the Conceptual Perceptions of Korean Medicine Students regarding Phlegm Pattern

PubMed Central

Kim, Hyungsuk; Park, Young-Jae; Park, Young-Bae

2013-01-01

Individuals may perceive the concepts in Korean medicine pattern classification differently because it is performed according to the integration of a variety of information. Therefore, analysis about individual perspective is very important for examining the cross-sectional perspective state of Korean medicine concepts and developing both the clinical guideline including diagnosis and the curriculum of Korean medicine colleges. Moreover, because this conceptual difference is thought to begin with college education, it is worthwhile to observe students' viewpoints. So, we suggested multivariate analysis to explore the dimensional structure of Korean medicine students' conceptual perceptions regarding phlegm pattern. We surveyed 326 students divided into 5 groups based on their year of study. Data were analyzed using multidimensional scaling and factor analysis. Within-group difference was the smallest for third-year students, who have received Korean medicine education in full for the first time. With the exception of first-year students, the conceptual map revealed that each group's mean perceptions of phlegm pattern were distributed in almost linear fashion. To determine the effect of education, we investigated the preference rankings and scores of each symptom. We also extracted factors to identify latent variables and to compare the between-group conceptual characteristics regarding phlegm pattern. PMID:24062789

Extracting galactic structure parameters from multivariated density estimation

NASA Technical Reports Server (NTRS)

Chen, B.; Creze, M.; Robin, A.; Bienayme, O.

1992-01-01

Multivariate statistical analysis, including includes cluster analysis (unsupervised classification), discriminant analysis (supervised classification) and principle component analysis (dimensionlity reduction method), and nonparameter density estimation have been successfully used to search for meaningful associations in the 5-dimensional space of observables between observed points and the sets of simulated points generated from a synthetic approach of galaxy modelling. These methodologies can be applied as the new tools to obtain information about hidden structure otherwise unrecognizable, and place important constraints on the space distribution of various stellar populations in the Milky Way. In this paper, we concentrate on illustrating how to use nonparameter density estimation to substitute for the true densities in both of the simulating sample and real sample in the five-dimensional space. In order to fit model predicted densities to reality, we derive a set of equations which include n lines (where n is the total number of observed points) and m (where m: the numbers of predefined groups) unknown parameters. A least-square estimation will allow us to determine the density law of different groups and components in the Galaxy. The output from our software, which can be used in many research fields, will also give out the systematic error between the model and the observation by a Bayes rule.

Risk Factors of Porcine Cysticercosis in the Eastern Cape Province, South Africa

PubMed Central

Krecek, Rosina Claudia; Mohammed, Hamish; Michael, Lynne Margaret; Schantz, Peter Mullineaux; Ntanjana, Lulama; Morey, Liesl; Werre, Stephen Rakem; Willingham, Arve Lee

2012-01-01

There is a high prevalence of Taenia solium taeniosis/cysticercosis in humans and pigs in the Eastern Cape Province (ECP) of South Africa. The objective of this study was to identify risk factors of porcine cysticercosis in select districts of the ECP. Data were collected in 2003 by interviewing 217 pig producers from the area. Blood samples were collected from 261 of their pigs, which were tested using two enzyme-linked immunosorbent assays (ELISA) for the presence of antibodies to cysticercosis. Frequencies of both owner- and pig-level characteristics were determined. For pig-level analysis, all bivariable and multivariable associations were determined using the surveylogistic procedure of the SAS/STAT® software to accommodate for the intraclass correlation that exists for clusters of pigs within one owner and for clusters of owners within a district. All tests for significance were performed at the α = 0.05 level, and adjusted odds ratios (aOR) and 95% confidence intervals (CI) were determined. Among the respondents, 48% of their households lacked a latrine, 98% slaughtered pigs at home, and 99% indicated that meat inspection services were not available. On bivariable analysis, there was a significant association between porcine infection and district (p = 0.003), breed (p = 0.041) and the absence of a latrine (p = 0.006). On multivariable analysis, the absence of a latrine was the only variable significantly associated with porcine infection (aOR = 1.89; 95% CI = 1.07, 3.35) (p = 0.028). The increased odds of porcine infection with households lacking a latrine contributes to our understanding of the transmission of this parasite in the ECP. Determining and addressing the risk factors for T. solium infection can potentially lower the very high prevalence in humans and pigs in this endemic area. PMID:22655065

Socioeconomic and sociocultural determinants of psychological distress and quality of life among patients with psoriasis in a selected multi-ethnic Malaysian population.

PubMed

Kwan, Zhenli; Bong, Yii Bonn; Tan, Leng Leng; Lim, Shu Xian; Yong, Adrian Sze Wai; Ch'ng, Chin Chwen; Tan, Maw Pin; Thevarajah, Suganthi; Ismail, Rokiah

2017-02-01

Patients with psoriasis may have increased risk of psychological comorbidities. This cross-sectional study aimed at determining associations between sociocultural and socioeconomic factors with the Depression Anxiety Stress Scale (DASS) scores and the Dermatology Life Quality Index (DLQI) scores. Adult patients with psoriasis were recruited from a Dermatology outpatient clinic via convenience sampling. Interviews were conducted regarding socio-demographic factors and willing subjects were requested to complete the DASS and DLQI questionnaires. The Pearson χ 2 test, Fisher's exact test and multivariate logistic regression were used for statistical analysis to determine independent predictors of depression, anxiety, stress and severe impairment of quality of life. Unadjusted analysis revealed that depression was associated with Indian ethnicity (p = .041) and severe impairment of quality of life was associated with Indian ethnicity (p = .032), higher education (p = .013), higher income (p = .042), and employment status (p = .014). Multivariate analysis revealed that Indian ethnicity was a predictor of depression (p = .024). For stress, tertiary level of education (p = .020) was an independent risk factor while a higher monthly income was a protective factor (p = .042). The ethnic Indians and Malays were significantly more likely than the ethnic Chinese to suffer reduced quality of life (p = .001 and p = .006 respectively) and subjects with tertiary education were more likely to have severe impairment of quality of life (p = .002). Our study was unique in determining sociocultural influences on psychological complications of psoriasis in a South East Asian population. This has provided invaluable insight into factors predictive of adverse effects of psoriasis on psychological distress and quality of life in our patient population. Future studies should devise interventions to specifically target at risk groups in the development of strategies to reduce morbidity associated with psoriasis.

Why are our children wasting: Determinants of wasting among under 5s in Ghana.

PubMed

Darteh, Eugene Kofuor Maafo; Acquah, Evelyn; Darteh, Florie

2017-09-01

Wasting is one of the indicators of malnutrition known to contribute to the deaths occurring from childhood malnutrition. It is the measure of body mass in relation to body length used to explain recent nutritional status. This paper examines the determinants of wasting among under 5s in Ghana. Data were drawn from the 2014 Ghana Demographic and Health Survey children's records file to examine the determinants of wasting among children. A total of 2720 children under 5 years with valid anthropometric data were used. Data on wasting were collected by measuring the weight and height of all children under 5 years of age. Bi-variate and multi-variate statistics are used to examine the determinants of wasting. The bi-variate analysis showed significant differences ( p < 0.001) in the prevalence of wasting among under 5s according to age of the child, region, and wealth status. On the other hand, the multi-variate analysis revealed that the odds of wasting were lower among children aged 24-35 months (Odds ratio (OR) = 0.37; p < 0.001), those from households of the middle wealth quintile (OR = 0.49, p < 0.05) and with health insurance (OR = 0.70; p < 0.10). Programmes and policies aimed at ensuring the survival of children during the first 24 months of life should be strengthened to reduce the risk of wasting among under 5s. Also, efforts should be made by the relevant government agencies and other stakeholders to strengthen the socio-economic status of mothers to enable them to provide adequate nutrition and improve access to health insurance for their children in order to reduce the incidence of wasting among these children.

Orthotopic bladder substitution in men revisited: identification of continence predictors.

PubMed

Koraitim, M M; Atta, M A; Foda, M K

2006-11-01

We determined the impact of the functional characteristics of the neobladder and urethral sphincter on continence results, and determined the most significant predictors of continence. A total of 88 male patients 29 to 70 years old underwent orthotopic bladder substitution with tubularized ileocecal segment (40) and detubularized sigmoid (25) or ileum (23). Uroflowmetry, cystometry and urethral pressure profilometry were performed at 13 to 36 months (mean 19) postoperatively. The correlation between urinary continence and 28 urodynamic variables was assessed. Parameters that correlated significantly with continence were entered into a multivariate analysis using a logistic regression model to determine the most significant predictors of continence. Maximum urethral closure pressure was the only parameter that showed a statistically significant correlation with diurnal continence. Nocturnal continence had not only a statistically significant positive correlation with maximum urethral closure pressure, but also statistically significant negative correlations with maximum contraction amplitude, and baseline pressure at mid and maximum capacity. Three of these 4 parameters, including maximum urethral closure pressure, maximum contraction amplitude and baseline pressure at mid capacity, proved to be significant predictors of continence on multivariate analysis. While daytime continence is determined by maximum urethral closure pressure, during the night it is the net result of 2 forces that have about equal influence but in opposite directions, that is maximum urethral closure pressure vs maximum contraction amplitude plus baseline pressure at mid capacity. Two equations were derived from the logistic regression model to predict the probability of continence after orthotopic bladder substitution, including Z1 (diurnal) = 0.605 + 0.0085 maximum urethral closure pressure and Z2 (nocturnal) = 0.841 + 0.01 [maximum urethral closure pressure - (maximum contraction amplitude + baseline pressure at mid capacity)].

Study of Orbitally Excited $$B_{(s)}$$ Mesons and Evidence for a New $$B\\pi$$ Resonance with the CDF II Detector

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

Kambeitz, Manuel

This thesis presents an analysis of excited states of B0, B+ and B0 s mesons, decaying to B mesons while emitting a pion or kaon. They are reconstructed from their decay products and a selection is performed to discard wrongly reconstructed B(s) mesons with the multivariate analysis software NeuroBayes, as described in chapter 5. In the training process, the sPlot method and measured and simulated data are used. Chapter 6 describes how the properties of excited B(s) are determined by an unbinned maximum likelihood t to their mass spectra. The systematic uncertainties determined in this analysis are described in chaptermore » 7. The results of this thesis are presented in chapter 8 and a conclusion is given in chapter 9. The results shown in this thesis have been published before in [1].« less

MULTIVARIATE CURVE RESOLUTION OF NMR SPECTROSCOPY METABONOMIC DATA

EPA Science Inventory

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

Characterizing multivariate decoding models based on correlated EEG spectral features

PubMed Central

McFarland, Dennis J.

2013-01-01

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

Drunk driving detection based on classification of multivariate time series.

PubMed

Li, Zhenlong; Jin, Xue; Zhao, Xiaohua

2015-09-01

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

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

PubMed

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

2015-12-30

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

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

PubMed Central

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

2015-01-01

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

Distribution, genetic and cardiovascular determinants of FVIII:c - Data from the population-based Gutenberg Health Study.

PubMed

Hermanns, M Iris; Grossmann, Vera; Spronk, Henri M H; Schulz, Andreas; Jünger, Claus; Laubert-Reh, Dagmar; Mazur, Johanna; Gori, Tommaso; Zeller, Tanja; Pfeiffer, Norbert; Beutel, Manfred; Blankenberg, Stefan; Münzel, Thomas; Lackner, Karl J; Ten Cate-Hoek, Arina J; Ten Cate, Hugo; Wild, Philipp S

2015-01-01

Elevated levels of c are associated with risk for both venous and arterial thromboembolism. However, no population-based study on the sex-specific distribution and reference ranges of plasma c and its cardiovascular determinants is available. c was analyzed in a randomly selected sample of 2533 males and 2440 females from the Gutenberg Health Study in Germany. Multivariable regression analyses for c were performed under adjustment for genetic determinants, cardiovascular risk factors and cardiovascular disease. Females (126.6% (95% CI: 125.2/128)) showed higher c levels than males (121.2% (119.8/122.7)). c levels increased with age in both sexes (ß per decade: 5.67% (4.22/7.13) male, 6.15% (4.72/7.57) female; p<0.001). Sex-specific reference limits and categories indicating the grade of deviation from the reference were calculated, and nomograms for c were created. c was approximately 25% higher in individuals with non-O blood type. Adjusted for sex and age, ABO-blood group accounted for 18.3% of c variation. In multivariable analysis, c was notably positively associated with diabetes mellitus, obesity, hypertension and dyslipidemia and negatively with current smoking. In a fully adjusted multivariable model, the strongest associations observed were of elevated c with diabetes and peripheral artery disease in both sexes and with obesity in males. Effects of SNPs in the vWF, STAB2 and SCARA5 gene were stronger in females than in males. The use of nomograms for valuation of c might be useful to identify high-risk cohorts for thromboembolism. Additionally, the prospective evaluation of c as a risk predictor becomes feasible. Copyright © 2015. Published by Elsevier Ireland Ltd.

Hydrothermal contamination of public supply wells in Napa and Sonoma Valleys, California

USGS Publications Warehouse

Forrest, Matthew J.; Kulongoski, Justin T.; Edwards, Matthew S.; Farrar, Christopher D.; Belitz, Kenneth; Norris, Richard D.

2013-01-01

Groundwater chemistry and isotope data from 44 public supply wells in the Napa and Sonoma Valleys, California were determined to investigate mixing of relatively shallow groundwater with deeper hydrothermal fluids. Multivariate analyses including Cluster Analyses, Multidimensional Scaling (MDS), Principal Components Analyses (PCA), Analysis of Similarities (ANOSIM), and Similarity Percentage Analyses (SIMPER) were used to elucidate constituent distribution patterns, determine which constituents are significantly associated with these hydrothermal systems, and investigate hydrothermal contamination of local groundwater used for drinking water. Multivariate statistical analyses were essential to this study because traditional methods, such as mixing tests involving single species (e.g. Cl or SiO2) were incapable of quantifying component proportions due to mixing of multiple water types. Based on these analyses, water samples collected from the wells were broadly classified as fresh groundwater, saline waters, hydrothermal fluids, or mixed hydrothermal fluids/meteoric water wells. The Multivariate Mixing and Mass-balance (M3) model was applied in order to determine the proportion of hydrothermal fluids, saline water, and fresh groundwater in each sample. Major ions, isotopes, and physical parameters of the waters were used to characterize the hydrothermal fluids as Na–Cl type, with significant enrichment in the trace elements As, B, F and Li. Five of the wells from this study were classified as hydrothermal, 28 as fresh groundwater, two as saline water, and nine as mixed hydrothermal fluids/meteoric water wells. The M3 mixing-model results indicated that the nine mixed wells contained between 14% and 30% hydrothermal fluids. Further, the chemical analyses show that several of these mixed-water wells have concentrations of As, F and B that exceed drinking-water standards or notification levels due to contamination by hydrothermal fluids.

