Ripening of salami: assessment of colour and aspect evolution using image analysis and multivariate image analysis.

PubMed

Fongaro, Lorenzo; Alamprese, Cristina; Casiraghi, Ernestina

2015-03-01

During ripening of salami, colour changes occur due to oxidation phenomena involving myoglobin. Moreover, shrinkage due to dehydration results in aspect modifications, mainly ascribable to fat aggregation. The aim of this work was the application of image analysis (IA) and multivariate image analysis (MIA) techniques to the study of colour and aspect changes occurring in salami during ripening. IA results showed that red, green, blue, and intensity parameters decreased due to the development of a global darker colour, while Heterogeneity increased due to fat aggregation. By applying MIA, different salami slice areas corresponding to fat and three different degrees of oxidised meat were identified and quantified. It was thus possible to study the trend of these different areas as a function of ripening, making objective an evaluation usually performed by subjective visual inspection. Copyright © 2014 Elsevier Ltd. All rights reserved.

Real-Time Onboard Global Nonlinear Aerodynamic Modeling from Flight Data

NASA Technical Reports Server (NTRS)

Brandon, Jay M.; Morelli, Eugene A.

2014-01-01

Flight test and modeling techniques were developed to accurately identify global nonlinear aerodynamic models onboard an aircraft. The techniques were developed and demonstrated during piloted flight testing of an Aermacchi MB-326M Impala jet aircraft. Advanced piloting techniques and nonlinear modeling techniques based on fuzzy logic and multivariate orthogonal function methods were implemented with efficient onboard calculations and flight operations to achieve real-time maneuver monitoring and analysis, and near-real-time global nonlinear aerodynamic modeling and prediction validation testing in flight. Results demonstrated that global nonlinear aerodynamic models for a large portion of the flight envelope were identified rapidly and accurately using piloted flight test maneuvers during a single flight, with the final identified and validated models available before the aircraft landed.

Estimating global distribution of boreal, temperate, and tropical tree plant functional types using clustering techniques

NASA Astrophysics Data System (ADS)

Wang, Audrey; Price, David T.

2007-03-01

A simple integrated algorithm was developed to relate global climatology to distributions of tree plant functional types (PFT). Multivariate cluster analysis was performed to analyze the statistical homogeneity of the climate space occupied by individual tree PFTs. Forested regions identified from the satellite-based GLC2000 classification were separated into tropical, temperate, and boreal sub-PFTs for use in the Canadian Terrestrial Ecosystem Model (CTEM). Global data sets of monthly minimum temperature, growing degree days, an index of climatic moisture, and estimated PFT cover fractions were then used as variables in the cluster analysis. The statistical results for individual PFT clusters were found consistent with other global-scale classifications of dominant vegetation. As an improvement of the quantification of the climatic limitations on PFT distributions, the results also demonstrated overlapping of PFT cluster boundaries that reflected vegetation transitions, for example, between tropical and temperate biomes. The resulting global database should provide a better basis for simulating the interaction of climate change and terrestrial ecosystem dynamics using global vegetation models.

Advanced spectral methods for climatic time series

USGS Publications Warehouse

Ghil, M.; Allen, M.R.; Dettinger, M.D.; Ide, K.; Kondrashov, D.; Mann, M.E.; Robertson, A.W.; Saunders, A.; Tian, Y.; Varadi, F.; Yiou, P.

2002-01-01

The analysis of univariate or multivariate time series provides crucial information to describe, understand, and predict climatic variability. The discovery and implementation of a number of novel methods for extracting useful information from time series has recently revitalized this classical field of study. Considerable progress has also been made in interpreting the information so obtained in terms of dynamical systems theory. In this review we describe the connections between time series analysis and nonlinear dynamics, discuss signal- to-noise enhancement, and present some of the novel methods for spectral analysis. The various steps, as well as the advantages and disadvantages of these methods, are illustrated by their application to an important climatic time series, the Southern Oscillation Index. This index captures major features of interannual climate variability and is used extensively in its prediction. Regional and global sea surface temperature data sets are used to illustrate multivariate spectral methods. Open questions and further prospects conclude the review.

Quantifying the Value of Downscaled Climate Model Information for Adaptation Decisions: When is Downscaling a Smart Decision?

NASA Astrophysics Data System (ADS)

Terando, A. J.; Wootten, A.; Eaton, M. J.; Runge, M. C.; Littell, J. S.; Bryan, A. M.; Carter, S. L.

2015-12-01

Two types of decisions face society with respect to anthropogenic climate change: (1) whether to enact a global greenhouse gas abatement policy, and (2) how to adapt to the local consequences of current and future climatic changes. The practice of downscaling global climate models (GCMs) is often used to address (2) because GCMs do not resolve key features that will mediate global climate change at the local scale. In response, the development of downscaling techniques and models has accelerated to aid decision makers seeking adaptation guidance. However, quantifiable estimates of the value of information are difficult to obtain, particularly in decision contexts characterized by deep uncertainty and low system-controllability. Here we demonstrate a method to quantify the additional value that decision makers could expect if research investments are directed towards developing new downscaled climate projections. As a proof of concept we focus on a real-world management problem: whether to undertake assisted migration for an endangered tropical avian species. We also take advantage of recently published multivariate methods that account for three vexing issues in climate impacts modeling: maximizing climate model quality information, accounting for model dependence in ensembles of opportunity, and deriving probabilistic projections. We expand on these global methods by including regional (Caribbean Basin) and local (Puerto Rico) domains. In the local domain, we test whether a high resolution (2km) dynamically downscaled GCM reduces the multivariate error estimate compared to the original coarse-scale GCM. Initial tests show little difference between the downscaled and original GCM multivariate error. When propagated through to a species population model, the Value of Information analysis indicates that the expected utility that would accrue to the manager (and species) if this downscaling were completed may not justify the cost compared to alternative actions.

Discerning mild cognitive impairment and Alzheimer Disease from normal aging: morphologic characterization based on univariate and multivariate models.

PubMed

Liao, Weiqi; Long, Xiaojing; Jiang, Chunxiang; Diao, Yanjun; Liu, Xin; Zheng, Hairong; Zhang, Lijuan

2014-05-01

Differentiating mild cognitive impairment (MCI) and Alzheimer Disease (AD) from healthy aging remains challenging. This study aimed to explore the cerebral structural alterations of subjects with MCI or AD as compared to healthy elderly based on the individual and collective effects of cerebral morphologic indices using univariate and multivariate analyses. T1-weighted images (T1WIs) were retrieved from Alzheimer Disease Neuroimaging Initiative database for 116 subjects who were categorized into groups of healthy aging, MCI, and AD. Analysis of covariance (ANCOVA) and multivariate analysis of covariance (MANCOVA) were performed to explore the intergroup morphologic alterations indexed by surface area, curvature index, cortical thickness, and subjacent white matter volume with age and sex controlled as covariates, in 34 parcellated gyri regions of interest (ROIs) for both cerebral hemispheres based on the T1WI. Statistical parameters were mapped on the anatomic images to facilitate visual inspection. Global rather than region-specific structural alterations were revealed in groups of MCI and AD relative to healthy elderly using MANCOVA. ANCOVA revealed that the cortical thickness decreased more prominently in entorhinal, temporal, and cingulate cortices and was positively correlated with patients' cognitive performance in AD group but not in MCI. The temporal lobe features marked atrophy of white matter during the disease dynamics. Significant intercorrelations were observed among the morphologic indices with univariate analysis for given ROIs. Significant global structural alterations were identified in MCI and AD based on MANCOVA model with improved sensitivity. The intercorrelation among the morphologic indices may dampen the use of individual morphological parameter in featuring cerebral structural alterations. Decrease in cortical thickness is not reflective of the cognitive performance at the early stage of AD. Copyright © 2014 AUR. Published by Elsevier Inc. All rights reserved.

Statistical polarization in greenhouse gas emissions: Theory and evidence.

PubMed

Remuzgo, Lorena; Trueba, Carmen

2017-11-01

The current debate on climate change is over whether global warming can be limited in order to lessen its impacts. In this sense, evidence of a decrease in the statistical polarization in greenhouse gas (GHG) emissions could encourage countries to establish a stronger multilateral climate change agreement. Based on the interregional and intraregional components of the multivariate generalised entropy measures (Maasoumi, 1986), Gigliarano and Mosler (2009) proposed to study the statistical polarization concept from a multivariate view. In this paper, we apply this approach to study the evolution of such phenomenon in the global distribution of the main GHGs. The empirical analysis has been carried out for the time period 1990-2011, considering an endogenous grouping of countries (Aghevli and Mehran, 1981; Davies and Shorrocks, 1989). Most of the statistical polarization indices showed a slightly increasing pattern that was similar regardless of the number of groups considered. Finally, some policy implications are commented. Copyright © 2017 Elsevier Ltd. All rights reserved.

FGWAS: Functional genome wide association analysis.

PubMed

Huang, Chao; Thompson, Paul; Wang, Yalin; Yu, Yang; Zhang, Jingwen; Kong, Dehan; Colen, Rivka R; Knickmeyer, Rebecca C; Zhu, Hongtu

2017-10-01

Functional phenotypes (e.g., subcortical surface representation), which commonly arise in imaging genetic studies, have been used to detect putative genes for complexly inherited neuropsychiatric and neurodegenerative disorders. However, existing statistical methods largely ignore the functional features (e.g., functional smoothness and correlation). The aim of this paper is to develop a functional genome-wide association analysis (FGWAS) framework to efficiently carry out whole-genome analyses of functional phenotypes. FGWAS consists of three components: a multivariate varying coefficient model, a global sure independence screening procedure, and a test procedure. Compared with the standard multivariate regression model, the multivariate varying coefficient model explicitly models the functional features of functional phenotypes through the integration of smooth coefficient functions and functional principal component analysis. Statistically, compared with existing methods for genome-wide association studies (GWAS), FGWAS can substantially boost the detection power for discovering important genetic variants influencing brain structure and function. Simulation studies show that FGWAS outperforms existing GWAS methods for searching sparse signals in an extremely large search space, while controlling for the family-wise error rate. We have successfully applied FGWAS to large-scale analysis of data from the Alzheimer's Disease Neuroimaging Initiative for 708 subjects, 30,000 vertices on the left and right hippocampal surfaces, and 501,584 SNPs. Copyright © 2017 Elsevier Inc. All rights reserved.

A cross-sectional study on factors associated with patient-physician discordance in global assessment of patients with axial spondyloarthritis: an Asian perspective.

PubMed

Wang, Charmaine Tze May; Fong, Warren; Kwan, Yu Heng; Phang, Jie Kie; Lui, Nai Lee; Leung, Ying Ying; Thumboo, Julian; Cheung, Peter P

2018-06-19

To identify the factors associated with patient-physician discordance in patients with axial spondyloarthritis (axSpA) in an Asian population. A cross-sectional study was conducted in two tertiary referral centers in Singapore. Patients with axSpA who fulfilled Assessment in Ankylosing Spondylitis International Working Group 2009 criteria for axSpA were included in the study. Socio-demographics, clinical, laboratory and patient-reported outcomes data were collected during study visits from 2014 to 2015. We performed univariate and multivariate linear regression analyses to evaluate the factors associated with patient-physician discordance, which we defined as the difference between Patient Global Assessment and Physician Global Assessment. Included in the study were 298 axSpA patients: 82% male, 81% Chinese, median age 40 (20-78) years, median disease duration 9 (0.1-48) years. 80% were on non-steroidal anti-inflammatory drugs and 23% on biologics. In univariate analysis, current age (β: 0.18, ρ = 0.06), duration of disease (β: 0.34, ρ = 0.03), post-secondary education level (β: -10.82, ρ = 0.03), global pain score (β: 0.33, ρ < 0.01), Bath Ankylosing Spondylitis Functional Index (β: 2.80, ρ < 0.01), Ankylosing Spondylitis Disease Activity Score C-reactive protein (β: 4.63, ρ < 0.01) and current use of biologics (β: 10.97, ρ < 0.01) were associated with patient-physician discordance. In multivariate analysis, global pain score (β: 0.32, ρ < 0.01), post-secondary education level (β: -12.80, ρ = 0.01) and current biologics use (β: 16.21, ρ < 0.01) were associated with patient-physician discordance. Higher global pain score, lower educational level and current biologics use were associated with greater patient-physician discordance. These factors should be considered during shared decision making. © 2018 Asia Pacific League of Associations for Rheumatology and John Wiley & Sons Australia, Ltd.

Structural changes in cross-border liabilities: A multidimensional approach

NASA Astrophysics Data System (ADS)

Araújo, Tanya; Spelta, Alessandro

2014-01-01

We study the international interbank market through a geometric analysis of empirical data. The geometric analysis of the time series of cross-country liabilities shows that the systematic information of the interbank international market is contained in a space of small dimension. Geometric spaces of financial relations across countries are developed, for which the space volume, multivariate skewness and multivariate kurtosis are computed. The behavior of these coefficients reveals an important modification acting in the financial linkages since 1997 and allows us to relate the shape of the geometric space that emerges in recent years to the globally turbulent period that has characterized financial systems since the late 1990s. Here we show that, besides a persistent decrease in the volume of the geometric space since 1997, the observation of a generalized increase in the values of the multivariate skewness and kurtosis sheds some light on the behavior of cross-border interdependencies during periods of financial crises. This was found to occur in such a systematic fashion, that these coefficients may be used as a proxy for systemic risk.

Quasi-biennial (QBO), annual (AO), and semi-annual oscillation (SAO) in stratospheric SCIAMACHY O3, NO2, and BrO limb data using a multivariate least squares approach

NASA Astrophysics Data System (ADS)

Dikty, Sebastian; von Savigny, Christian; Sinnhuber, Bjoern-Martin; Rozanov, Alexej; Weber, Mark; Burrows, John P.

We use SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartog-raphY) ozone, nitrogen dioxide and bromine oxide profiles (20-50 km altitude, 2003-2008) to quantify the amplitudes of QBO, AO, and SAO signals with the help of a simple multivariate regression model. The analysis is being carried out with SCIAMACHY data covering all lat-itudes with the exception of polar nights, when measurements are not available. The overall global yield is approximately 10,000 profiles per month, which are binned into 10-steps with one zonal mean profile being calculated per day and per latitude bin.

Multivariate Bioclimatic Ecosystem Change Approaches

DTIC Science & Technology

2015-02-06

course the sandy soils of the Sandhills will not migrate. This observation suggests that a new nomenclature for ecosystems must be developed if...Coast Sandhills. At that time period, not only will the climate be similar, but the soil character will also be similar. Therefore about the year 2115...Disaggregation of global circulation model outputs decision and policy analysis. Working Paper No. 2. Cali, Colombia : International Centre for Tropical

The role of objective cognitive dysfunction in subjective cognitive complaints after stroke.

PubMed

van Rijsbergen, M W A; Mark, R E; Kop, W J; de Kort, P L M; Sitskoorn, M M

2017-03-01

Objective cognitive performance (OCP) is often impaired in patients post-stroke but the consequences of OCP for patient-reported subjective cognitive complaints (SCC) are poorly understood. We performed a detailed analysis on the association between post-stroke OCP and SCC. Assessments of OCP and SCC were obtained in 208 patients 3 months after stroke. OCP was evaluated using conventional and ecologically valid neuropsychological tests. Levels of SCC were measured using the CheckList for Cognitive and Emotional (CLCE) consequences following stroke inventory. Multivariate hierarchical regression analyses were used to evaluate the association of OCP with CLCE scores adjusting for age, sex and intelligence quotient. Analyses were performed to examine the global extent of OCP dysfunction (based on the total number of impaired neuropsychological tests, i.e. objective cognitive impairment index) and for each OCP test separately using the raw neuropsychological (sub)test scores. The objective cognitive impairment index for global OCP was positively correlated with the CLCE score (Spearman's rho = 0.22, P = 0.003), which remained significant in multivariate adjusted models (β = 0.25, P = 0.01). Results for the separate neuropsychological tests indicated that only one task (the ecologically valid Rivermead Behavioural Memory Test) was independently associated with the CLCE in multivariate adjusted models (β = -0.34, P < 0.001). Objective neuropsychological test performance, as measured by the global dysfunction index or an ecologically valid memory task, was associated with SCC. These data suggest that cumulative deficits in multiple cognitive domains contribute to subjectively experienced poor cognitive abilities in daily life in patients post-stroke. © 2016 EAN.

Phobic Anxiety and Plasma Levels of Global Oxidative Stress in Women.

PubMed

Hagan, Kaitlin A; Wu, Tianying; Rimm, Eric B; Eliassen, A Heather; Okereke, Olivia I

2015-01-01

Psychological distress has been hypothesized to be associated with adverse biologic states such as higher oxidative stress and inflammation. Yet, little is known about associations between a common form of distress - phobic anxiety - and global oxidative stress. Thus, we related phobic anxiety to plasma fluorescent oxidation products (FlOPs), a global oxidative stress marker. We conducted a cross-sectional analysis among 1,325 women (aged 43-70 years) from the Nurses' Health Study. Phobic anxiety was measured using the Crown-Crisp Index (CCI). Adjusted least-squares mean log-transformed FlOPs were calculated across phobic categories. Logistic regression models were used to calculate odds ratios (OR) comparing the highest CCI category (≥6 points) vs. lower scores, across FlOPs quartiles. No association was found between phobic anxiety categories and mean FlOP levels in multivariable adjusted linear models. Similarly, in multivariable logistic regression models there were no associations between FlOPs quartiles and likelihood of being in the highest phobic category. Comparing women in the highest vs. lowest FlOPs quartiles: FlOP_360: OR=0.68 (95% CI: 0.40-1.15); FlOP_320: OR=0.99 (95% CI: 0.61-1.61); FlOP_400: OR=0.92 (95% CI: 0.52, 1.63). No cross-sectional association was found between phobic anxiety and a plasma measure of global oxidative stress in this sample of middle-aged and older women.

The double-edged sword of electronic health records: implications for patient disclosure.

PubMed

Campos-Castillo, Celeste; Anthony, Denise L

2015-04-01

Electronic health record (EHR) systems are linked to improvements in quality of care, yet also privacy and security risks. Results from research studies are mixed about whether patients withhold personal information from their providers to protect against the perceived EHR privacy and security risks. This study seeks to reconcile the mixed findings by focusing on whether accounting for patients' global ratings of care reveals a relationship between EHR provider-use and patient non-disclosure. A nationally representative sample from the 2012 Health Information National Trends Survey was analyzed using bivariate and multivariable logit regressions to examine whether global ratings of care suppress the relationship between EHR provider-use and patient non-disclosure. 13% of respondents reported having ever withheld information from a provider because of privacy/security concerns. Bivariate analysis showed that withholding information was unrelated to whether respondents' providers used an EHR. Multivariable analysis showed that accounting for respondents' global ratings of care revealed a positive relationship between having a provider who uses an EHR and withholding information. After accounting for global ratings of care, findings suggest that patients may non-disclose to providers to protect against the perceived EHR privacy and security risks. Despite evidence that EHRs inhibit patient disclosure, their advantages for promoting quality of care may outweigh the drawbacks. Clinicians should leverage the EHR's value in quality of care and discuss patients' privacy concerns during clinic visits, while policy makers should consider how to address the real and perceived privacy and security risks of EHRs. © The Author 2014. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Identification of adverse events that have a negative impact on quality of life in a clinical trial comparing docetaxel versus S-1 with cisplatin in lung cancer.

PubMed

Aotani, Eriko; Hamano, Tetsutaro; Gemma, Akihiko; Takeuchi, Masahiro; Takebayashi, Toru; Kobayashi, Kunihiko

2016-10-01

In the CATS (Cisplatin And TS-1) randomized trial comparing cisplatin plus either docetaxel (DP arm) or TS-1 (SP arm) in lung cancer, efficacy was found to be equivalent but the global quality of life (QOL) score was higher in the SP arm. The purpose of the current study was to identify which of the adverse events (AEs) contributed to the deterioration of QOL. QOL and AE data from the CATS trial were used to quantitatively analyze the relationship between deterioration of QOL score and occurrence of AEs. Subtracted values of the QOL score from post-chemotherapy to pre-chemotherapy were fully compared between patients with or without each AE (Student's t test, significance level = 0.001). Multivariate linear regression analysis was also performed. Analysis of variance was performed to identify whether grade of AE(s) might be significantly correlated with the deterioration of the QOL score (significance level of 0.05). As expected, gastrointestinal (GI) toxicities were associated with worsening of a variety of QOL items in both trial arms, detected by both univariate and multivariate analysis (p < 0.001 and p < 0.0001, respectively). Multivariate analysis unpredictably indicated that an increase in serum bilirubin level was the only AE that was uniquely associated with worsening of physical functioning (p = 0.0002), cognitive functioning (p < 0.0001), and financial problems (p = 0.0005) in the DP arm, although not in the SP arm. GI toxicities tended to be prolonged in the SP arm. An increase in serum bilirubin level may contribute to the worse global QOL of subjects in the DP arm in the CATS trial. The method we used here may be a unique approach to identify unpredictable AE(s) that worsen the QOL of patients treated by chemotherapy.

Environmental assessment of Al-Hammar Marsh, Southern Iraq.

PubMed

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

2017-02-01

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

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

PubMed

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

2015-03-25

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

Source Evaluation and Trace Metal Contamination in Benthic Sediments from Equatorial Ecosystems Using Multivariate Statistical Techniques

PubMed Central

Benson, Nsikak U.; Asuquo, Francis E.; Williams, Akan B.; Essien, Joseph P.; Ekong, Cyril I.; Akpabio, Otobong; Olajire, Abaas A.

2016-01-01

Trace metals (Cd, Cr, Cu, Ni and Pb) concentrations in benthic sediments were analyzed through multi-step fractionation scheme to assess the levels and sources of contamination in estuarine, riverine and freshwater ecosystems in Niger Delta (Nigeria). The degree of contamination was assessed using the individual contamination factors (ICF) and global contamination factor (GCF). Multivariate statistical approaches including principal component analysis (PCA), cluster analysis and correlation test were employed to evaluate the interrelationships and associated sources of contamination. The spatial distribution of metal concentrations followed the pattern Pb>Cu>Cr>Cd>Ni. Ecological risk index by ICF showed significant potential mobility and bioavailability for Cu, Cu and Ni. The ICF contamination trend in the benthic sediments at all studied sites was Cu>Cr>Ni>Cd>Pb. The principal component and agglomerative clustering analyses indicate that trace metals contamination in the ecosystems was influenced by multiple pollution sources. PMID:27257934

Monitoring Quality of Biotherapeutic Products Using Multivariate Data Analysis.

PubMed

Rathore, Anurag S; Pathak, Mili; Jain, Renu; Jadaun, Gaurav Pratap Singh

2016-07-01

Monitoring the quality of pharmaceutical products is a global challenge, heightened by the implications of letting subquality drugs come to the market on public safety. Regulatory agencies do their due diligence at the time of approval as per their prescribed regulations. However, product quality needs to be monitored post-approval as well to ensure patient safety throughout the product life cycle. This is particularly complicated for biotechnology-based therapeutics where seemingly minor changes in process and/or raw material attributes have been shown to have a significant effect on clinical safety and efficacy of the product. This article provides a perspective on the topic of monitoring the quality of biotech therapeutics. In the backdrop of challenges faced by the regulatory agencies, the potential use of multivariate data analysis as a tool for effective monitoring has been proposed. Case studies using data from several insulin biosimilars have been used to illustrate the key concepts.

Reverse left ventricular remodeling after acute myocardial infarction: the prognostic impact of left ventricular global torsion.

PubMed

Spinelli, Letizia; Morisco, Carmine; Assante di Panzillo, Emiliano; Izzo, Raffaele; Trimarco, Bruno

2013-04-01

Reverse left ventricular (LV) remodeling (>10 % reduction in LV end-systolic volume) may occur in patients recovering for acute ST-elevation myocardial infarction (STEMI), undergoing percutaneous revascularization of infarct-related coronary artery (PCI). To detect whether LV global torsion obtained by two-dimensional speckle-tracking echocardiography was predictive of reverse LV remodeling, 75 patients with first anterior wall STEMI were studied before (T1) and after PCI (T2) and at 6-month follow-up. Two-year clinical follow-up was also accomplished. LV volumes and both LV sphericity index and conic index were obtained by three-dimensional echocardiography. Reverse remodeling was observed in 25 patients (33 %). By multivariate analysis, independent predictors of reverse LV remodeling were: LV conic index, T2 LV torsion and Δ torsion (difference between T2 and T1 LV torsion expressed as percentage of this latter). According to receiver operating characteristic analysis, 1.34°/cm for T2 LV torsion (sensitivity 88 % and specificity 80 %) and 54 % for Δ torsion (sensitivity 92 % and specificity 82 %) were the optimal cutoff values in predicting reverse LV remodeling. In up to 24 month follow-up, 4 non-fatal re-infarction, 7 hospitalization for heart failure and 4 cardiac deaths occurred. By multivariate Cox analysis, the best variable significantly associated with event-free survival rate was reverse LV remodeling with a hazard ratio = 9.9 (95 % confidence interval, 7.9-31.4, p < 0.01). In conclusion, reverse LV remodeling occurring after anterior wall STEMI is associated with favorable long-term outcome. The improvement of global LV torsion following coronary artery revascularization is the major predictor of reverse LV remodeling.

Building global models for fat and total protein content in raw milk based on historical spectroscopic data in the visible and short-wave near infrared range.

PubMed

Melenteva, Anastasiia; Galyanin, Vladislav; Savenkova, Elena; Bogomolov, Andrey

2016-07-15

A large set of fresh cow milk samples collected from many suppliers over a large geographical area in Russia during a year has been analyzed by optical spectroscopy in the range 400-1100 nm in accordance with previously developed scatter-based technique. The global (i.e. resistant to seasonal, genetic, regional and other variations of the milk composition) models for fat and total protein content, which were built using partial least-squares (PLS) regression, exhibit satisfactory prediction performances enabling their practical application in the dairy. The root mean-square errors of prediction (RMSEP) were 0.09 and 0.10 for fat and total protein content, respectively. The issues of raw milk analysis and multivariate modelling based on the historical spectroscopic data have been considered and approaches to the creation of global models and their transfer between the instruments have been proposed. Availability of global models should significantly facilitate the dissemination of optical spectroscopic methods for the laboratory and in-line quantitative milk analysis. Copyright © 2016. Published by Elsevier Ltd.

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

NASA Astrophysics Data System (ADS)

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.

Multivariate detrending of fMRI signal drifts for real-time multiclass pattern classification.

PubMed

Lee, Dongha; Jang, Changwon; Park, Hae-Jeong

2015-03-01

Signal drift in functional magnetic resonance imaging (fMRI) is an unavoidable artifact that limits classification performance in multi-voxel pattern analysis of fMRI. As conventional methods to reduce signal drift, global demeaning or proportional scaling disregards regional variations of drift, whereas voxel-wise univariate detrending is too sensitive to noisy fluctuations. To overcome these drawbacks, we propose a multivariate real-time detrending method for multiclass classification that involves spatial demeaning at each scan and the recursive detrending of drifts in the classifier outputs driven by a multiclass linear support vector machine. Experiments using binary and multiclass data showed that the linear trend estimation of the classifier output drift for each class (a weighted sum of drifts in the class-specific voxels) was more robust against voxel-wise artifacts that lead to inconsistent spatial patterns and the effect of online processing than voxel-wise detrending. The classification performance of the proposed method was significantly better, especially for multiclass data, than that of voxel-wise linear detrending, global demeaning, and classifier output detrending without demeaning. We concluded that the multivariate approach using classifier output detrending of fMRI signals with spatial demeaning preserves spatial patterns, is less sensitive than conventional methods to sample size, and increases classification performance, which is a useful feature for real-time fMRI classification. Copyright © 2014 Elsevier Inc. All rights reserved.

Global health inequalities and breast cancer: an impending public health problem for developing countries.

PubMed

Igene, Helen

2008-01-01

The aim of the study was to provide information on the global health inequality pattern produced by the increasing incidence of breast cancer and its relationship with the health expenditure of developing countries with emphasis on sub-Saharan Africa. It examines the difference between the health expenditure of developed and developing countries, and how this affects breast cancer incidence and mortality. The data collected from the World Health Organization and World Bank were examined, using bivariate analysis, through scatter-plots and Pearson's product moment correlation coefficient. Multivariate analysis was carried out by multiple regression analysis. National income, health expenditure affects breast cancer incidence, particularly between the developed and developing countries. However, these factors do not adequately explain variations in mortality rates. The study reveals the risk posed to developing countries to solving the present and predicted burden of breast cancer, currently characterized by late presentation, inadequate health care systems, and high mortality. Findings from this study contribute to the knowledge of the burden of disease in developing countries, especially sub-Saharan Africa, and how that is related to globalization and health inequalities.

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

PubMed

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

2012-07-06

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

Phobic Anxiety and Plasma Levels of Global Oxidative Stress in Women

PubMed Central

Hagan, Kaitlin A.; Wu, Tianying; Rimm, Eric B.; Eliassen, A. Heather; Okereke, Olivia I.

2015-01-01

Background and Objectives Psychological distress has been hypothesized to be associated with adverse biologic states such as higher oxidative stress and inflammation. Yet, little is known about associations between a common form of distress – phobic anxiety – and global oxidative stress. Thus, we related phobic anxiety to plasma fluorescent oxidation products (FlOPs), a global oxidative stress marker. Methods We conducted a cross-sectional analysis among 1,325 women (aged 43-70 years) from the Nurses’ Health Study. Phobic anxiety was measured using the Crown-Crisp Index (CCI). Adjusted least-squares mean log-transformed FlOPs were calculated across phobic categories. Logistic regression models were used to calculate odds ratios (OR) comparing the highest CCI category (≥6 points) vs. lower scores, across FlOPs quartiles. Results No association was found between phobic anxiety categories and mean FlOP levels in multivariable adjusted linear models. Similarly, in multivariable logistic regression models there were no associations between FlOPs quartiles and likelihood of being in the highest phobic category. Comparing women in the highest vs. lowest FlOPs quartiles: FlOP_360: OR=0.68 (95% CI: 0.40-1.15); FlOP_320: OR=0.99 (95% CI: 0.61-1.61); FlOP_400: OR=0.92 (95% CI: 0.52, 1.63). Conclusions No cross-sectional association was found between phobic anxiety and a plasma measure of global oxidative stress in this sample of middle-aged and older women. PMID:26635425

Accelerating policy decisions to adopt haemophilus influenzae type B vaccine: a global, multivariable analysis.

PubMed

Shearer, Jessica C; Stack, Meghan L; Richmond, Marcie R; Bear, Allyson P; Hajjeh, Rana A; Bishai, David M

2010-03-16

Adoption of new and underutilized vaccines by national immunization programs is an essential step towards reducing child mortality. Policy decisions to adopt new vaccines in high mortality countries often lag behind decisions in high-income countries. Using the case of Haemophilus influenzae type b (Hib) vaccine, this paper endeavors to explain these delays through the analysis of country-level economic, epidemiological, programmatic and policy-related factors, as well as the role of the Global Alliance for Vaccines and Immunisation (GAVI Alliance). Data for 147 countries from 1990 to 2007 were analyzed in accelerated failure time models to identify factors that are associated with the time to decision to adopt Hib vaccine. In multivariable models that control for Gross National Income, region, and burden of Hib disease, the receipt of GAVI support speeded the time to decision by a factor of 0.37 (95% CI 0.18-0.76), or 63%. The presence of two or more neighboring country adopters accelerated decisions to adopt by a factor of 0.50 (95% CI 0.33-0.75). For each 1% increase in vaccine price, decisions to adopt are delayed by a factor of 1.02 (95% CI 1.00-1.04). Global recommendations and local studies were not associated with time to decision. This study substantiates previous findings related to vaccine price and presents new evidence to suggest that GAVI eligibility is associated with accelerated decisions to adopt Hib vaccine. The influence of neighboring country decisions was also highly significant, suggesting that approaches to support the adoption of new vaccines should consider supply- and demand-side factors.

COMT Val158Met polymorphism is associated with post-traumatic stress disorder and functional outcome following mild traumatic brain injury

PubMed Central

Winkler, Ethan A.; Yue, John K.; Ferguson, Adam R.; Temkin, Nancy R.; Stein, Murray B.; Barber, Jason; Yuh, Esther L.; Sharma, Sourabh; Satris, Gabriela G.; McAllister, Thomas W.; Rosand, Jonathan; Sorani, Marco D.; Lingsma, Hester F.; Tarapore, Phiroz E.; Burchard, Esteban G.; Hu, Donglei; Eng, Celeste; Wang, Kevin K.W.; Mukherjee, Pratik; Okonkwo, David O.; Diaz-Arrastia, Ramon; Manley, Geoffrey T.

2017-01-01

Mild traumatic brain injury (mTBI) results in variable clinical trajectories and outcomes. The source of variability remains unclear, but may involve genetic variations, such as single nucleotide polymorphisms (SNPs). A SNP in catechol-o-methyltransferase (COMT) is suggested to influence development of post-traumatic stress disorder (PTSD), but its role in TBI remains unclear. Here, we utilize the Transforming Research and Clinical Knowledge in Traumatic Brain Injury Pilot (TRACK-TBI Pilot) study to investigate whether the COMT Val158Met polymorphism is associated with PTSD and global functional outcome as measured by the PTSD Checklist – Civilian Version and Glasgow Outcome Scale Extended (GOSE), respectively. Results in 93 predominately Caucasian subjects with mTBI show that the COMT Met158 allele is associated with lower incidence of PTSD (univariate odds ratio (OR) of 0.25, 95% CI [0.09–0.69]) and higher GOSE scores (univariate OR 2.87, 95% CI [1.20–6.86]) 6-months following injury. The COMT Val158Met genotype and PTSD association persists after controlling for race (multivariable OR of 0.29, 95% CI [0.10–0.83]) and pre-existing psychiatric disorders/substance abuse (multivariable OR of 0.32, 95% CI [0.11–0.97]). PTSD emerged as a strong predictor of poorer outcome on GOSE (multivariable OR 0.09, 95% CI [0.03–0.26]), which persists after controlling for age, GCS, and race. When accounting for PTSD in multivariable analysis, the association of COMT genotype and GOSE did not remain significant (multivariable OR 1.73, 95% CI [0.69–4.35]). Whether COMT genotype indirectly influences global functional outcome through PTSD remains to be determined and larger studies in more diverse populations are needed to confirm these findings. PMID:27769642

COMT Val158Met polymorphism is associated with post-traumatic stress disorder and functional outcome following mild traumatic brain injury.

PubMed

Winkler, Ethan A; Yue, John K; Ferguson, Adam R; Temkin, Nancy R; Stein, Murray B; Barber, Jason; Yuh, Esther L; Sharma, Sourabh; Satris, Gabriela G; McAllister, Thomas W; Rosand, Jonathan; Sorani, Marco D; Lingsma, Hester F; Tarapore, Phiroz E; Burchard, Esteban G; Hu, Donglei; Eng, Celeste; Wang, Kevin K W; Mukherjee, Pratik; Okonkwo, David O; Diaz-Arrastia, Ramon; Manley, Geoffrey T

2017-01-01

Mild traumatic brain injury (mTBI) results in variable clinical trajectories and outcomes. The source of variability remains unclear, but may involve genetic variations, such as single nucleotide polymorphisms (SNPs). A SNP in catechol-o-methyltransferase (COMT) is suggested to influence development of post-traumatic stress disorder (PTSD), but its role in TBI remains unclear. Here, we utilize the Transforming Research and Clinical Knowledge in Traumatic Brain Injury Pilot (TRACK-TBI Pilot) study to investigate whether the COMT Val 158 Met polymorphism is associated with PTSD and global functional outcome as measured by the PTSD Checklist - Civilian Version and Glasgow Outcome Scale Extended (GOSE), respectively. Results in 93 predominately Caucasian subjects with mTBI show that the COMT Met 158 allele is associated with lower incidence of PTSD (univariate odds ratio (OR) of 0.25, 95% CI [0.09-0.69]) and higher GOSE scores (univariate OR 2.87, 95% CI [1.20-6.86]) 6-months following injury. The COMT Val 158 Met genotype and PTSD association persists after controlling for race (multivariable OR of 0.29, 95% CI [0.10-0.83]) and pre-existing psychiatric disorders/substance abuse (multivariable OR of 0.32, 95% CI [0.11-0.97]). PTSD emerged as a strong predictor of poorer outcome on GOSE (multivariable OR 0.09, 95% CI [0.03-0.26]), which persists after controlling for age, GCS, and race. When accounting for PTSD in multivariable analysis, the association of COMT genotype and GOSE did not remain significant (multivariable OR 1.73, 95% CI [0.69-4.35]). Whether COMT genotype indirectly influences global functional outcome through PTSD remains to be determined and larger studies in more diverse populations are needed to confirm these findings. Copyright © 2016 Elsevier Ltd. All rights reserved.

Multicollinearity in prognostic factor analyses using the EORTC QLQ-C30: identification and impact on model selection.

PubMed

Van Steen, Kristel; Curran, Desmond; Kramer, Jocelyn; Molenberghs, Geert; Van Vreckem, Ann; Bottomley, Andrew; Sylvester, Richard

2002-12-30

Clinical and quality of life (QL) variables from an EORTC clinical trial of first line chemotherapy in advanced breast cancer were used in a prognostic factor analysis of survival and response to chemotherapy. For response, different final multivariate models were obtained from forward and backward selection methods, suggesting a disconcerting instability. Quality of life was measured using the EORTC QLQ-C30 questionnaire completed by patients. Subscales on the questionnaire are known to be highly correlated, and therefore it was hypothesized that multicollinearity contributed to model instability. A correlation matrix indicated that global QL was highly correlated with 7 out of 11 variables. In a first attempt to explore multicollinearity, we used global QL as dependent variable in a regression model with other QL subscales as predictors. Afterwards, standard diagnostic tests for multicollinearity were performed. An exploratory principal components analysis and factor analysis of the QL subscales identified at most three important components and indicated that inclusion of global QL made minimal difference to the loadings on each component, suggesting that it is redundant in the model. In a second approach, we advocate a bootstrap technique to assess the stability of the models. Based on these analyses and since global QL exacerbates problems of multicollinearity, we therefore recommend that global QL be excluded from prognostic factor analyses using the QLQ-C30. The prognostic factor analysis was rerun without global QL in the model, and selected the same significant prognostic factors as before. Copyright 2002 John Wiley & Sons, Ltd.

Efficient Global Aerodynamic Modeling from Flight Data

NASA Technical Reports Server (NTRS)

Morelli, Eugene A.

2012-01-01

A method for identifying global aerodynamic models from flight data in an efficient manner is explained and demonstrated. A novel experiment design technique was used to obtain dynamic flight data over a range of flight conditions with a single flight maneuver. Multivariate polynomials and polynomial splines were used with orthogonalization techniques and statistical modeling metrics to synthesize global nonlinear aerodynamic models directly and completely from flight data alone. Simulation data and flight data from a subscale twin-engine jet transport aircraft were used to demonstrate the techniques. Results showed that global multivariate nonlinear aerodynamic dependencies could be accurately identified using flight data from a single maneuver. Flight-derived global aerodynamic model structures, model parameter estimates, and associated uncertainties were provided for all six nondimensional force and moment coefficients for the test aircraft. These models were combined with a propulsion model identified from engine ground test data to produce a high-fidelity nonlinear flight simulation very efficiently. Prediction testing using a multi-axis maneuver showed that the identified global model accurately predicted aircraft responses.

Examining the association between late-life leisure activity participation and global cognitive decline in community-dwelling elderly Chinese in Hong Kong.

PubMed

Leung, Grace Tak Yu; Fung, Ada Wai Tung; Tam, Cindy Woon Chi; Lui, Victor Wing Cheong; Chiu, Helen Fung Kum; Chan, Wai Man; Lam, Linda Chiu Wa

2011-01-01

This study examines the association between late-life leisure activity participation and global cognitive decline in community-dwelling elderly Chinese in Hong Kong. Five hundred and five participants, not clinically demented at the baseline, were analysed in the follow-up study of a population-based community survey among Hong Kong Chinese aged 60 and over. Information regarding leisure activity participation, global cognitive function and important sociodemographic variables was collected. Late life leisure activity profiles were classified into intellectual, social, physical and recreational categories, and were measured by total hours per week, total frequency and total number of subtypes. Multivariate logistic regression analyses were used to evaluate the association between leisure activity participation at the baseline and the incidence of global cognitive decline at the 22-month follow-up. The incidence of global cognitive decline was defined as a one-point drop in z-score of the Cantonese version of the mini-mental state examination (CMMSE). At the follow-up, a higher level of participation in intellectual activities was significantly associated with a lower incidence of global cognitive decline as measured by both the total hours per week (multivariate-adjusted OR 0.97 (95% CI 0.94-0.99, p=0.003)), and the total number of subtypes (multivariate-adjusted OR 0.74 (95% CI 0.58-0.95, p=0.018)). A higher level of late-life intellectual activity participation was associated with less global cognitive decline among community-dwelling elderly Chinese in Hong Kong. Copyright © 2010 John Wiley & Sons, Ltd.

The Japanese Histologic Classification and T-score in the Oxford Classification system could predict renal outcome in Japanese IgA nephropathy patients.

PubMed

Kaihan, Ahmad Baseer; Yasuda, Yoshinari; Katsuno, Takayuki; Kato, Sawako; Imaizumi, Takahiro; Ozeki, Takaya; Hishida, Manabu; Nagata, Takanobu; Ando, Masahiko; Tsuboi, Naotake; Maruyama, Shoichi

2017-12-01

The Oxford Classification is utilized globally, but has not been fully validated. In this study, we conducted a comparative analysis between the Oxford Classification and Japanese Histologic Classification (JHC) to predict renal outcome in Japanese patients with IgA nephropathy (IgAN). A retrospective cohort study including 86 adult IgAN patients was conducted. The Oxford Classification and the JHC were evaluated by 7 independent specialists. The JHC, MEST score in the Oxford Classification, and crescents were analyzed in association with renal outcome, defined as a 50% increase in serum creatinine. In multivariate analysis without the JHC, only the T score was significantly associated with renal outcome. While, a significant association was revealed only in the JHC on multivariate analysis with JHC. The JHC and T score in the Oxford Classification were associated with renal outcome among Japanese patients with IgAN. Superiority of the JHC as a predictive index should be validated with larger study population and cohort studies in different ethnicities.

