A comparison of bivariate and univariate QTL mapping in livestock populations

Directory of Open Access Journals (Sweden)

Sorensen Daniel

2003-11-01

Full Text Available Abstract This study presents a multivariate, variance component-based QTL mapping model implemented via restricted maximum likelihood (REML. The method was applied to investigate bivariate and univariate QTL mapping analyses, using simulated data. Specifically, we report results on the statistical power to detect a QTL and on the precision of parameter estimates using univariate and bivariate approaches. The model and methodology were also applied to study the effectiveness of partitioning the overall genetic correlation between two traits into a component due to many genes of small effect, and one due to the QTL. It is shown that when the QTL has a pleiotropic effect on two traits, a bivariate analysis leads to a higher statistical power of detecting the QTL and to a more precise estimate of the QTL's map position, in particular in the case when the QTL has a small effect on the trait. The increase in power is most marked in cases where the contributions of the QTL and of the polygenic components to the genetic correlation have opposite signs. The bivariate REML analysis can successfully partition the two components contributing to the genetic correlation between traits.

Comparison of spectrum normalization techniques for univariate ...

Indian Academy of Sciences (India)

Laser-induced breakdown spectroscopy; univariate study; normalization models; stainless steel; standard error of prediction. Abstract. Analytical performance of six different spectrum normalization techniques, namelyinternal normalization, normalization with total light, normalization with background along with their ...

Univaried models in the series of temperature of the air

International Nuclear Information System (INIS)

Leon Aristizabal Gloria esperanza

2000-01-01

The theoretical framework for the study of the air's temperature time series is the theory of stochastic processes, particularly those known as ARIMA, that make it possible to carry out a univaried analysis. ARIMA models are built in order to explain the structure of the monthly temperatures corresponding to the mean, the absolute maximum, absolute minimum, maximum mean and minimum mean temperatures, for four stations in Colombia. By means of those models, the possible evolution of the latter variables is estimated with predictive aims in mind. The application and utility of the models is discussed

Comparison between the univariate and multivariate analysis on the partial characterization of the endoglucanase produced in the solid state fermentation by Aspergillus oryzae ATCC 10124.

Science.gov (United States)

de Brito, Aila Riany; Santos Reis, Nadabe Dos; Silva, Tatielle Pereira; Ferreira Bonomo, Renata Cristina; Trovatti Uetanabaro, Ana Paula; de Assis, Sandra Aparecida; da Silva, Erik Galvão Paranhos; Aguiar-Oliveira, Elizama; Oliveira, Julieta Rangel; Franco, Marcelo

2017-11-26

Endoglucanase production by Aspergillus oryzae ATCC 10124 cultivated in rice husks or peanut shells was optimized by experimental design as a function of humidity, time, and temperature. The optimum temperature for the endoglucanase activity was estimated by a univariate analysis (one factor at the time) as 50°C (rice husks) and 60°C (peanut shells), however, by a multivariate analysis (synergism of factors), it was determined a different temperature (56°C) for endoglucanase from peanut shells. For the optimum pH, values determined by univariate and multivariate analysis were 5 and 5.2 (rice husk) and 5 and 7.6 (peanut shells). In addition, the best half-lives were observed at 50°C as 22.8 hr (rice husks) and 7.3 hr (peanut shells), also, 80% of residual activities was obtained between 30 and 50°C for both substrates, and the pH stability was improved at 5-7 (rice hulls) and 6-9 (peanut shells). Both endoglucanases obtained presented different characteristics as a result of the versatility of fungi in different substrates.

Trend and forecasting rate of cancer deaths at a public university hospital using univariate modeling

Science.gov (United States)

Ismail, A.; Hassan, Noor I.

2013-09-01

Cancer is one of the principal causes of death in Malaysia. This study was performed to determine the pattern of rate of cancer deaths at a public hospital in Malaysia over an 11 year period from year 2001 to 2011, to determine the best fitted model of forecasting the rate of cancer deaths using Univariate Modeling and to forecast the rates for the next two years (2012 to 2013). The medical records of the death of patients with cancer admitted at this Hospital over 11 year's period were reviewed, with a total of 663 cases. The cancers were classified according to 10th Revision International Classification of Diseases (ICD-10). Data collected include socio-demographic background of patients such as registration number, age, gender, ethnicity, ward and diagnosis. Data entry and analysis was accomplished using SPSS 19.0 and Minitab 16.0. The five Univariate Models used were Naïve with Trend Model, Average Percent Change Model (ACPM), Single Exponential Smoothing, Double Exponential Smoothing and Holt's Method. The overall 11 years rate of cancer deaths showed that at this hospital, Malay patients have the highest percentage (88.10%) compared to other ethnic groups with males (51.30%) higher than females. Lung and breast cancer have the most number of cancer deaths among gender. About 29.60% of the patients who died due to cancer were aged 61 years old and above. The best Univariate Model used for forecasting the rate of cancer deaths is Single Exponential Smoothing Technique with alpha of 0.10. The forecast for the rate of cancer deaths shows a horizontally or flat value. The forecasted mortality trend remains at 6.84% from January 2012 to December 2013. All the government and private sectors and non-governmental organizations need to highlight issues on cancer especially lung and breast cancers to the public through campaigns using mass media, media electronics, posters and pamphlets in the attempt to decrease the rate of cancer deaths in Malaysia.

A comparison of multivariate and univariate time series approaches to modelling and forecasting emergency department demand in Western Australia.

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Aboagye-Sarfo, Patrick; Mai, Qun; Sanfilippo, Frank M; Preen, David B; Stewart, Louise M; Fatovich, Daniel M

2015-10-01

To develop multivariate vector-ARMA (VARMA) forecast models for predicting emergency department (ED) demand in Western Australia (WA) and compare them to the benchmark univariate autoregressive moving average (ARMA) and Winters' models. Seven-year monthly WA state-wide public hospital ED presentation data from 2006/07 to 2012/13 were modelled. Graphical and VARMA modelling methods were used for descriptive analysis and model fitting. The VARMA models were compared to the benchmark univariate ARMA and Winters' models to determine their accuracy to predict ED demand. The best models were evaluated by using error correction methods for accuracy. Descriptive analysis of all the dependent variables showed an increasing pattern of ED use with seasonal trends over time. The VARMA models provided a more precise and accurate forecast with smaller confidence intervals and better measures of accuracy in predicting ED demand in WA than the ARMA and Winters' method. VARMA models are a reliable forecasting method to predict ED demand for strategic planning and resource allocation. While the ARMA models are a closely competing alternative, they under-estimated future ED demand. Copyright © 2015 Elsevier Inc. All rights reserved.

Regression Is a Univariate General Linear Model Subsuming Other Parametric Methods as Special Cases.

Science.gov (United States)

Vidal, Sherry

Although the concept of the general linear model (GLM) has existed since the 1960s, other univariate analyses such as the t-test and the analysis of variance models have remained popular. The GLM produces an equation that minimizes the mean differences of independent variables as they are related to a dependent variable. From a computer printout…

Comparison of different Methods for Univariate Time Series Imputation in R

OpenAIRE

Moritz, Steffen; Sardá, Alexis; Bartz-Beielstein, Thomas; Zaefferer, Martin; Stork, Jörg

2015-01-01

Missing values in datasets are a well-known problem and there are quite a lot of R packages offering imputation functions. But while imputation in general is well covered within R, it is hard to find functions for imputation of univariate time series. The problem is, most standard imputation techniques can not be applied directly. Most algorithms rely on inter-attribute correlations, while univariate time series imputation needs to employ time dependencies. This paper provides an overview of ...

A comparison between univariate probabilistic and multivariate (logistic regression) methods for landslide susceptibility analysis: the example of the Febbraro valley (Northern Alps, Italy)

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Rossi, M.; Apuani, T.; Felletti, F.

2009-04-01

The aim of this paper is to compare the results of two statistical methods for landslide susceptibility analysis: 1) univariate probabilistic method based on landslide susceptibility index, 2) multivariate method (logistic regression). The study area is the Febbraro valley, located in the central Italian Alps, where different types of metamorphic rocks croup out. On the eastern part of the studied basin a quaternary cover represented by colluvial and secondarily, by glacial deposits, is dominant. In this study 110 earth flows, mainly located toward NE portion of the catchment, were analyzed. They involve only the colluvial deposits and their extension mainly ranges from 36 to 3173 m2. Both statistical methods require to establish a spatial database, in which each landslide is described by several parameters that can be assigned using a main scarp central point of landslide. The spatial database is constructed using a Geographical Information System (GIS). Each landslide is described by several parameters corresponding to the value of main scarp central point of the landslide. Based on bibliographic review a total of 15 predisposing factors were utilized. The width of the intervals, in which the maps of the predisposing factors have to be reclassified, has been defined assuming constant intervals to: elevation (100 m), slope (5 °), solar radiation (0.1 MJ/cm2/year), profile curvature (1.2 1/m), tangential curvature (2.2 1/m), drainage density (0.5), lineament density (0.00126). For the other parameters have been used the results of the probability-probability plots analysis and the statistical indexes of landslides site. In particular slope length (0 ÷ 2, 2 ÷ 5, 5 ÷ 10, 10 ÷ 20, 20 ÷ 35, 35 ÷ 260), accumulation flow (0 ÷ 1, 1 ÷ 2, 2 ÷ 5, 5 ÷ 12, 12 ÷ 60, 60 ÷27265), Topographic Wetness Index 0 ÷ 0.74, 0.74 ÷ 1.94, 1.94 ÷ 2.62, 2.62 ÷ 3.48, 3.48 ÷ 6,00, 6.00 ÷ 9.44), Stream Power Index (0 ÷ 0.64, 0.64 ÷ 1.28, 1.28 ÷ 1.81, 1.81 ÷ 4.20, 4.20 ÷ 9

Validated univariate and multivariate spectrophotometric methods for the determination of pharmaceuticals mixture in complex wastewater

Science.gov (United States)

Riad, Safaa M.; Salem, Hesham; Elbalkiny, Heba T.; Khattab, Fatma I.

2015-04-01

Five, accurate, precise, and sensitive univariate and multivariate spectrophotometric methods were developed for the simultaneous determination of a ternary mixture containing Trimethoprim (TMP), Sulphamethoxazole (SMZ) and Oxytetracycline (OTC) in waste water samples collected from different cites either production wastewater or livestock wastewater after their solid phase extraction using OASIS HLB cartridges. In univariate methods OTC was determined at its λmax 355.7 nm (0D), while (TMP) and (SMZ) were determined by three different univariate methods. Method (A) is based on successive spectrophotometric resolution technique (SSRT). The technique starts with the ratio subtraction method followed by ratio difference method for determination of TMP and SMZ. Method (B) is successive derivative ratio technique (SDR). Method (C) is mean centering of the ratio spectra (MCR). The developed multivariate methods are principle component regression (PCR) and partial least squares (PLS). The specificity of the developed methods is investigated by analyzing laboratory prepared mixtures containing different ratios of the three drugs. The obtained results are statistically compared with those obtained by the official methods, showing no significant difference with respect to accuracy and precision at p = 0.05.

Combinatorial bounds on the α-divergence of univariate mixture models

KAUST Repository

Nielsen, Frank; Sun, Ke

2017-01-01

We derive lower- and upper-bounds of α-divergence between univariate mixture models with components in the exponential family. Three pairs of bounds are presented in order with increasing quality and increasing computational cost. They are verified

Nuisance forecasting. Univariate modelling and very-short-term forecasting of winter smog episodes; Immissionsprognose. Univariate Modellierung und Kuerzestfristvorhersage von Wintersmogsituationen

Energy Technology Data Exchange (ETDEWEB)

Schlink, U.

1996-12-31

The work evaluates specifically the nuisance data provided by the measuring station in the centre of Leipig during the period from 1980 to 1993, with the aim to develop an algorithm for making very short-term forecasts of excessive nuisances. Forecasting was to be univariate, i.e., based exclusively on the half-hourly readings of SO{sub 2} concentrations taken in the past. As shown by Fourier analysis, there exist three main and mutually independent spectral regions: the high-frequency sector (period < 12 hours) of unstable irregularities, the seasonal sector with the periods of 24 and 12 hours, and the low-frequency sector (period > 24 hours). After breaking the measuring series up into components, the low-frequency sector is termed trend component, or trend for short. For obtaining the components, a Kalman filter is used. It was found that smog episodes are most adequately described by the trend component. This is therefore more closely investigated. The phase representation then shows characteristic trajectories of the trends. (orig./KW) [Deutsch] In der vorliegende Arbeit wurden speziell die Immissionsdaten der Messstation Leipzig-Mitte des Zeitraumes 1980-1993 mit dem Ziel der Erstellung eines Algorithmus fuer die Kuerzestfristprognose von Ueberschreitungssituationen untersucht. Die Prognosestellung sollte allein anhand der in der Vergangenheit registrierten Halbstundenwerte der SO{sub 2}-Konzentration, also univariat erfolgen. Wie die Fourieranalyse zeigt, gibt es drei wesentliche und voneinander unabhaengige Spektralbereiche: Den hochfrequenten Bereich (Periode <12 Stunden) der instabilen Irregularitaeten, den saisonalen Anteil mit den Perioden von 24 und 12 Stunden und den niedrigfrequenten Bereich (Periode >24 Stunden). Letzterer wird nach einer Zerlegung der Messreihe in Komponenten als Trendkomponente (oder kurz Trend) bezeichnet. Fuer die Komponentenzerlegung wird ein Kalman-Filter verwendet. Es stellt sich heraus, dass Smogepisoden am deutlichsten

Acceleration techniques in the univariate Lipschitz global optimization

Science.gov (United States)

Sergeyev, Yaroslav D.; Kvasov, Dmitri E.; Mukhametzhanov, Marat S.; De Franco, Angela

2016-10-01

Univariate box-constrained Lipschitz global optimization problems are considered in this contribution. Geometric and information statistical approaches are presented. The novel powerful local tuning and local improvement techniques are described in the contribution as well as the traditional ways to estimate the Lipschitz constant. The advantages of the presented local tuning and local improvement techniques are demonstrated using the operational characteristics approach for comparing deterministic global optimization algorithms on the class of 100 widely used test functions.

QRS complex detection based on continuous density hidden Markov models using univariate observations

Science.gov (United States)

Sotelo, S.; Arenas, W.; Altuve, M.

2018-04-01

In the electrocardiogram (ECG), the detection of QRS complexes is a fundamental step in the ECG signal processing chain since it allows the determination of other characteristics waves of the ECG and provides information about heart rate variability. In this work, an automatic QRS complex detector based on continuous density hidden Markov models (HMM) is proposed. HMM were trained using univariate observation sequences taken either from QRS complexes or their derivatives. The detection approach is based on the log-likelihood comparison of the observation sequence with a fixed threshold. A sliding window was used to obtain the observation sequence to be evaluated by the model. The threshold was optimized by receiver operating characteristic curves. Sensitivity (Sen), specificity (Spc) and F1 score were used to evaluate the detection performance. The approach was validated using ECG recordings from the MIT-BIH Arrhythmia database. A 6-fold cross-validation shows that the best detection performance was achieved with 2 states HMM trained with QRS complexes sequences (Sen = 0.668, Spc = 0.360 and F1 = 0.309). We concluded that these univariate sequences provide enough information to characterize the QRS complex dynamics from HMM. Future works are directed to the use of multivariate observations to increase the detection performance.

Effect Sizes for Research Univariate and Multivariate Applications

CERN Document Server

Grissom, Robert J

2011-01-01

Noted for its comprehensive coverage, this greatly expanded new edition now covers the use of univariate and multivariate effect sizes. Many measures and estimators are reviewed along with their application, interpretation, and limitations. Noted for its practical approach, the book features numerous examples using real data for a variety of variables and designs, to help readers apply the material to their own data. Tips on the use of SPSS, SAS, R, and S-Plus are provided. The book's broad disciplinary appeal results from its inclusion of a variety of examples from psychology, medicine, educa

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

Science.gov (United States)

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.

Combinatorial bounds on the α-divergence of univariate mixture models

KAUST Repository

Nielsen, Frank

2017-06-20

We derive lower- and upper-bounds of α-divergence between univariate mixture models with components in the exponential family. Three pairs of bounds are presented in order with increasing quality and increasing computational cost. They are verified empirically through simulated Gaussian mixture models. The presented methodology generalizes to other divergence families relying on Hellinger-type integrals.

Characteristics of genomic signatures derived using univariate methods and mechanistically anchored functional descriptors for predicting drug- and xenobiotic-induced nephrotoxicity.

Science.gov (United States)

Shi, Weiwei; Bugrim, Andrej; Nikolsky, Yuri; Nikolskya, Tatiana; Brennan, Richard J

2008-01-01

ABSTRACT The ideal toxicity biomarker is composed of the properties of prediction (is detected prior to traditional pathological signs of injury), accuracy (high sensitivity and specificity), and mechanistic relationships to the endpoint measured (biological relevance). Gene expression-based toxicity biomarkers ("signatures") have shown good predictive power and accuracy, but are difficult to interpret biologically. We have compared different statistical methods of feature selection with knowledge-based approaches, using GeneGo's database of canonical pathway maps, to generate gene sets for the classification of renal tubule toxicity. The gene set selection algorithms include four univariate analyses: t-statistics, fold-change, B-statistics, and RankProd, and their combination and overlap for the identification of differentially expressed probes. Enrichment analysis following the results of the four univariate analyses, Hotelling T-square test, and, finally out-of-bag selection, a variant of cross-validation, were used to identify canonical pathway maps-sets of genes coordinately involved in key biological processes-with classification power. Differentially expressed genes identified by the different statistical univariate analyses all generated reasonably performing classifiers of tubule toxicity. Maps identified by enrichment analysis or Hotelling T-square had lower classification power, but highlighted perturbed lipid homeostasis as a common discriminator of nephrotoxic treatments. The out-of-bag method yielded the best functionally integrated classifier. The map "ephrins signaling" performed comparably to a classifier derived using sparse linear programming, a machine learning algorithm, and represents a signaling network specifically involved in renal tubule development and integrity. Such functional descriptors of toxicity promise to better integrate predictive toxicogenomics with mechanistic analysis, facilitating the interpretation and risk assessment of

An exercise in model validation: Comparing univariate statistics and Monte Carlo-based multivariate statistics

International Nuclear Information System (INIS)

Weathers, J.B.; Luck, R.; Weathers, J.W.

2009-01-01

The complexity of mathematical models used by practicing engineers is increasing due to the growing availability of sophisticated mathematical modeling tools and ever-improving computational power. For this reason, the need to define a well-structured process for validating these models against experimental results has become a pressing issue in the engineering community. This validation process is partially characterized by the uncertainties associated with the modeling effort as well as the experimental results. The net impact of the uncertainties on the validation effort is assessed through the 'noise level of the validation procedure', which can be defined as an estimate of the 95% confidence uncertainty bounds for the comparison error between actual experimental results and model-based predictions of the same quantities of interest. Although general descriptions associated with the construction of the noise level using multivariate statistics exists in the literature, a detailed procedure outlining how to account for the systematic and random uncertainties is not available. In this paper, the methodology used to derive the covariance matrix associated with the multivariate normal pdf based on random and systematic uncertainties is examined, and a procedure used to estimate this covariance matrix using Monte Carlo analysis is presented. The covariance matrices are then used to construct approximate 95% confidence constant probability contours associated with comparison error results for a practical example. In addition, the example is used to show the drawbacks of using a first-order sensitivity analysis when nonlinear local sensitivity coefficients exist. Finally, the example is used to show the connection between the noise level of the validation exercise calculated using multivariate and univariate statistics.

An exercise in model validation: Comparing univariate statistics and Monte Carlo-based multivariate statistics

Energy Technology Data Exchange (ETDEWEB)

Weathers, J.B. [Shock, Noise, and Vibration Group, Northrop Grumman Shipbuilding, P.O. Box 149, Pascagoula, MS 39568 (United States)], E-mail: James.Weathers@ngc.com; Luck, R. [Department of Mechanical Engineering, Mississippi State University, 210 Carpenter Engineering Building, P.O. Box ME, Mississippi State, MS 39762-5925 (United States)], E-mail: Luck@me.msstate.edu; Weathers, J.W. [Structural Analysis Group, Northrop Grumman Shipbuilding, P.O. Box 149, Pascagoula, MS 39568 (United States)], E-mail: Jeffrey.Weathers@ngc.com

2009-11-15

The complexity of mathematical models used by practicing engineers is increasing due to the growing availability of sophisticated mathematical modeling tools and ever-improving computational power. For this reason, the need to define a well-structured process for validating these models against experimental results has become a pressing issue in the engineering community. This validation process is partially characterized by the uncertainties associated with the modeling effort as well as the experimental results. The net impact of the uncertainties on the validation effort is assessed through the 'noise level of the validation procedure', which can be defined as an estimate of the 95% confidence uncertainty bounds for the comparison error between actual experimental results and model-based predictions of the same quantities of interest. Although general descriptions associated with the construction of the noise level using multivariate statistics exists in the literature, a detailed procedure outlining how to account for the systematic and random uncertainties is not available. In this paper, the methodology used to derive the covariance matrix associated with the multivariate normal pdf based on random and systematic uncertainties is examined, and a procedure used to estimate this covariance matrix using Monte Carlo analysis is presented. The covariance matrices are then used to construct approximate 95% confidence constant probability contours associated with comparison error results for a practical example. In addition, the example is used to show the drawbacks of using a first-order sensitivity analysis when nonlinear local sensitivity coefficients exist. Finally, the example is used to show the connection between the noise level of the validation exercise calculated using multivariate and univariate statistics.

Which DTW Method Applied to Marine Univariate Time Series Imputation

OpenAIRE

Phan , Thi-Thu-Hong; Caillault , Émilie; Lefebvre , Alain; Bigand , André

2017-01-01

International audience; Missing data are ubiquitous in any domains of applied sciences. Processing datasets containing missing values can lead to a loss of efficiency and unreliable results, especially for large missing sub-sequence(s). Therefore, the aim of this paper is to build a framework for filling missing values in univariate time series and to perform a comparison of different similarity metrics used for the imputation task. This allows to suggest the most suitable methods for the imp...

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

Science.gov (United States)

Barimani, Shirin; Kleinebudde, Peter

2017-10-01

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

Men and women show similar survival outcome in stage IV breast cancer.

Science.gov (United States)

Wu, San-Gang; Zhang, Wen-Wen; Liao, Xu-Lin; Sun, Jia-Yuan; Li, Feng-Yan; Su, Jing-Jun; He, Zhen-Yu

2017-08-01

To evaluate the clinicopathological features, patterns of distant metastases, and survival outcome between stage IV male breast cancer (MBC) and female breast cancer (FBC). Patients diagnosed with stage IV MBC and FBC between 2010 and 2013 were included using the Surveillance, Epidemiology, and End Results program. Univariate and multivariate Cox regression analyses were used to analyze risk factors for overall survival (OS). A total of 4997 patients were identified, including 60 MBC and 4937 FBC. Compared with FBC, patients with MBC were associated with a significantly higher rate of estrogen receptor-positive, progesterone receptor-positive, unmarried, lung metastases, and a lower frequency of liver metastases. Univariate and multivariate analyses showed no significant difference in OS between MBC and FBC. In the propensity score-matched population, there was also no difference in survival between MBC and FBC. Multivariate analysis of MBC showed that OS was longer for patients aged 50-69 years and with estrogen receptor-positive disease. There was no significant difference in survival outcome between stage IV MBC and FBC, but significant differences in clinicopathological features and patterns of metastases between the genders. Copyright © 2017 Elsevier Ltd. All rights reserved.

The Use of Univariate and Multivariate Analyses in the Geochemical Exploration, Ravanj Lead Mine, Delijan, Iran

Directory of Open Access Journals (Sweden)

Mostafa Nejadhadad

2017-11-01

Full Text Available A geochemical exploration program was applied to recognize the anomalous geochemical haloes at the Ravanj lead mine, Delijan, Iran. Sampling of unweathered rocks were undertaken across rock exposures on a 10 × 10 meter grid (n = 302 as well as the accessible parts of underground mine A (n = 42. First, the threshold values of all elements were determined using the cut-off values used in the exploratory data analysis (EDA method. Then, for further studies, elements with lognormal distributions (Pb, Zn, Ag, As, Cd, Co, Cu, Sb, S, Sr, Th, Ba, Bi, Fe, Ni and Mn were selected. Robustness against outliers is achieved by application of central log ratio transformation to address the closure problems with compositional data prior to principle components analysis (PCA. Results of these analyses show that, in the Ravanj deposit, Pb mineralization is characterized by a Pb-Ba-Ag-Sb ± Zn ± Cd association. The supra-mineralization haloes are characterized by barite and tetrahedrite in a Ba- Th- Ag- Cu- Sb- As- Sr association and sub-mineralization haloes are comprised of pyrite and tetrahedrite, probably reflecting a Fe-Cu-As-Bi-Ni-Co-Mo-Mn association. Using univariate and multivariate geostatistical analyses (e.g., EDA and robust PCA, four anomalies were detected and mapped in Block A of the Ravanj deposit. Anomalies 1 and 2 are around the ancient orebodies. Anomaly 3 is located in a thin bedded limestone-shale intercalation unit that does not show significant mineralization. Drilling of the fourth anomaly suggested a low grade, non-economic Pb mineralization.

Cellulose I crystallinity determination using FT-Raman spectroscopy : univariate and multivariate methods

Science.gov (United States)

Umesh P. Agarwal; Richard S. Reiner; Sally A. Ralph

2010-01-01

Two new methods based on FT–Raman spectroscopy, one simple, based on band intensity ratio, and the other using a partial least squares (PLS) regression model, are proposed to determine cellulose I crystallinity. In the simple method, crystallinity in cellulose I samples was determined based on univariate regression that was first developed using the Raman band...

Modeling the potential risk factors of bovine viral diarrhea prevalence in Egypt using univariable and multivariable logistic regression analyses

Directory of Open Access Journals (Sweden)

Abdelfattah M. Selim

2018-03-01

Full Text Available Aim: The present cross-sectional study was conducted to determine the seroprevalence and potential risk factors associated with Bovine viral diarrhea virus (BVDV disease in cattle and buffaloes in Egypt, to model the potential risk factors associated with the disease using logistic regression (LR models, and to fit the best predictive model for the current data. Materials and Methods: A total of 740 blood samples were collected within November 2012-March 2013 from animals aged between 6 months and 3 years. The potential risk factors studied were species, age, sex, and herd location. All serum samples were examined with indirect ELIZA test for antibody detection. Data were analyzed with different statistical approaches such as Chi-square test, odds ratios (OR, univariable, and multivariable LR models. Results: Results revealed a non-significant association between being seropositive with BVDV and all risk factors, except for species of animal. Seroprevalence percentages were 40% and 23% for cattle and buffaloes, respectively. OR for all categories were close to one with the highest OR for cattle relative to buffaloes, which was 2.237. Likelihood ratio tests showed a significant drop of the -2LL from univariable LR to multivariable LR models. Conclusion: There was an evidence of high seroprevalence of BVDV among cattle as compared with buffaloes with the possibility of infection in different age groups of animals. In addition, multivariable LR model was proved to provide more information for association and prediction purposes relative to univariable LR models and Chi-square tests if we have more than one predictor.

Forecasting electric vehicles sales with univariate and multivariate time series models: The case of China.

Science.gov (United States)

Zhang, Yong; Zhong, Miner; Geng, Nana; Jiang, Yunjian

2017-01-01

The market demand for electric vehicles (EVs) has increased in recent years. Suitable models are necessary to understand and forecast EV sales. This study presents a singular spectrum analysis (SSA) as a univariate time-series model and vector autoregressive model (VAR) as a multivariate model. Empirical results suggest that SSA satisfactorily indicates the evolving trend and provides reasonable results. The VAR model, which comprised exogenous parameters related to the market on a monthly basis, can significantly improve the prediction accuracy. The EV sales in China, which are categorized into battery and plug-in EVs, are predicted in both short term (up to December 2017) and long term (up to 2020), as statistical proofs of the growth of the Chinese EV industry.

Robust assignment of cancer subtypes from expression data using a uni-variate gene expression average as classifier

International Nuclear Information System (INIS)

Lauss, Martin; Frigyesi, Attila; Ryden, Tobias; Höglund, Mattias

2010-01-01

Genome wide gene expression data is a rich source for the identification of gene signatures suitable for clinical purposes and a number of statistical algorithms have been described for both identification and evaluation of such signatures. Some employed algorithms are fairly complex and hence sensitive to over-fitting whereas others are more simple and straight forward. Here we present a new type of simple algorithm based on ROC analysis and the use of metagenes that we believe will be a good complement to existing algorithms. The basis for the proposed approach is the use of metagenes, instead of collections of individual genes, and a feature selection using AUC values obtained by ROC analysis. Each gene in a data set is assigned an AUC value relative to the tumor class under investigation and the genes are ranked according to these values. Metagenes are then formed by calculating the mean expression level for an increasing number of ranked genes, and the metagene expression value that optimally discriminates tumor classes in the training set is used for classification of new samples. The performance of the metagene is then evaluated using LOOCV and balanced accuracies. We show that the simple uni-variate gene expression average algorithm performs as well as several alternative algorithms such as discriminant analysis and the more complex approaches such as SVM and neural networks. The R package rocc is freely available at http://cran.r-project.org/web/packages/rocc/index.html

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

Science.gov (United States)

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

2017-08-01

Birds litter contains unutilized nitrogen in the form of uric acid that is converted into ammonia; a fact that does not only affect poultry performance but also has a negative effect on people's health around the farm and contributes in the environmental degradation. The influence of microclimatic ammonia emissions on Ross and Hubbard broilers reared in different housing systems at two consecutive seasons (fall and winter) was evaluated using a discriminant function analysis to differentiate between Ross and Hubbard breeds. A total number of 400 air samples were collected and analyzed for ammonia levels during the experimental period. Data were analyzed using univariate and multivariate statistical methods. Ammonia levels were significantly higher (p0.05) were found between the two farms in body weight, body weight gain, feed intake, feed conversion ratio, and performance index (PI) of broilers. Body weight; weight gain and PI had increased values (pbroiler breed. Ammonia emissions were positively (although weekly) correlated with the ambient relative humidity (r=0.383; p0.05). Test of significance of discriminant function analysis did not show a classification based on the studied traits suggesting that they cannot been used as predictor variables. The percentage of correct classification was 52% and it was improved after deletion of highly correlated traits to 57%. The study revealed that broiler's growth was negatively affected by increased microclimatic ammonia concentrations and recommended the analysis of broilers' growth performance parameters data using multivariate discriminant function analysis.

[Retrospective statistical analysis of clinical factors of recurrence in chronic subdural hematoma: correlation between univariate and multivariate analysis].

Science.gov (United States)

Takayama, Motoharu; Terui, Keita; Oiwa, Yoshitsugu

2012-10-01

Chronic subdural hematoma is common in elderly individuals and surgical procedures are simple. The recurrence rate of chronic subdural hematoma, however, varies from 9.2 to 26.5% after surgery. The authors studied factors of the recurrence using univariate and multivariate analyses in patients with chronic subdural hematoma We retrospectively reviewed 239 consecutive cases of chronic subdural hematoma who received burr-hole surgery with irrigation and closed-system drainage. We analyzed the relationships between recurrence of chronic subdural hematoma and factors such as sex, age, laterality, bleeding tendency, other complicated diseases, density on CT, volume of the hematoma, residual air in the hematoma cavity, use of artificial cerebrospinal fluid. Twenty-one patients (8.8%) experienced a recurrence of chronic subdural hematoma. Multiple logistic regression found that the recurrence rate was higher in patients with a large volume of the residual air, and was lower in patients using artificial cerebrospinal fluid. No statistical differences were found in bleeding tendency. Techniques to reduce the air in the hematoma cavity are important for good outcome in surgery of chronic subdural hematoma. Also, the use of artificial cerebrospinal fluid reduces recurrence of chronic subdural hematoma. The surgical procedures can be the same for patients with bleeding tendencies.

Lower bounds on the run time of the univariate marginal distribution algorithm on OneMax

DEFF Research Database (Denmark)

Krejca, Martin S.; Witt, Carsten

2017-01-01

The Univariate Marginal Distribution Algorithm (UMDA), a popular estimation of distribution algorithm, is studied from a run time perspective. On the classical OneMax benchmark function, a lower bound of Ω(μ√n + n log n), where μ is the population size, on its expected run time is proved...... values maintained by the algorithm, including carefully designed potential functions. These techniques may prove useful in advancing the field of run time analysis for estimation of distribution algorithms in general........ This is the first direct lower bound on the run time of the UMDA. It is stronger than the bounds that follow from general black-box complexity theory and is matched by the run time of many evolutionary algorithms. The results are obtained through advanced analyses of the stochastic change of the frequencies of bit...

Univariate and multiple linear regression analyses for 23 single nucleotide polymorphisms in 14 genes predisposing to chronic glomerular diseases and IgA nephropathy in Han Chinese.

Science.gov (United States)

Wang, Hui; Sui, Weiguo; Xue, Wen; Wu, Junyong; Chen, Jiejing; Dai, Yong

2014-09-01

Immunoglobulin A nephropathy (IgAN) is a complex trait regulated by the interaction among multiple physiologic regulatory systems and probably involving numerous genes, which leads to inconsistent findings in genetic studies. One possibility of failure to replicate some single-locus results is that the underlying genetics of IgAN nephropathy is based on multiple genes with minor effects. To learn the association between 23 single nucleotide polymorphisms (SNPs) in 14 genes predisposing to chronic glomerular diseases and IgAN in Han males, the 23 SNPs genotypes of 21 Han males were detected and analyzed with a BaiO gene chip, and their associations were analyzed with univariate analysis and multiple linear regression analysis. Analysis showed that CTLA4 rs231726 and CR2 rs1048971 revealed a significant association with IgAN. These findings support the multi-gene nature of the etiology of IgAN and propose a potential gene-gene interactive model for future studies.

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

Science.gov (United States)

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

2017-01-01

Background and Aim: Birds litter contains unutilized nitrogen in the form of uric acid that is converted into ammonia; a fact that does not only affect poultry performance but also has a negative effect on people’s health around the farm and contributes in the environmental degradation. The influence of microclimatic ammonia emissions on Ross and Hubbard broilers reared in different housing systems at two consecutive seasons (fall and winter) was evaluated using a discriminant function analysis to differentiate between Ross and Hubbard breeds. Materials and Methods: A total number of 400 air samples were collected and analyzed for ammonia levels during the experimental period. Data were analyzed using univariate and multivariate statistical methods. Results: Ammonia levels were significantly higher (p0.05) were found between the two farms in body weight, body weight gain, feed intake, feed conversion ratio, and performance index (PI) of broilers. Body weight; weight gain and PI had increased values (pbroiler breed. Ammonia emissions were positively (although weekly) correlated with the ambient relative humidity (r=0.383; p0.05). Test of significance of discriminant function analysis did not show a classification based on the studied traits suggesting that they cannot been used as predictor variables. The percentage of correct classification was 52% and it was improved after deletion of highly correlated traits to 57%. Conclusion: The study revealed that broiler’s growth was negatively affected by increased microclimatic ammonia concentrations and recommended the analysis of broilers’ growth performance parameters data using multivariate discriminant function analysis. PMID:28919677

Wind Speed Prediction Using a Univariate ARIMA Model and a Multivariate NARX Model

Directory of Open Access Journals (Sweden)

Erasmo Cadenas

2016-02-01

Full Text Available Two on step ahead wind speed forecasting models were compared. A univariate model was developed using a linear autoregressive integrated moving average (ARIMA. This method’s performance is well studied for a large number of prediction problems. The other is a multivariate model developed using a nonlinear autoregressive exogenous artificial neural network (NARX. This uses the variables: barometric pressure, air temperature, wind direction and solar radiation or relative humidity, as well as delayed wind speed. Both models were developed from two databases from two sites: an hourly average measurements database from La Mata, Oaxaca, Mexico, and a ten minute average measurements database from Metepec, Hidalgo, Mexico. The main objective was to compare the impact of the various meteorological variables on the performance of the multivariate model of wind speed prediction with respect to the high performance univariate linear model. The NARX model gave better results with improvements on the ARIMA model of between 5.5% and 10. 6% for the hourly database and of between 2.3% and 12.8% for the ten minute database for mean absolute error and mean squared error, respectively.

R package imputeTestbench to compare imputations methods for univariate time series

OpenAIRE

Bokde, Neeraj; Kulat, Kishore; Beck, Marcus W; Asencio-Cortés, Gualberto

2016-01-01

This paper describes the R package imputeTestbench that provides a testbench for comparing imputation methods for missing data in univariate time series. The imputeTestbench package can be used to simulate the amount and type of missing data in a complete dataset and compare filled data using different imputation methods. The user has the option to simulate missing data by removing observations completely at random or in blocks of different sizes. Several default imputation methods are includ...

Study of Ecotype and Sowing Date Interaction in Cumin (Cuminum cyminum L. using Different Univariate Stability Parameters

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J Ghanbari

2017-06-01

Full Text Available Introduction Cumin is one of the most important medicinal plants in Iran and today, it is in the second level of popularity between spices in the world after black pepper. Cumin is an aromatic plant used as flavoring and seasoning agent in foods. Cumin seeds have been found to possess significant biological and have been used for treatment of toothache, dyspepsia, diarrhoea, epilepsy and jaundice. Knowledge of GEI is advantageous to have a cultivar that gives consistently high yield in a broad range of environments and to increase efficiency of breeding program and selection of best genotypes. A genotype that has stable trait expression across environments contributes little to GEI and its performance should be more predictable from the main several statistical methods have been proposed for stability analysis, with the aim of explaining the information contained in the GEI. Regression technique was proposed by Finlay and Wilkinson (1963 and was improved by Eberhart and Russell (1966. Generally, genotype stability was estimated by the slope of and deviation from the regression line for each of the genotypes. This is a popular method in stability analysis and has been applied in many crops. Non-parametric methods (rank mean (R, standard deviation rank (SDR and yield index ratio (YIR, environmental variance (S2i and genotypic variation coefficient (CVi Wricke's ecovalence and Shukla's stability variance (Shukla, 1972 have been used to determine genotype-by-environment interaction in many studies. This study was aimed to evaluate the ecotype × sowing date interaction in cumin and to evaluation of genotypic response of cumin to different sowing dates using univariate stability parameters. Materials and Methods In order to study of ecotype × sowing date interaction, different cumin ecotypes: Semnan, Fars, Yazd, Golestan, Khorasan-Razavi, Khorasan-Shomali, Khorasan-Jonoubi, Isfahan and Kerman in 5 different sowing dates (26th December, 10th January

In situ calibration using univariate analyses based on the onboard ChemCam targets: first prediction of Martian rock and soil compositions

International Nuclear Information System (INIS)

Fabre, C.; Cousin, A.; Wiens, R.C.; Ollila, A.; Gasnault, O.; Maurice, S.; Sautter, V.; Forni, O.; Lasue, J.; Tokar, R.; Vaniman, D.; Melikechi, N.