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

ERIC Educational Resources Information Center

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

2016-01-01

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

Multivariate Relationships of Specific Impression Cues with Teacher Expectations and Dyadic Interactions in Elementary Physical Education Classes.

ERIC Educational Resources Information Center

Martinek, Thomas J.; Karper, William B.

1984-01-01

This study determined multivariate relationships of the impression cues of attractiveness and effort with teacher expectations and dyadic interaction in two groups of elementary school children. (Author/JMK)

Predictive value of clinical scoring and simplified gait analysis for acetabulum fractures.

PubMed

Braun, Benedikt J; Wrona, Julian; Veith, Nils T; Rollman, Mika; Orth, Marcel; Herath, Steven C; Holstein, Jörg H; Pohlemann, Tim

2016-12-01

Fractures of the acetabulum show a high, long-term complication rate. The aim of the present study was to determine the predictive value of clinical scoring and standardized, simplified gait analysis on the outcome after these fractures. Forty-one patients with acetabular fractures treated between 2008 and 2013 and available, standardized video recorded aftercare were identified from a prospective database. A visual gait score was used to determine the patients walking abilities 6-m postoperatively. Clinical (Merle d'Aubigne and Postel score, visual analogue scale pain, EQ5d) and radiological scoring (Kellgren-Lawrence score, postoperative computed tomography, and Matta classification) were used to perform correlation and multivariate regression analysis. The average patient age was 48 y (range, 15-82 y), six female patients were included in the study. Mean follow-up was 1.6 y (range, 1-2 y). Moderate correlation between the gait score and outcome (versus EQ5d: r s  = 0.477; versus Merle d'Aubigne: r s  = 0.444; versus Kellgren-Lawrence: r s  = -0.533), as well as high correlation between the Merle d'Aubigne score and outcome were seen (versus EQ5d: r s  = 0.575; versus Merle d'Aubigne: r s  = 0.776; versus Kellgren-Lawrence: r s  = -0.419). Using a multivariate regression model, the 6 m gait score (B = -0.299; P < 0.05) and early osteoarthritis development (B = 1.026; P < 0.05) were determined as predictors of final osteoarthritis. A good fit of the regression model was seen (R 2  = 904). Easy and available clinical scoring (gait score/Merle d'Aubigne) can predict short-term radiological and functional outcome after acetabular fractures with sufficient accuracy. Decisions on further treatment and interventions could be based on simplified gait analysis. Copyright © 2016 Elsevier Inc. All rights reserved.

Practical determination of aortic valve calcium volume score on contrast-enhanced computed tomography prior to transcatheter aortic valve replacement and impact on paravalvular regurgitation: Elucidating optimal threshold cutoffs.

PubMed

Bettinger, Nicolas; Khalique, Omar K; Krepp, Joseph M; Hamid, Nadira B; Bae, David J; Pulerwitz, Todd C; Liao, Ming; Hahn, Rebecca T; Vahl, Torsten P; Nazif, Tamim M; George, Isaac; Leon, Martin B; Einstein, Andrew J; Kodali, Susheel K

The threshold for the optimal computed tomography (CT) number in Hounsfield Units (HU) to quantify aortic valvular calcium on contrast-enhanced scans has not been standardized. Our aim was to find the most accurate threshold to predict paravalvular regurgitation (PVR) after transcatheter aortic valve replacement (TAVR). 104 patients who underwent TAVR with the CoreValve prosthesis were studied retrospectively. Luminal attenuation (LA) in HU was measured at the level of the aortic annulus. Calcium volume score for the aortic valvular complex was measured using 6 threshold cutoffs (650 HU, 850 HU, LA × 1.25, LA × 1.5, LA+50, LA+100). Receiver-operating characteristic (ROC) analysis was performed to assess the predictive value for > mild PVR (n = 16). Multivariable analysis was performed to determine the accuracy to predict > mild PVR after adjustment for depth and perimeter oversizing. ROC analysis showed lower area under the curve (AUC) values for fixed threshold cutoffs (650 or 850 HU) compared to thresholds relative to LA. The LA+100 threshold had the highest AUC (0.81), and AUC was higher than all studied protocols, other than the LA x 1.25 and LA + 50 protocols, where the difference approached statistical significance (p = 0.05, and 0.068, respectively). Multivariable analysis showed calcium volume determined by the LAx1.25, LAx1.5, LA+50, and LA+ 100 HU protocols to independently predict PVR. Calcium volume scoring thresholds which are relative to LA are more predictive of PVR post-TAVR than those which use fixed cutoffs. A threshold of LA+100 HU had the highest predictive value. Copyright © 2017 Society of Cardiovascular Computed Tomography. Published by Elsevier Inc. All rights reserved.

Environmental assessment of Al-Hammar Marsh, Southern Iraq.

PubMed

Al-Gburi, Hind Fadhil Abdullah; Al-Tawash, Balsam Salim; Al-Lafta, Hadi Salim

2017-02-01

(a) To determine the spatial distributions and levels of major and minor elements, as well as heavy metals, in water, sediment, and biota (plant and fish) in Al-Hammar Marsh, southern Iraq, and ultimately to supply more comprehensive information for policy-makers to manage the contaminants input into the marsh so that their concentrations do not reach toxic levels. (b) to characterize the seasonal changes in the marsh surface water quality. (c) to address the potential environmental risk of these elements by comparison with the historical levels and global quality guidelines (i.e., World Health Organization (WHO) standard limits). (d) to define the sources of these elements (i.e., natural and/or anthropogenic) using combined multivariate statistical techniques such as Principal Component Analysis (PCA) and Agglomerative Hierarchical Cluster Analysis (AHCA) along with pollution analysis (i.e., enrichment factor analysis). Water, sediment, plant, and fish samples were collected from the marsh, and analyzed for major and minor ions, as well as heavy metals, and then compared to historical levels and global quality guidelines (WHO guidelines). Then, multivariate statistical techniques, such as PCA and AHCA, were used to determine the element sourcing. Water analyses revealed unacceptable values for almost all physio-chemical and biological properties, according to WHO standard limits for drinking water. Almost all major ions and heavy metal concentrations in water showed a distinct decreasing trend at the marsh outlet station compared to other stations. In general, major and minor ions, as well as heavy metals exhibit higher concentrations in winter than in summer. Sediment analyses using multivariate statistical techniques revealed that Mg, Fe, S, P, V, Zn, As, Se, Mo, Co, Ni, Cu, Sr, Br, Cd, Ca, N, Mn, Cr, and Pb were derived from anthropogenic sources, while Al, Si, Ti, K, and Zr were primarily derived from natural sources. Enrichment factor analysis gave results compatible with multivariate statistical techniques findings. Analysis of heavy metals in plant samples revealed that there is no pollution in plants in Al-Hammar Marsh. However, the concentrations of heavy metals in fish samples showed that all samples were contaminated by Pb, Mn, and Ni, while some samples were contaminated by Pb, Mn, and Ni. Decreasing of Tigris and Euphrates discharges during the past decades due to drought conditions and upstream damming, as well as the increasing stress of wastewater effluents from anthropogenic activities, led to degradation of the downstream Al-Hammar Marsh water quality in terms of physical, chemical, and biological properties. As such properties were found to consistently exceed the historical and global quality objectives. However, element concentration decreasing trend at the marsh outlet station compared to other stations indicate that the marsh plays an important role as a natural filtration and bioremediation system. Higher element concentrations in winter were due to runoff from the washing of the surrounding Sabkha during flooding by winter rainstorms. Finally, the high concentrations of heavy metals in fish samples can be attributed to bioaccumulation and biomagnification processes.

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.

Integration of vessel traits, wood density, and height in angiosperm shrubs and trees.

PubMed

Martínez-Cabrera, Hugo I; Schenk, H Jochen; Cevallos-Ferriz, Sergio R S; Jones, Cynthia S

2011-05-01

Trees and shrubs tend to occupy different niches within and across ecosystems; therefore, traits related to their resource use and life history are expected to differ. Here we analyzed how growth form is related to variation in integration among vessel traits, wood density, and height. We also considered the ecological and evolutionary consequences of such differences. In a sample of 200 woody plant species (65 shrubs and 135 trees) from Argentina, Mexico, and the United States, standardized major axis (SMA) regression, correlation analyses, and ANOVA were used to determine whether relationships among traits differed between growth forms. The influence of phylogenetic relationships was examined with a phylogenetic ANOVA and phylogenetically independent contrasts (PICs). A principal component analysis was conducted to determine whether trees and shrubs occupy different portions of multivariate trait space. Wood density did not differ between shrubs and trees, but there were significant differences in vessel diameter, vessel density, theoretical conductivity, and as expected, height. In addition, relationships between vessel traits and wood density differed between growth forms. Trees showed coordination among vessel traits, wood density, and height, but in shrubs, wood density and vessel traits were independent. These results hold when phylogenetic relationships were considered. In the multivariate analyses, these differences translated as significantly different positions in multivariate trait space occupied by shrubs and trees. Differences in trait integration between growth forms suggest that evolution of growth form in some lineages might be associated with the degree of trait interrelation.

CpG island methylator phenotype identifies high risk patients among microsatellite stable BRAF mutated colorectal cancers

PubMed Central

Vedeld, Hege Marie; Merok, Marianne; Jeanmougin, Marine; Danielsen, Stine A.; Honne, Hilde; Presthus, Gro Kummeneje; Svindland, Aud; Sjo, Ole H.; Hektoen, Merete; Eknæs, Mette; Nesbakken, Arild; Lothe, Ragnhild A.

2017-01-01

The prognostic value of CpG island methylator phenotype (CIMP) in colorectal cancer remains unsettled. We aimed to assess the prognostic value of this phenotype analyzing a total of 1126 tumor samples obtained from two Norwegian consecutive colorectal cancer series. CIMP status was determined by analyzing the 5‐markers CAGNA1G, IGF2, NEUROG1, RUNX3 and SOCS1 by quantitative methylation specific PCR (qMSP). The effect of CIMP on time to recurrence (TTR) and overall survival (OS) were determined by uni‐ and multivariate analyses. Subgroup analyses were conducted according to MSI and BRAF mutation status, disease stage, and also age at time of diagnosis (<60, 60‐74, ≥75 years). Patients with CIMP positive tumors demonstrated significantly shorter TTR and worse OS compared to those with CIMP negative tumors (multivariate hazard ratio [95% CI] 1.86 [1.31‐2.63] and 1.89 [1.34‐2.65], respectively). In stratified analyses, CIMP tumors showed significantly worse outcome among patients with microsatellite stable (MSS, P < 0.001), and MSS BRAF mutated tumors (P < 0.001), a finding that persisted in patients with stage II, III or IV disease, and that remained significant in multivariate analysis (P < 0.01). Consistent results were found for all three age groups. To conclude, CIMP is significantly associated with inferior outcome for colorectal cancer patients, and can stratify the poor prognostic patients with MSS BRAF mutated tumors. PMID:28542846

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

Tobacco and alcohol use in adolescents with unplanned pregnancies: relation with family structure, tobacco and alcohol use at home and by friends.

PubMed

Francisco, Vazquez-Nava; Carlos, Vazquez-Rodríguez; Eliza, Vazquez-Rodriguez; Octelina, Castillo-Ruiz; Maria, Iribar Ibabe

2016-03-01

Recent publications show that smoking and alcohol use among adolescents with unplanned pregnancy is increasing and the causes need to be further studied. To determine the association between living in a non-intact family household and the presence of smokers and consumers of alcoholic beverages in the adolescents' environment with smoking and consuming alcoholic beverages in adolescents with unplanned pregnancies. A cross-sectional study was carried out among 785 pregnant adolescents, aged 13-19 years. Data was collected by trained interviewers using a self-administered questionnaire. The association was determined using multivariate logistic regression analysis. In adolescents with unplanned pregnancies, the prevalence of active smoking was 21.2% and of alcohol consumption, 41.5%. The percentage of smoking at home was 57.4% and alcohol consumption, 77.5%. Approximately, 80.3% of adolescents with unplanned pregnancies had friends who smoked and 90.6% consumed alcoholic beverages. Multivariate logistic regression analysis shows that having friends who smoke or who consume alcoholic beverages is the most important risk factor for substance use in adolescents with unplanned pregnancies. Smoking and alcohol consumption at home are not associated with smoking in adolescents with unplanned pregnancies. Socializing with friends who smoke and/or consume alcoholic beverages constitutes the most important risk factor for substance use among adolescents with unplanned pregnancies.

In-situ determination of metallic variation and multi-association in single particles by combining synchrotron microprobe, sequential chemical extraction and multivariate statistical analysis.

PubMed

Zhu, Yu-Min; Zhang, Hua; Fan, Shi-Suo; Wang, Si-Jia; Xia, Yi; Shao, Li-Ming; He, Pin-Jing

2014-07-15

Due to the heterogeneity of metal distribution, it is challenging to identify the speciation, source and fate of metals in solid samples at micro scales. To overcome these challenges single particles of air pollution control residues were detected in situ by synchrotron microprobe after each step of chemical extraction and analyzed by multivariate statistical analysis. Results showed that Pb, Cu and Zn co-existed as acid soluble fractions during chemical extraction, regardless of their individual distribution as chlorides or oxides in the raw particles. Besides the forms of Fe2O3, MnO2 and FeCr2O4, Fe, Mn, Cr and Ni were closely associated with each other, mainly as reducible fractions. In addition, the two groups of metals had interrelations with the Si-containing insoluble matrix. The binding could not be directly detected by micro-X-ray diffraction (μ-XRD) and XRD, suggesting their partial existence as amorphous forms or in the solid solution. The combined method on single particles can effectively determine metallic multi-associations and various extraction behaviors that could not be identified by XRD, μ-XRD or X-ray absorption spectroscopy. The results are useful for further source identification and migration tracing of heavy metals. Copyright © 2014 Elsevier B.V. All rights reserved.