Automatic and objective oral cancer diagnosis by Raman spectroscopic detection of keratin with multivariate curve resolution analysis

NASA Astrophysics Data System (ADS)

Chen, Po-Hsiung; Shimada, Rintaro; Yabumoto, Sohshi; Okajima, Hajime; Ando, Masahiro; Chang, Chiou-Tzu; Lee, Li-Tzu; Wong, Yong-Kie; Chiou, Arthur; Hamaguchi, Hiro-O.

2016-01-01

We have developed an automatic and objective method for detecting human oral squamous cell carcinoma (OSCC) tissues with Raman microspectroscopy. We measure 196 independent Raman spectra from 196 different points of one oral tissue sample and globally analyze these spectra using a Multivariate Curve Resolution (MCR) analysis. Discrimination of OSCC tissues is automatically and objectively made by spectral matching comparison of the MCR decomposed Raman spectra and the standard Raman spectrum of keratin, a well-established molecular marker of OSCC. We use a total of 24 tissue samples, 10 OSCC and 10 normal tissues from the same 10 patients, 3 OSCC and 1 normal tissues from different patients. Following the newly developed protocol presented here, we have been able to detect OSCC tissues with 77 to 92% sensitivity (depending on how to define positivity) and 100% specificity. The present approach lends itself to a reliable clinical diagnosis of OSCC substantiated by the “molecular fingerprint” of keratin.

Graphite Web: web tool for gene set analysis exploiting pathway topology

PubMed Central

Sales, Gabriele; Calura, Enrica; Martini, Paolo; Romualdi, Chiara

2013-01-01

Graphite web is a novel web tool for pathway analyses and network visualization for gene expression data of both microarray and RNA-seq experiments. Several pathway analyses have been proposed either in the univariate or in the global and multivariate context to tackle the complexity and the interpretation of expression results. These methods can be further divided into ‘topological’ and ‘non-topological’ methods according to their ability to gain power from pathway topology. Biological pathways are, in fact, not only gene lists but can be represented through a network where genes and connections are, respectively, nodes and edges. To this day, the most used approaches are non-topological and univariate although they miss the relationship among genes. On the contrary, topological and multivariate approaches are more powerful, but difficult to be used by researchers without bioinformatic skills. Here we present Graphite web, the first public web server for pathway analysis on gene expression data that combines topological and multivariate pathway analyses with an efficient system of interactive network visualizations for easy results interpretation. Specifically, Graphite web implements five different gene set analyses on three model organisms and two pathway databases. Graphite Web is freely available at http://graphiteweb.bio.unipd.it/. PMID:23666626

Global Profiling and Novel Structure Discovery Using Multiple Neutral Loss/Precursor Ion Scanning Combined with Substructure Recognition and Statistical Analysis (MNPSS): Characterization of Terpene-Conjugated Curcuminoids in Curcuma longa as a Case Study.

PubMed

Qiao, Xue; Lin, Xiong-hao; Ji, Shuai; Zhang, Zheng-xiang; Bo, Tao; Guo, De-an; Ye, Min

2016-01-05

To fully understand the chemical diversity of an herbal medicine is challenging. In this work, we describe a new approach to globally profile and discover novel compounds from an herbal extract using multiple neutral loss/precursor ion scanning combined with substructure recognition and statistical analysis. Turmeric (the rhizomes of Curcuma longa L.) was used as an example. This approach consists of three steps: (i) multiple neutral loss/precursor ion scanning to obtain substructure information; (ii) targeted identification of new compounds by extracted ion current and substructure recognition; and (iii) untargeted identification using total ion current and multivariate statistical analysis to discover novel structures. Using this approach, 846 terpecurcumins (terpene-conjugated curcuminoids) were discovered from turmeric, including a number of potentially novel compounds. Furthermore, two unprecedented compounds (terpecurcumins X and Y) were purified, and their structures were identified by NMR spectroscopy. This study extended the application of mass spectrometry to global profiling of natural products in herbal medicines and could help chemists to rapidly discover novel compounds from a complex matrix.

The relationship of self-concept and perceived athletic competence to physical activity level and gender among Turkish early adolescents.

PubMed

Aşçi, F H; Koşar, S N; Işler, A K

2001-01-01

The purpose of this study was to examine the self-concept and perceived athletic competence of Turkish early adolescents in relation to physical activity level and gender. Self-concept was assessed using the Piers-Harris Children's Self-Concept Scale, and perceived athletic competence was assessed by means of the Athletic Competence subscale of Harter's Self-Perception Profile for Children. In addition, the Weekly Activity Checklist was used for assessing physical activity level. Males and females were assigned to low and high physical activity level groups based on their mean scores. Multivariate analysis of variance revealed significant main effects for gender and physical activity level, but there was no significant gender by physical activity interaction. Univariate analysis demonstrated a significant main effect for physical activity level on perceived athletic competence but not global self-concept. In addition, univariate analysis did not reveal a significant difference in either global self-concept or perceived athletic competence with respect to gender.

User Selection Criteria of Airspace Designs in Flexible Airspace Management

NASA Technical Reports Server (NTRS)

Lee, Hwasoo E.; Lee, Paul U.; Jung, Jaewoo; Lai, Chok Fung

2011-01-01

A method for identifying global aerodynamic models from flight data in an efficient manner is explained and demonstrated. A novel experiment design technique was used to obtain dynamic flight data over a range of flight conditions with a single flight maneuver. Multivariate polynomials and polynomial splines were used with orthogonalization techniques and statistical modeling metrics to synthesize global nonlinear aerodynamic models directly and completely from flight data alone. Simulation data and flight data from a subscale twin-engine jet transport aircraft were used to demonstrate the techniques. Results showed that global multivariate nonlinear aerodynamic dependencies could be accurately identified using flight data from a single maneuver. Flight-derived global aerodynamic model structures, model parameter estimates, and associated uncertainties were provided for all six nondimensional force and moment coefficients for the test aircraft. These models were combined with a propulsion model identified from engine ground test data to produce a high-fidelity nonlinear flight simulation very efficiently. Prediction testing using a multi-axis maneuver showed that the identified global model accurately predicted aircraft responses.

Global assessment of surfing conditions: seasonal, interannual and long-term variability

NASA Astrophysics Data System (ADS)

Espejo, A.; Losada, I.; Mendez, F.

2012-12-01

International surfing destinations owe a great debt to specific combinations of wind-wave, thermal conditions and local bathymetry. As surf quality depends on a vast number of geophysical variables, a multivariable standardized index on the basis of expert judgment is proposed to analyze surf resource in a worldwide domain. Data needed is obtained by combining several datasets (reanalyses): 60-year satellite-calibrated spectral wave hindcast (GOW, WaveWatchIII), wind fields from NCEP/NCAR, global sea surface temperature from ERSST.v3b, and global tides from TPXO7.1. A summary of the global surf resource is presented, which highlights the high degree of variability in surfable events. According to general atmospheric circulation, results show that west facing low to middle latitude coasts are more suitable for surfing, especially those in Southern Hemisphere. Month to month analysis reveals strong seasonal changes in the occurrence of surfable events, enhancing those in North Atlantic or North Pacific. Interannual variability is investigated by comparing occurrence values with global and regional climate patterns showing a great influence at both, global and regional scales. Analysis of long term trends shows an increase in the probability of surfable events over the west facing coasts on the planet (i.e. + 30 hours/year in California). The resulting maps provide useful information for surfers and surf related stakeholders, coastal planning, education, and basic research.; Figure 1. Global distribution of medium quality (a) and high quality surf conditions probability (b).

Environmental controls on microbial abundance and activity on the greenland ice sheet: a multivariate analysis approach.

PubMed

Stibal, Marek; Telling, Jon; Cook, Joe; Mak, Ka Man; Hodson, Andy; Anesio, Alexandre M

2012-01-01

Microbes in supraglacial ecosystems have been proposed to be significant contributors to regional and possibly global carbon cycling, and quantifying the biogeochemical cycling of carbon in glacial ecosystems is of great significance for global carbon flow estimations. Here we present data on microbial abundance and productivity, collected along a transect across the ablation zone of the Greenland ice sheet (GrIS) in summer 2010. We analyse the relationships between the physical, chemical and biological variables using multivariate statistical analysis. Concentrations of debris-bound nutrients increased with distance from the ice sheet margin, as did both cell numbers and activity rates before reaching a peak (photosynthesis) or a plateau (respiration, abundance) between 10 and 20 km from the margin. The results of productivity measurements suggest an overall net autotrophy on the GrIS and support the proposed role of ice sheet ecosystems in carbon cycling as regional sinks of CO(2) and places of production of organic matter that can be a potential source of nutrients for downstream ecosystems. Principal component analysis based on chemical and biological data revealed three clusters of sites, corresponding to three 'glacier ecological zones', confirmed by a redundancy analysis (RDA) using physical data as predictors. RDA using data from the largest 'bare ice zone' showed that glacier surface slope, a proxy for melt water flow, accounted for most of the variation in the data. Variation in the chemical data was fully explainable by the determined physical variables. Abundance of phototrophic microbes and their proportion in the community were identified as significant controls of the carbon cycling-related microbial processes.

Appraising the Corporate Sustainability Reports - Text Mining and Multi-Discriminatory Analysis

NASA Astrophysics Data System (ADS)

Modapothala, J. R.; Issac, B.; Jayamani, E.

The voluntary disclosure of the sustainability reports by the companies attracts wider stakeholder groups. Diversity in these reports poses challenge to the users of information and regulators. This study appraises the corporate sustainability reports as per GRI (Global Reporting Initiative) guidelines (the most widely accepted and used) across all industrial sectors. Text mining is adopted to carry out the initial analysis with a large sample size of 2650 reports. Statistical analyses were performed for further investigation. The results indicate that the disclosures made by the companies differ across the industrial sectors. Multivariate Discriminant Analysis (MDA) shows that the environmental variable is a greater significant contributing factor towards explanation of sustainability report.

Fast and Accurate Multivariate Gaussian Modeling of Protein Families: Predicting Residue Contacts and Protein-Interaction Partners

PubMed Central

Feinauer, Christoph; Procaccini, Andrea; Zecchina, Riccardo; Weigt, Martin; Pagnani, Andrea

2014-01-01

In the course of evolution, proteins show a remarkable conservation of their three-dimensional structure and their biological function, leading to strong evolutionary constraints on the sequence variability between homologous proteins. Our method aims at extracting such constraints from rapidly accumulating sequence data, and thereby at inferring protein structure and function from sequence information alone. Recently, global statistical inference methods (e.g. direct-coupling analysis, sparse inverse covariance estimation) have achieved a breakthrough towards this aim, and their predictions have been successfully implemented into tertiary and quaternary protein structure prediction methods. However, due to the discrete nature of the underlying variable (amino-acids), exact inference requires exponential time in the protein length, and efficient approximations are needed for practical applicability. Here we propose a very efficient multivariate Gaussian modeling approach as a variant of direct-coupling analysis: the discrete amino-acid variables are replaced by continuous Gaussian random variables. The resulting statistical inference problem is efficiently and exactly solvable. We show that the quality of inference is comparable or superior to the one achieved by mean-field approximations to inference with discrete variables, as done by direct-coupling analysis. This is true for (i) the prediction of residue-residue contacts in proteins, and (ii) the identification of protein-protein interaction partner in bacterial signal transduction. An implementation of our multivariate Gaussian approach is available at the website http://areeweb.polito.it/ricerca/cmp/code. PMID:24663061

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

PubMed

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

2018-02-01

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

Global Tree Range Shifts Under Forecasts from Two Alternative GCMs Using Two Future Scenarios

NASA Astrophysics Data System (ADS)

Hargrove, W. W.; Kumar, J.; Potter, K. M.; Hoffman, F. M.

2013-12-01

Global shifts in the environmentally suitable ranges of 215 tree species were predicted under forecasts from two GCMs (the Parallel Climate Model (PCM), and the Hadley Model), each under two IPCC future climatic scenarios (A1 and B1), each at two future dates (2050 and 2100). The analysis considers all global land surface at a resolution of 4 km2. A statistical multivariate clustering procedure was used to quantitatively delineate 30 thousand environmentally homogeneous ecoregions across present and 8 potential future global locations at once, using global maps of 17 environmental characteristics describing temperature, precipitation, soils, topography and solar insolation. Presence of each tree species on Forest Inventory Analysis (FIA) plots and in Global Biodiversity Information Facility (GBIF) samples was used to select a subset of suitable ecoregions from the full set of 30 thousand. Once identified, this suitable subset of ecoregions was compared to the known current range of the tree species under present conditions. Predicted present ranges correspond well with current understanding for all but a few of the 215 tree species. The subset of suitable ecoregions for each tree species can then be tracked into the future to determine whether the suitable home range for this species remains the same, moves, grows, shrinks, or disappears under each model/scenario combination. Occurrence and growth performance measurements for various tree species across the U.S. are limited to FIA plots. We present a new, general-purpose empirical imputation method which associates sparse measurements of dependent variables with particular multivariate clustered combinations of the independent variables, and then estimates values for unmeasured clusters, based on directional proximity in multidimensional data space, at both the cluster and map-cell levels of resolution. Using Associative Clustering, we scaled up the FIA point measurements into contonuous maps that show the expected growth and suitability for individual tree species across the continental US. Maps were generated for each tree species showing the Minimum Required Movement (MRM) straight-line distance from each currently suitable location to the geographically nearest "lifeboat" location having suitable conditions in the future. Locations that are the closest "lifeboats" for many MRM propagules originating from wide surrounding areas may constitute high-priority preservation targets as a refugium against climatic change.

An Exploratory Study of Fatigue and Physical Activity in Canadian Thyroid Cancer Patients.

PubMed

Alhashemi, Ahmad; Jones, Jennifer M; Goldstein, David P; Mina, Daniel Santa; Thabane, Lehana; Sabiston, Catherine M; Chang, Eugene K; Brierley, James D; Sawka, Anna M

2017-09-01

Fatigue is common among cancer survivors, but fatigue in thyroid cancer (TC) survivors may be under-appreciated. This study investigated the severity and prevalence of moderate and severe fatigue in TC survivors. Potential predictive factors, including physical activity, were explored. A cross-sectional, written, self-administered TC patient survey and retrospective chart review were performed in an outpatient academic Endocrinology clinic in Toronto, Canada. The primary outcome measure was the global fatigue score measured by the Brief Fatigue Inventory (BFI). Physical activity was evaluated using the International Physical Activity Questionnaire-7 day (IPAQ-7). Predictors of BFI global fatigue score were explored in univariate analyses and a multivariable linear regression model. The response rate was 63.1% (205/325). Three-quarters of the respondents were women (152/205). The mean age was 52.5 years, and the mean time since first TC surgery was 6.8 years. The mean global BFI score was 3.5 (standard deviation 2.4) out of 10 (10 is worst). The prevalence of moderate-severe fatigue (global BFI score 4.1-10 out of 10) was 41.4% (84/203). Individuals who were unemployed or unable to work due to disability reported significantly higher levels of fatigue compared to the rest of the study population, in uni-and multivariable analyses. Furthermore, increased physical activity was associated with reduced fatigue in uni- and multivariable analyses. Other socio-demographic, disease, or biochemical variables were not significantly associated with fatigue in the multivariable model. Moderate or severe fatigue was reported in about 4/10 TC survivors. Independent predictors of worse fatigue included unemployment and reduced physical activity.

Hyperconnectivity in juvenile myoclonic epilepsy: a network analysis.

PubMed

Caeyenberghs, K; Powell, H W R; Thomas, R H; Brindley, L; Church, C; Evans, J; Muthukumaraswamy, S D; Jones, D K; Hamandi, K

2015-01-01

Juvenile myoclonic epilepsy (JME) is a common idiopathic (genetic) generalized epilepsy (IGE) syndrome characterized by impairments in executive and cognitive control, affecting independent living and psychosocial functioning. There is a growing consensus that JME is associated with abnormal function of diffuse brain networks, typically affecting frontal and fronto-thalamic areas. Using diffusion MRI and a graph theoretical analysis, we examined bivariate (network-based statistic) and multivariate (global and local) properties of structural brain networks in patients with JME (N = 34) and matched controls. Neuropsychological assessment was performed in a subgroup of 14 patients. Neuropsychometry revealed impaired visual memory and naming in JME patients despite a normal full scale IQ (mean = 98.6). Both JME patients and controls exhibited a small world topology in their white matter networks, with no significant differences in the global multivariate network properties between the groups. The network-based statistic approach identified one subnetwork of hyperconnectivity in the JME group, involving primary motor, parietal and subcortical regions. Finally, there was a significant positive correlation in structural connectivity with cognitive task performance. Our findings suggest that structural changes in JME patients are distributed at a network level, beyond the frontal lobes. The identified subnetwork includes key structures in spike wave generation, along with primary motor areas, which may contribute to myoclonic jerks. We conclude that analyzing the affected subnetworks may provide new insights into understanding seizure generation, as well as the cognitive deficits observed in JME patients.

Hyperconnectivity in juvenile myoclonic epilepsy: A network analysis

PubMed Central

Caeyenberghs, K.; Powell, H.W.R.; Thomas, R.H.; Brindley, L.; Church, C.; Evans, J.; Muthukumaraswamy, S.D.; Jones, D.K.; Hamandi, K.

2014-01-01

Objective Juvenile myoclonic epilepsy (JME) is a common idiopathic (genetic) generalized epilepsy (IGE) syndrome characterized by impairments in executive and cognitive control, affecting independent living and psychosocial functioning. There is a growing consensus that JME is associated with abnormal function of diffuse brain networks, typically affecting frontal and fronto-thalamic areas. Methods Using diffusion MRI and a graph theoretical analysis, we examined bivariate (network-based statistic) and multivariate (global and local) properties of structural brain networks in patients with JME (N = 34) and matched controls. Neuropsychological assessment was performed in a subgroup of 14 patients. Results Neuropsychometry revealed impaired visual memory and naming in JME patients despite a normal full scale IQ (mean = 98.6). Both JME patients and controls exhibited a small world topology in their white matter networks, with no significant differences in the global multivariate network properties between the groups. The network-based statistic approach identified one subnetwork of hyperconnectivity in the JME group, involving primary motor, parietal and subcortical regions. Finally, there was a significant positive correlation in structural connectivity with cognitive task performance. Conclusions Our findings suggest that structural changes in JME patients are distributed at a network level, beyond the frontal lobes. The identified subnetwork includes key structures in spike wave generation, along with primary motor areas, which may contribute to myoclonic jerks. We conclude that analyzing the affected subnetworks may provide new insights into understanding seizure generation, as well as the cognitive deficits observed in JME patients. PMID:25610771

Effect of noise in principal component analysis with an application to ozone pollution

NASA Astrophysics Data System (ADS)

Tsakiri, Katerina G.

This thesis analyzes the effect of independent noise in principal components of k normally distributed random variables defined by a covariance matrix. We prove that the principal components as well as the canonical variate pairs determined from joint distribution of original sample affected by noise can be essentially different in comparison with those determined from the original sample. However when the differences between the eigenvalues of the original covariance matrix are sufficiently large compared to the level of the noise, the effect of noise in principal components and canonical variate pairs proved to be negligible. The theoretical results are supported by simulation study and examples. Moreover, we compare our results about the eigenvalues and eigenvectors in the two dimensional case with other models examined before. This theory can be applied in any field for the decomposition of the components in multivariate analysis. One application is the detection and prediction of the main atmospheric factor of ozone concentrations on the example of Albany, New York. Using daily ozone, solar radiation, temperature, wind speed and precipitation data, we determine the main atmospheric factor for the explanation and prediction of ozone concentrations. A methodology is described for the decomposition of the time series of ozone and other atmospheric variables into the global term component which describes the long term trend and the seasonal variations, and the synoptic scale component which describes the short term variations. By using the Canonical Correlation Analysis, we show that solar radiation is the only main factor between the atmospheric variables considered here for the explanation and prediction of the global and synoptic scale component of ozone. The global term components are modeled by a linear regression model, while the synoptic scale components by a vector autoregressive model and the Kalman filter. The coefficient of determination, R2, for the prediction of the synoptic scale ozone component was found to be the highest when we consider the synoptic scale component of the time series for solar radiation and temperature. KEY WORDS: multivariate analysis; principal component; canonical variate pairs; eigenvalue; eigenvector; ozone; solar radiation; spectral decomposition; Kalman filter; time series prediction

Variations of global gravity waves derived from 14 years of SABER temperature observations

NASA Astrophysics Data System (ADS)

Liu, Xiao; Yue, Jia; Xu, Jiyao; Garcia, Rolando R.; Russell, James M.; Mlynczak, Martin; Wu, Dong L.; Nakamura, Takuji

2017-06-01

The global gravity wave (GW) potential energy (PE) per unit mass is derived from SABER (Sounding of the Atmosphere using Broadband Emission Radiometry) temperature profiles over the past 14 years (2002-2015). Since the SABER data cover longer than one solar cycle, multivariate linear regression is applied to calculate the trend (means linear trend from 2002 to 2015) of global GW PE and the responses of global GW PE to solar activity, to QBO (quasi-biennial oscillation) and to ENSO (El Niño-Southern Oscillation). We find a significant positive trend of GW PE at around 50°N during July from 2002 to 2015, in agreement with ground-based radar observations at a similar latitude but from 1990 to 2010. Both the monthly and the deseasonalized trends of GW PE are significant near 50°S. Specifically, the deseasonalized trend of GW PE has a positive peak of 12-15% per decade at 40°S-50°S and below 60 km, which suggests that eddy diffusion is increasing in some places. A significant positive trend of GW PE near 50°S could be due to the strengthening of the polar stratospheric jets, as documented from Modern Era Retrospective-analysis for Research and Applications wind data. The response of GW PE to solar activity is negative in the lower and middle latitudes. The response of GW PE to QBO (as indicated by 30 hPa zonal winds over the equator) is negative in the tropical upper stratosphere and extends to higher latitudes at higher altitudes. The response of GW PE to ENSO (as indicated by the Multivariate ENSO Index) is positive in the tropical upper stratosphere.

Impact of Reproductive History and Exogenous Hormone Use on Cognitive Function in Midlife and Late Life

PubMed Central

Karim, Roksana; Dang, Ha; Henderson, Victor W.; Hodis, Howard N.; St John, Jan; Brinton, Roberta D.; Mack, Wendy J.

2016-01-01

Background/objectives Given the potent role of sex hormones on brain chemistry and function, we investigated the association of reproductive history indicators of hormonal exposures, including reproductive period, pregnancy, and use of hormonal contraceptives, on mid- and late-life cognition in postmenopausal women. Design Analysis of baseline data from two randomized clinical trials, the Women’s Isoflavone Soy Health (WISH) and the Early vs Late Intervention Trial of Estradiol (ELITE). Setting University academic research center Participants 830 naturally menopausal women Measurements Participants were uniformly evaluated with a cognitive battery and a structured reproductive history. Outcomes were composite scores for verbal episodic memory, executive functions, and global cognition. Reproductive variables included ages at pregnancies, menarche, and menopause, reproductive period, number of pregnancies, and use of hormones for contraception and menopausal symptoms. Multivariable linear regression evaluated associations between cognitive scores (dependent variable) and reproductive factors (independent variables), adjusting for age, race/ethnicity, income and education. Results On multivariable modeling, age at menarche ≥ 13 years of age was inversely associated with global cognition (p= 0.05). Last pregnancy after age 35 was positively associated with verbal memory (p=0.03). Use of hormonal contraceptives was positively associated with global cognition (p trend=0.04), and verbal memory (p trend=0.007). The association between hormonal contraceptive use and verbal memory and executive functions was strongest for more than 10 years of use. Reproductive period was positively associated with global cognition (p=0.04) and executive functions (p=0.04). Conclusion In this sample of healthy postmenopausal women, reproductive life events related to sex hormones, including earlier age at menarche, later age at last pregnancy, length of reproductive period, and use of oral contraceptives are positively related to aspects of cognition in later life. PMID:27996108

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

NASA Astrophysics Data System (ADS)

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

2014-12-01

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

Advanced Taste Sensors Based on Artificial Lipids with Global Selectivity to Basic Taste Qualities and High Correlation to Sensory Scores

PubMed Central

Kobayashi, Yoshikazu; Habara, Masaaki; Ikezazki, Hidekazu; Chen, Ronggang; Naito, Yoshinobu; Toko, Kiyoshi

2010-01-01

Effective R&D and strict quality control of a broad range of foods, beverages, and pharmaceutical products require objective taste evaluation. Advanced taste sensors using artificial-lipid membranes have been developed based on concepts of global selectivity and high correlation with human sensory score. These sensors respond similarly to similar basic tastes, which they quantify with high correlations to sensory score. Using these unique properties, these sensors can quantify the basic tastes of saltiness, sourness, bitterness, umami, astringency and richness without multivariate analysis or artificial neural networks. This review describes all aspects of these taste sensors based on artificial lipid, ranging from the response principle and optimal design methods to applications in the food, beverage, and pharmaceutical markets. PMID:22319306

Dancing with the Muses: dissociation and flow.

PubMed

Thomson, Paula; Jaque, S Victoria

2012-01-01

This study investigated dissociative psychological processes and flow (dispositional and state) in a group of professional and pre-professional dancers (n=74). In this study, high scores for global (Mdn=4.14) and autotelic (Mdn=4.50) flow suggest that dancing was inherently integrating and rewarding, although 17.6% of the dancers were identified as possibly having clinical levels of dissociation (Dissociative Experiences Scale-Taxon cutoff score≥20). The results of the multivariate analysis of variance indicated that subjects with high levels of dissociation had significantly lower levels of global flow (p<.05). Stepwise linear regression analyses demonstrated that dispositional flow negatively predicted the dissociative constructs of depersonalization and taxon (p<.05) but did not significantly predict the variance in absorption/imagination (p>.05). As hypothesized, dissociation and flow seem to operate as different mental processes.

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

PubMed Central

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

2014-01-01

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

Treatment effect of methylphenidate on intrinsic functional brain network in medication-naïve ADHD children: A multivariate analysis.

PubMed

Yoo, Jae Hyun; Kim, Dohyun; Choi, Jeewook; Jeong, Bumseok

2018-04-01

Methylphenidate is a first-line therapeutic option for treating attention-deficit/hyperactivity disorder (ADHD); however, elicited changes on resting-state functional networks (RSFNs) are not well understood. This study investigated the treatment effect of methylphenidate using a variety of RSFN analyses and explored the collaborative influences of treatment-relevant RSFN changes in children with ADHD. Resting-state functional magnetic resonance imaging was acquired from 20 medication-naïve ADHD children before methylphenidate treatment and twelve weeks later. Changes in large-scale functional connectivity were defined using independent component analysis with dual regression and graph theoretical analysis. The amplitude of low frequency fluctuation (ALFF) was measured to investigate local spontaneous activity alteration. Finally, significant findings were recruited to random forest regression to identify the feature subset that best explains symptom improvement. After twelve weeks of methylphenidate administration, large-scale connectivity was increased between the left fronto-parietal RSFN and the left insula cortex and the right fronto-parietal and the brainstem, while the clustering coefficient (CC) of the global network and nodes, the left fronto-parietal, cerebellum, and occipital pole-visual network, were decreased. ALFF was increased in the bilateral superior parietal cortex and decreased in the right inferior fronto-temporal area. The subset of the local and large-scale RSFN changes, including widespread ALFF changes, the CC of the global network and the cerebellum, could explain the 27.1% variance of the ADHD Rating Scale and 13.72% of the Conner's Parent Rating Scale. Our multivariate approach suggests that the neural mechanism of methylphenidate treatment could be associated with alteration of spontaneous activity in the superior parietal cortex or widespread brain regions as well as functional segregation of the large-scale intrinsic functional network.

Worldwide analysis of factors associated with medicines compendia publishing.

PubMed

Arguello, Blanca; Fernandez-Llimos, Fernando

2013-06-01

Medicines compendia, also called formularies, are the most commonly used drug information source among health care professionals. The aim was to identify the countries publishing medicines compendia and the socio-demographic factors associated to this fact. Additionally, we sought to determine the use of foreign compendia in countries lacking their own. Global web-based survey. Healthcare practitioners and researchers from 193 countries worldwide were invited to complete a web-based survey. The questionnaire investigated the existence of a national compendium, or the use of foreign compendia in the absence of one. Demographic and socioeconomic variables were used to predict compendia publishing through a multivariate analysis. Existence of national medicines compendia and foreign compendia used. Professionals from 132 countries completed the survey (response rate at a country level 68.4%, comprising 90.9% global population). Eighty-four countries (63.6%) reported publishing a medicines compendium. In the multivariate analysis, only two covariates had significant association with compendia publishing. Being a member of the Organisation for the Economic Cooperation and Development was the only variable positively associated with compendia publishing (OR = 37.5; 95% CI = 2.3:599.8). In contrast, the countries that listed French as an official language were less likely to publish a compendium (OR = 0.07; 95% CI = 0.007:0.585). Countries without national compendia reported using the British National Formulary most commonly, followed by the Dictionnaire Vidal. Publication of medicines compendia is associated with socio-economic development. Countries lacking a national compendium, use foreign compendia from higher-income countries. Creating an international medicines compendium under the leadership of the World Health Organisation, rather than merely a 'model', would reduce the risks of using information sources not-adapted to the necessities of developing countries.

Multi objective climate change impact assessment using multi downscaled climate scenarios

NASA Astrophysics Data System (ADS)

Rana, Arun; Moradkhani, Hamid

2016-04-01

Global Climate Models (GCMs) are often used to downscale the climatic parameters on a regional and global scale. In the present study, we have analyzed the changes in precipitation and temperature for future scenario period of 2070-2099 with respect to historical period of 1970-2000 from a set of statistically downscaled GCM projections for Columbia River Basin (CRB). Analysis is performed using 2 different statistically downscaled climate projections namely the Bias Correction and Spatial Downscaling (BCSD) technique generated at Portland State University and the Multivariate Adaptive Constructed Analogs (MACA) technique, generated at University of Idaho, totaling to 40 different scenarios. Analysis is performed on spatial, temporal and frequency based parameters in the future period at a scale of 1/16th of degree for entire CRB region. Results have indicated in varied degree of spatial change pattern for the entire Columbia River Basin, especially western part of the basin. At temporal scales, winter precipitation has higher variability than summer and vice-versa for temperature. Frequency analysis provided insights into possible explanation to changes in precipitation.

Global metabolic profiling procedures for urine using UPLC-MS.

PubMed

Want, Elizabeth J; Wilson, Ian D; Gika, Helen; Theodoridis, Georgios; Plumb, Robert S; Shockcor, John; Holmes, Elaine; Nicholson, Jeremy K

2010-06-01

The production of 'global' metabolite profiles involves measuring low molecular-weight metabolites (<1 kDa) in complex biofluids/tissues to study perturbations in response to physiological challenges, toxic insults or disease processes. Information-rich analytical platforms, such as mass spectrometry (MS), are needed. Here we describe the application of ultra-performance liquid chromatography-MS (UPLC-MS) to urinary metabolite profiling, including sample preparation, stability/storage and the selection of chromatographic conditions that balance metabolome coverage, chromatographic resolution and throughput. We discuss quality control and metabolite identification, as well as provide details of multivariate data analysis approaches for analyzing such MS data. Using this protocol, the analysis of a sample set in 96-well plate format, would take ca. 30 h, including 1 h for system setup, 1-2 h for sample preparation, 24 h for UPLC-MS analysis and 1-2 h for initial data processing. The use of UPLC-MS for metabolic profiling in this way is not faster than the conventional HPLC-based methods but, because of improved chromatographic performance, provides superior metabolome coverage.

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

PubMed

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

2013-05-01

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

Association between work role stressors and sleep quality.

PubMed

Iwasaki, S; Deguchi, Y; Inoue, K

2018-05-17

Work-related stressors are associated with low sleep quality. However, few studies have reported an association between role stressors and sleep quality. To elucidate the association between role stressors (including role conflict and ambiguity) and sleep quality. Cross-sectional study of daytime workers whose sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI). Work-related stressors, including role stressors, were assessed using the Generic Job Stress Questionnaire (GJSQ). The association between sleep quality and work-related stressors was investigated by logistic regression analysis. A total of 243 participants completed questionnaires were received (response rate 71%); 86 participants reported poor sleep quality, based on a global PSQI score ≥6. Multivariable logistic regression analysis revealed that higher role ambiguity was associated with global PSQI scores ≥6, and that role conflict was significantly associated with sleep problems, including sleep disturbance and daytime dysfunction. These results suggest that high role stress is associated with low sleep quality, and that this association should be considered an important determinant of the health of workers.

Multidisciplinary optimization of controlled space structures with global sensitivity equations

NASA Technical Reports Server (NTRS)

Padula, Sharon L.; James, Benjamin B.; Graves, Philip C.; Woodard, Stanley E.

1991-01-01

A new method for the preliminary design of controlled space structures is presented. The method coordinates standard finite element structural analysis, multivariable controls, and nonlinear programming codes and allows simultaneous optimization of the structures and control systems of a spacecraft. Global sensitivity equations are a key feature of this method. The preliminary design of a generic geostationary platform is used to demonstrate the multidisciplinary optimization method. Fifteen design variables are used to optimize truss member sizes and feedback gain values. The goal is to reduce the total mass of the structure and the vibration control system while satisfying constraints on vibration decay rate. Incorporating the nonnegligible mass of actuators causes an essential coupling between structural design variables and control design variables. The solution of the demonstration problem is an important step toward a comprehensive preliminary design capability for structures and control systems. Use of global sensitivity equations helps solve optimization problems that have a large number of design variables and a high degree of coupling between disciplines.

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

PubMed

Lee, Yeonok; Wu, Hulin

2012-01-01

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

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

PubMed

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

2015-01-01

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

Seroprevalence of Hepatitis B Virus Infection and Its Risk Factors in the West of Iran: A Population-based Study

PubMed Central

Alavian, Seyed Moayed; Tabatabaei, Seyed Vahid; Ghadimi, Teyyeb; Beedrapour, Farzam; Kafi-abad, Sedigheh Amini; Gharehbaghian, Ahmad; Abolghasemi, Hassan

2012-01-01

Introduction: Hepatitis B virus (HBV) infection is a serious global public health problem affecting billions of people globally. The lack of information of its seroprevalence among the general population is an obstacle for formulating effective policies to reduce the burden viral hepatitis. Therefore, this population based serological survey was conducted in Kurdistan province, where no epidemiological data was available to determine the prevalence and risk factors of HBV infection. Methods: 1613 healthy subjects were selected from all districts of Kurdistan province (in the western of Iran) using random cluster sampling. The subjects’ age ranged from 6 to 65 years old. Serum samples were tested for HBcAb, HBsAg and anti-HDV antibody. Screening tests were carried out by the third generation of ELISA. Various risk factors were recorded and multivariate analysis was performed. Results: The prevalence of HBsAg and HBcAb in Kurdistan was before 0.80% (95% CI 0.44; 1.34) and 5.02% (95% CI 4.03; 6.17), respectively. None of HBsAg carriers had positive anti-HDV antibody. Predictors of HBsAg or HBcAb in multivariate analysis were: older age and marriage. We did not find any significant differences between males and females. Conclusion: Our population based study suggests that intrafamilial HBV transmission plays a major role in HBV transmission in Kurdistan province. Furthermore, approximately 5% of general population in this province has prior exposure to HBV and less than 1% is HBsAg carriers. However, we could not find any case of HDV infection among them. PMID:23189228

Menopause is associated with self-reported poor sleep quality in women without vasomotor symptoms.

PubMed

Hung, Hao-Chang; Lu, Feng-Hwa; Ou, Horng-Yih; Wu, Jin-Shang; Yang, Yi-Ching; Chang, Chih-Jen

2014-08-01

The aim of this study was to investigate the relationship between menopause and self-reported sleep quality in Chinese women without vasomotor symptoms. Cross-sectional data were collected from a decoded database of the National Cheng Kung University Hospital. Menopause was defined as absence of menses for at least 12 months or a history of hysterectomy and oophorectomy. Self-reported sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI). A higher global PSQI score indicates poorer self-reported sleep quality, and a global PSQI score greater than 5 differentiates poor sleepers from good sleepers. Of the 1,088 women recruited, 353 (32.4%) were in postmenopause status. Postmenopausal women had higher mean (SD) global PSQI scores (8.0 [3.3] vs. 6.1 [2.2], P < 0.001) and a greater prevalence of poor sleepers (73.1% vs. 60.8%, P < 0.001) compared with premenopausal women. Multivariate linear regression analysis showed that menopause (β = 1.532; 95% CI, 1.135 to 1.949; P < 0.001) and snoring (β = 0.764; 95% CI, 0.299 to 1.228; P = 0.001) were positively associated with global PSQI scores, whereas long sleep duration (β = -0.791; 95% CI, -1.113 to -0.468; P < 0.001) was negatively associated with global PSQI scores. Multivariate logistic regression analyses showed that menopause (odds ratio, 1.453; 95% CI, 1.030 to 2.051; P < 0.05), long sleep duration (odds ratio, 0.545; 95% CI, 0.418 to 0.710; P < 0.001), and snoring (odds ratio, 2.022; 95% CI, 1.312 to 3.116; P = 0.001) were independent predictors of poor sleepers. Postmenopausal women without vasomotor symptoms have significantly higher global PSQI scores and a higher risk of being poor sleepers than premenopausal women. In addition, menopause and snoring are associated with an increased risk of poor self-reported sleep quality independently of cardiometabolic factors and lifestyle, whereas long sleep duration is associated with a decreased risk of poor self-reported sleep quality.

Multivariate analysis in thoracic research.

PubMed

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

2015-03-01

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

Multivariate multiscale entropy of financial markets

NASA Astrophysics Data System (ADS)

Lu, Yunfan; Wang, Jun

2017-11-01

In current process of quantifying the dynamical properties of the complex phenomena in financial market system, the multivariate financial time series are widely concerned. In this work, considering the shortcomings and limitations of univariate multiscale entropy in analyzing the multivariate time series, the multivariate multiscale sample entropy (MMSE), which can evaluate the complexity in multiple data channels over different timescales, is applied to quantify the complexity of financial markets. Its effectiveness and advantages have been detected with numerical simulations with two well-known synthetic noise signals. For the first time, the complexity of four generated trivariate return series for each stock trading hour in China stock markets is quantified thanks to the interdisciplinary application of this method. We find that the complexity of trivariate return series in each hour show a significant decreasing trend with the stock trading time progressing. Further, the shuffled multivariate return series and the absolute multivariate return series are also analyzed. As another new attempt, quantifying the complexity of global stock markets (Asia, Europe and America) is carried out by analyzing the multivariate returns from them. Finally we utilize the multivariate multiscale entropy to assess the relative complexity of normalized multivariate return volatility series with different degrees.

Real-life assessment of the validity of patient global impression of change in fibromyalgia.

PubMed

Rampakakis, Emmanouil; Ste-Marie, Peter A; Sampalis, John S; Karellis, Angeliki; Shir, Yoram; Fitzcharles, Mary-Ann

2015-01-01

Patient Global Rating of Change (GRC) scales are commonly used in routine clinical care given their ease of use, availability and short completion time. This analysis aimed at assessing the validity of Patient Global Impression of Change (PGIC), a GRC scale commonly used in fibromyalgia, in a Canadian real-life setting. 167 fibromyalgia patients with available PGIC data were recruited in 2005-2013 from a Canadian tertiary-care multidisciplinary clinic. In addition to PGIC, disease severity was assessed with: pain visual analogue scale (VAS); Patient Global Assessment (PGA); Fibromyalgia Impact Questionnaire (FIQ); Health Assessment Questionnaire (HAQ); McGill Pain Questionnaire; body map. Multivariate linear regression assessed the PGIC relationship with disease parameter improvement while adjusting for follow-up duration and baseline parameter levels. The Spearman's rank coefficient assessed parameter correlation. Higher PGIC scores were significantly (p<0.001) associated with greater improvement in pain, PGA, FIQ, HAQ and the body map. A statistically significant moderate positive correlation was observed between PGIC and FIQ improvement (r=0.423; p<0.001); correlation with all remaining disease severity measures was weak. Regression analysis confirmed a significant (p<0.001) positive association between improvement in all disease severity measures and PGIC. Baseline disease severity and follow-up duration were identified as significant independent predictors of PGIC rating. Despite that only a weak correlation was identified between PGIC and standard fibromyalgia outcomes improvement, in the absence of objective outcomes, PGIC remains a clinically relevant tool to assess perceived impact of disease management. However, our analysis suggests that outcome measures data should not be considered in isolation but, within the global clinical context.

Landscape Characterization and Representativeness Analysis for Understanding Sampling Network Coverage

DOE Data Explorer

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

2014-08-01

Sampling networks rarely conform to spatial and temporal ideals, often comprised of network sampling points which are unevenly distributed and located in less than ideal locations due to access constraints, budget limitations, or political conflict. Quantifying the global, regional, and temporal representativeness of these networks by quantifying the coverage of network infrastructure highlights the capabilities and limitations of the data collected, facilitates upscaling and downscaling for modeling purposes, and improves the planning efforts for future infrastructure investment under current conditions and future modeled scenarios. The work presented here utilizes multivariate spatiotemporal clustering analysis and representativeness analysis for quantitative landscape characterization and assessment of the Fluxnet, RAINFOR, and ForestGEO networks. Results include ecoregions that highlight patterns of bioclimatic, topographic, and edaphic variables and quantitative representativeness maps of individual and combined networks.