2014-01-01

Curiosity rover landed on August 6th, 2012 in Gale Crater, Mars and it possesses unique analytical capabilities to investigate the chemistry and mineralogy of the Martian soil. In particular, the LIBS technique is being used for the first time on another planet with the ChemCam instrument, and more than 75,000 spectra have been returned in the first year on Mars. Curiosity carries body-mounted calibration targets specially designed for the ChemCam instrument, some of which are homgeneous glasses and others that are fine-grained glass-ceramics. We present direct calibrations, using these onboard standards to infer elements and element ratios by ratioing relative peak areas. As the laser spot size is around 300 μm, the LIBS technique provides measurements of the silicate glass compositions representing homogeneous material and measurements of the ceramic targets that are comparable to fine-grained rock or soil. The laser energy and the auto-focus are controlled for all sequences used for calibration. The univariate calibration curves present relatively to very good correlation coefficients with low RSDs for major and ratio calibrations. Trace element calibration curves (Li, Sr, and Mn), down to several ppm, can be used as a rapid tool to draw attention to remarkable rocks and soils along the traverse. First comparisons to alpha-particle X-ray spectroscopy (APXS) data, on selected targets, show good agreement for most elements and for Mg# and Al/Si estimates. SiO 2 estimates using univariate cannot be yet used. Na 2 O and K 2 O estimates are relevant for high alkali contents, but probably under estimated due to the CCCT initial compositions. Very good results for CaO and Al 2 O 3 estimates and satisfactory results for FeO are obtained. - Highlights: • In situ LIBS univariate calibrations are done using the Curiosity onboard standards. • Major and minor element contents can be rapidly obtained. • Trace element contents can be used as a rapid tool along the

MULTIVARIANT AND UNIVARIANT INTERGROUP DIFFERENCES IN THE INVESTIGATED SPECIFIC MOTOR SPACE BETWEEN RESPONDENTS JUNIORS AND SENIORS MEMBERS OF THE MACEDONIAN NATIONAL KARATE TEAM

Directory of Open Access Journals (Sweden)

Kеnan Аsani

2013-07-01

Full Text Available The aim is to establish intergroup multivariant and univariant investigated differences in specific motor space between respondents juniors and seniors members of the Macedonian karate team. The sample of 30 male karate respondents covers juniors on 16,17 and seniors over 18 years.In the research were applied 20 specific motor tests. Based on Graph 1 where it is presented multivariant analysis of variance Manova and Anova can be noted that respondents juniors and seniors, although not belonging to the same population are not different in multivariant understudied area.W. lambda of .19, Rao-wool R - Approximation of 1.91 degrees of freedom df 1 = 20 and df 2 = 9 provides the level of significance of p =, 16. Based on univariant analysis for each variable separately can be seen that has been around intergroup statistically significant difference in seven SMAEGERI (kick in the sack with favoritism leg mae geri for 10 sec., SMAVASI (kick in the sack with favoritism foot mavashi geri by 10 sec., SUSIRO (kick in the sack with favoritism leg ushiro geri for 10 sec., SKIZAME (kick in the sack with favoritism hand kizame cuki for 10 sec., STAPNSR (taping with foot in sagital plane for 15 sec. SUDMNR (hitting a moving target with weaker hand and SUDMPN (hitting a moving target with favoritism foot of twenty applied manifest variables. There are no intergroup differences in multivariant investigated specific - motor space among the respondents juniors and seniors members of the Macedonian karate team. Based on univariant analysis for each variable separately can be seen that has been around intergroup statistically significant difference in seven SMAEGERI (kick in the sack with favoritism leg mae geri for 10 sec., SMAVASI (kick in the sack with favoritism foot mavashi geri by 10 sec., SUSIRO (kick in the sack with favoritism leg ushiro geri for 10 sec., SKIZAME (kick in the sack with favoritism hand kizame cuki for 10 sec., STAPNSR (taping with foot in

In situ calibration using univariate analyses based on the onboard ChemCam targets: first prediction of Martian rock and soil compositions

Energy Technology Data Exchange (ETDEWEB)

Fabre, C. [GeoRessources lab, Université de Lorraine, Nancy (France); Cousin, A.; Wiens, R.C. [Los Alamos National Laboratory, Los Alamos, NM (United States); Ollila, A. [University of NM, Albuquerque (United States); Gasnault, O.; Maurice, S. [IRAP, Toulouse (France); Sautter, V. [Museum National d' Histoire Naturelle, Paris (France); Forni, O.; Lasue, J. [IRAP, Toulouse (France); Tokar, R.; Vaniman, D. [Planetary Science Institute, Tucson, AZ (United States); Melikechi, N. [Delaware State University (United States)

2014-09-01

Curiosity rover landed on August 6th, 2012 in Gale Crater, Mars and it possesses unique analytical capabilities to investigate the chemistry and mineralogy of the Martian soil. In particular, the LIBS technique is being used for the first time on another planet with the ChemCam instrument, and more than 75,000 spectra have been returned in the first year on Mars. Curiosity carries body-mounted calibration targets specially designed for the ChemCam instrument, some of which are homgeneous glasses and others that are fine-grained glass-ceramics. We present direct calibrations, using these onboard standards to infer elements and element ratios by ratioing relative peak areas. As the laser spot size is around 300 μm, the LIBS technique provides measurements of the silicate glass compositions representing homogeneous material and measurements of the ceramic targets that are comparable to fine-grained rock or soil. The laser energy and the auto-focus are controlled for all sequences used for calibration. The univariate calibration curves present relatively to very good correlation coefficients with low RSDs for major and ratio calibrations. Trace element calibration curves (Li, Sr, and Mn), down to several ppm, can be used as a rapid tool to draw attention to remarkable rocks and soils along the traverse. First comparisons to alpha-particle X-ray spectroscopy (APXS) data, on selected targets, show good agreement for most elements and for Mg# and Al/Si estimates. SiO{sub 2} estimates using univariate cannot be yet used. Na{sub 2}O and K{sub 2}O estimates are relevant for high alkali contents, but probably under estimated due to the CCCT initial compositions. Very good results for CaO and Al{sub 2}O{sub 3} estimates and satisfactory results for FeO are obtained. - Highlights: • In situ LIBS univariate calibrations are done using the Curiosity onboard standards. • Major and minor element contents can be rapidly obtained. • Trace element contents can be used as a

The issue of multiple univariate comparisons in the context of neuroelectric brain mapping: an application in a neuromarketing experiment.

Science.gov (United States)

Vecchiato, G; De Vico Fallani, F; Astolfi, L; Toppi, J; Cincotti, F; Mattia, D; Salinari, S; Babiloni, F

2010-08-30

This paper presents some considerations about the use of adequate statistical techniques in the framework of the neuroelectromagnetic brain mapping. With the use of advanced EEG/MEG recording setup involving hundred of sensors, the issue of the protection against the type I errors that could occur during the execution of hundred of univariate statistical tests, has gained interest. In the present experiment, we investigated the EEG signals from a mannequin acting as an experimental subject. Data have been collected while performing a neuromarketing experiment and analyzed with state of the art computational tools adopted in specialized literature. Results showed that electric data from the mannequin's head presents statistical significant differences in power spectra during the visualization of a commercial advertising when compared to the power spectra gathered during a documentary, when no adjustments were made on the alpha level of the multiple univariate tests performed. The use of the Bonferroni or Bonferroni-Holm adjustments returned correctly no differences between the signals gathered from the mannequin in the two experimental conditions. An partial sample of recently published literature on different neuroscience journals suggested that at least the 30% of the papers do not use statistical protection for the type I errors. While the occurrence of type I errors could be easily managed with appropriate statistical techniques, the use of such techniques is still not so largely adopted in the literature. Copyright (c) 2010 Elsevier B.V. All rights reserved.

Automatic Image Segmentation Using Active Contours with Univariate Marginal Distribution

Directory of Open Access Journals (Sweden)

I. Cruz-Aceves

2013-01-01

Full Text Available This paper presents a novel automatic image segmentation method based on the theory of active contour models and estimation of distribution algorithms. The proposed method uses the univariate marginal distribution model to infer statistical dependencies between the control points on different active contours. These contours have been generated through an alignment process of reference shape priors, in order to increase the exploration and exploitation capabilities regarding different interactive segmentation techniques. This proposed method is applied in the segmentation of the hollow core in microscopic images of photonic crystal fibers and it is also used to segment the human heart and ventricular areas from datasets of computed tomography and magnetic resonance images, respectively. Moreover, to evaluate the performance of the medical image segmentations compared to regions outlined by experts, a set of similarity measures has been adopted. The experimental results suggest that the proposed image segmentation method outperforms the traditional active contour model and the interactive Tseng method in terms of segmentation accuracy and stability.

A Java-based fMRI processing pipeline evaluation system for assessment of univariate general linear model and multivariate canonical variate analysis-based pipelines.

Science.gov (United States)

Zhang, Jing; Liang, Lichen; Anderson, Jon R; Gatewood, Lael; Rottenberg, David A; Strother, Stephen C

2008-01-01

As functional magnetic resonance imaging (fMRI) becomes widely used, the demands for evaluation of fMRI processing pipelines and validation of fMRI analysis results is increasing rapidly. The current NPAIRS package, an IDL-based fMRI processing pipeline evaluation framework, lacks system interoperability and the ability to evaluate general linear model (GLM)-based pipelines using prediction metrics. Thus, it can not fully evaluate fMRI analytical software modules such as FSL.FEAT and NPAIRS.GLM. In order to overcome these limitations, a Java-based fMRI processing pipeline evaluation system was developed. It integrated YALE (a machine learning environment) into Fiswidgets (a fMRI software environment) to obtain system interoperability and applied an algorithm to measure GLM prediction accuracy. The results demonstrated that the system can evaluate fMRI processing pipelines with univariate GLM and multivariate canonical variates analysis (CVA)-based models on real fMRI data based on prediction accuracy (classification accuracy) and statistical parametric image (SPI) reproducibility. In addition, a preliminary study was performed where four fMRI processing pipelines with GLM and CVA modules such as FSL.FEAT and NPAIRS.CVA were evaluated with the system. The results indicated that (1) the system can compare different fMRI processing pipelines with heterogeneous models (NPAIRS.GLM, NPAIRS.CVA and FSL.FEAT) and rank their performance by automatic performance scoring, and (2) the rank of pipeline performance is highly dependent on the preprocessing operations. These results suggest that the system will be of value for the comparison, validation, standardization and optimization of functional neuroimaging software packages and fMRI processing pipelines.

Univariate and multivariate forecasting of hourly solar radiation with artificial intelligence techniques

Energy Technology Data Exchange (ETDEWEB)

Sfetsos, A. [7 Pirsou Str., Athens (Greece); Coonick, A.H. [Imperial Coll. of Science Technology and Medicine, Dept. of Electrical and Electronic Engineering, London (United Kingdom)

2000-07-01

This paper introduces a new approach for the forecasting of mean hourly global solar radiation received by a horizontal surface. In addition to the traditional linear methods, several artificial-intelligence-based techniques are studied. These include linear, feed-forward, recurrent Elman and Radial Basis neural networks alongside the adaptive neuro-fuzzy inference scheme. The problem is examined initially for the univariate case, and is extended to include additional meteorological parameters in the process of estimating the optimum model. The results indicate that the developed artificial intelligence models predict the solar radiation time series more effectively compared to the conventional procedures based on the clearness index. The forecasting ability of some models can be further enhanced with the use of additional meteorological parameters. (Author)

Analysis of Medication Errors in Simulated Pediatric Resuscitation by Residents

Directory of Open Access Journals (Sweden)

Evelyn Porter

2014-07-01

Full Text Available Introduction: The objective of our study was to estimate the incidence of prescribing medication errors specifically made by a trainee and identify factors associated with these errors during the simulated resuscitation of a critically ill child. Methods: The results of the simulated resuscitation are described. We analyzed data from the simulated resuscitation for the occurrence of a prescribing medication error. We compared univariate analysis of each variable to medication error rate and performed a separate multiple logistic regression analysis on the significant univariate variables to assess the association between the selected variables. Results: We reviewed 49 simulated resuscitations . The final medication error rate for the simulation was 26.5% (95% CI 13.7% - 39.3%. On univariate analysis, statistically significant findings for decreased prescribing medication error rates included senior residents in charge, presence of a pharmacist, sleeping greater than 8 hours prior to the simulation, and a visual analog scale score showing more confidence in caring for critically ill children. Multiple logistic regression analysis using the above significant variables showed only the presence of a pharmacist to remain significantly associated with decreased medication error, odds ratio of 0.09 (95% CI 0.01 - 0.64. Conclusion: Our results indicate that the presence of a clinical pharmacist during the resuscitation of a critically ill child reduces the medication errors made by resident physician trainees.

Monitoring endemic livestock diseases using laboratory diagnostic data: A simulation study to evaluate the performance of univariate process monitoring control algorithms.

Science.gov (United States)

Lopes Antunes, Ana Carolina; Dórea, Fernanda; Halasa, Tariq; Toft, Nils

2016-05-01

Surveillance systems are critical for accurate, timely monitoring and effective disease control. In this study, we investigated the performance of univariate process monitoring control algorithms in detecting changes in seroprevalence for endemic diseases. We also assessed the effect of sample size (number of sentinel herds tested in the surveillance system) on the performance of the algorithms. Three univariate process monitoring control algorithms were compared: Shewart p Chart(1) (PSHEW), Cumulative Sum(2) (CUSUM) and Exponentially Weighted Moving Average(3) (EWMA). Increases in seroprevalence were simulated from 0.10 to 0.15 and 0.20 over 4, 8, 24, 52 and 104 weeks. Each epidemic scenario was run with 2000 iterations. The cumulative sensitivity(4) (CumSe) and timeliness were used to evaluate the algorithms' performance with a 1% false alarm rate. Using these performance evaluation criteria, it was possible to assess the accuracy and timeliness of the surveillance system working in real-time. The results showed that EWMA and PSHEW had higher CumSe (when compared with the CUSUM) from week 1 until the end of the period for all simulated scenarios. Changes in seroprevalence from 0.10 to 0.20 were more easily detected (higher CumSe) than changes from 0.10 to 0.15 for all three algorithms. Similar results were found with EWMA and PSHEW, based on the median time to detection. Changes in the seroprevalence were detected later with CUSUM, compared to EWMA and PSHEW for the different scenarios. Increasing the sample size 10 fold halved the time to detection (CumSe=1), whereas increasing the sample size 100 fold reduced the time to detection by a factor of 6. This study investigated the performance of three univariate process monitoring control algorithms in monitoring endemic diseases. It was shown that automated systems based on these detection methods identified changes in seroprevalence at different times. Increasing the number of tested herds would lead to faster

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

Science.gov (United States)

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

2016-07-01

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

Investigating univariate temporal patterns for intrinsic connectivity networks based on complexity and low-frequency oscillation: a test-retest reliability study.

Science.gov (United States)

Wang, X; Jiao, Y; Tang, T; Wang, H; Lu, Z

2013-12-19

Intrinsic connectivity networks (ICNs) are composed of spatial components and time courses. The spatial components of ICNs were discovered with moderate-to-high reliability. So far as we know, few studies focused on the reliability of the temporal patterns for ICNs based their individual time courses. The goals of this study were twofold: to investigate the test-retest reliability of temporal patterns for ICNs, and to analyze these informative univariate metrics. Additionally, a correlation analysis was performed to enhance interpretability. Our study included three datasets: (a) short- and long-term scans, (b) multi-band echo-planar imaging (mEPI), and (c) eyes open or closed. Using dual regression, we obtained the time courses of ICNs for each subject. To produce temporal patterns for ICNs, we applied two categories of univariate metrics: network-wise complexity and network-wise low-frequency oscillation. Furthermore, we validated the test-retest reliability for each metric. The network-wise temporal patterns for most ICNs (especially for default mode network, DMN) exhibited moderate-to-high reliability and reproducibility under different scan conditions. Network-wise complexity for DMN exhibited fair reliability (ICC<0.5) based on eyes-closed sessions. Specially, our results supported that mEPI could be a useful method with high reliability and reproducibility. In addition, these temporal patterns were with physiological meanings, and certain temporal patterns were correlated to the node strength of the corresponding ICN. Overall, network-wise temporal patterns of ICNs were reliable and informative and could be complementary to spatial patterns of ICNs for further study. Copyright © 2013 IBRO. Published by Elsevier Ltd. All rights reserved.

Multivariate refined composite multiscale entropy analysis

International Nuclear Information System (INIS)

Humeau-Heurtier, Anne

2016-01-01

Multiscale entropy (MSE) has become a prevailing method to quantify signals complexity. MSE relies on sample entropy. However, MSE may yield imprecise complexity estimation at large scales, because sample entropy does not give precise estimation of entropy when short signals are processed. A refined composite multiscale entropy (RCMSE) has therefore recently been proposed. Nevertheless, RCMSE is for univariate signals only. The simultaneous analysis of multi-channel (multivariate) data often over-performs studies based on univariate signals. We therefore introduce an extension of RCMSE to multivariate data. Applications of multivariate RCMSE to simulated processes reveal its better performances over the standard multivariate MSE. - Highlights: • Multiscale entropy quantifies data complexity but may be inaccurate at large scale. • A refined composite multiscale entropy (RCMSE) has therefore recently been proposed. • Nevertheless, RCMSE is adapted to univariate time series only. • We herein introduce an extension of RCMSE to multivariate data. • It shows better performances than the standard multivariate multiscale entropy.

Application of Raman Spectroscopy and Univariate Modelling As a Process Analytical Technology for Cell Therapy Bioprocessing

Science.gov (United States)

Baradez, Marc-Olivier; Biziato, Daniela; Hassan, Enas; Marshall, Damian

2018-01-01

Cell therapies offer unquestionable promises for the treatment, and in some cases even the cure, of complex diseases. As we start to see more of these therapies gaining market authorization, attention is turning to the bioprocesses used for their manufacture, in particular the challenge of gaining higher levels of process control to help regulate cell behavior, manage process variability, and deliver product of a consistent quality. Many processes already incorporate the measurement of key markers such as nutrient consumption, metabolite production, and cell concentration, but these are often performed off-line and only at set time points in the process. Having the ability to monitor these markers in real-time using in-line sensors would offer significant advantages, allowing faster decision-making and a finer level of process control. In this study, we use Raman spectroscopy as an in-line optical sensor for bioprocess monitoring of an autologous T-cell immunotherapy model produced in a stirred tank bioreactor system. Using reference datasets generated on a standard bioanalyzer, we develop chemometric models from the Raman spectra for glucose, glutamine, lactate, and ammonia. These chemometric models can accurately monitor donor-specific increases in nutrient consumption and metabolite production as the primary T-cell transition from a recovery phase and begin proliferating. Using a univariate modeling approach, we then show how changes in peak intensity within the Raman spectra can be correlated with cell concentration and viability. These models, which act as surrogate markers, can be used to monitor cell behavior including cell proliferation rates, proliferative capacity, and transition of the cells to a quiescent phenotype. Finally, using the univariate models, we also demonstrate how Raman spectroscopy can be applied for real-time monitoring. The ability to measure these key parameters using an in-line Raman optical sensor makes it possible to have immediate

Application of Raman Spectroscopy and Univariate Modelling As a Process Analytical Technology for Cell Therapy Bioprocessing.

Science.gov (United States)

Baradez, Marc-Olivier; Biziato, Daniela; Hassan, Enas; Marshall, Damian

2018-01-01

Cell therapies offer unquestionable promises for the treatment, and in some cases even the cure, of complex diseases. As we start to see more of these therapies gaining market authorization, attention is turning to the bioprocesses used for their manufacture, in particular the challenge of gaining higher levels of process control to help regulate cell behavior, manage process variability, and deliver product of a consistent quality. Many processes already incorporate the measurement of key markers such as nutrient consumption, metabolite production, and cell concentration, but these are often performed off-line and only at set time points in the process. Having the ability to monitor these markers in real-time using in-line sensors would offer significant advantages, allowing faster decision-making and a finer level of process control. In this study, we use Raman spectroscopy as an in-line optical sensor for bioprocess monitoring of an autologous T-cell immunotherapy model produced in a stirred tank bioreactor system. Using reference datasets generated on a standard bioanalyzer, we develop chemometric models from the Raman spectra for glucose, glutamine, lactate, and ammonia. These chemometric models can accurately monitor donor-specific increases in nutrient consumption and metabolite production as the primary T-cell transition from a recovery phase and begin proliferating. Using a univariate modeling approach, we then show how changes in peak intensity within the Raman spectra can be correlated with cell concentration and viability. These models, which act as surrogate markers, can be used to monitor cell behavior including cell proliferation rates, proliferative capacity, and transition of the cells to a quiescent phenotype. Finally, using the univariate models, we also demonstrate how Raman spectroscopy can be applied for real-time monitoring. The ability to measure these key parameters using an in-line Raman optical sensor makes it possible to have immediate

Application of Raman Spectroscopy and Univariate Modelling As a Process Analytical Technology for Cell Therapy Bioprocessing

Directory of Open Access Journals (Sweden)

Marc-Olivier Baradez

2018-03-01

Full Text Available Cell therapies offer unquestionable promises for the treatment, and in some cases even the cure, of complex diseases. As we start to see more of these therapies gaining market authorization, attention is turning to the bioprocesses used for their manufacture, in particular the challenge of gaining higher levels of process control to help regulate cell behavior, manage process variability, and deliver product of a consistent quality. Many processes already incorporate the measurement of key markers such as nutrient consumption, metabolite production, and cell concentration, but these are often performed off-line and only at set time points in the process. Having the ability to monitor these markers in real-time using in-line sensors would offer significant advantages, allowing faster decision-making and a finer level of process control. In this study, we use Raman spectroscopy as an in-line optical sensor for bioprocess monitoring of an autologous T-cell immunotherapy model produced in a stirred tank bioreactor system. Using reference datasets generated on a standard bioanalyzer, we develop chemometric models from the Raman spectra for glucose, glutamine, lactate, and ammonia. These chemometric models can accurately monitor donor-specific increases in nutrient consumption and metabolite production as the primary T-cell transition from a recovery phase and begin proliferating. Using a univariate modeling approach, we then show how changes in peak intensity within the Raman spectra can be correlated with cell concentration and viability. These models, which act as surrogate markers, can be used to monitor cell behavior including cell proliferation rates, proliferative capacity, and transition of the cells to a quiescent phenotype. Finally, using the univariate models, we also demonstrate how Raman spectroscopy can be applied for real-time monitoring. The ability to measure these key parameters using an in-line Raman optical sensor makes it possible

Univariate time series modeling and an application to future claims amount in SOCSO's invalidity pension scheme

Science.gov (United States)

Chek, Mohd Zaki Awang; Ahmad, Abu Bakar; Ridzwan, Ahmad Nur Azam Ahmad; Jelas, Imran Md.; Jamal, Nur Faezah; Ismail, Isma Liana; Zulkifli, Faiz; Noor, Syamsul Ikram Mohd

2012-09-01

The main objective of this study is to forecast the future claims amount of Invalidity Pension Scheme (IPS). All data were derived from SOCSO annual reports from year 1972 - 2010. These claims consist of all claims amount from 7 benefits offered by SOCSO such as Invalidity Pension, Invalidity Grant, Survivors Pension, Constant Attendance Allowance, Rehabilitation, Funeral and Education. Prediction of future claims of Invalidity Pension Scheme will be made using Univariate Forecasting Models to predict the future claims among workforce in Malaysia.

Univariate Lp and ɭ p Averaging, 0 < p < 1, in Polynomial Time by Utilization of Statistical Structure

Directory of Open Access Journals (Sweden)

John E. Lavery

2012-10-01

Full Text Available We present evidence that one can calculate generically combinatorially expensive Lp and lp averages, 0 < p < 1, in polynomial time by restricting the data to come from a wide class of statistical distributions. Our approach differs from the approaches in the previous literature, which are based on a priori sparsity requirements or on accepting a local minimum as a replacement for a global minimum. The functionals by which Lp averages are calculated are not convex but are radially monotonic and the functionals by which lp averages are calculated are nearly so, which are the keys to solvability in polynomial time. Analytical results for symmetric, radially monotonic univariate distributions are presented. An algorithm for univariate lp averaging is presented. Computational results for a Gaussian distribution, a class of symmetric heavy-tailed distributions and a class of asymmetric heavy-tailed distributions are presented. Many phenomena in human-based areas are increasingly known to be represented by data that have large numbers of outliers and belong to very heavy-tailed distributions. When tails of distributions are so heavy that even medians (L1 and l1 averages do not exist, one needs to consider using lp minimization principles with 0 < p < 1.

Pleiotropy analysis of quantitative traits at gene level by multivariate functional linear models.

Science.gov (United States)

Wang, Yifan; Liu, Aiyi; Mills, James L; Boehnke, Michael; Wilson, Alexander F; Bailey-Wilson, Joan E; Xiong, Momiao; Wu, Colin O; Fan, Ruzong

2015-05-01

In genetics, pleiotropy describes the genetic effect of a single gene on multiple phenotypic traits. A common approach is to analyze the phenotypic traits separately using univariate analyses and combine the test results through multiple comparisons. This approach may lead to low power. Multivariate functional linear models are developed to connect genetic variant data to multiple quantitative traits adjusting for covariates for a unified analysis. Three types of approximate F-distribution tests based on Pillai-Bartlett trace, Hotelling-Lawley trace, and Wilks's Lambda are introduced to test for association between multiple quantitative traits and multiple genetic variants in one genetic region. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and optimal sequence kernel association test (SKAT-O). Extensive simulations were performed to evaluate the false positive rates and power performance of the proposed models and tests. We show that the approximate F-distribution tests control the type I error rates very well. Overall, simultaneous analysis of multiple traits can increase power performance compared to an individual test of each trait. The proposed methods were applied to analyze (1) four lipid traits in eight European cohorts, and (2) three biochemical traits in the Trinity Students Study. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and SKAT-O for the three biochemical traits. The approximate F-distribution tests of the proposed functional linear models are more sensitive than those of the traditional multivariate linear models that in turn are more sensitive than SKAT-O in the univariate case. The analysis of the four lipid traits and the three biochemical traits detects more association than SKAT-O in the univariate case. © 2015 WILEY PERIODICALS, INC.

Detection of biomarkers for Hepatocellular Carcinoma using a hybrid univariate gene selection methods

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Abdel Samee Nagwan M

2012-08-01

Full Text Available Abstract Background Discovering new biomarkers has a great role in improving early diagnosis of Hepatocellular carcinoma (HCC. The experimental determination of biomarkers needs a lot of time and money. This motivates this work to use in-silico prediction of biomarkers to reduce the number of experiments required for detecting new ones. This is achieved by extracting the most representative genes in microarrays of HCC. Results In this work, we provide a method for extracting the differential expressed genes, up regulated ones, that can be considered candidate biomarkers in high throughput microarrays of HCC. We examine the power of several gene selection methods (such as Pearson’s correlation coefficient, Cosine coefficient, Euclidean distance, Mutual information and Entropy with different estimators in selecting informative genes. A biological interpretation of the highly ranked genes is done using KEGG (Kyoto Encyclopedia of Genes and Genomes pathways, ENTREZ and DAVID (Database for Annotation, Visualization, and Integrated Discovery databases. The top ten genes selected using Pearson’s correlation coefficient and Cosine coefficient contained six genes that have been implicated in cancer (often multiple cancers genesis in previous studies. A fewer number of genes were obtained by the other methods (4 genes using Mutual information, 3genes using Euclidean distance and only one gene using Entropy. A better result was obtained by the utilization of a hybrid approach based on intersecting the highly ranked genes in the output of all investigated methods. This hybrid combination yielded seven genes (2 genes for HCC and 5 genes in different types of cancer in the top ten genes of the list of intersected genes. Conclusions To strengthen the effectiveness of the univariate selection methods, we propose a hybrid approach by intersecting several of these methods in a cascaded manner. This approach surpasses all of univariate selection methods when

Univariate and Bivariate Empirical Mode Decomposition for Postural Stability Analysis

Directory of Open Access Journals (Sweden)

Jacques Duchêne

2008-05-01

Full Text Available The aim of this paper was to compare empirical mode decomposition (EMD and two new extended methods of  EMD named complex empirical mode decomposition (complex-EMD and bivariate empirical mode decomposition (bivariate-EMD. All methods were used to analyze stabilogram center of pressure (COP time series. The two new methods are suitable to be applied to complex time series to extract complex intrinsic mode functions (IMFs before the Hilbert transform is subsequently applied on the IMFs. The trace of the analytic IMF in the complex plane has a circular form, with each IMF having its own rotation frequency. The area of the circle and the average rotation frequency of IMFs represent efficient indicators of the postural stability status of subjects. Experimental results show the effectiveness of these indicators to identify differences in standing posture between groups.

Guaranteed Bounds on Information-Theoretic Measures of Univariate Mixtures Using Piecewise Log-Sum-Exp Inequalities

KAUST Repository

Nielsen, Frank

2016-12-09

Information-theoreticmeasures, such as the entropy, the cross-entropy and the Kullback-Leibler divergence between two mixture models, are core primitives in many signal processing tasks. Since the Kullback-Leibler divergence of mixtures provably does not admit a closed-form formula, it is in practice either estimated using costly Monte Carlo stochastic integration, approximated or bounded using various techniques. We present a fast and generic method that builds algorithmically closed-form lower and upper bounds on the entropy, the cross-entropy, the Kullback-Leibler and the α-divergences of mixtures. We illustrate the versatile method by reporting our experiments for approximating the Kullback-Leibler and the α-divergences between univariate exponential mixtures, Gaussian mixtures, Rayleigh mixtures and Gamma mixtures.

Importance Performance Analysis as a Trade Show Performance Evaluation and Benchmarking Tool

OpenAIRE

Tafesse, Wondwesen; Skallerud, Kåre; Korneliussen, Tor

2010-01-01

Author's accepted version (post-print). The purpose of this study is to introduce importance performance analysis as a trade show performance evaluation and benchmarking tool. Importance performance analysis considers exhibitors’ performance expectation and perceived performance in unison to evaluate and benchmark trade show performance. The present study uses data obtained from exhibitors of an international trade show to demonstrate how importance performance analysis can be used to eval...

Bivariate Rainfall and Runoff Analysis Using Shannon Entropy Theory

Science.gov (United States)

Rahimi, A.; Zhang, L.

2012-12-01

Rainfall-Runoff analysis is the key component for many hydrological and hydraulic designs in which the dependence of rainfall and runoff needs to be studied. It is known that the convenient bivariate distribution are often unable to model the rainfall-runoff variables due to that they either have constraints on the range of the dependence or fixed form for the marginal distributions. Thus, this paper presents an approach to derive the entropy-based joint rainfall-runoff distribution using Shannon entropy theory. The distribution derived can model the full range of dependence and allow different specified marginals. The modeling and estimation can be proceeded as: (i) univariate analysis of marginal distributions which includes two steps, (a) using the nonparametric statistics approach to detect modes and underlying probability density, and (b) fitting the appropriate parametric probability density functions; (ii) define the constraints based on the univariate analysis and the dependence structure; (iii) derive and validate the entropy-based joint distribution. As to validate the method, the rainfall-runoff data are collected from the small agricultural experimental watersheds located in semi-arid region near Riesel (Waco), Texas, maintained by the USDA. The results of unviariate analysis show that the rainfall variables follow the gamma distribution, whereas the runoff variables have mixed structure and follow the mixed-gamma distribution. With this information, the entropy-based joint distribution is derived using the first moments, the first moments of logarithm transformed rainfall and runoff, and the covariance between rainfall and runoff. The results of entropy-based joint distribution indicate: (1) the joint distribution derived successfully preserves the dependence between rainfall and runoff, and (2) the K-S goodness of fit statistical tests confirm the marginal distributions re-derived reveal the underlying univariate probability densities which further

Effects of univariate and multivariate regression on the accuracy of hydrogen quantification with laser-induced breakdown spectroscopy

Science.gov (United States)

Ytsma, Cai R.; Dyar, M. Darby

2018-01-01

Hydrogen (H) is a critical element to measure on the surface of Mars because its presence in mineral structures is indicative of past hydrous conditions. The Curiosity rover uses the laser-induced breakdown spectrometer (LIBS) on the ChemCam instrument to analyze rocks for their H emission signal at 656.6 nm, from which H can be quantified. Previous LIBS calibrations for H used small data sets measured on standards and/or manufactured mixtures of hydrous minerals and rocks and applied univariate regression to spectra normalized in a variety of ways. However, matrix effects common to LIBS make these calibrations of limited usefulness when applied to the broad range of compositions on the Martian surface. In this study, 198 naturally-occurring hydrous geological samples covering a broad range of bulk compositions with directly-measured H content are used to create more robust prediction models for measuring H in LIBS data acquired under Mars conditions. Both univariate and multivariate prediction models, including partial least square (PLS) and the least absolute shrinkage and selection operator (Lasso), are compared using several different methods for normalization of H peak intensities. Data from the ChemLIBS Mars-analog spectrometer at Mount Holyoke College are compared against spectra from the same samples acquired using a ChemCam-like instrument at Los Alamos National Laboratory and the ChemCam instrument on Mars. Results show that all current normalization and data preprocessing variations for quantifying H result in models with statistically indistinguishable prediction errors (accuracies) ca. ± 1.5 weight percent (wt%) H2O, limiting the applications of LIBS in these implementations for geological studies. This error is too large to allow distinctions among the most common hydrous phases (basalts, amphiboles, micas) to be made, though some clays (e.g., chlorites with ≈ 12 wt% H2O, smectites with 15-20 wt% H2O) and hydrated phases (e.g., gypsum with ≈ 20

Segmentation of Coronary Angiograms Using Gabor Filters and Boltzmann Univariate Marginal Distribution Algorithm

Directory of Open Access Journals (Sweden)

Fernando Cervantes-Sanchez

2016-01-01

Full Text Available This paper presents a novel method for improving the training step of the single-scale Gabor filters by using the Boltzmann univariate marginal distribution algorithm (BUMDA in X-ray angiograms. Since the single-scale Gabor filters (SSG are governed by three parameters, the optimal selection of the SSG parameters is highly desirable in order to maximize the detection performance of coronary arteries while reducing the computational time. To obtain the best set of parameters for the SSG, the area (Az under the receiver operating characteristic curve is used as fitness function. Moreover, to classify vessel and nonvessel pixels from the Gabor filter response, the interclass variance thresholding method has been adopted. The experimental results using the proposed method obtained the highest detection rate with Az=0.9502 over a training set of 40 images and Az=0.9583 with a test set of 40 images. In addition, the experimental results of vessel segmentation provided an accuracy of 0.944 with the test set of angiograms.

Reciprocal Benefits of Mass-Univariate and Multivariate Modeling in Brain Mapping: Applications to Event-Related Functional MRI, H215O-, and FDG-PET

Directory of Open Access Journals (Sweden)

James R. Moeller

2006-01-01

Full Text Available In brain mapping studies of sensory, cognitive, and motor operations, specific waveforms of dynamic neural activity are predicted based on theoretical models of human information processing. For example in event-related functional MRI (fMRI, the general linear model (GLM is employed in mass-univariate analyses to identify the regions whose dynamic activity closely matches the expected waveforms. By comparison multivariate analyses based on PCA or ICA provide greater flexibility in detecting spatiotemporal properties of experimental data that may strongly support alternative neuroscientific explanations. We investigated conjoint multivariate and mass-univariate analyses that combine the capabilities to (1 verify activation of neural machinery we already understand and (2 discover reliable signatures of new neural machinery. We examined combinations of GLM and PCA that recover latent neural signals (waveforms and footprints with greater accuracy than either method alone. Comparative results are illustrated with analyses of real fMRI data, adding to Monte Carlo simulation support.

Individual patient data meta-analysis shows no association between the SNP rs1800469 in TGFB and late radiotherapy toxicity

International Nuclear Information System (INIS)

Barnett, Gillian C.; Elliott, Rebecca M.; Alsner, Jan; Andreassen, Christian N.; Abdelhay, Osama; Burnet, Neil G.; Chang-Claude, Jenny; Coles, Charlotte E.; Gutiérrez-Enríquez, Sara; Fuentes-Raspall, Maria J.; Alonso-Muñoz, Maria C.; Kerns, Sarah; Raabe, Annette; Symonds, R. Paul; Seibold, Petra; Talbot, Chris J.; Wenz, Frederik; Wilkinson, Jennifer; Yarnold, John; Dunning, Alison M.