The Impact of Risk Factors of Chronic Pancreatitis on Secretin Pancreatic Function Testing: Results of a 20-Year Study.

PubMed

Kothari, Darshan; Ketwaroo, Gyanprakash; Freedman, Steven D; Sheth, Sunil G

2017-08-01

The aim of this study was to determine the effect of established risk factors on the outcome of secretin pancreatic function testing (sPFT) in patients undergoing work-up for suspected chronic pancreatitis. We completed a retrospective review of patients who underwent sPFT for suspected chronic pancreatitis over 20 years. We compared peak bicarbonate concentrations between groups and completed univariate and multivariate analyses to determine associations between risk factors and positive sPFT results (peak bicarbonate <80 mEq/L). Forty-three of 162 patients had positive sPFT results. There were significant differences in peak bicarbonate concentrations in patients with and without recurrent acute pancreatitis (RAP) and with local complications from acute pancreatitis (AP) (P ≤ 0.05). The bicarbonate concentration in patients with and without other risk factors such as tobacco use, alcohol use, and family history of pancreatitis was not significantly different. Female sex, a history of AP, and a history of RAP were associated with positive sPFT results on univariate analysis (P ≤ 0.05). On multivariate analysis, sex and RAP remained significant. Our study demonstrates that female sex, history of AP and RAP, and AP with local complications are associated with positive sPFT results or lower peak bicarbonate concentration. However, other risk factors do not impact the results of sPFT.

Overexpression of epithelial cell adhesion molecule protein is associated with favorable prognosis in an unselected cohort of ovarian cancer patients.

PubMed

Battista, Marco Johannes; Cotarelo, Cristina; Jakobi, Sina; Steetskamp, Joscha; Makris, Georgios; Sicking, Isabel; Weyer, Veronika; Schmidt, Marcus

2014-07-01

The aim of this study was to evaluate the prognostic influence of epithelial cell adhesion molecule (EpCAM) in an unselected cohort of ovarian cancer (OC) patients. Expression of EpCAM was determined by immunohistochemistry in an unselected cohort of 117 patients with OC. Univariable and multivariable Cox regression analyses adjusted for age, tumor stage, histological grading, histological subtype, postoperative tumor burden and completeness of chemotherapy were performed in order to determine the prognostic influence of EpCAM. The Kaplan-Meier method is used to estimate survival rates. Univariable Cox regression analysis showed that overexpression of EpCAM is associated with favorable prognosis in terms of progression-free survival (PFS) (p = 0.011) and disease-specific survival (DSS) (p = 0.003). In multivariable Cox regression analysis, overexpression of EpCAM retains its significance independent of established prognostic factors for longer PFS [hazard ratios (HR) 0.408, 95 % confidence interval (CI) 0.197-0.846, p = 0.003] but not for PFS (HR 0.666, 95 % CI 0.366-1.212, p = 0.183). Kaplan-Meier plots demonstrate an influence on 5-year PFS rates (0 vs. 27.6 %, p = 0.048) and DSS rates (11.8 vs. 54.0 %, p = 0.018). These findings support the hypothesis that the expression of EpCAM is associated with favorable prognosis in OC.

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

NASA Astrophysics Data System (ADS)

Abeysekara, Saman; Damiran, Daalkhaijav; Yu, Peiqiang

2013-02-01

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

Examining a Comprehensive Model of Disaster-Related Posttraumatic Stress Disorder in Systematically Studied Survivors of 10 Disasters

PubMed Central

Oliver, Julianne; Pandya, Anand

2012-01-01

Objectives. Using a comprehensive disaster model, we examined predictors of posttraumatic stress disorder (PTSD) in combined data from 10 different disasters. Methods. The combined sample included data from 811 directly exposed survivors of 10 disasters between 1987 and 1995. We used consistent methods across all 10 disaster samples, including full diagnostic assessment. Results. In multivariate analyses, predictors of PTSD were female gender, younger age, Hispanic ethnicity, less education, ever-married status, predisaster psychopathology, disaster injury, and witnessing injury or death; exposure through death or injury to friends or family members and witnessing the disaster aftermath did not confer additional PTSD risk. Intentionally caused disasters associated with PTSD in bivariate analysis did not independently predict PTSD in multivariate analysis. Avoidance and numbing symptoms represented a PTSD marker. Conclusions. Despite confirming some previous research findings, we found no associations between PTSD and disaster typology. Prospective research is needed to determine whether early avoidance and numbing symptoms identify individuals likely to develop PTSD later. Our findings may help identify at-risk populations for treatment research. PMID:22897543

Computed tomography findings associated with the risk for emergency ventral hernia repair.

PubMed

Mueck, Krislynn M; Holihan, Julie L; Mo, Jiandi; Flores-Gonzales, Juan R; Ko, Tien C; Kao, Lillian S; Liang, Mike K

2017-07-01

Conventional wisdom teaches that small hernia defects are more likely to incarcerate. We aim to identify radiographic features of ventral hernias associated with increased risk of bowel incarceration. We assessed all patients who underwent emergent ventral hernia repair for bowel complications from 2009 to 2015. Cases were matched 1:3 with elective controls. Computed tomography scans were reviewed to determine hernia characteristics. Univariate and multivariable analyses were performed to identify variables associated with emergent surgery. The cohort consisted of 88 patients and 264 controls. On univariate analysis, older age, higher ASA score, elevated BMI, ascites, larger hernias, small angle, and taller hernias were associated with emergent surgery. On multivariable analysis, morbid obesity, ascites, smaller angle, and taller hernias were independently associated with emergent surgery. The teaching that large defects do not incarcerate is inaccurate; bowel compromise occurs with ventral hernias of all sizes. Instead, taller height and smaller angle are associated with the need for emergent repair. Early elective repair should be considered for patients with hernia features concerning for increased risk of bowel compromise. Copyright © 2016 Elsevier Inc. All rights reserved.

Factors Associated with Routine Dental Attendance among Aboriginal Australians.

PubMed

Amarasena, Najith; Kapellas, Kostas; Skilton, Michael R; Maple-Brown, Louise J; Brown, Alex; Bartold, Mark; O'Dea, Kerin; Celermajer, David; Jamieson, Lisa M

2016-01-01

To determine factors associated with routine dental attendance in Aboriginal Australians. Data of 271 Aboriginal adults residing in Australia's Northern Territory were used. Routine dental attendance was defined as last visiting a dentist less than one year ago or visiting a dentist for a check-up. Both bivariate and multivariable analytical techniques were used. While 27% visited a dentist in the past year, 29% of these visited for a check-up. In bivariate analysis, being female, low psychological distress, and low clinical attachment loss (CAL) were associated with visiting a dentist within last year. Being aged younger than 39 years, male, no oral health impairment, being caries-free, low CAL, and low apolipoprotein B were associated with visiting for a check-up. Clinical attachment loss remained associated with visiting a dentist less than one year ago while being younger than 39 years and having no oral health impairment remained associated with usually visiting for a check-up in multivariable analysis. Younger age, no oral health impairment, and low CAL were associated with routine dental attendance among Indigenous Australians.

Factors Associated with Routine Dental Attendance among Aboriginal Australians.

PubMed

Amarasena, Najith; Kapellas, Kostas; Skilton, Michael R; Maple-Brown, Louise J; Brown, Alex; Bartold, Mark; O'Dea, Kerin; Celermajer, David; Jamieson, Lisa M

2016-02-01

To determine factors associated with routine dental attendance in Aboriginal Australians. Data of 271 Aboriginal adults residing in Australia's Northern Territory were used. Routine dental attendance was defined as last visiting a dentist less than one year ago or visiting a dentist for a check-up. Both bivariate and multivariable analytical techniques were used. While 27% visited a dentist in the past year, 29% of these visited for a check-up. In bivariate analysis, being female, low psychological distress, and low clinical attachment loss (CAL) were associated with visiting a dentist within last year. Being aged younger than 39 years, male, no oral health impairment, being caries-free, low CAL, and low apolipoprotein B were associated with visiting for a check-up. Clinical attachment loss remained associated with visiting a dentist less than one year ago while being younger than 39 years and having no oral health impairment remained associated with usually visiting for a check-up in multivariable analysis. Younger age, no oral health impairment, and low CAL were associated with routine dental attendance among Indigenous Australians.

Factors predictive of survival after stapler hepatectomy of hepatocellular carcinoma: a multivariate, single-center analysis.

PubMed

Hoffmann, Katrin; Müller-Bütow, Verena; Franz, Clemens; Hinz, Ulf; Longerich, Thomas; Büchler, Markus W; Schemmer, Peter

2014-02-01

New technical devices for hepatic parenchymal transection have improved perioperative safety and patient survival. The aim of the present study was to determine the oncological outcome after stapler hepatectomy in patients with HCC. Data of 95 patients who underwent stapler hepatectomy for HCC between 2001 and 2011 were analyzed retrospectively regarding clinical safety of the procedure and predictive factors for survial. Thirty-nine minor (≤2 segments) and 56 major (≥3 segments) hepatic resections were performed. The median survival was 47.5 months, after 36 months follow-up. Low grading, tumors ≥5 cm, multiple nodules and liver cirrhosis were predictors of decreased overall survival using multivariate analysis with hazard ratio(HR)=2.62, 2.41, 2.05, and 1.92 respectively. An estimated intra-operative blood loss of ≥1.2l was inversely correlated to disease free survival (HR=1.96). Stapler hepatectomy is a safe procedure in patients with HCC. Substantial intraoperative blood loss and the presence of cirrhosis independently predict the overall probability of patient survival. Intraoperative blood loss directly impacts HCC recurrence.

Correlates of HIV knowledge and Sexual risk behaviors among Female Military Personnel

PubMed Central

Essien, E. James; Monjok, Emmanuel; Chen, Hua; Abughosh, Susan; Ekong, Ernest; Peters, Ronald J.; Holmes, Laurens; Holstad, Marcia M.; Mgbere, Osaro

2010-01-01

Objective Uniformed services personnel are at an increased risk of HIV infection. We examined the HIV/AIDS knowledge and sexual risk behaviors among female military personnel to determine the correlates of HIV risk behaviors in this population. Method The study used a cross-sectional design to examine HIV/AIDS knowledge and sexual risk behaviors in a sample of 346 females drawn from two military cantonments in Southwestern Nigeria. Data was collected between 2006 and 2008. Using bivariate analysis and multivariate logistic regression, HIV/AIDS knowledge and sexual behaviors were described in relation to socio-demographic characteristics of the participants. Results Multivariate logistic regression analysis revealed that level of education and knowing someone with HIV/AIDS were significant (p<0.05) predictors of HIV knowledge in this sample. HIV prevention self-efficacy was significantly (P<0.05) predicted by annual income and race/ethnicity. Condom use attitudes were also significantly (P<0.05) associated with number of children, annual income, and number of sexual partners. Conclusion Data indicates the importance of incorporating these predictor variables into intervention designs. PMID:20387111

Dittrichia graveolens (L.) Greuter Essential Oil: Chemical Composition, Multivariate Analysis, and Antimicrobial Activity.

PubMed

Mitic, Violeta; Stankov Jovanovic, Vesna; Ilic, Marija; Jovanovic, Olga; Djordjevic, Aleksandra; Stojanovic, Gordana

2016-01-01

The chemical composition and in vitro antimicrobial activities of Dittrichia graveolens (L.) Greuter essential oil was studied. Moreover, using agglomerative hierarchical cluster (AHC) and principal component analyses (PCA), the interrelationships of the D. graveolens essential-oil profiles characterized so far (including the sample from this study) were investigated. To evaluate the chemical composition of the essential oil, GC-FID and GC/MS analyses were performed. Altogether, 54 compounds were identified, accounting for 92.9% of the total oil composition. The D. graveolens oil belongs to the monoterpenoid chemotype, with monoterpenoids comprising 87.4% of the totally identified compounds. The major components were borneol (43.6%) and bornyl acetate (38.3%). Multivariate analysis showed that the compounds borneol and bornyl acetate exerted the greatest influence on the spatial differences in the composition of the reported oils. The antimicrobial activity against five bacterial and one fungal strain was determined using a disk-diffusion assay. The studied essential oil was active only against Gram-positive bacteria. Copyright © 2016 Verlag Helvetica Chimica Acta AG, Zürich.

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

Hybrid least squares multivariate spectral analysis methods

DOEpatents

Haaland, David M.

2002-01-01

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

Multivariate generalized multifactor dimensionality reduction to detect gene-gene interactions

PubMed Central

2013-01-01

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

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

PubMed

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

2016-11-01

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

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

PubMed

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

2015-03-27

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

Analysis of charcoal blast furnace slags by laser-induced breakdown spectroscopy

DOE PAGES

Bhatt, Chet R.; Goueguel, Christian L.; Jain, Jinesh C.; ...

2017-09-22

Laser-induced breakdown spectroscopy (LIBS) was used for the analysis of charcoal blast furnace slags. Plasma was generated by an application of a 1064 nm wavelength Nd:YAG laser beam to the surface of pellets created from the slags. The presence of Al, Ca, Fe, K, Mg, Mn, and Si was determined by identifying their characteristic spectral signatures. Multivariate analysis was performed for the quantification of these elements. The predicted LIBS results were found in agreement with the inductively coupled plasma optical emission spectrometry analysis. The limit of detection for Al, Ca, Fe, K, Mg, Mn, and Si was calculated to bemore » 0.10%, 0.22%, 0.02%, 0.01%, 0.01%, 0.005%, and 0.18%, respectively.« less

Analysis of charcoal blast furnace slags by laser-induced breakdown spectroscopy

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

Bhatt, Chet R.; Goueguel, Christian L.; Jain, Jinesh C.