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

PubMed

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

2014-11-01

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

Peritoneal Dialysate Glucose Load and Systemic Glucose Metabolism in Non-Diabetics: Results from the GLOBAL Fluid Cohort Study

PubMed Central

Chess, James; Do, Jun-Young; Noh, Hyunjin; Lee, Hi-Bahl; Kim, Yong-Lim; Summers, Angela; Williams, Paul Ford; Davison, Sara; Dorval, Marc

2016-01-01

Background and Objectives Glucose control is a significant predictor of mortality in diabetic peritoneal dialysis (PD) patients. During PD, the local toxic effects of intra-peritoneal glucose are well recognized, but despite large amounts of glucose being absorbed, the systemic effects of this in non-diabetic patients are not clear. We sought to clarify whether dialysate glucose has an effect upon systemic glucose metabolism. Methods and Materials We analysed the Global Fluid Study cohort, a prospective, observational cohort study initiated in 2002. A subset of 10 centres from 3 countries with high data quality were selected (368 incident and 272 prevalent non-diabetic patients), with multilevel, multivariable analysis of the reciprocal of random glucose levels, and a stratified-by-centre Cox survival analysis. Results The median follow up was 5.6 and 6.4 years respectively in incident and prevalent patients. On multivariate analysis, serum glucose increased with age (β = -0.007, 95%CI -0.010, -0.004) and decreased with higher serum sodium (β = 0.002, 95%CI 0.0005, 0.003) in incident patients and increased with dialysate glucose (β = -0.0002, 95%CI -0.0004, -0.00006) in prevalent patients. Levels suggested undiagnosed diabetes in 5.4% of prevalent patients. Glucose levels predicted death in unadjusted analyses of both incident and prevalent groups but in an adjusted survival analysis they did not (for random glucose 6–10 compared with <6, Incident group HR 0.92, 95%CI 0.58, 1.46, Prevalent group HR 1.42, 95%CI 0.86, 2.34). Conclusions In prevalent non-diabetic patients, random glucose levels at a diabetic level are under-recognised and increase with dialysate glucose load. Random glucose levels predict mortality in unadjusted analyses, but this association has not been proven in adjusted analyses. PMID:27249020

Multivariate Methods for Meta-Analysis of Genetic Association Studies.

PubMed

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

2018-01-01

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

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

ERIC Educational Resources Information Center

Grochowalski, Joseph H.

2015-01-01

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

A vision for an ultra-high resolution integrated water cycle observation and prediction system

NASA Astrophysics Data System (ADS)

Houser, P. R.

2013-05-01

Society's welfare, progress, and sustainable economic growth—and life itself—depend on the abundance and vigorous cycling and replenishing of water throughout the global environment. The water cycle operates on a continuum of time and space scales and exchanges large amounts of energy as water undergoes phase changes and is moved from one part of the Earth system to another. We must move toward an integrated observation and prediction paradigm that addresses broad local-to-global science and application issues by realizing synergies associated with multiple, coordinated observations and prediction systems. A central challenge of a future water and energy cycle observation strategy is to progress from single variable water-cycle instruments to multivariable integrated instruments in electromagnetic-band families. The microwave range in the electromagnetic spectrum is ideally suited for sensing the state and abundance of water because of water's dielectric properties. Eventually, a dedicated high-resolution water-cycle microwave-based satellite mission may be possible based on large-aperture antenna technology that can harvest the synergy that would be afforded by simultaneous multichannel active and passive microwave measurements. A partial demonstration of these ideas can even be realized with existing microwave satellite observations to support advanced multivariate retrieval methods that can exploit the totality of the microwave spectral information. The simultaneous multichannel active and passive microwave retrieval would allow improved-accuracy retrievals that are not possible with isolated measurements. Furthermore, the simultaneous monitoring of several of the land, atmospheric, oceanic, and cryospheric states brings synergies that will substantially enhance understanding of the global water and energy cycle as a system. The multichannel approach also affords advantages to some constituent retrievals—for instance, simultaneous retrieval of vegetation biomass would improve soil-moisture retrieval by avoiding the need for auxiliary vegetation information. This multivariable water-cycle observation system must be integrated with high-resolution, application relevant prediction systems to optimize their information content and utility is addressing critical water cycle issues. One such vision is a real-time ultra-high resolution locally-moasiced global land modeling and assimilation system, that overlays regional high-fidelity information over a baseline global land prediction system. Such a system would provide the best possible local information for use in applications, while integrating and sharing information globally for diagnosing larger water cycle variability. In a sense, this would constitute a hydrologic telecommunication system, where the best local in-situ gage, Doppler radar, and weather station can be shared internationally, and integrated in a consistent manner with global observation platforms like the multivariable water cycle mission. To realize such a vision, large issues must be addressed, such as international data sharing policy, model-observation integration approaches that maintain local extremes while achieving global consistency, and methods for establishing error estimates and uncertainty.

Predicting microbiologically defined infection in febrile neutropenic episodes in children: global individual participant data multivariable meta-analysis

PubMed Central

Phillips, Robert S; Sung, Lillian; Amman, Roland A; Riley, Richard D; Castagnola, Elio; Haeusler, Gabrielle M; Klaassen, Robert; Tissing, Wim J E; Lehrnbecher, Thomas; Chisholm, Julia; Hakim, Hana; Ranasinghe, Neil; Paesmans, Marianne; Hann, Ian M; Stewart, Lesley A

2016-01-01

Background: Risk-stratified management of fever with neutropenia (FN), allows intensive management of high-risk cases and early discharge of low-risk cases. No single, internationally validated, prediction model of the risk of adverse outcomes exists for children and young people. An individual patient data (IPD) meta-analysis was undertaken to devise one. Methods: The ‘Predicting Infectious Complications in Children with Cancer' (PICNICC) collaboration was formed by parent representatives, international clinical and methodological experts. Univariable and multivariable analyses, using random effects logistic regression, were undertaken to derive and internally validate a risk-prediction model for outcomes of episodes of FN based on clinical and laboratory data at presentation. Results: Data came from 22 different study groups from 15 countries, of 5127 episodes of FN in 3504 patients. There were 1070 episodes in 616 patients from seven studies available for multivariable analysis. Univariable analyses showed associations with microbiologically defined infection (MDI) in many items, including higher temperature, lower white cell counts and acute myeloid leukaemia, but not age. Patients with osteosarcoma/Ewings sarcoma and those with more severe mucositis were associated with a decreased risk of MDI. The predictive model included: malignancy type, temperature, clinically ‘severely unwell', haemoglobin, white cell count and absolute monocyte count. It showed moderate discrimination (AUROC 0.723, 95% confidence interval 0.711–0.759) and good calibration (calibration slope 0.95). The model was robust to bootstrap and cross-validation sensitivity analyses. Conclusions: This new prediction model for risk of MDI appears accurate. It requires prospective studies assessing implementation to assist clinicians and parents/patients in individualised decision making. PMID:26954719

Multivariate meta-analysis: potential and promise.

PubMed

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

2011-09-10

The multivariate random effects model is a generalization of the standard univariate model. Multivariate meta-analysis is becoming more commonly used and the techniques and related computer software, although continually under development, are now in place. In order to raise awareness of the multivariate methods, and discuss their advantages and disadvantages, we organized a one day 'Multivariate meta-analysis' event at the Royal Statistical Society. In addition to disseminating the most recent developments, we also received an abundance of comments, concerns, insights, critiques and encouragement. This article provides a balanced account of the day's discourse. By giving others the opportunity to respond to our assessment, we hope to ensure that the various view points and opinions are aired before multivariate meta-analysis simply becomes another widely used de facto method without any proper consideration of it by the medical statistics community. We describe the areas of application that multivariate meta-analysis has found, the methods available, the difficulties typically encountered and the arguments for and against the multivariate methods, using four representative but contrasting examples. We conclude that the multivariate methods can be useful, and in particular can provide estimates with better statistical properties, but also that these benefits come at the price of making more assumptions which do not result in better inference in every case. Although there is evidence that multivariate meta-analysis has considerable potential, it must be even more carefully applied than its univariate counterpart in practice. Copyright © 2011 John Wiley & Sons, Ltd.

Usage of multivariate geostatistics in interpolation processes for meteorological precipitation maps

NASA Astrophysics Data System (ADS)

Gundogdu, Ismail Bulent

2017-01-01

Long-term meteorological data are very important both for the evaluation of meteorological events and for the analysis of their effects on the environment. Prediction maps which are constructed by different interpolation techniques often provide explanatory information. Conventional techniques, such as surface spline fitting, global and local polynomial models, and inverse distance weighting may not be adequate. Multivariate geostatistical methods can be more significant, especially when studying secondary variables, because secondary variables might directly affect the precision of prediction. In this study, the mean annual and mean monthly precipitations from 1984 to 2014 for 268 meteorological stations in Turkey have been used to construct country-wide maps. Besides linear regression, the inverse square distance and ordinary co-Kriging (OCK) have been used and compared to each other. Also elevation, slope, and aspect data for each station have been taken into account as secondary variables, whose use has reduced errors by up to a factor of three. OCK gave the smallest errors (1.002 cm) when aspect was included.

Biological markers of fertility (inhibin-B) in HIV-infected men: influence of HIV infection and antiretroviral therapy.

PubMed

Moreno-Pérez, O; Boix, V; Merino, E; Picó, A; Reus, S; Alfayate, R; Giner, L; Mirete, R; Sánchez-Payá, J; Portilla, J

2016-06-01

Inhibin B (IB) levels and the IB: follicle-stimulating hormone (FSH) ratio (IFR), biomarkers of global Sertoli cell function, show a strong relationship with male fertility. The aim of the study was to examine the prevalence of impaired fertility potential in HIV-infected men and the influence of antiretroviral therapy (ART) on fertility biomarkers. A cross-sectional study with sequential sampling was carried out. A total of 169 clinically stable patients in a cohort of HIV-infected men undergoing regular ambulatory assessment in a tertiary hospital were included. The mean [± standard deviation (SD)] age of the patients was 42.6 ± 8.1 years, all were clinically stable, 61.5% had disease classified as Centers for Disease Control and Prevention (CDC) stage A, and were na?ve to ART or had not had any changes to ART for 6 months (91.1%). Morning baseline IB and FSH concentrations were measured using an enzyme-linked immunosorbent assay (ELISA) and an electrochemiluminescent immunoassay (ECLIA), respectively. A multivariate logistic regression model was used to identify factors associated with impaired fertility, defined as IB < 119 pg/mL or IFR < 23.5. The mean (± SD) IB level was 250 ± 103 pg/mL, the median [interquartile range (IQR)] FSH concentration was 5.1 (3.3-7.8) UI/L and the median (IQR) IFR was 46.1 (26.3-83.7). The prevalence of impaired fertility was 21.9% [95% confidence interval (CI) 16.3-20.7%]. Negative correlations of body mass index and waist: hip ratio with FSH and IB levels were observed (P < 0.01), while a sedentary lifestyle and previous nevirapine exposure were associated with a decreased risk of IB levels ≤ 25th percentile in multivariate analysis. Only older age, as a risk factor, and sedentary lifestyle, with a protective effect, were independently associated with impaired fertility in multivariate analysis. Global testicular Sertoli cell function and fertility potential, assessed indirectly through serum IB levels and IB: FSH ratio, appear to be well maintained in HIV-infected men and not damaged by ART. © 2015 British HIV Association.

Joint spatiotemporal variability of global sea surface temperatures and global Palmer drought severity index values

USGS Publications Warehouse

Apipattanavis, S.; McCabe, G.J.; Rajagopalan, B.; Gangopadhyay, S.

2009-01-01

Dominant modes of individual and joint variability in global sea surface temperatures (SST) and global Palmer drought severity index (PDSI) values for the twentieth century are identified through a multivariate frequency domain singular value decomposition. This analysis indicates that a secular trend and variability related to the El Niño–Southern Oscillation (ENSO) are the dominant modes of variance shared among the global datasets. For the SST data the secular trend corresponds to a positive trend in Indian Ocean and South Atlantic SSTs, and a negative trend in North Pacific and North Atlantic SSTs. The ENSO reconstruction shows a strong signal in the tropical Pacific, North Pacific, and Indian Ocean regions. For the PDSI data, the secular trend reconstruction shows high amplitudes over central Africa including the Sahel, whereas the regions with strong ENSO amplitudes in PDSI are the southwestern and northwestern United States, South Africa, northeastern Brazil, central Africa, the Indian subcontinent, and Australia. An additional significant frequency, multidecadal variability, is identified for the Northern Hemisphere. This multidecadal frequency appears to be related to the Atlantic multidecadal oscillation (AMO). The multidecadal frequency is statistically significant in the Northern Hemisphere SST data, but is statistically nonsignificant in the PDSI data.

Multivariate Models for Normal and Binary Responses in Intervention Studies

ERIC Educational Resources Information Center

Pituch, Keenan A.; Whittaker, Tiffany A.; Chang, Wanchen

2016-01-01

Use of multivariate analysis (e.g., multivariate analysis of variance) is common when normally distributed outcomes are collected in intervention research. However, when mixed responses--a set of normal and binary outcomes--are collected, standard multivariate analyses are no longer suitable. While mixed responses are often obtained in…

The distance between Mars and Venus: measuring global sex differences in personality.

PubMed

Del Giudice, Marco; Booth, Tom; Irwing, Paul

2012-01-01

Sex differences in personality are believed to be comparatively small. However, research in this area has suffered from significant methodological limitations. We advance a set of guidelines for overcoming those limitations: (a) measure personality with a higher resolution than that afforded by the Big Five; (b) estimate sex differences on latent factors; and (c) assess global sex differences with multivariate effect sizes. We then apply these guidelines to a large, representative adult sample, and obtain what is presently the best estimate of global sex differences in personality. Personality measures were obtained from a large US sample (N = 10,261) with the 16PF Questionnaire. Multigroup latent variable modeling was used to estimate sex differences on individual personality dimensions, which were then aggregated to yield a multivariate effect size (Mahalanobis D). We found a global effect size D = 2.71, corresponding to an overlap of only 10% between the male and female distributions. Even excluding the factor showing the largest univariate ES, the global effect size was D = 1.71 (24% overlap). These are extremely large differences by psychological standards. The idea that there are only minor differences between the personality profiles of males and females should be rejected as based on inadequate methodology.

The Distance Between Mars and Venus: Measuring Global Sex Differences in Personality

PubMed Central

Del Giudice, Marco; Booth, Tom; Irwing, Paul

2012-01-01

Background Sex differences in personality are believed to be comparatively small. However, research in this area has suffered from significant methodological limitations. We advance a set of guidelines for overcoming those limitations: (a) measure personality with a higher resolution than that afforded by the Big Five; (b) estimate sex differences on latent factors; and (c) assess global sex differences with multivariate effect sizes. We then apply these guidelines to a large, representative adult sample, and obtain what is presently the best estimate of global sex differences in personality. Methodology/Principal Findings Personality measures were obtained from a large US sample (N = 10,261) with the 16PF Questionnaire. Multigroup latent variable modeling was used to estimate sex differences on individual personality dimensions, which were then aggregated to yield a multivariate effect size (Mahalanobis D). We found a global effect size D = 2.71, corresponding to an overlap of only 10% between the male and female distributions. Even excluding the factor showing the largest univariate ES, the global effect size was D = 1.71 (24% overlap). These are extremely large differences by psychological standards. Significance The idea that there are only minor differences between the personality profiles of males and females should be rejected as based on inadequate methodology. PMID:22238596

Multidisciplinary optimization of a controlled space structure using 150 design variables

NASA Technical Reports Server (NTRS)

James, Benjamin B.

1993-01-01

A controls-structures interaction design method is presented. The method coordinates standard finite-element structural analysis, multivariable controls, and nonlinear programming codes and allows simultaneous optimization of the structure and control system of a spacecraft. Global sensitivity equations are used to account for coupling between the disciplines. Use of global sensitivity equations helps solve optimization problems that have a large number of design variables and a high degree of coupling between disciplines. The preliminary design of a generic geostationary platform is used to demonstrate the multidisciplinary optimization method. Design problems using 15, 63, and 150 design variables to optimize truss member sizes and feedback gain values are solved and the results are presented. The goal is to reduce the total mass of the structure and the vibration control system while satisfying constraints on vibration decay rate. Incorporation of the nonnegligible mass of actuators causes an essential coupling between structural design variables and control design variables.

A Look Inside HIV Resistance through Retroviral Protease Interaction Maps

PubMed Central

Kontijevskis, Aleksejs; Prusis, Peteris; Petrovska, Ramona; Yahorava, Sviatlana; Mutulis, Felikss; Mutule, Ilze; Komorowski, Jan; Wikberg, Jarl E. S

2007-01-01

Retroviruses affect a large number of species, from fish and birds to mammals and humans, with global socioeconomic negative impacts. Here the authors report and experimentally validate a novel approach for the analysis of the molecular networks that are involved in the recognition of substrates by retroviral proteases. Using multivariate analysis of the sequence-based physiochemical descriptions of 61 retroviral proteases comprising wild-type proteases, natural mutants, and drug-resistant forms of proteases from nine different viral species in relation to their ability to cleave 299 substrates, the authors mapped the physicochemical properties and cross-dependencies of the amino acids of the proteases and their substrates, which revealed a complex molecular interaction network of substrate recognition and cleavage. The approach allowed a detailed analysis of the molecular–chemical mechanisms involved in substrate cleavage by retroviral proteases. PMID:17352531

Disentangling synergistic climate drivers on the evolution of two species of planktonic foraminifera on regional and global scales

NASA Astrophysics Data System (ADS)

Brombacher, A.; Wilson, P. A.; Bailey, I.; Ezard, T. H. G.

2016-02-01

Evolution is driven by a combination of biotic and abiotic factors. When quantifying the effects of abiotic drivers, evolutionary change is generally described as a response to a single environmental parameter assumed to represent global climate. However, climate is a complex system of many interacting factors and characterized by high regional variability. Therefore, to understand the role of climate in evolutionary change, we need to consider multiple environmental parameters, across local, regional and global scales, as well as their interactions. The deep-sea microfossil record is sufficiently complete that sufficiently continuous multivariate climatic and multivariate trait data can be obtained from the same samples. Here we present morphological records of the planktonic foraminifera species Globoconella puncticulata and Truncorotalia crassaformis over a 500,000-year interval directly preceding the extinction of G. puncticulata (2.41 Ma). Material was collected from five North Atlantic sites (ODP Sites 659 [18° N], 925 [3° N] and 981 [55° N], IODP Site U1313 [41° N] and DSDP Site 606 [37° N]). Test size and shape of over 35,000 individuals were measured and compared to site-specific records of sea surface temperature, primary productivity and marine aeolian dust deposition, as well as to global records of ice volume, ocean circulation and atmospheric CO2, and all two-way interactions. Morphological parameters respond weakly to individual climate parameters. Once interactions among all studied climate parameters were incorporated, abiotic change explained around 35% of the evolutionary variance. Observed covariances between environmental parameters vary strongly with glacial-interglacial cyclicity, implying that the relationships among climate variables and their relative influences on evolutionary change varied through time. This time dependence cautions against unfettered use of dimension reduction techniques, such as principal components analysis, to extract a single, supposedly dominant, proxy. Furthermore species' responses differed between geographic locations, impressing the need to test how interactions among multiple climate variables at different regional settings shape the biotic microevolutionary response to local and global abiotic change.

Deconstructing multivariate decoding for the study of brain function.

PubMed

Hebart, Martin N; Baker, Chris I

2017-08-04

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

Multivariate meta-analysis: Potential and promise

PubMed Central

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

2011-01-01

The multivariate random effects model is a generalization of the standard univariate model. Multivariate meta-analysis is becoming more commonly used and the techniques and related computer software, although continually under development, are now in place. In order to raise awareness of the multivariate methods, and discuss their advantages and disadvantages, we organized a one day ‘Multivariate meta-analysis’ event at the Royal Statistical Society. In addition to disseminating the most recent developments, we also received an abundance of comments, concerns, insights, critiques and encouragement. This article provides a balanced account of the day's discourse. By giving others the opportunity to respond to our assessment, we hope to ensure that the various view points and opinions are aired before multivariate meta-analysis simply becomes another widely used de facto method without any proper consideration of it by the medical statistics community. We describe the areas of application that multivariate meta-analysis has found, the methods available, the difficulties typically encountered and the arguments for and against the multivariate methods, using four representative but contrasting examples. We conclude that the multivariate methods can be useful, and in particular can provide estimates with better statistical properties, but also that these benefits come at the price of making more assumptions which do not result in better inference in every case. Although there is evidence that multivariate meta-analysis has considerable potential, it must be even more carefully applied than its univariate counterpart in practice. Copyright © 2011 John Wiley & Sons, Ltd. PMID:21268052

Global Fund grant programmes: an analysis of evaluation scores.

PubMed

Radelet, Steven; Siddiqi, Bilal

2007-05-26

The Global Fund to Fight AIDS, Tuberculosis and Malaria evaluates programme performance after 2 years to help decide whether to continue funding. We aimed to identify the correlation between programme evaluation scores and characteristics of the programme, the health sector, and the recipient country. We obtained data on the first 140 Global Fund grants evaluated in 2006, and analysed 134 of these. We used an ordered probit multivariate analysis to link evaluation scores to different characteristics, allowing us to record the association between changes in those characteristics and the probability of a programme receiving a particular evaluation score. Programmes that had government agencies as principal recipients, had a large amount of funding, were focused on malaria, had weak initial proposals, or were evaluated by the accounting firm KPMG, scored lowest. Countries with a high number of doctors per head, high measles immunisation rates, few health-sector donors, and high disease-prevalence rates had higher evaluation scores. Poor countries, those with small government budget deficits, and those that have or have had socialist governments also received higher scores. Our results show associations, not causality, and they focus on evaluation scores rather than actual performance of the programmes. Yet they provide some early indications of characteristics that can help the Global Fund identify and monitor programmes that might be at risk. The results should not be used to influence the distribution of funding, but rather to allocate resources for oversight and risk management.

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

PubMed

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

2013-10-01

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

Multivariate Longitudinal Analysis with Bivariate Correlation Test

PubMed Central

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

2016-01-01

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

Multivariate Longitudinal Analysis with Bivariate Correlation Test.

PubMed

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

2016-01-01

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

An analysis of human-induced land transformations in the San Francisco Bay/Sacramento area

USGS Publications Warehouse

Kirtland, David A.; Gaydos, L.J.; Clarke, Keith; DeCola, Lee; Acevedo, William; Bell, Cindy

1994-01-01

Part of the U.S. Geological Survey's Global Change Research Program involvesstudying the area from the Pacific Ocean to the Sierra foothills to enhance understanding ofthe role that human activities play in global change. The study investigates the ways thathumans transform the land and the effects that changing the landscape may have on regionaland global systems. To accomplish this research, scientists are compiling records ofhistorical transformations in the region's land cover over the last 140 years, developing asimulation model to predict land cover change, and assembling a digital data set to analyzeand describe land transformations. The historical data regarding urban growth focusattention on the significant change the region underwent from 1850 to 1990. Animation isused to visualize a time series of the change in land cover. The historical change is beingused to calibrate a prototype cellular automata model, developed to predict changes in urbanland cover 100 years into the future. Future urban growth scenarios will be developed foranalyzing possible human-induced impacts on land cover at a regional scale. These data aidin documenting and understanding human-induced land transformations from both historical andpredictive perspectives. A descriptive analysis of the region is used to investigate therelationships among data characteristic of the region. These data consist of multilayertopography, climate, vegetation, and population data for a 256-km2 region of centralCalifornia. A variety of multivariate analysis tools are used to integrate the data inraster format from map contours, interpolated climate observations, satellite observations,and population estimates.

The role of country-to-region assignments in global integrated modeling of energy, agriculture, land use, and climate

NASA Astrophysics Data System (ADS)

Kyle, P.; Patel, P.; Calvin, K. V.

2014-12-01

Global integrated assessment models used for understanding the linkages between the future energy, agriculture, and climate systems typically represent between 8 and 30 geopolitical macro-regions, balancing the benefits of geographic resolution with the costs of additional data collection, processing, analysis, and computing resources. As these models are continually being improved and updated in order to address new questions for the research and policy communities, it is worth examining the consequences of the country-to-region mapping schemes used for model results. This study presents an application of a data processing system built for the GCAM integrated assessment model that allows any country-to-region assignments, with a minimum of four geopolitical regions and a maximum of 185. We test ten different mapping schemes, including the specific mappings used in existing major integrated assessment models. We also explore the impacts of clustering nations into regions according to the similarity of the structure of each nation's energy and agricultural sectors, as indicated by multivariate analysis. Scenarios examined include a reference scenario, a low-emissions scenario, and scenarios with agricultural and buildings sector climate change impacts. We find that at the global level, the major output variables (primary energy, agricultural land use) are surprisingly similar regardless of regional assignments, but at finer geographic scales, differences are pronounced. We suggest that enhancing geographic resolution is advantageous for analysis of climate impacts on the buildings and agricultural sectors, due to the spatial heterogeneity of these drivers.

Probabilistic, meso-scale flood loss modelling

NASA Astrophysics Data System (ADS)

Kreibich, Heidi; Botto, Anna; Schröter, Kai; Merz, Bruno

2016-04-01

Flood risk analyses are an important basis for decisions on flood risk management and adaptation. However, such analyses are associated with significant uncertainty, even more if changes in risk due to global change are expected. Although uncertainty analysis and probabilistic approaches have received increased attention during the last years, they are still not standard practice for flood risk assessments and even more for flood loss modelling. State of the art in flood loss modelling is still the use of simple, deterministic approaches like stage-damage functions. Novel probabilistic, multi-variate flood loss models have been developed and validated on the micro-scale using a data-mining approach, namely bagging decision trees (Merz et al. 2013). In this presentation we demonstrate and evaluate the upscaling of the approach to the meso-scale, namely on the basis of land-use units. The model is applied in 19 municipalities which were affected during the 2002 flood by the River Mulde in Saxony, Germany (Botto et al. submitted). The application of bagging decision tree based loss models provide a probability distribution of estimated loss per municipality. Validation is undertaken on the one hand via a comparison with eight deterministic loss models including stage-damage functions as well as multi-variate models. On the other hand the results are compared with official loss data provided by the Saxon Relief Bank (SAB). The results show, that uncertainties of loss estimation remain high. Thus, the significant advantage of this probabilistic flood loss estimation approach is that it inherently provides quantitative information about the uncertainty of the prediction. References: Merz, B.; Kreibich, H.; Lall, U. (2013): Multi-variate flood damage assessment: a tree-based data-mining approach. NHESS, 13(1), 53-64. Botto A, Kreibich H, Merz B, Schröter K (submitted) Probabilistic, multi-variable flood loss modelling on the meso-scale with BT-FLEMO. Risk Analysis.

Multidisciplinary optimization of a controlled space structure using 150 design variables

NASA Technical Reports Server (NTRS)

James, Benjamin B.

1992-01-01

A general optimization-based method for the design of large space platforms through integration of the disciplines of structural dynamics and control is presented. The method uses the global sensitivity equations approach and is especially appropriate for preliminary design problems in which the structural and control analyses are tightly coupled. The method is capable of coordinating general purpose structural analysis, multivariable control, and optimization codes, and thus, can be adapted to a variety of controls-structures integrated design projects. The method is used to minimize the total weight of a space platform while maintaining a specified vibration decay rate after slewing maneuvers.

Multivariate Regression Analysis and Slaughter Livestock,

DTIC Science & Technology

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

On the importance of local dynamics in statokinesigram: A multivariate approach for postural control evaluation in elderly.

PubMed

Bargiotas, Ioannis; Audiffren, Julien; Vayatis, Nicolas; Vidal, Pierre-Paul; Buffat, Stephane; Yelnik, Alain P; Ricard, Damien

2018-01-01

The fact that almost one third of population >65 years-old has at least one fall per year, makes the risk-of-fall assessment through easy-to-use measurements an important issue in current clinical practice. A common way to evaluate posture is through the recording of the center-of-pressure (CoP) displacement (statokinesigram) with force platforms. Most of the previous studies, assuming homogeneous statokinesigrams in quiet standing, used global parameters in order to characterize the statokinesigrams. However the latter analysis provides little information about local characteristics of statokinesigrams. In this study, we propose a multidimensional scoring approach which locally characterizes statokinesigrams on small time-periods, or blocks, while highlighting those which are more indicative to the general individual's class (faller/non-faller). Moreover, this information can be used to provide a global score in order to evaluate the postural control and classify fallers/non-fallers. We evaluate our approach using the statokinesigram of 126 community-dwelling elderly (78.5 ± 7.7 years). Participants were recorded with eyes open and eyes closed (25 seconds each acquisition) and information about previous falls was collected. The performance of our findings are assessed using the receiver operating characteristics (ROC) analysis and the area under the curve (AUC). The results show that global scores provided by splitting statokinesigrams in smaller blocks and analyzing them locally, classify fallers/non-fallers more effectively (AUC = 0.77 ± 0.09 instead of AUC = 0.63 ± 0.12 for global analysis when splitting is not used). These promising results indicate that such methodology might provide supplementary information about the risk of fall of an individual and be of major usefulness in assessment of balance-related diseases such as Parkinson's disease.

On the importance of local dynamics in statokinesigram: A multivariate approach for postural control evaluation in elderly

PubMed Central

Audiffren, Julien; Vayatis, Nicolas; Vidal, Pierre-Paul; Buffat, Stephane; Yelnik, Alain P.; Ricard, Damien

2018-01-01

The fact that almost one third of population >65 years-old has at least one fall per year, makes the risk-of-fall assessment through easy-to-use measurements an important issue in current clinical practice. A common way to evaluate posture is through the recording of the center-of-pressure (CoP) displacement (statokinesigram) with force platforms. Most of the previous studies, assuming homogeneous statokinesigrams in quiet standing, used global parameters in order to characterize the statokinesigrams. However the latter analysis provides little information about local characteristics of statokinesigrams. In this study, we propose a multidimensional scoring approach which locally characterizes statokinesigrams on small time-periods, or blocks, while highlighting those which are more indicative to the general individual’s class (faller/non-faller). Moreover, this information can be used to provide a global score in order to evaluate the postural control and classify fallers/non-fallers. We evaluate our approach using the statokinesigram of 126 community-dwelling elderly (78.5 ± 7.7 years). Participants were recorded with eyes open and eyes closed (25 seconds each acquisition) and information about previous falls was collected. The performance of our findings are assessed using the receiver operating characteristics (ROC) analysis and the area under the curve (AUC). The results show that global scores provided by splitting statokinesigrams in smaller blocks and analyzing them locally, classify fallers/non-fallers more effectively (AUC = 0.77 ± 0.09 instead of AUC = 0.63 ± 0.12 for global analysis when splitting is not used). These promising results indicate that such methodology might provide supplementary information about the risk of fall of an individual and be of major usefulness in assessment of balance-related diseases such as Parkinson’s disease. PMID:29474402

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

NASA Astrophysics Data System (ADS)

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

2018-06-01

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

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

PubMed

Dinç, Erdal; Ozdemir, Abdil

2005-01-01

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

Hand grip strength and associated factors in non-institutionalised men and women 50 years and older in South Africa.

PubMed

Ramlagan, Shandir; Peltzer, Karl; Phaswana-Mafuya, Nancy

2014-01-07

Little is known about the prevalence, predictors and gender differences in hand grip strength of older adults in Africa. This study aims to investigate social and health differences in hand grip strength among older adults in a national probability sample of older South Africans who participated in the Study of Global Ageing and Adults Health (SAGE wave 1) in 2008. We conducted a national population-based cross-sectional study with a sample of 3840 men and women aged 50 years or older in South Africa. The questionnaire included socio-demographic characteristics, health variables, and anthropometric measurements. Linear multivariate regression analysis was performed to assess the association of social factors, health variables and grip strength. The mean overall hand grip strength was 37.9 kgs for men (mean age 61.1 years, SD = 9.1) and 31.5 kgs for women (mean age 62.0 years, SD = 9.7). In multivariate analysis among men, greater height, not being underweight and lower functional disability was associated with greater grip strength, and among women, greater height, better cognitive functioning, and lower functional disability were associated with greater grip strength. Greater height and lower functional disability were found for both older South African men and women to be significantly associated with grip strength.

Noninvasive assessment of the risk of tobacco abuse in oral mucosa using fluorescence spectroscopy: a clinical approach

NASA Astrophysics Data System (ADS)

Nazeer, Shaiju S.; Asish, Rajashekharan; Venugopal, Chandrashekharan; Anita, Balan; Gupta, Arun Kumar; Jayasree, Ramapurath S.

2014-05-01

Tobacco abuse and alcoholism cause cancer, emphysema, and heart disease, which contribute to high death rates, globally. Society pays a significant cost for these habits whose first demonstration in many cases is in the oral cavity. Oral cavity disorders are highly curable if a screening procedure is available to diagnose them in the earliest stages. The aim of the study is to identify the severity of tobacco abuse, in oral cavity, as reflected by the emission from endogenous fluorophores and the chromophore hemoglobin. A group who had no tobacco habits and another with a history of tobacco abuse were included in this study. To compare the results with a pathological condition, a group of leukoplakia patients were also included. Emission from porphyrin and the spectral filtering modulation effect of hemoglobin were collected from different sites. Multivariate analysis strengthened the spectral features with a sensitivity of 60% to 100% and a specificity of 76% to 100% for the discrimination. Total hemoglobin and porphyrin levels of habitués and leukoplakia groups were comparable, indicating the alarming situation about the risk of tobacco abuse. Results prove that fluorescence spectroscopy along with multivariate analysis is an effective noninvasive tool for the early diagnosis of pathological changes due to tobacco abuse.

Investigation on the antidepressant effect of sea buckthorn seed oil through the GC-MS-based metabolomics approach coupled with multivariate analysis.

PubMed

Tian, Jun-sheng; Liu, Cai-chun; Xiang, Huan; Zheng, Xiao-fen; Peng, Guo-jiang; Zhang, Xiang; Du, Guan-hua; Qin, Xue-mei

2015-11-01

Depression is one of the prevalent and serious mental disorders and the number of depressed patients has been on the rise globally during the recent decades. Sea buckthorn seed oil from traditional Chinese medicine (TCM) is edible and has been widely used for treatment of different diseases for a long time. However, there are few published reports on the antidepressant effect of sea buckthorn seed oil. With the objective of finding potential biomarkers of the therapeutic response of sea buckthorn seed oil in chronic unpredictable mild stress (CUMS) rats, urine metabolomics based on gas chromatography-mass spectrometry (GC-MS) coupled with multivariate analysis was applied. In this study, we discovered a higher level of pimelic acid as well as palmitic acid and a lower level of suberic acid, citrate, phthalic acid, cinnamic acid and Sumiki's acid in urine of rats exposed to CUMS procedures after sea buckthorn seed oil was administered. These changes of metabolites are involved in energy metabolism, fatty acid metabolism and other metabolic pathways as well as in the synthesis of neurotransmitters and it is helpful to facilitate the efficacy evaluation and mechanism elucidating the effect of sea buckthorn seed oil for depression management.

Safety and effectiveness of olanzapine in monotherapy: a multivariate analysis of a naturalistic study.

PubMed

Ciudad, Antonio; Gutiérrez, Miguel; Cañas, Fernando; Gibert, Juan; Gascón, Josep; Carrasco, José-Luis; Bobes, Julio; Gómez, Juan-Carlos; Alvarez, Enrique

2005-07-01

This study investigated safety and effectiveness of olanzapine in monotherapy compared with conventional antipsychotics in treatment of acute inpatients with schizophrenia. This was a prospective, comparative, nonrandomized, open-label, multisite, observational study of Spanish inpatients with an acute episode of schizophrenia. Data included safety assessments with an extrapyramidal symptoms (EPS) questionnaire and the report of spontaneous adverse events, plus clinical assessments with the Brief Psychiatric Rating Scale (BPRS) and the Clinical Global Impressions-Severity of Illness (CGI-S). A multivariate methodology was used to more adequately determine which factors can influence safety and effectiveness of olanzapine in monotherapy. 339 patients treated with olanzapine in monotherapy (OGm) and 385 patients treated with conventional antipsychotics (CG) were included in the analysis. Treatment-emergent EPS were significantly higher in the CG (p<0.0001). Response rate was significantly higher in the OGm (p=0.005). Logistic regression analyses revealed that the only variable significantly correlated with treatment-emergent EPS and clinical response was treatment strategy, with patients in OGm having 1.5 times the probability of obtaining a clinical response and patients in CG having 5 times the risk of developing EPS. In this naturalistic study olanzapine in monotherapy was better-tolerated and at least as effective as conventional antipsychotics.

Seroepidemiology of HBV infection in South-East of iran; a population based study.

PubMed

Salehi, M; Alavian, S M; Tabatabaei, S V; Izadi, Sh; Sanei Moghaddam, E; Amini Kafi-Abad, S; Gharehbaghian, A; Khosravi, S; Abolghasemi, H

2012-05-01

Hepatitis B virus (HBV) infection is a major risk factor of cirrhosis and hepatocellular carcinoma affecting billions of people globally. Since information on its prevalence in general population is mandatory for formulating effective policies, this population based serological survey was conducted in Sistan and Baluchistan, where no previous epidemiological data were available. Using random cluster sampling 3989 healthy subjects were selected from 9 districts of Sistan and Baluchistan Province in southeastern Iran. The subjects' age ranged from 6 to 65 years old. Serum samples were tested for HBcAb, HBsAg. Screening tests were carried out by the third generation of ELISA. Various risk factors were recorded and multivariate analysis was performed. The prevalence of HBsAg and HBcAb in Sistan and Baluchistan was 3.38% (95% CI 2.85; 3.98) and 23.58% (95% CI 22.29; 24.93) respectively. We found 8 cases of positive anti-HDV antibody. Predictors of HBsAg or HBcAb in multivariate analysis were age, marital status and addiction. The rate of HBV infection in Sistan and Baluchistan was higher than other parts of Iran. Approximately 25% of general population in this province had previous exposure to HBV and 3% were HBsAg carriers. Intrafamilial and addiction were major routes of HBV transmission in this province.

Fetal growth restriction and maternal smoking in the Macedonian Roma population: a causality dilemma.

PubMed

Walfisch, Asnat; Nikolovski, Sotir; Talevska, Bilijana; Hallak, Mordechai

2013-06-01

Macedonia is one of the top five countries globally in reported smoking rates. Over 10 % of the population consists of the underprivileged Roma minority. We aimed to determine whether Roma ethnicity is an independent risk factor for adverse pregnancy outcome or merely mediating maternal smoking. Maternal data were retrieved from the perinatal computerized database for all deliveries during 2007-2011 at the only Clinical Hospital in Bitola, Macedonia. Multivariable regression models were constructed to control for confounders. Of nearly 7,000 deliveries, 8.65 % were of maternal Roma ethnicity and 40 % of the Romani women admitted to regularly smoke during pregnancy. Both Roma ethnicity and maternal smoking were significantly associated with the absence of maternal education, history of abortions and intra uterine growth restriction (IUGR) in the univariate analysis. Both maternal Roma ethnicity (OR 2.46, 95 % CI 1.79-3.38) and smoking status (OR 1.37, 95 % CI 1.02-1.85) were found to be independent predictors of IUGR using the multivariate analysis. Lower birthweight and smaller head circumference were both independently associated with Roma ethnicity and smoking. Underprivileged ethnic background is a significant risk factor for IUGR, independent of maternal smoking status. To the best of our knowledge, this is the first publication focusing on pregnancy outcome in Romani Macedonian parturients.

Untargeted Identification of Wood Type-Specific Markers in Particulate Matter from Wood Combustion.

PubMed

Weggler, Benedikt A; Ly-Verdu, Saray; Jennerwein, Maximilian; Sippula, Olli; Reda, Ahmed A; Orasche, Jürgen; Gröger, Thomas; Jokiniemi, Jorma; Zimmermann, Ralf

2016-09-20

Residential wood combustion emissions are one of the major global sources of particulate and gaseous organic pollutants. However, the detailed chemical compositions of these emissions are poorly characterized due to their highly complex molecular compositions, nonideal combustion conditions, and sample preparation steps. In this study, the particulate organic emissions from a masonry heater using three types of wood logs, namely, beech, birch, and spruce, were chemically characterized using thermal desorption in situ derivatization coupled to a GCxGC-ToF/MS system. Untargeted data analyses were performed using the comprehensive measurements. Univariate and multivariate chemometric tools, such as analysis of variance (ANOVA), principal component analysis (PCA), and ANOVA simultaneous component analysis (ASCA), were used to reduce the data to highly significant and wood type-specific features. This study reveals substances not previously considered in the literature as meaningful markers for differentiation among wood types.

Multivariate analysis: A statistical approach for computations

NASA Astrophysics Data System (ADS)

Michu, Sachin; Kaushik, Vandana

2014-10-01

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

Tracking brain states under general anesthesia by using global coherence analysis.

PubMed

Cimenser, Aylin; Purdon, Patrick L; Pierce, Eric T; Walsh, John L; Salazar-Gomez, Andres F; Harrell, Priscilla G; Tavares-Stoeckel, Casie; Habeeb, Kathleen; Brown, Emery N

2011-05-24

Time and frequency domain analyses of scalp EEG recordings are widely used to track changes in brain states under general anesthesia. Although these analyses have suggested that different spatial patterns are associated with changes in the state of general anesthesia, the extent to which these patterns are spatially coordinated has not been systematically characterized. Global coherence, the ratio of the largest eigenvalue to the sum of the eigenvalues of the cross-spectral matrix at a given frequency and time, has been used to analyze the spatiotemporal dynamics of multivariate time-series. Using 64-lead EEG recorded from human subjects receiving computer-controlled infusions of the anesthetic propofol, we used surface Laplacian referencing combined with spectral and global coherence analyses to track the spatiotemporal dynamics of the brain's anesthetic state. During unconsciousness the spectrograms in the frontal leads showed increasing α (8-12 Hz) and δ power (0-4 Hz) and in the occipital leads δ power greater than α power. The global coherence detected strong coordinated α activity in the occipital leads in the awake state that shifted to the frontal leads during unconsciousness. It revealed a lack of coordinated δ activity during both the awake and unconscious states. Although strong frontal power during general anesthesia-induced unconsciousness--termed anteriorization--is well known, its possible association with strong α range global coherence suggests highly coordinated spatial activity. Our findings suggest that combined spectral and global coherence analyses may offer a new approach to tracking brain states under general anesthesia.