2012-01-01

Background and purpose: Reported associations between risk of radiation-induced normal tissue injury and single nucleotide polymorphisms (SNPs) in TGFB1, encoding the pro-fibrotic cytokine transforming growth factor-beta 1 (TGF-β1), remain controversial. To overcome publication bias, the international Radiogenomics Consortium collected and analysed individual patient level data from both published and unpublished studies. Materials and methods: TGFB1 SNP rs1800469 c.-1347T>C (previously known as C-509T) genotype, treatment-related data, and clinically-assessed fibrosis (measured at least 2 years after therapy) were available in 2782 participants from 11 cohorts. All received adjuvant breast radiotherapy. Associations between late fibrosis or overall toxicity, reported by STAT (Standardised Total Average Toxicity) score, and rs1800469 genotype were assessed. Results: No statistically significant associations between either fibrosis or overall toxicity and rs1800469 genotype were observed with univariate or multivariate regression analysis. The multivariate odds ratio (OR), obtained from meta-analysis, for an increase in late fibrosis grade with each additional rare allele of rs1800469 was 0.98 (95% Confidence Interval (CI) 0.85–1.11). This CI is sufficiently narrow to rule out any clinically relevant effect on toxicity risk in carriers vs. non-carriers with a high probability. Conclusion: This meta-analysis has not confirmed previous reports of association between fibrosis or overall toxicity and rs1800469 genotype in breast cancer patients. It has demonstrated successful collaboration within the Radiogenomics Consortium.

A Course in Time Series Analysis

CERN Document Server

Peña, Daniel; Tsay, Ruey S

2011-01-01

New statistical methods and future directions of research in time series A Course in Time Series Analysis demonstrates how to build time series models for univariate and multivariate time series data. It brings together material previously available only in the professional literature and presents a unified view of the most advanced procedures available for time series model building. The authors begin with basic concepts in univariate time series, providing an up-to-date presentation of ARIMA models, including the Kalman filter, outlier analysis, automatic methods for building ARIMA models, a

Migraineurs without aura show microstructural abnormalities in the cerebellum and frontal lobe.

Science.gov (United States)

Granziera, C; Romascano, D; Daducci, A; Roche, A; Vincent, M; Krueger, G; Hadjikhani, N

2013-12-01

The involvement of the cerebellum in migraine pathophysiology is not well understood. We used a biparametric approach at high-field MRI (3 T) to assess the structural integrity of the cerebellum in 15 migraineurs with aura (MWA), 23 migraineurs without aura (MWoA), and 20 healthy controls (HC). High-resolution T1 relaxation maps were acquired together with magnetization transfer images in order to probe microstructural and myelin integrity. Clusterwise analysis was performed on T1 and magnetization transfer ratio (MTR) maps of the cerebellum of MWA, MWoA, and HC using an ANOVA and a non-parametric clusterwise permutation F test, with age and gender as covariates and correction for familywise error rate. In addition, mean MTR and T1 in frontal regions known to be highly connected to the cerebellum were computed. Clusterwise comparison among groups showed a cluster of lower MTR in the right Crus I of MWoA patients vs. HC and MWA subjects (p = 0.04). Univariate and bivariate analysis on T1 and MTR contrasts showed that MWoA patients had longer T1 and lower MTR in the right and left pars orbitalis compared to MWA (p < 0.01 and 0.05, respectively), but no differences were found with HC. Lower MTR and longer T1 point at a loss of macromolecules and/or micro-edema in Crus I and pars orbitalis in MWoA patients vs. HC and vs. MWA. The pathophysiological implications of these findings are discussed in light of recent literature.

Real-time prediction of extreme ambient carbon monoxide concentrations due to vehicular exhaust emissions using univariate linear stochastic models

International Nuclear Information System (INIS)

Sharma, P.; Khare, M.

2000-01-01

Historical data of the time-series of carbon monoxide (CO) concentration was analysed using Box-Jenkins modelling approach. Univariate Linear Stochastic Models (ULSMs) were developed to examine the degree of prediction possible for situations where only a limited data set, restricted only to the past record of pollutant data are available. The developed models can be used to provide short-term, real-time forecast of extreme CO concentrations for an Air Quality Control Region (AQCR), comprising a major traffic intersection in a Central Business District of Delhi City, India. (author)

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

Science.gov (United States)

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.

Gastric cancers of Western European and African patients show different patterns of genomic instability

Directory of Open Access Journals (Sweden)

Mulder Chris JJ

2011-01-01

Full Text Available Abstract Background Infection with H. pylori is important in the etiology of gastric cancer. Gastric cancer is infrequent in Africa, despite high frequencies of H. pylori infection, referred to as the African enigma. Variation in environmental and host factors influencing gastric cancer risk between different populations have been reported but little is known about the biological differences between gastric cancers from different geographic locations. We aim to study genomic instability patterns of gastric cancers obtained from patients from United Kingdom (UK and South Africa (SA, in an attempt to support the African enigma hypothesis at the biological level. Methods DNA was isolated from 67 gastric adenocarcinomas, 33 UK patients, 9 Caucasian SA patients and 25 native SA patients. Microsatellite instability and chromosomal instability were analyzed by PCR and microarray comparative genomic hybridization, respectively. Data was analyzed by supervised univariate and multivariate analyses as well as unsupervised hierarchical cluster analysis. Results Tumors from Caucasian and native SA patients showed significantly more microsatellite instable tumors (p Conclusions Gastric cancers from SA and UK patients show differences in genetic instability patterns, indicating possible different biological mechanisms in patients from different geographical origin. This is of future clinical relevance for stratification of gastric cancer therapy.

Multivariate analysis with LISREL

CERN Document Server

Jöreskog, Karl G; Y Wallentin, Fan

2016-01-01

This book traces the theory and methodology of multivariate statistical analysis and shows how it can be conducted in practice using the LISREL computer program. It presents not only the typical uses of LISREL, such as confirmatory factor analysis and structural equation models, but also several other multivariate analysis topics, including regression (univariate, multivariate, censored, logistic, and probit), generalized linear models, multilevel analysis, and principal component analysis. It provides numerous examples from several disciplines and discusses and interprets the results, illustrated with sections of output from the LISREL program, in the context of the example. The book is intended for masters and PhD students and researchers in the social, behavioral, economic and many other sciences who require a basic understanding of multivariate statistical theory and methods for their analysis of multivariate data. It can also be used as a textbook on various topics of multivariate statistical analysis.

Evaluating statistical and clinical significance of intervention effects in single-case experimental designs: an SPSS method to analyze univariate data.

Science.gov (United States)

Maric, Marija; de Haan, Else; Hogendoorn, Sanne M; Wolters, Lidewij H; Huizenga, Hilde M

2015-03-01

Single-case experimental designs are useful methods in clinical research practice to investigate individual client progress. Their proliferation might have been hampered by methodological challenges such as the difficulty applying existing statistical procedures. In this article, we describe a data-analytic method to analyze univariate (i.e., one symptom) single-case data using the common package SPSS. This method can help the clinical researcher to investigate whether an intervention works as compared with a baseline period or another intervention type, and to determine whether symptom improvement is clinically significant. First, we describe the statistical method in a conceptual way and show how it can be implemented in SPSS. Simulation studies were performed to determine the number of observation points required per intervention phase. Second, to illustrate this method and its implications, we present a case study of an adolescent with anxiety disorders treated with cognitive-behavioral therapy techniques in an outpatient psychotherapy clinic, whose symptoms were regularly assessed before each session. We provide a description of the data analyses and results of this case study. Finally, we discuss the advantages and shortcomings of the proposed method. Copyright © 2014. Published by Elsevier Ltd.

Prognostic and survival analysis of 837 Chinese colorectal cancer patients.

Science.gov (United States)

Yuan, Ying; Li, Mo-Dan; Hu, Han-Guang; Dong, Cai-Xia; Chen, Jia-Qi; Li, Xiao-Fen; Li, Jing-Jing; Shen, Hong

2013-05-07

To develop a prognostic model to predict survival of patients with colorectal cancer (CRC). Survival data of 837 CRC patients undergoing surgery between 1996 and 2006 were collected and analyzed by univariate analysis and Cox proportional hazard regression model to reveal the prognostic factors for CRC. All data were recorded using a standard data form and analyzed using SPSS version 18.0 (SPSS, Chicago, IL, United States). Survival curves were calculated by the Kaplan-Meier method. The log rank test was used to assess differences in survival. Univariate hazard ratios and significant and independent predictors of disease-specific survival and were identified by Cox proportional hazard analysis. The stepwise procedure was set to a threshold of 0.05. Statistical significance was defined as P analysis suggested age, preoperative obstruction, serum carcinoembryonic antigen level at diagnosis, status of resection, tumor size, histological grade, pathological type, lymphovascular invasion, invasion of adjacent organs, and tumor node metastasis (TNM) staging were positive prognostic factors (P analysis showed a significant statistical difference in 3-year survival among these groups: LNR1, 73%; LNR2, 55%; and LNR3, 42% (P analysis results showed that histological grade, depth of bowel wall invasion, and number of metastatic lymph nodes were the most important prognostic factors for CRC if we did not consider the interaction of the TNM staging system (P < 0.05). When the TNM staging was taken into account, histological grade lost its statistical significance, while the specific TNM staging system showed a statistically significant difference (P < 0.0001). The overall survival of CRC patients has improved between 1996 and 2006. LNR is a powerful factor for estimating the survival of stage III CRC patients.

a meta-analysis

Indian Academy of Sciences (India)

Chrissa G. Tsiara

2018-03-13

Mar 13, 2018 ... a meta-analysis of case–control studies was conducted. Univariate and ...... recent hepatitis C virus: potential benefit for ribavirin use in. HCV/HIV ... C/G polymorphism in breast pathologies and in HIV-infected patients.

Who uses nursing theory? A univariate descriptive analysis of five years' research articles.

Science.gov (United States)

Bond, A Elaine; Eshah, Nidal Farid; Bani-Khaled, Mohammed; Hamad, Atef Omar; Habashneh, Samira; Kataua', Hussein; al-Jarrah, Imad; Abu Kamal, Andaleeb; Hamdan, Falastine Rafic; Maabreh, Roqia

2011-06-01

Since the early 1950s, nursing leaders have worked diligently to build the Scientific Discipline of Nursing, integrating Theory, Research and Practice. Recently, the role of theory has again come into question, with some scientists claiming nurses are not using theory to guide their research, with which to improve practice. The purposes of this descriptive study were to determine: (i) Were nursing scientists' research articles in leading nursing journals based on theory? (ii) If so, were the theories nursing theories or borrowed theories? (iii) Were the theories integrated into the studies, or were they used as organizing frameworks? Research articles from seven top ISI journals were analysed, excluding regularly featured columns, meta-analyses, secondary analysis, case studies and literature reviews. The authors used King's dynamic Interacting system and Goal Attainment Theory as an organizing framework. They developed consensus on how to identify the integration of theory, searching the Title, Abstract, Aims, Methods, Discussion and Conclusion sections of each research article, whether quantitative or qualitative. Of 2857 articles published in the seven journals from 2002 to, and including, 2006, 2184 (76%) were research articles. Of the 837 (38%) authors who used theories, 460 (55%) used nursing theories, 377 (45%) used other theories: 776 (93%) of those who used theory integrated it into their studies, including qualitative studies, while 51 (7%) reported they used theory as an organizing framework for their studies. Closer analysis revealed theory principles were implicitly implied, even in research reports that did not explicitly report theory usage. Increasing numbers of nursing research articles (though not percentagewise) continue to be guided by theory, and not always by nursing theory. Newer nursing research methods may not explicitly state the use of nursing theory, though it is implicitly implied. © 2010 The Authors. Scandinavian Journal of Caring

Improving the performance of univariate control charts for abnormal detection and classification

Science.gov (United States)

Yiakopoulos, Christos; Koutsoudaki, Maria; Gryllias, Konstantinos; Antoniadis, Ioannis

2017-03-01

Bearing failures in rotating machinery can cause machine breakdown and economical loss, if no effective actions are taken on time. Therefore, it is of prime importance to detect accurately the presence of faults, especially at their early stage, to prevent sequent damage and reduce costly downtime. The machinery fault diagnosis follows a roadmap of data acquisition, feature extraction and diagnostic decision making, in which mechanical vibration fault feature extraction is the foundation and the key to obtain an accurate diagnostic result. A challenge in this area is the selection of the most sensitive features for various types of fault, especially when the characteristics of failures are difficult to be extracted. Thus, a plethora of complex data-driven fault diagnosis methods are fed by prominent features, which are extracted and reduced through traditional or modern algorithms. Since most of the available datasets are captured during normal operating conditions, the last decade a number of novelty detection methods, able to work when only normal data are available, have been developed. In this study, a hybrid method combining univariate control charts and a feature extraction scheme is introduced focusing towards an abnormal change detection and classification, under the assumption that measurements under normal operating conditions of the machinery are available. The feature extraction method integrates the morphological operators and the Morlet wavelets. The effectiveness of the proposed methodology is validated on two different experimental cases with bearing faults, demonstrating that the proposed approach can improve the fault detection and classification performance of conventional control charts.

Impact of BCL2 and p53 on postmastectomy radiotherapy response in high-risk breast cancer. A subgroup analysis of DBCG82 b&c

DEFF Research Database (Denmark)

Kyndi, Marianne; Sørensen, Flemming Brandt; Knudsen, Helle

2008-01-01

-Meier probability plots showed a significantly improved overall survival after PMRT for the BCL2 positive subgroup, whereas practically no survival improvement was seen after PMRT for the BCL2 negative subgroup. In multivariate analysis of OS, however, no significant interaction was found between BCL2......PURPOSE: To examine p53 and BCL2 expression in high-risk breast cancer patients randomized to postmastectomy radiotherapy (PMRT). PATIENTS AND METHODS: The present analysis included 1 000 of 3 083 high-risk breast cancer patients randomly assigned to PMRT in the DBCG82 b&c studies. Tissue...... tests, Kaplan-Meier probability plots, Log-rank test, and Cox univariate and multivariate regression analyses. RESULTS: p53 accumulation was not significantly associated with increased overall mortality, DM or LRR probability in univariate or multivariate Cox regression analyses. Kaplan...

Impact of BCL2 and p53 on postmastectomy radiotherapy response in high-risk breast cancer. A subgroup analysis of DBCG82 b

DEFF Research Database (Denmark)

Kyndi, M.; Sorensen, F.B.; Alsner, J.

2008-01-01

-Meier probability plots showed a significantly improved overall survival after PMRT for the BCL2 positive subgroup, whereas practically no survival improvement was seen after PMRT for the BCL2 negative subgroup. In multivariate analysis of OS, however, no significant interaction was found between BCL2......Purpose. To examine p53 and BCL2 expression in high-risk breast cancer patients randomized to postmastectomy radiotherapy (PMRT). Patients and methods. The present analysis included 1000 of 3 083 high-risk breast cancer patients randomly assigned to PMRT in the DBCG82 b&c studies. Tissue microarray......, Kaplan-Meier probability plots, Log-rank test, and Cox univariate and multivariate regression analyses. Results. p53 accumulation was not significantly associated with increased overall mortality, DM or LRR probability in univariate or multivariate Cox regression analyses. Kaplan-Meier probability plots...

Uncertainty Visualization Using Copula-Based Analysis in Mixed Distribution Models.

Science.gov (United States)

Hazarika, Subhashis; Biswas, Ayan; Shen, Han-Wei

2018-01-01

Distributions are often used to model uncertainty in many scientific datasets. To preserve the correlation among the spatially sampled grid locations in the dataset, various standard multivariate distribution models have been proposed in visualization literature. These models treat each grid location as a univariate random variable which models the uncertainty at that location. Standard multivariate distributions (both parametric and nonparametric) assume that all the univariate marginals are of the same type/family of distribution. But in reality, different grid locations show different statistical behavior which may not be modeled best by the same type of distribution. In this paper, we propose a new multivariate uncertainty modeling strategy to address the needs of uncertainty modeling in scientific datasets. Our proposed method is based on a statistically sound multivariate technique called Copula, which makes it possible to separate the process of estimating the univariate marginals and the process of modeling dependency, unlike the standard multivariate distributions. The modeling flexibility offered by our proposed method makes it possible to design distribution fields which can have different types of distribution (Gaussian, Histogram, KDE etc.) at the grid locations, while maintaining the correlation structure at the same time. Depending on the results of various standard statistical tests, we can choose an optimal distribution representation at each location, resulting in a more cost efficient modeling without significantly sacrificing on the analysis quality. To demonstrate the efficacy of our proposed modeling strategy, we extract and visualize uncertain features like isocontours and vortices in various real world datasets. We also study various modeling criterion to help users in the task of univariate model selection.

Fat stigmatization in television shows and movies: a content analysis.

Science.gov (United States)

Himes, Susan M; Thompson, J Kevin

2007-03-01

To examine the phenomenon of fat stigmatization messages presented in television shows and movies, a content analysis was used to quantify and categorize fat-specific commentary and humor. Fat stigmatization vignettes were identified using a targeted sampling procedure, and 135 scenes were excised from movies and television shows. The material was coded by trained raters. Reliability indices were uniformly high for the seven categories (percentage agreement ranged from 0.90 to 0.98; kappas ranged from 0.66 to 0.94). Results indicated that fat stigmatization commentary and fat humor were often verbal, directed toward another person, and often presented directly in the presence of the overweight target. Results also indicated that male characters were three times more likely to engage in fat stigmatization commentary or fat humor than female characters. To our knowledge, these findings provide the first information regarding the specific gender, age, and types of fat stigmatization that occur frequently in movies and television shows. The stimuli should prove useful in future research examining the role of individual difference factors (e.g., BMI) in the reaction to viewing such vignettes.

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

Science.gov (United States)

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.

Meta-Analysis in Stata Using Gllamm

Science.gov (United States)

Bagos, Pantelis G.

2015-01-01

There are several user-written programs for performing meta-analysis in Stata (Stata Statistical Software: College Station, TX: Stata Corp LP). These include metan, metareg, mvmeta, and glst. However, there are several cases for which these programs do not suffice. For instance, there is no software for performing univariate meta-analysis with…

Resective surgical approach shows a high performance in the management of advanced cases of bisphosphonate-related osteonecrosis of the jaws: a retrospective survey of 347 cases.

Science.gov (United States)

Graziani, Filippo; Vescovi, Paolo; Campisi, Giuseppina; Favia, Gianfranco; Gabriele, Mario; Gaeta, Giovanni Maria; Gennai, Stefano; Goia, Franco; Miccoli, Mario; Peluso, Franco; Scoletta, Matteo; Solazzo, Luigi; Colella, Giuseppe

2012-11-01

The aim of this study was to evaluate the results of the surgical treatment of bisphosphonate-related osteonecrosis of the jaw (BRONJ) in a large cohort. A retrospective cohort multicenter study was designed. Patients were enrolled if they were diagnosed with BRONJ and received operative treatment. Data on demographic, health status, perioperative, and surgical factors were collected retrospectively. The primary outcome variable was a change in BRONJ staging (improvement, worsening, or no change). Interventions were grouped by local debridement and resective surgery. Data were collected for other variables as cofactors. Univariate analysis and logistic regressions were then performed. Of the 347 BRONJ-affected subjects, 59% showed improvement, 30% showed no change, and 11% showed worsening. Improvement was observed in 49% of cases treated with local debridement and 68% of cases treated with resective surgery. Multivariate analysis indicated that maxillary location, resective surgery, and no additional corticosteroid treatment were associated with a positive outcome. Surgical treatment of BRONJ appeared to be more effective when resective procedures were performed. Nonetheless, other factors, such as the absence of symptoms and the types of drug administration, should be taken into account before clinical decisions are made. Copyright © 2012 American Association of Oral and Maxillofacial Surgeons. Published by Elsevier Inc. All rights reserved.

A comparison of two follow-up analyses after multiple analysis of variance, analysis of variance, and descriptive discriminant analysis: A case study of the program effects on education-abroad programs

Science.gov (United States)

Alvin H. Yu; Garry. Chick

2010-01-01

This study compared the utility of two different post-hoc tests after detecting significant differences within factors on multiple dependent variables using multivariate analysis of variance (MANOVA). We compared the univariate F test (the Scheffé method) to descriptive discriminant analysis (DDA) using an educational-tour survey of university study-...

The discriminatory analysis about factors correlative with the early hypothyroidism after 131I therapy for hyperthyroidism

International Nuclear Information System (INIS)

Xiong Lingjing; Liang Changhua; Deng Haoyu; Li Xinhui; Hu Shuo

2002-01-01

Objective: To explore the factors correlative with the early hypothyroidism after 131 I therapy for Graves' hyperthyroidism so as to cure it and decrease the early hypothyroidism occurring and prevent it from becoming irreversible hypothyroidism. Methods: Logistic regression discriminatory analysis by introducing multiple factors from group data and forward stepwise selection of 11 independent variables of 240 hyperthyroidism patients from clinical data and 1 dependent variable from follow-up data after 131 I therapy was conducted. Univariate analysis of each observed factor was performed, too. Results: (1)The results of multivariate analysis showed that the age of patients, the weight of thyroid, the suffering situation, the curve of 131 I absorption rate and the giving 131 I dosage/g thyroid tissue were correlated to early hypothyroidism. The results of univariate analysis showed that the weight of thyroid, the highest absorption of 131 I, the total treatment dosage of 131 I were correlated to early hypothyroidism. (2) The logistic regression equation was statistically significant. (3) The positive and negative predicting accuracy of the early hypothyroidism occurring was 64.08 %, 78.83 %, respectively, the overall predicting accuracy was 72.50%. Conclusions: The dosage of 131 I for treatment of hyperthyroid is the key factor according to the five correlative factors which are relating to the early hypothyroidism and the discriminatory classification. Enhanced follow-up and in time supplement of thyroid hormone are important measures for preventing the early hypothyroidism from becoming irreversible hypothyroidism

Comparison of spectrum normalization techniques for univariate ...

Indian Academy of Sciences (India)

2016-02-29

Feb 29, 2016 ... 1Fuel Chemistry Division, Bhabha Atomic Research Centre, Mumbai 400 085, India. 2Department of ... their three-point smoothing methods were studied using LIBS for quantification of Cr, Mn and Ni ... nique for the qualitative and quantitative analysis of the samples. .... SEP is a type of mean square error.

Analysis on risk factors for post-stroke emotional incontinence

Directory of Open Access Journals (Sweden)

Xiao-chun ZHANG

2018-01-01

Full Text Available Objective To investigate the occurrence rate and related risk factors for post-stroke emotional incontinence (PSEI. Methods The clinical data [sex, age, body mass index (BMI, education, marital status, medical history (hypertension, heart disease, diabetes, hyperlipemia, smoking and drinking and family history of stroke] of 162 stroke patients were recorded. Serum homocysteine (Hcy level was examined. Head CT and/or MRI were used to indicate stroke subtype, site of lesion and number of lesion. Diagnostic and Statistical Manual of Mental Disorders Fifth Edition (DSM-Ⅴ Chinese version and Hamilton Depression Rating Scale-17 Items (HAMD-17 were used to evaluate the degree of depression. House diagnostic standard was used to diagnose PSEI. Univariate and multivariate backward Logistic regression analysis was used to screen related risk factor for PSEI. Spearman rank correlation analysis was used to discuss the correlation between PSEI and post-stroke depression (PSD. Results Among 162 stroke patients, 12 cases were diagnosed as PSEI (7.41% . The ratio of age < 60 years in PSEI group was significantly higher than non-PSEI group (P = 0.045. The ratio of smoking in PSEI group was significantly lower than non-PSEI group (P = 0.036. Univariate and multivariate backward Logistic regression analysis showed age < 60 years was independent risk factor for PSEI (OR = 4.000, 95%CI: 1.149-13.924; P = 0.029. Ten cases were combined with PSD in 12 PSEI patients, and the co-morbidity rate of PSEI and PSD was83.33%. Spearman rank correlation analysis showed PSEI was positively related to PSD (rs = 0.305, P = 0.000. Conclusions PSEI is common affective disorder in stroke patients, which easily happens in patients under 60 years of age. DOI: 10.3969/j.issn.1672-6731.2017.12.010

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

Science.gov (United States)

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.

Prevalence of gestational diabetes mellitus in Europe: A meta-analysis.

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Eades, Claire E; Cameron, Dawn M; Evans, Josie M M

2017-07-01

Estimates of the prevalence of gestational diabetes vary widely. It is important to have a clear understanding of the prevalence of this condition to be able to plan interventions and health care provision. This paper describes a meta-analysis of primary research data reporting the prevalence of gestational diabetes mellitus in the general pregnant population of developed countries in Europe. Four electronic databases were systematically searched in May 2016. English language articles reporting gestational diabetes mellitus prevalence using universal screening in general pregnant population samples from developed countries in Europe were included. All papers identified by the search were screened by one author, and then half screened independently by a second author and half by a third author. Data were extracted by one author. Values for the measures of interest were combined using a random effects model and analysis of the effects of moderator variables was carried out. A total of 3258 abstracts were screened, with 40 studies included in the review. Overall prevalence of gestational diabetes mellitus was 5.4% (3.8-7.8). Maternal age, year of data collection, country, area of Europe, week of gestation at testing, and diagnostic criteria were found to have a significant univariate effect on GDM prevalence, and area, week of gestation at testing and year of data collection remained statistically significant in multivariate analysis. Quality category was significant in multivariate but not univariate analysis. This meta-analysis shows prevalence of GDM that is at the upper end of previous estimates in Europe. Copyright © 2017 Elsevier B.V. All rights reserved.

Prognostic impact of the level of nodal involvement: retrospective analysis of patients with advanced oral squamous cell carcinoma.

Science.gov (United States)

Murakami, R; Nakayama, H; Semba, A; Hiraki, A; Nagata, M; Kawahara, K; Shiraishi, S; Hirai, T; Uozumi, H; Yamashita, Y

2017-01-01

We retrospectively evaluated the prognostic impact of the level of nodal involvement in patients with advanced oral squamous cell carcinoma (SCC). Between 2005 and 2010, 105 patients with clinical stage III or IV oral SCC had chemoradiotherapy preoperatively. Clinical (cN) and pathological nodal (pN) involvement was primarily at levels Ib and II. We defined nodal involvement at levels Ia and III-V as anterior and inferior extensions, respectively, and recorded such findings as extensive. With respect to pretreatment variables (age, clinical stage, clinical findings of the primary tumour, and nodal findings), univariate analysis showed that extensive cN was the only significant factor for overall survival (hazard ratio [HR], 3.27; 95% CI 1.50 to 7.13; p=0.001). Univariate analysis showed that all pN findings, including the nodal classification (invaded nodes, multiple, and contralateral) and extensive involvement were significant, and multivariate analysis confirmed that extensive pN (HR 4.71; 95% CI 1.85 to 11.97; p=0.001) and multiple pN (HR 2.59; 95% CI 1.10 to 6.09; p=0.029) were independent predictors of overall survival. Assessment based on the level of invaded neck nodes may be a better predictor of survival than the current nodal classification. Copyright © 2016 The British Association of Oral and Maxillofacial Surgeons. Published by Elsevier Ltd. All rights reserved.

Preoperative C-reactive protein as a prognostic predictor for upper tract urothelial carcinoma: A systematic review and meta-analysis.

Science.gov (United States)

Luo, You; Fu, Sheng Jun; She, Dong Li; Xiong, H U; Yang, L I

2015-07-01

Upper tract urothelial carcinoma (UTUC) is a relatively rare and highly aggressive tumor. However, the prognosis of UTUC is rarely predicted accurately due to the lack of reliable biomarkers. C-reactive protein (CRP) has been found to be correlated with several types of cancer. In this study, we performed a systematic review and meta-analysis to determine the association between CRP levels and prognosis in UTUC. A computerized search was conducted through PubMed, Embase, Web of Science, the Cochrane Library and CBM databases to identify clinical studies that have evaluated the association between preoperative CRP levels and prognosis of UTUC. The prognostic outcomes included recurrence-free survival (RFS), cancer-specific survival (CSS) and overall survival (OS). We extracted and synthesized corresponding hazard ratios (HRs) and confidence intervals (CIs) using Review Manager 5.3 software. We identified 7 retrospective cohort studies including a total of 1,919 patients and analyzed these studies using univariate and multivariate models. Our meta-analysis results revealed that RFS and CSS were significantly different between patients with elevated CRP levels and those with low CRP levels (P<0.0001 and P<0.00001, respectively); however, that was not the case for OS (P=0.22) in the multivariate or the univariate model. The pooled HR of RFS was 2.90 (95% CI: 1.87-4.51, P<0.00001) in the univariate analysis and 1.57 (95% CI: 1.26-1.97, P<0.0001) in the multivariate analysis. The pooled HRs of CSS were 2.78 (95% CI: 1.75-4.43, P<0.0001) and 1.64 (95% CI: 1.32-2.03, P<0.00001) in the univariate and multivariate analysis, respectively. However, the pooled HRs of OS were not significant in the univariate [1.24 (95% CI: 0.72-2.15, P=0.43)] or the multivariate analysis [1.24 (95% CI: 0.88-1.75, P=0.22)]. In conclusion, our meta-analysis results suggested that CRP level may be a prognostic predictor in UTUC.

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

Science.gov (United States)

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

2009-01-01

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

Outcome predictors in the management of intramedullary classic ependymoma: An integrative survival analysis.

Science.gov (United States)

Wang, Yinqing; Cai, Ranze; Wang, Rui; Wang, Chunhua; Chen, Chunmei

2018-06-01

This is a retrospective study.The aim of this study was to illustrate the survival outcomes of patients with classic ependymoma (CE) and identify potential prognostic factors.CE is the most common category of spinal ependymomas, but few published studies have discussed predictors of the survival outcome.A Boolean search of the PubMed, Embase, and OVID databases was conducted by 2 investigators independently. The objects were intramedullary grade II ependymoma according to 2007 WHO classification. Univariate Kaplan-Meier analysis and Log-Rank tests were performed to identify variables associated with progression-free survival (PFS) or overall survival (OS). Multivariate Cox regression was performed to assess hazard ratios (HRs) with 95% confidence intervals (95% CIs). Statistical analysis was performed by SPSS version 23.0 (IBM Corp.) with statistical significance defined as P analysis showed that patients who had undergone total resection (TR) had better PFS and OS than those with subtotal resection (STR) and biopsy (P = .002, P = .004, respectively). Within either univariate or multivariate analysis (P = .000, P = .07, respectively), histological type was an independent prognostic factor for PFS of CE [papillary type: HR 0.002, 95% CI (0.000-0.073), P = .001, tanycytic type: HR 0.010, 95% CI (0.000-0.218), P = .003].It was the first integrative analysis of CE to elucidate the correlation between kinds of factors and prognostic outcomes. Definite histological type and safely TR were foundation of CE's management. 4.

The use of principal components and univariate charts to control multivariate processes

Directory of Open Access Journals (Sweden)

Marcela A. G. Machado

2008-04-01

Full Text Available In this article, we evaluate the performance of the T² chart based on the principal components (PC X chart and the simultaneous univariate control charts based on the original variables (SU charts or based on the principal components (SUPC charts. The main reason to consider the PC chart lies on the dimensionality reduction. However, depending on the disturbance and on the way the original variables are related, the chart is very slow in signaling, except when all variables are negatively correlated and the principal component is wisely selected. Comparing the SU , the SUPC and the T² charts we conclude that the SU X charts (SUPC charts have a better overall performance when the variables are positively (negatively correlated. We also develop the expression to obtain the power of two S² charts designed for monitoring the covariance matrix. These joint S² charts are, in the majority of the cases, more efficient than the generalized variance chart.Neste artigo, avaliamos o desempenho do gráfico de T² baseado em componentes principais (gráfico PC e dos gráficos de controle simultâneos univariados baseados nas variáveis originais (gráfico SU X ou baseados em componentes principais (gráfico SUPC. A principal razão para o uso do gráfico PC é a redução de dimensionalidade. Entretanto, dependendo da perturbação e da correlação entre as variáveis originais, o gráfico é lento em sinalizar, exceto quando todas as variáveis são negativamente correlacionadas e a componente principal é adequadamente escolhida. Comparando os gráficos SU X, SUPC e T² concluímos que o gráfico SU X (gráfico SUPC tem um melhor desempenho global quando as variáveis são positivamente (negativamente correlacionadas. Desenvolvemos também uma expressão para obter o poder de detecção de dois gráficos de S² projetados para controlar a matriz de covariâncias. Os gráficos conjuntos de S² são, na maioria dos casos, mais eficientes que o gr

[Analysis of the risk factors of acute respiratory distress syndrome of Berlin new definition in patients with sepsis in emergency department].

Science.gov (United States)

Qiao, Liang; Liu, Zhi

2015-07-01

To discuss the risk factors of acute respiratory distress syndrome (ARDS) in patients with sepsis in emergency department. 312 patients with sepsis admitted to Department of Emergency of China Medical University Affiliated First Hospital were retrospectively analyzed, and they were divided into two groups according to development of ARDS, which was defined according to the Berlin new definition. The age, gender, vital signs, laboratory results, underlying disease, the mortality in emergency department sepsis (MEDS) score and lung injury prediction score (LIPS) were collected. Univariate analysis was done for each parameter. Statistical significance results were evaluated by multivariate logistic regression analysis. Receiver operating characteristic (ROC) curve was plotted to analyze the predictive value of the parameter for ARDS. The incidence of sepsis-related ARDS was 11.2% (35/312). Within 35 cases of ARDS, there were 10 cases of mild ARDS, 18 cases of moderate ARDS, and 7 cases of severe ARDS. Univariate analysis showed that age (t=-2.134, P=0.035), oxygenation index (t=-4.245, P=0.001), arterial lactate (Lac, t=6.245, Pemergency department, which may be a reference for the early clinical diagnosis of ARDS.

Panel data analysis using EViews

CERN Document Server

Agung, I Gusti Ngurah

2013-01-01

A comprehensive and accessible guide to panel data analysis using EViews software This book explores the use of EViews software in creating panel data analysis using appropriate empirical models and real datasets. Guidance is given on developing alternative descriptive statistical summaries for evaluation and providing policy analysis based on pool panel data. Various alternative models based on panel data are explored, including univariate general linear models, fixed effect models and causal models, and guidance on the advantages and disadvantages of each one is given. Panel Data Analysis

Will initiatives to promote hydroelectricity consumption be effective? Evidence from univariate and panel LM unit root tests with structural breaks

International Nuclear Information System (INIS)

Lean, Hooi Hooi; Smyth, Russell

2014-01-01

This paper examines whether initiatives to promote hydroelectricity consumption are likely to be effective by applying univariate and panel Lagrange Multiplier (LM) unit root tests to hydroelectricity consumption in 55 countries over the period 1965–2011. We find that for the panel, as well as about four-fifths of individual countries, that hydroelectricity consumption is stationary. This result implies that shocks to hydroelectricity consumption in most countries will only result in temporary deviations from the long-run growth path. An important consequence of this finding is that initiatives designed to have permanent positive effects on hydroelectricity consumption, such as large-scale dam construction, are unlikely to be effective in increasing the share of hydroelectricity, relative to consumption of fossil fuels. - Highlights: • Applies unit root tests to hydroelectricity consumption. • Hydroelectricity consumption is stationary. • Shocks to hydroelectricity consumption result in temporary deviations from the long-run growth path

Analysis of risk factors for non-anastomotic biliary stricture following liver transplantation

Directory of Open Access Journals (Sweden)

WU Xiaofeng

2013-06-01

Full Text Available ObjectiveTo investigate the risk factors for non-anastomotic biliary stricture (NABS following liver transplantation. MethodsA retrospective analysis was performed on 175 patients who underwent liver transplantation from January 2004 to December 2010 to analyze the risk factors for NABS, which included sex, age, primary disease, blood type, T-tube placement, acute rejection, biliary tract infection, cytomegalovirus infection, Child-Pugh score, cold ischemia time, warm ischemia time, duration of anhepatic phase, and mean hepatic artery blood flow within one week after operation. These patients were divided into early group, who underwent operation from January 2004 to December 2006, and late group, who underwent operation from January 2007 to December 2010; each group was further divided into two subgroups according to whether they developed NABS. The risk factors for NABS were determined by univariate and multivariate logistic regression analyses. ResultsThe univariate logistic regression analysis showed that the risk factors for NABS were biliary tract infection, T-tube placement, and acute rejection in the early group (P＜0.05 and that acute rejection was the risk factor in the late group (P=0003. The multivariate logistic regression analysis showed that acute rejection was significantly associated with NABS in the early group (P=0.014. ConclusionThe risk factors for NABS following liver transplantation from January 2004 to December 2006; biliary tract infection and T-tube placement could be prevented by perioperative interventions, thus reducing the incidence of NABS. The incidence of acute rejection was reduced from January 2007 to December 2010, but it was still significantly associated with NABS.

Evaluation of genetic diversity among soybean (Glycine max) genotypes using univariate and multivariate analysis.

Science.gov (United States)

Oliveira, M M; Sousa, L B; Reis, M C; Silva Junior, E G; Cardoso, D B O; Hamawaki, O T; Nogueira, A P O

2017-05-31

The genetic diversity study has paramount importance in breeding programs; hence, it allows selection and choice of the parental genetic divergence, which have the agronomic traits desired by the breeder. This study aimed to characterize the genetic divergence between 24 soybean genotypes through their agronomic traits, using multivariate clustering methods to select the potential genitors for the promising hybrid combinations. Six agronomic traits evaluated were number of days to flowering and maturity, plant height at flowering and maturity, insertion height of the first pod, and yield. The genetic divergence evaluated by multivariate analysis that esteemed first the Mahalanobis' generalized distance (D 2 ), then the clustering using Tocher's optimization methods, and then the unweighted pair group method with arithmetic average (UPGMA). Tocher's optimization method and the UPGMA agreed with the groups' constitution between each other, the formation of eight distinct groups according Tocher's method and seven distinct groups using UPGMA. The trait number of days for flowering (45.66%) was the most efficient to explain dissimilarity between genotypes, and must be one of the main traits considered by the breeder in the moment of genitors choice in soybean-breeding programs. The genetic variability allowed the identification of dissimilar genotypes and with superior performances. The hybridizations UFU 18 x UFUS CARAJÁS, UFU 15 x UFU 13, and UFU 13 x UFUS CARAJÁS are promising to obtain superior segregating populations, which enable the development of more productive genotypes.