Laser-induced breakdown spectroscopy (LIBS) was used for the analysis of charcoal blast furnace slags. Plasma was generated by an application of a 1064 nm wavelength Nd:YAG laser beam to the surface of pellets created from the slags. The presence of Al, Ca, Fe, K, Mg, Mn, and Si was determined by identifying their characteristic spectral signatures. Multivariate analysis was performed for the quantification of these elements. The predicted LIBS results were found in agreement with the inductively coupled plasma optical emission spectrometry analysis. The limit of detection for Al, Ca, Fe, K, Mg, Mn, and Si was calculated to bemore » 0.10%, 0.22%, 0.02%, 0.01%, 0.01%, 0.005%, and 0.18%, respectively.« less

Prevalence and determinants of cardiovascular disease risk factors among the residents of urban community housing projects in Malaysia.

PubMed

Amiri, Mohammadreza; Majid, Hazreen Abdul; Hairi, FarizahMohd; Thangiah, Nithiah; Bulgiba, Awang; Su, Tin Tin

2014-01-01

The objectives are to assess the prevalence and determinants of cardiovascular disease (CVD) risk factors among the residents of Community Housing Projects in metropolitan Kuala Lumpur, Malaysia. By using simple random sampling, we selected and surveyed 833 households which comprised of 3,722 individuals. Out of the 2,360 adults, 50.5% participated in blood sampling and anthropometric measurement sessions. Uni and bivariate data analysis and multivariate binary logistic regression were applied to identify demographic and socioeconomic determinants of the existence of having at least one CVD risk factor. As a Result, while obesity (54.8%), hypercholesterolemia (51.5%), and hypertension (39.3%) were the most common CVD risk factors among the low-income respondents, smoking (16.3%), diabetes mellitus (7.8%) and alcohol consumption (1.4%) were the least prevalent. Finally, the results from the multivariate binary logistic model illustrated that compared to the Malays, the Indians were 41% less likely to have at least one of the CVD risk factors (OR = 0.59; 95% CI: 0.37 - 0.93). In Conclusion, the low-income individuals were at higher risk of developing CVDs. Prospective policies addressing preventive actions and increased awareness focusing on low-income communities are highly recommended and to consider age, gender, ethnic backgrounds, and occupation classes.

Chemometric methods for the simultaneous determination of some water-soluble vitamins.

PubMed

Mohamed, Abdel-Maaboud I; Mohamed, Horria A; Mohamed, Niveen A; El-Zahery, Marwa R

2011-01-01

Two spectrophotometric methods, derivative and multivariate methods, were applied for the determination of binary, ternary, and quaternary mixtures of the water-soluble vitamins thiamine HCI (I), pyridoxine HCI (II), riboflavin (III), and cyanocobalamin (IV). The first method is divided into first derivative and first derivative of ratio spectra methods, and the second into classical least squares and principal components regression methods. Both methods are based on spectrophotometric measurements of the studied vitamins in 0.1 M HCl solution in the range of 200-500 nm for all components. The linear calibration curves were obtained from 2.5-90 microg/mL, and the correlation coefficients ranged from 0.9991 to 0.9999. These methods were applied for the analysis of the following mixtures: (I) and (II); (I), (II), and (III); (I), (II), and (IV); and (I), (II), (III), and (IV). The described methods were successfully applied for the determination of vitamin combinations in synthetic mixtures and dosage forms from different manufacturers. The recovery ranged from 96.1 +/- 1.2 to 101.2 +/- 1.0% for derivative methods and 97.0 +/- 0.5 to 101.9 +/- 1.3% for multivariate methods. The results of the developed methods were compared with those of reported methods, and gave good accuracy and precision.

Association between Breast Cancer Recurrence and Cellular Dissociation Assessed Using Fine-Needle Aspiration.

PubMed

Koike, Etsuko; Iwaya, Keiichi; Watanabe, Akinori; Miyake, Shinji; Sato, Eiichi; Ishikawa, Takashi

2016-01-01

To determine the associations between breast cancer recurrence and cytological findings of fine-needle aspiration cytology (FNAC). The study included 117 women who had undergone a modified radical mastectomy for invasive ductal carcinoma of the breast. FNAC samples of these patients were reexamined, and cytological findings, such as cellular dissociation, nuclear pleomorphism, nuclear atypia, chromatin pattern, and nuclear size, were scored. Uni- and multivariate analyses were performed to determine the prognostic significance of the cytological findings. Corresponding cancer tissues were immunostained for estrogen receptor, progesterone receptor, human epidermal growth factor 2 (HER2), p53, and E-cadherin to determine their associations with cytological findings. Coexpression of Arp2 and WAVE2 was also examined immunohistochemically as a cell locomotion signal. Cellular dissociation (p = 0.0259) and nuclear size (p = 0.0417) were significantly associated with cancer recurrence. Multivariate analysis showed that cellular dissociation and histological grade were significant independent predictors of cancer recurrence. Cellular dissociation was found to be associated with coexpression of Arp2 and WAVE2 (p = 0.0356) and HER2 (p = 0.0469). The cytological finding of cell dissociation was associated with the activation of Arp2 and WAVE2 signals and was an independent predictor of recurrence. © 2016 S. Karger AG, Basel.

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.

Clinical factors associated with postoperative hydronephrosis after ureteroscopic lithotripsy.

PubMed

Kim, Sun Woo; Ahn, Ji Hoon; Yim, Sang Un; Cho, Yang Hyun; Shin, Bo Sung; Chung, Ho Seok; Hwang, Eu Chang; Yu, Ho Song; Oh, Kyung Jin; Kim, Sun-Ouck; Jung, Seung Il; Kang, Taek Won; Kwon, Dong Deuk; Park, Kwangsung

2016-09-01

This study aimed to determine the predictors of ipsilateral hydronephrosis after ureteroscopic lithotripsy for ureteral calculi. From January 2010 to December 2014, a total of 204 patients with ureteral calculi who underwent ureteroscopic lithotripsy were reviewed. Patients with lack of clinical data, presence of ureteral rupture, and who underwent simultaneous percutaneous nephrolithotomy (PNL) were excluded. Postoperative hydronephrosis was determined via computed tomographic scan or renal ultrasonography, at 6 months after ureteroscopic lithotripsy. Multivariable analysis was performed to determine clinical factors associated with ipsilateral hydronephrosis. A total of 137 patients were enrolled in this study. The mean age of the patients was 58.8±14.2 years and the mean stone size was 10.0±4.6 mm. The stone-free rate was 85.4%. Overall, 44 of the 137 patients (32.1%) had postoperative hydronephrosis. Significant differences between the hydronephrosis and nonhydronephrosis groups were noted in terms of stone location, preoperative hydronephrosis, impacted stone, operation time, and ureteral stent duration (all, p<0.05). On multivariable analysis, increasing preoperative diameter of the hydronephrotic kidney (adjusted odds ratio [OR], 1.21; 95% confidence interval [CI], 1.12-1.31; p=0.001) and impacted stone (adjusted OR, 3.01; 95% CI, 1.15-7.61; p=0.031) independently predicted the occurrence of postoperative hydronpehrosis. Large preoperative diameter of the hydronephrotic kidney and presence of impacted stones were associated with hydronephrosis after ureteroscopic stone removal. Therefore, patients with these predictive factors undergo more intensive imaging follow-up in order to prevent renal deterioration due to postoperative hydronephrosis.

Clinical factors associated with postoperative hydronephrosis after ureteroscopic lithotripsy

PubMed Central

Kim, Sun Woo; Ahn, Ji Hoon; Yim, Sang Un; Cho, Yang Hyun; Shin, Bo Sung; Chung, Ho Seok; Yu, Ho Song; Oh, Kyung Jin; Kim, Sun-Ouck; Jung, Seung Il; Kang, Taek Won; Kwon, Dong Deuk; Park, Kwangsung

2016-01-01

Purpose This study aimed to determine the predictors of ipsilateral hydronephrosis after ureteroscopic lithotripsy for ureteral calculi. Materials and Methods From January 2010 to December 2014, a total of 204 patients with ureteral calculi who underwent ureteroscopic lithotripsy were reviewed. Patients with lack of clinical data, presence of ureteral rupture, and who underwent simultaneous percutaneous nephrolithotomy (PNL) were excluded. Postoperative hydronephrosis was determined via computed tomographic scan or renal ultrasonography, at 6 months after ureteroscopic lithotripsy. Multivariable analysis was performed to determine clinical factors associated with ipsilateral hydronephrosis. Results A total of 137 patients were enrolled in this study. The mean age of the patients was 58.8±14.2 years and the mean stone size was 10.0±4.6 mm. The stone-free rate was 85.4%. Overall, 44 of the 137 patients (32.1%) had postoperative hydronephrosis. Significant differences between the hydronephrosis and nonhydronephrosis groups were noted in terms of stone location, preoperative hydronephrosis, impacted stone, operation time, and ureteral stent duration (all, p<0.05). On multivariable analysis, increasing preoperative diameter of the hydronephrotic kidney (adjusted odds ratio [OR], 1.21; 95% confidence interval [CI], 1.12–1.31; p=0.001) and impacted stone (adjusted OR, 3.01; 95% CI, 1.15–7.61; p=0.031) independently predicted the occurrence of postoperative hydronpehrosis. Conclusions Large preoperative diameter of the hydronephrotic kidney and presence of impacted stones were associated with hydronephrosis after ureteroscopic stone removal. Therefore, patients with these predictive factors undergo more intensive imaging follow-up in order to prevent renal deterioration due to postoperative hydronephrosis. PMID:27617316

Multicomponent kinetic spectrophotometric determination of pefloxacin and norfloxacin in pharmaceutical preparations and human plasma samples with the aid of chemometrics

NASA Astrophysics Data System (ADS)

Ni, Yongnian; Wang, Yong; Kokot, Serge

2008-10-01

A spectrophotometric method for the simultaneous determination of the important pharmaceuticals, pefloxacin and its structurally similar metabolite, norfloxacin, is described for the first time. The analysis is based on the monitoring of a kinetic spectrophotometric reaction of the two analytes with potassium permanganate as the oxidant. The measurement of the reaction process followed the absorbance decrease of potassium permanganate at 526 nm, and the accompanying increase of the product, potassium manganate, at 608 nm. It was essential to use multivariate calibrations to overcome severe spectral overlaps and similarities in reaction kinetics. Calibration curves for the individual analytes showed linear relationships over the concentration ranges of 1.0-11.5 mg L -1 at 526 and 608 nm for pefloxacin, and 0.15-1.8 mg L -1 at 526 and 608 nm for norfloxacin. Various multivariate calibration models were applied, at the two analytical wavelengths, for the simultaneous prediction of the two analytes including classical least squares (CLS), principal component regression (PCR), partial least squares (PLS), radial basis function-artificial neural network (RBF-ANN) and principal component-radial basis function-artificial neural network (PC-RBF-ANN). PLS and PC-RBF-ANN calibrations with the data collected at 526 nm, were the preferred methods—%RPE T ˜ 5, and LODs for pefloxacin and norfloxacin of 0.36 and 0.06 mg L -1, respectively. Then, the proposed method was applied successfully for the simultaneous determination of pefloxacin and norfloxacin present in pharmaceutical and human plasma samples. The results compared well with those from the alternative analysis by HPLC.

Leptospira Exposure and Waste Pickers: A Case-Control Seroprevalence Study in Durango, Mexico.

PubMed

Alvarado-Esquivel, Cosme; Hernandez-Tinoco, Jesus; Sanchez-Anguiano, Luis Francisco; Ramos-Nevarez, Agar; Cerrillo-Soto, Sandra Margarita; Guido-Arreola, Carlos Alberto

2015-08-01

Infection with Leptospira may occur by contact with Leptospira-infected animals. Waste pickers are in contact with rodents and dogs while picking in the garbage. Whether waste pickers are at risk for Leptospira infection is largely unknown. This study was aimed to determine the association of Leptospira IgG seroprevalence with the occupation of waste picking, and to determine the epidemiological characteristics of the waste pickers with Leptospira exposure. Through a case-control study, we determined the seroprevalence of anti-Leptospira IgG antibodies in 90 waste pickers and 90 age- and gender-matched control subjects in Durango City, Mexico using an enzyme immunoassay. Data were analyzed by bivariate and multivariate analyses. The prevalence of anti-Leptospira IgG antibodies was similar in waste pickers (4/90: 4.4%) to that in control subjects (5/90: 5.6%) (P = 1.00). Bivariate analysis showed that Leptospira exposure in waste pickers was associated with increasing age (P = 0.009), no education (P = 0.008), and consumption of rat meat (P = 0.04). However, these associations were no longer found by multivariate analysis. Leptospira exposure in waste pickers was not associated with health status, duration in the activity, wearing hand gloves and facemasks, history of injuries with sharp material of the garbage, or contact with animals or soil. This is the first study about Leptospira exposure in waste pickers. Results suggest that waste pickers are not at increasing risk for Leptospira exposure in Durango City, Mexico. Further research with a larger sample size to elucidate the association of Leptospira exposure with waste picking activity is needed.

Cardiovascular Risk Reduction is Important for Improving Patient and Graft Survival After Ligation and Bypass Surgery for Popliteal Artery Aneurysm.

PubMed

Dattani, N; Ali, M; Aber, A; Kannan, R Yap; Choke, E C; Bown, M J; Sayers, R D; Davies, R S

2017-07-01

To report outcomes following ligation and bypass (LGB) surgery for popliteal artery aneurysm (PAA) and study factors influencing patient and graft survival. A retrospective review of patients undergoing LGB surgery for PAA between September 1999 and August 2012 at a tertiary referral vascular unit was performed. Primary graft patency (PGP), primary-assisted graft patency (PAGP), and secondary graft patency (SGP) rates were calculated using survival analyses. Patient, graft aneurysm-free survival (GAFS), aneurysm reperfusion-free survival (ARFS), and amputation-free survival (AFS) rates were also calculated. Log-rank testing and Cox proportional hazards modeling were used to perform univariate and multivariate analysis of influencing factors, respectively. Eighty-four LGB repairs in 69 patients (mean age 71.3 years, 68 males) were available for study. The 5-year PGP, PAGP, SGP, and patient survival rates were 58.1%, 84.4%, 85.2%, and 81.1%, respectively. On multivariate analysis, the principal determinants of PGP were urgency of operation ( P = .009) and smoking status ( P = .019). The principal determinants of PAGP were hyperlipidemia status ( P = .048) and of SGP were hyperlipidemia ( P = .042) and cerebrovascular disease (CVD) status ( P = .045). The principal determinants of patient survival were previous myocardial infarction ( P = .004) and CVD ( P = .001). The 5-year GAFS, ARFS, and AFS rates were 87.9%, 91.6%, and 96.1%, respectively. This study has shown that traditional cardiovascular risk factors, such as a smoking and ischemic heart disease, are the most important predictors of early graft failure and patient death following LGB surgery for PAA.