Multivariate Cluster Analysis.

ERIC Educational Resources Information Center

McRae, Douglas J.

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

A dynamic factor model of the evaluation of the financial crisis in Turkey.

PubMed

Sezgin, F; Kinay, B

2010-01-01

Factor analysis has been widely used in economics and finance in situations where a relatively large number of variables are believed to be driven by few common causes of variation. Dynamic factor analysis (DFA) which is a combination of factor and time series analysis, involves autocorrelation matrices calculated from multivariate time series. Dynamic factor models were traditionally used to construct economic indicators, macroeconomic analysis, business cycles and forecasting. In recent years, dynamic factor models have become more popular in empirical macroeconomics. They have more advantages than other methods in various respects. Factor models can for instance cope with many variables without running into scarce degrees of freedom problems often faced in regression-based analysis. In this study, a model which determines the effect of the global crisis on Turkey is proposed. The main aim of the paper is to analyze how several macroeconomic quantities show an alteration before the evolution of the crisis and to decide if a crisis can be forecasted or not.

Multivariate Statistical Inference of Lightning Occurrence, and Using Lightning Observations

NASA Technical Reports Server (NTRS)

Boccippio, Dennis

2004-01-01

Two classes of multivariate statistical inference using TRMM Lightning Imaging Sensor, Precipitation Radar, and Microwave Imager observation are studied, using nonlinear classification neural networks as inferential tools. The very large and globally representative data sample provided by TRMM allows both training and validation (without overfitting) of neural networks with many degrees of freedom. In the first study, the flashing / or flashing condition of storm complexes is diagnosed using radar, passive microwave and/or environmental observations as neural network inputs. The diagnostic skill of these simple lightning/no-lightning classifiers can be quite high, over land (above 80% Probability of Detection; below 20% False Alarm Rate). In the second, passive microwave and lightning observations are used to diagnose radar reflectivity vertical structure. A priori diagnosis of hydrometeor vertical structure is highly important for improved rainfall retrieval from either orbital radars (e.g., the future Global Precipitation Mission "mothership") or radiometers (e.g., operational SSM/I and future Global Precipitation Mission passive microwave constellation platforms), we explore the incremental benefit to such diagnosis provided by lightning observations.

Relationship between Oral Malodor and the Global Composition of Indigenous Bacterial Populations in Saliva ▿

PubMed Central

Takeshita, Toru; Suzuki, Nao; Nakano, Yoshio; Shimazaki, Yoshihiro; Yoneda, Masahiro; Hirofuji, Takao; Yamashita, Yoshihisa

2010-01-01

Oral malodor develops mostly from the metabolic activities of indigenous bacterial populations within the oral cavity, but whether healthy or oral malodor-related patterns of the global bacterial composition exist remains unclear. In this study, the bacterial compositions in the saliva of 240 subjects complaining of oral malodor were divided into groups based on terminal-restriction fragment length polymorphism (T-RFLP) profiles using hierarchical cluster analysis, and the patterns of the microbial community composition of those exhibiting higher and lower malodor were explored. Four types of bacterial community compositions were detected (clusters I, II, III, and IV). Two parameters for measuring oral malodor intensity (the concentration of volatile sulfur compounds in mouth air and the organoleptic score) were noticeably lower in cluster I than in the other clusters. Using multivariate analysis, the differences in the levels of oral malodor were significant after adjustment for potential confounding factors such as total bacterial count, mean periodontal pocket depth, and tongue coating score (P < 0.001). Among the four clusters with different proportions of indigenous members, the T-RFLP profiles of cluster I were implicated as the bacterial populations with higher proportions of Streptococcus, Granulicatella, Rothia, and Treponema species than those of the other clusters. These results clearly correlate the global composition of indigenous bacterial populations with the severity of oral malodor. PMID:20228112

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

PubMed

Bonetti, Jennifer; Quarino, Lawrence

2014-05-01

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

Alternatives for Jet Engine Control

NASA Technical Reports Server (NTRS)

Leake, R. J.; Sain, M. K.

1976-01-01

Approaches are developed as alternatives to current design methods which rely heavily on linear quadratic and Riccati equation methods. The main alternatives are discussed in two broad categories, local multivariable frequency domain methods and global nonlinear optimal methods.

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

PubMed Central

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

2012-01-01

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

Analyzing Multiple Outcomes in Clinical Research Using Multivariate Multilevel Models

PubMed Central

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

2014-01-01

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

Current Literature Review of Registered Nurses' Competency in the Global Community.

PubMed

Liu, Ying; Aungsuroch, Yupin

2018-03-01

In order to enhance international standards of nursing service, this article aims to analyze the English full-text peer-reviewed published articles from the past 10 years that describe contemporary registered nurses' (RNs') competency in the global community. An integrative review of literature was conducted between June 2016 and January 2017. A systematic search was completed using four databases (Science Direct, Scopus, Web of Science, and the Cumulative Index to Nursing and Allied Health Literature) that covered the years between 2007 and 2017, and used the key words nurs * OR (staff nurs * ) OR (register nurs * ) AND competenc * AND international OR global. Ultimately, 32 studies meeting inclusion and exclusion criteria were selected for analysis. Nursing competency trended towards definitions using a holistic lens and behavior statements reflecting the skills, knowledge, attitudes, and judgment required for effective performance in the nursing profession. By using inductive content analysis, 11 components emerged. Additionally, six instruments were found to measure generalist RNs' competencies across countries. The variables related to generalist nursing competency included sociodemographic variables, professional-related variables, and work environment variables. This review provides the research evidence for updating definitions, components, measurements, and variables related to RNs' competency in the global community. Further research should consider cross-cultural validation of instruments and influencing factors related to nursing competency. The components and measurements identified in this review can be used by nursing administrators to select or evaluate qualified nurses. The multivariables related to nursing competency can assistant hospital administrators to recognize and find effective ways to improve nursing competency. © 2018 Sigma Theta Tau International.

Fish Intake, Genetic Predisposition to Alzheimer Disease, and Decline in Global Cognition and Memory in 5 Cohorts of Older Persons.

PubMed

Samieri, Cécilia; Morris, Martha-Clare; Bennett, David A; Berr, Claudine; Amouyel, Philippe; Dartigues, Jean-François; Tzourio, Christophe; Chasman, Daniel I; Grodstein, Francine

2018-05-01

Fish are a primary source of long-chain omega-3 fatty acids, which may help delay cognitive aging. We pooled participants from the French Three-City study and 4 US cohorts (Nurses' Health Study, Women's Health Study, Chicago Health and Aging Project, and Rush Memory and Aging Project) for whom diet and cognitive data were available (n = 23,688 white persons, aged ≥65 years, 88% female, baseline year range of 1992-1999, and median follow-up range of 3.9-9.1 years) to investigate the relationship of fish intake to cognitive decline and examine interactions with genes related to Alzheimer disease. We estimated cohort-specific associations between fish and change in composite scores of global cognition and episodic memory using linear mixed models, and we pooled results using inverse-variance weighted meta-analysis. In multivariate analyses, higher fish intake was associated with slower decline in both global cognition and memory (P for trend ≤ 0.031). Consuming ≥4 servings/week versus <1 serving/week of fish was associated with a lower rate of memory decline: 0.018 (95% confidence interval: 0.004, 0.032) standard units, an effect estimate equivalent to that found for 4 years of age. For global cognition, no comparisons of higher versus low fish intake reached statistical significance. In this meta-analysis, higher fish intake was associated with a lower rate of memory decline. We found no evidence of effect modification by genes associated with Alzheimer disease.

Multivariate Feature Selection of Image Descriptors Data for Breast Cancer with Computer-Assisted Diagnosis

PubMed Central

Galván-Tejada, Carlos E.; Zanella-Calzada, Laura A.; Galván-Tejada, Jorge I.; Celaya-Padilla, José M.; Gamboa-Rosales, Hamurabi; Garza-Veloz, Idalia; Martinez-Fierro, Margarita L.

2017-01-01

Breast cancer is an important global health problem, and the most common type of cancer among women. Late diagnosis significantly decreases the survival rate of the patient; however, using mammography for early detection has been demonstrated to be a very important tool increasing the survival rate. The purpose of this paper is to obtain a multivariate model to classify benign and malignant tumor lesions using a computer-assisted diagnosis with a genetic algorithm in training and test datasets from mammography image features. A multivariate search was conducted to obtain predictive models with different approaches, in order to compare and validate results. The multivariate models were constructed using: Random Forest, Nearest centroid, and K-Nearest Neighbor (K-NN) strategies as cost function in a genetic algorithm applied to the features in the BCDR public databases. Results suggest that the two texture descriptor features obtained in the multivariate model have a similar or better prediction capability to classify the data outcome compared with the multivariate model composed of all the features, according to their fitness value. This model can help to reduce the workload of radiologists and present a second opinion in the classification of tumor lesions. PMID:28216571

Multivariate Feature Selection of Image Descriptors Data for Breast Cancer with Computer-Assisted Diagnosis.

PubMed

Galván-Tejada, Carlos E; Zanella-Calzada, Laura A; Galván-Tejada, Jorge I; Celaya-Padilla, José M; Gamboa-Rosales, Hamurabi; Garza-Veloz, Idalia; Martinez-Fierro, Margarita L

2017-02-14

Breast cancer is an important global health problem, and the most common type of cancer among women. Late diagnosis significantly decreases the survival rate of the patient; however, using mammography for early detection has been demonstrated to be a very important tool increasing the survival rate. The purpose of this paper is to obtain a multivariate model to classify benign and malignant tumor lesions using a computer-assisted diagnosis with a genetic algorithm in training and test datasets from mammography image features. A multivariate search was conducted to obtain predictive models with different approaches, in order to compare and validate results. The multivariate models were constructed using: Random Forest, Nearest centroid, and K-Nearest Neighbor (K-NN) strategies as cost function in a genetic algorithm applied to the features in the BCDR public databases. Results suggest that the two texture descriptor features obtained in the multivariate model have a similar or better prediction capability to classify the data outcome compared with the multivariate model composed of all the features, according to their fitness value. This model can help to reduce the workload of radiologists and present a second opinion in the classification of tumor lesions.

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

NASA Technical Reports Server (NTRS)

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

1980-01-01

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

Methods for presentation and display of multivariate data

NASA Technical Reports Server (NTRS)

Myers, R. H.

1981-01-01

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

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

ERIC Educational Resources Information Center

Warne, Russell T.

2014-01-01

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

ATR-FTIR membrane-based sensor for the simultaneous determination of surfactant and oil total indices in industrial degreasing baths.

PubMed

Lucena, Rafael; Cárdenas, Soledad; Gallego, Mercedes; Valcárcel, Miguel

2006-03-01

Monitoring the exhaustion of alkaline degreasing baths is one of the main aspects in metal mechanizing industrial process control. The global level of surfactant, and mainly grease, can be used as ageing indicators. In this paper, an attenuated total reflection-Fourier transform infrared (ATR-FTIR) membrane-based sensor is presented for the determination of these parameters. The system is based on a micro-liquid-liquid extraction of the analytes through a polymeric membrane from the aqueous to the organic solvent layer which is in close contact with the internal reflection element and continuously monitored. Samples are automatically processed using a simple, robust sequential injection analysis (SIA) configuration, on-line coupled to the instrument. The global signal obtained for both families of compounds are processed via a multivariate calibration technique (partial least squares, PLS). Excellent correlation was obtained for the values given by the proposed method compared to those of the gravimetric reference one with very low error values for both calibration and validation.

Effect of non-stationary climate on infectious gastroenteritis transmission in Japan.

PubMed

Onozuka, Daisuke

2014-06-03

Local weather factors are widely considered to influence the transmission of infectious gastroenteritis. Few studies, however, have examined the non-stationary relationships between global climatic factors and transmission of infectious gastroenteritis. We analyzed monthly data for cases of infectious gastroenteritis in Fukuoka, Japan from 2000 to 2012 using cross-wavelet coherency analysis to assess the pattern of associations between indices for the Indian Ocean Dipole (IOD) and El Niño Southern Oscillation (ENSO). Infectious gastroenteritis cases were non-stationary and significantly associated with the IOD and ENSO (Multivariate ENSO Index [MEI], Niño 1 + 2, Niño 3, Niño 4, and Niño 3.4) for a period of approximately 1 to 2 years. This association was non-stationary and appeared to have a major influence on the synchrony of infectious gastroenteritis transmission. Our results suggest that non-stationary patterns of association between global climate factors and incidence of infectious gastroenteritis should be considered when developing early warning systems for epidemics of infectious gastroenteritis.

Relationship of soft drink consumption to global overweight, obesity, and diabetes: a cross-national analysis of 75 countries.

PubMed

Basu, Sanjay; McKee, Martin; Galea, Gauden; Stuckler, David

2013-11-01

We estimated the relationship between soft drink consumption and obesity and diabetes worldwide. We used multivariate linear regression to estimate the association between soft drink consumption and overweight, obesity, and diabetes prevalence in 75 countries, controlling for other foods (cereals, meats, fruits and vegetables, oils, and total calories), income, urbanization, and aging. Data were obtained from the Euromonitor Global Market Information Database, the World Health Organization, and the International Diabetes Federation. Bottled water consumption, which increased with per-capita income in parallel to soft drink consumption, served as a natural control group. Soft drink consumption increased globally from 9.5 gallons per person per year in 1997 to 11.4 gallons in 2010. A 1% rise in soft drink consumption was associated with an additional 4.8 overweight adults per 100 (adjusted B; 95% confidence interval [CI] = 3.1, 6.5), 2.3 obese adults per 100 (95% CI = 1.1, 3.5), and 0.3 adults with diabetes per 100 (95% CI = 0.1, 0.8). These findings remained robust in low- and middle-income countries. Soft drink consumption is significantly linked to overweight, obesity, and diabetes worldwide, including in low- and middle-income countries.

Multivariate Analysis and Machine Learning in Cerebral Palsy Research

PubMed Central

Zhang, Jing

2017-01-01

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

Multivariate Analysis and Machine Learning in Cerebral Palsy Research.

PubMed

Zhang, Jing

2017-01-01

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

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

PubMed

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

2016-06-24

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

Quality by design case study: an integrated multivariate approach to drug product and process development.

PubMed

Huang, Jun; Kaul, Goldi; Cai, Chunsheng; Chatlapalli, Ramarao; Hernandez-Abad, Pedro; Ghosh, Krishnendu; Nagi, Arwinder

2009-12-01

To facilitate an in-depth process understanding, and offer opportunities for developing control strategies to ensure product quality, a combination of experimental design, optimization and multivariate techniques was integrated into the process development of a drug product. A process DOE was used to evaluate effects of the design factors on manufacturability and final product CQAs, and establish design space to ensure desired CQAs. Two types of analyses were performed to extract maximal information, DOE effect & response surface analysis and multivariate analysis (PCA and PLS). The DOE effect analysis was used to evaluate the interactions and effects of three design factors (water amount, wet massing time and lubrication time), on response variables (blend flow, compressibility and tablet dissolution). The design space was established by the combined use of DOE, optimization and multivariate analysis to ensure desired CQAs. Multivariate analysis of all variables from the DOE batches was conducted to study relationships between the variables and to evaluate the impact of material attributes/process parameters on manufacturability and final product CQAs. The integrated multivariate approach exemplifies application of QbD principles and tools to drug product and process development.

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

ERIC Educational Resources Information Center

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

2009-01-01

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

Dangers in Using Analysis of Covariance Procedures.

ERIC Educational Resources Information Center

Campbell, Kathleen T.

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

Predictors of mistimed, and unwanted pregnancies among women of childbearing age in Rufiji, Kilombero, and Ulanga districts of Tanzania.

PubMed

Exavery, Amon; Kanté, Almamy Malick; Njozi, Mustafa; Tani, Kassimu; Doctor, Henry V; Hingora, Ahmed; Phillips, James F

2014-08-08

While unintended pregnancies pose a serious threat to the health and well-being of families globally, characteristics of Tanzanian women who conceive unintentionally are rarely documented. This analysis identifies factors associated with unintended pregnancies-both mistimed and unwanted-in three rural districts of Tanzania. A cross-sectional survey of 2,183 random households was conducted in three Tanzanian districts of Rufiji, Kilombero, and Ulanga in 2011 to assess women's health behavior and service utilization patterns. These households produced 3,127 women age 15+ years from which 2,199 gravid women aged 15-49 were selected for the current analysis. Unintended pregnancies were identified as either mistimed (wanted later) or unwanted (not wanted at all). Correlates of mistimed, and unwanted pregnancies were identified through Chi-squared tests to assess associations and multinomial logistic regression for multivariate analysis. Mean age of the participants was 32.1 years. While 54.1% of the participants reported that their most recent pregnancy was intended, 32.5% indicated their most recent pregnancy as mistimed and 13.4% as unwanted. Multivariate analysis revealed that young age (<20 years), and single marital status were significant predictors of both mistimed and unwanted pregnancies. Lack of inter-partner communication about family planning increased the risk of mistimed pregnancy significantly, and multi-gravidity was shown to significantly increase the risk of unwanted pregnancy. About one half of women in Rufiji, Kilombero, and Ulanga districts of Tanzania conceive unintentionally. Women, especially the most vulnerable should be empowered to avoid pregnancy at their own will and discretion.

A Polyhedral Outer-approximation, Dynamic-discretization optimization solver, 1.x

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

Bent, Rusell; Nagarajan, Harsha; Sundar, Kaarthik

2017-09-25

In this software, we implement an adaptive, multivariate partitioning algorithm for solving mixed-integer nonlinear programs (MINLP) to global optimality. The algorithm combines ideas that exploit the structure of convex relaxations to MINLPs and bound tightening procedures

Use of multivariate measures of disability in health surveys.

PubMed Central

Charlton, J R; Patrick, D L; Peach, H

1983-01-01

It has been claimed that the aggregation of information from several areas of life into a small set of global measures has certain advantages for describing disability. Global measures of disability were constructed from a modified version of an existing health survey instrument and the sickness impact profile (SIP) and their properties were tested. The disability items grouped satisfactorily into five global measures (physical, psychosocial, eating, communication, and work). All disability measures (global and original category scores) were poor predictors of service use by individuals but were related as expected to age and number of medical conditions. The global measures generally had lower standard errors and better repeatability. All scores exhibit J-shaped distributions for cross sectional data but the change in global measures over time was consistent with the normal distribution. Preferably, both global and category measures should be used for comparing changes over time between groups of individuals. PMID:6655420

Surfing wave climate variability

NASA Astrophysics Data System (ADS)

Espejo, Antonio; Losada, Iñigo J.; Méndez, Fernando J.

2014-10-01

International surfing destinations are highly dependent on specific combinations of wind-wave formation, thermal conditions and local bathymetry. Surf quality depends on a vast number of geophysical variables, and analyses of surf quality require the consideration of the seasonal, interannual and long-term variability of surf conditions on a global scale. A multivariable standardized index based on expert judgment is proposed for this purpose. This index makes it possible to analyze surf conditions objectively over a global domain. A summary of global surf resources based on a new index integrating existing wave, wind, tides and sea surface temperature databases is presented. According to general atmospheric circulation and swell propagation patterns, results show that west-facing low to middle-latitude coasts are more suitable for surfing, especially those in the Southern Hemisphere. Month-to-month analysis reveals strong seasonal variations in the occurrence of surfable events, enhancing the frequency of such events in the North Atlantic and the North Pacific. Interannual variability was investigated by comparing occurrence values with global and regional modes of low-frequency climate variability such as El Niño and the North Atlantic Oscillation, revealing their strong influence at both the global and the regional scale. Results of the long-term trends demonstrate an increase in the probability of surfable events on west-facing coasts around the world in recent years. The resulting maps provide useful information for surfers, the surf tourism industry and surf-related coastal planners and stakeholders.

Tracking brain states under general anesthesia by using global coherence analysis

PubMed Central

Cimenser, Aylin; Purdon, Patrick L.; Pierce, Eric T.; Walsh, John L.; Salazar-Gomez, Andres F.; Harrell, Priscilla G.; Tavares-Stoeckel, Casie; Habeeb, Kathleen; Brown, Emery N.

2011-01-01

Time and frequency domain analyses of scalp EEG recordings are widely used to track changes in brain states under general anesthesia. Although these analyses have suggested that different spatial patterns are associated with changes in the state of general anesthesia, the extent to which these patterns are spatially coordinated has not been systematically characterized. Global coherence, the ratio of the largest eigenvalue to the sum of the eigenvalues of the cross-spectral matrix at a given frequency and time, has been used to analyze the spatiotemporal dynamics of multivariate time-series. Using 64-lead EEG recorded from human subjects receiving computer-controlled infusions of the anesthetic propofol, we used surface Laplacian referencing combined with spectral and global coherence analyses to track the spatiotemporal dynamics of the brain's anesthetic state. During unconsciousness the spectrograms in the frontal leads showed increasing α (8–12 Hz) and δ power (0–4 Hz) and in the occipital leads δ power greater than α power. The global coherence detected strong coordinated α activity in the occipital leads in the awake state that shifted to the frontal leads during unconsciousness. It revealed a lack of coordinated δ activity during both the awake and unconscious states. Although strong frontal power during general anesthesia-induced unconsciousness—termed anteriorization—is well known, its possible association with strong α range global coherence suggests highly coordinated spatial activity. Our findings suggest that combined spectral and global coherence analyses may offer a new approach to tracking brain states under general anesthesia. PMID:21555565

Regional vertical total electron content (VTEC) modeling together with satellite and receiver differential code biases (DCBs) using semi-parametric multivariate adaptive regression B-splines (SP-BMARS)

NASA Astrophysics Data System (ADS)

Durmaz, Murat; Karslioglu, Mahmut Onur

2015-04-01

There are various global and regional methods that have been proposed for the modeling of ionospheric vertical total electron content (VTEC). Global distribution of VTEC is usually modeled by spherical harmonic expansions, while tensor products of compactly supported univariate B-splines can be used for regional modeling. In these empirical parametric models, the coefficients of the basis functions as well as differential code biases (DCBs) of satellites and receivers can be treated as unknown parameters which can be estimated from geometry-free linear combinations of global positioning system observables. In this work we propose a new semi-parametric multivariate adaptive regression B-splines (SP-BMARS) method for the regional modeling of VTEC together with satellite and receiver DCBs, where the parametric part of the model is related to the DCBs as fixed parameters and the non-parametric part adaptively models the spatio-temporal distribution of VTEC. The latter is based on multivariate adaptive regression B-splines which is a non-parametric modeling technique making use of compactly supported B-spline basis functions that are generated from the observations automatically. This algorithm takes advantage of an adaptive scale-by-scale model building strategy that searches for best-fitting B-splines to the data at each scale. The VTEC maps generated from the proposed method are compared numerically and visually with the global ionosphere maps (GIMs) which are provided by the Center for Orbit Determination in Europe (CODE). The VTEC values from SP-BMARS and CODE GIMs are also compared with VTEC values obtained through calibration using local ionospheric model. The estimated satellite and receiver DCBs from the SP-BMARS model are compared with the CODE distributed DCBs. The results show that the SP-BMARS algorithm can be used to estimate satellite and receiver DCBs while adaptively and flexibly modeling the daily regional VTEC.

Eat, sleep, work, play: associations of weight status and health-related behaviors among young adult college students.

PubMed

Quick, Virginia; Byrd-Bredbenner, Carol; White, Adrienne A; Brown, Onikia; Colby, Sarah; Shoff, Suzanne; Lohse, Barbara; Horacek, Tanya; Kidd, Tanda; Greene, Geoffrey

2014-01-01

To examine relationships of sleep, eating, and exercise behaviors; work time pressures; and sociodemographic characteristics by weight status (healthy weight [body mass index or BMI < 25] vs. overweight [BMI ≥ 25]) of young adults. Cross-sectional. Nine U.S. universities. Enrolled college students (N = 1252; 18-24 years; 80% white; 59% female). Survey included the Pittsburgh Sleep Quality Index (PSQI), Three-Factor Eating Questionnaire (TFEQ), Satter Eating Competence Inventory (ecSI), National Cancer Institute Fruit/Vegetable Screener, International Physical Activity Questionnaire, Work Time Pressure items, and sociodemographic characteristics. Chi-square and t-tests determined significant bivariate associations of sociodemographics, sleep behaviors, eating behaviors, physical activity behavior, and work time pressures with weight status (i.e., healthy vs. overweight/obese). Statistically significant bivariate associations with weight status were then entered into a multivariate logistic regression model that estimated associations with being overweight/obese. Sex (female), race (nonwhite), older age, higher Global PSQI score, lower ecSI total score, and higher TFEQ Emotional Eating Scale score were significantly (p < .05) associated with overweight/obesity in bivariate analyses. Multivariate logistic regression analysis showed that sex (female; odds ratio [OR] = 2.05, confidence interval [CI] = 1.54-2.74), older age (OR = 1.35, CI = 1.21-1.50), higher Global PSQI score (OR = 1.07, CI = 1.01-1.13), and lower ecSI score (OR = .96, CI = .94-.98), were significantly (p < .05) associated with overweight/obesity. Findings suggest that obesity prevention interventions for college students should include an education component to emphasize the importance of overall sleep quality and improving eating competence.

Development and Validation of a Novel Scoring System for Predicting Technical Success of Chronic Total Occlusion Percutaneous Coronary Interventions: The PROGRESS CTO (Prospective Global Registry for the Study of Chronic Total Occlusion Intervention) Score.

PubMed

Christopoulos, Georgios; Kandzari, David E; Yeh, Robert W; Jaffer, Farouc A; Karmpaliotis, Dimitri; Wyman, Michael R; Alaswad, Khaldoon; Lombardi, William; Grantham, J Aaron; Moses, Jeffrey; Christakopoulos, Georgios; Tarar, Muhammad Nauman J; Rangan, Bavana V; Lembo, Nicholas; Garcia, Santiago; Cipher, Daisha; Thompson, Craig A; Banerjee, Subhash; Brilakis, Emmanouil S

2016-01-11

This study sought to develop a novel parsimonious score for predicting technical success of chronic total occlusion (CTO) percutaneous coronary intervention (PCI) performed using the hybrid approach. Predicting technical success of CTO PCI can facilitate clinical decision making and procedural planning. We analyzed clinical and angiographic parameters from 781 CTO PCIs included in PROGRESS CTO (Prospective Global Registry for the Study of Chronic Total Occlusion Intervention) using a derivation and validation cohort (2:1 sampling ratio). Variables with strong association with technical success in multivariable analysis were assigned 1 point, and a 4-point score was developed from summing all points. The PROGRESS CTO score was subsequently compared with the J-CTO (Multicenter Chronic Total Occlusion Registry in Japan) score in the validation cohort. Technical success was 92.9%. On multivariable analysis, factors associated with technical success included proximal cap ambiguity (beta coefficient [b] = 0.88), moderate/severe tortuosity (b = 1.18), circumflex artery CTO (b = 0.99), and absence of "interventional" collaterals (b = 0.88). The resulting score demonstrated good calibration and discriminatory capacity in the derivation (Hosmer-Lemeshow chi-square = 2.633; p = 0.268, and receiver-operator characteristic [ROC] area = 0.778) and validation (Hosmer-Lemeshow chi-square = 5.333; p = 0.070, and ROC area = 0.720) subset. In the validation cohort, the PROGRESS CTO and J-CTO scores performed similarly in predicting technical success (ROC area 0.720 vs. 0.746, area under the curve difference = 0.026, 95% confidence interval = -0.093 to 0.144). The PROGRESS CTO score is a novel useful tool for estimating technical success in CTO PCI performed using the hybrid approach. Copyright © 2016 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

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

PubMed

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

2016-01-01

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

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

PubMed

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

2016-07-01

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

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

PubMed Central

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

2016-01-01

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

Maximum covariance analysis to identify intraseasonal oscillations over tropical Brazil

NASA Astrophysics Data System (ADS)

Barreto, Naurinete J. C.; Mesquita, Michel d. S.; Mendes, David; Spyrides, Maria H. C.; Pedra, George U.; Lucio, Paulo S.

2017-09-01

A reliable prognosis of extreme precipitation events in the tropics is arguably challenging to obtain due to the interaction of meteorological systems at various time scales. A pivotal component of the global climate variability is the so-called intraseasonal oscillations, phenomena that occur between 20 and 100 days. The Madden-Julian Oscillation (MJO), which is directly related to the modulation of convective precipitation in the equatorial belt, is considered the primary oscillation in the tropical region. The aim of this study is to diagnose the connection between the MJO signal and the regional intraseasonal rainfall variability over tropical Brazil. This is achieved through the development of an index called Multivariate Intraseasonal Index for Tropical Brazil (MITB). This index is based on Maximum Covariance Analysis (MCA) applied to the filtered daily anomalies of rainfall data over tropical Brazil against a group of covariates consisting of: outgoing longwave radiation and the zonal component u of the wind at 850 and 200 hPa. The first two MCA modes, which were used to create the { MITB}_1 and { MITB}_2 indices, represent 65 and 16 % of the explained variance, respectively. The combined multivariate index was able to satisfactorily represent the pattern of intraseasonal variability over tropical Brazil, showing that there are periods of activation and inhibition of precipitation connected with the pattern of MJO propagation. The MITB index could potentially be used as a diagnostic tool for intraseasonal forecasting.

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

PubMed

Kaphle, Dinesh; Lewallen, Susan

2017-10-01

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

Hand grip strength and associated factors in non-institutionalised men and women 50 years and older in South Africa

PubMed Central

2014-01-01

Background Little is known about the prevalence, predictors and gender differences in hand grip strength of older adults in Africa. This study aims to investigate social and health differences in hand grip strength among older adults in a national probability sample of older South Africans who participated in the Study of Global Ageing and Adults Health (SAGE wave 1) in 2008. Methods We conducted a national population-based cross-sectional study with a sample of 3840 men and women aged 50 years or older in South Africa. The questionnaire included socio-demographic characteristics, health variables, and anthropometric measurements. Linear multivariate regression analysis was performed to assess the association of social factors, health variables and grip strength. Results The mean overall hand grip strength was 37.9 kgs for men (mean age 61.1 years, SD = 9.1) and 31.5 kgs for women (mean age 62.0 years, SD = 9.7). In multivariate analysis among men, greater height, not being underweight and lower functional disability was associated with greater grip strength, and among women, greater height, better cognitive functioning, and lower functional disability were associated with greater grip strength. Conclusions Greater height and lower functional disability were found for both older South African men and women to be significantly associated with grip strength. PMID:24393403

Dopamine-Related Disruption of Functional Topography of Striatal Connections in Unmedicated Patients With Schizophrenia.

PubMed

Horga, Guillermo; Cassidy, Clifford M; Xu, Xiaoyan; Moore, Holly; Slifstein, Mark; Van Snellenberg, Jared X; Abi-Dargham, Anissa

2016-08-01

Despite the well-established role of striatal dopamine in psychosis, current views generally agree that cortical dysfunction is likely necessary for the emergence of psychotic symptoms. The topographic organization of striatal-cortical connections is central to gating and integration of higher-order information, so a disruption of such topography via dysregulated dopamine could lead to cortical dysfunction in schizophrenia. However, this hypothesis remains to be tested using multivariate methods ascertaining the global pattern of striatal connectivity and without the confounding effects of antidopaminergic medication. To examine whether the pattern of brain connectivity across striatal subregions is abnormal in unmedicated patients with schizophrenia and whether this abnormality relates to psychotic symptoms and extrastriatal dopaminergic transmission. In this multimodal, case-control study, we obtained resting-state functional magnetic resonance imaging data from 18 unmedicated patients with schizophrenia and 24 matched healthy controls from the New York State Psychiatric Institute. A subset of these (12 and 17, respectively) underwent positron emission tomography with the dopamine D2 receptor radiotracer carbon 11-labeled FLB457 before and after amphetamine administration. Data were acquired between June 16, 2011, and February 25, 2014. Data analysis was performed from September 1, 2014, to January 11, 2016. Group differences in the striatal connectivity pattern (assessed via multivariable logistic regression) across striatal subregions, the association between the multivariate striatal connectivity pattern and extrastriatal baseline D2 receptor binding potential and its change after amphetamine administration, and the association between the multivariate connectivity pattern and the severity of positive symptoms evaluated with the Positive and Negative Syndrome Scale. Of the patients with schizophrenia (mean [SEM] age, 35.6 [11.8] years), 9 (50%) were male and 9 (50%) were female. Of the controls (mean [SEM] age, 33.7 [8.8] years), 10 (42%) were male and 14 (58%) were female. Patients had an abnormal pattern of striatal connectivity, which included abnormal caudate connections with a distributed set of associative cortex regions (χ229 = 53.55, P = .004). In patients, more deviation from the multivariate pattern of striatal connectivity found in controls correlated specifically with more severe positive symptoms (ρ = -0.77, P = .002). Striatal connectivity also correlated with baseline binding potential across cortical and extrastriatal subcortical regions (t25 = 3.01, P = .01, Bonferroni corrected) but not with its change after amphetamine administration. Using a multimodal, circuit-level interrogation of striatal-cortical connections, it was demonstrated that the functional topography of these connections is globally disrupted in unmedicated patients with schizophrenia. These findings suggest that striatal-cortical dysconnectivity may underlie the effects of dopamine dysregulation on the pathophysiologic mechanism of psychotic symptoms.

Using Interactive Graphics to Teach Multivariate Data Analysis to Psychology Students

ERIC Educational Resources Information Center

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

2011-01-01

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

Relationships between Participants' International Prostate Symptom Score and BPH Impact Index Changes and Global Ratings of Change in a Trial of Phytotherapy for Men with Lower Urinary Tract Symptoms

PubMed Central

Barry, Michael J.; Cantor, Alan; Roehrborn, Claus G.

2014-01-01

Purpose To relate changes in AUA Symptom Index (AUASI) scores with bother measures and global ratings of change among men with lower urinary tract symptoms enrolled in a trial of saw palmetto. Materials and Methods To be eligible, men were ≥45 years old, had ajpeak uroflow ≥4 ml/sec, and an AUASI score ≥ 8 and ≤ 24. Participants self-administered the AUASI, IPSS quality of life item (IPSS QoL), BPH Impact Index (BII) and two global change questions at baseline and 24, 48, and 72 weeks. Results Among 357 participants, global ratings of “a little better” were associated with mean decreases in AUASI scores from 2.8 to 4.1 points, across three time points. The analogous range for mean decreases in BII scores was 1.0 to 1.7 points, and for the IPSS QoL item 0.5 to 0.8 points. At 72 weeks, for the first global change question, each change measure could discriminate between participants rating themselves at least a little better versus unchanged or worse 70-72% of the time. A multivariable model increased discrimination to 77%. For the second global change question, each change measure correctly discriminated ratings of at least a little better versus unchanged or worse 69-74% of the time, and a multivariable model increased discrimination to 79%. Conclusions Changes in AUASI scores could discriminate between participants rating themselves at least a little better versus unchanged or worse. Our findings support the practice of powering studies to detect group mean differences in AUASI scores of at least 3 points. PMID:23017510

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

PubMed

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

2011-10-01

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

The CONCEPTS Global Ice-Ocean Prediction System: Establishing an Environmental Prediction Capability in Canada

NASA Astrophysics Data System (ADS)

Pellerin, Pierre; Smith, Gregory; Testut, Charles-Emmanuel; Surcel Colan, Dorina; Roy, Francois; Reszka, Mateusz; Dupont, Frederic; Lemieux, Jean-Francois; Beaudoin, Christiane; He, Zhongjie; Belanger, Jean-Marc; Deacu, Daniel; Lu, Yimin; Buehner, Mark; Davidson, Fraser; Ritchie, Harold; Lu, Youyu; Drevillon, Marie; Tranchant, Benoit; Garric, Gilles

2015-04-01

Here we describe a new system implemented recently at the Canadian Meteorological Centre (CMC) entitled the Global Ice Ocean Prediction System (GIOPS). GIOPS provides ice and ocean analyses and 10 day forecasts daily at 00GMT on a global 1/4° resolution grid. GIOPS includes a full multivariate ocean data assimilation system that combines satellite observations of sea level anomaly and sea surface temperature (SST) together with in situ observations of temperature and salinity. In situ observations are obtained from a variety of sources including: the Argo network of autonomous profiling floats, moorings, ships of opportunity, marine mammals and research cruises. Ocean analyses are blended with sea ice analyses produced by the Global Ice Analysis System.. GIOPS has been developed as part of the Canadian Operational Network of Coupled Environmental PredicTion Systems (CONCEPTS) tri-departmental initiative between Environment Canada, Fisheries and Oceans Canada and National Defense. The development of GIOPS was made through a partnership with Mercator-Océan, a French operational oceanography group. Mercator-Océan provided the ocean data assimilation code and assistance with the system implementation. GIOPS has undergone a rigorous evaluation of the analysis, trial and forecast fields demonstrating its capacity to provide high-quality products in a robust and reliable framework. In particular, SST and ice concentration forecasts demonstrate a clear benefit with respect to persistence. These results support the use of GIOPS products within other CMC operational systems, and more generally, as part of a Government of Canada marine core service. Impact of a two-way coupling between the GEM atmospheric model and NEMO-CICE ocean-ice model will also be presented.

Stream biogeochemical resilience in the age of Anthropocene

NASA Astrophysics Data System (ADS)

Dong, H.; Creed, I. F.

2017-12-01

Recent evidence indicates that biogeochemical cycles are being pushed beyond the tolerance limits of the earth system in the age of the Anthropocene placing terrestrial and aquatic ecosystems at risk. Here, we explored the question: Is there empirical evidence of global atmospheric changes driving losses in stream biogeochemical resilience towards a new normal? Stream biogeochemical resilience is the process of returning to equilibrium conditions after a disturbance and can be measured using three metrics: reactivity (the highest initial response after a disturbance), return rate (the rate of return to equilibrium condition after reactive changes), and variance of the stationary distribution (the signal to noise ratio). Multivariate autoregressive models were used to derive the three metrics for streams along a disturbance gradient - from natural systems where global drivers would dominate, to relatively managed or modified systems where global and local drivers would interact. We observed a loss of biogeochemical resilience in all streams. The key biogeochemical constituent(s) that may be driving loss of biogeochemical resilience were identified from the time series of the stream biogeochemical constituents. Non-stationary trends (detected by Mann-Kendall analysis) and stationary cycles (revealed through Morlet wavelet analysis) were removed, and the standard deviation (SD) of the remaining residuals were analyzed to determine if there was an increase in SD over time that would indicate a pending shift towards a new normal. We observed that nitrate-N and total phosphorus showed behaviours indicative of a pending shift in natural and managed forest systems, but not in agricultural systems. This study provides empirical support that stream ecosystems are showing signs of exceeding planetary boundary tolerance levels and shifting towards a "new normal" in response to global changes, which can be exacerbated by local management activities. Future work will consider the potential for cascading effects on downstream systems.

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

PubMed Central

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

2013-01-01

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

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

PubMed Central

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

2012-01-01

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

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

ERIC Educational Resources Information Center

Gaotlhobogwe, Michael; Laugharne, Janet; Durance, Isabelle

2011-01-01

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

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

PubMed

Thulin, M

2016-09-10

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

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

PubMed

Makambi, Kepher H; Seung, Hyunuk

2015-01-01

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

[Results of the ocular hypertension treatment study and the confocal scanning laser ophthalmoscopy ancillary study and evaluation of the heidelberg retina tomograph].

PubMed

Klatt, K; Schmidt, E; Scheuerle, A F

2008-04-01

The Ocular Hypertension Treatment Study (OHTS) has shown that analyzing changes of the optic disc configuration is superior to evaluating visual field findings for the early detection of primary open angle glaucoma. The Confocal Scanning Laser Ophthalmoscopy Ancillary Study (CSLO) is the first study to reveal that certain topographic baseline measurements of the optic disc are significantly associated with the development of primary open angle glaucoma in patients with ocular hypertension. An abnormally increased "mean height contour" value proved to be the individual parameter connected with the highest risk. The reliability of the Moorfields Regression Analysis of certain individual sectors during early detection of a primary angle glaucoma is higher than that of the global measurement. The temporal superior and inferior as well as the nasal inferior sectors have the highest positive predictive values and the largest risks in both univariate and multivariate analysis.

Research, science and technology parks: A global comparison of best practices

NASA Astrophysics Data System (ADS)

Ruiz Villacres, Hugo D.

The purpose of this study was to determine if significant differences exist in the evaluation of effectiveness and efficiency between North American, European, and Asian research parks (RPs). Park directors and staff responded to 25 questions from the Survey for Research, Science and Technology Parks. Effectiveness was measured by director's perception of the RP's contribution to economic growth and job creation. Efficiency was evaluated by the interactions between local universities and research parks, assessment of the ecosystem's basic characteristics, and the culture of innovation in the ecosystem. A stratified sampling procedure from a population of 793 parks was used; analysis of variance (ANOVA) and multivariate analysis of variance (MANOVA) were used to test for significance. 130 RPs from three continents participated in this study. No significant differences were found in the evaluation of RPs' directors on effectiveness and efficiency of RPs.

Multivariate analysis of dietary patterns in 939 Swiss adults: sociodemographic parameters and alcohol consumption profiles.

PubMed

Gex-Fabry, M; Raymond, L; Jeanneret, O

1988-09-01

A dietary survey of 939 Swiss adults, randomly selected from the population of Geneva and its surrounding communities, was performed according to the history method. A factor analysis, using average weekly intakes for 33 food variables, reveals three principal components of the diet: satiating capacity, healthfulness and culinary complexity. These characteristics, together with the energy content of the diet, were analysed for differences according to sex, age, relative weight index, birthplace, marital status and occupation. All of these sociodemographic variables influence some dimension of dietary habits. Alcohol consumption is positively associated with satiating, protein rich diets, but energy intake from foods does not significantly differ between various groups of abstainers and drinkers. Although the energy contribution of alcoholic beverages is globally additive, we suggest that cultural and societal norms may modulate the relationship of alcohol and diet.