Multivariate Meta-Analysis Using Individual Participant Data

Science.gov (United States)

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…

Objective Bayesian Analysis of Skew- t Distributions

KAUST Repository

BRANCO, MARCIA D'ELIA; GENTON, MARC G.; LISEO, BRUNERO

2012-01-01

We study the Jeffreys prior and its properties for the shape parameter of univariate skew-t distributions with linear and nonlinear Student's t skewing functions. In both cases, we show that the resulting priors for the shape parameter are symmetric

Analysis of association of clinical aspects and IL1B tagSNPs with severe preeclampsia.

Science.gov (United States)

Leme Galvão, Larissa Paes; Menezes, Filipe Emanuel; Mendonca, Caio; Barreto, Ikaro; Alvim-Pereira, Claudia; Alvim-Pereira, Fabiano; Gurgel, Ricardo

2016-01-01

This study investigates the association between IL1B genotypes using a tag SNP (single polymorphism) approach, maternal and environmental factors in Brazilian women with severe preeclampsia. A case-control study with a total of 456 patients (169 preeclamptic women and 287 controls) was conducted in the two reference maternity hospitals of Sergipe state, Northeast Brazil. A questionnaire was administered and DNA was extracted to genotype the population for four tag SNPs of the IL1Beta: rs 1143643, rs 1143633, rs 1143634 and rs 1143630. Haplotype association analysis and p-values were calculated using the THESIAS test. Odds ratio (OR) estimation, confidence interval (CI) and multivariate logistic regression were performed. High pregestational body mass index (pre-BMI), first gestation, cesarean section, more than six medical visits, low level of consciousness on admission and TC and TT genotype in rs1143630 of IL1Beta showed association with the preeclamptic group in univariate analysis. After multivariate logistic regression pre-BMI, first gestation and low level of consciousness on admission remained associated. We identified an association between clinical variables and preeclampsia. Univariate analysis suggested that inflammatory process-related genes, such as IL1B, may be involved and should be targeted in further studies. The identification of the genetic background involved in preeclampsia host response modulation is mandatory in order to understand the preeclampsia process.

Transforming Parent-Child Interaction in Family Routines: Longitudinal Analysis with Families of Children with Developmental Disabilities.

Science.gov (United States)

Lucyshyn, Joseph M; Fossett, Brenda; Bakeman, Roger; Cheremshynski, Christy; Miller, Lynn; Lohrmann, Sharon; Binnendyk, Lauren; Khan, Sophia; Chinn, Stephen; Kwon, Samantha; Irvin, Larry K

2015-12-01

The efficacy and consequential validity of an ecological approach to behavioral intervention with families of children with developmental disabilities was examined. The approach aimed to transform coercive into constructive parent-child interaction in family routines. Ten families participated, including 10 mothers and fathers and 10 children 3-8 years old with developmental disabilities. Thirty-six family routines were selected (2 to 4 per family). Dependent measures included child problem behavior, routine steps completed, and coercive and constructive parent-child interaction. For each family, a single case, multiple baseline design was employed with three phases: baseline, intervention, and follow-up. Visual analysis evaluated the functional relation between intervention and improvements in child behavior and routine participation. Nonparametric tests across families evaluated the statistical significance of these improvements. Sequential analyses within families and univariate analyses across families examined changes from baseline to intervention in the percentage and odds ratio of coercive and constructive parent-child interaction. Multiple baseline results documented functional or basic effects for 8 of 10 families. Nonparametric tests showed these changes to be significant. Follow-up showed durability at 11 to 24 months postintervention. Sequential analyses documented the transformation of coercive into constructive processes for 9 of 10 families. Univariate analyses across families showed significant improvements in 2- and 4-step coercive and constructive processes but not in odds ratio. Results offer evidence of the efficacy of the approach and consequential validity of the ecological unit of analysis, parent-child interaction in family routines. Future studies should improve efficiency, and outcomes for families experiencing family systems challenges.

Clinical Characteristics and Predictors of Outcome for Onconeural Antibody-Associated Disorders: A Retrospective Analysis

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Shaohua Liao

2017-11-01

Full Text Available ObjectiveTo describe and analyze the clinical characteristics, laboratory data, management, and outcome of patients with onconeural antibody-associated disorders (OAAD and identify predictors for poor outcome.MethodsThis was a retrospective review of all patients with potential OAAD, who were hospitalized in Jinan General Hospital between September 2009 and July 2017. We clarified the diagnosis, collected comprehensive information and categorized patients into three groups: paraneoplastic neurological disorders (PNDs, autoimmune encephalitis (AE, and possible OAAD. Within the three groups, we analyzed a range of clinical and laboratory parameters and used univariate and multivariate regression analysis to identify predictors for poor outcome [modified Rankin Scale (mRS = 3–6].ResultsFrom 158 patients, we identified 70 who fulfilled the criteria for OAAD, including 44 men (62.9% and 26 women (37.1%. There were 38 patients (54.3% in the PNDs group, 14 patients (20% in the AE group, and 18 patients (25.7% in the possible OAAD group. After the last follow-up, 14 (36.8%, 9 (64.2%, and 12 (66.7% had a good outcome (mRS = 0–2. However, 6 (15.8%, 2 (14.3%, and 3 (16.7% died, respectively. Univariate analysis showed that duration prior to the hospital (p = 0.0224 and urinary incontinence/retention (p = 0.0043 were associated with poor outcome (mRS = 3–6. After multivariate regression analysis, urinary incontinence/retention (p = 0.0388 and an immunocompromised state (p = 0.0247 remained as significant factors for poor outcome.ConclusionUrinary incontinence/retention and an immunocompromised state represent significant predictors of a worse prognosis for patients with OAAD. By contrast, cerebrospinal fluid analysis showed that serum autoantibodies and tumor markers, the function of crucial organs, electrophysiology, and radiological findings were not associated with a poor outcome.

A Pilot Study of Reasons and Risk Factors for "No-Shows" in a Pediatric Neurology Clinic.

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Guzek, Lindsay M; Fadel, William F; Golomb, Meredith R

2015-09-01

Missed clinic appointments lead to decreased patient access, worse patient outcomes, and increased healthcare costs. The goal of this pilot study was to identify reasons for and risk factors associated with missed pediatric neurology outpatient appointments ("no-shows"). This was a prospective cohort study of patients scheduled for 1 week of clinic. Data on patient clinical and demographic information were collected by record review; data on reasons for missed appointments were collected by phone interviews. Univariate and multivariate analyses were conducted using chi-square tests and multiple logistic regression to assess risk factors for missed appointments. Fifty-nine (25%) of 236 scheduled patients were no-shows. Scheduling conflicts (25.9%) and forgetting (20.4%) were the most common reasons for missed appointments. When controlling for confounding factors in the logistic regression, Medicaid (odds ratio 2.36), distance from clinic, and time since appointment was scheduled were associated with missed appointments. Further work in this area is needed. © The Author(s) 2014.

A comparisonof lymphocytic thyroiditis with papillary thyroid carcinoma showing suspicious ultrasonographic findings in a background of heterogeneous parenchyma

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Nam, Sang Yu; Shin, Jung Hee; Ko, Eun Young; Hahn, Soo Yeon [Dept. of Radiology, Samsung Medicine Center, Sungkyunkwan University School of Medicine, Seoul (Korea, Republic of)

2015-01-15

The aim of this study was to compare ultrasonographic features in patients with lymphocytic thyroiditis (LT) and papillary thyroid carcinoma (PTC) having suspicious thyroid nodule(s) in a background of heterogeneous parenchyma and to determine the clinical and radiological predictors of malignancy. We reviewed the cases of 100 patients who underwent ultrasonography between April 2011 and October 2012, and showed suspicious thyroid nodule(s) in a background of heterogeneous parenchyma. Eight patients who did not undergo ultrasonography-guided fineneedle aspiration cytology (FNAC) and 34 cases of follow-up ultrasonography after initial FNAC were excluded. We compared the benign and malignant nodules in terms of their clinical and radiological factors. For the 58 nodules including 31 LTs (53.4%) and 27 PTCs (46.6%), the mean tumor sizes of the two groups were 0.96 cm for LT and 0.97 cm for PTC. A univariate analysis revealed that PTCs were more frequent in patients younger than 45 years and having microcalcifications than was LT. An independent predictor of PTC after adjustment was an age of <45 years. LT mimics malignancy in a background of heterogeneous parenchyma on ultrasonography. A young age of <45 years is the most important predictor of malignancy in this condition.

Historical and future drought in Bangladesh using copula-based bivariate regional frequency analysis

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Mortuza, Md Rubayet; Moges, Edom; Demissie, Yonas; Li, Hong-Yi

2018-02-01

The study aims at regional and probabilistic evaluation of bivariate drought characteristics to assess both the past and future drought duration and severity in Bangladesh. The procedures involve applying (1) standardized precipitation index to identify drought duration and severity, (2) regional frequency analysis to determine the appropriate marginal distributions for both duration and severity, (3) copula model to estimate the joint probability distribution of drought duration and severity, and (4) precipitation projections from multiple climate models to assess future drought trends. Since drought duration and severity in Bangladesh are often strongly correlated and do not follow same marginal distributions, the joint and conditional return periods of droughts are characterized using the copula-based joint distribution. The country is divided into three homogeneous regions using Fuzzy clustering and multivariate discordancy and homogeneity measures. For given severity and duration values, the joint return periods for a drought to exceed both values are on average 45% larger, while to exceed either value are 40% less than the return periods from the univariate frequency analysis, which treats drought duration and severity independently. These suggest that compared to the bivariate drought frequency analysis, the standard univariate frequency analysis under/overestimate the frequency and severity of droughts depending on how their duration and severity are related. Overall, more frequent and severe droughts are observed in the west side of the country. Future drought trend based on four climate models and two scenarios showed the possibility of less frequent drought in the future (2020-2100) than in the past (1961-2010).

Meta-Analysis of Effect Sizes Reported at Multiple Time Points Using General Linear Mixed Model

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Musekiwa, Alfred; Manda, Samuel O. M.; Mwambi, Henry G.; Chen, Ding-Geng

2016-01-01

Meta-analysis of longitudinal studies combines effect sizes measured at pre-determined time points. The most common approach involves performing separate univariate meta-analyses at individual time points. This simplistic approach ignores dependence between longitudinal effect sizes, which might result in less precise parameter estimates. In this paper, we show how to conduct a meta-analysis of longitudinal effect sizes where we contrast different covariance structures for dependence between effect sizes, both within and between studies. We propose new combinations of covariance structures for the dependence between effect size and utilize a practical example involving meta-analysis of 17 trials comparing postoperative treatments for a type of cancer, where survival is measured at 6, 12, 18 and 24 months post randomization. Although the results from this particular data set show the benefit of accounting for within-study serial correlation between effect sizes, simulations are required to confirm these results. PMID:27798661

Elements of nonlinear time series analysis and forecasting

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De Gooijer, Jan G

2017-01-01

This book provides an overview of the current state-of-the-art of nonlinear time series analysis, richly illustrated with examples, pseudocode algorithms and real-world applications. Avoiding a “theorem-proof” format, it shows concrete applications on a variety of empirical time series. The book can be used in graduate courses in nonlinear time series and at the same time also includes interesting material for more advanced readers. Though it is largely self-contained, readers require an understanding of basic linear time series concepts, Markov chains and Monte Carlo simulation methods. The book covers time-domain and frequency-domain methods for the analysis of both univariate and multivariate (vector) time series. It makes a clear distinction between parametric models on the one hand, and semi- and nonparametric models/methods on the other. This offers the reader the option of concentrating exclusively on one of these nonlinear time series analysis methods. To make the book as user friendly as possible...

Survival benefit of radiotherapy to patients with small cell esophagus carcinoma: an analysis of Surveillance Epidemiology and End Results (SEER) data.

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Song, Yaqi; Wang, Wanwei; Tao, Guangzhou; Zhu, Weiguo; Zhou, Xilei; Pan, Peng

2016-03-29

Small cell esophageal carcinoma (SCEC) is a rare malignant tumor. So far, few studies are found to research the effect of radiotherapy (RT) to it. This study is designed to explore the prognostic factors, and analyze survival benefit of RT to patients with SCEC. Patients with SCEC were more likely to be in female, older, higher disease stage than those with non-small cell esophageal carcinoma. RT was used in more than 50% SCEC patients. RT tended be reduced as the disease stage raise in SCEC. Univariate and multivariate analysis showed that age, year, disease stage, and RT were the prognostic factors of survival (P 0.05) and nearly 30% risks of death in distant stage (P > 0.05). SCEC patients between 1973 and 2012 were searched from the Surveillance Epidemiology and End Results (SEER) data. Clinical factors including age, year, sex, race, stage, surgery, and RT were summarized. Univariate and multivariate analysis were performed to explore the independent prognostic factors of SCEC. Cox regression survival analysis was performed to evaluate the effect of RT to SCEC based on different stages. Stage, age, year, and RT are independent prognostic factors of SCEC. Survival benefit of RT exists in any disease stage, but is only statistically significant in localized stage of SCEC.

Relationship between climatic variables and the variation in bulk tank milk composition using canonical correlation analysis.

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Stürmer, Morgana; Busanello, Marcos; Velho, João Pedro; Heck, Vanessa Isabel; Haygert-Velho, Ione Maria Pereira

2018-06-04

A number of studies have addressed the relations between climatic variables and milk composition, but these works used univariate statistical approaches. In our study, we used a multivariate approach (canonical correlation) to study the impact of climatic variables on milk composition, price, and monthly milk production at a dairy farm using bulk tank milk data. Data on milk composition, price, and monthly milk production were obtained from a dairy company that purchased the milk from the farm, while climatic variable data were obtained from the National Institute of Meteorology (INMET). The data are from January 2014 to December 2016. Univariate correlation analysis and canonical correlation analysis were performed. Few correlations between the climatic variables and milk composition were found using a univariate approach. However, using canonical correlation analysis, we found a strong and significant correlation (r c  = 0.95, p value = 0.0029). Lactose, ambient temperature measures (mean, minimum, and maximum), and temperature-humidity index (THI) were found to be the most important variables for the canonical correlation. Our study indicated that 10.2% of the variation in milk composition, pricing, and monthly milk production can be explained by climatic variables. Ambient temperature variables, together with THI, seem to have the most influence on variation in milk composition.

Handbook of univariate and multivariate data analysis and interpretation with SPSS

CERN Document Server

Ho, Robert

2006-01-01

Many statistics texts tend to focus more on the theory and mathematics underlying statistical tests than on their applications and interpretation. This can leave readers with little understanding of how to apply statistical tests or how to interpret their findings. While the SPSS statistical software has done much to alleviate the frustrations of social science professionals and students who must analyze data, they still face daunting challenges in selecting the proper tests, executing the tests, and interpreting the test results.With emphasis firmly on such practical matters, this handbook se

A Quantitative Analysis of the Extrinsic and Intrinsic Turnover Factors of Relational Database Support Professionals

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Takusi, Gabriel Samuto

2010-01-01

This quantitative analysis explored the intrinsic and extrinsic turnover factors of relational database support specialists. Two hundred and nine relational database support specialists were surveyed for this research. The research was conducted based on Hackman and Oldham's (1980) Job Diagnostic Survey. Regression analysis and a univariate ANOVA…

Fast Detection of Copper Content in Rice by Laser-Induced Breakdown Spectroscopy with Uni- and Multivariate Analysis

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Ye, Lanhan; Song, Kunlin; Shen, Tingting

2018-01-01

Fast detection of heavy metals is very important for ensuring the quality and safety of crops. Laser-induced breakdown spectroscopy (LIBS), coupled with uni- and multivariate analysis, was applied for quantitative analysis of copper in three kinds of rice (Jiangsu rice, regular rice, and Simiao rice). For univariate analysis, three pre-processing methods were applied to reduce fluctuations, including background normalization, the internal standard method, and the standard normal variate (SNV). Linear regression models showed a strong correlation between spectral intensity and Cu content, with an R2 more than 0.97. The limit of detection (LOD) was around 5 ppm, lower than the tolerance limit of copper in foods. For multivariate analysis, partial least squares regression (PLSR) showed its advantage in extracting effective information for prediction, and its sensitivity reached 1.95 ppm, while support vector machine regression (SVMR) performed better in both calibration and prediction sets, where Rc2 and Rp2 reached 0.9979 and 0.9879, respectively. This study showed that LIBS could be considered as a constructive tool for the quantification of copper contamination in rice. PMID:29495445

Proteome Analysis of the Plant Pathogenic Fungus Monilinia laxa Showing Host Specificity

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Olja Bregar

2012-01-01

Full Text Available Brown rot fungus Monilinia laxa (Aderh. & Ruhl. Honey is an important plant pathogen in stone and pome fruits in Europe. We applied a proteomic approach in a study of M. laxa isolates obtained from apples and apricots in order to show the host specifity of the isolates and to analyse differentially expressed proteins in terms of host specifity, fungal pathogenicity and identification of candidate proteins for diagnostic marker development. Extracted mycelium proteins were separated by 2-D electrophoresis (2-DE and visualized by Coomassie staining in a non-linear pH range of 3–11 and Mr of 14–116 kDa. We set up a 2-DE reference map of M. laxa, resolving up to 800 protein spots, and used it for image analysis. The average technical coefficient of variance (13 % demonstrated a high reproducibility of protein extraction and 2-D polyacrylamide gel electrophoresis (2-DE PAGE, and the average biological coefficient of variance (23 % enabled differential proteomic analysis of the isolates. Multivariate statistical analysis (principal component analysis discriminated isolates from two different hosts, providing new data that support the existence of a M. laxa specialized form f. sp. mali, which infects only apples. A total of 50 differentially expressed proteins were further analyzed by LC-MS/MS, yielding 41 positive identifications. The identified mycelial proteins were functionally classified into 6 groups: amino acid and protein metabolism, energy production, carbohydrate metabolism, stress response, fatty acid metabolism and other proteins. Some proteins expressed only in apple isolates have been described as virulence factors in other fungi. The acetolactate synthase was almost 11-fold more abundant in apple-specific isolates than in apricot isolates and it might be implicated in M. laxa host specificity. Ten proteins identified only in apple isolates are potential candidates for the development of M. laxa host-specific diagnostic markers.

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

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

2016-05-01

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

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

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

2001-01-01

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

Time series analysis in the social sciences the fundamentals

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Shin, Youseop

2017-01-01

Times Series Analysis in the Social Sciences is a practical and highly readable introduction written exclusively for students and researchers whose mathematical background is limited to basic algebra. The book focuses on fundamental elements of time series analysis that social scientists need to understand so they can employ time series analysis for their research and practice. Through step-by-step explanations and using monthly violent crime rates as case studies, this book explains univariate time series from the preliminary visual analysis through the modeling of seasonality, trends, and re

Statistical Analysis of Research Data | Center for Cancer Research

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Recent advances in cancer biology have resulted in the need for increased statistical analysis of research data. The Statistical Analysis of Research Data (SARD) course will be held on April 5-6, 2018 from 9 a.m.-5 p.m. at the National Institutes of Health's Natcher Conference Center, Balcony C on the Bethesda Campus. SARD is designed to provide an overview on the general principles of statistical analysis of research data.  The first day will feature univariate data analysis, including descriptive statistics, probability distributions, one- and two-sample inferential statistics.

Appearance matters: neural correlates of food choice and packaging aesthetics.

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Laura N Van der Laan

Full Text Available Neuro-imaging holds great potential for predicting choice behavior from brain responses. In this study we used both traditional mass-univariate and state-of-the-art multivariate pattern analysis to establish which brain regions respond to preferred packages and to what extent neural activation patterns can predict realistic low-involvement consumer choices. More specifically, this was assessed in the context of package-induced binary food choices. Mass-univariate analyses showed that several regions, among which the bilateral striatum, were more strongly activated in response to preferred food packages. Food choices could be predicted with an accuracy of up to 61.2% by activation patterns in brain regions previously found to be involved in healthy food choices (superior frontal gyrus and visual processing (middle occipital gyrus. In conclusion, this study shows that mass-univariate analysis can detect small package-induced differences in product preference and that MVPA can successfully predict realistic low-involvement consumer choices from functional MRI data.

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

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Meng, Wei; Jiang, Yangyang; Ma, Jie

2017-01-01

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

Objective Bayesian Analysis of Skew- t Distributions

KAUST Repository

BRANCO, MARCIA D'ELIA

2012-02-27

We study the Jeffreys prior and its properties for the shape parameter of univariate skew-t distributions with linear and nonlinear Student\\'s t skewing functions. In both cases, we show that the resulting priors for the shape parameter are symmetric around zero and proper. Moreover, we propose a Student\\'s t approximation of the Jeffreys prior that makes an objective Bayesian analysis easy to perform. We carry out a Monte Carlo simulation study that demonstrates an overall better behaviour of the maximum a posteriori estimator compared with the maximum likelihood estimator. We also compare the frequentist coverage of the credible intervals based on the Jeffreys prior and its approximation and show that they are similar. We further discuss location-scale models under scale mixtures of skew-normal distributions and show some conditions for the existence of the posterior distribution and its moments. Finally, we present three numerical examples to illustrate the implications of our results on inference for skew-t distributions. © 2012 Board of the Foundation of the Scandinavian Journal of Statistics.

Flood risk analysis for flood control and sediment transportation in sandy regions: A case study in the Loess Plateau, China

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Guo, Aijun; Chang, Jianxia; Wang, Yimin; Huang, Qiang; Zhou, Shuai

2018-05-01

Traditional flood risk analysis focuses on the probability of flood events exceeding the design flood of downstream hydraulic structures while neglecting the influence of sedimentation in river channels on regional flood control systems. This work advances traditional flood risk analysis by proposing a univariate and copula-based bivariate hydrological risk framework which incorporates both flood control and sediment transport. In developing the framework, the conditional probabilities of different flood events under various extreme precipitation scenarios are estimated by exploiting the copula-based model. Moreover, a Monte Carlo-based algorithm is designed to quantify the sampling uncertainty associated with univariate and bivariate hydrological risk analyses. Two catchments located on the Loess plateau are selected as study regions: the upper catchments of the Xianyang and Huaxian stations (denoted as UCX and UCH, respectively). The univariate and bivariate return periods, risk and reliability in the context of uncertainty for the purposes of flood control and sediment transport are assessed for the study regions. The results indicate that sedimentation triggers higher risks of damaging the safety of local flood control systems compared with the event that AMF exceeds the design flood of downstream hydraulic structures in the UCX and UCH. Moreover, there is considerable sampling uncertainty affecting the univariate and bivariate hydrologic risk evaluation, which greatly challenges measures of future flood mitigation. In addition, results also confirm that the developed framework can estimate conditional probabilities associated with different flood events under various extreme precipitation scenarios aiming for flood control and sediment transport. The proposed hydrological risk framework offers a promising technical reference for flood risk analysis in sandy regions worldwide.

Developing a univariate approach to phase-I monitoring of fuzzy quality profiles

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Kazem Noghondarian

2012-10-01

Full Text Available In many real-world applications, the quality of a process or a particular product can be characterized by a functional relationship called profile. A profile builds the relationships between a response quality characteristic and one or more explanatory variables. Monitoring the quality of a profile is implemented to understand and to verify the stability of this functional relationship over time. In some real applications, a fuzzy linear regression model can represent the profile adequately where the response quality characteristic is fuzzy. The purpose of this paper is to develop an approach for monitoring process/product profiles in fuzzy environment. A model in fuzzy linear regression is developed to construct the quality profiles by using linear programming and then fuzzy individuals and moving-range (I-MR control charts are developed to monitor both intercept and slope of fuzzy profiles to achieve an in-control process. A case study in customer satisfaction is presented to show the application of our approach and to express the sensitivity analysis of parameters for building a fuzzy profile.

Analysis of Prognostic Factors in 541 Female Patients with Advanced Non-small Cell Lung Cancer

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Meina WU

2011-03-01

Full Text Available Background and objective As there is a sharp increase in the incidence of lung cancer in women in recent years, it has brought broad concerns with its unique clinical and epidemiological characteristics and better prognosis. The aim of this study is to analyze the clinical data of women with advanced non-small cell lung cancer (NSCLC retrospectively to explore the prognostic factors. Methods Clinical data of 541 female patients with advanced NSCLC were collected and followed up till death. The primary endpoint is overall survival (OS. SPSS 11.0 statistical analysis software was used for univariate and multivariate analysis. Results The mean age is 59 years (20 years-86 years, adenocarcinoma account for 80.2% (434/541. The median OS was 15 months (95%CI: 13.87-16.13, and 1, 2, 5-year survival rates were 58.8%, 23.7% and 3.20% respectively. Univariate analysis showed that clinical stage, ECOG score, weight loss, clinical symptoms, liver/bone/brain metastasis and received more than one chemotherapy regimen, good response to the first-line chemotherapy, EGFR-TKI targeted therapy and radiotherapy treatment were significantly correlated with the OS and survival rate (P < 0.05. Combined with multivariate analysis, weight loss before treatment, ECOG score, received EGFR-TKI targeted therapy and response to first-line chemotherapy were independent prognostic factor for survival (P < 0.05. Conclusion There is a higher percentage of adenocarcinoma in female NSCLC. Weight loss before treatment, ECOG score, EGFR-TKI targeted therapy and response to first-line chemotherapy may become independent prognostic factors for survival of female patients with advanced NSCLC.

Fast Detection of Copper Content in Rice by Laser-Induced Breakdown Spectroscopy with Uni- and Multivariate Analysis

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Fei Liu

2018-02-01

Full Text Available Fast detection of heavy metals is very important for ensuring the quality and safety of crops. Laser-induced breakdown spectroscopy (LIBS, coupled with uni- and multivariate analysis, was applied for quantitative analysis of copper in three kinds of rice (Jiangsu rice, regular rice, and Simiao rice. For univariate analysis, three pre-processing methods were applied to reduce fluctuations, including background normalization, the internal standard method, and the standard normal variate (SNV. Linear regression models showed a strong correlation between spectral intensity and Cu content, with an R 2 more than 0.97. The limit of detection (LOD was around 5 ppm, lower than the tolerance limit of copper in foods. For multivariate analysis, partial least squares regression (PLSR showed its advantage in extracting effective information for prediction, and its sensitivity reached 1.95 ppm, while support vector machine regression (SVMR performed better in both calibration and prediction sets, where R c 2 and R p 2 reached 0.9979 and 0.9879, respectively. This study showed that LIBS could be considered as a constructive tool for the quantification of copper contamination in rice.

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

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Binod Neupane

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

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

International Nuclear Information System (INIS)

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

2015-01-01

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. (orig.) [de

Disability in physical education textbooks: an analysis of image content.

Science.gov (United States)

Táboas-Pais, María Inés; Rey-Cao, Ana

2012-10-01

The aim of this paper is to show how images of disability are portrayed in physical education textbooks for secondary schools in Spain. The sample was composed of 3,316 images published in 36 textbooks by 10 publishing houses. A content analysis was carried out using a coding scheme based on categories employed in other similar studies and adapted to the requirements of this study with additional categories. The variables were camera angle, gender, type of physical activity, field of practice, space, and level. Univariate and bivariate descriptive analyses were also carried out. The Pearson chi-square statistic was used to identify associations between the variables. Results showed a noticeable imbalance between people with disabilities and people without disabilities, and women with disabilities were less frequently represented than men with disabilities. People with disabilities were depicted as participating in a very limited variety of segregated, competitive, and elite sports activities.

Aortic and Hepatic Contrast Enhancement During Hepatic-Arterial and Portal Venous Phase Computed Tomography Scanning: Multivariate Linear Regression Analysis Using Age, Sex, Total Body Weight, Height, and Cardiac Output.

Science.gov (United States)

Masuda, Takanori; Nakaura, Takeshi; Funama, Yoshinori; Higaki, Toru; Kiguchi, Masao; Imada, Naoyuki; Sato, Tomoyasu; Awai, Kazuo

We evaluated the effect of the age, sex, total body weight (TBW), height (HT) and cardiac output (CO) of patients on aortic and hepatic contrast enhancement during hepatic-arterial phase (HAP) and portal venous phase (PVP) computed tomography (CT) scanning. This prospective study received institutional review board approval; prior informed consent to participate was obtained from all 168 patients. All were examined using our routine protocol; the contrast material was 600 mg/kg iodine. Cardiac output was measured with a portable electrical velocimeter within 5 minutes of starting the CT scan. We calculated contrast enhancement (per gram of iodine: [INCREMENT]HU/gI) of the abdominal aorta during the HAP and of the liver parenchyma during the PVP. We performed univariate and multivariate linear regression analysis between all patient characteristics and the [INCREMENT]HU/gI of aortic- and liver parenchymal enhancement. Univariate linear regression analysis demonstrated statistically significant correlations between the [INCREMENT]HU/gI and the age, sex, TBW, HT, and CO (all P linear regression analysis showed that only the TBW and CO were of independent predictive value (P linear regression analysis only the TBW and CO were significantly correlated with aortic and liver parenchymal enhancement; the age, sex, and HT were not. The CO was the only independent factor affecting aortic and liver parenchymal enhancement at hepatic CT when the protocol was adjusted for the TBW.

3D spatially-adaptive canonical correlation analysis: Local and global methods.

Science.gov (United States)

Yang, Zhengshi; Zhuang, Xiaowei; Sreenivasan, Karthik; Mishra, Virendra; Curran, Tim; Byrd, Richard; Nandy, Rajesh; Cordes, Dietmar

2018-04-01

Local spatially-adaptive canonical correlation analysis (local CCA) with spatial constraints has been introduced to fMRI multivariate analysis for improved modeling of activation patterns. However, current algorithms require complicated spatial constraints that have only been applied to 2D local neighborhoods because the computational time would be exponentially increased if the same method is applied to 3D spatial neighborhoods. In this study, an efficient and accurate line search sequential quadratic programming (SQP) algorithm has been developed to efficiently solve the 3D local CCA problem with spatial constraints. In addition, a spatially-adaptive kernel CCA (KCCA) method is proposed to increase accuracy of fMRI activation maps. With oriented 3D spatial filters anisotropic shapes can be estimated during the KCCA analysis of fMRI time courses. These filters are orientation-adaptive leading to rotational invariance to better match arbitrary oriented fMRI activation patterns, resulting in improved sensitivity of activation detection while significantly reducing spatial blurring artifacts. The kernel method in its basic form does not require any spatial constraints and analyzes the whole-brain fMRI time series to construct an activation map. Finally, we have developed a penalized kernel CCA model that involves spatial low-pass filter constraints to increase the specificity of the method. The kernel CCA methods are compared with the standard univariate method and with two different local CCA methods that were solved by the SQP algorithm. Results show that SQP is the most efficient algorithm to solve the local constrained CCA problem, and the proposed kernel CCA methods outperformed univariate and local CCA methods in detecting activations for both simulated and real fMRI episodic memory data. Copyright © 2017 Elsevier Inc. All rights reserved.

Combined optimal-pathlengths method for near-infrared spectroscopy analysis

International Nuclear Information System (INIS)

Liu Rong; Xu Kexin; Lu Yanhui; Sun Huili

2004-01-01

Near-infrared (NIR) spectroscopy is a rapid, reagent-less and nondestructive analytical technique, which is being increasingly employed for quantitative application in chemistry, pharmaceutics and food industry, and for the optical analysis of biological tissue. The performance of NIR technology greatly depends on the abilities to control and acquire data from the instrument and to calibrate and analyse data. Optical pathlength is a key parameter of the NIR instrument, which has been thoroughly discussed in univariate quantitative analysis in the presence of photometric errors. Although multiple wavelengths can provide more chemical information, it is difficult to determine a single pathlength that is suitable for each wavelength region. A theoretical investigation of a selection procedure for multiple pathlengths, called the combined optimal-pathlengths (COP) method, is identified in this paper and an extensive comparison with the single pathlength method is also performed on simulated and experimental NIR spectral data sets. The results obtained show that the COP method can greatly improve the prediction accuracy in NIR spectroscopy quantitative analysis

Prognostic value of PINP, bone alkaline phosphatase, CTX-I, and YKL-40 in patients with metastatic prostate carcinoma

DEFF Research Database (Denmark)

Brasso, Klaus; Christensen, Ib Jarle; Johansen, Julia S.

2006-01-01

%, BAP (55%), CTX-I (33%), and YKL-40 (43%). Univariate analysis showed an association to survival: PINP (HR = 1.6, P 4, P 4, P = 0.004). In multivariate Cox analysis performance status, WHO grade, Soloway score, PINP...

Applied quantitative analysis in the social sciences

CERN Document Server

Petscher, Yaacov; Compton, Donald L

2013-01-01

To say that complex data analyses are ubiquitous in the education and social sciences might be an understatement. Funding agencies and peer-review journals alike require that researchers use the most appropriate models and methods for explaining phenomena. Univariate and multivariate data structures often require the application of more rigorous methods than basic correlational or analysis of variance models. Additionally, though a vast set of resources may exist on how to run analysis, difficulties may be encountered when explicit direction is not provided as to how one should run a model

A kernel version of spatial factor analysis

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg

2009-01-01

. Schölkopf et al. introduce kernel PCA. Shawe-Taylor and Cristianini is an excellent reference for kernel methods in general. Bishop and Press et al. describe kernel methods among many other subjects. Nielsen and Canty use kernel PCA to detect change in univariate airborne digital camera images. The kernel...... version of PCA handles nonlinearities by implicitly transforming data into high (even infinite) dimensional feature space via the kernel function and then performing a linear analysis in that space. In this paper we shall apply kernel versions of PCA, maximum autocorrelation factor (MAF) analysis...

Multivariate meta-analysis: Potential and promise

Science.gov (United States)

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

Statistical Analysis Of Reconnaissance Geochemical Data From ...

African Journals Online (AJOL)

, Co, Mo, Hg, Sb, Tl, Sc, Cr, Ni, La, W, V, U, Th, Bi, Sr and Ga in 56 stream sediment samples collected from Orle drainage system were subjected to univariate and multivariate statistical analyses. The univariate methods used include ...

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

Science.gov (United States)

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…

Retropubic radical prostatectomy: Clinicopathological observations and outcome analysis of 428 consecutive patients

Directory of Open Access Journals (Sweden)

Jagdeesh N Kulkarni

2011-01-01

Full Text Available Aim : We report the outcome analysis of retropubic radical prostatectomy (RRP performed in 428 patients in terms of pathological findings, complications, and survival. Materials and Methods : Systematically recorded case reports forms of consecutive 428 RRPs done over a 14-year period were analyzed using the SPSS 14 software. Secondary analysis was done to evaluate era specific (pre and post 2002 changes in clinical features and survivals. Results : Seven-year overall survival (OAS, cancer-specific survival (CSS, and event-free survival (EFS was 83.2%, 82.8%, and 69.8% respectively in our series. Era-specific survival showed higher CSS post 2002, and there was an increase in presentation with organ-confined disease. Univariate and multivariate analysis showed statistically significant impact on era specific outcome. With the improvement in techniques decrease in complications rate and increase in quality of life was noted. Conclusions : Our series spanning over decade demonstrates that RRP is viable option to offer cure to organ-confined carcinoma prostate. Further, there is evidence of stage migration and improvements in outcome in post 2002 patients. Although our series is modest in number, the success rates and outcome data matches those reported in the literature.

Development of a Univariate Membrane-Based Mid-Infrared Method for Protein Quantitation and Total Lipid Content Analysis of Biological Samples

Directory of Open Access Journals (Sweden)

Ivona Strug

2014-01-01

Full Text Available Biological samples present a range of complexities from homogeneous purified protein to multicomponent mixtures. Accurate qualification of such samples is paramount to downstream applications. We describe the development of an MIR spectroscopy-based analytical method offering simultaneous protein quantitation (0.25–5 mg/mL and analysis of total lipid or detergent species, as well as the identification of other biomolecules present in biological samples. The method utilizes a hydrophilic PTFE membrane engineered for presentation of aqueous samples in a dried format compatible with fast infrared analysis. Unlike classical quantification techniques, the reported method is amino acid sequence independent and thus applicable to complex samples of unknown composition. By comparison to existing platforms, this MIR-based method enables direct quantification using minimal sample volume (2 µL; it is well-suited where repeat access and limited sample size are critical parameters. Further, accurate results can be derived without specialized training or knowledge of IR spectroscopy. Overall, the simplified application and analysis system provides a more cost-effective alternative to high-throughput IR systems for research laboratories with minimal throughput demands. In summary, the MIR-based system provides a viable alternative to current protein quantitation methods; it also uniquely offers simultaneous qualification of other components, notably lipids and detergents.

Univariate and Cross Tabulation Analysis of Construction Accidents in the Aegean Region

OpenAIRE

BARADAN, Selim; AKBOĞA, Özge; ÇETİNKAYA, Ufuk; USMEN, Mümtaz A.

2016-01-01

It is crucial toinvestigate case studies and analyze accident statistics to establish safetyand health culture in the construction industry, which exhibits high fatalityrates. However, it is difficult to find reliable and accurate constructionaccidents data in Turkeydue to inadequate accident reporting and recordkeeping system, which hindersstatistical safety research. Therefore, an independent database was generatedby using inspection reports in this research study. Data mining was performed...

Average-case analysis of numerical problems

CERN Document Server

2000-01-01

The average-case analysis of numerical problems is the counterpart of the more traditional worst-case approach. The analysis of average error and cost leads to new insight on numerical problems as well as to new algorithms. The book provides a survey of results that were mainly obtained during the last 10 years and also contains new results. The problems under consideration include approximation/optimal recovery and numerical integration of univariate and multivariate functions as well as zero-finding and global optimization. Background material, e.g. on reproducing kernel Hilbert spaces and random fields, is provided.