Determination of Rare Earth Elements in Geological Samples Using Laser-Induced Breakdown Spectroscopy (LIBS).

PubMed

Bhatt, Chet R; Jain, Jinesh C; Goueguel, Christian L; McIntyre, Dustin L; Singh, Jagdish P

2018-01-01

Laser-induced breakdown spectroscopy (LIBS) was used to detect rare earth elements (REEs) in natural geological samples. Low and high intensity emission lines of Ce, La, Nd, Y, Pr, Sm, Eu, Gd, and Dy were identified in the spectra recorded from the samples to claim the presence of these REEs. Multivariate analysis was executed by developing partial least squares regression (PLS-R) models for the quantification of Ce, La, and Nd. Analysis of unknown samples indicated that the prediction results of these samples were found comparable to those obtained by inductively coupled plasma mass spectrometry analysis. Data support that LIBS has potential to quantify REEs in geological minerals/ores.

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

An investigation of gender and grade-level differences in middle school students' attitudes about science, in science process skills ability, and in parental expectations of their children's science performance

NASA Astrophysics Data System (ADS)

White, Terri Renee'

The primary purpose of the study was to examine different variables (i.e. science process skill ability, science attitudes, and parents' levels of expectation for their children in science, which may impinge on science education differently for males and females in grades five, seven, and nine. The research question addressed by the study was: What are the differences between science process skill ability, science attitudes, and parents' levels of expectation in science on the academic success of fifth, seventh, and ninth graders in science and do effects differ according to gender and grade level? The subjects included fifth, seven, and ninth grade students ( n = 543) and their parents (n = 474) from six rural, public elementary schools and two rural, public middle schools in Southern Mississippi. A two-way (grade x gender) multivariate analysis of variance (MANOVA) was used to determine the differences in science process skill abilities of females and males in grade five, seven, and nine. An additional separate two-way multivariate analysis of variance (grade x gender) was also used to determine the differences in science attitudes of males and females in grade five, seven, and nine. A separate analysis of variance (PPSEX [parent's gender]) with the effects being parents' gender was used to determine differences in parents' levels of expectation for their childrens' performance in science. An additional separate analysis of variance (SSEX [student's gender]) with the effects being the gender of the student was also used to determine differences in parents' levels of expectation for their childrens' performance in science. Results of the analyses indicated significant main effects for grade level (p < .001) and gender (p < .001) on the TIPS II. There was no significant grade by gender interaction on the TIPS II. Results for the TOSRA also indicated a significant main effect for grade (p < .001) and the interaction of grade by sex ( p < .001). On variable ATT 5 (enjoyment of science lessons), males' attitudes toward science decreased across the grade levels; whereas, females decreased from grade five to seven, but showed a significant increase from grade seven to nine. Results from the analysis of variance with the parent's gender as the main effect showed no significant difference. The analysis of variance with student's gender as the main effect showed no significant difference.

Method of determining the optimal dilution ratio for fluorescence fingerprint of food constituents.

PubMed

Trivittayasil, Vipavee; Tsuta, Mizuki; Kokawa, Mito; Yoshimura, Masatoshi; Sugiyama, Junichi; Fujita, Kaori; Shibata, Mario

2015-01-01

Quantitative determination by fluorescence spectroscopy is possible because of the linear relationship between the intensity of emitted fluorescence and the fluorophore concentration. However, concentration quenching may cause the relationship to become nonlinear, and thus, the optimal dilution ratio has to be determined. In the case of fluorescence fingerprint (FF) measurement, fluorescence is measured under multiple wavelength conditions and a method of determining the optimal dilution ratio for multivariate data such as FFs has not been reported. In this study, the FFs of mixed solutions of tryptophan and epicatechin of different concentrations and composition ratios were measured. Principal component analysis was applied, and the resulting loading plots were found to contain useful information about each constituent. The optimal concentration ranges could be determined by identifying the linear region of the PC score plotted against total concentration.

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

NASA Astrophysics Data System (ADS)

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

2008-05-01

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

SUGGESTIONS FOR OPTIMIZED PLANNING OF MULTIVARIATE MONITORING OF ATMOSPHERIC POLLUTION

EPA Science Inventory

Recent work in factor analysis of multivariate data sets has shown that variables with little signal should not be included in the factor analysis. Work also shows that rotational ambiguity is reduced if sources impacting a receptor have both large and small contributions. Thes...

Multivariate Meta-Analysis Using Individual Participant Data

ERIC Educational Resources Information Center

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

2015-01-01

When combining results across related studies, a multivariate meta-analysis allows the joint synthesis of correlated effect estimates from multiple outcomes. Joint synthesis can improve efficiency over separate univariate syntheses, may reduce selective outcome reporting biases, and enables joint inferences across the outcomes. A common issue is…

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

PubMed Central

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

2013-01-01

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

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

PubMed

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

2013-10-15

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

Effect of noise in principal component analysis with an application to ozone pollution

NASA Astrophysics Data System (ADS)

Tsakiri, Katerina G.

This thesis analyzes the effect of independent noise in principal components of k normally distributed random variables defined by a covariance matrix. We prove that the principal components as well as the canonical variate pairs determined from joint distribution of original sample affected by noise can be essentially different in comparison with those determined from the original sample. However when the differences between the eigenvalues of the original covariance matrix are sufficiently large compared to the level of the noise, the effect of noise in principal components and canonical variate pairs proved to be negligible. The theoretical results are supported by simulation study and examples. Moreover, we compare our results about the eigenvalues and eigenvectors in the two dimensional case with other models examined before. This theory can be applied in any field for the decomposition of the components in multivariate analysis. One application is the detection and prediction of the main atmospheric factor of ozone concentrations on the example of Albany, New York. Using daily ozone, solar radiation, temperature, wind speed and precipitation data, we determine the main atmospheric factor for the explanation and prediction of ozone concentrations. A methodology is described for the decomposition of the time series of ozone and other atmospheric variables into the global term component which describes the long term trend and the seasonal variations, and the synoptic scale component which describes the short term variations. By using the Canonical Correlation Analysis, we show that solar radiation is the only main factor between the atmospheric variables considered here for the explanation and prediction of the global and synoptic scale component of ozone. The global term components are modeled by a linear regression model, while the synoptic scale components by a vector autoregressive model and the Kalman filter. The coefficient of determination, R2, for the prediction of the synoptic scale ozone component was found to be the highest when we consider the synoptic scale component of the time series for solar radiation and temperature. KEY WORDS: multivariate analysis; principal component; canonical variate pairs; eigenvalue; eigenvector; ozone; solar radiation; spectral decomposition; Kalman filter; time series prediction

Analysis of Binary Multivariate Longitudinal Data via 2-Dimensional Orbits: An Application to the Agincourt Health and Socio-Demographic Surveillance System in South Africa

PubMed Central

Visaya, Maria Vivien; Sherwell, David; Sartorius, Benn; Cromieres, Fabien

2015-01-01

We analyse demographic longitudinal survey data of South African (SA) and Mozambican (MOZ) rural households from the Agincourt Health and Socio-Demographic Surveillance System in South Africa. In particular, we determine whether absolute poverty status (APS) is associated with selected household variables pertaining to socio-economic determination, namely household head age, household size, cumulative death, adults to minor ratio, and influx. For comparative purposes, households are classified according to household head nationality (SA or MOZ) and APS (rich or poor). The longitudinal data of each of the four subpopulations (SA rich, SA poor, MOZ rich, and MOZ poor) is a five-dimensional space defined by binary variables (questions), subjects, and time. We use the orbit method to represent binary multivariate longitudinal data (BMLD) of each household as a two-dimensional orbit and to visualise dynamics and behaviour of the population. At each time step, a point (x, y) from the orbit of a household corresponds to the observation of the household, where x is a binary sequence of responses and y is an ordering of variables. The ordering of variables is dynamically rearranged such that clusters and holes associated to least and frequently changing variables in the state space respectively, are exposed. Analysis of orbits reveals information of change at both individual- and population-level, change patterns in the data, capacity of states in the state space, and density of state transitions in the orbits. Analysis of household orbits of the four subpopulations show association between (i) households headed by older adults and rich households, (ii) large household size and poor households, and (iii) households with more minors than adults and poor households. Our results are compared to other methods of BMLD analysis. PMID:25919116

Influence of intravenous opioid dose on postoperative ileus.

PubMed

Barletta, Jeffrey F; Asgeirsson, Theodor; Senagore, Anthony J

2011-07-01

Intravenous opioids represent a major component in the pathophysiology of postoperative ileus (POI). However, the most appropriate measure and threshold to quantify the association between opioid dose (eg, average daily, cumulative, maximum daily) and POI remains unknown. To evaluate the relationship between opioid dose, POI, and length of stay (LOS) and identify the opioid measure that was most strongly associated with POI. Consecutive patients admitted to a community teaching hospital who underwent elective colorectal surgery by any technique with an enhanced-recovery protocol postoperatively were retrospectively identified. Patients were excluded if they received epidural analgesia, developed a major intraabdominal complication or medical complication, or had a prolonged workup prior to surgery. Intravenous opioid doses were quantified and converted to hydromorphone equivalents. Classification and regression tree (CART) analysis was used to determine the dosing threshold for the opioid measure most associated with POI and define high versus low use of opioids. Risk factors for POI and prolonged LOS were determined through multivariate analysis. The incidence of POI in 279 patients was 8.6%. CART analysis identified a maximum daily intravenous hydromorphone dose of 2 mg or more as the opioid measure most associated with POI. Multivariate analysis revealed maximum daily hydromorphone dose of 2 mg or more (p = 0.034), open surgical technique (p = 0.045), and days of intravenous narcotic therapy (p = 0.003) as significant risk factors for POI. Variables associated with increased LOS were POI (p < 0.001), maximum daily hydromorphone dose of 2 mg or more (p < 0.001), and age (p = 0.005); laparoscopy (p < 0.001) was associated with a decreased LOS. Intravenous opioid therapy is significantly associated with POI and prolonged LOS, particularly when the maximum hydromorphone dose per day exceeds 2 mg. Clinicians should consider alternative, nonopioid-based pain management options when this occurs.

Tumor invasiveness defined by IASLC/ATS/ERS classification of ground-glass nodules can be predicted by quantitative CT parameters.

PubMed

Zhou, Qian-Jun; Zheng, Zhi-Chun; Zhu, Yong-Qiao; Lu, Pei-Ji; Huang, Jia; Ye, Jian-Ding; Zhang, Jie; Lu, Shun; Luo, Qing-Quan

2017-05-01

To investigate the potential value of CT parameters to differentiate ground-glass nodules between noninvasive adenocarcinoma and invasive pulmonary adenocarcinoma (IPA) as defined by IASLC/ATS/ERS classification. We retrospectively reviewed 211 patients with pathologically proved stage 0-IA lung adenocarcinoma which appeared as subsolid nodules, from January 2012 to January 2013 including 137 pure ground glass nodules (pGGNs) and 74 part-solid nodules (PSNs). Pathological data was classified under the 2011 IASLC/ATS/ERS classification. Both quantitative and qualitative CT parameters were used to determine the tumor invasiveness between noninvasive adenocarcinomas and IPAs. There were 154 noninvasive adenocarcinomas and 57 IPAs. In pGGNs, CT size and area, one-dimensional mean CT value and bubble lucency were significantly different between noninvasive adenocarcinomas and IPAs on univariate analysis. Multivariate regression and ROC analysis revealed that CT size and one-dimensional mean CT value were predictive of noninvasive adenocarcinomas compared to IPAs. Optimal cutoff value was 13.60 mm (sensitivity, 75.0%; specificity, 99.6%), and -583.60 HU (sensitivity, 68.8%; specificity, 66.9%). In PSNs, there were significant differences in CT size and area, solid component area, solid proportion, one-dimensional mean and maximum CT value, three-dimensional (3D) mean CT value between noninvasive adenocarcinomas and IPAs on univariate analysis. Multivariate and ROC analysis showed that CT size and 3D mean CT value were significantly differentiators. Optimal cutoff value was 19.64 mm (sensitivity, 53.7%; specificity, 93.9%), -571.63 HU (sensitivity, 85.4%; specificity, 75.8%). For pGGNs, CT size and one-dimensional mean CT value are determinants for tumor invasiveness. For PSNs, tumor invasiveness can be predicted by CT size and 3D mean CT value.

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

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

Loveday, D.L.; Craggs, C.

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

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

PubMed

Maione, Camila; Barbosa, Rommel Melgaço

2018-01-24

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

Patterns and determinants of maternity care in Damascus.

PubMed

Bashour, H; Abdulsalam, A; Al-Faisal, W; Cheikha, S

2008-01-01

This descriptive study was designed to describe the patterns and determinants of maternity care among Syrian women living in Damascus. All 39 birth registers in 2 large provinces were used to recruit 500 mothers of healthy newborns. Mothers were interviewed in their homes using a semistructured questionnaire. Multivariate analysis of the determinants of the frequency of use of antenatal care showed the following variables were significant: urban residence and visit to antenatal care in the 1st trimester. The significant variables for an early visit to antenatal care were the woman's level of education; being pregnant with the 1st baby; and number of visits to antenatal care. Being young (age < 20 years) also correlated with early timing of the 1st antenatal visit.

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 ...

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.