Unemployment and prostate cancer mortality in the OECD, 1990–2009

PubMed Central

Maruthappu, Mahiben; Watkins, Johnathan; Taylor, Abigail; Williams, Callum; Ali, Raghib; Zeltner, Thomas; Atun, Rifat

2015-01-01

The global economic downturn has been associated with increased unemployment in many countries. Insights into the impact of unemployment on specific health conditions remain limited. We determined the association between unemployment and prostate cancer mortality in members of the Organisation for Economic Co-operation and Development (OECD). We used multivariate regression analysis to assess the association between changes in unemployment and prostate cancer mortality in OECD member states between 1990 and 2009. Country-specific differences in healthcare infrastructure, population structure, and population size were controlled for and lag analyses conducted. Several robustness checks were also performed. Time trend analyses were used to predict the number of excess deaths from prostate cancer following the 2008 global recession. Between 1990 and 2009, a 1% rise in unemployment was associated with an increase in prostate cancer mortality. Lag analysis showed a continued increase in mortality years after unemployment rises. The association between unemployment and prostate cancer mortality remained significant in robustness checks with 46 controls. Eight of the 21 OECD countries for which a time trend analysis was conducted, exhibited an estimated excess of prostate cancer deaths in at least one of 2008, 2009, or 2010, based on 2000–2007 trends. Rises in unemployment are associated with significant increases in prostate cancer mortality. Initiatives that bolster employment may help to minimise prostate cancer mortality during times of economic hardship. PMID:26045715

Unemployment and prostate cancer mortality in the OECD, 1990-2009.

PubMed

Maruthappu, Mahiben; Watkins, Johnathan; Taylor, Abigail; Williams, Callum; Ali, Raghib; Zeltner, Thomas; Atun, Rifat

2015-01-01

The global economic downturn has been associated with increased unemployment in many countries. Insights into the impact of unemployment on specific health conditions remain limited. We determined the association between unemployment and prostate cancer mortality in members of the Organisation for Economic Co-operation and Development (OECD). We used multivariate regression analysis to assess the association between changes in unemployment and prostate cancer mortality in OECD member states between 1990 and 2009. Country-specific differences in healthcare infrastructure, population structure, and population size were controlled for and lag analyses conducted. Several robustness checks were also performed. Time trend analyses were used to predict the number of excess deaths from prostate cancer following the 2008 global recession. Between 1990 and 2009, a 1% rise in unemployment was associated with an increase in prostate cancer mortality. Lag analysis showed a continued increase in mortality years after unemployment rises. The association between unemployment and prostate cancer mortality remained significant in robustness checks with 46 controls. Eight of the 21 OECD countries for which a time trend analysis was conducted, exhibited an estimated excess of prostate cancer deaths in at least one of 2008, 2009, or 2010, based on 2000-2007 trends. Rises in unemployment are associated with significant increases in prostate cancer mortality. Initiatives that bolster employment may help to minimise prostate cancer mortality during times of economic hardship.

Metabolic dependence of green tea on plucking positions revisited: a metabolomic study.

PubMed

Lee, Jang-Eun; Lee, Bum-Jin; Hwang, Jeong-Ah; Ko, Kwang-Sup; Chung, Jin-Oh; Kim, Eun-Hee; Lee, Sang-Jun; Hong, Young-Shick

2011-10-12

The dependence of global green tea metabolome on plucking positions was investigated through (1)H nuclear magnetic resonance (NMR) analysis coupled with multivariate statistical data set. Pattern recognition methods, such as principal component analysis (PCA) and orthogonal projection on latent structure-discriminant analysis (OPLS-DA), were employed for a finding metabolic discrimination among fresh green tea leaves plucked at different positions from young to old leaves. In addition to clear metabolic discrimination among green tea leaves, elevations in theanine, caffeine, and gallic acid levels but reductions in catechins, such as epicatechin (EC), epigallocatechin (EGC), epicatechin-3-gallate (ECG), and epigallocatechin-3-gallate (EGCG), glucose, and sucrose levels were observed, as the green tea plant grows up. On the other hand, the younger the green tea leaf is, the more theanine, caffeine, and gallic acid but the lesser catechins accumlated in the green tea leaf, revealing a reverse assocation between theanine and catechins levels due to incorporaton of theanine into catechins with growing up green tea plant. Moreover, as compared to the tea leaf, the observation of marked high levels of theanine and low levels of catechins in green tea stems exhibited a distinct tea plant metabolism between the tea leaf and the stem. This metabolomic approach highlights taking insight to global metabolic dependence of green tea leaf on plucking position, thereby providing distinct information on green tea production with specific tea quality.

Multivariate Autoregressive Modeling and Granger Causality Analysis of Multiple Spike Trains

PubMed Central

Krumin, Michael; Shoham, Shy

2010-01-01

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

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

PubMed

Jackson, Daniel; Riley, Richard D

2014-02-20

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

Population risk perceptions of global warming in Australia.

PubMed

Agho, Kingsley; Stevens, Garry; Taylor, Mel; Barr, Margo; Raphael, Beverley

2010-11-01

According to the World Health Organisation (WHO), global warming has the potential to dramatically disrupt some of life's essential requirements for health, water, air and food. Understanding how Australians perceive the risk of global warming is essential for climate change policy and planning. The aim of this study was to determine the prevalence of, and socio-demographic factors associated with, high levels of perceived likelihood that global warming would worsen, concern for self and family and reported behaviour changes. A module of questions on global warming was incorporated into the New South Wales Population Health Survey in the second quarter of 2007. This Computer Assisted Telephone Interview (CATI) was completed by a representative sample of 2004 adults. The weighted sample was comparable to the Australian population. Bivariate and multivariate statistical analyses were conducted to examine the socio-demographic and general health factors. Overall 62.1% perceived that global warming was likely to worsen; 56.3% were very or extremely concerned that they or their family would be directly affected by global warming; and 77.6% stated that they had made some level of change to the way they lived their lives, because of the possibility of global warming. After controlling for confounding factors, multivariate analyses revealed that those with high levels of psychological distress were 2.17 (Adjusted Odds Ratio (AOR)=2.17; CI: 1.16-4.03; P=0.015) times more likely to be concerned about global warming than those with low psychological distress levels. Those with a University degree or equivalent and those who lived in urban areas were significantly more likely to think that global warming would worsen compared to those without a University degree or equivalent and those who lived in the rural areas. Females were significantly (AOR=1.69; CI: 1.23-2.33; P=0.001) more likely to report they had made changes to the way they lived their lives due to the risk of global warming. A high proportion of respondents reported that they perceived that global warming would worsen, were concerned that it would affect them and their families and had already made changes in their lives because of it. These findings support a readiness in the population to deal with global warming. Future research and programs are needed to investigate population-level strategies for future action. Crown Copyright © 2010. Published by Elsevier Inc. All rights reserved.

The NRL relocatable ocean/acoustic ensemble forecast system

NASA Astrophysics Data System (ADS)

Rowley, C.; Martin, P.; Cummings, J.; Jacobs, G.; Coelho, E.; Bishop, C.; Hong, X.; Peggion, G.; Fabre, J.

2009-04-01

A globally relocatable regional ocean nowcast/forecast system has been developed to support rapid implementation of new regional forecast domains. The system is in operational use at the Naval Oceanographic Office for a growing number of regional and coastal implementations. The new system is the basis for an ocean acoustic ensemble forecast and adaptive sampling capability. We present an overview of the forecast system and the ocean ensemble and adaptive sampling methods. The forecast system consists of core ocean data analysis and forecast modules, software for domain configuration, surface and boundary condition forcing processing, and job control, and global databases for ocean climatology, bathymetry, tides, and river locations and transports. The analysis component is the Navy Coupled Ocean Data Assimilation (NCODA) system, a 3D multivariate optimum interpolation system that produces simultaneous analyses of temperature, salinity, geopotential, and vector velocity using remotely-sensed SST, SSH, and sea ice concentration, plus in situ observations of temperature, salinity, and currents from ships, buoys, XBTs, CTDs, profiling floats, and autonomous gliders. The forecast component is the Navy Coastal Ocean Model (NCOM). The system supports one-way nesting and multiple assimilation methods. The ensemble system uses the ensemble transform technique with error variance estimates from the NCODA analysis to represent initial condition error. Perturbed surface forcing or an atmospheric ensemble is used to represent errors in surface forcing. The ensemble transform Kalman filter is used to assess the impact of adaptive observations on future analysis and forecast uncertainty for both ocean and acoustic properties.

Multivariate missing data in hydrology - Review and applications

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

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

DTIC Science & Technology

1993-06-18

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

Hot spots of multivariate extreme anomalies in Earth observations

NASA Astrophysics Data System (ADS)

Flach, M.; Sippel, S.; Bodesheim, P.; Brenning, A.; Denzler, J.; Gans, F.; Guanche, Y.; Reichstein, M.; Rodner, E.; Mahecha, M. D.

2016-12-01

Anomalies in Earth observations might indicate data quality issues, extremes or the change of underlying processes within a highly multivariate system. Thus, considering the multivariate constellation of variables for extreme detection yields crucial additional information over conventional univariate approaches. We highlight areas in which multivariate extreme anomalies are more likely to occur, i.e. hot spots of extremes in global atmospheric Earth observations that impact the Biosphere. In addition, we present the year of the most unusual multivariate extreme between 2001 and 2013 and show that these coincide with well known high impact extremes. Technically speaking, we account for multivariate extremes by using three sophisticated algorithms adapted from computer science applications. Namely an ensemble of the k-nearest neighbours mean distance, a kernel density estimation and an approach based on recurrences is used. However, the impact of atmosphere extremes on the Biosphere might largely depend on what is considered to be normal, i.e. the shape of the mean seasonal cycle and its inter-annual variability. We identify regions with similar mean seasonality by means of dimensionality reduction in order to estimate in each region both the `normal' variance and robust thresholds for detecting the extremes. In addition, we account for challenges like heteroscedasticity in Northern latitudes. Apart from hot spot areas, those anomalies in the atmosphere time series are of particular interest, which can only be detected by a multivariate approach but not by a simple univariate approach. Such an anomalous constellation of atmosphere variables is of interest if it impacts the Biosphere. The multivariate constellation of such an anomalous part of a time series is shown in one case study indicating that multivariate anomaly detection can provide novel insights into Earth observations.

Multivariate analysis for scanning tunneling spectroscopy data

NASA Astrophysics Data System (ADS)

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

2018-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

Advertising of tobacco products at point of sale: who are more exposed in Brazil?

PubMed

Ferreira-Gomes, Adriana Bacelar; Moura, Lenildo de; Araújo-Andrade, Silvânia Suely de; Lacerda-Mendes, Felipe; Perez, Cristina A; Abaakouk, Zohra

2017-01-01

To describe the adult population perception of cigarette advertising at point of sale, according their tobacco-use status and socio-demographic characteristics such as sex, age, race/color, region, household location and schooling. A multivariable analysis was carried out using data from the Global Adult Tobacco Survey in 2008 and the National Health Survey in 2013. Both surveys showed that among nonsmokers: women, young adults and those who had over 10 years of schooling had more frequently noticed advertising of cigarettes at point of sale. It was also observed that among the population with fewer years of schooling these proportions increased significantly. A measure that completely bans tobacco advertising would be more effective to protect the vulnerable groups from tobacco consumption.

Global Risk Score and Clinical SYNTAX Score as Predictors of Clinical Outcomes of Patients Undergoing Unprotected Left Main Percutaneous Catheter Intervention

PubMed Central

Cuenza, Lucky; Collado, Marianne P.; Ho Khe Sui, James

2017-01-01

Background Risk stratification is an important component of left main percutaneous catheter intervention (PCI) which has emerged as a feasible alternative to cardiac surgery. We sought to compare the clinical SYNTAX score and the global risk score in predicting outcomes of patients undergoing unprotected left main PCI in our institution. Methods Clinical, angiographic and procedural characteristics of 92 patients who underwent unprotected left main PCI (mean age 62 ± 12.1 years) were analyzed. Patients were risk stratified into tertiles of high, intermediate and low risk using the global risk score (GRS) and the clinical SYNTAX score (CSS) and were prospectively followed up at 1 year for the occurrence of major adverse cardiovascular events (MACEs), defined as a composite of all cause mortality, cardiac mortality, non-fatal myocardial infarction, stroke, coronary artery bypass, and target vessel revascularization. Results There were 26 (28.2%) who experienced MACEs, of which 10 (10.8%) patients died. Multivariable hazards analysis showed that the GRS (hazard ratio (HR) = 5.5, P = 0.001) and CSS (HR = 4.3, P = 0.001) were both independent predictors of MACEs. Kaplan-Meier analysis showed higher incidence of MACEs with the intermediate and higher risk categories compared to those classified as low risk. Receiver-operator characteristic analysis showed that the GRS has better discriminatory ability than the CSS in the prediction of 1 year MACEs (0.891 vs. 0.743, P = 0.007). Conclusion The GRS and CSS are predictive of outcomes after left main PCI. The GRS appears to have superior predictive and prognostic utility compared to the CSS. This study emphasizes the importance of combining both anatomic and clinical variables for optimum prognostication and management decisions in left main PCI. PMID:29317974

Association of tobacco advertising, promotion and sponsorship (TAPS) exposure and cigarette use among Nigerian adolescents: implications for current practices, products and policies.

PubMed

Chido-Amajuoyi, Onyema G; Mantey, Dale S; Clendennen, Stephanie L; Pérez, Adriana

2017-01-01

This study investigates the association between exposure to tobacco advertising, promotion and sponsorship (TAPS) and cigarette use behaviours among adolescents in five Nigerian regions. This is imperative given a 2015 WHO report on the global tobacco epidemic, revealing Nigeria has not met any of the MPOWER TAPS ban indicators instituted since 2008. Secondary data analysis of the 2008 Global Youth Tobacco Survey for Nigeria. Participants were 1399 adolescents, representative of 5 Nigerian regions. Weighted multivariable logistic regression models were used to assess the relationship between TAPS exposure and (1) past 30-day (current) cigarette use, (2) ever cigarette use and (3) susceptibility to use cigarettes among never cigarette users. Sensitivity analysis via complete case analysis and multiple imputation were conducted. Ninety-five per cent of Nigerian adolescents reported exposure to TAPS. Among adolescents who had never smoked, 15% were susceptible to use cigarettes. Cumulative TAPS exposure was significantly associated with both an increased odds of current cigarette use (AOR: 1.73; 95% CI 1.09 to2.99) and ever cigarette use (AOR: 1.29; 95% CI 1.15 to1.45); as well as increased susceptibility to cigarette smoking (AOR: 1.18; 95% CI 1.03 to 1.34), among non-smokers. Given study results, the emergence of new tobacco products and novel platforms for TAPS globally, implementation of existing policies and enhancement of efforts to attain comprehensive bans on all forms of direct and indirect TAPS in line with article 13 of the WHO Framework Convention on Tobacco Control are needed to reduce TAPS exposure and curtail tobacco use in Nigeria.

Association of tobacco advertising, promotion and sponsorship (TAPS) exposure and cigarette use among Nigerian adolescents: implications for current practices, products and policies

PubMed Central

Mantey, Dale S; Clendennen, Stephanie L; Pérez, Adriana

2017-01-01

Background This study investigates the association between exposure to tobacco advertising, promotion and sponsorship (TAPS) and cigarette use behaviours among adolescents in five Nigerian regions. This is imperative given a 2015 WHO report on the global tobacco epidemic, revealing Nigeria has not met any of the MPOWER TAPS ban indicators instituted since 2008. Methods Secondary data analysis of the 2008 Global Youth Tobacco Survey for Nigeria. Participants were 1399 adolescents, representative of 5 Nigerian regions. Weighted multivariable logistic regression models were used to assess the relationship between TAPS exposure and (1) past 30-day (current) cigarette use, (2) ever cigarette use and (3) susceptibility to use cigarettes among never cigarette users. Sensitivity analysis via complete case analysis and multiple imputation were conducted. Results Ninety-five per cent of Nigerian adolescents reported exposure to TAPS. Among adolescents who had never smoked, 15% were susceptible to use cigarettes. Cumulative TAPS exposure was significantly associated with both an increased odds of current cigarette use (AOR: 1.73; 95% CI 1.09 to2.99) and ever cigarette use (AOR: 1.29; 95% CI 1.15 to1.45); as well as increased susceptibility to cigarette smoking (AOR: 1.18; 95% CI 1.03 to 1.34), among non-smokers. Conclusion Given study results, the emergence of new tobacco products and novel platforms for TAPS globally, implementation of existing policies and enhancement of efforts to attain comprehensive bans on all forms of direct and indirect TAPS in line with article 13 of the WHO Framework Convention on Tobacco Control are needed to reduce TAPS exposure and curtail tobacco use in Nigeria. PMID:29082014

Multivariate Analysis of Schools and Educational Policy.

ERIC Educational Resources Information Center

Kiesling, Herbert J.

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

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

NASA Technical Reports Server (NTRS)

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

1993-01-01

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

Current globalization of drug interventional clinical trials: characteristics and associated factors, 2011-2013.

PubMed

Jeong, Sohyun; Sohn, Minji; Kim, Jae Hyun; Ko, Minoh; Seo, Hee-Won; Song, Yun-Kyoung; Choi, Boyoon; Han, Nayoung; Na, Han-Sung; Lee, Jong Gu; Kim, In-Wha; Oh, Jung Mi; Lee, Euni

2017-06-21

Clinical trial globalization is a major trend for industry-sponsored clinical trials. There has been a shift in clinical trial sites towards emerging regions of Eastern Europe, Latin America, Asia, the Middle East, and Africa. Our study objectives were to evaluate the current characteristics of clinical trials and to find out the associated multiple factors which could explain clinical trial globalization and its implications for clinical trial globalization in 2011-2013. The data elements of "phase," "recruitment status," "type of sponsor," "age groups," and "design of trial" from 30 countries were extracted from the ClinicalTrials.gov website. Ten continental representative countries including the USA were selected and the design elements were compared to those of the USA. Factors associated with trial site distribution were chosen for a multilinear regression analysis. The USA, Germany, France, Canada, and United Kingdom were the "top five" countries which frequently held clinical trials. The design elements from nine continental representative countries were quite different from those of the USA; phase 1 trials were more prevalent in India (OR 1.517, p < 0.001) while phase 3 trials were much more prevalent in all nine representative countries than in the USA. A larger number of "child" age group trials was performed in Poland (OR 1.852, p < 0.001), Israel (OR 1.546, p = 0.005), and South Africa (OR 1.963, p < 0.001) than in the USA. Multivariate analysis showed that health care expenditure per capita, Economic Freedom Index, Human Capital Index, and Intellectual Property Rights Index could explain the variance of regional distribution of clinical trials by 63.6%. The globalization of clinical trials in the emerging regions of Asia, South Africa, and Eastern Europe developed in parallel with the factors of economic drive, population for recruitment, and regulatory constraints.

Predictors of mistimed, and unwanted pregnancies among women of childbearing age in Rufiji, Kilombero, and Ulanga districts of Tanzania

PubMed Central

2014-01-01

Background While unintended pregnancies pose a serious threat to the health and well-being of families globally, characteristics of Tanzanian women who conceive unintentionally are rarely documented. This analysis identifies factors associated with unintended pregnancies—both mistimed and unwanted—in three rural districts of Tanzania. Methods A cross-sectional survey of 2,183 random households was conducted in three Tanzanian districts of Rufiji, Kilombero, and Ulanga in 2011 to assess women’s health behavior and service utilization patterns. These households produced 3,127 women age 15+ years from which 2,199 gravid women aged 15–49 were selected for the current analysis. Unintended pregnancies were identified as either mistimed (wanted later) or unwanted (not wanted at all). Correlates of mistimed, and unwanted pregnancies were identified through Chi-squared tests to assess associations and multinomial logistic regression for multivariate analysis. Results Mean age of the participants was 32.1 years. While 54.1% of the participants reported that their most recent pregnancy was intended, 32.5% indicated their most recent pregnancy as mistimed and 13.4% as unwanted. Multivariate analysis revealed that young age (<20 years), and single marital status were significant predictors of both mistimed and unwanted pregnancies. Lack of inter-partner communication about family planning increased the risk of mistimed pregnancy significantly, and multi-gravidity was shown to significantly increase the risk of unwanted pregnancy. Conclusions About one half of women in Rufiji, Kilombero, and Ulanga districts of Tanzania conceive unintentionally. Women, especially the most vulnerable should be empowered to avoid pregnancy at their own will and discretion. PMID:25102924

Application of multivariate statistical analysis and hydrochemical and isotopic investigations for evaluation of groundwater quality and its suitability for drinking and agriculture purposes: case of Oum Ali-Thelepte aquifer, central Tunisia.

PubMed

Hassen, Imen; Hamzaoui-Azaza, Fadoua; Bouhlila, Rachida

2016-03-01

Groundwater plays a dominant role in arid regions; it is among the most available water resources in Tunisia. Located in northwestern Tunisia, Oum Ali-Thelepte is a deep Miocene sedimentary aquifer, where groundwater is the most important source of water supply. The aim of the study is to investigate the hydrochemical processes leading to mineralization and to assess water quality with respect to agriculture and drinking for a better management of groundwater resources. To achieve such objectives, water analysis was carried out on 16 groundwater samples collected during January-February 2014. Stable isotopes and 26 hydrochemical parameters were examined. The interpretation of these analytical data showed that the concentrations of major and trace elements were within the permissible level for human use. The distribution of mineral processes in this aquifer was identified using conventional classification techniques, suggesting that the water facies gradually changes from Ca-HCO3 to Mg-SO4 type and are controlled by water-rock interaction. These results were endorsed using multivariate statistical methods such as principal component analysis and cluster analysis. The sustainability of groundwater for drinking and irrigation was assessed based on the water quality index (WQI) and on Wilcox and Richards's diagrams. This aquifer has been classified as "excellent water" serving good irrigation in the area. As for the stable isotope, the measurements showed that groundwater samples lay between global meteoric water line (GMWL) and LMWL; hence, this arrangement signifies that the recharge of the Oum Ali-Thelepte aquifer is ensured by rainwater infiltration through mountains in the border of the aquifer without evaporation effects.

A spatial analysis of the association between restaurant density and body mass index in Canadian adults.

PubMed

Hollands, Simon; Campbell, M Karen; Gilliland, Jason; Sarma, Sisira

2013-10-01

To investigate the association between fast-food restaurant density and adult body mass index (BMI) in Canada. Individual-level BMI and confounding variables were obtained from the 2007-2008 Canadian Community Health Survey master file. Locations of the fast-food and full-service chain restaurants and other non-chain restaurants were obtained from the 2008 Infogroup Canada business database. Food outlet density (fast-food, full-service and other) per 10,000 population was calculated for each Forward Sortation Area (FSA). Global (Moran's I) and local indicators of spatial autocorrelation of BMI were assessed. Ordinary least squares (OLS) and spatial auto-regressive error (SARE) methods were used to assess the association between local food environment and adult BMI in Canada. Global and local spatial autocorrelation of BMI were found in our univariate analysis. We found that OLS and SARE estimates were very similar in our multivariate models. An additional fast-food restaurant per 10,000 people at the FSA-level is associated with a 0.022kg/m(2) increase in BMI. On the other hand, other restaurant density is negatively related to BMI. Fast-food restaurant density is positively associated with BMI in Canada. Results suggest that restricting availability of fast-food in local neighborhoods may play a role in obesity prevention. © 2013.

Urine Metabolomics Analysis for Biomarker Discovery and Detection of Jaundice Syndrome in Patients With Liver Disease*

PubMed Central

Wang, Xijun; Zhang, Aihua; Han, Ying; Wang, Ping; Sun, Hui; Song, Gaochen; Dong, Tianwei; Yuan, Ye; Yuan, Xiaoxia; Zhang, Miao; Xie, Ning; Zhang, He; Dong, Hui; Dong, Wei

2012-01-01

Metabolomics is a powerful new technology that allows for the assessment of global metabolic profiles in easily accessible biofluids and biomarker discovery in order to distinguish between diseased and nondiseased status information. Deciphering the molecular networks that distinguish diseases may lead to the identification of critical biomarkers for disease aggressiveness. However, current diagnostic methods cannot predict typical Jaundice syndrome (JS) in patients with liver disease and little is known about the global metabolomic alterations that characterize JS progression. Emerging metabolomics provides a powerful platform for discovering novel biomarkers and biochemical pathways to improve diagnostic, prognostication, and therapy. Therefore, the aim of this study is to find the potential biomarkers from JS disease by using a nontarget metabolomics method, and test their usefulness in human JS diagnosis. Multivariate data analysis methods were utilized to identify the potential biomarkers. Interestingly, 44 marker metabolites contributing to the complete separation of JS from matched healthy controls were identified. Metabolic pathways (Impact-value≥0.10) including alanine, aspartate, and glutamate metabolism and synthesis and degradation of ketone bodies were found to be disturbed in JS patients. This study demonstrates the possibilities of metabolomics as a diagnostic tool in diseases and provides new insight into pathophysiologic mechanisms. PMID:22505723

Global metabolomics reveals potential urinary biomarkers of esophageal squamous cell carcinoma for diagnosis and staging

NASA Astrophysics Data System (ADS)

Xu, Jing; Chen, Yanhua; Zhang, Ruiping; He, Jiuming; Song, Yongmei; Wang, Jingbo; Wang, Huiqing; Wang, Luhua; Zhan, Qimin; Abliz, Zeper

2016-10-01

We performed a metabolomics study using liquid chromatography-mass spectrometry (LC-MS) combined with multivariate data analysis (MVDA) to discriminate global urine profiles in urine samples from esophageal squamous cell carcinoma (ESCC) patients and healthy controls (NC). Our work evaluated the feasibility of employing urine metabolomics for the diagnosis and staging of ESCC. The satisfactory classification between the healthy controls and ESCC patients was obtained using the MVDA model, and obvious classification of early-stage and advanced-stage patients was also observed. The results suggest that the combination of LC-MS analysis and MVDA may have potential applications for ESCC diagnosis and staging. We then conducted LC-MS/MS experiments to identify the potential biomarkers with large contributions to the discrimination. A total of 83 potential diagnostic biomarkers for ESCC were screened out, and 19 potential biomarkers were identified; the variations between the differences in staging using these potential biomarkers were further analyzed. These biomarkers may not be unique to ESCCs, but instead result from any malignant disease. To further elucidate the pathophysiology of ESCC, we studied related metabolic pathways and found that ESCC is associated with perturbations of fatty acid β-oxidation and the metabolism of amino acids, purines, and pyrimidines.

Graph theoretical analysis of EEG functional network during multi-workload flight simulation experiment in virtual reality environment.

PubMed

Shengqian Zhang; Yuan Zhang; Yu Sun; Thakor, Nitish; Bezerianos, Anastasios

2017-07-01

The research field of mental workload has attracted abundant researchers as mental workload plays a crucial role in real-life performance and safety. While previous studies have examined the neural correlates of mental workload in 2D scenarios (i.e., presenting stimuli on a computer screen (CS) environment using univariate methods (e.g., EEG channel power), it is still unclear of the findings of one that uses multivariate approach using graphical theory and the effects of a 3D environment (i.e., presenting stimuli on a Virtual Reality (VR)). In this study, twenty subjects undergo flight simulation in both CS and VR environment with three stages each. After preprocessing, the Electroencephalogram (EEG) signals were a connectivity matrix based on Phase Lag Index (PLI) will be constructed. Graph theory analysis then will be applied based on their global efficiency, local efficiency and nodal efficiency on both alpha and theta band. For global efficiency and local efficiency, VR values are generally lower than CS in both bands. For nodal efficiency, the regions that show at least marginally significant decreases are very different for CS and VR. These findings suggest that 3D simulation effects a higher mental workload than 2D simulation and that they each involved a different brain region.

Analysis of fracture healing in osteopenic bone caused by disuse: experimental study.

PubMed

Paiva, A G; Yanagihara, G R; Macedo, A P; Ramos, J; Issa, J P M; Shimano, A C

2016-03-01

Osteoporosis has become a serious global public health issue. Hence, osteoporotic fracture healing has been investigated in several previous studies because there is still controversy over the effect osteoporosis has on the healing process. The current study aimed to analyze two different periods of bone healing in normal and osteopenic rats. Sixty, 7-week-old female Wistar rats were randomly divided into four groups: unrestricted and immobilized for 2 weeks after osteotomy (OU2), suspended and immobilized for 2 weeks after osteotomy (OS2), unrestricted and immobilized for 6 weeks after osteotomy (OU6), and suspended and immobilized for 6 weeks after osteotomy (OS6). Osteotomy was performed in the middle third of the right tibia 21 days after tail suspension, when the osteopenic condition was already set. The fractured limb was then immobilized by orthosis. Tibias were collected 2 and 6 weeks after osteotomy, and were analyzed by bone densitometry, mechanical testing, and histomorphometry. Bone mineral density values from bony calluses were significantly lower in the 2-week post-osteotomy groups compared with the 6-week post-osteotomy groups (multivariate general linear model analysis, P<0.000). Similarly, the mechanical properties showed that animals had stronger bones 6 weeks after osteotomy compared with 2 weeks after osteotomy (multivariate general linear model analysis, P<0.000). Histomorphometry indicated gradual bone healing. Results showed that osteopenia did not influence the bone healing process, and that time was an independent determinant factor regardless of whether the fracture was osteopenic. This suggests that the body is able to compensate for the negative effects of suspension.

Aligning US health and immigration policy to reduce the incidence of tuberculosis.

PubMed

Blewett, L A; Marmor, S; Pintor, J K; Boudreaux, M

2014-04-01

Tuberculosis (TB) is a significant public health issue, claiming 1.4 million lives worldwide in 2011. Using data from the 2009-2010 National Health Interview Survey, we examine variation in 'having heard of TB' (HTB) by global region of birth and health insurance status. Cross-sectional analysis with bivariate comparisons and multivariate logistic regression to evaluate how adults differed in reported HTB, controlling for global region of birth. HTB rates ranged from 63.4% of adults born in Asia to 88.6% born in Europe. Uninsured immigrants had the lowest rate of HTB, ranging from a low of 50.1% of uninsured adults born in Asia to 77.6% born in Europe and 90.8% of US-born uninsured adults. Longer length of time in the United States (>5 years) was significantly associated with increased likelihood of HTB, as did being of Asian race/ethnicity and being male. Those with private health insurance coverage had the highest rates of HTB. To reduce persistent TB, public health program directors and policy makers must 1) recognize the variation in HTB by global region of birth and prioritize areas with the lowest HTB rates, and 2) reduce barriers to health insurance coverage by eliminating the 5-year ban for public program coverage for new immigrants.

Relationship of Soft Drink Consumption to Global Overweight, Obesity, and Diabetes: A Cross-National Analysis of 75 Countries

PubMed Central

McKee, Martin; Galea, Gauden; Stuckler, David

2013-01-01

Objectives. We estimated the relationship between soft drink consumption and obesity and diabetes worldwide. Methods. We used multivariate linear regression to estimate the association between soft drink consumption and overweight, obesity, and diabetes prevalence in 75 countries, controlling for other foods (cereals, meats, fruits and vegetables, oils, and total calories), income, urbanization, and aging. Data were obtained from the Euromonitor Global Market Information Database, the World Health Organization, and the International Diabetes Federation. Bottled water consumption, which increased with per-capita income in parallel to soft drink consumption, served as a natural control group. Results. Soft drink consumption increased globally from 9.5 gallons per person per year in 1997 to 11.4 gallons in 2010. A 1% rise in soft drink consumption was associated with an additional 4.8 overweight adults per 100 (adjusted B; 95% confidence interval [CI] = 3.1, 6.5), 2.3 obese adults per 100 (95% CI = 1.1, 3.5), and 0.3 adults with diabetes per 100 (95% CI = 0.1, 0.8). These findings remained robust in low- and middle-income countries. Conclusions. Soft drink consumption is significantly linked to overweight, obesity, and diabetes worldwide, including in low- and middle-income countries. PMID:23488503

Combining p-values in replicated single-case experiments with multivariate outcome.

PubMed

Solmi, Francesca; Onghena, Patrick

2014-01-01

Interest in combining probabilities has a long history in the global statistical community. The first steps in this direction were taken by Ronald Fisher, who introduced the idea of combining p-values of independent tests to provide a global decision rule when multiple aspects of a given problem were of interest. An interesting approach to this idea of combining p-values is the one based on permutation theory. The methods belonging to this particular approach exploit the permutation distributions of the tests to be combined, and use a simple function to combine probabilities. Combining p-values finds a very interesting application in the analysis of replicated single-case experiments. In this field the focus, while comparing different treatments effects, is more articulated than when just looking at the means of the different populations. Moreover, it is often of interest to combine the results obtained on the single patients in order to get more global information about the phenomenon under study. This paper gives an overview of how the concept of combining p-values was conceived, and how it can be easily handled via permutation techniques. Finally, the method of combining p-values is applied to a simulated replicated single-case experiment, and a numerical illustration is presented.

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

PubMed

Ferreira, Ana P; Tobyn, Mike

2015-01-01

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

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

PubMed

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

2016-11-30

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

Instrumental Neutron Activation Analysis and Multivariate Statistics for Pottery Provenance

NASA Astrophysics Data System (ADS)

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

2004-06-01

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

A Study of Effects of MultiCollinearity in the Multivariable Analysis

PubMed Central

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

2015-01-01

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

A Study of Effects of MultiCollinearity in the Multivariable Analysis.

PubMed

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

2014-10-01

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

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

PubMed

Olswold, Curtis; de Andrade, Mariza

2003-12-31

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

Prediction of significant conduction disease through noninvasive assessment of cardiac calcification.

PubMed

Mainigi, Sumeet K; Chebrolu, Lakshmi Hima Bindu; Romero-Corral, Abel; Mehta, Vinay; Machado, Rodolfo Rozindo; Konecny, Tomas; Pressman, Gregg S

2012-10-01

Cardiac calcification is associated with coronary artery disease, arrhythmias, conduction disease, and adverse cardiac events. Recently, we have described an echocardiographic-based global cardiac calcification scoring system. The objective of this study was to evaluate the severity of cardiac calcification in patients with permanent pacemakers as based on this scoring system. Patients with a pacemaker implanted within the 2-year study period with a previous echocardiogram were identified and underwent blinded global cardiac calcium scoring. These patients were compared to matched control patients without a pacemaker who also underwent calcium scoring. The study group consisted of 49 patients with pacemaker implantation who were compared to 100 matched control patients. The mean calcium score in the pacemaker group was 3.3 ± 2.9 versus 1.8 ± 2.0 (P = 0.006) in the control group. Univariate and multivariate analysis revealed glomerular filtration rate and calcium scoring to be significant predictors of the presence of a pacemaker. Echocardiographic-based calcium scoring correlates with the presence of severe conduction disease requiring a pacemaker. © 2012, Wiley Periodicals, Inc.

A Systematic Global Mapping of the Radiation Field at Aviation Altitudes

NASA Technical Reports Server (NTRS)

Stassinopoulos, E. G.; Stauffer, C. A.; Brucker, G. J.

2003-01-01

This paper presents early results from aircraft measurements made by a Low-LET Radiation Spectrometer (LoLRS), as part of a long-range effort to study the complex dynamics of the atmospheric radiation field. For this purpose, a comprehensive data base is being generated to enable a multivariable global mapping (and eventually modeling) of doses and Linear-Energy-Transfer (LET) spectra at aviation altitudes. To accomplish this, a methodical collection of data from the LoLRS (and other instruments), is planned over extended periods of time, in a manner that complements some previous isolated and sporadic measurements by other workers, with the objective to generate a detailed long-range description of the cosmic-ray induced particle environment and to study its variability and dependence on atmospheric thickness, magnetic latitude, L-shell or rigidity, space weather, solar particle events, solar cycle effects, magnetic field variation, diurnal and seasonal effects, and atmospheric weather. Analysis of initial data indicates that the dose is rising with increasing altitude and increasing magnetic latitude. Comparison of total doses with predictions is in good agreement.

Global Mercury Observatory System (GMOS): measurements of atmospheric mercury in Celestun, Yucatan, Mexico during 2012.

PubMed

Velasco, Antonio; Arcega-Cabrera, Flor; Oceguera-Vargas, Ismael; Ramírez, Martha; Ortinez, Abraham; Umlauf, Gunther; Sena, Fabrizio

2016-09-01

Within the Global Mercury Observation System (GMOS) project, long-term continuous measurements of total gaseous mercury (TGM) were carried out by a monitoring station located at Celestun, Yucatan, Mexico, a coastal site along the Gulf of Mexico. The measurements covered the period from January 28th to October 17th, 2012. TGM data, at the Celestun site, were obtained using a high-resolution mercury vapor analyzer. TGM data show values from 0.50 to 2.82 ng/m(3) with an annual average concentration of 1.047 ± 0.271 ng/m(3). Multivariate analyses of TGM and meteorological variables suggest that TGM is correlated with the vertical air mass distribution in the atmosphere, which is influenced by diurnal variations in temperature and relative humidity. Diurnal variation is characterized by higher nighttime mercury concentrations, which might be influenced by convection currents between sea and land. The back trajectory analysis confirmed that local sources do not significantly influence TGM variations. This study shows that TGM monitoring at the Celestun site fulfills GMOS goals for a background site.

The Influence of Body Mass Index on Outcomes in Ureteroscopy: Results from the Clinical Research Office of Endourological Society URS Global Study.

PubMed

Krambeck, Amy; Wijnstok, Nienke; Olbert, Peter; Mitroi, George; Bariol, Simon; Shah, Hemendra N; El-Abd, Ahmed S; Onal, Bulent; de la Rosette, Jean

2017-01-01

Although ureteroscopy (URS) has been established as a viable treatment for stones in obese patients, its safety and success has not been fully elucidated. The current study describes the worldwide prevalence of obesity in patients with urolithiasis and examines trends in URS outcomes, safety, and efficacy. This study utilized the Clinical Research Office of the Endourological Society (CROES) URS Global Study, which was a prospective, multicenter study including 11,885 patients treated with URS for urinary stones at 1 of 114 urology departments across 32 countries. The relationship between body mass index (BMI), diabetes, and creatinine, with retreatment, stone-free rates, complications, and long hospital stay, was examined with a multivariate logistic regression analyses. Of the 10,099 URS patients with BMI data, 17.4% were obese and 2.2% were super obese. Overall, 86.7% patients were stone free and 16.8% required retreatment. Higher BMI was associated with lower stone-free rates, and any deviation from normal weight was associated with higher retreatment rates. In multivariate analysis controlling for several variables including stone size, the association between BMI and lower stone-free rates with higher retreatment rates persisted. Intraoperative complications occurred in 518 (5.1%) patients, and 343 (3.4%) experienced a postoperative complication. Postoperative complications were more frequent in the underweight and super obese subjects, and there was no relationship between BMI and intraoperative complications. Although URS for stone disease was found to be an overall safe procedure for obese and super obese patients, efficacy of the procedure may be lower compared with normal-weight subjects and higher retreatment rates may be necessary.

The prognostic value of standardized reference values for speckle-tracking global longitudinal strain in hypertrophic cardiomyopathy.

PubMed

Hartlage, Gregory R; Kim, Jonathan H; Strickland, Patrick T; Cheng, Alan C; Ghasemzadeh, Nima; Pernetz, Maria A; Clements, Stephen D; Williams, B Robinson

2015-03-01

Speckle-tracking left ventricular global longitudinal strain (GLS) assessment may provide substantial prognostic information for hypertrophic cardiomyopathy (HCM) patients. Reference values for GLS have been recently published. We aimed to evaluate the prognostic value of standardized reference values for GLS in HCM patients. An analysis of HCM clinic patients who underwent GLS was performed. GLS was defined as normal (more negative or equal to -16%) and abnormal (less negative than -16%) based on recently published reference values. Patients were followed for a composite of events including heart failure hospitalization, sustained ventricular arrhythmia, and all-cause death. The power of GLS to predict outcomes was assessed relative to traditional clinical and echocardiographic variables present in HCM. 79 HCM patients were followed for a median of 22 months (interquartile range 9-30 months) after imaging. During follow-up, 15 patients (19%) met the primary outcome. Abnormal GLS was the only echocardiographic variable independently predictive of the primary outcome [multivariate Hazard ratio 5.05 (95% confidence interval 1.09-23.4, p = 0.038)]. When combined with traditional clinical variables, abnormal GLS remained independently predictive of the primary outcome [multivariate Hazard ratio 5.31 (95 % confidence interval 1.18-24, p = 0.030)]. In a model including the strongest clinical and echocardiographic predictors of the primary outcome, abnormal GLS demonstrated significant incremental benefit for risk stratification [net reclassification improvement 0.75 (95 % confidence interval 0.21-1.23, p < 0.0001)]. Abnormal GLS is an independent predictor of adverse outcomes in HCM patients. Standardized use of GLS may provide significant incremental value over traditional variables for risk stratification.

Health-related quality-of-life parameters as independent prognostic factors in advanced or metastatic bladder cancer.

PubMed

Roychowdhury, D F; Hayden, A; Liepa, A M

2003-02-15

This retrospective analysis examined prognostic significance of health-related quality-of-life (HRQoL) parameters combined with baseline clinical factors on outcomes (overall survival, time to progressive disease, and time to treatment failure) in bladder cancer. Outcome and HRQoL (European Organization for Research and Treatment of Cancer Quality of Life Questionnaire C30) data were collected prospectively in a phase III study assessing gemcitabine and cisplatin versus methotrexate, vinblastine, doxorubicin, and cisplatin in locally advanced or metastatic bladder cancer. Prespecified baseline clinical factors (performance status, tumor-node-metastasis staging, visceral metastases [VM], alkaline phosphatase [AP] level, number of metastatic sites, prior radiotherapy, disease measurability, sex, time from diagnosis, and sites of disease) and selected HRQoL parameters (global QoL; all functional scales; symptoms: pain, fatigue, insomnia, dyspnea, anorexia) were evaluated using Cox's proportional hazards model. Factors with individual prognostic value (P <.05) on outcomes in univariate models were assessed for joint prognostic value in a multivariate model. A final model was developed using a backward selection strategy. Patients with baseline HRQoL were included (364 of 405, 90%). The final model predicted longer survival with low/normal AP levels, no VM, high physical functioning, low role functioning, and no anorexia. Positive prognostic factors for time to progressive disease were good performance status, low/normal AP levels, no VM, and minimal fatigue; for time to treatment failure, they were low/normal AP levels, minimal fatigue, and no anorexia. Global QoL was a significant predictor of outcome in univariate analyses but was not retained in the multivariate model. HRQoL parameters are independent prognostic factors for outcome in advanced bladder cancer; their prognostic importance needs further evaluation.