Serial Expression Analysis: a web tool for the analysis of serial gene expression data

Science.gov (United States)

Nueda, Maria José; Carbonell, José; Medina, Ignacio; Dopazo, Joaquín; Conesa, Ana

2010-01-01

Serial transcriptomics experiments investigate the dynamics of gene expression changes associated with a quantitative variable such as time or dosage. The statistical analysis of these data implies the study of global and gene-specific expression trends, the identification of significant serial changes, the comparison of expression profiles and the assessment of transcriptional changes in terms of cellular processes. We have created the SEA (Serial Expression Analysis) suite to provide a complete web-based resource for the analysis of serial transcriptomics data. SEA offers five different algorithms based on univariate, multivariate and functional profiling strategies framed within a user-friendly interface and a project-oriented architecture to facilitate the analysis of serial gene expression data sets from different perspectives. SEA is available at sea.bioinfo.cipf.es. PMID:20525784

Which factors enhance positive drug reimbursement recommendation in Scotland? A retrospective analysis 2006-2013.

Science.gov (United States)

Charokopou, Mata; Majer, Istvan M; Raad, Johan de; Broekhuizen, Stefan; Postma, Maarten; Heeg, Bart

2015-03-01

To identify the factors that influence the Scottish Medicines Consortium (SMC) in deciding whether to accept pharmaceutical technologies for use within the Scottish health care system. A database of SMC submissions between 2006 and 2013 was created, containing a range of clinical, economic, and other factors extracted from published health technology assessment reports. A binomial outcome variable was used, defined as the decision to "accept for use" or "not recommend" a technology. Univariate and multivariate analyses were conducted to assess the impact by means of odds ratios (ORs) of the submitted evidence on the recommendation decision. Out of 463 applications, 265 were accepted for use (57%) and 198 (43%) were not recommended for use within National Health Service Scotland. Univariate analyses showed that 13 variables significantly affected the SMC decision. Of these 13 variables, 7 variables were shown to have a meaningful impact in the multivariate analysis. Four of these concerned the outcome of cost-effectiveness analyses; the fact that a submission was supported by a cost-minimization analysis was the strongest positive variable (OR = 10.30) and a submission showing a product not being cost-effective (i.e., incremental cost-effectiveness ratio above £30,000/quality-adjusted life-year gained) was the strongest negative predictor (OR = 0.47). The other variables concerned whether the submission was related to a product indicated for a nervous system disease (OR = 0.41), whether it was indicated for nonchronic use (OR = 1.66), and whether the submission was performed by a big company (OR = 2.83). This study demonstrated that the outcome of cost-effectiveness analyses is an important factor affecting the SMC's reimbursement recommendation decision. Copyright © 2015. Published by Elsevier Inc.

Analysis of spectra from portable handheld gamma-ray spectrometry for terrain comparative assessment

International Nuclear Information System (INIS)

Dias, Flávio; Lima, Marco; Sanjurjo-Sánchez, Jorge; Alves, Carlos

2016-01-01

Geological characteristics can have impacts on societal development by, e.g., geotechnical issues and radiological hazard levels. Due to urban sprawl, there is an increasing need for detailed geological assessment. In this work are analysed data from portable handheld gamma-ray spectra (K, eU and eTh) obtained in granitic and Silurian metaclastic outcrops as well as in an profile, roughly N–S, on soil covered terrains transecting a mapped contact between these rock types (the profile's northern extremity is at locations mapped as granite). Estimations from gamma-ray spectra were studied by univariate and multivariate analyses. K, eU and eTh values were higher on granite in relation to Silurian metaclastic rocks. The northern extremity of the profile showed clearly higher contents of eTh and this contrast was supported by univariate statistical tools (normality plot and Wilk–Shapiro test; boxplots). A ternary plot with the contribution of the elements to gamma-ray absorbed dose showed the separation of granite from Silurian metaclastic rocks with the former being nearer the eTh vertex. The points in the northern extremity of the profile are nearer the eTh vertex than the other points on the profile. These visual suggestions were supported by hierarchical cluster analysis, which was able to differentiate between granite and metaclastic outcrops and separate portions of the profile located on different terrains. Portable gamma-ray spectrometry showed, hence, the potential to distinguish granite and metaclastic terrains at a scale useful for engineering works. These results can also be useful for a first comparative zoning of radiological hazards (which are higher for granite). - Highlights: • Contents of K, eU and eTh were estimated by portable gamma-ray spectra. • Spectra were acquired on a profile across a soil covered granite/metaclastic contact. • Spectra were also collected on granite and Silurian metaclastic outcrops. • Obtained estimations were

What marketing scholars should know about time series analysis : time series applications in marketing

NARCIS (Netherlands)

Horváth, Csilla; Kornelis, Marcel; Leeflang, Peter S.H.

2002-01-01

In this review, we give a comprehensive summary of time series techniques in marketing, and discuss a variety of time series analysis (TSA) techniques and models. We classify them in the sets (i) univariate TSA, (ii) multivariate TSA, and (iii) multiple TSA. We provide relevant marketing

Analysis of risk factors for rebleeding after splenectomy and pericardial devascularization in treatment of portal hypertension due to liver cirrhosis

Directory of Open Access Journals (Sweden)

ZHANG Lei

2015-03-01

Full Text Available ObjectiveTo investigate the possible risk factors for rebleeding after splenectomy and pericardial devascularization in the treatment of portal hypertension due to liver cirrhosis, and to provide a certain basis for reducing the incidence of digestive tract re-hemorrhage for these patients. MethodsA retrospective analysis was performed on 238 cirrhotic patients with portal hypertension who underwent splenectomy and pericardial devascularization in the First Hospital of Lanzhou University from December 2003 to December 2013. These patients were divided into postoperative rebleeding group (n=32 and non-bleeding group (n=206. Univariate analysis (t test or chi-square test and multivariate logistic regression analysis were performed to investigate the risk factors for rebleeding after splenectomy and pericardial devascularization. ResultsOf the 32 patients with postoperative rebleeding, 17 had esophagogastric variceal bleeding, 11 had bleeding due to portal hypertensive gastropathy, and 4 had stress ulcer bleeding. The univariate analysis showed that there were significant differences between the two groups in the following factors: Child-Pugh classification of liver function, degree of liver cirrhosis evaluated intraoperatively, pathological changes of the gastric mucosa, platelet count, prothrombin time (PT, activated partial thromboplastin time (APTT, and presence of diabetes (all P<0.05. The multivariate logistic regression analysis suggested that the significant independent influential factors for postoperative rebleeding were presence of diabetes, Child-Pugh classification of liver function, degree of liver cirrhosis evaluated intraoperatively, diffuse lesion of the gastric mucosa, PT, and APTT. ConclusionFor cirrhotic patients with portal hypertension, the appropriate methods for managing these risk factors are of great clinical significance for preventing rebleeding after splenectomy and pericardial devascularization.

The Impact of Multiple Endpoint Dependency on "Q" and "I"[superscript 2] in Meta-Analysis

Science.gov (United States)

Thompson, Christopher Glen; Becker, Betsy Jane

2014-01-01

A common assumption in meta-analysis is that effect sizes are independent. When correlated effect sizes are analyzed using traditional univariate techniques, this assumption is violated. This research assesses the impact of dependence arising from treatment-control studies with multiple endpoints on homogeneity measures "Q" and…

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

International Nuclear Information System (INIS)

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

2012-01-01

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 CaCO 3 (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. - Highlights: ► Did the detection of strontium, cerium, and cesium in CaCO 3 and graphite matrices. ► The detection of these elements was performed in a systematic manner. ► Univariate calibration curves were used to determine strontium detection. ► Univariate and multivariate statistical analysis built improved statistical models. ► Limits of detection are comparable or better in case of cerium and cesium.

Prognostic factors for long-term outcome after percutaneous thermal ablation for hepatocellular carcinoma: a survival analysis of 137 consecutive patients

Energy Technology Data Exchange (ETDEWEB)

Xu, H.-X. [Department of Medical Ultrasonics, First Affiliated Hospital, Sun Yat-Sen University, Guangzhou (China); Lu, M.-D. [Department of Hepatobiliary Surgery, First Affiliated Hospital, Sun Yat-Sen University, Guangzhou (China); Xie, X.-Y. [Department of Medical Ultrasonics, First Affiliated Hospital, Sun Yat-Sen University, Guangzhou (China); Yin, X.-Y. [Department of Hepatobiliary Surgery, First Affiliated Hospital, Sun Yat-Sen University, Guangzhou (China); Kuang, M. [Department of Hepatobiliary Surgery, First Affiliated Hospital, Sun Yat-Sen University, Guangzhou (China); Chen, J.-W. [Department of Hepatobiliary Surgery, First Affiliated Hospital, Sun Yat-Sen University, Guangzhou (China); Xu, Z.-F. [Department of Medical Ultrasonics, First Affiliated Hospital, Sun Yat-Sen University, Guangzhou (China); Liu, G.-J. [Department of Medical Ultrasonics, First Affiliated Hospital, Sun Yat-Sen University, Guangzhou (China)

2005-09-01

AIM: To identify prognostic factors for long-term outcome for patients with hepatocellular carcinoma (HCC) after percutaneous microwave or radiofrequency ablation. MATERIALS AND METHODS: In total, 137 consecutive patients with HCC underwent microwave or radiofrequency ablation with curative intent; 16 possible prognostic factors were evaluated for their association with overall survival (OS) and disease-free survival (DFS) using univariate and multivariate analysis. RESULTS: The median OS and DFS were 27.0 months and 8.2 months, respectively. OS rates for all patients at 1, 2, 3, 4 and 5 years were 73.9%, 52.1%, 42.8%, 26.2% and 20.1%, respectively. DFS rates at 1, 2, 3 and 4 years were 38.1%, 21.9%, 18.8%, and 14.1%, respectively. Pretreatment serum alpha-fetoprotein (AFP) >200 ng/ml, pretreatment serum albumin {<=}35 g/dl, liver function Child's class C and incomplete ablation were found to be significant predictors for OS by univariate analysis. Using multivariate analysis, incomplete ablation was identified to be the most significant independent predictor for OS. Other independent predictors for OS were serum albumin level, serum AFP level and Child-Pugh classification. Recurrence after hepatectomy and prothrombin time >14 s were identified to be significant predictors for DFS by univariate analysis, and the former was the only independent predictor for DFS by multivariate analysis. CONCLUSION: Prognosis for patients with HCC after thermal ablation with curative intent was determined by treatment response to ablation, pretreatment serum AFP, and liver function reserve. Tumour response to treatment was the most predictive factor for long-term survival and was related to tumour size, thus careful selection of patients for ablation therapy is recommended.

Prognostic factors for long-term outcome after percutaneous thermal ablation for hepatocellular carcinoma: a survival analysis of 137 consecutive patients

International Nuclear Information System (INIS)

Xu, H.-X.; Lu, M.-D.; Xie, X.-Y.; Yin, X.-Y.; Kuang, M.; Chen, J.-W.; Xu, Z.-F.; Liu, G.-J.

2005-01-01

AIM: To identify prognostic factors for long-term outcome for patients with hepatocellular carcinoma (HCC) after percutaneous microwave or radiofrequency ablation. MATERIALS AND METHODS: In total, 137 consecutive patients with HCC underwent microwave or radiofrequency ablation with curative intent; 16 possible prognostic factors were evaluated for their association with overall survival (OS) and disease-free survival (DFS) using univariate and multivariate analysis. RESULTS: The median OS and DFS were 27.0 months and 8.2 months, respectively. OS rates for all patients at 1, 2, 3, 4 and 5 years were 73.9%, 52.1%, 42.8%, 26.2% and 20.1%, respectively. DFS rates at 1, 2, 3 and 4 years were 38.1%, 21.9%, 18.8%, and 14.1%, respectively. Pretreatment serum alpha-fetoprotein (AFP) >200 ng/ml, pretreatment serum albumin ≤35 g/dl, liver function Child's class C and incomplete ablation were found to be significant predictors for OS by univariate analysis. Using multivariate analysis, incomplete ablation was identified to be the most significant independent predictor for OS. Other independent predictors for OS were serum albumin level, serum AFP level and Child-Pugh classification. Recurrence after hepatectomy and prothrombin time >14 s were identified to be significant predictors for DFS by univariate analysis, and the former was the only independent predictor for DFS by multivariate analysis. CONCLUSION: Prognosis for patients with HCC after thermal ablation with curative intent was determined by treatment response to ablation, pretreatment serum AFP, and liver function reserve. Tumour response to treatment was the most predictive factor for long-term survival and was related to tumour size, thus careful selection of patients for ablation therapy is recommended

Do patients with very few brain metastases from breast cancer benefit from whole-brain radiotherapy in addition to radiosurgery?

International Nuclear Information System (INIS)

Rades, Dirk; Huttenlocher, Stefan; Hornung, Dagmar; Blanck, Oliver; Schild, Steven E; Fischer, Dorothea

2014-01-01

An important issue in palliative radiation oncology is the whether whole-brain radiotherapy should be added to radiosurgery when treating a limited number of brain metastases. To optimize personalized treatment of cancer patients with brain metastases, the value of whole-brain radiotherapy should be described separately for each tumor entity. This study investigated the role of whole-brain radiotherapy added to radiosurgery in breast cancer patients. Fifty-eight patients with 1–3 brain metastases from breast cancer were included in this retrospective study. Of these patients, 30 were treated with radiosurgery alone and 28 with radiosurgery plus whole-brain radiotherapy. Both groups were compared for local control of the irradiated metastases, freedom from new brain metastases and survival. Furthermore, eight additional factors were analyzed including dose of radiosurgery, age at radiotherapy, Eastern Cooperative Oncology Group (ECOG) performance score, number of brain metastases, maximum diameter of all brain metastases, site of brain metastases, extra-cranial metastases and the time from breast cancer diagnosis to radiotherapy. The treatment regimen had no significant impact on local control in the univariate analysis (p = 0.59). Age ≤59 years showed a trend towards improved local control on univariate (p = 0.066) and multivariate analysis (p = 0.07). On univariate analysis, radiosurgery plus whole-brain radiotherapy (p = 0.040) and ECOG 0–1 (p = 0.012) showed positive associations with freedom from new brain metastases. Both treatment regimen (p = 0.039) and performance status (p = 0.028) maintained significance on multivariate analysis. ECOG 0–1 was positively correlated with survival on univariate analysis (p < 0.001); age ≤59 years showed a strong trend (p = 0.054). On multivariate analysis, performance status (p < 0.001) and age (p = 0.041) were significant. In breast cancer patients with few brain metastases, radiosurgery plus whole

Prognostic factors in children and adolescents with acute myeloid leukemia (excluding children with Down syndrome and acute promyelocytic leukemia): univariate and recursive partitioning analysis of patients treated on Pediatric Oncology Group (POG) Study 8821.

Science.gov (United States)

Chang, M; Raimondi, S C; Ravindranath, Y; Carroll, A J; Camitta, B; Gresik, M V; Steuber, C P; Weinstein, H

2000-07-01

The purpose of the paper was to define clinical or biological features associated with the risk for treatment failure for children with acute myeloid leukemia. Data from 560 children and adolescents with newly diagnosed acute myeloid leukemia who entered the Pediatric Oncology Group Study 8821 from June 1988 to March 1993 were analyzed by univariate and recursive partitioning methods. Children with Down syndrome or acute promyelocytic leukemia were excluded from the study. Factors examined included age, number of leukocytes, sex, FAB morphologic subtype, cytogenetic findings, and extramedullary disease at the time of diagnosis. The overall event-free survival (EFS) rate at 4 years was 32.7% (s.e. = 2.2%). Age > or =2 years, fewer than 50 x 10(9)/I leukocytes, and t(8;21) or inv(16), and normal chromosomes were associated with higher rates of EFS (P value = 0.003, 0.049, 0.0003, 0.031, respectively), whereas the M5 subtype of AML (P value = 0.0003) and chromosome abnormalities other than t(8;21) and inv(16) were associated with lower rates of EFS (P value = 0.0001). Recursive partitioning analysis defined three groups of patients with widely varied prognoses: female patients with t(8;21), inv(16), or a normal karyotype (n = 89) had the best prognosis (4-year EFS = 55.1%, s.e. = 5.7%); male patients with t(8;21), inv(16) or normal chromosomes (n = 106) had an intermediate prognosis (4-year EFS = 38.1%, s.e. = 5.3%); patients with chromosome abnormalities other than t(8;21) and inv(16) (n = 233) had the worst prognosis (4-year EFS = 27.0%, s.e. = 3.2%). One hundred and thirty-two patients (24%) could not be grouped because of missing cytogenetic data, mainly due to inadequate marrow samples. The results suggest that pediatric patients with acute myeloid leukemia can be categorized into three potential risk groups for prognosis and that differences in sex and chromosomal abnormalities are associated with differences in estimates of EFS. These results are tentative and

Association of Body Mass Index and Serum Adipocytokines with ...

African Journals Online (AJOL)

Results: Univariate analysis showed that EMR exposure, age at presentation >30 years, nulliparity, artificial lactation and increased BMI were significantly associated with breast cancer risk (p30 years (p= 0.02) ...

Analysis of Factors Related to Hypopituitarism in Patients with Nonsellar Intracranial Tumor.

Science.gov (United States)

Lu, Song-Song; Gu, Jian-Jun; Luo, Xiao-Hong; Zhang, Jian-He; Wang, Shou-Sen

2017-09-01

Previous studies have suggested that postoperative hypopituitarism in patients with nonsellar intracranial tumors is caused by traumatic surgery. However, with development of minimally invasive and precise neurosurgical techniques, the degree of injury to brain tissue has been reduced significantly, especially for parenchymal tumors. Therefore, understanding preexisting hypopituitarism and related risk factors can improve perioperative management for patients with nonsellar intracranial tumors. Chart data were collected retrospectively from 83 patients with nonsellar intracranial tumors admitted to our hospital from May 2014 to April 2015. Pituitary function of each subject was determined based on results of preoperative serum pituitary hormone analysis. Univariate and multivariate logistic regression methods were used to analyze relationships between preoperative hypopituitarism and factors including age, sex, history of hypertension and secondary epilepsy, course of disease, tumor mass effect, site of tumor, intracranial pressure (ICP), cerebrospinal fluid content, and pituitary morphology. A total of 30 patients (36.14%) presented with preoperative hypopituitarism in either 1 axis or multiple axes; 23 (27.71%) were affected in 1 axis, and 7 (8.43%) were affected in multiple axes. Univariate analysis showed that risk factors for preoperative hypopituitarism in patients with a nonsellar intracranial tumor include an acute or subacute course (≤3 months), intracranial hypertension (ICP >200 mm H 2 O), and mass effect (P hypopituitarism in patients with nonsellar intracranial tumors (P hypopituitarism is high in patients with nonsellar intracranial tumors. The occurrence of hypopituitarism is correlated with factors including an acute or subacute course (≤3 months), intracranial hypertension (ICP >200 mm H 2 O), and mass effect (P hypopituitarism. Copyright © 2017 Elsevier Inc. All rights reserved.

Longitudinal categorical data analysis

CERN Document Server

Sutradhar, Brajendra C

2014-01-01

This is the first book in longitudinal categorical data analysis with parametric correlation models developed based on dynamic relationships among repeated categorical responses. This book is a natural generalization of the longitudinal binary data analysis to the multinomial data setup with more than two categories. Thus, unlike the existing books on cross-sectional categorical data analysis using log linear models, this book uses multinomial probability models both in cross-sectional and longitudinal setups. A theoretical foundation is provided for the analysis of univariate multinomial responses, by developing models systematically for the cases with no covariates as well as categorical covariates, both in cross-sectional and longitudinal setups. In the longitudinal setup, both stationary and non-stationary covariates are considered. These models have also been extended to the bivariate multinomial setup along with suitable covariates. For the inferences, the book uses the generalized quasi-likelihood as w...

Proteomic Analysis Shows Constitutive Secretion of MIF and p53-associated Activity of COX-2−/− Lung Fibroblasts

Directory of Open Access Journals (Sweden)

Mandar Dave

2017-12-01

Full Text Available The differential expression of two closelyassociated cyclooxygenase isozymes, COX-1 and COX-2, exhibited functions beyond eicosanoid metabolism. We hypothesized that COX-1 or COX-2 knockout lung fibroblasts may display altered protein profiles which may allow us to further differentiate the functional roles of these isozymes at the molecular level. Proteomic analysis shows constitutive production of macrophage migration inhibitory factor (MIF in lung fibroblasts derived from COX-2−/− but not wild-type (WT or COX-1−/− mice. MIF was spontaneously released in high levels into the extracellular milieu of COX2−/− fibroblasts seemingly from the preformed intracellular stores, with no change in the basal gene expression of MIF. The secretion and regulation of MIF in COX-2−/− was “prostaglandin-independent.” GO analysis showed that concurrent with upregulation of MIF, there is a significant surge in expression of genes related to fibroblast growth, FK506 binding proteins, and isomerase activity in COX-2−/− cells. Furthermore, COX-2−/− fibroblasts also exhibit a significant increase in transcriptional activity of various regulators, antagonists, and co-modulators of p53, as well as in the expression of oncogenes and related transcripts. Integrative Oncogenomics Cancer Browser (IntroGen analysis shows downregulation of COX-2 and amplification of MIF and/or p53 activity during development of glioblastomas, ependymoma, and colon adenomas. These data indicate the functional role of the MIF-COX-p53 axis in inflammation and cancer at the genomic and proteomic levels in COX-2-ablated cells. This systematic analysis not only shows the proinflammatory state but also unveils a molecular signature of a pro-oncogenic state of COX-1 in COX-2 ablated cells.

Does mental health staffing level affect antipsychotic prescribing? Analysis of Italian national statistics.

Science.gov (United States)

Starace, Fabrizio; Mungai, Francesco; Barbui, Corrado

2018-01-01

In mental healthcare, one area of major concern identified by health information systems is variability in antipsychotic prescribing. While most studies have investigated patient- and prescriber-related factors as possible reasons for such variability, no studies have investigated facility-level characteristics. The present study ascertained whether staffing level is associated with antipsychotic prescribing in community mental healthcare. A cross-sectional analysis of data extracted from the Italian national mental health information system was carried out. For each Italian region, it collects data on the availability and use of mental health facilities. The rate of individuals exposed to antipsychotic drugs was tested for evidence of association with the rate of mental health staff availability by means of univariate and multivariate analyses. In Italy there were on average nearly 60 mental health professionals per 100,000 inhabitants, with wide regional variations (range 21 to 100). The average rate of individuals prescribed antipsychotic drugs was 2.33%, with wide regional variations (1.04% to 4.01%). Univariate analysis showed that the rate of individuals prescribed antipsychotic drugs was inversely associated with the rate of mental health professionals available in Italian regions (Kendall's tau -0.438, p = 0.006), with lower rates of antipsychotic prescriptions in regions with higher rates of mental health professionals. After adjustment for possible confounders, the total availability of mental health professionals was still inversely associated with the rate of individuals exposed to antipsychotic drugs. The evidence that staffing level was inversely associated with antipsychotic prescribing indicates that any actions aimed at decreasing variability in antipsychotic prescribing need to take into account aspects related to the organization of the mental health system.

Clinical analysis of urinary tract infection in patients undergoing transurethral resection of the prostate.

Science.gov (United States)

Li, Y-H; Li, G-Q; Guo, S-M; Che, Y-N; Wang, X; Cheng, F-T

2017-10-01

To analyze the related influencing factors of urinary tract infection in patients undergoing transurethral resection of the prostate (TURP). A total of 343 patients with benign prostatic hyperplasia admitted to this hospital from January 2013 to December 2016, were selected and treated by TURP. Patients were divided into infection group and non-infection group according to the occurrence of urinary tract infection after operation. The possible influencing factors were collected to perform univariate and multivariate logistic regression analysis. There were 53 cases with urinary tract infection after operation among 343 patients with benign prostatic hyperplasia, accounting for 15.5%. The univariate analysis displayed that the occurrence of urinary tract infection in patients undergoing TURP was closely associated with patient's age ≥ 65 years old, complicated diabetes, catheterization for urinary retention before operation, no use of antibiotics before operation and postoperative indwelling catheter duration ≥ 5 d (p urinary tract infection in patients receiving TURP (p urinary tract infection after TURP, while preoperative prophylactic utilization of anti-infective drugs can reduce the occurrence of postoperative urinary tract infection.

Handbook of Time Series Analysis Recent Theoretical Developments and Applications

CERN Document Server

Schelter, Björn; Timmer, Jens

2006-01-01

This handbook provides an up-to-date survey of current research topics and applications of time series analysis methods written by leading experts in their fields. It covers recent developments in univariate as well as bivariate and multivariate time series analysis techniques ranging from physics' to life sciences' applications. Each chapter comprises both methodological aspects and applications to real world complex systems, such as the human brain or Earth's climate. Covering an exceptionally broad spectrum of topics, beginners, experts and practitioners who seek to understand the latest de

Natural radionuclides in effluents release by a deactivated uranium mine

Energy Technology Data Exchange (ETDEWEB)

Pereira, Wagner S.; Kelecom, Alphonse; Silva, Ademir X.; Lopes, José M.; Pinto, Carlos E.C.; Py Júnior, Delcy A.; Antunes, Marcos M., E-mail: pereiraws@gmail.com, E-mail: caerjbr@gmail.com, E-mail: wspereira@inb.gov.br, E-mail: delcy@inb.gov.br, E-mail: Antunes@inb.gov.br, E-mail: lararapls@hotmail.com, E-mail: Ademir@nuclear.ufrj.br, E-mail: marqueslopes@yahoo.com.br [Universidade Veiga de Almeida (UVA), Rio de Janeiro, RJ (Brazil); Indústrias Nucleares do Brasil (COMAP.N/FCN/INB), Resende RJ (Brazil). Fábrica de Combustível Nuclear. Coordenação de Meio Ambiente e Proteção Radiológica Ambiental; Universidade Federal Fluminense (LARARA-PLS/UFF), Niterói, RJ (Brazil). Laboratório de Radiobiologia e Radiometria; Coordenacao de Pos-Graduacao e Pesquisa de Engenharia (COPPE/UFRJ), Rio de Janeiro, RJ (Brazil). Programa de Engenharia Nuclear

2017-07-01

The Ore Treatment Unit (OTU) is a mine and deactivated uranium plant in the city of Caldas, Minas Gerais, Brazil. This facility possesses three points of release of liquid effluents containing radionuclides: point 014, 025 and 076. At these points, the values of activity concentrations (AC) of the radionuclides U{sub nat}, {sup 226}Ra, {sup 210}Pb, {sup 232}Th and {sup 228}Ra were analyzed in 2012. The evaluation of point 014 by univariate statistics pointed four groups. [U{sub nat} > {sup 228}Ra > ({sup 226}Ra = {sup 210}Pb) >{sup 232}Th]. The multivariate statistics separated the radionuclides into two groups: [(U{sub nat} and {sup 232}Th) and ({sup 226}Ra, {sup 228}Ra and {sup 210}Pb)]. At point 025, the univariate statistics described three groups: [Un{sub at} > ({sup 228}Ra = {sup 210}Pb) > ({sup 226}Ra = {sup 232}Th)] and the multivariate analysis also described three but different groups: [(U{sub nat} and {sup 228}Ra), ({sup 226}Ra and {sup 210}Pb) and {sup 232}Th]. In turn, point 076 showed another behavior. The univariate analysis showed only two groups: [(U{sub nat}) > ({sup 226}Ra, {sup 228}Ra, {sup 210}Pb, {sup 232}Th)]. Differently, the multivariate statistics defined three groups: [(U{sub nat} and {sup 232}Th), ({sup 226}Ra and {sup 228}Ra) and {sup 210}Pb].Thus, statistical analysis showed that each point has releases of effluents with different characteristics. Both the behaviors of releases, based on multivariate statistics, and of the AC magnitudes, based on the univariate statistics, are different between the points. The only common features were the greater magnitude of uranium and the smaller magnitude of thorium. (author)

Risk Factors for Neovascular Glaucoma After Proton Beam Therapy of Uveal Melanoma: A Detailed Analysis of Tumor and Dose–Volume Parameters

International Nuclear Information System (INIS)

Mishra, Kavita K.; Daftari, Inder K.; Weinberg, Vivian; Cole, Tia; Quivey, Jeanne M.; Castro, Joseph R.; Phillips, Theodore L.; Char, Devron H.

2013-01-01

Purpose: To determine neovascular glaucoma (NVG) incidence and identify contributing tumor and dosing factors in uveal melanoma patients treated with proton beam radiation therapy (PBRT). Methods and Materials: A total of 704 PBRT patients treated by a single surgeon (DHC) for uveal melanoma (1996-2010) were reviewed for NVG in our prospectively maintained database. All patients received 56 GyE in 4 fractions. Median follow-up was 58.3 months. Analyses included the Kaplan-Meier method to estimate NVG distributions, univariate log–rank tests, and Cox's proportional hazards multivariate analysis using likelihood ratio tests to identify independent risk factors of NVG among patient, tumor, and dose–volume histogram parameters. Results: The 5-year PBRT NVG rate was 12.7% (95% confidence interval [CI] 10.2%-15.9%). The 5-year rate of enucleation due to NVG was 4.9% (95% CI 3.4%-7.2%). Univariately, the NVG rate increased significantly with larger tumor diameter (P 30% of the lens or ciliary body received ≥50% dose (≥28 GyE), there was a higher probability of NVG (P 0%-30% vs >30%) (P=.01), and optic nerve length treated to ≥90% Dose (≤1 mm vs >1 mm) (P=.02). Conclusions: Our current PBRT patients experience a low rate of NVG and resultant enucleation compared with historical data. The present analysis shows that tumor height, diameter, and anterior as well as posterior critical structure dose–volume parameters may be used to predict NVG risk

Conventional Versus Automated Implantation of Loose Seeds in Prostate Brachytherapy: Analysis of Dosimetric and Clinical Results

International Nuclear Information System (INIS)

Genebes, Caroline; Filleron, Thomas; Graff, Pierre; Jonca, Frédéric; Huyghe, Eric; Thoulouzan, Matthieu; Soulie, Michel; Malavaud, Bernard; Aziza, Richard; Brun, Thomas; Delannes, Martine; Bachaud, Jean-Marc

2013-01-01

Purpose: To review the clinical outcome of I-125 permanent prostate brachytherapy (PPB) for low-risk and intermediate-risk prostate cancer and to compare 2 techniques of loose-seed implantation. Methods and Materials: 574 consecutive patients underwent I-125 PPB for low-risk and intermediate-risk prostate cancer between 2000 and 2008. Two successive techniques were used: conventional implantation from 2000 to 2004 and automated implantation (Nucletron, FIRST system) from 2004 to 2008. Dosimetric and biochemical recurrence-free (bNED) survival results were reported and compared for the 2 techniques. Univariate and multivariate analysis researched independent predictors for bNED survival. Results: 419 (73%) and 155 (27%) patients with low-risk and intermediate-risk disease, respectively, were treated (median follow-up time, 69.3 months). The 60-month bNED survival rates were 95.2% and 85.7%, respectively, for patients with low-risk and intermediate-risk disease (P=.04). In univariate analysis, patients treated with automated implantation had worse bNED survival rates than did those treated with conventional implantation (P<.0001). By day 30, patients treated with automated implantation showed lower values of dose delivered to 90% of prostate volume (D90) and volume of prostate receiving 100% of prescribed dose (V100). In multivariate analysis, implantation technique, Gleason score, and V100 on day 30 were independent predictors of recurrence-free status. Grade 3 urethritis and urinary incontinence were observed in 2.6% and 1.6% of the cohort, respectively, with no significant differences between the 2 techniques. No grade 3 proctitis was observed. Conclusion: Satisfactory 60-month bNED survival rates (93.1%) and acceptable toxicity (grade 3 urethritis <3%) were achieved by loose-seed implantation. Automated implantation was associated with worse dosimetric and bNED survival outcomes

Conventional Versus Automated Implantation of Loose Seeds in Prostate Brachytherapy: Analysis of Dosimetric and Clinical Results

Energy Technology Data Exchange (ETDEWEB)

Genebes, Caroline, E-mail: genebes.caroline@claudiusregaud.fr [Radiation Oncology Department, Institut Claudius Regaud, Toulouse (France); Filleron, Thomas; Graff, Pierre [Radiation Oncology Department, Institut Claudius Regaud, Toulouse (France); Jonca, Frédéric [Department of Urology, Clinique Ambroise Paré, Toulouse (France); Huyghe, Eric; Thoulouzan, Matthieu; Soulie, Michel; Malavaud, Bernard [Department of Urology and Andrology, CHU Rangueil, Toulouse (France); Aziza, Richard; Brun, Thomas; Delannes, Martine; Bachaud, Jean-Marc [Radiation Oncology Department, Institut Claudius Regaud, Toulouse (France)

2013-11-15

Purpose: To review the clinical outcome of I-125 permanent prostate brachytherapy (PPB) for low-risk and intermediate-risk prostate cancer and to compare 2 techniques of loose-seed implantation. Methods and Materials: 574 consecutive patients underwent I-125 PPB for low-risk and intermediate-risk prostate cancer between 2000 and 2008. Two successive techniques were used: conventional implantation from 2000 to 2004 and automated implantation (Nucletron, FIRST system) from 2004 to 2008. Dosimetric and biochemical recurrence-free (bNED) survival results were reported and compared for the 2 techniques. Univariate and multivariate analysis researched independent predictors for bNED survival. Results: 419 (73%) and 155 (27%) patients with low-risk and intermediate-risk disease, respectively, were treated (median follow-up time, 69.3 months). The 60-month bNED survival rates were 95.2% and 85.7%, respectively, for patients with low-risk and intermediate-risk disease (P=.04). In univariate analysis, patients treated with automated implantation had worse bNED survival rates than did those treated with conventional implantation (P<.0001). By day 30, patients treated with automated implantation showed lower values of dose delivered to 90% of prostate volume (D90) and volume of prostate receiving 100% of prescribed dose (V100). In multivariate analysis, implantation technique, Gleason score, and V100 on day 30 were independent predictors of recurrence-free status. Grade 3 urethritis and urinary incontinence were observed in 2.6% and 1.6% of the cohort, respectively, with no significant differences between the 2 techniques. No grade 3 proctitis was observed. Conclusion: Satisfactory 60-month bNED survival rates (93.1%) and acceptable toxicity (grade 3 urethritis <3%) were achieved by loose-seed implantation. Automated implantation was associated with worse dosimetric and bNED survival outcomes.

Advertising Discourse Analysis of FES stores: Killing Love, Cowards Show

Directory of Open Access Journals (Sweden)

Cristian Venegas Ahumada

2013-08-01

Full Text Available The objective is to analyze the structural and photographic discourse of the Autumn-Winter campaign 2008 of FES stores for young people. This was done by a semiotic theory and a critical-structural methodology of discourse. An analysis of 4 advertising photographs was done, and at once an analysis of the discourse “FES says no to violence against Women”, which explains the campaign’s target. The result is: The discourse was subjected to production condition (society of control and makes advertising a way to homogenize subjectivity of masses to consume. Recognition conditions demonstrate that this advertising discourse of symbolic violence means a type of violation of Men and Women Rights. An action like this requires commitment of Psychology in order to promote the social humanizing change, by means of university teaching and professional tasks.

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

Science.gov (United States)

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

2012-01-01

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

Multivariate survival analysis and competing risks

CERN Document Server

Crowder, Martin J

2012-01-01

Multivariate Survival Analysis and Competing Risks introduces univariate survival analysis and extends it to the multivariate case. It covers competing risks and counting processes and provides many real-world examples, exercises, and R code. The text discusses survival data, survival distributions, frailty models, parametric methods, multivariate data and distributions, copulas, continuous failure, parametric likelihood inference, and non- and semi-parametric methods. There are many books covering survival analysis, but very few that cover the multivariate case in any depth. Written for a graduate-level audience in statistics/biostatistics, this book includes practical exercises and R code for the examples. The author is renowned for his clear writing style, and this book continues that trend. It is an excellent reference for graduate students and researchers looking for grounding in this burgeoning field of research.

Cost effectiveness analysis of immunotherapy in patients with grass pollen allergic rhinoconjunctivitis in Germany.

Science.gov (United States)

Westerhout, K Y; Verheggen, B G; Schreder, C H; Augustin, M

2012-01-01

An economic evaluation was conducted to assess the outcomes and costs as well as cost-effectiveness of the following grass-pollen immunotherapies: OA (Oralair; Stallergenes S.A., Antony, France) vs GRZ (Grazax; ALK-Abelló, Hørsholm, Denmark), and ALD (Alk Depot SQ; ALK-Abelló) (immunotherapy agents alongside symptomatic medication) and symptomatic treatment alone for grass pollen allergic rhinoconjunctivitis. The costs and outcomes of 3-year treatment were assessed for a period of 9 years using a Markov model. Treatment efficacy was estimated using an indirect comparison of available clinical trials with placebo as a common comparator. Estimates for immunotherapy discontinuation, occurrence of asthma, health state utilities, drug costs, resource use, and healthcare costs were derived from published sources. The analysis was conducted from the insurant's perspective including public and private health insurance payments and co-payments by insurants. Outcomes were reported as quality-adjusted life years (QALYs) and symptom-free days. The uncertainty around incremental model results was tested by means of extensive deterministic univariate and probabilistic multivariate sensitivity analyses. In the base case analysis the model predicted a cost-utility ratio of OA vs symptomatic treatment of €14,728 per QALY; incremental costs were €1356 (95%CI: €1230; €1484) and incremental QALYs 0.092 (95%CI: 0.052; 0.140). OA was the dominant strategy compared to GRZ and ALD, with estimated incremental costs of -€1142 (95%CI: -€1255; -€1038) and -€54 (95%CI: -€188; €85) and incremental QALYs of 0.015 (95%CI: -0.025; 0.056) and 0.027 (95%CI: -0.022; 0.075), respectively. At a willingness-to-pay threshold of €20,000, the probability of OA being the most cost-effective treatment was predicted to be 79%. Univariate sensitivity analyses show that incremental outcomes were moderately sensitive to changes in efficacy estimates. The main study limitation was the

Self-confidence in financial analysis: a study of younger and older male professional analysts.