Understanding characteristics in multivariate traffic flow time series from complex network structure

NASA Astrophysics Data System (ADS)

Yan, Ying; Zhang, Shen; Tang, Jinjun; Wang, Xiaofei

2017-07-01

Discovering dynamic characteristics in traffic flow is the significant step to design effective traffic managing and controlling strategy for relieving traffic congestion in urban cities. A new method based on complex network theory is proposed to study multivariate traffic flow time series. The data were collected from loop detectors on freeway during a year. In order to construct complex network from original traffic flow, a weighted Froenius norm is adopt to estimate similarity between multivariate time series, and Principal Component Analysis is implemented to determine the weights. We discuss how to select optimal critical threshold for networks at different hour in term of cumulative probability distribution of degree. Furthermore, two statistical properties of networks: normalized network structure entropy and cumulative probability of degree, are utilized to explore hourly variation in traffic flow. The results demonstrate these two statistical quantities express similar pattern to traffic flow parameters with morning and evening peak hours. Accordingly, we detect three traffic states: trough, peak and transitional hours, according to the correlation between two aforementioned properties. The classifying results of states can actually represent hourly fluctuation in traffic flow by analyzing annual average hourly values of traffic volume, occupancy and speed in corresponding hours.

Interpreting support vector machine models for multivariate group wise analysis in neuroimaging

PubMed Central

Gaonkar, Bilwaj; Shinohara, Russell T; Davatzikos, Christos

2015-01-01

Machine learning based classification algorithms like support vector machines (SVMs) have shown great promise for turning a high dimensional neuroimaging data into clinically useful decision criteria. However, tracing imaging based patterns that contribute significantly to classifier decisions remains an open problem. This is an issue of critical importance in imaging studies seeking to determine which anatomical or physiological imaging features contribute to the classifier’s decision, thereby allowing users to critically evaluate the findings of such machine learning methods and to understand disease mechanisms. The majority of published work addresses the question of statistical inference for support vector classification using permutation tests based on SVM weight vectors. Such permutation testing ignores the SVM margin, which is critical in SVM theory. In this work we emphasize the use of a statistic that explicitly accounts for the SVM margin and show that the null distributions associated with this statistic are asymptotically normal. Further, our experiments show that this statistic is a lot less conservative as compared to weight based permutation tests and yet specific enough to tease out multivariate patterns in the data. Thus, we can better understand the multivariate patterns that the SVM uses for neuroimaging based classification. PMID:26210913

Ethnicity Is an Independent Determinant of Age-Specific PSA Level: Findings from a Multiethnic Asian Setting

PubMed Central

Sothilingam, Selvalingam; Malek, Rohan; Sundram, Murali; Hisham Bahadzor, Badrul; Ong, Teng Aik; Ng, Keng Lim; Sivalingam, Sivaprakasam; Razack, Azad Hassan Abdul

2014-01-01

Objectives To study the baseline PSA profile and determine the factors influencing the PSA levels within a multiethnic Asian setting. Materials and Methods We conducted a cross-sectional study of 1054 men with no clinical evidence of prostate cancer, prostate surgery or 5α-reductase inhibitor treatment of known prostate conditions. The serum PSA concentration of each subject was assayed. Potential factors associated with PSA level including age, ethnicity, height, weight, family history of prostate cancer, lower urinary tract voiding symptoms (LUTS), prostate volume and digital rectal examination (DRE) were evaluated using univariable and multivariable analysis. Results There were 38 men (3.6%) found to have a PSA level above 4 ng/ml and 1016 (96.4%) with a healthy PSA (≤4 ng/ml). The median PSA level of Malay, Chinese and Indian men was 1.00 ng/ml, 1.16 ng/ml and 0.83 ng/ml, respectively. Indians had a relatively lower median PSA level and prostate volume than Malays and Chinese, who shared a comparable median PSA value across all 10-years age groups. The PSA density was fairly similar amongst all ethnicities. Further analysis showed that ethnicity, weight and prostate volume were independent factors associated with age specific PSA level in the multivariable analysis (p<0.05). Conclusion These findings support the concept that the baseline PSA level varies between different ethnicities across all age groups. In addition to age and prostate volume, ethnicity may also need to be taken into account when investigating serum PSA concentrations in the multiethnic Asian population. PMID:25111507

Root Cause Analysis of Gastroduodenal Ulceration After Yttrium-90 Radioembolization

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

Lam, Marnix G. E. H.; Banerjee, Subhas; Louie, John D.

IntroductionA root cause analysis was performed on the occurrence of gastroduodenal ulceration after hepatic radioembolization (RE). We aimed to identify the risk factors in the treated population and to determine the specific mechanism of nontarget RE in individual cases. Methods: The records of 247 consecutive patients treated with yttrium-90 RE for primary (n = 90) or metastatic (n = 157) liver cancer using either resin (n = 181) or glass (n = 66) microspheres were reviewed. All patients who developed a biopsy-proven microsphere-induced gastroduodenal ulcer were identified. Univariate and multivariate analyses were performed on baseline parameters and procedural data tomore » determine possible risk factors in the total population. Individual cases were analyzed to ascertain the specific cause, including identification of the culprit vessel(s) leading to extrahepatic deposition of the microspheres. Results: Eight patients (3.2 %) developed a gastroduodenal ulcer. Stasis during injection was the strongest independent risk factor (p = 0.004), followed by distal origin of the gastroduodenal artery (p = 0.004), young age (p = 0.040), and proximal injection of the microspheres (p = 0.043). Prolonged administrations, pain during administration, whole liver treatment, and use of resin microspheres also showed interrelated trends in multivariate analysis. Retrospective review of intraprocedural and postprocedural imaging showed a probable or possible culprit vessel, each a tiny complex collateral vessel, in seven patients. Conclusion: Proximal administrations and those resulting in stasis of flow presented increased risk for gastroduodenal ulceration. Patients who had undergone bevacizumab therapy were at high risk for developing stasis.« less

Isomorphic red blood cells using automated urine flow cytometry is a reliable method in diagnosis of bladder cancer.

PubMed

Muto, Satoru; Sugiura, Syo-Ichiro; Nakajima, Akiko; Horiuchi, Akira; Inoue, Masahiro; Saito, Keisuke; Isotani, Shuji; Yamaguchi, Raizo; Ide, Hisamitsu; Horie, Shigeo

2014-10-01

We aimed to identify patients with a chief complaint of hematuria who could safely avoid unnecessary radiation and instrumentation in the diagnosis of bladder cancer (BC), using automated urine flow cytometry to detect isomorphic red blood cells (RBCs) in urine. We acquired urine samples from 134 patients over the age of 35 years with a chief complaint of hematuria and a positive urine occult blood test or microhematuria. The data were analyzed using the UF-1000i (®) (Sysmex Co., Ltd., Kobe, Japan) automated urine flow cytometer to determine RBC morphology, which was classified as isomorphic or dysmorphic. The patients were divided into two groups (BC versus non-BC) for statistical analysis. Multivariate logistic regression analysis was used to determine the predictive value of flow cytometry versus urine cytology, the bladder tumor antigen test, occult blood in urine test, and microhematuria test. BC was confirmed in 26 of 134 patients (19.4 %). The area under the curve for RBC count using the automated urine flow cytometer was 0.94, representing the highest reference value obtained in this study. Isomorphic RBCs were detected in all patients in the BC group. On multivariate logistic regression analysis, only isomorphic RBC morphology was significantly predictive for BC (p < 0.001). Analytical parameters such as sensitivity, specificity, positive predictive value, and negative predictive value of isomorphic RBCs in urine were 100.0, 91.7, 74.3, and 100.0 %, respectively. Detection of urinary isomorphic RBCs using automated urine flow cytometry is a reliable method in the diagnosis of BC with hematuria.

Risk factors for Staphylococcus aureus postpartum breast abscess.

PubMed

Branch-Elliman, Westyn; Golen, Toni H; Gold, Howard S; Yassa, David S; Baldini, Linda M; Wright, Sharon B

2012-01-01

Staphylococcus aureus (SA) breast abscesses are a complication of the postpartum period. Risk factors for postpartum SA breast abscesses are poorly defined, and literature is conflicting. Whether risk factors for methicillin-resistant SA (MRSA) and methicillin-susceptible SA (MSSA) infections differ is unknown. We describe novel risk factors associated with postpartum breast abscesses and the changing epidemiology of this infection. We conducted a cohort study with a nested case-control study (n = 216) involving all patients with culture-confirmed SA breast abscess among >30 000 deliveries at our academic tertiary care center from 2003 through 2010. Data were collected from hospital databases and through abstraction from medical records. All SA cases were compared with both nested controls and full cohort controls. A subanalysis was completed to determine whether risk factors for MSSA and MRSA breast abscess differ. Univariate analysis was completed using Student's t test, Wilcoxon rank-sum test, and analysis of variance, as appropriate. A multivariable stepwise logistic regression was used to determine final adjusted results for both the case-control and the cohort analyses. Fifty-four cases of culture-confirmed abscess were identified: 30 MRSA and 24 MSSA. Risk factors for postpartum SA breast abscess in multivariable analysis include in-hospital identification of a mother having difficulty breastfeeding (odds ratio, 5.00) and being a mother employed outside the home (odds ratio, 2.74). Risk factors did not differ between patients who developed MRSA and MSSA infections. MRSA is an increasingly important pathogen in postpartum women; risk factors for postpartum SA breast abscess have not changed with the advent of community-associated MRSA.

Barriers to preemptive renal transplantation: a single center questionnaire study.

PubMed

Knight, Richard J; Teeter, Larry D; Graviss, Edward A; Patel, Samir J; DeVos, Jennifer M; Moore, Linda W; Gaber, A Osama

2015-03-01

Preemptive transplantation results in excellent patient and graft survival yet most transplant candidates are referred for transplantation after initiation of dialysis. The goal of this study was to determine barriers to preemptive renal transplantation. A nonvalidated questionnaire was administered to prospective kidney transplant recipients to determine factors that hindered or favored referral for transplantation before the initiation of dialysis. One hundred ninety-seven subjects referred for a primary renal transplant completed the questionnaire. Ninety-one subjects (46%) had been informed of preemptive transplantation before referral, and 80 (41%) were predialysis at the time of evaluation. The median time from diagnosis of renal disease to referral was 60 months (range, 2-444 months). In bivariate analysis, among other factors, knowledge of preemptive transplantation was highly associated (odds ratio=94.69) with referral before initiation of dialysis. Given the strong association between knowledge of preemptive transplantation and predialysis referral, this variable was not included in the multivariate analysis. Using multivariate logistic regression analysis, white recipient race, referral by a transplant nephrologist, recipient employment, and the diagnosis of polycystic kidney disease were significantly associated with presentation to the pretransplant clinic before initiation of dialysis. The principle barrier to renal transplantation referral before dialysis was patient education regarding the option of preemptive transplantation. Factors significantly associated with referral before dialysis were the diagnosis of polycystic kidney disease, white recipient race, referral by a transplant nephrologist, and employed status. Greater effort should be applied to patient education regarding preemptive transplantation early after the diagnosis of end-stage renal disease.

Temporal performance assessment of wastewater treatment plants by using multivariate statistical analysis.

PubMed

Ebrahimi, Milad; Gerber, Erin L; Rockaway, Thomas D

2017-05-15

For most water treatment plants, a significant number of performance data variables are attained on a time series basis. Due to the interconnectedness of the variables, it is often difficult to assess over-arching trends and quantify operational performance. The objective of this study was to establish simple and reliable predictive models to correlate target variables with specific measured parameters. This study presents a multivariate analysis of the physicochemical parameters of municipal wastewater. Fifteen quality and quantity parameters were analyzed using data recorded from 2010 to 2016. To determine the overall quality condition of raw and treated wastewater, a Wastewater Quality Index (WWQI) was developed. The index summarizes a large amount of measured quality parameters into a single water quality term by considering pre-established quality limitation standards. To identify treatment process performance, the interdependencies between the variables were determined by using Principal Component Analysis (PCA). The five extracted components from the 15 variables accounted for 75.25% of total dataset information and adequately represented the organic, nutrient, oxygen demanding, and ion activity loadings of influent and effluent streams. The study also utilized the model to predict quality parameters such as Biological Oxygen Demand (BOD), Total Phosphorus (TP), and WWQI. High accuracies ranging from 71% to 97% were achieved for fitting the models with the training dataset and relative prediction percentage errors less than 9% were achieved for the testing dataset. The presented techniques and procedures in this paper provide an assessment framework for the wastewater treatment monitoring programs. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

PubMed

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

1999-01-01

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

Determinants of survival after liver resection for metastatic colorectal carcinoma.

PubMed

Parau, Angela; Todor, Nicolae; Vlad, Liviu

2015-01-01

Prognostic factors for survival after liver resection for metastatic colorectal cancer identified up to date are quite inconsistent with a great inter-study variability. In this study we aimed to identify predictors of outcome in our patient population. A series of 70 consecutive patients from the oncological hepatobiliary database, who had undergone curative hepatic surgical resection for hepatic metastases of colorectal origin, operated between 2006 and 2011, were identified. At 44.6 months (range 13.7-73), 30 of 70 patients (42.85%) were alive. Patient demographics, primary tumor and liver tumor factors, operative factors, pathologic findings, recurrence patterns, disease-free survival (DFS), overall survival (OS) and cancer-specific survival (CSS) were analyzed. Clinicopathologic variables were tested using univariate and multivariate analyses. The 3-year CSS after first hepatic resection was 54%. Median CSS survival after first hepatic resection was 40.2 months. Median CSS after second hepatic resection was 24.2 months. The 3-year DFS after first hepatic resection was 14%. Median disease free survival after first hepatic resection was 18 months. The 3-year DFS after second hepatic resection was 27% and median DFS after second hepatic resection 12 months. The 30-day mortality and morbidity rate after first hepatic resection was 5.71% and 12.78%, respectively. In univariate analysis CSS was significantly reduced for the following factors: age >53 years, advanced T stage of primary tumor, moderately- poorly differentiated tumor, positive and narrow resection margin, preoperative CEA level >30 ng/ml, DFS <18 months. Perioperative chemotherapy related to metastasectomy showed a trend in improving CSS (p=0.07). Perioperative chemotherapy improved DFS in a statistically significant way (p=0.03). Perioperative chemotherapy and achievement of resection margins beyond 1 mm were the major determinants of both CSS and DFS after first liver resection in multivariate analysis. In our series predictors of outcome in multivariate analysis were resection margins beyond 1mm and perioperative chemotherapy. Studies on larger population and analyses of additional clinicopathologic factors like genetic markers could contribute to development of clinical scoring models to assess the risk of relapse and survival.