Multivariate frequency domain analysis of protein dynamics

NASA Astrophysics Data System (ADS)

Matsunaga, Yasuhiro; Fuchigami, Sotaro; Kidera, Akinori

2009-03-01

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

Imaging of polysaccharides in the tomato cell wall with Raman microspectroscopy

PubMed Central

2014-01-01

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

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

PubMed Central

Jackson, Daniel; Riley, Richard D

2014-01-01

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

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

PubMed Central

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

2013-01-01

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

Biostatistics Series Module 10: Brief Overview of Multivariate Methods.

PubMed

Hazra, Avijit; Gogtay, Nithya

2017-01-01

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

Capital market based warning indicators of bank runs

NASA Astrophysics Data System (ADS)

Vakhtina, Elena; Wosnitza, Jan Henrik

2015-01-01

In this investigation, we examine the univariate as well as the multivariate capabilities of the log-periodic [super-exponential] power law (LPPL) for the prediction of bank runs. The research is built upon daily CDS spreads of 40 international banks for the period from June 2007 to March 2010, i.e. at the heart of the global financial crisis. For this time period, 20 of the financial institutions received federal bailouts and are labeled as defaults while the remaining institutions are categorized as non-defaults. The employed multivariate pattern recognition approach represents a modification of the CORA3 algorithm. The approach is found to be robust regardless of reasonable changes of its inputs. Despite the fact that distinct alarm indices for banks do not clearly demonstrate predictive capabilities of the LPPL, the synchronized alarm indices confirm the multivariate discriminative power of LPPL patterns in CDS spread developments acknowledged by bootstrap intervals with 70% confidence level.

Corruption kills: estimating the global impact of corruption on children deaths.

PubMed

Hanf, Matthieu; Van-Melle, Astrid; Fraisse, Florence; Roger, Amaury; Carme, Bernard; Nacher, Mathieu

2011-01-01

Information on the global risk factors of children mortality is crucial to guide global efforts to improve survival. Corruption has been previously shown to significantly impact on child mortality. However no recent quantification of its current impact is available. The impact of corruption was assessed through crude Pearson's correlation, univariate and multivariate linear models coupling national under-five mortality rates in 2008 to the national "perceived level of corruption" (CPI) and a large set of adjustment variables measured during the same period. The final multivariable model (adjusted R(2)= 0.89) included the following significant variables: percentage of people with improved sanitation (p.value<0.001), logarithm of total health expenditure (p.value = 0.006), Corruption Perception Index (p.value<0.001), presence of an arid climate on the national territory (p = 0.006), and the dependency ratio (p.value<0.001). A decrease in CPI of one point (i.e. a more important perceived corruption) was associated with an increase in the log of national under-five mortality rate of 0.0644. According to this result, it could be roughly hypothesized that more than 140000 annual children deaths could be indirectly attributed to corruption. Global response to children mortality must involve a necessary increase in funds available to develop water and sanitation access and purchase new methods for prevention, management, and treatment of major diseases drawing the global pattern of children deaths. However without paying regard to the anti-corruption mechanisms needed to ensure their proper use, it will also provide further opportunity for corruption. Policies and interventions supported by governments and donors must integrate initiatives that recognise how they are inter-related.

Corruption Kills: Estimating the Global Impact of Corruption on Children Deaths

PubMed Central

Hanf, Matthieu; Van-Melle, Astrid; Fraisse, Florence; Roger, Amaury; Carme, Bernard; Nacher, Mathieu

2011-01-01

Background Information on the global risk factors of children mortality is crucial to guide global efforts to improve survival. Corruption has been previously shown to significantly impact on child mortality. However no recent quantification of its current impact is available. Methods The impact of corruption was assessed through crude Pearson's correlation, univariate and multivariate linear models coupling national under-five mortality rates in 2008 to the national “perceived level of corruption” (CPI) and a large set of adjustment variables measured during the same period. Findings The final multivariable model (adjusted R2 = 0.89) included the following significant variables: percentage of people with improved sanitation (p.value<0.001), logarithm of total health expenditure (p.value = 0.006), Corruption Perception Index (p.value<0.001), presence of an arid climate on the national territory (p = 0.006), and the dependency ratio (p.value<0.001). A decrease in CPI of one point (i.e. a more important perceived corruption) was associated with an increase in the log of national under-five mortality rate of 0.0644. According to this result, it could be roughly hypothesized that more than 140000 annual children deaths could be indirectly attributed to corruption. Interpretations Global response to children mortality must involve a necessary increase in funds available to develop water and sanitation access and purchase new methods for prevention, management, and treatment of major diseases drawing the global pattern of children deaths. However without paying regard to the anti-corruption mechanisms needed to ensure their proper use, it will also provide further opportunity for corruption. Policies and interventions supported by governments and donors must integrate initiatives that recognise how they are inter-related. PMID:22073233

Single-trial decoding of auditory novelty responses facilitates the detection of residual consciousness

PubMed Central

King, J.R.; Faugeras, F.; Gramfort, A.; Schurger, A.; El Karoui, I.; Sitt, J.D.; Rohaut, B.; Wacongne, C.; Labyt, E.; Bekinschtein, T.; Cohen, L.; Naccache, L.; Dehaene, S.

2017-01-01

Detecting residual consciousness in unresponsive patients is a major clinical concern and a challenge for theoretical neuroscience. To tackle this issue, we recently designed a paradigm that dissociates two electro-encephalographic (EEG) responses to auditory novelty. Whereas a local change in pitch automatically elicits a mismatch negativity (MMN), a change in global sound sequence leads to a late P300b response. The latter component is thought to be present only when subjects consciously perceive the global novelty. Unfortunately, it can be difficult to detect because individual variability is high, especially in clinical recordings. Here, we show that multivariate pattern classifiers can extract subject-specific EEG patterns and predict single-trial local or global novelty responses. We first validate our method with 38 high-density EEG, MEG and intracranial EEG recordings. We empirically demonstrate that our approach circumvents the issues associated with multiple comparisons and individual variability while improving the statistics. Moreover, we confirm in control subjects that local responses are robust to distraction whereas global responses depend on attention. We then investigate 104 vegetative state (VS), minimally conscious state (MCS) and conscious state (CS) patients recorded with high-density EEG. For the local response, the proportion of significant decoding scores (M = 60%) does not vary with the state of consciousness. By contrast, for the global response, only 14% of the VS patients' EEG recordings presented a significant effect, compared to 31% in MCS patients' and 52% in CS patients'. In conclusion, single-trial multivariate decoding of novelty responses provides valuable information in non-communicating patients and paves the way towards real-time monitoring of the state of consciousness. PMID:23859924

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

PubMed

Pourahmadi, Mohsen; Noorbaloochi, Siamak

2016-04-01

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

Synthesis of a control model for a liquid nitrogen cooled, closed circuit, cryogenic nitrogen wind tunnel and its validation

NASA Technical Reports Server (NTRS)

Balakrishna, S.; Goglia, G. L.

1979-01-01

The details of the efforts to synthesize a control-compatible multivariable model of a liquid nitrogen cooled, gaseous nitrogen operated, closed circuit, cryogenic pressure tunnel are presented. The synthesized model was transformed into a real-time cryogenic tunnel simulator, and this model is validated by comparing the model responses to the actual tunnel responses of the 0.3 m transonic cryogenic tunnel, using the quasi-steady-state and the transient responses of the model and the tunnel. The global nature of the simple, explicit, lumped multivariable model of a closed circuit cryogenic tunnel is demonstrated.

Data analysis techniques

NASA Technical Reports Server (NTRS)

Park, Steve

1990-01-01

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

Chemical structure of wood charcoal by infrared spectroscopy and multivariate analysis

Treesearch

Nicole Labbe; David Harper; Timothy Rials; Thomas Elder

2006-01-01

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

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

PubMed

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

2015-01-01

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

The Emerging Field of Quantitative Blood Metabolomics for Biomarker Discovery in Critical Illnesses

PubMed Central

Serkova, Natalie J.; Standiford, Theodore J.

2011-01-01

Metabolomics, a science of systems biology, is the global assessment of endogenous metabolites within a biologic system and represents a “snapshot” reading of gene function, enzyme activity, and the physiological landscape. Metabolite detection, either individual or grouped as a metabolomic profile, is usually performed in cells, tissues, or biofluids by either nuclear magnetic resonance spectroscopy or mass spectrometry followed by sophisticated multivariate data analysis. Because loss of metabolic homeostasis is common in critical illness, the metabolome could have many applications, including biomarker and drug target identification. Metabolomics could also significantly advance our understanding of the complex pathophysiology of acute illnesses, such as sepsis and acute lung injury/acute respiratory distress syndrome. Despite this potential, the clinical community is largely unfamiliar with the field of metabolomics, including the methodologies involved, technical challenges, and, most importantly, clinical uses. Although there is evidence of successful preclinical applications, the clinical usefulness and application of metabolomics in critical illness is just beginning to emerge, the advancement of which hinges on linking metabolite data to known and validated clinically relevant indices. In addition, other important aspects, such as patient selection, sample collection, and processing, as well as the needed multivariate data analysis, have to be taken into consideration before this innovative approach to biomarker discovery can become a reliable tool in the intensive care unit. The purpose of this review is to begin to familiarize clinicians with the field of metabolomics and its application for biomarker discovery in critical illnesses such as sepsis. PMID:21680948

Depression and associated factors in older adults in South Africa.

PubMed

Peltzer, Karl; Phaswana-Mafuya, Nancy

2013-01-18

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

Forecasting daily source air quality using multivariate statistical analysis and radial basis function networks.

PubMed

Sun, Gang; Hoff, Steven J; Zelle, Brian C; Nelson, Minda A

2008-12-01

It is vital to forecast gas and particle matter concentrations and emission rates (GPCER) from livestock production facilities to assess the impact of airborne pollutants on human health, ecological environment, and global warming. Modeling source air quality is a complex process because of abundant nonlinear interactions between GPCER and other factors. The objective of this study was to introduce statistical methods and radial basis function (RBF) neural network to predict daily source air quality in Iowa swine deep-pit finishing buildings. The results show that four variables (outdoor and indoor temperature, animal units, and ventilation rates) were identified as relative important model inputs using statistical methods. It can be further demonstrated that only two factors, the environment factor and the animal factor, were capable of explaining more than 94% of the total variability after performing principal component analysis. The introduction of fewer uncorrelated variables to the neural network would result in the reduction of the model structure complexity, minimize computation cost, and eliminate model overfitting problems. The obtained results of RBF network prediction were in good agreement with the actual measurements, with values of the correlation coefficient between 0.741 and 0.995 and very low values of systemic performance indexes for all the models. The good results indicated the RBF network could be trained to model these highly nonlinear relationships. Thus, the RBF neural network technology combined with multivariate statistical methods is a promising tool for air pollutant emissions modeling.

Risk Factors for Acute Unintentional Poisoning among Children Aged 1–5 Years in the Rural Community of Sri Lanka

PubMed Central

Jayamanne, Shaluka F.; Jayasinghe, Chamilka Y.

2017-01-01

Background Acute poisoning in children is a major preventable cause of morbidity and mortality in both developed and developing countries. However, there is a wide variation in patterns of poisoning and related risk factors across different geographic regions globally. This hospital based case-control study identifies the risk factors of acute unintentional poisoning among children aged 1−5 years of the rural community in a developing Asian country. Methods This hospital based case-control study included 600 children. Each group comprised three hundred children and all children were recruited at Anuradhapura Teaching Hospital, Sri Lanka, over two years (from February 2012 to January 2014). The two groups were compared to identify the effect of 23 proposed risk factors for unintentional poisoning using multivariate analysis in a binary logistic regression model. Results Multivariate analysis identified eight risk factors which were significantly associated with unintentional poisoning. The strongest risk factors were inadequate supervision (95% CI: 15.4–52.6), employed mother (95% CI: 2.9–17.5), parental concern of lack of family support (95% CI: 3.65–83.3), and unsafe storage of household poisons (95% CI: 1.5–4.9). Conclusions Since inadequate supervision, unsafe storage, and unsafe environment are the strongest risk factors for childhood unintentional poisoning, the effect of community education to enhance vigilance, safe storage, and assurance of safe environment should be evaluated. PMID:28932247

Risk Factors for Venous Thromboembolism in Chronic Obstructive Pulmonary Disease

PubMed Central

Kim, Victor; Goel, Nishant; Gangar, Jinal; Zhao, Huaqing; Ciccolella, David E.; Silverman, Edwin K.; Crapo, James D.; Criner, Gerard J.

2014-01-01

Background: COPD patients are at increased risk for venous thromboembolism (VTE). VTE however remains under-diagnosed in this population and the clinical profile of VTE in COPD is unclear. Methods: Global initiative for chronic Obstructive Lung Disease (GOLD) stages II-IV participants in the COPD Genetic Epidemiology (COPDGene) study were divided into 2 groups: VTE+, those who reported a history of VTE by questionnaire, and VTE-, those who did not. We compared variables in these 2 groups with either t-test or chi-squared test for continuous and categorical variables, respectively. We performed a univariate logistic regression for VTE, and then a multivariate logistic regression using the significant predictors of interest in the univariate analysis to ascertain the determinants of VTE. Results: The VTE+ group was older, more likely to be Caucasian, had a higher body mass index (BMI), smoking history, used oxygen, had a lower 6-minute walk distance, worse quality of life scores, and more dyspnea and respiratory exacerbations than the VTE- group. Lung function was not different between groups. A greater percentage of the VTE+ group described multiple medical comorbidities. On multivariate analysis, BMI, 6-minute walk distance, pneumothorax, peripheral vascular disease, and congestive heart failure significantly increased the odds for VTE by history. Conclusions: BMI, exercise capacity, and medical comorbidities were significantly associated with VTE in moderate to severe COPD. Clinicians should suspect VTE in patients who present with dyspnea and should consider possibilities other than infection as causes of COPD exacerbation. PMID:25844397

Multivariate analysis: greater insights into complex systems

USDA-ARS?s Scientific Manuscript database

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

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

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

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

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

A three-dimensional multivariate representation of atmospheric variability

NASA Astrophysics Data System (ADS)

Žagar, Nedjeljka; Jelić, Damjan; Blaauw, Marten; Jesenko, Blaž

2016-04-01

A recently developed MODES software has been applied to the ECMWF analyses and forecasts and to several reanalysis datasets to describe the global variability of the balanced and inertio-gravity (IG) circulation across many scales by considering both mass and wind field and the whole model depth. In particular, the IG spectrum, which has only recently become observable in global datasets, can be studied simultaneously in the mass field and wind field and considering the whole model depth. MODES is open-access software that performs the normal-mode function decomposition of the 3D global datasets. Its application to the ERA Interim dataset reveals several aspects of the large-scale circulation after it has been partitioned into the linearly balanced and IG components. The global energy distribution is dominated by the balanced energy while the IG modes contribute around 8% of the total wave energy. However, on subsynoptic scales IG energy dominates and it is associated with the main features of tropical variability on all scales. The presented energy distribution and features of the zonally-averaged and equatorial circulation provide a reference for the intercomparison of several reanalysis datasets and for the validation of climate models. Features of the global IG circulation are compared in ERA Interim, MERRA and JRA reanalysis datasets and in several CMIP5 models. Since October 2014 the operational medium-range forecasts of the European Centre for Medium-Range Weather Forecasts (ECMWF) have been analyzed by MODES daily and an online archive of all the outputs is available at http://meteo.fmf.uni-lj.si/MODES. New outputs are made available daily based on the 00 UTC run and subsequent 12-hour forecasts up to 240-hour forecast. In addition to the energy spectra and horizontal circulation on selected levels for the balanced and IG components, the equatorial Kelvin waves are presented in time and space as the most energetic tropical IG modes propagating vertically and along the equator from its main generation regions in the upper troposphere over the Indian and Pacific region. The validation of the 10-day ECMWF forecasts with analyses in the modal space suggests a lack of variability in the tropics in the medium range. Reference: Žagar, N. et al., 2015: Normal-mode function representation of global 3-D data sets: open-access software for the atmospheric research community. Geosci. Model Dev., 8, 1169-1195, doi:10.5194/gmd-8-1169-2015 Žagar, N., R. Buizza, and J. Tribbia, 2015: A three-dimensional multivariate modal analysis of atmospheric predictability with application to the ECMWF ensemble. J. Atmos. Sci., 72, 4423-4444 The MODES software is available from http://meteo.fmf.uni-lj.si/MODES.

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

PubMed

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

2017-12-01

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

Comparison of connectivity analyses for resting state EEG data

NASA Astrophysics Data System (ADS)

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

2017-06-01

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

Experts' perceptions on the entrepreneurial framework conditions

NASA Astrophysics Data System (ADS)

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

2017-11-01

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

Simulation of growth of Adirondack conifers in relation to global climate change

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

Pan, Y.; Raynal, D.J.

1993-06-01

Several conifer species grown in plantations in the southeastern Adirondack mountains of New York were chosen to model tree growth. In the models, annual xylem growth was decomposed into several components that reflect various intrinsic or extrinsic factors. Growth signals indicative of climatic effects were used to construct response functions using both multivariate analysis and Kalman filter methods. Two models were used to simulate tree growth response to future CO[sub 2]-induced climate change projected by GCMs. The comparable results of both models indicate that different conifer species have individualistic growth responses to future climatic change. The response behaviors of treesmore » are affected greatly by local stand conditions. The results suggest possible changes in future growth and distributions of naturally occurring conifers in this region.« less

MULTIVARIATE CURVE RESOLUTION OF NMR SPECTROSCOPY METABONOMIC DATA

EPA Science Inventory

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

Characterizing multivariate decoding models based on correlated EEG spectral features

PubMed Central

McFarland, Dennis J.

2013-01-01

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

Drunk driving detection based on classification of multivariate time series.

PubMed

Li, Zhenlong; Jin, Xue; Zhao, Xiaohua

2015-09-01

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

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

PubMed

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

2015-12-30

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

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

PubMed Central

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

2015-01-01

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

Beverage and culture. "Zhourat", a multivariate analysis of the globalization of a herbal tea from the Middle East.

PubMed

Obón, Concepción; Rivera, Diego; Alcaraz, Francisco; Attieh, Latiffa

2014-08-01

The "Zhourat" herbal tea consists of a blend of wild flowers, herbs, leaves and fruits and is a typical beverage of Lebanon and Syria. We aim to evaluate cultural significance of "Zhourat", to determine cultural standards for its formulation including key ingredients and to determine acceptable variability levels in terms of number of ingredients and their relative proportions, in summary what is "Zhourat" and what is not "Zhourat" from an ethnobotanical perspective. For this purpose we develop a novel methodology to describe and analyse patterns of variation of traditional multi-ingredient herbal formulations, beverages and teas and to identify key ingredients, which are characteristics of a particular culture and region and to interpret health claims for the mixture. Factor analysis and hierarchical clustering techniques were used to display similarities between samples whereas salience index was used to determine the main ingredients which could help to distinguish a standard traditional blend from a global market-addressed formulation. The study revealed 77 main ingredients belonging to 71 different species of vascular plants. In spite of the "Zhourat's" highly variable content, the salience analysis resulted in a determined set of key botanical components including Rosa x damascena Herrm., Althaea damascena Mouterde, Matricaria chamomilla L., Aloysia citrodora Palau, Zea mays L. and Elaeagnus angustifolia L. The major health claims for "Zhourat" as digestive, sedative and for respiratory problems are culturally coherent with the analysis of the traditional medicinal properties uses of its ingredients. Copyright © 2014 Elsevier Ltd. All rights reserved.

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

PubMed

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

2007-01-01

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

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

ERIC Educational Resources Information Center

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

2016-01-01

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

Global cardiovascular mortality risk in the adult Polish population: prospective assessment of the cohorts studied in multicentre national WOBASZ and WOBASZ Senior studies.

PubMed

Piotrowski, Walerian; Waśkiewicz, Anna; Cicha-Mikołajczyk, Alicja

2016-01-01

To develop a global cardiovascular disease (CVD) mortality risk model for the Polish population and to verify these data in the context of the SCORE risk algorithm. We analysed data obtained in two multicentre national population studies, the WOBASZ study which was conducted in 2003-2005 and included 14,769 subjects aged 20-74 years, and the WOBASZ Senior study which was conducted in 2007 and included 1096 subjects above 74 years of age. All these subjects were followed for survival status until 2012 and the cause of death was determined. The mean duration of follow-up was 8.2 years for WOBASZ study participants and about 5 years for WOBASZ Senior study participants. Overall, 1436 subjects died, including 568 due to CVD. For the purpose of our analysis of overall and CVD mortality, 15 established risk factors were selected. Survival was analysed separately in WOBASZ and WOBASZ Senior study participants. Statistical methods included descriptive statistics, Kaplan-Meier curves, Cox proportional hazard models, and the SCORE risk algorithm. Measure of incompatibility of the SCORE risk model to the Polish population was determined as the difference between mortality rates by the SCORE risk quartiles and the Cox approach. During the 8-year follow-up of the WOBASZ study population, mortality due to CVD was 38% among men and 31% among women. The most common causes of CVD mortality were ischaemic heart disease (IHD, 33%) followed by cerebro-vascular disease (17%) in men, and cerebrovascular disease (31%) followed by IHD (23%) in women. We found significant differences between men and women in regard to survival curves for both overall mortality and CVD mortality (p < 0.0001). For overall mortality among men and women, nearly all selected risk factors were shown to be significant in univariate analyses, except for high density lipoprotein cholesterol (HDL-C) level and the total cholesterol/HDL-C ratio in men, and smoking status in women. In multivariate analysis, independent predictors in men included age, glucose level, systolic blood pressure, and smoking status. In women, independent predictors were age, smoking status, and diabetes. During the 5-year follow-up of the WOBASZ Senior study population, mortality due to CVD was 48% among men and 58% among women. The most common cause of CVD mortality in both men and women was IHD (29% and 24%, respectively), followed by cerebrovascular disease (16% and 21%, respectively). We found significant differences between men and women in regard to survival curves for overall mortality (p < 0.0001) but not for CVD mortality (p = 0.0755). Due to the fact that survival curves for CVD mortality did not differ between men and women, we estimated the cut-off age for no survival difference in the WOBASZ study. By selecting the oldest patients and adding them to the WOBASZ Senior cohort, we obtained the cut-off age of 70 years above which the survival curves were not significantly different between men and women. In univariate analyses, independent predictors in men were age and creatinine level. These factors remained significant in multivariate analysis. In women above 74 years of age, independent predictors in univariate analyses included age, HDL-C level, creatinine level, total cholesterol/HDL-C ratio, and smoking status. Age, HDL-C level, creatinine level, and smoking status remained independent predictors of overall mortality in multivariate analysis. For CVD mortality, significant predictors were the same as for overall mortality. In women, significant predictors in uni- and multivariate analyses were age and smoking status. Overall disagreement between CVD mortality rates by the SCORE risk model and the Cox model was 5.7% in men and 2% in women. 1. Long-term follow-up of WOBASZ and WOBASZ Senior study participants allowed assessment of the inde-pendent association of the evaluated cardiovascular risk factors with CVD mortality in the Polish population. 2. Validation of the SCORE risk algorithm to estimate individual global CVD risk in the Polish population showed a high predictive value of this algorithm.

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

PubMed

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

2015-04-01

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

Multivariate meta-analysis using individual participant data

PubMed Central

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

2016-01-01

When combining results across related studies, a multivariate meta-analysis allows the joint synthesis of correlated effect estimates from multiple outcomes. Joint synthesis can improve efficiency over separate univariate syntheses, may reduce selective outcome reporting biases, and enables joint inferences across the outcomes. A common issue is that within-study correlations needed to fit the multivariate model are unknown from published reports. However, provision of individual participant data (IPD) allows them to be calculated directly. Here, we illustrate how to use IPD to estimate within-study correlations, using a joint linear regression for multiple continuous outcomes and bootstrapping methods for binary, survival and mixed outcomes. In a meta-analysis of 10 hypertension trials, we then show how these methods enable multivariate meta-analysis to address novel clinical questions about continuous, survival and binary outcomes; treatment–covariate interactions; adjusted risk/prognostic factor effects; longitudinal data; prognostic and multiparameter models; and multiple treatment comparisons. Both frequentist and Bayesian approaches are applied, with example software code provided to derive within-study correlations and to fit the models. PMID:26099484

NIPA-like domain containing 1 is a novel tumor-promoting factor in oral squamous cell carcinoma.

PubMed

Sasahira, Tomonori; Nishiguchi, Yukiko; Kurihara-Shimomura, Miyako; Nakashima, Chie; Kuniyasu, Hiroki; Kirita, Tadaaki

2018-05-01

In our previous global gene expression analysis, we identified NIPA-like domain containing 1 (NIPAL1), which encodes a magnesium transporter, as one of the most overexpressed genes in recurrent oral squamous cell carcinoma (OSCC). Although has been NIPAL1 linked with gout pathogenesis, little is known about its expression and function in human malignancies. In this study, we examined NIPAL1 expression in 192 cases of OSCC by immunohistochemistry and performed a functional analysis of human OSCC cells. NIPAL1 immunostaining was observed in 39 of 192 OSCC patients (20.3%). NIPAL1 expression correlated significantly with cancer cell intravsation (P = 0.0062), as well as with poorer disease-free survival in a Kaplan-Meier analysis (P < 0.0001). Moreover, a multivariate Cox proportional hazards model analysis revealed that NIPAL1 expression was an independent predictor of disease-free survival in OSCC (P < 0.0001). In a functional analysis, NIPAL1 regulated the growth and adhesion of OSCC tumor cells and endothelial cells. Our findings suggest that NIPAL1 might be a novel factor promoting OSCC tumorigenesis, as well as a useful molecular marker of OSCC.

Hybrid least squares multivariate spectral analysis methods

DOEpatents

Haaland, David M.

2002-01-01

A set of hybrid least squares multivariate spectral analysis methods in which spectral shapes of components or effects not present in the original calibration step are added in a following estimation or calibration step to improve the accuracy of the estimation of the amount of the original components in the sampled mixture. The "hybrid" method herein means a combination of an initial classical least squares analysis calibration step with subsequent analysis by an inverse multivariate analysis method. A "spectral shape" herein means normally the spectral shape of a non-calibrated chemical component in the sample mixture but can also mean the spectral shapes of other sources of spectral variation, including temperature drift, shifts between spectrometers, spectrometer drift, etc. The "shape" can be continuous, discontinuous, or even discrete points illustrative of the particular effect.

Population aging, macroeconomic changes, and global diabetes prevalence, 1990-2008.

PubMed

Sudharsanan, Nikkil; Ali, Mohammed K; Mehta, Neil K; Narayan, K M Venkat

2015-01-01

Diabetes is an important contributor to global morbidity and mortality. The contributions of population aging and macroeconomic changes to the growth in diabetes prevalence over the past 20 years are unclear. We used cross-sectional data on age- and sex-specific counts of people with diabetes by country, national population estimates, and country-specific macroeconomic variables for the years 1990, 2000, and 2008. Decomposition analysis was performed to quantify the contribution of population aging to the change in global diabetes prevalence between 1990 and 2008. Next, age-standardization was used to estimate the contribution of age composition to differences in diabetes prevalence between high-income (HIC) and low-to-middle-income countries (LMICs). Finally, we used non-parametric correlation and multivariate first-difference regression estimates to examine the relationship between macroeconomic changes and the change in diabetes prevalence between 1990 and 2008. Globally, diabetes prevalence grew by two percentage points between 1990 (7.4 %) and 2008 (9.4 %). Population aging was responsible for 19 % of the growth, with 81 % attributable to increases in the age-specific prevalences. In both LMICs and HICs, about half the growth in age-specific prevalences was from increasing levels of diabetes between ages 45-65 (51 % in HICs and 46 % in LMICs). After age-standardization, the difference in the prevalence of diabetes between LMICs and HICs was larger (1.9 % point difference in 1990; 1.5 % point difference in 2008). We found no evidence that macroeconomic changes were associated with the growth in diabetes prevalence. Population aging explains a minority of the recent growth in global diabetes prevalence. The increase in global diabetes between 1990 and 2008 was primarily due to an increase in the prevalence of diabetes at ages 45-65. We do not find evidence that basic indicators of economic growth, development, globalization, or urbanization were related to rising levels of diabetes between 1990 and 2008.

Multivariate generalized multifactor dimensionality reduction to detect gene-gene interactions

PubMed Central

2013-01-01

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

Evaluation of the Risk Factors for a Rotator Cuff Retear After Repair Surgery.

PubMed

Lee, Yeong Seok; Jeong, Jeung Yeol; Park, Chan-Deok; Kang, Seung Gyoon; Yoo, Jae Chul

2017-07-01

A retear is a significant clinical problem after rotator cuff repair. However, no study has evaluated the retear rate with regard to the extent of footprint coverage. To evaluate the preoperative and intraoperative factors for a retear after rotator cuff repair, and to confirm the relationship with the extent of footprint coverage. Cohort study; Level of evidence, 3. Data were retrospectively collected from 693 patients who underwent arthroscopic rotator cuff repair between January 2006 and December 2014. All repairs were classified into 4 types of completeness of repair according to the amount of footprint coverage at the end of surgery. All patients underwent magnetic resonance imaging (MRI) after a mean postoperative duration of 5.4 months. Preoperative demographic data, functional scores, range of motion, and global fatty degeneration on preoperative MRI and intraoperative variables including the tear size, completeness of rotator cuff repair, concomitant subscapularis repair, number of suture anchors used, repair technique (single-row or transosseous-equivalent double-row repair), and surgical duration were evaluated. Furthermore, the factors associated with failure using the single-row technique and transosseous-equivalent double-row technique were analyzed separately. The retear rate was 7.22%. Univariate analysis revealed that rotator cuff retears were affected by age; the presence of inflammatory arthritis; the completeness of rotator cuff repair; the initial tear size; the number of suture anchors; mean operative time; functional visual analog scale scores; Simple Shoulder Test findings; American Shoulder and Elbow Surgeons scores; and fatty degeneration of the supraspinatus, infraspinatus, and subscapularis. Multivariate logistic regression analysis revealed patient age, initial tear size, and fatty degeneration of the supraspinatus as independent risk factors for a rotator cuff retear. Multivariate logistic regression analysis of the single-row group revealed patient age and fatty degeneration of the supraspinatus as independent risk factors for a rotator cuff retear. Multivariate logistic regression analysis of the transosseous-equivalent double-row group revealed a frozen shoulder as an independent risk factor for a rotator cuff retear. Our results suggest that patient age, initial tear size, and fatty degeneration of the supraspinatus are independent risk factors for a rotator cuff retear, whereas the completeness of rotator cuff repair based on the extent of footprint coverage and repair technique are not.

Two-dimensional NMR spectroscopy strongly enhances soil organic matter composition analysis

NASA Astrophysics Data System (ADS)

Soucemarianadin, Laure; Erhagen, Björn; Öquist, Mats; Nilsson, Mats; Hedenström, Mattias; Schleucher, Jürgen

2016-04-01

Soil organic matter (SOM) is the largest terrestrial carbon pool and strongly affects soil properties. With climate change, understanding SOM processes and turnover and how they could be affected by increasing temperatures becomes critical. This is particularly key for organic soils as they represent a huge carbon pool in very sensitive ecosystems, like boreal ecosystems and peatlands. Nevertheless, characterization of SOM molecular composition, which is essential to elucidate soil carbon processes, is not easily achieved, and further advancements in that area are greatly needed. Solid-state one-dimensional (1D) 13C nuclear magnetic resonance (NMR) spectroscopy is often used to characterize its molecular composition, but only provides data on a few major functional groups, which regroup many different molecular fragments. For instance, in the carbohydrates region, signals of all monosaccharides present in many different polymers overlap. This overlap thwarts attempts to identify molecular moieties, resulting in insufficient information to characterize SOM composition. Here we show that two-dimensional (2D) liquid-state 1H-13C NMR spectra provided much richer data on the composition of boreal plant litter and organic surface soil. The 2D spectra indeed resolved overlaps observed in 1D 13C spectra and displayed signals from hundreds of identifiable molecular groups. For example, in the aromatics region, signals from individual lignin units could be recognized. It was hence possible to follow the fate of specific structural moieties in soils. We observed differences between litter and soil samples, and were able to relate them to the decomposition of identifiable moieties. Sample preparation and data acquisition were both simple and fast. Further, using multivariate data analysis, we aimed at linking the detailed chemical fingerprints of SOM to turnover rates in a soil incubation experiment. With the multivariate models, we were able to identify specific molecular moieties correlated to variability in the temperature response of organic matter decomposition, as assessed by Q10. Thus, 2D NMR methods, and their combination with multivariate analysis, can greatly improve analysis of litter and SOM composition, thereby facilitating elucidation of their roles in biogeochemical and ecological processes that are so critical to foresee associated feedback mechanisms on SOM turnover as a result of global environmental change.

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

PubMed

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

2001-11-01

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

Prediction of energy expenditure from heart rate and accelerometry in children and adolescents using multivariate adaptive regression splines modeling

USDA-ARS?s Scientific Manuscript database

Free-living measurements of 24-h total energy expenditure (TEE) and activity energy expenditure (AEE) are required to better understand the metabolic, physiological, behavioral, and environmental factors affecting energy balance and contributing to the global epidemic of childhood obesity. The spec...

Does national expenditure on research and development influence stroke outcomes?

PubMed

Kim, Young Dae; Jung, Yo Han; Norrving, Bo; Ovbiagele, Bruce; Saposnik, Gustavo

2017-10-01

Background Expenditure on research and development is a macroeconomic indicator representative of national investment. International organizations use this indicator to compare international research and development activities. Aim We investigated whether differences in expenditures on research and development at the country level may influence the incidence of stroke and stroke mortality. Methods We compared stroke metrics with absolute amount of gross domestic expenditure on R&D (GERD) per-capita adjusted for purchasing power parity (aGERD) and relative amount of GERD as percent of gross domestic product (rGERD). Sources included official data from the UNESCO, the World Health Organization, the World Bank, and population-based studies. We used correlation analysis and multivariable linear regression modeling. Results Overall, data on stroke mortality rate and GERD were available from 66 countries for two periods (2002 and 2008). Age-standardized stroke mortality rate was associated with aGERD (r = -0.708 in 2002 and r = -0.730 in 2008) or rGERD (r = -0.545 in 2002 and r = -0.657 in 2008) (all p < 0.001). Multivariable analysis showed a lower aGERD and rGERD were independently and inversely associated with higher stroke mortality (all p < 0.05). The estimated prevalence of hypertension, diabetes, or obesity was higher in countries with lower aGERD. The analysis of 27 population-based studies showed consistent inverse associations between aGERD or rGERD and incident risk of stroke and 30-day case fatality. Conclusions There is higher stroke mortality among countries with lower expenditures in research and development. While this study does not prove causality, it suggests a potential area to focus efforts to improve global stroke outcomes.

A coupled geochemical and biogeochemical approach to characterize the bioreactivity of dissolved organic matter from a headwater stream

NASA Astrophysics Data System (ADS)

Sleighter, Rachel L.; Cory, Rose M.; Kaplan, Louis A.; Abdulla, Hussain A. N.; Hatcher, Patrick G.

2014-08-01

The bioreactivity or susceptibility of dissolved organic matter (DOM) to microbial degradation in streams and rivers is of critical importance to global change studies, but a comprehensive understanding of DOM bioreactivity has been elusive due, in part, to the stunningly diverse assemblages of organic molecules within DOM. We approach this problem by employing a range of techniques to characterize DOM as it flows through biofilm reactors: dissolved organic carbon (DOC) concentrations, excitation emission matrix spectroscopy (EEMs), and ultrahigh resolution mass spectrometry. The EEMs and mass spectral data were analyzed using a combination of multivariate statistical approaches. We found that 45% of stream water DOC was biodegraded by microorganisms, including 31-45% of the humic DOC. This bioreactive DOM separated into two different groups: (1) H/C centered at 1.5 with O/C 0.1-0.5 or (2) low H/C of 0.5-1.0 spanning O/C 0.2-0.7 that were positively correlated (Spearman ranking) with chromophoric and fluorescent DOM (CDOM and FDOM, respectively). DOM that was more recalcitrant and resistant to microbial degradation aligned tightly in the center of the van Krevelen space (H/C 1.0-1.5, O/C 0.25-0.6) and negatively correlated (Spearman ranking) with CDOM and FDOM. These findings were supported further by principal component analysis and 2-D correlation analysis of the relative magnitudes of the mass spectral peaks assigned to molecular formulas. This study demonstrates that our approach of processing stream water through bioreactors followed by EEMs and FTICR-MS analyses, in combination with multivariate statistical analysis, allows for precise, robust characterization of compound bioreactivity and associated molecular level composition.

Blended learning in situated contexts: 3-year evaluation of an online peer review project.

PubMed

Bridges, S; Chang, J W W; Chu, C H; Gardner, K

2014-08-01

Situated and sociocultural perspectives on learning indicate that the design of complex tasks supported by educational technologies holds potential for dental education in moving novices towards closer approximation of the clinical outcomes of their expert mentors. A cross-faculty-, student-centred, web-based project in operative dentistry was established within the Universitas 21 (U21) network of higher education institutions to support university goals for internationalisation in clinical learning by enabling distributed interactions across sites and institutions. This paper aims to present evaluation of one dental faculty's project experience of curriculum redesign for deeper student learning. A mixed-method case study approach was utilised. Three cohorts of second-year students from a 5-year bachelor of dental surgery (BDS) programme were invited to participate in annual surveys and focus group interviews on project completion. Survey data were analysed for differences between years using multivariate logistical regression analysis. Thematic analysis of questionnaire open responses and interview transcripts was conducted. Multivariate logistic regression analysis noted significant differences across items over time indicating learning improvements, attainment of university aims and the positive influence of redesign. Students perceived the enquiry-based project as stimulating and motivating, and building confidence in operative techniques. Institutional goals for greater understanding of others and lifelong learning showed improvement over time. Despite positive scores, students indicated global citizenship and intercultural understanding were conceptually challenging. Establishment of online student learning communities through a blended approach to learning stimulated motivation and intellectual engagement, thereby supporting a situated approach to cognition. Sociocultural perspectives indicate that novice-expert interactions supported student development of professional identities. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Individual and socioeconomic factors associated with childhood immunization coverage in Nigeria

PubMed Central

Oleribe, Obinna; Kumar, Vibha; Awosika-Olumo, Adebowale; Taylor-Robinson, Simon David

2017-01-01

Introduction Immunization is the world’s most successful and cost-effective public health intervention as it prevents over 2 million deaths annually. However, over 2 million deaths still occur yearly from Vaccine preventable diseases, the majority of which occur in sub-Saharan Africa. Nigeria is a major contributor of global childhood deaths from VPDs. Till date, Nigeria still has wild polio virus in circulation. The objective of this study was to identify the individual and socioeconomic factors associated with immunization coverage in Nigeria through a secondary dataset analysis of Nigeria Demographic and Health Survey (NDHS), 2013. Methods A quantitative analysis of the 2013 NDHS dataset was performed. Ethical approvals were obtained from Walden University IRB and the National Health Research Ethics Committee of Nigeria. The dataset was downloaded, validated for completeness and analyzed using univariate, bivariate and multivariate statistics. Results Of 27,571 children aged 0 to 59 months, 22.1% had full vaccination, and 29% never received any vaccination. Immunization coverage was significantly associated with childbirth order, delivery place, child number, and presence or absence of a child health card. Maternal age, geographical location, education, religion, literacy, wealth index, marital status, and occupation were significantly associated with immunization coverage. Paternal education, occupation, and age were also significantly associated with coverage. Respondent's age, educational attainment and wealth index remained significantly related to immunization coverage at 95% confidence interval in multivariate analysis. Conclusion The study highlights child, parental and socioeconomic barriers to successful immunization programs in Nigeria. These findings need urgent attention, given the re-emergence of wild poliovirus in Nigeria. An effective, efficient, sustainable, accessible, and acceptable immunization program for children should be designed, developed and undertaken in Nigeria with adequate strategies put in place to implement them. PMID:28690734

Individual and socioeconomic factors associated with childhood immunization coverage in Nigeria.

PubMed

Oleribe, Obinna; Kumar, Vibha; Awosika-Olumo, Adebowale; Taylor-Robinson, Simon David

2017-01-01

Immunization is the world's most successful and cost-effective public health intervention as it prevents over 2 million deaths annually. However, over 2 million deaths still occur yearly from Vaccine preventable diseases, the majority of which occur in sub-Saharan Africa. Nigeria is a major contributor of global childhood deaths from VPDs. Till date, Nigeria still has wild polio virus in circulation. The objective of this study was to identify the individual and socioeconomic factors associated with immunization coverage in Nigeria through a secondary dataset analysis of Nigeria Demographic and Health Survey (NDHS), 2013. A quantitative analysis of the 2013 NDHS dataset was performed. Ethical approvals were obtained from Walden University IRB and the National Health Research Ethics Committee of Nigeria. The dataset was downloaded, validated for completeness and analyzed using univariate, bivariate and multivariate statistics. Of 27,571 children aged 0 to 59 months, 22.1% had full vaccination, and 29% never received any vaccination. Immunization coverage was significantly associated with childbirth order, delivery place, child number, and presence or absence of a child health card. Maternal age, geographical location, education, religion, literacy, wealth index, marital status, and occupation were significantly associated with immunization coverage. Paternal education, occupation, and age were also significantly associated with coverage. Respondent's age, educational attainment and wealth index remained significantly related to immunization coverage at 95% confidence interval in multivariate analysis. The study highlights child, parental and socioeconomic barriers to successful immunization programs in Nigeria. These findings need urgent attention, given the re-emergence of wild poliovirus in Nigeria. An effective, efficient, sustainable, accessible, and acceptable immunization program for children should be designed, developed and undertaken in Nigeria with adequate strategies put in place to implement them.