Science.gov (United States)

Webster, R L; Ellis, T S

2001-06-01

Measures of reported self-confidence in performing financial analysis by 59 professional male analysts, 31 born between 1946 and 1964 and 28 born between 1965 and 1976, were investigated and reported. Self-confidence in one's ability is important in the securities industry because it affects recommendations and decisions to buy, sell, and hold securities. The respondents analyzed a set of multiyear corporate financial statements and reported their self-confidence in six separate financial areas. Data from the 59 male financial analysts were tallied and analyzed using both univariate and multivariate statistical tests. Rated self-confidence was not significantly different for the younger and the older men. These results are not consistent with a similar prior study of female analysts in which younger women showed significantly higher self-confidence than older women.

#13ReasonsWhy Health Professionals and Educators are Tweeting: A Systematic Analysis of Uses and Perceptions of Show Content and Learning Outcomes.

Science.gov (United States)

Walker, Kimberly K; Burns, Kelli

2018-04-27

This study is a content analysis of health professionals' and educators' tweets about a popular Netflix show that depicts teen suicide: 13 Reasons Why. A content analysis of 740 tweets was conducted to determine the main themes associated with professionals' and educators' tweets about the show, as well as the valence of the tweets. Additionally, a thematic analysis of linked content in tweets (n = 178) was conducted to explore additional content shared about the show and modeling outcomes. Results indicated the largest percentage of tweets was related to social learning, particularly about outcomes that could occur from viewing the show. The valence of the tweets about outcomes was more positive than negative. However, linked materials commonly circulated in tweets signified greater concern with unintended learning outcomes. Some of the linked content included media guidelines for reporting on suicide with recommendations that entertainment producers follow the guidelines. This study emphasizes the importance of including social learning objectives in future typologies of Twitter uses and demonstrates the importance of examining linked content in Twitter studies.

Mediating the effect of self-care management intervention in type 2 diabetes: A meta-analysis of 47 randomised controlled trials

DEFF Research Database (Denmark)

Minet, Lisbeth; Møller, Sine; Vach, Werner

2010-01-01

OBJECTIVE: To perform a meta-analysis assessing the effects of self-care management interventions in improving glycaemic control in type 2 diabetes by analysing the impact of different study characteristics on the effect size. METHODS: A literature search in eight scientific databases up...... to November 2007 included original studies of randomised controlled trials involving adult patients diagnosed with type 2 diabetes and evaluating a self-care management intervention. RESULTS: The 47 included studies yielded 7677 participants. The analysis showed a 0.36% (95% CI 0.21-0.51) improvement...... in glycaemic control in people who received self-care management treatment. In the univariate meta-regression sample size (effect size 0.42%, p=0.007) and follow-up period (effect size 0.49%, p=0.017) were identified to have significant effect on the effect size in favour of small studies and short follow...

Impact of BCL2 and p53 on postmastectomy radiotherapy response in high-risk breast cancer. A subgroup analysis of DBCG82 b and c

International Nuclear Information System (INIS)

Kyndi, M.; Alsner, J.; Nielsen, H.M.; Overgaard, J.; Soerensen, F.B.; Knudsen, H.; Overgaard, M.

2008-01-01

Purpose. To examine p53 and BCL2 expression in high-risk breast cancer patients randomized to postmastectomy radiotherapy (PMRT). Patients and methods. The present analysis included 1 000 of 3 083 high-risk breast cancer patients randomly assigned to PMRT in the DBCG82 b and c studies. Tissue microarray sections were stained with immunohistochemistry for p53 and BCL2. Median potential follow-up was 17 years. Clinical endpoints were locoregional recurrence (LRR), distant metastases (DM), overall mortality, and overall survival (OS). Statistical analyses included Kappa statistics, χ2 or exact tests, Kaplan-Meier probability plots, Log-rank test, and Cox univariate and multivariate regression analyses. Results. p53 accumulation was not significantly associated with increased overall mortality, DM or LRR probability in univariate or multivariate Cox regression analyses. Kaplan-Meier probability plots showed reduced OS and improved DM and LRR probabilities after PMRT within subgroups of both p53 negative and p53 positive patients. Negative BCL2 expression was significantly associated with increased overall mortality, DM and LRR probability in multivariate Cox regression analyses. Kaplan-Meier probability plots showed a significantly improved overall survival after PMRT for the BCL2 positive subgroup, whereas practically no survival improvement was seen after PMRT for the BCL2 negative subgroup. In multivariate analysis of OS, however, no significant interaction was found between BCL2 and randomization status. Significant reductions in LRR probability after PMRT were recorded within both the BCL2 positive and BCL2 negative subgroups. Conclusion. p53 was not associated with survival after radiotherapy in high-risk breast cancer, but BCL2 might be

Exact analysis of discrete data

CERN Document Server

Hirji, Karim F

2005-01-01

Researchers in fields ranging from biology and medicine to the social sciences, law, and economics regularly encounter variables that are discrete or categorical in nature. While there is no dearth of books on the analysis and interpretation of such data, these generally focus on large sample methods. When sample sizes are not large or the data are otherwise sparse, exact methods--methods not based on asymptotic theory--are more accurate and therefore preferable.This book introduces the statistical theory, analysis methods, and computation techniques for exact analysis of discrete data. After reviewing the relevant discrete distributions, the author develops the exact methods from the ground up in a conceptually integrated manner. The topics covered range from univariate discrete data analysis, a single and several 2 x 2 tables, a single and several 2 x K tables, incidence density and inverse sampling designs, unmatched and matched case -control studies, paired binary and trinomial response models, and Markov...

Heterogeneity of Glucose Metabolism in Esophageal Cancer Measured by Fractal Analysis of Fluorodeoxyglucose Positron Emission Tomography Image: Correlation between Metabolic Heterogeneity and Survival.

Science.gov (United States)

Tochigi, Toru; Shuto, Kiyohiko; Kono, Tsuguaki; Ohira, Gaku; Tohma, Takayuki; Gunji, Hisashi; Hayano, Koichi; Narushima, Kazuo; Fujishiro, Takeshi; Hanaoka, Toshiharu; Akutsu, Yasunori; Okazumi, Shinichi; Matsubara, Hisahiro

2017-01-01

Intratumoral heterogeneity is a well-recognized characteristic feature of cancer. The purpose of this study is to assess the heterogeneity of the intratumoral glucose metabolism using fractal analysis, and evaluate its prognostic value in patients with esophageal squamous cell carcinoma (ESCC). 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) studies of 79 patients who received curative surgery were evaluated. FDG-PET images were analyzed using fractal analysis software, where differential box-counting method was employed to calculate the fractal dimension (FD) of the tumor lesion. Maximum standardized uptake value (SUVmax) and FD were compared with overall survival (OS). The median SUVmax and FD of ESCCs in this cohort were 13.8 and 1.95, respectively. In univariate analysis performed using Cox's proportional hazard model, T stage and FD showed significant associations with OS (p = 0.04, p heterogeneity measured by fractal analysis can be a novel imaging biomarker for survival in patients with ESCC. © 2016 S. Karger AG, Basel.

Genomic analysis of codon usage shows influence of mutation pressure, natural selection, and host features on Marburg virus evolution.

Science.gov (United States)

Nasrullah, Izza; Butt, Azeem M; Tahir, Shifa; Idrees, Muhammad; Tong, Yigang

2015-08-26

The Marburg virus (MARV) has a negative-sense single-stranded RNA genome, belongs to the family Filoviridae, and is responsible for several outbreaks of highly fatal hemorrhagic fever. Codon usage patterns of viruses reflect a series of evolutionary changes that enable viruses to shape their survival rates and fitness toward the external environment and, most importantly, their hosts. To understand the evolution of MARV at the codon level, we report a comprehensive analysis of synonymous codon usage patterns in MARV genomes. Multiple codon analysis approaches and statistical methods were performed to determine overall codon usage patterns, biases in codon usage, and influence of various factors, including mutation pressure, natural selection, and its two hosts, Homo sapiens and Rousettus aegyptiacus. Nucleotide composition and relative synonymous codon usage (RSCU) analysis revealed that MARV shows mutation bias and prefers U- and A-ended codons to code amino acids. Effective number of codons analysis indicated that overall codon usage among MARV genomes is slightly biased. The Parity Rule 2 plot analysis showed that GC and AU nucleotides were not used proportionally which accounts for the presence of natural selection. Codon usage patterns of MARV were also found to be influenced by its hosts. This indicates that MARV have evolved codon usage patterns that are specific to both of its hosts. Moreover, selection pressure from R. aegyptiacus on the MARV RSCU patterns was found to be dominant compared with that from H. sapiens. Overall, mutation pressure was found to be the most important and dominant force that shapes codon usage patterns in MARV. To our knowledge, this is the first detailed codon usage analysis of MARV and extends our understanding of the mechanisms that contribute to codon usage and evolution of MARV.

Factor analysis shows association between family activity environment and children's health behaviour.

Science.gov (United States)

Hendrie, Gilly A; Coveney, John; Cox, David N

2011-12-01

To characterise the family activity environment in a questionnaire format, assess the questionnaire's reliability and describe its predictive ability by examining the relationships between the family activity environment and children's health behaviours - physical activity, screen time and fruit and vegetable intake. This paper describes the creation of a tool, based on previously validated scales, adapted from the food domain. Data are from 106 children and their parents (Adelaide, South Australia). Factor analysis was used to characterise factors within the family activity environment. Pearson-Product Moment correlations between the family environment and child outcomes, controlling for demographic variation, were examined. Three factors described the family activity environment - parental activity involvement, opportunity for role modelling and parental support for physical activity - and explained 37.6% of the variance. Controlling for demographic factors, the scale was significantly correlated with children's health behaviour - physical activity (r=0.27), screen time (r=-0.24) and fruit and vegetable intake (r=0.34). The family activity environment questionnaire shows high internal consistency and moderate predictive ability. This study has built on previous research by taking a more comprehensive approach to measuring the family activity environment. This research suggests the family activity environment should be considered in family-based health promotion interventions. © 2011 The Authors. ANZJPH © 2011 Public Health Association of Australia.

Neural network analysis in pharmacogenetics of mood disorders

Directory of Open Access Journals (Sweden)

Serretti Alessandro

2004-12-01

Full Text Available Abstract Background The increasing number of available genotypes for genetic studies in humans requires more advanced techniques of analysis. We previously reported significant univariate associations between gene polymorphisms and antidepressant response in mood disorders. However the combined analysis of multiple gene polymorphisms and clinical variables requires the use of non linear methods. Methods In the present study we tested a neural network strategy for a combined analysis of two gene polymorphisms. A Multi Layer Perceptron model showed the best performance and was therefore selected over the other networks. One hundred and twenty one depressed inpatients treated with fluvoxamine in the context of previously reported pharmacogenetic studies were included. The polymorphism in the transcriptional control region upstream of the 5HTT coding sequence (SERTPR and in the Tryptophan Hydroxylase (TPH gene were analysed simultaneously. Results A multi layer perceptron network composed by 1 hidden layer with 7 nodes was chosen. 77.5 % of responders and 51.2% of non responders were correctly classified (ROC area = 0.731 – empirical p value = 0.0082. Finally, we performed a comparison with traditional techniques. A discriminant function analysis correctly classified 34.1 % of responders and 68.1 % of non responders (F = 8.16 p = 0.0005. Conclusions Overall, our findings suggest that neural networks may be a valid technique for the analysis of gene polymorphisms in pharmacogenetic studies. The complex interactions modelled through NN may be eventually applied at the clinical level for the individualized therapy.

Radiographic analysis of odontogenic cysts showing displacement of the mandibular canal

International Nuclear Information System (INIS)

Cho, Bong Hae

2003-01-01

To assess the radiographic findings of odontogenic cysts showing displacement of the mandibular canal using computed tomographic (CT) and panoramic images. CT and panoramic images of 63 odontogenic cysts (27 dentigerous, 16 odontogenic keratocysts, and 20 radicular cysts) were analyzed to evaluate the following parameters: the dimension and shape of the cysts, and the effect of the cysts on the mandibular canal and cortical plates. Of the 63 cysts examined in the study, 35 (55.6%) showed inferior displacement of the mandibular canal and 46 (73.0%) showed perforation of the canal. There were statistically significant differenced between CT and panoramic images in depicting displacement and perforation of the mandibular canal. Cortical expansion was seen in 46 cases (73.0%) and cortical perforation in 23 cases (36.5%). The radicular cysts showed cortical expansion and perforation less frequently than the other cyst groups. Large cysts of mandible should be evaluated by multiplanar CT images in order to detect the mandibular canal and cortical bone involvement.

A Novel and Effective Multivariate Method for Compositional Analysis using Laser Induced Breakdown Spectroscopy

International Nuclear Information System (INIS)

Wang, W; Qi, H; Ayhan, B; Kwan, C; Vance, S

2014-01-01

Compositional analysis is important to interrogate spectral samples for direct analysis of materials in agriculture, environment and archaeology, etc. In this paper, multi-variate analysis (MVA) techniques are coupled with laser induced breakdown spectroscopy (LIBS) to estimate quantitative elemental compositions and determine the type of the sample. In particular, we present a new multivariate analysis method for composition analysis, referred to as s pectral unmixing . The LIBS spectrum of a testing sample is considered as a linear mixture with more than one constituent signatures that correspond to various chemical elements. The signature library is derived from regression analysis using training samples or is manually set up with the information from an elemental LIBS spectral database. A calibration step is used to make all the signatures in library to be homogeneous with the testing sample so as to avoid inhomogeneous signatures that might be caused by different sampling conditions. To demonstrate the feasibility of the proposed method, we compare it with the traditional partial least squares (PLS) method and the univariate method using a standard soil data set with elemental concentration measured a priori. The experimental results show that the proposed method holds great potential for reliable and effective elemental concentration estimation

Multi-criteria evaluation of CMIP5 GCMs for climate change impact analysis

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Ahmadalipour, Ali; Rana, Arun; Moradkhani, Hamid; Sharma, Ashish

2017-04-01

Climate change is expected to have severe impacts on global hydrological cycle along with food-water-energy nexus. Currently, there are many climate models used in predicting important climatic variables. Though there have been advances in the field, there are still many problems to be resolved related to reliability, uncertainty, and computing needs, among many others. In the present work, we have analyzed performance of 20 different global climate models (GCMs) from Climate Model Intercomparison Project Phase 5 (CMIP5) dataset over the Columbia River Basin (CRB) in the Pacific Northwest USA. We demonstrate a statistical multicriteria approach, using univariate and multivariate techniques, for selecting suitable GCMs to be used for climate change impact analysis in the region. Univariate methods includes mean, standard deviation, coefficient of variation, relative change (variability), Mann-Kendall test, and Kolmogorov-Smirnov test (KS-test); whereas multivariate methods used were principal component analysis (PCA), singular value decomposition (SVD), canonical correlation analysis (CCA), and cluster analysis. The analysis is performed on raw GCM data, i.e., before bias correction, for precipitation and temperature climatic variables for all the 20 models to capture the reliability and nature of the particular model at regional scale. The analysis is based on spatially averaged datasets of GCMs and observation for the period of 1970 to 2000. Ranking is provided to each of the GCMs based on the performance evaluated against gridded observational data on various temporal scales (daily, monthly, and seasonal). Results have provided insight into each of the methods and various statistical properties addressed by them employed in ranking GCMs. Further; evaluation was also performed for raw GCM simulations against different sets of gridded observational dataset in the area.

DCE-MRI texture analysis with tumor subregion partitioning for predicting Ki-67 status of estrogen receptor-positive breast cancers

KAUST Repository

Fan, Ming

2017-12-08

Breast tumor heterogeneity is related to risk factors that lead to worse prognosis, yet such heterogeneity has not been well studied.To predict the Ki-67 status of estrogen receptor (ER)-positive breast cancer patients via analysis of tumor heterogeneity with subgroup identification based on patterns of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).Retrospective study.Seventy-seven breast cancer patients with ER-positive breast cancer were investigated, of whom 51 had low Ki-67 expression.T1 -weighted 3.0T DCE-MR images.Each tumor was partitioned into multiple subregions using three methods based on patterns of dynamic enhancement: 1) time to peak (TTP), 2) peak enhancement rate (PER), and 3) kinetic pattern clustering (KPC). In each tumor subregion, 18 texture features were computed.Univariate and multivariate logistic regression analyses were performed using a leave-one-out-based cross-validation (LOOCV) method. The partitioning results were compared with the same feature extraction methods across the whole tumor.In the univariate analysis, the best-performing feature was the texture statistic of sum variance in the tumor subregion with early TTP for differentiating between patients with high and low Ki-67 expression (area under the receiver operating characteristic curves, AUC = 0.748). Multivariate analysis showed that features from the tumor subregion associated with early TTP yielded the highest performance (AUC = 0.807) among the subregions for predicting the Ki-67 status. Among all regions, the tumor area with high PER at a precontrast MR image achieved the highest performance (AUC = 0.722), while the subregion that exhibited the highest overall enhancement rate based on KPC had an AUC of 0.731. These three models based on intratumoral texture analysis significantly (P < 0.01) outperformed the model using features from the whole tumor (AUC = 0.59).Texture analysis of intratumoral heterogeneity has the potential to serve as a valuable

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

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

2017-11-01

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

A multi-centre cohort study shows no association between experienced violence and labour dystocia in nulliparous women at term

Science.gov (United States)

2011-01-01

Background Although both labour dystocia and domestic violence during pregnancy are associated with adverse maternal and fetal outcome, evidence in support of a possible association between experiences of domestic violence and labour dystocia is sparse. The aim of this study was to investigate whether self-reported history of violence or experienced violence during pregnancy is associated with increased risk of labour dystocia in nulliparous women at term. Methods A population-based multi-centre cohort study. A self-administrated questionnaire collected at 37 weeks of gestation from nine obstetric departments in Denmark. The total cohort comprised 2652 nulliparous women, among whom 985 (37.1%) met the protocol criteria for dystocia. Results Among the total cohort, 940 (35.4%) women reported experience of violence, and among these, 66 (2.5%) women reported exposure to violence during their first pregnancy. Further, 39.5% (n = 26) of those had never been exposed to violence before. Univariate logistic regression analysis showed no association between history of violence or experienced violence during pregnancy and labour dystocia at term, crude OR 0.91, 95% CI (0.77-1.08), OR 0.90, 95% CI (0.54-1.50), respectively. However, violence exposed women consuming alcoholic beverages during late pregnancy had increased odds of labour dystocia, crude OR 1.45, 95% CI (1.07-1.96). Conclusions Our findings indicate that nulliparous women who have a history of violence or experienced violence during pregnancy do not appear to have a higher risk of labour dystocia at term, according to the definition of labour dystocia in this study. Additional research on this topic would be beneficial, including further evaluation of the criteria for labour dystocia. PMID:21338523

A multi-centre cohort study shows no association between experienced violence and labour dystocia in nulliparous women at term.

Science.gov (United States)

Finnbogadóttir, Hafrún; Dejin-Karlsson, Elisabeth; Dykes, Anna-Karin

2011-02-21

Although both labour dystocia and domestic violence during pregnancy are associated with adverse maternal and fetal outcome, evidence in support of a possible association between experiences of domestic violence and labour dystocia is sparse. The aim of this study was to investigate whether self-reported history of violence or experienced violence during pregnancy is associated with increased risk of labour dystocia in nulliparous women at term. A population-based multi-centre cohort study. A self-administrated questionnaire collected at 37 weeks of gestation from nine obstetric departments in Denmark. The total cohort comprised 2652 nulliparous women, among whom 985 (37.1%) met the protocol criteria for dystocia. Among the total cohort, 940 (35.4%) women reported experience of violence, and among these, 66 (2.5%) women reported exposure to violence during their first pregnancy. Further, 39.5% (n = 26) of those had never been exposed to violence before. Univariate logistic regression analysis showed no association between history of violence or experienced violence during pregnancy and labour dystocia at term, crude OR 0.91, 95% CI (0.77-1.08), OR 0.90, 95% CI (0.54-1.50), respectively. However, violence exposed women consuming alcoholic beverages during late pregnancy had increased odds of labour dystocia, crude OR 1.45, 95% CI (1.07-1.96). Our findings indicate that nulliparous women who have a history of violence or experienced violence during pregnancy do not appear to have a higher risk of labour dystocia at term, according to the definition of labour dystocia in this study. Additional research on this topic would be beneficial, including further evaluation of the criteria for labour dystocia.

C-reactive protein gene polymorphisms and myocardial infarction risk: a meta-analysis and meta-regression.

Science.gov (United States)

Zhu, Yanbin; Liu, Tongku; He, Haitao; Sun, Yuqing; Zhuo, Fengling

2013-12-01

C-reactive protein (CRP), the classic acute-phase protein, plays an important role in the etiology of myocardial infarction (MI). Emerging evidence has shown that the common polymorphisms in the CRP gene may influence an individual's susceptibility to MI; but individually published studies showed inconclusive results. This meta-analysis aimed to derive a more precise estimation of the associations between CRP gene polymorphisms and MI risk. A literature search of PubMed, Embase, Web of Science, and China BioMedicine (CBM) databases was conducted on articles published before June 1st, 2013. Crude odds ratio (OR) with 95% confidence interval (CI) were calculated. Nine case-control studies were included with a total of 2992 MI patients and 4711 healthy controls. The meta-analysis results indicated that CRP rs3093059 (T>C) polymorphism was associated with decreased risk of MI, especially among Asian populations. However, similar associations were not observed in CRP rs1800947 (G>C) and rs2794521 (G>A) polymorphisms (all p>0.05) among both Asian and Caucasian populations. Univariate and multivariate meta-regression analyses showed that ethnicity may be a major source of heterogeneity. No publication bias was detected in this meta-analysis. In conclusion, the current meta-analysis indicates that CRP rs3093059 (T>C) polymorphism may be associated with decreased risk of MI, especially among Asian populations.

Logistic regression analysis of prognostic factors in 106 acute-on-chronic liver failure patients with hepatic encephalopathy

Directory of Open Access Journals (Sweden)

CUI Yanping

2014-10-01

Full Text Available ObjectiveTo analyze the prognostic factors in acute-on-chronic liver failure (ACLF patients with hepatic encephalopathy (HE and to explore the risk factors for prognosis. MethodsA retrospective analysis was performed on 106 ACLF patients with HE who were hospitalized in our hospital from January 2010 to July 2013. The patients were divided into improved group and deteriorated group. The univariate indicators including age, sex, laboratory indicators ［total bilirubin (TBil, albumin (Alb, alanine aminotransferase (ALT, aspartate amino-transferase (AST, and prothrombin time activity (PTA］, the stage of HE, complications ［persistent hyponatremia, digestive tract bleeding, hepatorenal syndrome (HRS, ascites, infection, and spontaneous bacterial peritonitis (SBP］, and plasma exchange were analyzed by chi-square test or t-test. Indicators with statistical significance were subsequently analyzed by binary logistic regression. ResultsUnivariate analysis showed that ALT (P=0.009, PTA (P=0.043, the stage of HE (P=0.000, and HRS (P=0.003 were significantly different between the two groups, whereas differences in age, sex, TBil, Alb, AST, persistent hyponatremia, digestive tract bleeding, ascites, infection, SBP, and plasma exchange were not statistically significant (P＞0.05. Binary logistic regression demonstrated that PTA (b=-0097, P=0.025, OR=0.908, HRS (b=2.279, P=0.007, OR=9.764, and the stage of HE (b=1873, P=0.000, OR=6.510 were prognostic factors in ACLF patients with HE. ConclusionThe stage of HE, HRS, and PTA are independent influential factors for the prognosis in ACLF patients with HE. Reduced PTA, advanced HE stage, and the presence of HRS indicate worse prognosis.

From root to fruit: RNA-Seq analysis shows that arbuscular mycorrhizal symbiosis may affect tomato fruit metabolism.

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Zouari, Inès; Salvioli, Alessandra; Chialva, Matteo; Novero, Mara; Miozzi, Laura; Tenore, Gian Carlo; Bagnaresi, Paolo; Bonfante, Paola

2014-03-21

Tomato (Solanum lycopersicum) establishes a beneficial symbiosis with arbuscular mycorrhizal (AM) fungi. The formation of the mycorrhizal association in the roots leads to plant-wide modulation of gene expression. To understand the systemic effect of the fungal symbiosis on the tomato fruit, we used RNA-Seq to perform global transcriptome profiling on Moneymaker tomato fruits at the turning ripening stage. Fruits were collected at 55 days after flowering, from plants colonized with Funneliformis mosseae and from control plants, which were fertilized to avoid responses related to nutrient deficiency. Transcriptome analysis identified 712 genes that are differentially expressed in fruits from mycorrhizal and control plants. Gene Ontology (GO) enrichment analysis of these genes showed 81 overrepresented functional GO classes. Up-regulated GO classes include photosynthesis, stress response, transport, amino acid synthesis and carbohydrate metabolism functions, suggesting a general impact of fungal symbiosis on primary metabolisms and, particularly, on mineral nutrition. Down-regulated GO classes include cell wall, metabolism and ethylene response pathways. Quantitative RT-PCR validated the RNA-Seq results for 12 genes out of 14 when tested at three fruit ripening stages, mature green, breaker and turning. Quantification of fruit nutraceutical and mineral contents produced values consistent with the expression changes observed by RNA-Seq analysis. This RNA-Seq profiling produced a novel data set that explores the intersection of mycorrhization and fruit development. We found that the fruits of mycorrhizal plants show two transcriptomic "signatures": genes characteristic of a climacteric fleshy fruit, and genes characteristic of mycorrhizal status, like phosphate and sulphate transporters. Moreover, mycorrhizal plants under low nutrient conditions produce fruits with a nutrient content similar to those from non-mycorrhizal plants under high nutrient conditions

Quantitative Analysis of "1"8F-Fluorodeoxyglucose Positron Emission Tomography Identifies Novel Prognostic Imaging Biomarkers in Locally Advanced Pancreatic Cancer Patients Treated With Stereotactic Body Radiation Therapy

International Nuclear Information System (INIS)

Cui, Yi; Song, Jie; Pollom, Erqi; Alagappan, Muthuraman; Shirato, Hiroki; Chang, Daniel T.; Koong, Albert C.; Li, Ruijiang

2016-01-01

Purpose: To identify prognostic biomarkers in pancreatic cancer using high-throughput quantitative image analysis. Methods and Materials: In this institutional review board–approved study, we retrospectively analyzed images and outcomes for 139 locally advanced pancreatic cancer patients treated with stereotactic body radiation therapy (SBRT). The overall population was split into a training cohort (n=90) and a validation cohort (n=49) according to the time of treatment. We extracted quantitative imaging characteristics from pre-SBRT "1"8F-fluorodeoxyglucose positron emission tomography, including statistical, morphologic, and texture features. A Cox proportional hazard regression model was built to predict overall survival (OS) in the training cohort using 162 robust image features. To avoid over-fitting, we applied the elastic net to obtain a sparse set of image features, whose linear combination constitutes a prognostic imaging signature. Univariate and multivariate Cox regression analyses were used to evaluate the association with OS, and concordance index (CI) was used to evaluate the survival prediction accuracy. Results: The prognostic imaging signature included 7 features characterizing different tumor phenotypes, including shape, intensity, and texture. On the validation cohort, univariate analysis showed that this prognostic signature was significantly associated with OS (P=.002, hazard ratio 2.74), which improved upon conventional imaging predictors including tumor volume, maximum standardized uptake value, and total legion glycolysis (P=.018-.028, hazard ratio 1.51-1.57). On multivariate analysis, the proposed signature was the only significant prognostic index (P=.037, hazard ratio 3.72) when adjusted for conventional imaging and clinical factors (P=.123-.870, hazard ratio 0.53-1.30). In terms of CI, the proposed signature scored 0.66 and was significantly better than competing prognostic indices (CI 0.48-0.64, Wilcoxon rank sum test P<1e-6

Quantitative Analysis of {sup 18}F-Fluorodeoxyglucose Positron Emission Tomography Identifies Novel Prognostic Imaging Biomarkers in Locally Advanced Pancreatic Cancer Patients Treated With Stereotactic Body Radiation Therapy

Energy Technology Data Exchange (ETDEWEB)

Cui, Yi [Department of Radiation Oncology, Stanford University, Palo Alto, California (United States); Global Institution for Collaborative Research and Education, Hokkaido University, Sapporo (Japan); Song, Jie; Pollom, Erqi; Alagappan, Muthuraman [Department of Radiation Oncology, Stanford University, Palo Alto, California (United States); Shirato, Hiroki [Global Institution for Collaborative Research and Education, Hokkaido University, Sapporo (Japan); Chang, Daniel T.; Koong, Albert C. [Department of Radiation Oncology, Stanford University, Palo Alto, California (United States); Stanford Cancer Institute, Stanford, California (United States); Li, Ruijiang, E-mail: rli2@stanford.edu [Department of Radiation Oncology, Stanford University, Palo Alto, California (United States); Global Institution for Collaborative Research and Education, Hokkaido University, Sapporo (Japan); Stanford Cancer Institute, Stanford, California (United States)

2016-09-01

Purpose: To identify prognostic biomarkers in pancreatic cancer using high-throughput quantitative image analysis. Methods and Materials: In this institutional review board–approved study, we retrospectively analyzed images and outcomes for 139 locally advanced pancreatic cancer patients treated with stereotactic body radiation therapy (SBRT). The overall population was split into a training cohort (n=90) and a validation cohort (n=49) according to the time of treatment. We extracted quantitative imaging characteristics from pre-SBRT {sup 18}F-fluorodeoxyglucose positron emission tomography, including statistical, morphologic, and texture features. A Cox proportional hazard regression model was built to predict overall survival (OS) in the training cohort using 162 robust image features. To avoid over-fitting, we applied the elastic net to obtain a sparse set of image features, whose linear combination constitutes a prognostic imaging signature. Univariate and multivariate Cox regression analyses were used to evaluate the association with OS, and concordance index (CI) was used to evaluate the survival prediction accuracy. Results: The prognostic imaging signature included 7 features characterizing different tumor phenotypes, including shape, intensity, and texture. On the validation cohort, univariate analysis showed that this prognostic signature was significantly associated with OS (P=.002, hazard ratio 2.74), which improved upon conventional imaging predictors including tumor volume, maximum standardized uptake value, and total legion glycolysis (P=.018-.028, hazard ratio 1.51-1.57). On multivariate analysis, the proposed signature was the only significant prognostic index (P=.037, hazard ratio 3.72) when adjusted for conventional imaging and clinical factors (P=.123-.870, hazard ratio 0.53-1.30). In terms of CI, the proposed signature scored 0.66 and was significantly better than competing prognostic indices (CI 0.48-0.64, Wilcoxon rank sum test P<1e-6

Tomato Fruits Show Wide Phenomic Diversity but Fruit Developmental Genes Show Low Genomic Diversity.

Directory of Open Access Journals (Sweden)

Vijee Mohan

Full Text Available Domestication of tomato has resulted in large diversity in fruit phenotypes. An intensive phenotyping of 127 tomato accessions from 20 countries revealed extensive morphological diversity in fruit traits. The diversity in fruit traits clustered the accessions into nine classes and identified certain promising lines having desirable traits pertaining to total soluble salts (TSS, carotenoids, ripening index, weight and shape. Factor analysis of the morphometric data from Tomato Analyzer showed that the fruit shape is a complex trait shared by several factors. The 100% variance between round and flat fruit shapes was explained by one discriminant function having a canonical correlation of 0.874 by stepwise discriminant analysis. A set of 10 genes (ACS2, COP1, CYC-B, RIN, MSH2, NAC-NOR, PHOT1, PHYA, PHYB and PSY1 involved in various plant developmental processes were screened for SNP polymorphism by EcoTILLING. The genetic diversity in these genes revealed a total of 36 non-synonymous and 18 synonymous changes leading to the identification of 28 haplotypes. The average frequency of polymorphism across the genes was 0.038/Kb. Significant negative Tajima'D statistic in two of the genes, ACS2 and PHOT1 indicated the presence of rare alleles in low frequency. Our study indicates that while there is low polymorphic diversity in the genes regulating plant development, the population shows wider phenotype diversity. Nonetheless, morphological and genetic diversity of the present collection can be further exploited as potential resources in future.

Recruitment Methods and Show Rates to a Prostate Cancer Early Detection Program for High-Risk Men: A Comprehensive Analysis

Science.gov (United States)

Giri, Veda N.; Coups, Elliot J.; Ruth, Karen; Goplerud, Julia; Raysor, Susan; Kim, Taylor Y.; Bagden, Loretta; Mastalski, Kathleen; Zakrzewski, Debra; Leimkuhler, Suzanne; Watkins-Bruner, Deborah

2009-01-01

Purpose Men with a family history (FH) of prostate cancer (PCA) and African American (AA) men are at higher risk for PCA. Recruitment and retention of these high-risk men into early detection programs has been challenging. We report a comprehensive analysis on recruitment methods, show rates, and participant factors from the Prostate Cancer Risk Assessment Program (PRAP), which is a prospective, longitudinal PCA screening study. Materials and Methods Men 35–69 years are eligible if they have a FH of PCA, are AA, or have a BRCA1/2 mutation. Recruitment methods were analyzed with respect to participant demographics and show to the first PRAP appointment using standard statistical methods Results Out of 707 men recruited, 64.9% showed to the initial PRAP appointment. More individuals were recruited via radio than from referral or other methods (χ2 = 298.13, p < .0001). Men recruited via radio were more likely to be AA (p<0.001), less educated (p=0.003), not married or partnered (p=0.007), and have no FH of PCA (p<0.001). Men recruited via referrals had higher incomes (p=0.007). Men recruited via referral were more likely to attend their initial PRAP visit than those recruited by radio or other methods (χ2 = 27.08, p < .0001). Conclusions This comprehensive analysis finds that radio leads to higher recruitment of AA men with lower socioeconomic status. However, these are the high-risk men that have lower show rates for PCA screening. Targeted motivational measures need to be studied to improve show rates for PCA risk assessment for these high-risk men. PMID:19758657

Idiopathic Pulmonary Fibrosis: Data-driven Textural Analysis of Extent of Fibrosis at Baseline and 15-Month Follow-up.

Science.gov (United States)

Humphries, Stephen M; Yagihashi, Kunihiro; Huckleberry, Jason; Rho, Byung-Hak; Schroeder, Joyce D; Strand, Matthew; Schwarz, Marvin I; Flaherty, Kevin R; Kazerooni, Ella A; van Beek, Edwin J R; Lynch, David A

2017-10-01

Purpose To evaluate associations between pulmonary function and both quantitative analysis and visual assessment of thin-section computed tomography (CT) images at baseline and at 15-month follow-up in subjects with idiopathic pulmonary fibrosis (IPF). Materials and Methods This retrospective analysis of preexisting anonymized data, collected prospectively between 2007 and 2013 in a HIPAA-compliant study, was exempt from additional institutional review board approval. The extent of lung fibrosis at baseline inspiratory chest CT in 280 subjects enrolled in the IPF Network was evaluated. Visual analysis was performed by using a semiquantitative scoring system. Computer-based quantitative analysis included CT histogram-based measurements and a data-driven textural analysis (DTA). Follow-up CT images in 72 of these subjects were also analyzed. Univariate comparisons were performed by using Spearman rank correlation. Multivariate and longitudinal analyses were performed by using a linear mixed model approach, in which models were compared by using asymptotic χ 2 tests. Results At baseline, all CT-derived measures showed moderate significant correlation (P pulmonary function. At follow-up CT, changes in DTA scores showed significant correlation with changes in both forced vital capacity percentage predicted (ρ = -0.41, P pulmonary function (P fibrosis at CT yields an index of severity that correlates with visual assessment and functional change in subjects with IPF. © RSNA, 2017.

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

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Lee, Yeonok; Wu, Hulin

2012-01-01

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

A consistent framework for Horton regression statistics that leads to a modified Hack's law

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Furey, P.R.; Troutman, B.M.

2008-01-01

A statistical framework is introduced that resolves important problems with the interpretation and use of traditional Horton regression statistics. The framework is based on a univariate regression model that leads to an alternative expression for Horton ratio, connects Horton regression statistics to distributional simple scaling, and improves the accuracy in estimating Horton plot parameters. The model is used to examine data for drainage area A and mainstream length L from two groups of basins located in different physiographic settings. Results show that confidence intervals for the Horton plot regression statistics are quite wide. Nonetheless, an analysis of covariance shows that regression intercepts, but not regression slopes, can be used to distinguish between basin groups. The univariate model is generalized to include n > 1 dependent variables. For the case where the dependent variables represent ln A and ln L, the generalized model performs somewhat better at distinguishing between basin groups than two separate univariate models. The generalized model leads to a modification of Hack's law where L depends on both A and Strahler order ??. Data show that ?? plays a statistically significant role in the modified Hack's law expression. ?? 2008 Elsevier B.V.