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.

What factors increase Dutch child health care professionals' adherence to a national guideline on preventing child abuse and neglect?

PubMed

Konijnendijk, Annemieke A J; Boere-Boonekamp, Magda M; Fleuren, Margot A H; Haasnoot, Maria E; Need, Ariana

2016-03-01

Guidelines to support health care professionals in early detection of, and responses to, suspected Child Abuse and Neglect (CAN) have become increasingly widely available. Yet little is known about professionals' adherence to these guidelines or the determinants that affect their uptake. This study used a cross-sectional design to assess the adherence of Dutch Child Health Care (CHC) professionals to seven key activities described in a national guideline on preventing CAN. This study also examined the presence and strengths of determinants of guideline adherence. Online questionnaires were filled in between May and July 2013 by 164 CHC professionals. Adherence was defined as the extent to which professionals performed each of seven key activities when they suspected CAN. Thirty-three determinants were measured in relation to the guideline, the health professional, the organisational context and the socio-political context. Bivariate and multivariate regression analyses tested associations between determinants and guideline adherence. Most of the responding CHC professionals were aware of the guideline and its content (83.7%). Self-reported rates of full adherence varied between 19.5% and 42.7%. Stronger habit to use the guideline was the only determinant associated with higher adherence rates in the multivariate analysis. Understanding guideline adherence and associated determinants is essential for developing implementation strategies that can stimulate adherence. Although CHC professionals in this sample were aware of the guideline, they did not always adhere to its key recommended activities. To increase adherence, tailored interventions should primarily focus on enhancing habit strength. Copyright © 2015 Elsevier Ltd. All rights reserved.

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.

Determination of fragrance content in perfume by Raman spectroscopy and multivariate calibration

NASA Astrophysics Data System (ADS)

Godinho, Robson B.; Santos, Mauricio C.; Poppi, Ronei J.

2016-03-01

An alternative methodology is herein proposed for determination of fragrance content in perfumes and their classification according to the guidelines established by fine perfume manufacturers. The methodology is based on Raman spectroscopy associated with multivariate calibration, allowing the determination of fragrance content in a fast, nondestructive, and sustainable manner. The results were considered consistent with the conventional method, whose standard error of prediction values was lower than the 1.0%. This result indicates that the proposed technology is a feasible analytical tool for determination of the fragrance content in a hydro-alcoholic solution for use in manufacturing, quality control and regulatory agencies.

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

Cytologic separation of branchial cleft cyst from metastatic cystic squamous cell carcinoma: A multivariate analysis of nineteen cytomorphologic features.

PubMed

Layfield, Lester J; Esebua, Magda; Schmidt, Robert L

2016-07-01

The separation of branchial cleft cysts from metastatic cystic squamous cell carcinomas in adults can be clinically and cytologically challenging. Diagnostic accuracy for separation is reported to be as low as 75% prompting some authors to recommend frozen section evaluation of suspected branchial cleft cysts before resection. We evaluated 19 cytologic features to determine which were useful in this distinction. Thirty-three cases (21 squamous carcinoma and 12 branchial cysts) of histologically confirmed cystic lesions of the lateral neck were graded for the presence or absence of 19 cytologic features by two cytopathologists. The cytologic features were analyzed for agreement between observers and underwent multivariate analysis for correlation with the diagnosis of carcinoma. Interobserver agreement was greatest for increased nuclear/cytoplasmic (N/C) ratio, pyknotic nuclei, and irregular nuclear membranes. Recursive partitioning analysis showed increased N/C ratio, small clusters of cells, and irregular nuclear membranes were the best discriminators. The distinction of branchial cleft cysts from cystic squamous cell carcinoma is cytologically difficult. Both digital image analysis and p16 testing have been suggested as aids in this separation, but analysis of cytologic features remains the main method for diagnosis. In an analysis of 19 cytologic features, we found that high nuclear cytoplasmic ratio, irregular nuclear membranes, and small cell clusters were most helpful in their distinction. Diagn. Cytopathol. 2016;44:561-567. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Investigating conflict in ICUs - Is the clinicians’ perspective enough?

PubMed Central

Schuster, Rachel A.; Hong, Seo Yeon; Arnold, Robert M.; White, Douglas B.

2013-01-01

Objective Most studies have assessed conflict between clinicians and surrogate decision makers in ICUs from only clinicians’ perspectives. It is unknown if surrogates’ perceptions differ from clinicians’. We sought to determine the degree of agreement between physicians and surrogates about conflict, and to identify predictors of physician-surrogate conflict. Design Prospective cohort study. Setting Four ICUs of two hospitals in San Francisco, California. Patients 230 surrogate decision makers and 100 physicians of 175 critically ill patients. Measurements Questionnaires addressing participants’ perceptions of whether there was physician-surrogate conflict, as well as attitudes and preferences about clinician-surrogate communication; kappa scores to quantify physician-surrogate concordance about the presence of conflict; and hierarchical multivariate modeling to determine predictors of conflict. Main Results Either the physician or surrogate identified conflict in 63% of cases. Physicians were less likely to perceive conflict than surrogates (27.8% vs 42.3%; p=0.007). Agreement between physicians and surrogates about conflict was poor (kappa = 0.14). Multivariable analysis with surrogate-assessed conflict as the outcome revealed that higher levels of surrogates’ satisfaction with physicians’ bedside manner were associated with lower odds of conflict (OR: 0.75 per 1 point increase in satisfaction, 95% CI 0.59–0.96). Multivariable analysis with physician-assessed conflict as the outcome revealed that the surrogate having felt discriminated against in the healthcare setting was associated with higher odds of conflict (OR 17.5, 95% CI 1.6–190.1) while surrogates’ satisfaction with physicians’ bedside manner was associated with lower odds of conflict (0–10 scale, OR 0.76 per 1 point increase, 95% CI 0.58–0.99). Conclusions Conflict between physicians and surrogates is common in ICUs. There is little agreement between physicians and surrogates about whether physician-surrogate conflict has occurred. Further work is needed to develop reliable and valid methods to assess conflict. In the interim, future studies should assess conflict from the perspective of both clinicians and surrogates. PMID:24434440

Dissolution testing of isoniazid, rifampicin, pyrazinamide and ethambutol tablets using near-infrared spectroscopy (NIRS) and multivariate calibration.

PubMed

de Oliveira Neves, Ana Carolina; Soares, Gustavo Mesquita; de Morais, Stéphanie Cavalcante; da Costa, Fernanda Saadna Lopes; Porto, Dayanne Lopes; de Lima, Kássio Michell Gomes

2012-01-05

This work utilized the near-infrared spectroscopy (NIRS) and multivariate calibration to measure the percentage drug dissolution of four active pharmaceutical ingredients (APIs) (isoniazid, rifampicin, pyrazinamide and ethambutol) in finished pharmaceutical products produced in the Federal University of Rio Grande do Norte (Brazil). The conventional analytical method employed in quality control tests of the dissolution by the pharmaceutical industry is high-performance liquid chromatography (HPLC). The NIRS is a reliable method that offers important advantages for the large-scale production of tablets and for non-destructive analysis. NIR spectra of 38 samples (in triplicate) were measured using a Bomen FT-NIR 160 MB in the range 1100-2500nm. Each spectrum was the average of 50 scans obtained in the diffuse reflectance mode. The dissolution test, which was initially carried out in 900mL of 0.1N hydrochloric acid at 37±0.5°C, was used to determine the percentage a drug that dissolved from each tablet measured at the same time interval (45min) at pH 6.8. The measurement of the four API was performed by HPLC (Shimadzu, Japan) in the gradiente mode. The influence of various spectral pretreatments (Savitzky-Golay smoothing, Multiplicative Scatter Correction (MSC), and Savitzky-Golay derivatives) and multivariate analysis using the partial least squares (PLS) regression algorithm was calculated by the Unscrambler 9.8 (Camo) software. The correlation coefficient (R(2)) for the HPLC determination versus predicted values (NIRS) ranged from 0.88 to 0.98. The root-mean-square error of prediction (RMSEP) obtained from PLS models were 9.99%, 8.63%, 8.57% and 9.97% for isoniazid, rifampicin, ethambutol and pyrazinamide, respectively, indicating that the NIR method is an effective and non-destructive tool for measurement of drug dissolution from tablets. Crown Copyright © 2011. Published by Elsevier B.V. All rights reserved.

Species classification and bioactive ingredients accumulation of BaiJiangCao based on characteristic inorganic elements analysis by inductively coupled plasma-mass spectrometry and multivariate analysis

PubMed Central

Wen-Lan, Li; Xue, Zhang; Xin-Xin, Yang; Shuai, Wang; Lin, Zhao; Huan-Jun, Zhao; Yong-Rui, Bao; Chen-Feng, Ji; Ning, Chen; Zheng, Xiang

2015-01-01

Background: Patrinia scabiosaefolia Fisch and Patrinia villosa (Thunb.) Juss., two species herbs with the same Chinese name “BaiJiangCao”, are important ancient herbal medicines widely used for more than 2000 years. The clinical application of two species herb is confused due to the difficult identification. Objective: The objective was to authenticate the species of BaiJiangCao and analyze the accumulation of bioactive ingredients based on characteristic inorganic elements analysis. Materials and Methods: Content of 32 inorganic elements in BaiJiangCao from different habitats were determined by inductively coupled plasma-mass spectrometry (ICP-MS), and the characteristic inorganic elements were picked to distinguish the species of the herb by principal component analysis and cluster analysis. Contents of two bioactive ingredients, luteoloside, and oleanolic acid, in the samples, were also analyzed by high-performance liquid chromatography method. Relationship between accumulation of bioactive ingredients and content of macroelements in BaiJiangCao was established by statistics. Results: A 4 macroelements (Na, Mg, K, Fe) in 32 determined inorganic elements were picked for characteristic inorganic elements. Content of Na, Mg, K and Fe showed positive correlations with that of luteoloside, content of Na, Mg showed positive correlations with that of oleanolic acid, but content of K and Fe showed negative correlations with that of oleanolic acid. Conclusion: It is for the first time to utilize the characteristic inorganic elements as an index to classify the herb species by the method of ICP-MS and multivariate analysis. And it is also the first report to investigate the influence of inorganic elements in herb on the accumulation of bioactive components which could affect the pharmacological efficacy of the herb medicine. And this method could also be utilized in research of corresponding aspects. PMID:26600721

Survival analysis of platinum-refractory patients with advanced esophageal cancer treated with docetaxel or best supportive care alone: a retrospective study.

PubMed

Moriwaki, T; Kajiwara, T; Matsumoto, T; Suzuki, H; Hiroshima, Y; Matsuda, K; Hirai, S; Yamamoto, Y; Yamada, T; Sugaya, A; Kobayashi, M; Endo, S; Ishige, K; Nishina, T; Hyodo, I

2014-01-01

The survival benefit of second-line chemotherapy with docetaxel in platinum-refractory patients with advanced esophageal cancer (AEC) remains unclear. A retrospective analysis of AEC patients with Eastern Cooperative Oncology Group performance status (PS)≤2 was performed, and major organ functions were preserved, who determined to receive docetaxel or best supportive care (BSC) alone after failure of platinum-based chemotherapy. The post-progression survival (PPS), defined as survival time after disease progression following platinum-based chemotherapy, was analyzed by multivariate Cox regression analysis using factors identified as significant in univariate analysis of various 20 characteristics (age, sex, PS, primary tumor location, etc) including Glasgow prognostic score (GPS), which is a well-known prognostic factor in many malignant tumors. Sixty-six and 45 patients were determined to receive docetaxel and BSC between January 2007 and December 2011, respectively. The median PPS was 5.4 months (95% confidence interval [CI] 4.8-6.0) in the docetaxel group and 3.3 months (95% CI 2.5-4.0) in the BSC group (hazard ratio [HR] 0.56, 95% CI 0.38-0.84, P=0.005). Univariate analysis revealed six significant factors: treatment, PS, GPS, number of metastatic organs, liver metastasis, and bone metastasis. Multivariate analysis including these significant factors revealed three independent prognostic factors: docetaxel treatment (HR 0.62, 95% CI 0.39-0.99, P=0.043), better GPS (HR 0.61, 95% CI 0.46-0.81, P=0.001), and no bone metastasis (HR 0.31, 95% CI 0.15-0.68, P=0.003). There was a trend for PPS in favor of the docetaxel group compared with patients who refused docetaxel treatment in the BSC group (adjusted HR 0.61, 95% CI 0.29-1.29, P=0.20). Docetaxel treatment may have prolonged survival in platinum-refractory patients with AEC. © 2014 International Society for Diseases of the Esophagus.

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

Wong, Jonathan; Xu, Beibei; Moores Cancer Center, University of California San Diego, La Jolla, California

Purpose/Objective: Palliative radiation therapy represents an important treatment option among patients with advanced cancer, although research shows decreased use among older patients. This study evaluated age-related patterns of palliative radiation use among an elderly Medicare population. Methods and Materials: We identified 63,221 patients with metastatic lung, breast, prostate, or colorectal cancer diagnosed between 2000 and 2007 from the Surveillance, Epidemiology, and End Results (SEER)-Medicare linked database. Receipt of palliative radiation therapy was extracted from Medicare claims. Multivariate Poisson regression analysis determined residual age-related disparity in the receipt of palliative radiation therapy after controlling for confounding covariates including age-related differences inmore » patient and demographic covariates, length of life, and patient preferences for aggressive cancer therapy. Results: The use of radiation decreased steadily with increasing patient age. Forty-two percent of patients aged 66 to 69 received palliative radiation therapy. Rates of palliative radiation decreased to 38%, 32%, 24%, and 14% among patients aged 70 to 74, 75 to 79, 80 to 84, and over 85, respectively. Multivariate analysis found that confounding covariates attenuated these findings, although the decreased relative rate of palliative radiation therapy among the elderly remained clinically and statistically significant. On multivariate analysis, compared to patients 66 to 69 years old, those aged 70 to 74, 75 to 79, 80 to 84, and over 85 had a 7%, 15%, 25%, and 44% decreased rate of receiving palliative radiation, respectively (all P<.0001). Conclusions: Age disparity with palliative radiation therapy exists among older cancer patients. Further research should strive to identify barriers to palliative radiation among the elderly, and extra effort should be made to give older patients the opportunity to receive this quality of life-enhancing treatment at the end of life.« less

Predictors of persistent pain after total knee arthroplasty: a systematic review and meta-analysis.