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

DTIC Science & Technology

1982-12-20

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

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

NASA Astrophysics Data System (ADS)

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

2008-05-01

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

SUGGESTIONS FOR OPTIMIZED PLANNING OF MULTIVARIATE MONITORING OF ATMOSPHERIC POLLUTION

EPA Science Inventory

Recent work in factor analysis of multivariate data sets has shown that variables with little signal should not be included in the factor analysis. Work also shows that rotational ambiguity is reduced if sources impacting a receptor have both large and small contributions. Thes...

Multivariate Meta-Analysis Using Individual Participant Data

ERIC Educational Resources Information Center

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

2015-01-01

When combining results across related studies, a multivariate meta-analysis allows the joint synthesis of correlated effect estimates from multiple outcomes. Joint synthesis can improve efficiency over separate univariate syntheses, may reduce selective outcome reporting biases, and enables joint inferences across the outcomes. A common issue is…

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

PubMed Central

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

2013-01-01

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

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

PubMed

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

2013-10-15

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

Relevant Feature Set Estimation with a Knock-out Strategy and Random Forests

PubMed Central

Ganz, Melanie; Greve, Douglas N.; Fischl, Bruce; Konukoglu, Ender

2015-01-01

Group analysis of neuroimaging data is a vital tool for identifying anatomical and functional variations related to diseases as well as normal biological processes. The analyses are often performed on a large number of highly correlated measurements using a relatively smaller number of samples. Despite the correlation structure, the most widely used approach is to analyze the data using univariate methods followed by post-hoc corrections that try to account for the data’s multivariate nature. Although widely used, this approach may fail to recover from the adverse effects of the initial analysis when local effects are not strong. Multivariate pattern analysis (MVPA) is a powerful alternative to the univariate approach for identifying relevant variations. Jointly analyzing all the measures, MVPA techniques can detect global effects even when individual local effects are too weak to detect with univariate analysis. Current approaches are successful in identifying variations that yield highly predictive and compact models. However, they suffer from lessened sensitivity and instabilities in identification of relevant variations. Furthermore, current methods’ user-defined parameters are often unintuitive and difficult to determine. In this article, we propose a novel MVPA method for group analysis of high-dimensional data that overcomes the drawbacks of the current techniques. Our approach explicitly aims to identify all relevant variations using a “knock-out” strategy and the Random Forest algorithm. In evaluations with synthetic datasets the proposed method achieved substantially higher sensitivity and accuracy than the state-of-the-art MVPA methods, and outperformed the univariate approach when the effect size is low. In experiments with real datasets the proposed method identified regions beyond the univariate approach, while other MVPA methods failed to replicate the univariate results. More importantly, in a reproducibility study with the well-known ADNI dataset the proposed method yielded higher stability and power than the univariate approach. PMID:26272728

Discovery of safety biomarkers for atorvastatin in rat urine using mass spectrometry based metabolomics combined with global and targeted approach.

PubMed

Kumar, Bhowmik Salil; Lee, Young-Joo; Yi, Hong Jae; Chung, Bong Chul; Jung, Byung Hwa

2010-02-19

In order to develop a safety biomarker for atorvastatin, this drug was orally administrated to hyperlipidemic rats, and a metabolomic study was performed. Atorvastatin was given in doses of either 70 mg kg(-1) day(-1) or 250 mg kg(-1) day(-1) for a period of 7 days (n=4 for each group). To evaluate any abnormal effects of the drug, physiological and plasma biochemical parameters were measured and histopathological tests were carried out. Safety biomarkers were derived by comparing these parameters and using both global and targeted metabolic profiling. Global metabolic profiling was performed using liquid chromatography/time of flight/mass spectrometry (LC/TOF/MS) with multivariate data analysis. Several safety biomarker candidates that included various steroids and amino acids were discovered as a result of global metabolic profiling, and they were also confirmed by targeted metabolic profiling using gas chromatography/mass spectrometry (GC/MS) and capillary electrophoresis/mass spectrometry (CE/MS). Serum biochemical and histopathological tests were used to detect abnormal drug reactions in the liver after repeating oral administration of atorvastatin. The metabolic differences between control and the drug-treated groups were compared using PLS-DA score plots. These results were compared with the physiological and plasma biochemical parameters and the results of a histopathological test. Estrone, cortisone, proline, cystine, 3-ureidopropionic acid and histidine were proposed as potential safety biomarkers related with the liver toxicity of atorvastatin. These results indicate that the combined application of global and targeted metabolic profiling could be a useful tool for the discovery of drug safety biomarkers. Copyright 2009 Elsevier B.V. All rights reserved.

Predictive monitoring and diagnosis of periodic air pollution in a subway station.

PubMed

Kim, YongSu; Kim, MinJung; Lim, JungJin; Kim, Jeong Tai; Yoo, ChangKyoo

2010-11-15

The purpose of this study was to develop a predictive monitoring and diagnosis system for the air pollutants in a subway system using a lifting technique with a multiway principal component analysis (MPCA) which monitors the periodic patterns of the air pollutants and diagnoses the sources of the contamination. The basic purpose of this lifting technique was to capture the multivariate and periodic characteristics of all of the indoor air samples collected during each day. These characteristics could then be used to improve the handling of strong periodic fluctuations in the air quality environment in subway systems and will allow important changes in the indoor air quality to be quickly detected. The predictive monitoring approach was applied to a real indoor air quality dataset collected by telemonitoring systems (TMS) that indicated some periodic variations in the air pollutants and multivariate relationships between the measured variables. Two monitoring models--global and seasonal--were developed to study climate change in Korea. The proposed predictive monitoring method using the lifted model resulted in fewer false alarms and missed faults due to non-stationary behavior than that were experienced with the conventional methods. This method could be used to identify the contributions of various pollution sources. Copyright © 2010 Elsevier B.V. All rights reserved.

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

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

Loveday, D.L.; Craggs, C.

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

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

PubMed

Maione, Camila; Barbosa, Rommel Melgaço

2018-01-24

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

An examination on the influence of small and medium enterprise (SME) stakeholder on green supply chain management practices

NASA Astrophysics Data System (ADS)

Shahlan, M. Z.; Sidek, A. A.; Suffian, S. A.; Hazza, M. H. F. A.; Daud, M. R. C.

2018-01-01

In this paper, climate change and global warming are the biggest current issues in the industrial sectors. The green supply chain managements (GSCM) is one of the crucial input to these issues. Effective GSCM can potentially secure the organization’s competitive advantage and improve the environmental performance of the network activities. In this study, the aim is to investigate and examine how a small and medium enterprises (SMEs) stakeholder pressure and top management influence green supply chain management practices. The study is further advance green supply chain management research in Malaysia focusing on SMEs manufacturing sector using structural equation modelling. Structural equation modelling is a multivariate statistical analysis technique used to examine structural relationship. It is the combination of factor analysis and multi regression analysis and used to analyse structural relationship between measure variable and latent factor. This research found that top management support and stakeholder pressure is the major influence for SMEs to adopt green supply chain management. The research also found that top management is fully mediate with the relationship between stakeholder pressure and monitoring supplier environmental performance.

Regional origin assignment of red wines from Valencia (Spain) by (2)H NMR and (13)C IRMS stable isotope analysis of fermentative ethanol.

PubMed

Giménez-Miralles, J E; Salazar, D M; Solana, I

1999-07-01

The use of the stable hydrogen and carbon isotope ratios of fermentative ethanol as suitable environmental fingerprints for the regional origin identification of red wines from Valencia (Spain) has been explored. Monovarietal Vitis vinifera L. cvs. Bobal, Tempranillo, and Monastrell wines have been investigated by (2)H NMR and (13)C IRMS for the natural ranges of site-specific (2)H/(1)H ratios and global delta(13)C values of ethanol over three vintage years. Statistically significant interregional and interannual (2)H and (13)C abundance differences have been noticed, which are interpreted in terms of environmental and ecophysiological factors of isotope content variation. Multivariate discriminant analysis is shown to provide a convenient means for integration of the classifying information, high discriminating abilities being demonstrated for the (2)H and (13)C fingerprints of ethanol. Reasonable differentiation results are achieved at a microregional scale in terms of geographic provenance and even grapevine genotypic features.

A chaotic model for the epidemic of Ebola virus disease in West Africa (2013-2016)

NASA Astrophysics Data System (ADS)

Mangiarotti, Sylvain; Peyre, Marisa; Huc, Mireille

2016-11-01

An epidemic of Ebola Virus Disease (EVD) broke out in Guinea in December 2013. It was only identified in March 2014 while it had already spread out in Liberia and Sierra Leone. The spill over of the disease became uncontrollable and the epidemic could not be stopped before 2016. The time evolution of this epidemic is revisited here with the global modeling technique which was designed to obtain the deterministic models from single time series. A generalized formulation of this technique for multivariate time series is introduced. It is applied to the epidemic of EVD in West Africa focusing on the period between March 2014 and January 2015, that is, before any detected signs of weakening. Data gathered by the World Health Organization, based on the official publications of the Ministries of Health of the three main countries involved in this epidemic, are considered in our analysis. Two observed time series are used: the daily numbers of infections and deaths. A four-dimensional model producing a very complex dynamical behavior is obtained. The model is tested in order to investigate its skills and drawbacks. Our global analysis clearly helps to distinguish three main stages during the epidemic. A characterization of the obtained attractor is also performed. In particular, the topology of the chaotic attractor is analyzed and a skeleton is obtained for its structure.

Using large-scale Granger causality to study changes in brain network properties in the Clinically Isolated Syndrome (CIS) stage of multiple sclerosis

NASA Astrophysics Data System (ADS)

Abidin, Anas Z.; Chockanathan, Udaysankar; DSouza, Adora M.; Inglese, Matilde; Wismüller, Axel

2017-03-01

Clinically Isolated Syndrome (CIS) is often considered to be the first neurological episode associated with Multiple sclerosis (MS). At an early stage the inflammatory demyelination occurring in the CNS can manifest as a change in neuronal metabolism, with multiple asymptomatic white matter lesions detected in clinical MRI. Such damage may induce topological changes of brain networks, which can be captured by advanced functional MRI (fMRI) analysis techniques. We test this hypothesis by capturing the effective relationships of 90 brain regions, defined in the Automated Anatomic Labeling (AAL) atlas, using a large-scale Granger Causality (lsGC) framework. The resulting networks are then characterized using graph-theoretic measures that quantify various network topology properties at a global as well as at a local level. We study for differences in these properties in network graphs obtained for 18 subjects (10 male and 8 female, 9 with CIS and 9 healthy controls). Global network properties captured trending differences with modularity and clustering coefficient (p<0.1). Additionally, local network properties, such as local efficiency and the strength of connections, captured statistically significant (p<0.01) differences in some regions of the inferior frontal and parietal lobe. We conclude that multivariate analysis of fMRI time-series can reveal interesting information about changes occurring in the brain in early stages of MS.

A methodology for designing robust multivariable nonlinear control systems. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Grunberg, D. B.

1986-01-01

A new methodology is described for the design of nonlinear dynamic controllers for nonlinear multivariable systems providing guarantees of closed-loop stability, performance, and robustness. The methodology is an extension of the Linear-Quadratic-Gaussian with Loop-Transfer-Recovery (LQG/LTR) methodology for linear systems, thus hinging upon the idea of constructing an approximate inverse operator for the plant. A major feature of the methodology is a unification of both the state-space and input-output formulations. In addition, new results on stability theory, nonlinear state estimation, and optimal nonlinear regulator theory are presented, including the guaranteed global properties of the extended Kalman filter and optimal nonlinear regulators.

Salience and Impact of Health Warning Label on Cigarette Packs in Vietnam: Findings From the Global Adult Tobacco Survey 2015.

PubMed

Ngoc Bich, Nguyen; Thu Ngan, Tran; Bao Giang, Kim; Thi Hai, Phan; Thi Thu Huyen, Doan; Ngoc Khue, Luong; Tuan Lam, Nguyen; Van Minh, Hoang; Thi Quynh Nga, Pham; The Quan, Nguyen; Loan, Vu Hoang

2018-04-13

Viet Nam is among the countries having highest rate of male smokers in the world. The country has joined the Global Tobacco Surveillance System since 2010. Under this system, two rounds of Global Adult Tobacco Survey (GATS) were conducted in 2010 and 2015. Those two surveys provide excellent comparable data on tobacco usage and its related aspects in Vietnam. This study using the data from GATS 2015 to examine the salience and impact of cigarette pack health warnings on quitting intention in Vietnam. The Vietnam GATS 2015 was a nationally representative survey in which 9,513 households were selected using two-stage random systematic sampling method. Results of multivariate analysis showed that the strongest predictor for quit intention because of health warnings was "ever made a quit attempt in the past 12 months" followed by "believes that tobacco smoking causes serious illness". Compared to GATS 2010, GATS 2015 observed the increase in salience of cigarette health warnings. However, the current pictorial health warnings are losing their impact on motivating intention to quit. The results highlight that it is time to start the rotation cycle to refresh the current health warning set. Actions to select a new and more impressive set of pictorial health warnings should be developed as soon as possible.

Identification of Reliable Components in Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS): a Data-Driven Approach across Metabolic Processes.

PubMed

Motegi, Hiromi; Tsuboi, Yuuri; Saga, Ayako; Kagami, Tomoko; Inoue, Maki; Toki, Hideaki; Minowa, Osamu; Noda, Tetsuo; Kikuchi, Jun

2015-11-04

There is an increasing need to use multivariate statistical methods for understanding biological functions, identifying the mechanisms of diseases, and exploring biomarkers. In addition to classical analyses such as hierarchical cluster analysis, principal component analysis, and partial least squares discriminant analysis, various multivariate strategies, including independent component analysis, non-negative matrix factorization, and multivariate curve resolution, have recently been proposed. However, determining the number of components is problematic. Despite the proposal of several different methods, no satisfactory approach has yet been reported. To resolve this problem, we implemented a new idea: classifying a component as "reliable" or "unreliable" based on the reproducibility of its appearance, regardless of the number of components in the calculation. Using the clustering method for classification, we applied this idea to multivariate curve resolution-alternating least squares (MCR-ALS). Comparisons between conventional and modified methods applied to proton nuclear magnetic resonance ((1)H-NMR) spectral datasets derived from known standard mixtures and biological mixtures (urine and feces of mice) revealed that more plausible results are obtained by the modified method. In particular, clusters containing little information were detected with reliability. This strategy, named "cluster-aided MCR-ALS," will facilitate the attainment of more reliable results in the metabolomics datasets.

Nutritional Intervention: A Secondary Analysis of Its Effect on Malnourished Colombian Pre-Schoolers.

ERIC Educational Resources Information Center

Bejar, Isaac I.

1981-01-01

Effects of nutritional supplementation on physical development of malnourished children was analyzed by univariate and multivariate methods for the analysis of repeated measures. Results showed that the nutritional treatment was successful, but it was necessary to resort to the multivariate approach. (Author/GK)

A Multivariate Descriptive Model of Motivation for Orthodontic Treatment.

ERIC Educational Resources Information Center

Hackett, Paul M. W.; And Others

1993-01-01

Motivation for receiving orthodontic treatment was studied among 109 young adults, and a multivariate model of the process is proposed. The combination of smallest scale analysis and Partial Order Scalogram Analysis by base Coordinates (POSAC) illustrates an interesting methodology for health treatment studies and explores motivation for dental…

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

ERIC Educational Resources Information Center

Grundmann, Matthias

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

Univariate Analysis of Multivariate Outcomes in Educational Psychology.

ERIC Educational Resources Information Center

Hubble, L. M.

1984-01-01

The author examined the prevalence of multiple operational definitions of outcome constructs and an estimate of the incidence of Type I error rates when univariate procedures were applied to multiple variables in educational psychology. Multiple operational definitions of constructs were advocated and wider use of multivariate analysis was…

Applied Statistics: From Bivariate through Multivariate Techniques [with CD-ROM

ERIC Educational Resources Information Center

Warner, Rebecca M.

2007-01-01

This book provides a clear introduction to widely used topics in bivariate and multivariate statistics, including multiple regression, discriminant analysis, MANOVA, factor analysis, and binary logistic regression. The approach is applied and does not require formal mathematics; equations are accompanied by verbal explanations. Students are asked…

Evaluation of Meterorite Amono Acid Analysis Data Using Multivariate Techniques

NASA Technical Reports Server (NTRS)

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

1999-01-01

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

MULTIVARIATE ANALYSIS ON LEVELS OF SELECTED METALS, PARTICULATE MATTER, VOC, AND HOUSEHOLD CHARACTERISTICS AND ACTIVITIES FROM THE MIDWESTERN STATES NHEXAS

EPA Science Inventory

Microenvironmental and biological/personal monitoring information were collected during the National Human Exposure Assessment Survey (NHEXAS), conducted in the six states comprising U.S. EPA Region Five. They have been analyzed by multivariate analysis techniques with general ...

Multivariate meta-analysis: a robust approach based on the theory of U-statistic.

PubMed

Ma, Yan; Mazumdar, Madhu

2011-10-30

Meta-analysis is the methodology for combining findings from similar research studies asking the same question. When the question of interest involves multiple outcomes, multivariate meta-analysis is used to synthesize the outcomes simultaneously taking into account the correlation between the outcomes. Likelihood-based approaches, in particular restricted maximum likelihood (REML) method, are commonly utilized in this context. REML assumes a multivariate normal distribution for the random-effects model. This assumption is difficult to verify, especially for meta-analysis with small number of component studies. The use of REML also requires iterative estimation between parameters, needing moderately high computation time, especially when the dimension of outcomes is large. A multivariate method of moments (MMM) is available and is shown to perform equally well to REML. However, there is a lack of information on the performance of these two methods when the true data distribution is far from normality. In this paper, we propose a new nonparametric and non-iterative method for multivariate meta-analysis on the basis of the theory of U-statistic and compare the properties of these three procedures under both normal and skewed data through simulation studies. It is shown that the effect on estimates from REML because of non-normal data distribution is marginal and that the estimates from MMM and U-statistic-based approaches are very similar. Therefore, we conclude that for performing multivariate meta-analysis, the U-statistic estimation procedure is a viable alternative to REML and MMM. Easy implementation of all three methods are illustrated by their application to data from two published meta-analysis from the fields of hip fracture and periodontal disease. We discuss ideas for future research based on U-statistic for testing significance of between-study heterogeneity and for extending the work to meta-regression setting. Copyright © 2011 John Wiley & Sons, Ltd.

Classical least squares multivariate spectral analysis

DOEpatents

Haaland, David M.

2002-01-01

An improved classical least squares multivariate spectral analysis method that adds spectral shapes describing non-calibrated components and system effects (other than baseline corrections) present in the analyzed mixture to the prediction phase of the method. These improvements decrease or eliminate many of the restrictions to the CLS-type methods and greatly extend their capabilities, accuracy, and precision. One new application of PACLS includes the ability to accurately predict unknown sample concentrations when new unmodeled spectral components are present in the unknown samples. Other applications of PACLS include the incorporation of spectrometer drift into the quantitative multivariate model and the maintenance of a calibration on a drifting spectrometer. Finally, the ability of PACLS to transfer a multivariate model between spectrometers is demonstrated.

Using Clustering to Establish Climate Regimes from PCM Output

NASA Technical Reports Server (NTRS)

Oglesby, Robert; Arnold, James E. (Technical Monitor); Hoffman, Forrest; Hargrove, W. W.; Erickson, D.

2002-01-01

A multivariate statistical clustering technique--based on the k-means algorithm of Hartigan has been used to extract patterns of climatological significance from 200 years of general circulation model (GCM) output. Originally developed and implemented on a Beowulf-style parallel computer constructed by Hoffman and Hargrove from surplus commodity desktop PCs, the high performance parallel clustering algorithm was previously applied to the derivation of ecoregions from map stacks of 9 and 25 geophysical conditions or variables for the conterminous U.S. at a resolution of 1 sq km. Now applied both across space and through time, the clustering technique yields temporally-varying climate regimes predicted by transient runs of the Parallel Climate Model (PCM). Using a business-as-usual (BAU) scenario and clustering four fields of significance to the global water cycle (surface temperature, precipitation, soil moisture, and snow depth) from 1871 through 2098, the authors' analysis shows an increase in spatial area occupied by the cluster or climate regime which typifies desert regions (i.e., an increase in desertification) and a decrease in the spatial area occupied by the climate regime typifying winter-time high latitude perma-frost regions. The patterns of cluster changes have been analyzed to understand the predicted variability in the water cycle on global and continental scales. In addition, representative climate regimes were determined by taking three 10-year averages of the fields 100 years apart for northern hemisphere winter (December, January, and February) and summer (June, July, and August). The result is global maps of typical seasonal climate regimes for 100 years in the past, for the present, and for 100 years into the future. Using three-dimensional data or phase space representations of these climate regimes (i.e., the cluster centroids), the authors demonstrate the portion of this phase space occupied by the land surface at all points in space and time. Any single spot on the globe will exist in one of these climate regimes at any single point in time. By incrementing time, that same spot will trace out a trajectory or orbit between and among these climate regimes (or atmospheric states) in phase (or state) space. When a geographic region enters a state it never previously visited, a climatic change is said to have occurred. Tracing out the entire trajectory of a single spot on the globe yields a 'manifold' in state space representing the shape of its predicted climate occupancy. This sort of analysis enables a researcher to more easily grasp the multivariate behavior of the climate system.

A suite of global reconstructed precipitation products and their error estimate by multivariate regression using empirical orthogonal functions: 1850-present

NASA Astrophysics Data System (ADS)

Shen, S. S.

2014-12-01

This presentation describes a suite of global precipitation products reconstructed by a multivariate regression method using an empirical orthogonal function (EOF) expansion. The sampling errors of the reconstruction are estimated for each product datum entry. The maximum temporal coverage is 1850-present and the spatial coverage is quasi-global (75S, 75N). The temporal resolution ranges from 5-day, monthly, to seasonal and annual. The Global Precipitation Climatology Project (GPCP) precipitation data from 1979-2008 are used to calculate the EOFs. The Global Historical Climatology Network (GHCN) gridded data are used to calculate the regression coefficients for reconstructions. The sampling errors of the reconstruction are analyzed in detail for different EOF modes. Our reconstructed 1900-2011 time series of the global average annual precipitation shows a 0.024 (mm/day)/100a trend, which is very close to the trend derived from the mean of 25 models of the CMIP5 (Coupled Model Intercomparison Project Phase 5). Our reconstruction examples of 1983 El Niño precipitation and 1917 La Niña precipitation (Figure 1) demonstrate that the El Niño and La Niña precipitation patterns are well reflected in the first two EOFs. The validation of our reconstruction results with GPCP makes it possible to use the reconstruction as the benchmark data for climate models. This will help the climate modeling community to improve model precipitation mechanisms and reduce the systematic difference between observed global precipitation, which hovers at around 2.7 mm/day for reconstructions and GPCP, and model precipitations, which have a range of 2.6-3.3 mm/day for CMIP5. Our precipitation products are publically available online, including digital data, precipitation animations, computer codes, readme files, and the user manual. This work is a joint effort between San Diego State University (Sam Shen, Nancy Tafolla, Barbara Sperberg, and Melanie Thorn) and University of Maryland (Phil Arkin, Tom Smith, Li Ren, and Li Dai) and supported in part by the U.S. National Science Foundation (Awards No. AGS-1015926 and AGS-1015957).

Estimation of myocardial flow reserve utilizing an ultrafast cardiac SPECT: Comparison with coronary angiography, fractional flow reserve, and the SYNTAX score.

PubMed

Miyagawa, Masao; Nishiyama, Yoshiko; Uetani, Teruyoshi; Ogimoto, Akiyoshi; Ikeda, Shuntaro; Ishimura, Hayato; Watanabe, Emiri; Tashiro, Rami; Tanabe, Yuki; Kido, Teruhito; Kurata, Akira; Mochizuki, Teruhito

2017-10-01

Quantitative assessment of myocardial flow reserve (MFR) by single photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) is challenging but may facilitate evaluation of multi-vessel coronary artery disease (CAD). We enrolled 153 patients with suspected or known CAD, referred for pharmacological stress MPI. They underwent a 99m Tc-perfusion stress/rest SPECT with an ultrafast cadmium-zinc-telluride (CZT) camera. Dynamic data were acquired and time-activity curves fitted to a 1-tissue compartment analysis with input function. K1 was assigned for stress and rest data. The MFR index (MFRi) was calculated as K1 stress/K1 at-rest. The findings were validated by invasive coronary angiography in 69 consecutive patients. The global MFRi was 1.46 (1.16-1.76), 1.33 (1.12-1.54), and 1.18 (1.01-1.35), for 1-vessel disease (VD), 2-VD, and 3-VD, respectively. In the 3-VD, global MFRi was lower than that in 0-VD (1.63 [1.22-2.04], P<0.0001) and 1-VD (P=0.003). Multivariate logistic regression analysis for 3-VD showed significant associations with smoking history (odds ratio [OR]: 4.4 [0.4-8.4]), left ventricular ejection fraction (OR: 61.6 [57.5-66.0]), and global MFRi (OR: 119.6 [111.5-127.7], P=0.002). A cut-off value of 1.3 yielded 93.3% sensitivity and 75.9% specificity for diagnosing 3-VD. Fractional flow reserve positively correlated with regional MFRi (r=0.62, P=0.008), and the SYNTAX score correlated negatively with global MFRi (r=0.567, P=0.0003). We developed and validated a clinically available method for MFR quantification by dynamic 99m Tc-perfusion SPECT utilizing a CZT camera, which improves the detectability of multi-vessel CAD. Copyright © 2017 Elsevier B.V. All rights reserved.

Relationship between global severity of patients with Alzheimer's disease and costs of care in Spain; results from the co-dependence study in Spain.

PubMed

Darbà, J; Kaskens, L; Lacey, L

2015-11-01

The objectives of this analysis were to examine how patients' global severity with Alzheimer's disease (AD) relates to costs of care and explore the incremental effects of global severity measured by the clinical dementia rating (CDR) scale on these costs for patients in Spain. The Codep-EA study is an 18-multicenter, cross-sectional, observational study among patients (343) with AD according to the CDR score and their caregivers in Spain. The data obtained included (in addition to clinical measures) also socio-demographic data concerning the patient and its caregiver. Cost analyses were based on resource use for medical care, social care, caregiver productivity losses, and informal caregiver time reported in the resource utilization in dementia (RUD). Lite instrument and a complementary questionnaire. Multivariate regression analysis was used to model the effects of global severity and other socio-demographic and clinical variables on cost of care. The mean (standard deviation) costs per patient over 6 months for direct medical, social care, indirect and informal care costs, were estimated at €1,028.1 (1,655.0), €843.8 (2,684.8), €464.2 (1,639.0) and €33,232.2 (30,898.9), respectively. Dementia severity, as having a CDR score 0.5, 2, or 3 with CDR score 1 being the reference group were all independently and significantly associated with informal care costs. Whereas having a CDR score of 2 was also significantly related with social care costs, a CDR score of 3 was associated with most cost components including direct medical, social care, and total costs, all compared to the reference group. The costs of care for patients with AD in Spain are substantial, with informal care accounting for the greatest part. Dementia severity, measured by CDR score, showed that with increasing severity of the disease, direct medical, social care, informal care and total costs augmented.

Characterizing multivariate decoding models based on correlated EEG spectral features.

PubMed

McFarland, Dennis J

2013-07-01

Multivariate decoding methods are popular techniques for analysis of neurophysiological data. The present study explored potential interpretative problems with these techniques when predictors are correlated. Data from sensorimotor rhythm-based cursor control experiments was analyzed offline with linear univariate and multivariate models. Features were derived from autoregressive (AR) spectral analysis of varying model order which produced predictors that varied in their degree of correlation (i.e., multicollinearity). The use of multivariate regression models resulted in much better prediction of target position as compared to univariate regression models. However, with lower order AR features interpretation of the spectral patterns of the weights was difficult. This is likely to be due to the high degree of multicollinearity present with lower order AR features. Care should be exercised when interpreting the pattern of weights of multivariate models with correlated predictors. Comparison with univariate statistics is advisable. While multivariate decoding algorithms are very useful for prediction their utility for interpretation may be limited when predictors are correlated. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Time Series Model Identification by Estimating Information.

DTIC Science & Technology

1982-11-01

principle, Applications of Statistics, P. R. Krishnaiah , ed., North-Holland: Amsterdam, 27-41. Anderson, T. W. (1971). The Statistical Analysis of Time Series...E. (1969). Multiple Time Series Modeling, Multivariate Analysis II, edited by P. Krishnaiah , Academic Press: New York, 389-409. Parzen, E. (1981...Newton, H. J. (1980). Multiple Time Series Modeling, II Multivariate Analysis - V, edited by P. Krishnaiah , North Holland: Amsterdam, 181-197. Shibata, R

Genomic Analysis of Complex Microbial Communities in Wounds

DTIC Science & Technology

2012-01-01

thoroughly in the ecology literature. Permutation Multivariate Analysis of Variance ( PerMANOVA ). We used PerMANOVA to test the null-hypothesis of no...difference between the bacterial communities found within a single wound compared to those from different patients (α = 0.05). PerMANOVA is a...permutation-based version of the multivariate analysis of variance (MANOVA). PerMANOVA uses the distances between samples to partition variance and

Right ventricular diastolic performance in children with pulmonary arterial hypertension associated with congenital heart disease: correlation of echocardiographic parameters with invasive reference standards by high-fidelity micromanometer catheter.

PubMed

Okumura, Kenichi; Slorach, Cameron; Mroczek, Dariusz; Dragulescu, Andreea; Mertens, Luc; Redington, Andrew N; Friedberg, Mark K

2014-05-01

Right ventricular diastolic dysfunction influences outcomes in pulmonary arterial hypertension (PAH), but echocardiographic parameters have not been investigated in relation to invasive reference standards in pediatric PAH. We investigated echocardiographic parameters of right ventricular diastolic function in children with PAH in relation to simultaneously measured invasive reference measures. We prospectively recruited children undergoing a clinically indicated cardiac catheterization for evaluation of PAH and pulmonary vasoreactivity testing. Echocardiography was performed simultaneously with invasive reference measurements by high-fidelity micromanometer catheter. For analysis, patients were divided into shunt and nonshunt groups. Sixteen children were studied. In the group as a whole, significant correlations were found among τ and tricuspid deceleration time, E', E/E', TimeE-E', A wave velocity, and global early and late diastolic strain rate. dp/dt minimum correlated significantly with late diastolic tricuspid annular velocity (A'), tissue Doppler imaging-derived systolic:diastolic duration ratio, and global late diastolic strain rate. End-diastolic pressure correlated significantly with tissue Doppler imaging-derived systolic:diastolic duration ratio. On multivariate analysis, tricuspid deceleration time, TimeE-E', and global early diastolic strain rate were independent predictors of τ, whereas tissue Doppler imaging-derived systolic:diastolic duration ratio was an independent predictor of dp/dt minimum. In general, correlations between echocardiographic and invasive parameters were better in the shunt group than in the nonshunt group. Echocardiography correlates with invasive reference measures of right ventricular diastolic function in children with PAH, although it does not differentiate between early versus late diastolic abnormalities. Newer echocardiographic techniques may have added value to assess right ventricular diastolic dysfunction in this population. © 2014 American Heart Association, Inc.

Global clinical response in Cushing’s syndrome patients treated with mifepristone

PubMed Central

Katznelson, Laurence; Loriaux, D Lynn; Feldman, David; Braunstein, Glenn D; Schteingart, David E; Gross, Coleman

2014-01-01

Objective Mifepristone, a glucocorticoid receptor antagonist, improves clinical status in patients with Cushing’s syndrome (CS). We examined the pattern, reliability and correlates of global clinical response (GCR) assessments during a 6-month clinical trial of mifepristone in CS. Design Post hoc analysis of secondary end-point data from a 24-week multicentre, open-label trial of mifepristone (300–1200mg daily) in CS. Intraclass correlation coefficient (ICC) was used to examine rater concordance, and drivers of clinical improvement were determined by multivariate regression analysis. Patients Forty-six adult patients with refractory CS along with diabetes mellitus type 2 or impaired glucose tolerance, and/or a diagnosis of hypertension. Measurements Global clinical assessment made by three independent reviewers using a three-point ordinal scale (+1 = improvement; 0=no change; −1=worsening) based on eight broad clinical categories including glucose control, lipids, blood pressure, body composition, clinical appearance, strength, psychiatric/cognitive symptoms and quality of life at Weeks 6, 10, 16, and 24. Results Positive GCR increased progressively over time with 88% of patients having improved at Week 24 (P<0·001). The full concordance among reviewers occurred in 76·6% of evaluations resulting in an ICC of 0·652 (P<0·001). Changes in body weight (P<0·0001), diastolic blood pressure (P<0·0001), two-hour postoral glucose challenge glucose concentration (P = 0·0003), and Cushingoid appearance (P=0·022) were strong correlates of GCR. Conclusions Mifepristone treatment for CS results in progressive clinical improvement. Overall agreement among clinical reviewers was substantial and determinants of positive GCR included change in weight, blood pressure, glucose levels and appearance. PMID:24102404

Defining global neuroendocrine gene expression patterns associated with reproductive seasonality in fish.

PubMed

Zhang, Dapeng; Xiong, Huiling; Mennigen, Jan A; Popesku, Jason T; Marlatt, Vicki L; Martyniuk, Christopher J; Crump, Kate; Cossins, Andrew R; Xia, Xuhua; Trudeau, Vance L

2009-06-05

Many vertebrates, including the goldfish, exhibit seasonal reproductive rhythms, which are a result of interactions between external environmental stimuli and internal endocrine systems in the hypothalamo-pituitary-gonadal axis. While it is long believed that differential expression of neuroendocrine genes contributes to establishing seasonal reproductive rhythms, no systems-level investigation has yet been conducted. In the present study, by analyzing multiple female goldfish brain microarray datasets, we have characterized global gene expression patterns for a seasonal cycle. A core set of genes (873 genes) in the hypothalamus were identified to be differentially expressed between May, August and December, which correspond to physiologically distinct stages that are sexually mature (prespawning), sexual regression, and early gonadal redevelopment, respectively. Expression changes of these genes are also shared by another brain region, the telencephalon, as revealed by multivariate analysis. More importantly, by examining one dataset obtained from fish in October who were kept under long-daylength photoperiod (16 h) typical of the springtime breeding season (May), we observed that the expression of identified genes appears regulated by photoperiod, a major factor controlling vertebrate reproductive cyclicity. Gene ontology analysis revealed that hormone genes and genes functionally involved in G-protein coupled receptor signaling pathway and transmission of nerve impulses are significantly enriched in an expression pattern, whose transition is located between prespawning and sexually regressed stages. The existence of seasonal expression patterns was verified for several genes including isotocin, ependymin II, GABA(A) gamma2 receptor, calmodulin, and aromatase b by independent samplings of goldfish brains from six seasonal time points and real-time PCR assays. Using both theoretical and experimental strategies, we report for the first time global gene expression patterns throughout a breeding season which may account for dynamic neuroendocrine regulation of seasonal reproductive development.

Defining Global Neuroendocrine Gene Expression Patterns Associated with Reproductive Seasonality in Fish

PubMed Central

Mennigen, Jan A.; Popesku, Jason T.; Marlatt, Vicki L.; Martyniuk, Christopher J.; Crump, Kate; Cossins, Andrew R.; Xia, Xuhua; Trudeau, Vance L.

2009-01-01

Background Many vertebrates, including the goldfish, exhibit seasonal reproductive rhythms, which are a result of interactions between external environmental stimuli and internal endocrine systems in the hypothalamo-pituitary-gonadal axis. While it is long believed that differential expression of neuroendocrine genes contributes to establishing seasonal reproductive rhythms, no systems-level investigation has yet been conducted. Methodology/Principal Findings In the present study, by analyzing multiple female goldfish brain microarray datasets, we have characterized global gene expression patterns for a seasonal cycle. A core set of genes (873 genes) in the hypothalamus were identified to be differentially expressed between May, August and December, which correspond to physiologically distinct stages that are sexually mature (prespawning), sexual regression, and early gonadal redevelopment, respectively. Expression changes of these genes are also shared by another brain region, the telencephalon, as revealed by multivariate analysis. More importantly, by examining one dataset obtained from fish in October who were kept under long-daylength photoperiod (16 h) typical of the springtime breeding season (May), we observed that the expression of identified genes appears regulated by photoperiod, a major factor controlling vertebrate reproductive cyclicity. Gene ontology analysis revealed that hormone genes and genes functionally involved in G-protein coupled receptor signaling pathway and transmission of nerve impulses are significantly enriched in an expression pattern, whose transition is located between prespawning and sexually regressed stages. The existence of seasonal expression patterns was verified for several genes including isotocin, ependymin II, GABAA gamma2 receptor, calmodulin, and aromatase b by independent samplings of goldfish brains from six seasonal time points and real-time PCR assays. Conclusions/Significance Using both theoretical and experimental strategies, we report for the first time global gene expression patterns throughout a breeding season which may account for dynamic neuroendocrine regulation of seasonal reproductive development. PMID:19503831

Kidney biopsy in AA amyloidosis: impact of histopathology on prognosis.

PubMed

Kendi Celebi, Zeynep; Kiremitci, Saba; Ozturk, Bengi; Akturk, Serkan; Erdogmus, Siyar; Duman, Neval; Ates, Kenan; Erturk, Sehsuvar; Nergizoglu, Gokhan; Kutlay, Sim; Sengul, Sule; Ensari, Arzu; Keven, Kenan

2017-09-01

In AA amyloidosis, while kidney biopsy is widely considered for diagnosis by clinicians, there is no evidence that the detailed investigation of renal histopathology can be utilized for the prognosis and clinical outcomes. In this study, we aimed to obtain whether histopathologic findings in kidney biopsy of AA amyloidosis might have prognostic and clinical value. This is a retrospective cohort study that included 38 patients who were diagnosed with AA amyloidosis by kidney biopsy between 2005 and 2013.The kidney biopsy specimens of patients were evaluated and graded for several characteristics of histopathological lesions and their relationship with renal outcomes. Segmental amyloid deposition in the kidney biopsy was seen in 29%, global amyloid deposition in 71, diffuse involvement of glomeruli in 84.2%, focal involvement in 7%, glomerular enlargement in 53%, tubular atrophy in 75% and interstitial fibrosis in 78% of patients. Histopathologically, glomerular enlargement, interstitial fibrosis, tubular atrophy, interstitial inflammation and global amyloid deposition were significantly associated with lower estimated glomerular filtration rate (eGFR) (p = .02, p < .001, p = .001, p = .009, p = .002, respectively) in univariate analysis. In multivariate analysis, tubular atrophy was the only predictor of eGFR (p = .019 B = -20.573). In the follow-up at an average of 27 months, 18 patients developed end-stage renal disease (ESRD). Among them, global amyloid deposition was the only risk factor for the development of ESRD (p = .01, OR = 18.750, %95 CI= 2.021-173.942). This is the first study showing that the histopathological findings in kidney biopsy of AA amyloidosis might have a prognostic and clinical value for renal outcomes.

In situ X-ray diffraction analysis of (CF x) n batteries: signal extraction by multivariate analysis

DOE PAGES

Rodriguez, Mark A.; Keenan, Michael R.; Nagasubramanian, Ganesan

2007-11-10

In this study, (CF x) n cathode reaction during discharge has been investigated using in situ X-ray diffraction (XRD). Mathematical treatment of the in situ XRD data set was performed using multivariate curve resolution with alternating least squares (MCR–ALS), a technique of multivariate analysis. MCR–ALS analysis successfully separated the relatively weak XRD signal intensity due to the chemical reaction from the other inert cell component signals. The resulting dynamic reaction component revealed the loss of (CF x) n cathode signal together with the simultaneous appearance of LiF by-product intensity. Careful examination of the XRD data set revealed an additional dynamicmore » component which may be associated with the formation of an intermediate compound during the discharge process.« less

Hybrid least squares multivariate spectral analysis methods

DOEpatents

Haaland, David M.

2004-03-23

A set of hybrid least squares multivariate spectral analysis methods in which spectral shapes of components or effects not present in the original calibration step are added in a following prediction or calibration step to improve the accuracy of the estimation of the amount of the original components in the sampled mixture. The hybrid method herein means a combination of an initial calibration step with subsequent analysis by an inverse multivariate analysis method. A spectral shape herein means normally the spectral shape of a non-calibrated chemical component in the sample mixture but can also mean the spectral shapes of other sources of spectral variation, including temperature drift, shifts between spectrometers, spectrometer drift, etc. The shape can be continuous, discontinuous, or even discrete points illustrative of the particular effect.

Effect of embryo culture media on percentage of males at birth.