Adaptive Analysis of Functional MRI Data

International Nuclear Information System (INIS)

Friman, Ola

2003-01-01

Functional Magnetic Resonance Imaging (fMRI) is a recently developed neuro-imaging technique with capacity to map neural activity with high spatial precision. To locate active brain areas, the method utilizes local blood oxygenation changes which are reflected as small intensity changes in a special type of MR images. The ability to non-invasively map brain functions provides new opportunities to unravel the mysteries and advance the understanding of the human brain, as well as to perform pre-surgical examinations in order to optimize surgical interventions. This dissertation introduces new approaches for the analysis of fMRI data. The detection of active brain areas is a challenging problem due to high noise levels and artifacts present in the data. A fundamental tool in the developed methods is Canonical Correlation Analysis (CCA). CCA is used in two novel ways. First as a method with the ability to fully exploit the spatio-temporal nature of fMRI data for detecting active brain areas. Established analysis approaches mainly focus on the temporal dimension of the data and they are for this reason commonly referred to as being mass-univariate. The new CCA detection method encompasses and generalizes the traditional mass-univariate methods and can in this terminology be viewed as a mass-multivariate approach. The concept of spatial basis functions is introduced as a spatial counterpart of the temporal basis functions already in use in fMRI analysis. The spatial basis functions implicitly perform an adaptive spatial filtering of the fMRI images, which significantly improves detection performance. It is also shown how prior information can be incorporated into the analysis by imposing constraints on the temporal and spatial models and a constrained version of CCA is devised to this end. A general Principal Component Analysis technique for generating and constraining temporal and spatial subspace models is proposed to be used in combination with the constrained CCA

Multivariate Analysis of the Predictors of Survival for Patients with Hepatocellular Carcinoma Undergoing Transarterial Chemoembolization: Focusing on Superselective Chemoembolization

International Nuclear Information System (INIS)

Ji, Suk Kyeong; Cho, Yun Ku; Ahn, Yong Sik; Kim, Mi Young; Park, Yoon Ok; Kim, Jae Kyun; Kim, Wan Tae

2008-01-01

While the prognostic factors of survival for patients with hepatocellular carcinoma (HCC) who underwent transarterial chemoembolization (TACE) are well known, the clinical significance of performing selective TACE for HCC patients has not been clearly documented. We tried to analyze the potential factors of disease-free survival for these patients, including the performance of selective TACE. A total of 151 patients with HCC who underwent TACE were retrospectively analyzed for their disease-free survival (a median follow- up of 23 months, range: 1-88 months). Univariate and multivariate analyses were performed for 20 potential factors by using the Cox proportional hazard model, including 19 baseline factors and one procedure-related factor (conventional versus selective TACE). The parameters that proved to be significant on the univariate analysis were subsequently tested with the multivariate model. Conventional or selective TACE was performed for 40 and 111 patients, respectively. Univariate and multivariate analyses revealed that tumor multiplicity, venous tumor thrombosis and selective TACE were the only three independent significant prognostic factors of disease-free survival (p = 0.002, 0.015 and 0.019, respectively). In our study, selective TACE was a favorable prognostic factor for the disease-free survival of patients with HCC who underwent TACE

HIV-1 phylogenetic analysis shows HIV-1 transits through the meninges to brain and peripheral tissues.

Science.gov (United States)

Lamers, Susanna L; Gray, Rebecca R; Salemi, Marco; Huysentruyt, Leanne C; McGrath, Michael S

2011-01-01

Brain infection by the human immunodeficiency virus type 1 (HIV-1) has been investigated in many reports with a variety of conclusions concerning the time of entry and degree of viral compartmentalization. To address these diverse findings, we sequenced HIV-1 gp120 clones from a wide range of brain, peripheral and meningeal tissues from five patients who died from several HIV-1 associated disease pathologies. High-resolution phylogenetic analysis confirmed previous studies that showed a significant degree of compartmentalization in brain and peripheral tissue subpopulations. Some intermixing between the HIV-1 subpopulations was evident, especially in patients that died from pathologies other than HIV-associated dementia. Interestingly, the major tissue harboring virus from both the brain and peripheral tissues was the meninges. These results show that (1) HIV-1 is clearly capable of migrating out of the brain, (2) the meninges are the most likely primary transport tissues, and (3) infected brain macrophages comprise an important HIV reservoir during highly active antiretroviral therapy. Copyright © 2010 Elsevier B.V. All rights reserved.

Natural radionuclides in effluents release by a deactivated uranium mine

International Nuclear Information System (INIS)

Pereira, Wagner S.; Kelecom, Alphonse; Silva, Ademir X.; Lopes, José M.; Pinto, Carlos E.C.; Py Júnior, Delcy A.; Antunes, Marcos M.; Indústrias Nucleares do Brasil; Universidade Federal Fluminense; Coordenacao de Pos-Graduacao e Pesquisa de Engenharia

2017-01-01

The Ore Treatment Unit (OTU) is a mine and deactivated uranium plant in the city of Caldas, Minas Gerais, Brazil. This facility possesses three points of release of liquid effluents containing radionuclides: point 014, 025 and 076. At these points, the values of activity concentrations (AC) of the radionuclides U_n_a_t, "2"2"6Ra, "2"1"0Pb, "2"3"2Th and "2"2"8Ra were analyzed in 2012. The evaluation of point 014 by univariate statistics pointed four groups. [U_n_a_t > "2"2"8Ra > ("2"2"6Ra = "2"1"0Pb) >"2"3"2Th]. The multivariate statistics separated the radionuclides into two groups: [(U_n_a_t and "2"3"2Th) and ("2"2"6Ra, "2"2"8Ra and "2"1"0Pb)]. At point 025, the univariate statistics described three groups: [Un_a_t > ("2"2"8Ra = "2"1"0Pb) > ("2"2"6Ra = "2"3"2Th)] and the multivariate analysis also described three but different groups: [(U_n_a_t and "2"2"8Ra), ("2"2"6Ra and "2"1"0Pb) and "2"3"2Th]. In turn, point 076 showed another behavior. The univariate analysis showed only two groups: [(U_n_a_t) > ("2"2"6Ra, "2"2"8Ra, "2"1"0Pb, "2"3"2Th)]. Differently, the multivariate statistics defined three groups: [(U_n_a_t and "2"3"2Th), ("2"2"6Ra and "2"2"8Ra) and "2"1"0Pb].Thus, statistical analysis showed that each point has releases of effluents with different characteristics. Both the behaviors of releases, based on multivariate statistics, and of the AC magnitudes, based on the univariate statistics, are different between the points. The only common features were the greater magnitude of uranium and the smaller magnitude of thorium. (author)

Using structural equation modeling for network meta-analysis.

Science.gov (United States)

Tu, Yu-Kang; Wu, Yun-Chun

2017-07-14

Network meta-analysis overcomes the limitations of traditional pair-wise meta-analysis by incorporating all available evidence into a general statistical framework for simultaneous comparisons of several treatments. Currently, network meta-analyses are undertaken either within the Bayesian hierarchical linear models or frequentist generalized linear mixed models. Structural equation modeling (SEM) is a statistical method originally developed for modeling causal relations among observed and latent variables. As random effect is explicitly modeled as a latent variable in SEM, it is very flexible for analysts to specify complex random effect structure and to make linear and nonlinear constraints on parameters. The aim of this article is to show how to undertake a network meta-analysis within the statistical framework of SEM. We used an example dataset to demonstrate the standard fixed and random effect network meta-analysis models can be easily implemented in SEM. It contains results of 26 studies that directly compared three treatment groups A, B and C for prevention of first bleeding in patients with liver cirrhosis. We also showed that a new approach to network meta-analysis based on the technique of unrestricted weighted least squares (UWLS) method can also be undertaken using SEM. For both the fixed and random effect network meta-analysis, SEM yielded similar coefficients and confidence intervals to those reported in the previous literature. The point estimates of two UWLS models were identical to those in the fixed effect model but the confidence intervals were greater. This is consistent with results from the traditional pairwise meta-analyses. Comparing to UWLS model with common variance adjusted factor, UWLS model with unique variance adjusted factor has greater confidence intervals when the heterogeneity was larger in the pairwise comparison. The UWLS model with unique variance adjusted factor reflects the difference in heterogeneity within each comparison

A cost analysis of implementing a behavioral weight loss intervention in community mental health settings: Results from the ACHIEVE trial.

Science.gov (United States)

Janssen, Ellen M; Jerome, Gerald J; Dalcin, Arlene T; Gennusa, Joseph V; Goldsholl, Stacy; Frick, Kevin D; Wang, Nae-Yuh; Appel, Lawrence J; Daumit, Gail L

2017-06-01

In the ACHIEVE randomized controlled trial, an 18-month behavioral intervention accomplished weight loss in persons with serious mental illness who attended community psychiatric rehabilitation programs. This analysis estimates costs for delivering the intervention during the study. It also estimates expected costs to implement the intervention more widely in a range of community mental health programs. Using empirical data, costs were calculated from the perspective of a community psychiatric rehabilitation program delivering the intervention. Personnel and travel costs were calculated using time sheet data. Rent and supply costs were calculated using rent per square foot and intervention records. A univariate sensitivity analysis and an expert-informed sensitivity analysis were conducted. With 144 participants receiving the intervention and a mean weight loss of 3.4 kg, costs of $95 per participant per month and $501 per kilogram lost in the trial were calculated. In univariate sensitivity analysis, costs ranged from $402 to $725 per kilogram lost. Through expert-informed sensitivity analysis, it was estimated that rehabilitation programs could implement the intervention for $68 to $85 per client per month. Costs of implementing the ACHIEVE intervention were in the range of other intensive behavioral weight loss interventions. Wider implementation of efficacious lifestyle interventions in community mental health settings will require adequate funding mechanisms. © 2017 The Obesity Society.

Correlation analysis and prognostic impact of 18F-FDG PET and excision repair cross-complementation group 1 (ERCC-1) expression in non-small cell lung cancer

International Nuclear Information System (INIS)

Jeong, Yong Hyu; Lee, Choong Kun; Jo, Kwan Hyeong; Hwang, Sang Hyun; Cha, Jong Tae; Lee, Jeong Won; Yun, Mi Jin; Cho, Arthur

2015-01-01

The aim of this study was to determine the relationship between [ 18 ]-2-fluoro-2-deoxy-D-glucose (FDG) uptake and excision repair cross-complementation group 1 (ERCC-1) expression and to evaluate the prognostic effect of these two factors in resectable non-small cell lung cancer (NSCLC) patients. We retrospectively reviewed 212 patients with resectable NSCLC who underwent FDG positron emission tomography/computed tomography (PET/CT) scan for cancer staging and ERCC-1 expression analysis between January 2008 to December 2011. All patients were then followed-up for survival analysis. Semiquantitative evaluation of ERCC-1 was performed with the H-scoring system and was correlated with maximum standardized uptake value (SUV max ) of NSCLC. Univariate and multivariate analyses were performed to evaluate for FDG uptake and ERCC-1 expression predicting overall survival. In 212 patients (139 male, median age 68 ± 9.11), 112 patients had ERCC-positive tumors and 100 patients had ERCC-negative tumors. There was no significant difference in SUV max between ERCC-1-positive tumors (8.02 ±5.40) and ERCC-1-negative tumors (7.57 ± 6.56, p = 0.584). All patients were followed-up for a median of 40.5 months (95 % confidence interval [CI], 38.5–42.2 months). Univariate analysis and multivariate analysis for all patients showed that both ERCC-1 expression (hazard ratio [HR], 2.78; 95 % CI, 1.20–6.47) and FDG uptake (HR, 4.50; 95 % CI, 2.07–9.77) independently predicted overall survival. We have found no statistical correlation between FDG uptake and ERCC-1 expression in NSCLC. However, both higher FDG uptake and positive ERCC-1 expression are independent predictive markers of prognosis, suggesting that both should be obtained during patient workup

Ischemic risk stratification by means of multivariate analysis of the heart rate variability

International Nuclear Information System (INIS)

Valencia, José F; Vallverdú, Montserrat; Caminal, Pere; Porta, Alberto; Voss, Andreas; Schroeder, Rico; Vázquez, Rafael; Bayés de Luna, Antonio

2013-01-01

In this work, a univariate and multivariate statistical analysis of indexes derived from heart rate variability (HRV) was conducted to stratify patients with ischemic dilated cardiomyopathy (IDC) in cardiac risk groups. Indexes conditional entropy, refined multiscale entropy (RMSE), detrended fluctuation analysis, time and frequency analysis, were applied to the RR interval series (beat-to-beat series), for single and multiscale complexity analysis of the HRV in IDC patients. Also, clinical parameters were considered. Two different end-points after a follow-up of three years were considered: (i) analysis A, with 151 survivor patients as a low risk group and 13 patients that suffered sudden cardiac death as a high risk group; (ii) analysis B, with 192 survivor patients as a low risk group and 30 patients that suffered cardiac mortality as a high risk group. A univariate and multivariate linear discriminant analysis was used as a statistical technique for classifying patients in risk groups. Sensitivity (Sen) and specificity (Spe) were calculated as diagnostic criteria in order to evaluate the performance of the indexes and their linear combinations. Sen and Spe values of 80.0% and 72.9%, respectively, were obtained during daytime by combining one clinical parameter and one index from RMSE, and during nighttime Sen = 80% and Spe = 73.4% were attained by combining one clinical factor and two indexes from RMSE. In particular, relatively long time scales were more relevant for classifying patients into risk groups during nighttime, while during daytime shorter scales performed better. The results suggest that the left atrial size, indexed to body surface and RMSE indexes are those that allow enhanced classification of ischemic patients in their respective risk groups, confirming that a single measurement is not enough to fully characterize ischemic risk patients and the clinical relevance of HRV complexity measures. (paper)

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

Science.gov (United States)

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

Metabolic tumor volume measured by F 18 FDG PET/CT can further stratify the prognosis of patients with stage IV Non Small Cell Lung Cancer

Energy Technology Data Exchange (ETDEWEB)

Yoo, Su Woong; Kim, Ja Hae; Chong, Ar I; Kwon, Seong Young; Min, Jung Joon; Song, Ho Chun; Bom, Hee Seung [Chonnam National Univ. Hwasun Hospital, Gwangju (Korea, Republic of)

2012-12-15

This study aimed to further stratify prognostic factors in patients with stage IV non small cell lung cancer (NSCLC) by measuring their metabolic tumor volume (MTV) using F 18 fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT). The subjects of this retrospective study were 57 patients with stage IV NSCLC. MTV, total lesion glycolysis (TLG), and maximum standardized uptake value (SUVmax) were measured on F 18 FDG PET/CT in both the primary lung lesion as well as metastatic lesions in torso. Optimal cutoff values of PET parameters were mea measured by receiver operating characteristic (ROC) curve anal analysis. Kaplan Meier survival (PET). The univariate and multivariate cox proportional hazards models were used to select the significant prognostic factors. Univariate analysis showed that both MTV and TLG of primary lung lesion (MTV lung and TLG lung) were significant factors for prediction of PFS ( <0.001 =0.038, respectively). Patients showing lower values of MTV lung and TLG lung than the cutoff values had significantly longer mean PFS than those with higher values. hazard ratios (95% confidence interval) of MTV lung and TLG lung measured by univariate analysis were 6.4 (2.5 16.3) and 2.4 (1.0 5.5), respectively. multivariate analysis revealed that MTV lung was the only significant factor for prediction of prognosis. Hazard ratio was 13,5 (1.6 111.1, =0,016). patients with stage IV NSCLC could be further stratified into subgroups of significantly better and worse prognosis by MTV of primary lung lesion.

Metabolic tumor volume measured by F 18 FDG PET/CT can further stratify the prognosis of patients with stage IV Non Small Cell Lung Cancer

International Nuclear Information System (INIS)

Yoo, Su Woong; Kim, Ja Hae; Chong, Ar I; Kwon, Seong Young; Min, Jung Joon; Song, Ho Chun; Bom, Hee Seung

2012-01-01

This study aimed to further stratify prognostic factors in patients with stage IV non small cell lung cancer (NSCLC) by measuring their metabolic tumor volume (MTV) using F 18 fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT). The subjects of this retrospective study were 57 patients with stage IV NSCLC. MTV, total lesion glycolysis (TLG), and maximum standardized uptake value (SUVmax) were measured on F 18 FDG PET/CT in both the primary lung lesion as well as metastatic lesions in torso. Optimal cutoff values of PET parameters were mea measured by receiver operating characteristic (ROC) curve anal analysis. Kaplan Meier survival (PET). The univariate and multivariate cox proportional hazards models were used to select the significant prognostic factors. Univariate analysis showed that both MTV and TLG of primary lung lesion (MTV lung and TLG lung) were significant factors for prediction of PFS ( <0.001 =0.038, respectively). Patients showing lower values of MTV lung and TLG lung than the cutoff values had significantly longer mean PFS than those with higher values. hazard ratios (95% confidence interval) of MTV lung and TLG lung measured by univariate analysis were 6.4 (2.5 16.3) and 2.4 (1.0 5.5), respectively. multivariate analysis revealed that MTV lung was the only significant factor for prediction of prognosis. Hazard ratio was 13,5 (1.6 111.1, =0,016). patients with stage IV NSCLC could be further stratified into subgroups of significantly better and worse prognosis by MTV of primary lung lesion

Dioscorea alata tuber proteome analysis shows over thirty dioscorin isoforms and novel tuber proteins.

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Sharma, Shruti; Gupta, Ravi; Deswal, Renu

2017-05-01

In Dioscorea, dioscorin (31 kDa) is the major storage protein constituting 85% of the total tuber proteins. An integrated proteomic and biochemical approach was used to understand the physiological role of dioscorin in the two contrasting growth stages (germinating and mature tuber). HPLC analysis showed 3 fold reduction in mannitol and 12.88 and 1.24 fold increase in sucrose and maltose in the germinating tuber. A 1.8 and 3 fold increase in sucrose phosphate synthase and mannitol dehydrogenase activity respectively was observed in the germinating tuber while a 2 fold higher invertase probably lowers the sucrose accumulation in the mature tuber. SDS-PAGE and 2-D maps of the mature and germinating tubers confirmed depletion (more than 50%) of dioscorin on germination. Dioscorin was purified using ion exchange and gel filtration chromatography with 43.32 fold purification and 38.16 yield. Out of a trail of 35 spots at 31 kDa only 12 spots (identified as dioscorin isoforms) were present in the 2D gel of the purified fraction. To search for other tuber proteins besides dioscorin, the unbound fractions of DEAE column were analysed by 2DGE. DREB 1A, caffeic acid 3-O-methyltransferase and Rab-1 small GTP binding protein were identified perhaps for the first time in the Dioscorea proteome. The interactome analysis revealed these to be involved in oxidative stress, carotenoid synthesis and vesicular transport. This is perhaps the first attempt to identify tuber proteome (although limited) and to understand the physiological significance of these proteins. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

FT-IR-cPAS—New Photoacoustic Measurement Technique for Analysis of Hot Gases: A Case Study on VOCs

Science.gov (United States)

Hirschmann, Christian Bernd; Koivikko, Niina Susanna; Raittila, Jussi; Tenhunen, Jussi; Ojala, Satu; Rahkamaa-Tolonen, Katariina; Marbach, Ralf; Hirschmann, Sarah; Keiski, Riitta Liisa

2011-01-01

This article describes a new photoacoustic FT-IR system capable of operating at elevated temperatures. The key hardware component is an optical-readout cantilever microphone that can work up to 200 °C. All parts in contact with the sample gas were put into a heated oven, incl. the photoacoustic cell. The sensitivity of the built photoacoustic system was tested by measuring 18 different VOCs. At 100 ppm gas concentration, the univariate signal to noise ratios (1σ, measurement time 25.5 min, at highest peak, optical resolution 8 cm−1) of the spectra varied from minimally 19 for o-xylene up to 329 for butyl acetate. The sensitivity can be improved by multivariate analyses over broad wavelength ranges, which effectively co-adds the univariate sensitivities achievable at individual wavelengths. The multivariate limit of detection (3σ, 8.5 min, full useful wavelength range), i.e., the best possible inverse analytical sensitivity achievable at optimum calibration, was calculated using the SBC method and varied from 2.60 ppm for dichloromethane to 0.33 ppm for butyl acetate. Depending on the shape of the spectra, which often only contain a few sharp peaks, the multivariate analysis improved the analytical sensitivity by 2.2 to 9.2 times compared to the univariate case. Selectivity and multi component ability were tested by a SBC calibration including 5 VOCs and water. The average cross selectivities turned out to be less than 2% and the resulting inverse analytical sensitivities of the 5 interfering VOCs was increased by maximum factor of 2.2 compared to the single component sensitivities. Water subtraction using SBC gave the true analyte concentration with a variation coefficient of 3%, although the sample spectra (methyl ethyl ketone, 200 ppm) contained water from 1,400 to 100k ppm and for subtraction only one water spectra (10k ppm) was used. The developed device shows significant improvement to the current state-of-the-art measurement methods used in industrial

STATCAT, Statistical Analysis of Parametric and Non-Parametric Data

International Nuclear Information System (INIS)

David, Hugh

1990-01-01

1 - Description of program or function: A suite of 26 programs designed to facilitate the appropriate statistical analysis and data handling of parametric and non-parametric data, using classical and modern univariate and multivariate methods. 2 - Method of solution: Data is read entry by entry, using a choice of input formats, and the resultant data bank is checked for out-of- range, rare, extreme or missing data. The completed STATCAT data bank can be treated by a variety of descriptive and inferential statistical methods, and modified, using other standard programs as required

Evaluation de la surdite professionnelle dans un departement du ...

African Journals Online (AJOL)

Of all ONIHL patients, 26,5% had tinnitus and 3,5% had complaints related to balance disorders. ONIHL was sensorineural, bilateral and symmetrical in 93% of cases. Univariate analysis showed no significant association between hearing loss, age, occupational exposure to noise and occupational group. Conclusion : To ...

Correlation analysis and prognostic impact of {sup 18}F-FDG PET and excision repair cross-complementation group 1 (ERCC-1) expression in non-small cell lung cancer

Energy Technology Data Exchange (ETDEWEB)

Jeong, Yong Hyu; Lee, Choong Kun; Jo, Kwan Hyeong; Hwang, Sang Hyun; Cha, Jong Tae; Lee, Jeong Won; Yun, Mi Jin; Cho, Arthur [Yonsei University College of Medicine, Seoul (Korea, Republic of)

2015-06-15

The aim of this study was to determine the relationship between [{sup 18}]-2-fluoro-2-deoxy-D-glucose (FDG) uptake and excision repair cross-complementation group 1 (ERCC-1) expression and to evaluate the prognostic effect of these two factors in resectable non-small cell lung cancer (NSCLC) patients. We retrospectively reviewed 212 patients with resectable NSCLC who underwent FDG positron emission tomography/computed tomography (PET/CT) scan for cancer staging and ERCC-1 expression analysis between January 2008 to December 2011. All patients were then followed-up for survival analysis. Semiquantitative evaluation of ERCC-1 was performed with the H-scoring system and was correlated with maximum standardized uptake value (SUV{sub max}) of NSCLC. Univariate and multivariate analyses were performed to evaluate for FDG uptake and ERCC-1 expression predicting overall survival. In 212 patients (139 male, median age 68 ± 9.11), 112 patients had ERCC-positive tumors and 100 patients had ERCC-negative tumors. There was no significant difference in SUV{sub max} between ERCC-1-positive tumors (8.02 ±5.40) and ERCC-1-negative tumors (7.57 ± 6.56, p = 0.584). All patients were followed-up for a median of 40.5 months (95 % confidence interval [CI], 38.5–42.2 months). Univariate analysis and multivariate analysis for all patients showed that both ERCC-1 expression (hazard ratio [HR], 2.78; 95 % CI, 1.20–6.47) and FDG uptake (HR, 4.50; 95 % CI, 2.07–9.77) independently predicted overall survival. We have found no statistical correlation between FDG uptake and ERCC-1 expression in NSCLC. However, both higher FDG uptake and positive ERCC-1 expression are independent predictive markers of prognosis, suggesting that both should be obtained during patient workup.

A multi-centre cohort study shows no association between experienced violence and labour dystocia in nulliparous women at term

Directory of Open Access Journals (Sweden)

Dykes Anna-Karin

2011-02-01

Full Text Available Abstract Background Although both labour dystocia and domestic violence during pregnancy are associated with adverse maternal and fetal outcome, evidence in support of a possible association between experiences of domestic violence and labour dystocia is sparse. The aim of this study was to investigate whether self-reported history of violence or experienced violence during pregnancy is associated with increased risk of labour dystocia in nulliparous women at term. Methods A population-based multi-centre cohort study. A self-administrated questionnaire collected at 37 weeks of gestation from nine obstetric departments in Denmark. The total cohort comprised 2652 nulliparous women, among whom 985 (37.1% met the protocol criteria for dystocia. Results Among the total cohort, 940 (35.4% women reported experience of violence, and among these, 66 (2.5% women reported exposure to violence during their first pregnancy. Further, 39.5% (n = 26 of those had never been exposed to violence before. Univariate logistic regression analysis showed no association between history of violence or experienced violence during pregnancy and labour dystocia at term, crude OR 0.91, 95% CI (0.77-1.08, OR 0.90, 95% CI (0.54-1.50, respectively. However, violence exposed women consuming alcoholic beverages during late pregnancy had increased odds of labour dystocia, crude OR 1.45, 95% CI (1.07-1.96. Conclusions Our findings indicate that nulliparous women who have a history of violence or experienced violence during pregnancy do not appear to have a higher risk of labour dystocia at term, according to the definition of labour dystocia in this study. Additional research on this topic would be beneficial, including further evaluation of the criteria for labour dystocia.

Post hoc analysis of plasma amino acid profiles: towards a specific pattern in autism spectrum disorder and intellectual disability.

Science.gov (United States)

Delaye, Jean-Baptiste; Patin, Franck; Lagrue, Emmanuelle; Le Tilly, Olivier; Bruno, Clement; Vuillaume, Marie-Laure; Raynaud, Martine; Benz-De Bretagne, Isabelle; Laumonnier, Frederic; Vourc'h, Patrick; Andres, Christian; Blasco, Helene

2018-01-01

Objectives Autism spectrum disorders and intellectual disability present a challenge for therapeutic and dietary management. We performed a re-analysis of plasma amino acid chromatography of children with autism spectrum disorders ( n = 22) or intellectual disability ( n = 29) to search for a metabolic signature that can distinguish individuals with these disorders from controls ( n = 30). Methods We performed univariate and multivariate analyses using different machine learning strategies, from the raw data of the amino acid chromatography. Finally, we analysed the metabolic pathways associated with discriminant biomarkers. Results Multivariate analysis revealed models to discriminate patients with autism spectrum disorders or intellectual disability and controls from plasma amino acid profiles ( P intellectual disability patients shared similar differences relative to controls, including lower glutamate ( P intellectual disability revealed the involvement of urea, 3-methyl-histidine and histidine metabolism. Biosigner analysis and univariate analysis confirmed the role of 3-methylhistidine ( P = 0.004), histidine ( P = 0.003), urea ( P = 0.0006) and lysine ( P = 0.002). Conclusions We revealed discriminant metabolic patterns between autism spectrum disorders, intellectual disability and controls. Amino acids known to play a role in neurotransmission were discriminant in the models comparing autism spectrum disorders or intellectual disability to controls, and histidine and b-alanine metabolism was specifically highlighted in the model.

Virological success after 12 and 24 months of antiretroviral therapy in sub-Saharan Africa: Comparing results of trials, cohorts and cross-sectional studies using a systematic review and meta-analysis.

Science.gov (United States)

Taieb, Fabien; Madec, Yoann; Cournil, Amandine; Delaporte, Eric

2017-01-01

UNAIDS recently defined the 90-90-90 target as a way to end the HIV epidemic. However, the proportion of virological success following antiretroviral therapy (ART) may not be as high as the anticipated 90%, and may in fact be highly heterogeneous. We aimed to describe the proportion of virological success in sub-Saharan Africa and to identify factors associated with the proportion of virological success. We performed a systematic review and meta-analysis focusing on the proportion of patients in sub-Saharan Africa who demonstrate virological success at 12 and 24 months since ART initiation, as well as at 6 and 36 months, where possible. Programme factors associated with the proportion of virological success were identified using meta-regression. Analyses were conducted using both on-treatment (OT) and intention-to-treat (ITT) approaches. Eighty-five articles were included in the meta-analysis, corresponding to 125 independent study populations. Using an on-treatment approach, the proportions (95% confidence interval (CI)) of virological success at 12 (n = 64) and at 24 (n = 32) months since ART initiation were 87.7% (81.3-91.0) and 83.7% (79.8-87.6), respectively. Univariate analysis indicated that the proportion of virological success was not different by study design. Multivariate analysis at 24 months showed that the proportion of virological success was significantly larger in studies conducted in public sector sites than in other sites (p = 0.045). Using an ITT approach, the proportions (95% CI) of virological success at 12 (n = 50) and at 24 (n = 20) months were 65.4% (61.8-69.1) and 56.8% (51.3-62.4), respectively. At 12 months, multivariate analysis showed that the proportion of success was significantly lower in cohort studies than in trials (63.0% vs. 71.1%; p = 0.017). At 24 months, univariate analysis demonstrated that the proportion of success was also lower in cohorts. Regardless of the time following ART initiation, and of the threshold, proportions

UniFIeD Univariate Frequency-based Imputation for Time Series Data

OpenAIRE

Friese, Martina; Stork, Jörg; Ramos Guerra, Ricardo; Bartz-Beielstein, Thomas; Thaker, Soham; Flasch, Oliver; Zaefferer, Martin

2013-01-01

This paper introduces UniFIeD, a new data preprocessing method for time series. UniFIeD can cope with large intervals of missing data. A scalable test function generator, which allows the simulation of time series with different gap sizes, is presented additionally. An experimental study demonstrates that (i) UniFIeD shows a significant better performance than simple imputation methods and (ii) UniFIeD is able to handle situations, where advanced imputation methods fail. The results are indep...

Selecting predictors for discriminant analysis of species performance: an example from an amphibious softwater plant.

Science.gov (United States)

Vanderhaeghe, F; Smolders, A J P; Roelofs, J G M; Hoffmann, M

2012-03-01

Selecting an appropriate variable subset in linear multivariate methods is an important methodological issue for ecologists. Interest often exists in obtaining general predictive capacity or in finding causal inferences from predictor variables. Because of a lack of solid knowledge on a studied phenomenon, scientists explore predictor variables in order to find the most meaningful (i.e. discriminating) ones. As an example, we modelled the response of the amphibious softwater plant Eleocharis multicaulis using canonical discriminant function analysis. We asked how variables can be selected through comparison of several methods: univariate Pearson chi-square screening, principal components analysis (PCA) and step-wise analysis, as well as combinations of some methods. We expected PCA to perform best. The selected methods were evaluated through fit and stability of the resulting discriminant functions and through correlations between these functions and the predictor variables. The chi-square subset, at P < 0.05, followed by a step-wise sub-selection, gave the best results. In contrast to expectations, PCA performed poorly, as so did step-wise analysis. The different chi-square subset methods all yielded ecologically meaningful variables, while probable noise variables were also selected by PCA and step-wise analysis. We advise against the simple use of PCA or step-wise discriminant analysis to obtain an ecologically meaningful variable subset; the former because it does not take into account the response variable, the latter because noise variables are likely to be selected. We suggest that univariate screening techniques are a worthwhile alternative for variable selection in ecology. © 2011 German Botanical Society and The Royal Botanical Society of the Netherlands.

Genome-wide analysis of adolescent psychotic-like experiences shows genetic overlap with psychiatric disorders.

Science.gov (United States)

Pain, Oliver; Dudbridge, Frank; Cardno, Alastair G; Freeman, Daniel; Lu, Yi; Lundstrom, Sebastian; Lichtenstein, Paul; Ronald, Angelica

2018-03-31

This study aimed to test for overlap in genetic influences between psychotic-like experience traits shown by adolescents in the community, and clinically-recognized psychiatric disorders in adulthood, specifically schizophrenia, bipolar disorder, and major depression. The full spectra of psychotic-like experience domains, both in terms of their severity and type (positive, cognitive, and negative), were assessed using self- and parent-ratings in three European community samples aged 15-19 years (Final N incl. siblings = 6,297-10,098). A mega-genome-wide association study (mega-GWAS) for each psychotic-like experience domain was performed. Single nucleotide polymorphism (SNP)-heritability of each psychotic-like experience domain was estimated using genomic-relatedness-based restricted maximum-likelihood (GREML) and linkage disequilibrium- (LD-) score regression. Genetic overlap between specific psychotic-like experience domains and schizophrenia, bipolar disorder, and major depression was assessed using polygenic risk score (PRS) and LD-score regression. GREML returned SNP-heritability estimates of 3-9% for psychotic-like experience trait domains, with higher estimates for less skewed traits (Anhedonia, Cognitive Disorganization) than for more skewed traits (Paranoia and Hallucinations, Parent-rated Negative Symptoms). Mega-GWAS analysis identified one genome-wide significant association for Anhedonia within IDO2 but which did not replicate in an independent sample. PRS analysis revealed that the schizophrenia PRS significantly predicted all adolescent psychotic-like experience trait domains (Paranoia and Hallucinations only in non-zero scorers). The major depression PRS significantly predicted Anhedonia and Parent-rated Negative Symptoms in adolescence. Psychotic-like experiences during adolescence in the community show additive genetic effects and partly share genetic influences with clinically-recognized psychiatric disorders, specifically schizophrenia and

[Risk factors for elevated serum total bile acid in preterm infants].

Science.gov (United States)

Song, Yan-Ting; Wang, Yong-Qin; Zhao, Yue-Hua; Zhu, Hai-Ling; Liu, Qian; Zhang, Xiao; Gao, Yi-Wen; Zhang, Wei-Ye; Sang, Yu-Tong

2018-03-01

To study the risk factors for elevated serum total bile acid (TBA) in preterm infants. A retrospective analysis was performed for the clinical data of 216 preterm infants who were admitted to the neonatal intensive care unit. According to the presence or absence of elevated TBA (TBA >24.8 μmol/L), the preterm infants were divided into elevated TBA group with 53 infants and non-elevated TBA group with 163 infants. A univariate analysis and an unconditional multivariate logistic regression analysis were used to investigate the risk factors for elevated TBA. The univariate analysis showed that there were significant differences between the elevated TBA group and the non-elevated TBA group in gestational age at birth, birth weight, proportion of small-for-gestational-age infants, proportion of infants undergoing ventilator-assisted ventilation, fasting time, parenteral nutrition time, and incidence of neonatal respiratory failure and sepsis (PTBA in preterm infants. Low birth weight and neonatal sepsis may lead to elevated TBA in preterm infants.

The prognostic role of mTOR and p-mTOR for survival in non-small cell lung cancer: a systematic review and meta-analysis.

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Lei Li

Full Text Available The mammalian target of rapamycin (mTOR and phosphorylated mTOR (p-mTOR are potential prognostic markers and therapeutic targets for non-small cell lung cancer (NSCLC. However, the association between mTOR/p-mTOR expression and NSCLC patients' prognosis remains controversial. Thus, a meta-analysis of existing studies evaluating the prognostic role of mTOR/p-mTOR expression for NSCLC was conducted.A systemically literature search was performed via Pubmed, Embase, Medline as well as CNKI (China National Knowledge Infrastructure. Studies were included that reported the hazard ratio (HR and 95%CI for the association between mTOR/p-mTOR expression and NSCLC patients' survival. Random-effects model was used to pool HRs.Ten eligible studies were included in this meta-analysis, with 4 about m-TOR and 7 about p-mTOR. For mTOR, the pooled HR of overall survival (OS was 1.00 (95%CI 0.5 to 1.99 by univariate analysis and 1.22 (95%CI 0.53 to 2.82 by multivariate analysis. For p-mTOR, the pooled HR was 1.39 (95%CI 0.97 to 1.98 by univariate analysis and 1.42 (95%CI 0.56 to 3.60 by multivariate analysis.The results indicated that no statistically significant association was found between mTOR/p-mTOR expression and NSCLC patients' prognosis.

Development and validation of a method for the determination of regulated fragrance allergens by High-Performance Liquid Chromatography and Parallel Factor Analysis 2.

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Pérez-Outeiral, Jessica; Elcoroaristizabal, Saioa; Amigo, Jose Manuel; Vidal, Maider

2017-12-01

This work presents the development and validation of a multivariate method for quantitation of 6 potentially allergenic substances (PAS) related to fragrances by ultrasound-assisted emulsification microextraction coupled with HPLC-DAD and PARAFAC2 in the presence of other 18 PAS. The objective is the extension of a previously proposed univariate method to be able to determine the 24 PAS currently considered as allergens. The suitability of the multivariate approach for the qualitative and quantitative analysis of the analytes is discussed through datasets of increasing complexity, comprising the assessment and validation of the method performance. PARAFAC2 showed to adequately model the data facing up different instrumental and chemical issues, such as co-elution profiles, overlapping spectra, unknown interfering compounds, retention time shifts and baseline drifts. Satisfactory quality parameters of the model performance were obtained (R 2 ≥0.94), as well as meaningful chromatographic and spectral profiles (r≥0.97). Moreover, low errors of prediction in external validation standards (below 15% in most cases) as well as acceptable quantification errors in real spiked samples (recoveries from 82 to 119%) confirmed the suitability of PARAFAC2 for resolution and quantification of the PAS. The combination of the previously proposed univariate approach, for the well-resolved peaks, with the developed multivariate method allows the determination of the 24 regulated PAS. Copyright © 2017 Elsevier B.V. All rights reserved.

Towards quantitative laser-induced breakdown spectroscopy analysis of soil samples

International Nuclear Information System (INIS)

Bousquet, B.; Sirven, J.-B.; Canioni, L.

2007-01-01

A quantitative analysis of chromium in soil samples is presented. Different emission lines related to chromium are studied in order to select the best one for quantitative features. Important matrix effects are demonstrated from one soil to the other, preventing any prediction of concentration in different soils on the basis of a univariate calibration curve. Finally, a classification of the LIBS data based on a series of Principal Component Analyses (PCA) is applied to a reduced dataset of selected spectral lines related to the major chemical elements in the soils. LIBS data of heterogeneous soils appear to be widely dispersed, which leads to a reconsideration of the sampling step in the analysis process

Fourier transform infrared spectroscopic imaging and multivariate regression for prediction of proteoglycan content of articular cartilage.

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Lassi Rieppo

Full Text Available Fourier Transform Infrared (FT-IR spectroscopic imaging has been earlier applied for the spatial estimation of the collagen and the proteoglycan (PG contents of articular cartilage (AC. However, earlier studies have been limited to the use of univariate analysis techniques. Current analysis methods lack the needed specificity for collagen and PGs. The aim of the present study was to evaluate the suitability of partial least squares regression (PLSR and principal component regression (PCR methods for the analysis of the PG content of AC. Multivariate regression models were compared with earlier used univariate methods and tested with a sample material consisting of healthy and enzymatically degraded steer AC. Chondroitinase ABC enzyme was used to increase the variation in PG content levels as compared to intact AC. Digital densitometric measurements of Safranin O-stained sections provided the reference for PG content. The results showed that multivariate regression models predict PG content of AC significantly better than earlier used absorbance spectrum (i.e. the area of carbohydrate region with or without amide I normalization or second derivative spectrum univariate parameters. Increased molecular specificity favours the use of multivariate regression models, but they require more knowledge of chemometric analysis and extended laboratory resources for gathering reference data for establishing the models. When true molecular specificity is required, the multivariate models should be used.