PubMed

Lewis, G N; Rice, D A; McNair, P J; Kluger, M

2015-04-01

Several studies have identified clinical, psychosocial, patient characteristic, and perioperative variables that are associated with persistent postsurgical pain; however, the relative effect of these variables has yet to be quantified. The aim of the study was to provide a systematic review and meta-analysis of predictor variables associated with persistent pain after total knee arthroplasty (TKA). Included studies were required to measure predictor variables prior to or at the time of surgery, include a pain outcome measure at least 3 months post-TKA, and include a statistical analysis of the effect of the predictor variable(s) on the outcome measure. Counts were undertaken of the number of times each predictor was analysed and the number of times it was found to have a significant relationship with persistent pain. Separate meta-analyses were performed to determine the effect size of each predictor on persistent pain. Outcomes from studies implementing uni- and multivariable statistical models were analysed separately. Thirty-two studies involving almost 30 000 patients were included in the review. Preoperative pain was the predictor that most commonly demonstrated a significant relationship with persistent pain across uni- and multivariable analyses. In the meta-analyses of data from univariate models, the largest effect sizes were found for: other pain sites, catastrophizing, and depression. For data from multivariate models, significant effects were evident for: catastrophizing, preoperative pain, mental health, and comorbidities. Catastrophizing, mental health, preoperative knee pain, and pain at other sites are the strongest independent predictors of persistent pain after TKA. © The Author 2014. Published by Oxford University Press on behalf of the British Journal of Anaesthesia. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Early impact of robot-assisted partial nephrectomy on renal function as assessed by renal scintigraphy.

PubMed

Luciani, Lorenzo G; Chiodini, Stefano; Donner, Davide; Cai, Tommaso; Vattovani, Valentino; Tiscione, Daniele; Giusti, Guido; Proietti, Silvia; Chierichetti, Franca; Malossini, Gianni

2016-06-01

To measure the early impact of robot-assisted partial nephrectomy (RAPN) on renal function as assessed by renal scan (Tc 99m-DTPA), addressing the issue of risk factors for ischemic damage to the kidney. All patients undergoing RAPN for cT1 renal masses between June 2013 and May 2014 were included in this prospective study. Renal function as expressed by glomerular filtration rate (GFR) was assessed by Technetium 99m-diethylenetriaminepentaacetic acid (Tc 99m-DTPA) renal scan preoperatively and postoperatively at 1 month in every patient. A multivariable analysis was used for the determination of independent factors predictive of GFR decrease of the operated kidney. Overall, 32 patients underwent RAPN in the time interval. Median tumor size, blood loss, and ischemia time were 4 cm, 200 mL, and 24 min, respectively. Two grade III complications occurred (postoperative bleeding in the renal fossa, urinoma). The GFR of the operated kidney decreased significantly from 51.7 ± 15.1 mL/min per 1.73 m(2) preoperatively to 40, 12 ± 12.4 mL/min per 1.73 m(2) 1 month postoperatively (p = 0.001) with a decrease of 22.4 %. On multivariable analysis, only tumor size (p = 0.05) was a predictor of GFR decrease of the operated kidney. Robotic-assisted partial nephrectomy had a detectable impact on early renal function in a series of relatively large tumors and prevailing intermediate nephrometric risk. A mean decrease of 22 % of GFR as assessed by renal scan in the operated kidney was found at 1 month postoperatively. In multivariable analysis, tumor size only was a significant predictor of renal function loss.

Predictors of unsuccessful outcome in cemented femoral revisions using bone impaction grafting; Cox regression analysis of 208 cases.

PubMed

Te Stroet, Martijn A J; Rijnen, Wim H C; Gardeniers, Jean W M; Schreurs, B Willem; Hannink, Gerjon

2016-09-29

Despite improvements in the technique of femoral impaction bone grafting, reconstruction failures still can occur. Therefore, the aim of our study was to determine risk factors for the endpoint re-revision for any reason. We used prospectively collected demographic, clinical and surgical data of all 202 patients who underwent 208 femoral revisions using the X-change Femoral Revision System (Stryker-Howmedica), fresh-frozen morcellised allograft and a cemented polished Exeter stem in our department from 1991 to 2007. Univariable and multivariable Cox regression analyses were performed to identify potential factors associated with re-revision. The mean follow-up was 10.6 (5-21) years. The cumulative re-revision rate was 6.3% (13/208). After univariable selection, sex, age, body mass index (BMI), American Association of Anesthesiologists (ASA) classification, type of removed femoral component, and mesh used for reconstruction were included in multivariable regression analysis.In the multivariable analysis, BMI was the only factor that was significantly associated with the risk of re-revision after bone impaction grafting (BMI ≥30 vs. BMI <30, HR = 6.54 [95% CI 1.89-22.65]; p = 0.003). BMI was the only factor associated with the risk of re-revision for any reason. Besides BMI also other factors, such as Endoklinik score and the type of removed femoral component, can provide guidance in the process of preclinical decision making. With the knowledge obtained from this study, preoperative patient selection, informed consent, and treatment protocols can be better adjusted to the individual patient who needs to undergo a femoral revision with impaction bone grafting.

Fast-NPS-A Markov Chain Monte Carlo-based analysis tool to obtain structural information from single-molecule FRET measurements

NASA Astrophysics Data System (ADS)

Eilert, Tobias; Beckers, Maximilian; Drechsler, Florian; Michaelis, Jens

2017-10-01

The analysis tool and software package Fast-NPS can be used to analyse smFRET data to obtain quantitative structural information about macromolecules in their natural environment. In the algorithm a Bayesian model gives rise to a multivariate probability distribution describing the uncertainty of the structure determination. Since Fast-NPS aims to be an easy-to-use general-purpose analysis tool for a large variety of smFRET networks, we established an MCMC based sampling engine that approximates the target distribution and requires no parameter specification by the user at all. For an efficient local exploration we automatically adapt the multivariate proposal kernel according to the shape of the target distribution. In order to handle multimodality, the sampler is equipped with a parallel tempering scheme that is fully adaptive with respect to temperature spacing and number of chains. Since the molecular surrounding of a dye molecule affects its spatial mobility and thus the smFRET efficiency, we introduce dye models which can be selected for every dye molecule individually. These models allow the user to represent the smFRET network in great detail leading to an increased localisation precision. Finally, a tool to validate the chosen model combination is provided. Programme Files doi:http://dx.doi.org/10.17632/7ztzj63r68.1 Licencing provisions: Apache-2.0 Programming language: GUI in MATLAB (The MathWorks) and the core sampling engine in C++ Nature of problem: Sampling of highly diverse multivariate probability distributions in order to solve for macromolecular structures from smFRET data. Solution method: MCMC algorithm with fully adaptive proposal kernel and parallel tempering scheme.

Effect of passive smoking on growth and infection rates of breast-fed and non-breast-fed infants.

PubMed

Yilmaz, Gonca; Hizli, Samil; Karacan, Candemir; Yurdakök, Kadriye; Coşkun, Turgay; Dilmen, Uğur

2009-06-01

The aim of the present study was to determine the effect of passive tobacco smoking on growth and infection rate of infants, and to evaluate whether breast-feeding might be protective against harmful effects of cigarette smoke. A cross-sectional study on 254 6-7-month-old infants was carried out. A questionnaire was given to mothers; and infants' head circumference, bodyweight, height, and urinary cotinine levels were measured. Multivariate analysis of factors influencing lower respiratory tract infections showed that smoking mothers increased the rate by 9.1-fold; breast-feeding decreased it by 3.3-fold; formula feeding at birth increased it by a factor of 15.2; another smoker at home increased it by a factor of 40.1. Multivariate analysis of factors influencing upper respiratory tract infections showed that smoking mothers increased the rate by a factor of 23; early formula feeding increased it by a factor of 62; breast-feeding decreased it by a factor of 5; smoking fathers increased it by a factor of 15. Multivariate analysis of factors influencing otitis media found that smoking mothers and fathers increased it by a factor of 9.4 and 6.15, respectively, and breast-feeding decreased it by a factor of 5.4. Tobacco smoke exposure of infants has negative consequences on growth, otitis media, and upper and lower respiratory tract infections. Breast-feeding promoted the growth of infants who were passively exposed to tobacco smoke and protected them against infections. Smoking should not be permitted in households with infants. When this is impossible, breast-feeding should be promoted to protect the infants against the health hazards of passive smoking.

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

Association between thoracic aortic disease and inguinal hernia.

PubMed

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

2014-08-21

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

Vitamin D insufficiency and subclinical atherosclerosis in non-diabetic males living with HIV.

PubMed

Portilla, Joaquín; Moreno-Pérez, Oscar; Serna-Candel, Carmen; Escoín, Corina; Alfayate, Rocio; Reus, Sergio; Merino, Esperanza; Boix, Vicente; Giner, Livia; Sánchez-Payá, José; Picó, Antonio

2014-01-01

Vitamin D insufficiency (VDI) has been associated with increased cardiovascular risk in the non-HIV population. This study evaluates the relationship among serum 25-hydroxyvitamin D [25(OH)D] levels, cardiovascular risk factors, adipokines, antiviral therapy (ART) and subclinical atherosclerosis in HIV-infected males. A cross-sectional study in ambulatory care was made in non-diabetic patients living with HIV. VDI was defined as 25(OH)D serum levels <75 nmol/L. Fasting lipids, glucose, inflammatory markers (tumour necrosis factor-α, interleukin-6, high-sensitivity C-reactive protein) and endothelial markers (plasminogen activator inhibitor-1, or PAI-I) were measured. The common carotid artery intima-media thickness (C-IMT) was determined. A multivariate logistic regression analysis was made to identify factors associated with the presence of VDI, while multivariate linear regression analysis was used to identify factors associated with common C-IMT. Eighty-nine patients were included (age 42 ± 8 years), 18.9% were in CDC (US Centers for Disease Control and Prevention) stage C and 75 were on ART. VDI was associated with ART exposure, sedentary lifestyle, higher triglycerides levels and PAI-I. In univariate analysis, VDI was associated with greater common C-IMT. The multivariate linear regression model, adjusted by confounding factors, revealed an independent association between common C-IMT and patient age, time of exposure to protease inhibitors (PIs) and impaired fasting glucose (IFG). In contrast, there were no independent associations between common C-IMT and VDI or inflammatory and endothelial markers. VDI was not independently associated with subclinical atherosclerosis in non-diabetic males living with HIV. Older age, a longer exposure to PIs, and IFG were independent factors associated with common C-IMT in this population.

Nitroglycerin mediated dilation evaluated by ultrasound is associated with sTWEAK in hemodialysis patients.

PubMed

Rusu, Crina Claudia; Ghervan, Liviu; Racasan, Simona; Kacsa, Ina; Moldovan, Diana; Potra, Alina; Bondor, Cosmina; Anton, Florin; Patiu, Ioan Mihai; Caprioara, Mirela Gherman

2016-03-01

The main cause of death in hemodialysis (HD) patients is cardiovascular disease. Ultrasound assessment of the brachial artery dysfunction is easily achievable and can non-invasively detect atherosclerosis in various stages. In HD patients the cardiovascular risk profile is different and the determinants of brachial arterial function can be distinct comparing with general population. The aim of the study is to assess the determinants of arterial brachial function (flow-mediated and nitroglycerin-mediated dilation) evaluated by ultrasound in HD patients and their relation with tumor necrosis factor-like weak inducer of apoptosis (sTWEAK) described as atherosclerotic marker in chronic kidney disease patients. We conducted a cross-sectional observational study on 54 hemodialysis patients. We recorded clinical and biological data and we measured sTWEAK serum levels by ELISA. We evaluated the arterial brachial function by measurement of flow-mediated and nitroglycerin-mediated dilation, using B mode ultrasound. The determinants of flow-mediated dilation were: Kt/V (r=0.47, p<0.001), LDL-cholesterol (r=0.29, p=0.04), and total cholesterol (r=0.31, p=0.02). Flow-mediated dilation correlated with nitroglycerin-mediated dilation (r=0.70, p<0.001). In multivariate analysis kt/V was the only significant predictor for flow-mediated dilation (p=0.04). Nitroglycerin-mediated dilation correlates with sTWEAK (r=-0.30, p=0.03), systolic blood pressure (r=-0.28, p=0.04) and pulse pressure (r=-0.31, p=0.02). In multivariate analysis sTWEAK was the only significant predictor for nitroglycerin-mediated dilation (p=0.04). The main determinant of nitroglycerin-mediated dilation was sTWEAK. In addition, decreased nitroglycerin-mediated dilation was associated with higher systolic blood pressure and pulse pressure. The main determinant of FMD was Kt/V. Increased flow-mediated dilation was associated with better dialysis efficiency and high total cholesterol and LDL-cholesterol.

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.

Component analysis and initial validity of the exercise fear avoidance scale.

PubMed

Wingo, Brooks C; Baskin, Monica; Ard, Jamy D; Evans, Retta; Roy, Jane; Vogtle, Laura; Grimley, Diane; Snyder, Scott

2013-01-01

To develop the Exercise Fear Avoidance Scale (EFAS) to measure fear of exercise-induced discomfort. We conducted principal component analysis to determine component structure and Cronbach's alpha to assess internal consistency of the EFAS. Relationships between EFAS scores, BMI, physical activity, and pain were analyzed using multivariate regression. The best fit was a 3-component structure: weight-specific fears, cardiorespiratory fears, and musculoskeletal fears. Cronbach's alpha for the EFAS was α=.86. EFAS scores significantly predicted BMI, physical activity, and PDI scores. Psychometric properties of this scale suggest it may be useful for tailoring exercise prescriptions to address fear of exercise-related discomfort.