Systems level analysis of systemic sclerosis shows a network of immune and profibrotic pathways connected with genetic polymorphisms.

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J Matthew Mahoney

2015-01-01

Full Text Available Systemic sclerosis (SSc is a rare systemic autoimmune disease characterized by skin and organ fibrosis. The pathogenesis of SSc and its progression are poorly understood. The SSc intrinsic gene expression subsets (inflammatory, fibroproliferative, normal-like, and limited are observed in multiple clinical cohorts of patients with SSc. Analysis of longitudinal skin biopsies suggests that a patient's subset assignment is stable over 6-12 months. Genetically, SSc is multi-factorial with many genetic risk loci for SSc generally and for specific clinical manifestations. Here we identify the genes consistently associated with the intrinsic subsets across three independent cohorts, show the relationship between these genes using a gene-gene interaction network, and place the genetic risk loci in the context of the intrinsic subsets. To identify gene expression modules common to three independent datasets from three different clinical centers, we developed a consensus clustering procedure based on mutual information of partitions, an information theory concept, and performed a meta-analysis of these genome-wide gene expression datasets. We created a gene-gene interaction network of the conserved molecular features across the intrinsic subsets and analyzed their connections with SSc-associated genetic polymorphisms. The network is composed of distinct, but interconnected, components related to interferon activation, M2 macrophages, adaptive immunity, extracellular matrix remodeling, and cell proliferation. The network shows extensive connections between the inflammatory- and fibroproliferative-specific genes. The network also shows connections between these subset-specific genes and 30 SSc-associated polymorphic genes including STAT4, BLK, IRF7, NOTCH4, PLAUR, CSK, IRAK1, and several human leukocyte antigen (HLA genes. Our analyses suggest that the gene expression changes underlying the SSc subsets may be long-lived, but mechanistically interconnected

Prospective Analysis of Surgical Bone Margins After Partial Foot Amputation in Diabetic Patients Admitted With Moderate to Severe Foot Infections.

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Schmidt, Brian M; McHugh, Jonathan B; Patel, Rajiv M; Wrobel, James S

2018-04-01

Osteomyelitis is common in diabetic foot infections and medical management can lead to poor outcomes. Surgical management involves sending histopathologic and microbiologic specimens which guides future intervention. We examined the effect of obtainment of surgical margins in patients undergoing forefoot amputations to identify patient characteristics associated with outcomes. Secondary aims included evaluating interobserver reliability of histopathologic data at both the distal-to and proximal-to surgical bone margin. Data were prospectively collected on 72 individuals and was pooled for analysis. Standardized method to retrieve intraoperative bone margins was established. A univariate analysis was performed. Negative outcomes, including major lower extremity amputation, wound dehiscence, reulceration, reamputation, or death were recorded. Viable proximal margins were obtained in 63 out of 72 cases (87.5%). Strong interobserver reliability of histopathology was recorded. Univariate analysis demonstrated preoperative platelets, albumin, probe-to-bone testing, absolute toe pressures, smaller wound surface area were associated with obtaining viable margins. Residual osteomyelitis resulted in readmission 2.6 times more often and more postoperative complications. Certain patients were significantly different in the viable margin group versus dirty margin group. High interobserver reliability was demonstrated. Obtainment of viable margins resulted in reduced rates of readmission and negative outcomes. Prognostic, Level I: Prospective.

Urine proteome analysis in Dent's disease shows high selective changes potentially involved in chronic renal damage.

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Santucci, Laura; Candiano, Giovanni; Anglani, Franca; Bruschi, Maurizio; Tosetto, Enrica; Cremasco, Daniela; Murer, Luisa; D'Ambrosio, Chiara; Scaloni, Andrea; Petretto, Andrea; Caridi, Gianluca; Rossi, Roberta; Bonanni, Alice; Ghiggeri, Gian Marco

2016-01-01

Definition of the urinary protein composition would represent a potential tool for diagnosis in many clinical conditions. The use of new proteomic technologies allows detection of genetic and post-trasductional variants that increase sensitivity of the approach but complicates comparison within a heterogeneous patient population. Overall, this limits research of urinary biomarkers. Studying monogenic diseases are useful models to address this issue since genetic variability is reduced among first- and second-degree relatives of the same family. We applied this concept to Dent's disease, a monogenic condition characterised by low-molecular-weight proteinuria that is inherited following an X-linked trait. Results are presented here on a combined proteomic approach (LC-mass spectrometry, Western blot and zymograms for proteases and inhibitors) to characterise urine proteins in a large family (18 members, 6 hemizygous patients, 6 carrier females, and 6 normals) with Dent's diseases due to the 1070G>T mutation of the CLCN5. Gene ontology analysis on more than 1000 proteins showed that several clusters of proteins characterised urine of affected patients compared to carrier females and normal subjects: proteins involved in extracellular matrix remodelling were the major group. Specific analysis on metalloproteases and their inhibitors underscored unexpected mechanisms potentially involved in renal fibrosis. Studying with new-generation techniques for proteomic analysis of the members of a large family with Dent's disease sharing the same molecular defect allowed highly repetitive results that justify conclusions. Identification in urine of proteins actively involved in interstitial matrix remodelling poses the question of active anti-fibrotic drugs in Dent's patients. Copyright © 2015 Elsevier B.V. All rights reserved.

CASAS: Cancer Survival Analysis Suite, a web based application.

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Rupji, Manali; Zhang, Xinyan; Kowalski, Jeanne

2017-01-01

We present CASAS, a shiny R based tool for interactive survival analysis and visualization of results. The tool provides a web-based one stop shop to perform the following types of survival analysis:  quantile, landmark and competing risks, in addition to standard survival analysis.  The interface makes it easy to perform such survival analyses and obtain results using the interactive Kaplan-Meier and cumulative incidence plots.  Univariate analysis can be performed on one or several user specified variable(s) simultaneously, the results of which are displayed in a single table that includes log rank p-values and hazard ratios along with their significance. For several quantile survival analyses from multiple cancer types, a single summary grid is constructed. The CASAS package has been implemented in R and is available via http://shinygispa.winship.emory.edu/CASAS/. The developmental repository is available at https://github.com/manalirupji/CASAS/.

Prognostic Value of microRNA-9 in Various Cancers: a Meta-analysis.

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Zhang, Yunyuan; Zhou, Jun; Sun, Meiling; Sun, Guirong; Cao, Yongxian; Zhang, Haiping; Tian, Runhua; Zhou, Lan; Duan, Liang; Chen, Xian; Lun, Limin

2017-07-01

Recently, there are more and more evidences from studies have revealed the association between microRNA-9 (miR-9) expression and outcome in multiple cancers, but inconsistent results have also been reported. It is necessary to rationalize a meta analysis of all available data to clarify the prognostic role of miR-9. Eligible studies were selected through multiple search strategies and the quality was assessed by MOOSE. Data was extracted from studies according to the key statistics index. All analyses were performed using STATA software. Twenty studies were selected in the meta-analysis to evaluate the prognostic role of miR-9 in multiple tumors. MiR-9 expression level was an independent prognostic biomarker for OS in tumor patients using multivariate and univariate analyses. High expression levels of miR-9 was demonstrated to associated with poor overall survival (OS) (HR = 2.23, 95 % CI: 1.56-3.17, P analysis showed that residence region (China and Japan), sample size, cancer type (solid or leukemia), follow-up months and analysis method (qPCR) did not alter the predictive value of miR-9 on OS in various cancers. Furthermore, no significant associations were detected for miR-9 expression and lymph node metastasis or distant metastasis. The present results suggest that promoted miR-9 expression is associated with poor OS in patients with general cancers.

Show-Bix &

DEFF Research Database (Denmark)

2014-01-01

The anti-reenactment 'Show-Bix &' consists of 5 dias projectors, a dial phone, quintophonic sound, and interactive elements. A responsive interface will enable the Dias projectors to show copies of original dias slides from the Show-Bix piece ”March på Stedet”, 265 images in total. The copies are...

Sex Differences in Relationship between Stress Responses and Lifestyle in Japanese Workers

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Akiko Suzuki

2014-03-01

Conclusion: This study showed that stress responses were related to lifestyle among women but not among men. Among women, stress responses were related to sleeping for shorter periods, whereas they were related to working long hours among men. In addition, stress responses were related to eating at night in the univariate analysis, although this relationship was not seen in the multivariate analysis, in either sex.

Multivariate analysis of prognostic factors for idiopathic sudden sensorineural hearing loss in children.

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Chung, Jae Ho; Cho, Seok Hyun; Jeong, Jin Hyeok; Park, Chul Won; Lee, Seung Hwan

2015-09-01

To evaluate clinical characteristics and possible associated factors of idiopathic sudden sensorineural hearing loss (ISSNHL) in children using univariate and multivariate analyses. A retrospective case series with comparisons. From January 2007 to December 2013, medical records of 37 pediatric ISSNHL patients were reviewed to assess hearing recovery rate and examine factors associated with prognosis (gender; side of hearing loss; opposite side hearing loss; treatment onset; presence of vertigo, tinnitus, and ear fullness; initial hearing threshold), using univariate and multivariate analysis, and compare them with 276 adult ISSNHL patients. Pediatric patients comprised only 6.6% of pediatric/adult cases of ISSNHL, and those below 10 years old were only 0.7%. The overall recovery rates (complete and partial) of the pediatric and adult patients were 57.4% and 47.2%, respectively. The complete recovery rate of the pediatric group (46.6%) was higher than that of the adult group (30.8%, P = .040). According to multivariate analysis, absence of tinnitus, later onset of treatment, and higher hearing threshold at initial presentation were associated with a poor prognosis in pediatric ISSNHL. The recovery rate of ISSNHL in pediatric patients is higher than in adults, and the presence of tinnitus and earlier treatment onset is associated with favorable outcomes. 4. © 2015 The American Laryngological, Rhinological and Otological Society, Inc.

The prognostic value of time parameters in adjuvant radiotherapy of head and neck cancer. A retrospective analysis of 138 patients

International Nuclear Information System (INIS)

Dietl, B.; Schaefer, C.; Koelbl, O.

2005-01-01

Purpose: to answer the question, how the parameters waiting time, radiation treatment time and overall treatment time (OTT) influenced the endpoints overall (OS), event-free (EFS) and local recurrence-free survival (LRFS) in patients with locally advanced head-and-neck cancer, who had received postoperative radiotherapy. Patients and methods: 138 patients were included into a retrospective analysis from 10/1993 to 05/2000. Besides the time parameters waiting time, radiation treatment time and OTT, tumor- and therapy-related parameters (T-, N-, R-status, grading, tumor site, surgical technique, and postoperative hemoglobin < 12 g/dl) with potential impact on the endpoints were investigated in the univariate analysis (Kaplan-Meier log-rank test). Individual parameters with a significant impact (p = 0.05) were subjected to a multivariate Cox regression analysis. Results: besides a postoperative hemoglobin value < 12 g/dl, in the univariate analysis an OTT ≥ 105 days negatively influenced all endpoints, as well as a radiation treatment time ≥ 60 days. On multivariate Cox regression analysis, postoperative hemoglobin < 12 g/dl and an OTT ≥ 105 days were identified as independent negative prognostic factors for all endpoints. Conclusion: the waiting time should be managed according to the ASARA (as short as reasonably achievable) recommendation, radiation treatment should not be protracted exceeding an overall treatment of 105 days. Generally, time parameters should be routinely included in the standard tumor documentation, thus facilitating further evaluation of these prognostically relevant factors. (orig.)

Multivariate Methods for Meta-Analysis of Genetic Association Studies.

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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.

Predictive factors for complete removal in soft tissue sarcomas: a retrospective analysis in a series of 592 cases.

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Sastre-Garau, X; Coindre, J M; Leroyer, A; Terrier, P; Ollivier, L; Stöckle, E; Bonichon, F; Collin, F; Le Doussal, V; Contesso, G; Vilain, M O; Jacquemier, J; Nguyen, B B

1997-07-01

In order to specify the indications for conservative surgery and preoperative therapeutic approaches of soft tissues sarcomas (STS), we looked for the clinico-pathological parameters associated with the failure to obtain a complete removal (CRm) of the tumor. We retrospectively analyzed a series of 592 cases of primary non-metastatic STS. Surgery was performed in 495 cases as a primary treatment and in 88 cases after chemo- or radiotherapy. Nine patients were treated by chemotherapy-radiotherapy. In a univariate analysis, 20 parameters were tested for their association with CRm. A multivariate analysis was then used to define the independent parameters linked to the achievement of a CRm. In the univariate analysis, 15 parameters were found to be linked to the achievement of a CRm. Three of them proved to be independent in the multivariate analysis: T in the TNM classification, tumor location, and tumor necrosis. By the combination of these risk factors, four groups of patients were defined, with respective rates of CRm of 97% (no factor), 95% (one factor), 70% (two factors), and 48% (three factors). The achievement of a CRm after surgery of STS depends not only on the accessibility of the lesion, but also on tumor aggressiveness, a reflection of which is necrosis. The detection of necrosis by imaging procedures may thus help predicting the resectability of tumors and defining the indications for neoadjuvant therapies, likely to broaden the use of conservative surgery.

Analysis of prognostic factors in stage IIB-IVA cervical carcinoma treated with radiation therapy: value of computed tomography

International Nuclear Information System (INIS)

Ogino, Ichiro; Okamoto, Naoyuki; Andoh, Kazuo; Kitamura, Tatsuo; Okajima, Hiroyuki; Matsubara, Sho

1997-01-01

Purpose: To define the influence of the tumor size measured by computed tomography (CT) and lymph node involvement detected by CT in patients treated with radiation therapy for Stage IIB-IVA carcinoma of intact uterine cervix. Methods and Materials: This was a retrospective analysis of 233 patients with uterine cervical cancer managed with both external irradiation and high-dose-rate intracavitary brachytherapy (HDR-ICR) at Kanagawa Cancer Center. The results were analyzed for the end points of absolute survival (AS), disease-free survival (DFS), pelvic control (PC), and central control (CC). The parameters of stage, CT-measured anterior-posterior (AP) cervix size, and CT-detected lymph node metastases were evaluated using univariate and multivariate analysis. Results: The stage, AP cervix size, and lymph node involvement were significant pretreatment factors in univariate analysis with respect to AS, DFS, PC, and CC. Multivariate analysis confirmed that significant risk was associated with certain prognostic parameters. Those in terms of AS, in order of decreasing significance, were lymph node involvement, AP cervix size, age, and total HDR-ICR dose. When DFS was studied, lymph node involvement and AP cervix size were demonstrated to have a significant effect. Stage and lymph node involvement significantly affected PC. Conclusion: Because the International Federation of Gynecological Obstetrics staging system fails to incorporate important prognostic information about tumor volume and lymph node involvement, CT-detected lymph node metastases as well as CT-measured cervix size should be determined as complementary additional prognostic measures

Exploring emergency department 4-hour target performance and cancelled elective operations: a regression analysis of routinely collected and openly reported NHS trust data.

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Keogh, Brad; Culliford, David; Guerrero-Ludueña, Richard; Monks, Thomas

2018-05-24

To quantify the effect of intrahospital patient flow on emergency department (ED) performance targets and indicate if the expectations set by the National Health Service (NHS) England 5-year forward review are realistic in returning emergency services to previous performance levels. Linear regression analysis of routinely reported trust activity and performance data using a series of cross-sectional studies. NHS trusts in England submitting routine nationally reported measures to NHS England. 142 acute non-specialist trusts operating in England between 2012 and 2016. The primary outcome measures were proportion of 4-hour waiting time breaches and cancelled elective operations. Univariate and multivariate linear regression models were used to show relationships between the outcome measures and various measures of trust activity including empty day beds, empty night beds, day bed to night bed ratio, ED conversion ratio and delayed transfers of care. Univariate regression results using the outcome of 4-hour breaches showed clear relationships with empty night beds and ED conversion ratio between 2012 and 2016. The day bed to night bed ratio showed an increasing ability to explain variation in performance between 2015 and 2016. Delayed transfers of care showed little evidence of an association. Multivariate model results indicated that the ability of patient flow variables to explain 4-hour target performance had reduced between 2012 and 2016 (19% to 12%), and had increased in explaining cancelled elective operations (7% to 17%). The flow of patients through trusts is shown to influence ED performance; however, performance has become less explainable by intratrust patient flow between 2012 and 2016. Some commonly stated explanatory factors such as delayed transfers of care showed limited evidence of being related. The results indicate some of the measures proposed by NHS England to reduce pressure on EDs may not have the desired impact on returning services to previous

FUNSTAT and statistical image representations

Science.gov (United States)

Parzen, E.

1983-01-01

General ideas of functional statistical inference analysis of one sample and two samples, univariate and bivariate are outlined. ONESAM program is applied to analyze the univariate probability distributions of multi-spectral image data.

Cross-Sectional Analysis of Neurocognitive Function, Retinopathy, and Retinal Thinning by Spectral-Domain Optical Coherence Tomography in Sickle Cell Patients.

Science.gov (United States)

Oltra, Erica Z; Chow, Clement C; Wubben, Thomas; Lim, Jennifer I; Chau, Felix Y; Moss, Heather E

2016-01-01

The purpose was to examine the relationship between neurocognitive function and two distinct forms of retinopathy in sickle cell disease. Patients with sickle cell disease (n = 44, age range: 19-56 years, 70% female) were prospectively recruited for this cross-sectional study. Retinopathy was characterized by: (1) Presence of focal retinal thinning on spectral domain optical coherence tomography and (2) determination of the sickle retinopathy stage on funduscopic exam based on Goldberg classification. Neurocognitive function was assessed using the Philadelphia Brief Assessment of Cognition (PBAC), a validated test of cognition. Univariate and multivariate analyses for PBAC score outcomes were performed. Retinal thinning and retinopathy stage were primary variables of interest and age, gender, genotype, education, and history of stroke were covariates. Univariate analysis revealed associations with total PBAC score and age (P = 0.049), history of stroke (P = 0.04), and genotype (P retinopathy stage were not associated with each other in this sample. Neither the presence of focal retinal thinning nor degree of retinopathy was associated with total PBAC score in univariate or multivariate analyses. We find an association between lower cognitive function and older age, history of stroke and sickle cell genotype SS in patients with sickle cell disease. Our data do not provide evidence to support an association between cognitive function and retinopathy in sickle cell patients.

Comportamento agressivo em shows musicais: analisando notícias de jornal impresso

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Carlos Eduardo Pimentel

2011-08-01

Full Text Available This paper reports a content analysis of the newspaper articles dealing with aggressive behavior in music shows. This analysis permits an understanding of the characteristics of these situations, the aggressive behaviors in shows, and the way in which the print media treat the subject. Despite of the newspapers’ influence on public opinion formation, that is, the transmission of attitudes and behavior repertoires, this kind of analysis is rarely reported in Brazilian social psychological literature. Some 31 newspaper articles were content analyzed as to aggressive behavior during music shows. Results indicate that aggressive behaviors occur in shows of most varied musical styles. Results are discussed on the basis of social cognitive theory.

Analysis of the Role of PET/CT SUVmax in Prognosis and Its Correlation with 
Clinicopathological Characteristics in Resectable Lung Squamous Cell Carcinoma

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Hongliang REN

2016-04-01

Full Text Available Background and objective Lung cancer is the leading cause of cancer death in men and women in the world, more than one-half of cases are diagnosed at a advanced stage, and the overall 5-year survival rate for lung cancer is 18%. Lung cancer is divided into non-small cell lung carcinoma (NSCLC and small cell lung carcinoma (SCLC. Approximately 80%-85% of cases are NSCLC which includes three main types: adenocarcinoma (40%, squamous cell carcinoma (SCC (20%-30%, and large cell carcinoma (10%. Although therapies that target driver mutations in adenocarcinomas are showing some promise, they are proving ineffective in smoking-related SCC. We need pay more attention to the diagnosis and treatment of SCC. 18F-FDG positron emission tomography (PET/computed tomography (CT has emerged as an accurate staging modality in lung cancer diagnosis. The aim of this study is to investigate the role of maximum standardized uptake value (SUVmax on PET-CT in prognosis and its correlation with clinicopathological characteristics in resectable SCC. Methods One hundred and eighty-two resectable SCC patients who underwent PET/CT imaging between May 2005 and October 2014 were enrolled into this retrospectively study. All the enrolled patients had underwent pulmonary resection with mediastinal lymph node dissection without preoperative chemotherapy or radiotherapy. Survival outcomes were analyzed using the Kaplan-Meier method and multivariate Cox proportional hazards model. Correlation between SUVmax and clinicopathological factors was analysed using Pearson correlation analysis and Spearman rank correlation analysis. Results The patients were divided into two groups on the basis of SUVmax 13.0 as cutoff value, and patients with SUVmax more than 13.0 had shorter median overall survival than patients less than 13.0 in univariate analysis (56 months vs 87 months; P=0.022. There was remarkable correlation between SUVmax and gender, tumor size, tumor-node-metastasis (TNM stage

Retrospective Analysis of Patient Presentations at the Sydney (Australia) Royal Easter Show from 2012 to 2014.

Science.gov (United States)

Crabtree, Nathan; Mo, Shirley; Ong, Leon; Jegathees, Thuvarahan; Wei, Daniel; Fahey, David; Liu, Jia Jenny

2017-04-01

Introduction Comprehensive studies on the relationship between patient demographics and subsequent treatment and disposition at a single mass-gathering event are lacking. The Sydney Royal Easter Show (SRES; Sydney Olympic Park, New South Wales, Australia) is an annual, 14-day, agricultural mass-gathering event occurring around the Easter weekend, attracting more than 800,000 patrons per year. In this study, patient records from the SRES were analyzed to examine relationships between weather, crowd size, day of week, and demographics on treatment and disposition. This information would help to predict factors affecting patient treatment and disposition to guide ongoing training of first responders and to evaluate the appropriateness of staffing skills mix at future events. Hypothesis Patient demographics, environmental factors, and attendance would influence the nature and severity of presentations at the SRES, which would influence staffing requirements. A retrospective analysis of 4,141 patient record forms was performed for patients who presented to St John Ambulance (Australian Capital Territory, Australia) at the SRES between 2012 and 2014 inclusive. Presentation type was classified using a previously published minimum data set. Data on weather and crowd size were obtained from the Australian Bureau of Meteorology (Melbourne, Victoria, Australia) and the SRES, respectively. Statistical analyses were performed using SPSS v22 (IBM; Armonk, New York USA). Between 2012 to 2014, over 2.5 million people attended the SRES with 4,141 patients treated onsite. As expected, the majority of presentations were injuries (49%) and illnesses (46%). Although patient demographics and presentation types did not change over time, the duration of treatment increased. A higher proportion of patients were discharged to hospital or home compared to the proportion of patients discharged back to the event. Patients from rural/regional locations (accounting for 15% of all patients) were

Sexual difference of human hyoid bones. Quantitative analysis of CT three-dimensional image

International Nuclear Information System (INIS)

Terashima, Yoshiharu; Izumi, Masahiro; Hanamura, Hajime; Takada, Yasushi

2007-01-01

We investigated sexual differences in hyoid bones of 50 dissected Japanese cadavers: 26 males (aged 52 to 101, averaged 81.9 years) and 24 females (aged 61 to 94, averaged 83.6 years). All extracted hyoid bones were scanned by multi-slice CT. Length of body, distance between bilateral greater horns, length of greater horns, distance between bilateral lesser horns, and length of lesser horns were measured on CT three-dimensional image, and were analyzed by univariate and multivariate statistics. t-tests showed significant sexual differences in all the dimensions; being about 20% longer in males than in females. In principal component analysis using five hyoid dimensions, factor 1, expressing the overall size of the bone, fairly separated each sex, but factors 2 and 3, expressing the shape, did not. Discriminant analysis by a stepwise model, using all the eight dimensions, classified sex rightly (88.6% of the bone) by a function of two dimensions: length of body and distance between bilateral tips of lesser horns. In conclusion, a sexual difference of the hyoid bone was evident in size rather than in shape. (author)

Preliminary Toxicity Analysis of 3-Dimensional Conformal Radiation Therapy Versus Intensity Modulated Radiation Therapy on the High-Dose Arm of the Radiation Therapy Oncology Group 0126 Prostate Cancer Trial

Energy Technology Data Exchange (ETDEWEB)

Michalski, Jeff M., E-mail: jmichalski@radonc.wustl.edu [Department of Radiation Oncology Washington University Medical Center, St. Louis, Missouri (United States); Yan, Yan [Radiation Therapy Oncology Group Statistical Center, Philadelphia, Pennsylvania (United States); Watkins-Bruner, Deborah [Emory University School of Nursing, Atlanta, Georgia (United States); Bosch, Walter R. [Department of Radiation Oncology Washington University Medical Center, St. Louis, Missouri (United States); Winter, Kathryn [Radiation Therapy Oncology Group Statistical Center, Philadelphia, Pennsylvania (United States); Galvin, James M. [Department of Radiation Oncology Thomas Jefferson University Hospital, Philadelphia, Pennsylvania (United States); Bahary, Jean-Paul [Department of Radiation Oncology Centre Hospitalier de l' Université de Montréal-Notre Dame, Montreal, QC (Canada); Morton, Gerard C. [Department of Radiation Oncology Toronto-Sunnybrook Regional Cancer Centre, Toronto, ON (Canada); Parliament, Matthew B. [Department of Oncology Cross Cancer Institute, Edmonton, AB (Canada); Sandler, Howard M. [Department of Radiation Oncology Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, California (United States)

2013-12-01

Purpose: To give a preliminary report of clinical and treatment factors associated with toxicity in men receiving high-dose radiation therapy (RT) on a phase 3 dose-escalation trial. Methods and Materials: The trial was initiated with 3-dimensional conformal RT (3D-CRT) and amended after 1 year to allow intensity modulated RT (IMRT). Patients treated with 3D-CRT received 55.8 Gy to a planning target volume that included the prostate and seminal vesicles, then 23.4 Gy to prostate only. The IMRT patients were treated to the prostate and proximal seminal vesicles to 79.2 Gy. Common Toxicity Criteria, version 2.0, and Radiation Therapy Oncology Group/European Organization for Research and Treatment of Cancer late morbidity scores were used for acute and late effects. Results: Of 763 patients randomized to the 79.2-Gy arm of Radiation Therapy Oncology Group 0126 protocol, 748 were eligible and evaluable: 491 and 257 were treated with 3D-CRT and IMRT, respectively. For both bladder and rectum, the volumes receiving 65, 70, and 75 Gy were significantly lower with IMRT (all P<.0001). For grade (G) 2+ acute gastrointestinal/genitourinary (GI/GU) toxicity, both univariate and multivariate analyses showed a statistically significant decrease in G2+ acute collective GI/GU toxicity for IMRT. There were no significant differences with 3D-CRT or IMRT for acute or late G2+ or 3+ GU toxicities. Univariate analysis showed a statistically significant decrease in late G2+ GI toxicity for IMRT (P=.039). On multivariate analysis, IMRT showed a 26% reduction in G2+ late GI toxicity (P=.099). Acute G2+ toxicity was associated with late G3+ toxicity (P=.005). With dose–volume histogram data in the multivariate analysis, RT modality was not significant, whereas white race (P=.001) and rectal V70 ≥15% were associated with G2+ rectal toxicity (P=.034). Conclusions: Intensity modulated RT is associated with a significant reduction in acute G2+ GI/GU toxicity. There is a trend for a

Increased risk for complications following removal of hardware in patients with liver disease, pilon or pelvic fractures: A regression analysis.

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Brown, Bryan D; Steinert, Justin N; Stelzer, John W; Yoon, Richard S; Langford, Joshua R; Koval, Kenneth J

2017-12-01

Indications for removing orthopedic hardware on an elective basis varies widely. Although viewed as a relatively benign procedure, there is a lack of data regarding overall complication rates after fracture fixation. The purpose of this study is to determine the overall short-term complication rate for elective removal of orthopedic hardware after fracture fixation and to identify associated risk factors. Adult patients indicated for elective hardware removal after fracture fixation between July 2012 and July 2016 were screened for inclusion. Inclusion criteria included patients with hardware related pain and/or impaired cosmesis with complete medical and radiographic records and at least 3-month follow-up. Exclusion criteria were those patients indicated for hardware removal for a diagnosis of malunion, non-union, and/or infection. Data collected included patient age, gender, anatomic location of hardware removed, body mass index, ASA score, and comorbidities. Overall complications, as well as complications requiring revision surgery were recorded. Statistical analysis was performed with SPSS 20.0, and included univariate and multivariate regression analysis. 391 patients (418 procedures) were included for analysis. Overall complication rates were 8.4%, with a 3.6% revision surgery rate. Univariate regression analysis revealed that patients who had liver disease were at significant risk for complication (p=0.001) and revision surgery (p=0.036). Multivariate regression analysis showed that: 1) patients who had liver disease were at significant risk of overall complication (p=0.001) and revision surgery (p=0.039); 2) Removal of hardware following fixation for a pilon had significantly increased risk for complication (p=0.012), but not revision surgery (p=0.43); and 3) Removal of hardware for pelvic fixation had a significantly increased risk for revision surgery (p=0.017). Removal of hardware following fracture fixation is not a risk-free procedure. Patients with

Prognostic value of the neutrophil to lymphocyte ratio in lung cancer: A meta-analysis.

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Yin, Yongmei; Wang, Jun; Wang, Xuedong; Gu, Lan; Pei, Hao; Kuai, Shougang; Zhang, Yingying; Shang, Zhongbo

2015-07-01

Recently, a series of studies explored the correlation between the neutrophil to lymphocyte ratio and the prognosis of lung cancer. However, the current opinion regarding the prognostic role of the neutrophil to lymphocyte ratio in lung cancer is inconsistent. We performed a meta-analysis of published articles to investigate the prognostic value of the neutrophil to lymphocyte ratio in lung cancer. The hazard ratio (HR) and its 95% confidence interval (CI) were calculated. An elevated neutrophil to lymphocyte ratio predicted worse overall survival, with a pooled HR of 1.243 (95%CI: 1.106-1.397; P(heterogeneity)=0.001) from multivariate studies and 1.867 (95%CI: 1.487-2.344; P(heterogeneity)=0.047) from univariate studies. Subgroup analysis showed that a high neutrophil to lymphocyte ratio yielded worse overall survival in non-small cell lung cancer (NSCLC) (HR=1.192, 95%CI: 1.061-1.399; P(heterogeneity)=0.003) as well as small cell lung cancer (SCLC) (HR=1.550, 95% CI: 1.156-2.077; P(heterogeneity)=0.625) in multivariate studies. The synthesized evidence from this meta-analysis of published articles demonstrated that an elevated neutrophil to lymphocyte ratio was a predictor of poor overall survival in patients with lung cancer.

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

Science.gov (United States)

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.

Using MetaboAnalyst 3.0 for Comprehensive Metabolomics Data Analysis.

Science.gov (United States)

Xia, Jianguo; Wishart, David S

2016-09-07

MetaboAnalyst (http://www.metaboanalyst.ca) is a comprehensive Web application for metabolomic data analysis and interpretation. MetaboAnalyst handles most of the common metabolomic data types from most kinds of metabolomics platforms (MS and NMR) for most kinds of metabolomics experiments (targeted, untargeted, quantitative). In addition to providing a variety of data processing and normalization procedures, MetaboAnalyst also supports a number of data analysis and data visualization tasks using a range of univariate, multivariate methods such as PCA (principal component analysis), PLS-DA (partial least squares discriminant analysis), heatmap clustering and machine learning methods. MetaboAnalyst also offers a variety of tools for metabolomic data interpretation including MSEA (metabolite set enrichment analysis), MetPA (metabolite pathway analysis), and biomarker selection via ROC (receiver operating characteristic) curve analysis, as well as time series and power analysis. This unit provides an overview of the main functional modules and the general workflow of the latest version of MetaboAnalyst (MetaboAnalyst 3.0), followed by eight detailed protocols. © 2016 by John Wiley & Sons, Inc. Copyright © 2016 John Wiley & Sons, Inc.

Online Italian fandoms of American TV shows

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Eleonora Benecchi

2015-06-01

Full Text Available The Internet has changed media fandom in two main ways: it helps fans connect with each other despite physical distance, leading to the formation of international fan communities; and it helps fans connect with the creators of the TV show, deepening the relationship between TV producers and international fandoms. To assess whether Italian fan communities active online are indeed part of transnational online communities and whether the Internet has actually altered their relationship with the creators of the original text they are devoted to, qualitative analysis and narrative interviews of 26 Italian fans of American TV shows were conducted to explore the fan-producer relationship. Results indicated that the online Italian fans surveyed preferred to stay local, rather than using geography-leveling online tools. Further, the sampled Italian fans' relationships with the show runners were mediated or even absent.

A comparison of univariate and multivariate methods for analyzing clinal variation in an invasive species

Czech Academy of Sciences Publication Activity Database

Edwards, K.R.; Bastlová, D.; Edwards-Jonášová, Magda; Květ, J.

2011-01-01

Roč. 674, č. 1 (2011), s. 119-131 ISSN 0018-8158 Institutional research plan: CEZ:AV0Z60870520 Keywords : common garden * life history traits * local adaptation * principal components analysis * purple loosestrife * redundancy analysis Subject RIV: EH - Ecology, Behaviour Impact factor: 1.784, year: 2011 http://www.springerlink.com/content/71r10n3367m98jxl/

Category Selectivity of Human Visual Cortex in Perception of Rubin Face–Vase Illusion

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Xiaogang Wang

2017-09-01

Full Text Available When viewing the Rubin face–vase illusion, our conscious perception spontaneously alternates between the face and the vase; this illusion has been widely used to explore bistable perception. Previous functional magnetic resonance imaging (fMRI studies have studied the neural mechanisms underlying bistable perception through univariate and multivariate pattern analyses; however, no studies have investigated the issue of category selectivity. Here, we used fMRI to investigate the neural mechanisms underlying the Rubin face–vase illusion by introducing univariate amplitude and multivariate pattern analyses. The results from the amplitude analysis suggested that the activity in the fusiform face area was likely related to the subjective face perception. Furthermore, the pattern analysis results showed that the early visual cortex (EVC and the face-selective cortex could discriminate the activity patterns of the face and vase perceptions. However, further analysis of the activity patterns showed that only the face-selective cortex contains the face information. These findings indicated that although the EVC and face-selective cortex activities could discriminate the visual information, only the activity and activity pattern in the face-selective areas contained the category information of face perception in the Rubin face–vase illusion.

Prevalence of premenstrual syndrome in Iran: A systematic review and meta-analysis

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Mehdi Ranjbaran

2017-11-01

Full Text Available Background: Premenstrual syndrome (PMS is a common disorder characterized by physical, mental and behavioral changes in the luteal phase of the menstrual cycle in the reproductive age women. Objective: The present study aimed to determine the overall prevalence of PMS in Iran by a systematic review and meta-analysis study. Materials and Methods: In this systematic review and meta-analysis, we searched international databases included ISI Web of Knowledge, PubMed/Medline, Scopus, Google Scholar, and also local databases including Iranmedex, Scientific Information Database, and Magiran for articles in English and Persian language published up to September 2016. We carried out data analysis with Stata version 11. We examined heterogeneity in the results of studies through I2 statistics and Chi-square based Q test. Also, we investigated the effects of potential heterogeneity factors in the prevalence of PMS by meta-regression. Results: We studied a total of 9147 reproductive-age women from 24 articles which entered to meta-analysis. Based on the result of random effect model, we estimated the overall prevalence of PMS 70.8% [95% CI: 63.8-77.7]. The results of subgroup analysis revealed that prevalence of PMS was 80.4% (95% CI; 66.9-93.9 among high school students, 68.9% (95% CI; 59.2-78.6 among university students, and 54.9% (95% CI; 51.6-58.2 in general population. Univariate meta-regression model showed that prevalence of PMS was decreased by increasing the age of subjects but this was not statistically significant (p=0.155. Conclusion: Our finding showed that PMS was prevalent in Iranian reproductive age women especially among high school students. More epidemiological research for determining factors that affect PMS prevalence seems essential.

Lymphatic vessel invasion detected by the endothelial lymphatic marker D2-40 (podoplanin is predictive of regional lymph node status and an independent prognostic factor in patients with resected esophageal cancer

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Jerzy Laudański

2011-04-01

Full Text Available The discovery of markers to lymphatic endothelial cells and the development of novel antibodies to these markers have brought increasing attention to the lymphatics and progress in the understanding of lymphangiogenesis and cancer metastasis. In this study, we investigate the presence of lymphatic vessel invasion (LVI detected by D2-40 immunohistochemical staining in resected esophageal cancer and correlated with clinicopathologic data and patient survival. Sixty nine patients, who had a primary resection of esophageal cancer, were analyzed by univariate and multivariate logistic regression, and univariate and multivariate survival analysis. The total rate of LVI was 72% (50/69. Positive LVI was significantly correlated with lymph node metastasis (p < 0.001, tumor size (p < 0.001, histological grading (p = 0.017, tumor depth (p = 0.001, and stage (p < 0.001. Multivariate logistic analysis identified LVI (p = 0.036 as a predictor of regional lymph node metastasis. On univariate survival analysis, patients with LVI had a significantly shorter disease-free survival, cancer-specific survival and overall survival. Multivariate analysis proved that LVI diagnosed by D2-40 is an independent prognostic factor of both disease-free survival (p = 0.04 and overall survival (p = 0.032 in resected esophageal cancer. These results show that LVI assessment identifies patients at high risk for regional lymph node metastasis and that LVI is an independent prognostic factor in patients with esophageal cancer. (Folia Histochemica et Cytobiologica 2011; Vol. 49, No. 1, pp. 90–97