PubMed

Zhu, Jinliang; Zhuang, Xinjie; Chen, Lixue; Liu, Ping; Qiao, Jie

2015-05-01

Does embryo culture medium influence the percentage of males at birth? The percentage of males delivered after ICSI cycles using G5™ medium was statistically significantly higher than after cycles where Global, G5™ PLUS, and Quinn's Advantage Media were used. Male and female embryos have different physiologies during preimplantation development. Manipulating the energy substrate and adding growth factors have a differential impact on the development of male and female embryos. This was a retrospective analysis of the percentage of males at birth, and included 4411 singletons born from fresh embryo transfer cycles between January 2011 and August 2013 at the Center for Reproductive Medicine of Third Hospital Peking University. Only singleton gestations were included. Participants were excluded if preimplantation genetic diagnosis, donor oocytes and donor sperm were used. The database between January 2011 and August 2013 was searched with unique medical record number, all patients were present in the database with only one cycle. Demographics, cycle characteristics and the percentage of male babies in the four culture media groups were compared with analysis of variance or χ(2) tests. Multivariable logistic regression was done to determine the association between the sex at birth and culture media after adjusting for other confounding factors, including parental age, parental BMI, type of infertility, parity, number of embryos transferred, number of early gestational sacs, cycles with testicular sperm aspiration (TESA)/percutaneous epididymal sperm aspiration (PESA)/testicular sperm extraction (TESE), number of oocytes retrieved, cycles with blastocyst transfers, and gestational age within ICSI group. Within the IVF group, the percentage of males at birth for G5™, Global, Quinn's and G5™ PLUS media were comparable (P > 0.05); however, within the ICSI group, the percentage of male babies in cycles using G5™(56.1%) was statistically significantly higher than in cycles that used Global (47.2%; P = 0.003), G5™ PLUS (47.7%; P = 0.005) or Quinn's media (45.0%; P = 0.009). There were no statistically significant differences in the percentage of males at birth between cycles that used Global, G5™ PLUS and Quinn's media (P > 0.05). Multivariable logistic regression indicated that culture media (G5™ versus Global, G5™ PLUS, and Quinn's) were significantly associated with the sex at birth (P = 0.008) after adjusting for parental age, parental BMI, type of infertility, parity, number of embryos transferred, number of early gestational sacs, cycles with TESA/PESA/TESE, number of oocytes retrieved, cycles with blastocyst transfers, and gestational age. This study was not a randomized controlled trial and allocation of treatment cycles over the four media was not completely at random. Cigarette smoking was not included in the current study because this confounding factor was not registered in our database. Moreover, intra-variability of sperm selection between the five embryologists may directly affect the percentage of males. Our study suggests that human embryogenesis responds differently to G5™, Global, G5™ PLUS and Quinn's Advantage Medium. This finding can be generalized to other commercial culture media. National Natural Science Foundation of China for Young Scholars (81300483 and 81200466). The authors have no conflicts of interest to declare. Not applicable. © The Author 2015. Published by Oxford University Press on behalf of the European Society of Human Reproduction and Embryology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Probabilistic flood damage modelling at the meso-scale

NASA Astrophysics Data System (ADS)

Kreibich, Heidi; Botto, Anna; Schröter, Kai; Merz, Bruno

2014-05-01

Decisions on flood risk management and adaptation are usually based on risk analyses. Such analyses are associated with significant uncertainty, even more if changes in risk due to global change are expected. Although uncertainty analysis and probabilistic approaches have received increased attention during the last years, they are still not standard practice for flood risk assessments. Most damage models have in common that complex damaging processes are described by simple, deterministic approaches like stage-damage functions. Novel probabilistic, multi-variate flood damage models have been developed and validated on the micro-scale using a data-mining approach, namely bagging decision trees (Merz et al. 2013). In this presentation we show how the model BT-FLEMO (Bagging decision Tree based Flood Loss Estimation MOdel) can be applied on the meso-scale, namely on the basis of ATKIS land-use units. The model is applied in 19 municipalities which were affected during the 2002 flood by the River Mulde in Saxony, Germany. The application of BT-FLEMO provides a probability distribution of estimated damage to residential buildings per municipality. Validation is undertaken on the one hand via a comparison with eight other damage models including stage-damage functions as well as multi-variate models. On the other hand the results are compared with official damage data provided by the Saxon Relief Bank (SAB). The results show, that uncertainties of damage estimation remain high. Thus, the significant advantage of this probabilistic flood loss estimation model BT-FLEMO is that it inherently provides quantitative information about the uncertainty of the prediction. Reference: Merz, B.; Kreibich, H.; Lall, U. (2013): Multi-variate flood damage assessment: a tree-based data-mining approach. NHESS, 13(1), 53-64.

Meteorological Research Institute multivariate ocean variational estimation (MOVE) system: Some early results

NASA Astrophysics Data System (ADS)

Usui, Norihisa; Ishizaki, Shiro; Fujii, Yosuke; Tsujino, Hiroyuki; Yasuda, Tamaki; Kamachi, Masafumi

The Meteorological Research Institute multivariate ocean variational estimation (MOVE) System has been developed as the next-generation ocean data assimilation system in Japan Meteorological Agency. A multivariate three-dimensional variational (3DVAR) analysis scheme with vertical coupled temperature salinity empirical orthogonal function modes is adopted. The MOVE system has two varieties, the global (MOVE-G) and North Pacific (MOVE-NP) systems. The equatorial Pacific and western North Pacific are analyzed with assimilation experiments using MOVE-G and -NP, respectively. In each system, the salinity and velocity fields are well reproduced, even in cases without salinity data. Changes in surface and subsurface zonal currents during the 1997/98 El Niño event are captured well, and their transports are reasonably consistent with in situ observations. For example, the eastward transport in the upper layer around the equator has 70 Sv in spring 1997 and weakens in spring 1998. With MOVE-NP, the Kuroshio transport has 25 Sv in the East China Sea, and 40 Sv crossing the ASUKA (Affiliated Surveys of the Kuroshio off Cape Ashizuri) line south of Japan. The variations in the Kuroshio transports crossing the ASUKA line agree well with observations. The Ryukyu Current System has a transport ranging from 6 Sv east of Taiwan to 17 Sv east of Amami. The Oyashio transport crossing the OICE (Oyashio Intensive observation line off Cape Erimo) line south of Hokkaido has 14 Sv southwestward (near shore) and 11 Sv northeastward (offshore). In the Kuroshio Oyashio transition area east of Japan, the eastward transport has 41 Sv (32 36°N) and 12 Sv (36 39°N) crossing the 145°E line.

Association of Low-Grade Albuminuria with Adverse Cardiac Mechanics: Findings from the HyperGEN Study

PubMed Central

Katz, Daniel H.; Selvaraj, Senthil; Aguilar, Frank G.; Martinez, Eva E.; Beussink, Lauren; Kim, Kwang-Youn A.; Peng, Jie; Sha, Jin; Irvin, Marguerite R.; Eckfeldt, John H.; Turner, Stephen T.; Freedman, Barry I.; Arnett, Donna K.; Shah, Sanjiv J.

2013-01-01

Introduction Albuminuria is a marker of endothelial dysfunction and has been associated with adverse cardiovascular outcomes. The reasons for this association are unclear, but may be due to the relationship between endothelial dysfunction and intrinsic myocardial dysfunction. Methods and Results In the HyperGEN study, a population- and family-based study of hypertension, we examined the relationship between urine albumin-to-creatinine ratio (UACR) and cardiac mechanics (N=1894, all of whom had normal left ventricular ejection fraction and wall motion). We performed speckle-tracking echocardiographic analysis to quantify global longitudinal, circumferential, and radial strain (GLS, GCS, and GRS, respectively), and early diastolic (e′) tissue velocities. We used E/e′ ratio as a marker of increased LV filling pressures. We used multivariable-adjusted linear mixed effect models to determine independent associations between UACR and cardiac mechanics. The mean age was 50±14 years, 59% were female, and 46% were African-American. Comorbidities were increasingly prevalent among higher UACR quartiles. Albuminuria was associated with GLS, GCS, GRS, e′ velocity, and E/e′ ratio on unadjusted analyses. After adjustment for covariates, UACR was independently associated with lower absolute GLS (multivariable-adjusted mean GLS [95% CI] for UACR Quartile 1 = 15.3 [15.0–15.5]% vs. UACR Q4 = 14.6 [14.3–14.9]%, P for trend <0.001) and increased E/e′ ratio (Q1 = 25.3 [23.5–27.1] vs. Q4 = 29.0 [27.0–31.0], P= 0.003). The association between UACR and GLS was present even in participants with UACR < 30 mg/g (P<0.001 after multivariable adjustment). Conclusions Albuminuria, even at low levels, is associated with adverse cardiac mechanics and higher E/e′ ratio. PMID:24077169

NIH disease funding levels and burden of disease.

PubMed

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

2011-02-24

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

Prevalence and Determinants of Suicide Ideation among Lebanese Adolescents: Results of the GSHS Lebanon 2005

ERIC Educational Resources Information Center

Mahfoud, Ziyad R.; Afifi, Rema A.; Haddad, Pascale H.; DeJong, Jocelyn

2011-01-01

The current study examined prevalence and risk factors for suicide ideation in 5038 Lebanese adolescents using Global School Health Survey data. Around 16% of Lebanese adolescents thought of suicide. Multivariate logistic regression models showed that risk factors for suicide ideation included poor mental health (felt lonely, felt worried, felt…

Global compositional variation among native and non-native regional insect assemblages emphasizes the importance of pathways

Treesearch

Andrew M. Liebhold; Takehiko Yamanaka; Alain Roques; Sylvie Augustin; Steven L. Chown; Eckehard G. Brockerhoff; Petr Pysek

2016-01-01

Insects are among the world's most ecologically and economically important invasive species. Here we assemble inventories of native and nonnative species from 20 world regions and contrast relative numbers among these species assemblages. Multivariate ordination indicates that the distribution of species among insect orders is completely different between native...

Multitemporal spatial pattern analysis of Tulum's tropical coastal landscape

NASA Astrophysics Data System (ADS)

Ramírez-Forero, Sandra Carolina; López-Caloca, Alejandra; Silván-Cárdenas, José Luis

2011-11-01

The tropical coastal landscape of Tulum in Quintana Roo, Mexico has a high ecological, economical, social and cultural value, it provides environmental and tourism services at global, national, regional and local levels. The landscape of the area is heterogeneous and presents random fragmentation patterns. In recent years, tourist services of the region has been increased promoting an accelerate expansion of hotels, transportation and recreation infrastructure altering the complex landscape. It is important to understand the environmental dynamics through temporal changes on the spatial patterns and to propose a better management of this ecological area to the authorities. This paper addresses a multi-temporal analysis of land cover changes from 1993 to 2000 in Tulum using Thematic Mapper data acquired by Landsat-5. Two independent methodologies were applied for the analysis of changes in the landscape and for the definition of fragmentation patterns. First, an Iteratively Multivariate Alteration Detection (IR-MAD) algorithm was used to detect and localize land cover change/no-change areas. Second, the post-classification change detection evaluated using the Support Vector Machine (SVM) algorithm. Landscape metrics were calculated from the results of IR-MAD and SVM. The analysis of the metrics indicated, among other things, a higher fragmentation pattern along roadways.

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

Treesearch

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

1996-01-01

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

Modeling Associations among Multivariate Longitudinal Categorical Variables in Survey Data: A Semiparametric Bayesian Approach

ERIC Educational Resources Information Center

Tchumtchoua, Sylvie; Dey, Dipak K.

2012-01-01

This paper proposes a semiparametric Bayesian framework for the analysis of associations among multivariate longitudinal categorical variables in high-dimensional data settings. This type of data is frequent, especially in the social and behavioral sciences. A semiparametric hierarchical factor analysis model is developed in which the…

Use of Multivariate Linkage Analysis for Dissection of a Complex Cognitive Trait

PubMed Central

Marlow, Angela J.; Fisher, Simon E.; Francks, Clyde; MacPhie, I. Laurence; Cherny, Stacey S.; Richardson, Alex J.; Talcott, Joel B.; Stein, John F.; Monaco, Anthony P.; Cardon, Lon R.

2003-01-01

Replication of linkage results for complex traits has been exceedingly difficult, owing in part to the inability to measure the precise underlying phenotype, small sample sizes, genetic heterogeneity, and statistical methods employed in analysis. Often, in any particular study, multiple correlated traits have been collected, yet these have been analyzed independently or, at most, in bivariate analyses. Theoretical arguments suggest that full multivariate analysis of all available traits should offer more power to detect linkage; however, this has not yet been evaluated on a genomewide scale. Here, we conduct multivariate genomewide analyses of quantitative-trait loci that influence reading- and language-related measures in families affected with developmental dyslexia. The results of these analyses are substantially clearer than those of previous univariate analyses of the same data set, helping to resolve a number of key issues. These outcomes highlight the relevance of multivariate analysis for complex disorders for dissection of linkage results in correlated traits. The approach employed here may aid positional cloning of susceptibility genes in a wide spectrum of complex traits. PMID:12587094

The association between body mass index and severe biliary infections: a multivariate analysis.

PubMed

Stewart, Lygia; Griffiss, J McLeod; Jarvis, Gary A; Way, Lawrence W

2012-11-01

Obesity has been associated with worse infectious disease outcomes. It is a risk factor for cholesterol gallstones, but little is known about associations between body mass index (BMI) and biliary infections. We studied this using factors associated with biliary infections. A total of 427 patients with gallstones were studied. Gallstones, bile, and blood (as applicable) were cultured. Illness severity was classified as follows: none (no infection or inflammation), systemic inflammatory response syndrome (fever, leukocytosis), severe (abscess, cholangitis, empyema), or multi-organ dysfunction syndrome (bacteremia, hypotension, organ failure). Associations between BMI and biliary bacteria, bacteremia, gallstone type, and illness severity were examined using bivariate and multivariate analysis. BMI inversely correlated with pigment stones, biliary bacteria, bacteremia, and increased illness severity on bivariate and multivariate analysis. Obesity correlated with less severe biliary infections. BMI inversely correlated with pigment stones and biliary bacteria; multivariate analysis showed an independent correlation between lower BMI and illness severity. Most patients with severe biliary infections had a normal BMI, suggesting that obesity may be protective in biliary infections. This study examined the correlation between BMI and biliary infection severity. Published by Elsevier Inc.

Multivariate meta-analysis using individual participant data.

PubMed

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

2015-06-01

When combining results across related studies, a multivariate meta-analysis allows the joint synthesis of correlated effect estimates from multiple outcomes. Joint synthesis can improve efficiency over separate univariate syntheses, may reduce selective outcome reporting biases, and enables joint inferences across the outcomes. A common issue is that within-study correlations needed to fit the multivariate model are unknown from published reports. However, provision of individual participant data (IPD) allows them to be calculated directly. Here, we illustrate how to use IPD to estimate within-study correlations, using a joint linear regression for multiple continuous outcomes and bootstrapping methods for binary, survival and mixed outcomes. In a meta-analysis of 10 hypertension trials, we then show how these methods enable multivariate meta-analysis to address novel clinical questions about continuous, survival and binary outcomes; treatment-covariate interactions; adjusted risk/prognostic factor effects; longitudinal data; prognostic and multiparameter models; and multiple treatment comparisons. Both frequentist and Bayesian approaches are applied, with example software code provided to derive within-study correlations and to fit the models. © 2014 The Authors. Research Synthesis Methods published by John Wiley & Sons, Ltd.

Multivariate Analysis As a Support for Diagnostic Flowcharts in Allergic Bronchopulmonary Aspergillosis: A Proof-of-Concept Study.

PubMed

Vitte, Joana; Ranque, Stéphane; Carsin, Ania; Gomez, Carine; Romain, Thomas; Cassagne, Carole; Gouitaa, Marion; Baravalle-Einaudi, Mélisande; Bel, Nathalie Stremler-Le; Reynaud-Gaubert, Martine; Dubus, Jean-Christophe; Mège, Jean-Louis; Gaudart, Jean

2017-01-01

Molecular-based allergy diagnosis yields multiple biomarker datasets. The classical diagnostic score for allergic bronchopulmonary aspergillosis (ABPA), a severe disease usually occurring in asthmatic patients and people with cystic fibrosis, comprises succinct immunological criteria formulated in 1977: total IgE, anti- Aspergillus fumigatus ( Af ) IgE, anti- Af "precipitins," and anti- Af IgG. Progress achieved over the last four decades led to multiple IgE and IgG(4) Af biomarkers available with quantitative, standardized, molecular-level reports. These newly available biomarkers have not been included in the current diagnostic criteria, either individually or in algorithms, despite persistent underdiagnosis of ABPA. Large numbers of individual biomarkers may hinder their use in clinical practice. Conversely, multivariate analysis using new tools may bring about a better chance of less diagnostic mistakes. We report here a proof-of-concept work consisting of a three-step multivariate analysis of Af IgE, IgG, and IgG4 biomarkers through a combination of principal component analysis, hierarchical ascendant classification, and classification and regression tree multivariate analysis. The resulting diagnostic algorithms might show the way for novel criteria and improved diagnostic efficiency in Af -sensitized patients at risk for ABPA.

Multivariate analysis of longitudinal rates of change.

PubMed

Bryan, Matthew; Heagerty, Patrick J

2016-12-10

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

[Prevalence of smoking among Colombian adolescents].

PubMed

Martínez-Torres, Javier; Peñuela Epalza, Martha

2017-03-01

Cigarette smoking is considered the most important preventable public health problem in developed countries, especially among adolescents. To determine the prevalence of cigarette smoking and associated factors in high school adolescents, from a Colombian city. The self-administered global tobacco youth survey (GTYS) was answered by 831 teenagers aged 14 ± 2 years (54% females). For data analysis, proportions were calculated; for associations, binary and multivariable logistic regression was applied. Fourteen percent of respondents declared that they had consumed at least one cigarette during the last 30 days. The life-time prevalence of tobacco use was 27.1%. Being older than thirteen years old, father’s academic level and having a smoker mother were factors associated with smoking. The prevalence of smoking in these adolescents was high. Age over 13 years and a smoking mother were associated with the cigarette smoking.

Exploration of the Medicinal Peptide Space.

PubMed

Gevaert, Bert; Stalmans, Sofie; Wynendaele, Evelien; Taevernier, Lien; Bracke, Nathalie; D'Hondt, Matthias; De Spiegeleer, Bart

2016-01-01

The chemical properties of peptide medicines, known as the 'medicinal peptide space' is considered a multi-dimensional subset of the global peptide space, where each dimension represents a chemical descriptor. These descriptors can be linked to biofunctional, medicinal properties to varying degrees. Knowledge of this space can increase the efficiency of the peptide-drug discovery and development process, as well as advance our understanding and classification of peptide medicines. For 245 peptide drugs, already available on the market or in clinical development, multivariate dataexploration was performed using peptide relevant physicochemical descriptors, their specific peptidedrug target and their clinical use. Our retrospective analysis indicates that clusters in the medicinal peptide space are located in a relatively narrow range of the physicochemical space: dense and empty regions were found, which can be explored for the discovery of novel peptide drugs.

A QC approach to the determination of day-to-day reproducibility and robustness of LC-MS methods for global metabolite profiling in metabonomics/metabolomics.

PubMed

Gika, Helen G; Theodoridis, Georgios A; Earll, Mark; Wilson, Ian D

2012-09-01

An approach to the determination of day-to-day analytical robustness of LC-MS-based methods for global metabolic profiling using a pooled QC sample is presented for the evaluation of metabonomic/metabolomic data. A set of 60 urine samples were repeatedly analyzed on five different days and the day-to-day reproducibility of the data obtained was determined. Multivariate statistical analysis was performed with the aim of evaluating variability and selected peaks were assessed and validated in terms of retention time stability, mass accuracy and intensity. The methodology enables the repeatability/reproducibility of extended analytical runs in large-scale studies to be determined, allowing the elimination of analytical (as opposed to biological) variability, in order to discover true patterns and correlations within the data. The day-to-day variability of the data revealed by this process suggested that, for this particular system, 3 days continuous operation was possible without the need for maintenance and cleaning. Variation was generally based on signal intensity changes over the 7-day period of the study, and was mainly a result of source contamination.

Brain metabolomic profiling of eastern honey bee (Apis cerana) infested with the mite Varroa destructor.

PubMed

Wu, Jiang-Li; Zhou, Chun-Xue; Wu, Peng-Jie; Xu, Jin; Guo, Yue-Qin; Xue, Fei; Getachew, Awraris; Xu, Shu-Fa

2017-01-01

The mite Varroa destructor is currently the greatest threat to apiculture as it is causing a global decrease in honey bee colonies. However, it rarely causes serious damage to its native hosts, the eastern honey bees Apis cerana. To better understand the mechanism of resistance of A. cerana against the V. destructor mite, we profiled the metabolic changes that occur in the honey bee brain during V. destructor infestation. Brain samples were collected from infested and control honey bees and then measured using an untargeted liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based global metabolomics method, in which 7918 and 7462 ions in ESI+ and ESI- mode, respectively, were successfully identified. Multivariate statistical analyses were applied, and 64 dysregulated metabolites, including fatty acids, amino acids, carboxylic acid, and phospholipids, amongst others, were identified. Pathway analysis further revealed that linoleic acid metabolism; propanoate metabolism; and glycine, serine, and threonine metabolism were acutely perturbed. The data obtained in this study offer insight into the defense mechanisms of A. cerana against V. destructor mites and provide a better method for understanding the synergistic effects of parasitism on honey bee colonies.

Brain metabolomic profiling of eastern honey bee (Apis cerana) infested with the mite Varroa destructor

PubMed Central

Wu, Peng-Jie; Xu, Jin; Guo, Yue-Qin; Xue, Fei; Getachew, Awraris; Xu, Shu-Fa

2017-01-01

The mite Varroa destructor is currently the greatest threat to apiculture as it is causing a global decrease in honey bee colonies. However, it rarely causes serious damage to its native hosts, the eastern honey bees Apis cerana. To better understand the mechanism of resistance of A. cerana against the V. destructor mite, we profiled the metabolic changes that occur in the honey bee brain during V. destructor infestation. Brain samples were collected from infested and control honey bees and then measured using an untargeted liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based global metabolomics method, in which 7918 and 7462 ions in ESI+ and ESI- mode, respectively, were successfully identified. Multivariate statistical analyses were applied, and 64 dysregulated metabolites, including fatty acids, amino acids, carboxylic acid, and phospholipids, amongst others, were identified. Pathway analysis further revealed that linoleic acid metabolism; propanoate metabolism; and glycine, serine, and threonine metabolism were acutely perturbed. The data obtained in this study offer insight into the defense mechanisms of A. cerana against V. destructor mites and provide a better method for understanding the synergistic effects of parasitism on honey bee colonies. PMID:28403242

Multivoxel patterns in face-sensitive temporal regions reveal an encoding schema based on detecting life in a face.

PubMed

Looser, Christine E; Guntupalli, Jyothi S; Wheatley, Thalia

2013-10-01

More than a decade of research has demonstrated that faces evoke prioritized processing in a 'core face network' of three brain regions. However, whether these regions prioritize the detection of global facial form (shared by humans and mannequins) or the detection of life in a face has remained unclear. Here, we dissociate form-based and animacy-based encoding of faces by using animate and inanimate faces with human form (humans, mannequins) and dog form (real dogs, toy dogs). We used multivariate pattern analysis of BOLD responses to uncover the representational similarity space for each area in the core face network. Here, we show that only responses in the inferior occipital gyrus are organized by global facial form alone (human vs dog) while animacy becomes an additional organizational priority in later face-processing regions: the lateral fusiform gyri (latFG) and right superior temporal sulcus. Additionally, patterns evoked by human faces were maximally distinct from all other face categories in the latFG and parts of the extended face perception system. These results suggest that once a face configuration is perceived, faces are further scrutinized for whether the face is alive and worthy of social cognitive resources.

Measles case fatality rate in Bihar, India, 2011-12.

PubMed

Murhekar, Manoj V; Ahmad, Mohammad; Shukla, Hemant; Abhishek, Kunwar; Perry, Robert T; Bose, Anindya S; Shimpi, Rahul; Kumar, Arun; Kaliaperumal, Kanagasabai; Sethi, Raman; Selvaraj, Vadivoo; Kamaraj, Pattabi; Routray, Satyabrata; Das, Vidya Nand; Menabde, Nata; Bahl, Sunil

2014-01-01

Updated estimates of measles case fatality rates (CFR) are critical for monitoring progress towards measles elimination goals. India accounted for 36% of total measles deaths occurred globally in 2011. We conducted a retrospective cohort study to estimate measles CFR and identify the risk factors for measles death in Bihar-one of the north Indian states historically known for its low vaccination coverage. We systematically selected 16 of the 31 laboratory-confirmed measles outbreaks occurring in Bihar during 1 October 2011 to 30 April 2012. All households of the villages/urban localities affected by these outbreaks were visited to identify measles cases and deaths. We calculated CFR and used multivariate analysis to identify risk factors for measles death. The survey found 3670 measles cases and 28 deaths (CFR: 0.78, 95% confidence interval: 0.47-1.30). CFR was higher among under-five children (1.22%) and children belonging to scheduled castes/tribes (SC/ST, 1.72%). On multivariate analysis, independent risk factors associated with measles death were age <5 years, SC/ST status and non-administration of vitamin A during illness. Outbreaks with longer interval between the occurrence of first case and notification of the outbreak also had a higher rate of deaths. Measles CFR in Bihar was low. To further reduce case fatality, health authorities need to ensure that SC/ST are targeted by the immunization programme and that outbreak investigations target for vitamin A treatment of cases in high risk groups such as SC/ST and young children and ensure regular visits by health-workers in affected villages to administer vitamin A to new cases.

Community-based tobacco cessation program among women in Mumbai, India.

PubMed

Mishra, G A; Kulkarni, S V; Majmudar, P V; Gupta, S D; Shastri, S S

2014-12-01

Globally tobacco epidemic kills nearly six million people annually. Consumption of tobacco products is on the rise in low- and middle-income countries. Tobacco is addictive; hence, tobacco users need support in quitting. Providing tobacco cessation services to women in community enabling them to quit tobacco, identifying factors associated with quitting and documenting the processes involved to establish a replicable "model tobacco cessation program." This is a community based tobacco cessation program of one year duration conducted among women in a low socioeconomic area of Mumbai, India. It involved three interventions conducted at three months interval, comprised of health education, games and counseling sessions and a post intervention follow-up. Uni and multivariate analysis was performed to find out association of various factors with quitting tobacco. The average compliance in three intervention rounds was 95.2%. The mean age at initiation of tobacco was 17.3 years. Tobacco use among family members and in the community was primary reasons for initiation and addiction to tobacco was an important factor for continuation, whereas health education and counseling seemed to be largely responsible for quitting. The quit rate at the end of the programme was 33.5%. Multivariate logistic regression analysis found that women in higher age groups and women consuming tobacco at multiple locations are less likely to quit tobacco. Changing cultural norms associated with smokeless tobacco, strict implementation of antitobacco laws in the community and work places and providing cessation support are important measures in preventing initiation and continuation of tobacco use among women in India.

Correlation of Biomarker Expression in Colonic Mucosa with Disease Phenotype in Crohn's Disease and Ulcerative Colitis.

PubMed

Bruno, Maria E C; Rogier, Eric W; Arsenescu, Razvan I; Flomenhoft, Deborah R; Kurkjian, Cathryn J; Ellis, Gavin I; Kaetzel, Charlotte S

2015-10-01

Inflammatory bowel diseases (IBD), including Crohn's disease (CD) and ulcerative colitis (UC), are characterized by chronic intestinal inflammation due to immunological, microbial, and environmental factors in genetically predisposed individuals. Advances in the diagnosis, prognosis, and treatment of IBD require the identification of robust biomarkers that can be used for molecular classification of diverse disease presentations. We previously identified five genes, RELA, TNFAIP3 (A20), PIGR, TNF, and IL8, whose mRNA levels in colonic mucosal biopsies could be used in a multivariate analysis to classify patients with CD based on disease behavior and responses to therapy. We compared expression of these five biomarkers in IBD patients classified as having CD or UC, and in healthy controls. Patients with CD were characterized as having decreased median expression of TNFAIP3, PIGR, and TNF in non-inflamed colonic mucosa as compared to healthy controls. By contrast, UC patients exhibited decreased expression of PIGR and elevated expression of IL8 in colonic mucosa compared to healthy controls. A multivariate analysis combining mRNA levels for all five genes resulted in segregation of individuals based on disease presentation (CD vs. UC) as well as severity, i.e., patients in remission versus those with acute colitis at the time of biopsy. We propose that this approach could be used as a model for molecular classification of IBD patients, which could further be enhanced by the inclusion of additional genes that are identified by functional studies, global gene expression analyses, and genome-wide association studies.

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

PubMed

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

2009-04-01

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

The association between temperature and mortality in tropical middle income Thailand from 1999 to 2008

NASA Astrophysics Data System (ADS)

Tawatsupa, Benjawan; Dear, Keith; Kjellstrom, Tord; Sleigh, Adrian

2014-03-01

We have investigated the association between tropical weather condition and age-sex adjusted death rates (ADR) in Thailand over a 10-year period from 1999 to 2008. Population, mortality, weather and air pollution data were obtained from four national databases. Alternating multivariable fractional polynomial (MFP) regression and stepwise multivariable linear regression analysis were used to sequentially build models of the associations between temperature variable and deaths, adjusted for the effects and interactions of age, sex, weather (6 variables), and air pollution (10 variables). The associations are explored and compared among three seasons (cold, hot and wet months) and four weather zones of Thailand (the North, Northeast, Central, and South regions). We found statistically significant associations between temperature and mortality in Thailand. The maximum temperature is the most important variable in predicting mortality. Overall, the association is nonlinear U-shape and 31 °C is the minimum-mortality temperature in Thailand. The death rates increase when maximum temperature increase with the highest rates in the North and Central during hot months. The final equation used in this study allowed estimation of the impact of a 4 °C increase in temperature as projected for Thailand by 2100; this analysis revealed that the heat-related deaths will increase more than the cold-related deaths avoided in the hot and wet months, and overall the net increase in expected mortality by region ranges from 5 to 13 % unless preventive measures were adopted. Overall, these results are useful for health impact assessment for the present situation and future public health implication of global climate change for tropical Thailand.

Alteration in metabolic signature and lipid metabolism in patients with angina pectoris and myocardial infarction.

PubMed

Park, Ju Yeon; Lee, Sang-Hak; Shin, Min-Jeong; Hwang, Geum-Sook

2015-01-01

Lipid metabolites are indispensable regulators of physiological and pathological processes, including atherosclerosis and coronary artery disease (CAD). However, the complex changes in lipid metabolites and metabolism that occur in patients with these conditions are incompletely understood. We performed lipid profiling to identify alterations in lipid metabolism in patients with angina and myocardial infarction (MI). Global lipid profiling was applied to serum samples from patients with CAD (angina and MI) and age-, sex-, and body mass index-matched healthy subjects using ultra-performance liquid chromatography/quadruple time-of-flight mass spectrometry and multivariate statistical analysis. A multivariate analysis showed a clear separation between the patients with CAD and normal controls. Lysophosphatidylcholine (lysoPC) and lysophosphatidylethanolamine (lysoPE) species containing unsaturated fatty acids and free fatty acids were associated with an increased risk of CAD, whereas species of lysoPC and lyso-alkyl PC containing saturated fatty acids were associated with a decreased risk. Additionally, PC species containing palmitic acid, diacylglycerol, sphingomyelin, and ceramide were associated with an increased risk of MI, whereas PE-plasmalogen and phosphatidylinositol species were associated with a decreased risk. In MI patients, we found strong positive correlation between lipid metabolites related to the sphingolipid pathway, sphingomyelin, and ceramide and acute inflammatory markers (high-sensitivity C-reactive protein). The results of this study demonstrate altered signatures in lipid metabolism in patients with angina or MI. Lipidomic profiling could provide the information to identity the specific lipid metabolites under the presence of disturbed metabolic pathways in patients with CAD.

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

PubMed

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

2018-02-01

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

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

PubMed

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

2017-10-01

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

454 pyrosequencing analysis on faecal samples from a randomized DBPC trial of colicky infants treated with Lactobacillus reuteri DSM 17938.

PubMed

Roos, Stefan; Dicksved, Johan; Tarasco, Valentina; Locatelli, Emanuela; Ricceri, Fulvio; Grandin, Ulf; Savino, Francesco

2013-01-01

To analyze the global microbial composition, using large-scale DNA sequencing of 16 S rRNA genes, in faecal samples from colicky infants given L. reuteri DSM 17938 or placebo. Twenty-nine colicky infants (age 10-60 days) were enrolled and randomly assigned to receive either Lactobacillus reuteri (10(8) cfu) or a placebo once daily for 21 days. Responders were defined as subjects with a decrease of 50% in daily crying time at day 21 compared with the starting point. The microbiota of faecal samples from day 1 and 21 were analyzed using 454 pyrosequencing. The primers: Bakt_341F and Bakt_805R, complemented with 454 adapters and sample specific barcodes were used for PCR amplification of the 16 S rRNA genes. The structure of the data was explored by using permutational multivariate analysis of variance and effects of different variables were visualized with ordination analysis. The infants' faecal microbiota were composed of Proteobacteria, Firmicutes, Actinobacteria and Bacteroidetes as the four main phyla. The composition of the microbiota in infants with colic had very high inter-individual variability with Firmicutes/Bacteroidetes ratios varying from 4000 to 0.025. On an individual basis, the microbiota was, however, relatively stable over time. Treatment with L. reuteri DSM 17938 did not change the global composition of the microbiota, but when comparing responders with non-responders the group responders had an increased relative abundance of the phyla Bacteroidetes and genus Bacteroides at day 21 compared with day 0. Furthermore, the phyla composition of the infants at day 21 could be divided into three enterotype groups, dominated by Firmicutes, Bacteroidetes, and Actinobacteria, respectively. L. reuteri DSM 17938 did not affect the global composition of the microbiota. However, the increase of Bacteroidetes in the responder infants indicated that a decrease in colicky symptoms was linked to changes of the microbiota. ClinicalTrials.gov NCT00893711.

A Statistical Discrimination Experiment for Eurasian Events Using a Twenty-Seven-Station Network

DTIC Science & Technology

1980-07-08

to test the effectiveness of a multivariate method of analysis for distinguishing earthquakes from explosions. The data base for the experiment...to test the effectiveness of a multivariate method of analysis for distinguishing earthquakes from explosions. The data base for the experiment...the weight assigned to each variable whenever a new one is added. Jennrich, R. I. (1977). Stepwise discriminant analysis , in Statistical Methods for

Is Heart Rate Variability Better Than Routine Vital Signs for Prehospital Identification of Major Hemorrhage

DTIC Science & Technology

2015-01-01

different PRBC transfusion volumes. We performed multivariate regression analysis using HRV metrics and routine vital signs to test the hypothesis that...study sponsors did not have any role in the study design, data collection, analysis and interpretation of data, report writing, or the decision to...primary outcome was hemorrhagic injury plus different PRBC transfusion volumes. We performed multivariate regression analysis using HRV metrics and

Multivariate optimum interpolation of surface pressure and winds over oceans

NASA Technical Reports Server (NTRS)

Bloom, S. C.

1984-01-01

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

Nonlinear multivariate and time series analysis by neural network methods

NASA Astrophysics Data System (ADS)

Hsieh, William W.

2004-03-01

Methods in multivariate statistical analysis are essential for working with large amounts of geophysical data, data from observational arrays, from satellites, or from numerical model output. In classical multivariate statistical analysis, there is a hierarchy of methods, starting with linear regression at the base, followed by principal component analysis (PCA) and finally canonical correlation analysis (CCA). A multivariate time series method, the singular spectrum analysis (SSA), has been a fruitful extension of the PCA technique. The common drawback of these classical methods is that only linear structures can be correctly extracted from the data. Since the late 1980s, neural network methods have become popular for performing nonlinear regression and classification. More recently, neural network methods have been extended to perform nonlinear PCA (NLPCA), nonlinear CCA (NLCCA), and nonlinear SSA (NLSSA). This paper presents a unified view of the NLPCA, NLCCA, and NLSSA techniques and their applications to various data sets of the atmosphere and the ocean (especially for the El Niño-Southern Oscillation and the stratospheric quasi-biennial oscillation). These data sets reveal that the linear methods are often too simplistic to describe real-world systems, with a tendency to scatter a single oscillatory phenomenon into numerous unphysical modes or higher harmonics, which can be largely alleviated in the new nonlinear paradigm.

Analysis and assessment on heavy metal sources in the coastal soils developed from alluvial deposits using multivariate statistical methods.

PubMed

Li, Jinling; He, Ming; Han, Wei; Gu, Yifan

2009-05-30

An investigation on heavy metal sources, i.e., Cu, Zn, Ni, Pb, Cr, and Cd in the coastal soils of Shanghai, China, was conducted using multivariate statistical methods (principal component analysis, clustering analysis, and correlation analysis). All the results of the multivariate analysis showed that: (i) Cu, Ni, Pb, and Cd had anthropogenic sources (e.g., overuse of chemical fertilizers and pesticides, industrial and municipal discharges, animal wastes, sewage irrigation, etc.); (ii) Zn and Cr were associated with parent materials and therefore had natural sources (e.g., the weathering process of parent materials and subsequent pedo-genesis due to the alluvial deposits). The effect of heavy metals in the soils was greatly affected by soil formation, atmospheric deposition, and human activities. These findings provided essential information on the possible sources of heavy metals, which would contribute to the monitoring and assessment process of agricultural soils in worldwide regions.

Application of multivariate statistical techniques for differentiation of ripe banana flour based on the composition of elements.

PubMed

Alkarkhi, Abbas F M; Ramli, Saifullah Bin; Easa, Azhar Mat

2009-01-01

Major (sodium, potassium, calcium, magnesium) and minor elements (iron, copper, zinc, manganese) and one heavy metal (lead) of Cavendish banana flour and Dream banana flour were determined, and data were analyzed using multivariate statistical techniques of factor analysis and discriminant analysis. Factor analysis yielded four factors explaining more than 81% of the total variance: the first factor explained 28.73%, comprising magnesium, sodium, and iron; the second factor explained 21.47%, comprising only manganese and copper; the third factor explained 15.66%, comprising zinc and lead; while the fourth factor explained 15.50%, comprising potassium. Discriminant analysis showed that magnesium and sodium exhibited a strong contribution in discriminating the two types of banana flour, affording 100% correct assignation. This study presents the usefulness of multivariate statistical techniques for analysis and interpretation of complex mineral content data from banana flour of different varieties.

Long-term forecasting of meteorological time series using Nonlinear Canonical Correlation Analysis (NLCCA)

NASA Astrophysics Data System (ADS)

Woldesellasse, H. T.; Marpu, P. R.; Ouarda, T.

2016-12-01

Wind is one of the crucial renewable energy sources which is expected to bring solutions to the challenges of clean energy and the global issue of climate change. A number of linear and nonlinear multivariate techniques has been used to predict the stochastic character of wind speed. A wind forecast with good accuracy has a positive impact on the reduction of electricity system cost and is essential for the effective grid management. Over the past years, few studies have been done on the assessment of teleconnections and its possible effects on the long-term wind speed variability in the UAE region. In this study Nonlinear Canonical Correlation Analysis (NLCCA) method is applied to study the relationship between global climate oscillation indices and meteorological variables, with a major emphasis on wind speed and wind direction, of Abu Dhabi, UAE. The wind dataset was obtained from six ground stations. The first mode of NLCCA is capable of capturing the nonlinear mode of the climate indices at different seasons, showing the symmetry between the warm states and the cool states. The strength of the nonlinear canonical correlation between the two sets of variables varies with the lead/lag time. The performance of the models is assessed by calculating error indices such as the root mean square error (RMSE) and Mean absolute error (MAE). The results indicated that NLCCA models provide more accurate information about the nonlinear intrinsic behaviour of the dataset of variables than linear CCA model in terms of the correlation and root mean square error. Key words: Nonlinear Canonical Correlation Analysis (NLCCA), Canonical Correlation Analysis, Neural Network, Climate Indices, wind speed, wind direction

PYCHEM: a multivariate analysis package for python.

PubMed

Jarvis, Roger M; Broadhurst, David; Johnson, Helen; O'Boyle, Noel M; Goodacre, Royston

2006-10-15

We have implemented a multivariate statistical analysis toolbox, with an optional standalone graphical user interface (GUI), using the Python scripting language. This is a free and open source project that addresses the need for a multivariate analysis toolbox in Python. Although the functionality provided does not cover the full range of multivariate tools that are available, it has a broad complement of methods that are widely used in the biological sciences. In contrast to tools like MATLAB, PyChem 2.0.0 is easily accessible and free, allows for rapid extension using a range of Python modules and is part of the growing amount of complementary and interoperable scientific software in Python based upon SciPy. One of the attractions of PyChem is that it is an open source project and so there is an opportunity, through collaboration, to increase the scope of the software and to continually evolve a user-friendly platform that has applicability across a wide range of analytical and post-genomic disciplines. http://sourceforge.net/projects/pychem

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

PubMed

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

2017-12-01

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

Multivariate longitudinal data analysis with censored and intermittent missing responses.

PubMed

Lin, Tsung-I; Lachos, Victor H; Wang, Wan-Lun

2018-05-08

The multivariate linear mixed model (MLMM) has emerged as an important analytical tool for longitudinal data with multiple outcomes. However, the analysis of multivariate longitudinal data could be complicated by the presence of censored measurements because of a detection limit of the assay in combination with unavoidable missing values arising when subjects miss some of their scheduled visits intermittently. This paper presents a generalization of the MLMM approach, called the MLMM-CM, for a joint analysis of the multivariate longitudinal data with censored and intermittent missing responses. A computationally feasible expectation maximization-based procedure is developed to carry out maximum likelihood estimation within the MLMM-CM framework. Moreover, the asymptotic standard errors of fixed effects are explicitly obtained via the information-based method. We illustrate our methodology by using simulated data and a case study from an AIDS clinical trial. Experimental results reveal that the proposed method is able to provide more satisfactory performance as compared with the traditional MLMM approach. Copyright © 2018 John Wiley & Sons, Ltd.

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

PubMed Central

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

2016-01-01

Multivariate and network meta-analysis have the potential for the estimated mean of one effect to borrow strength from the data on other effects of interest. The extent of this borrowing of strength is usually assessed informally. We present new mathematical definitions of ‘borrowing of strength’. Our main proposal is based on a decomposition of the score statistic, which we show can be interpreted as comparing the precision of estimates from the multivariate and univariate models. Our definition of borrowing of strength therefore emulates the usual informal assessment. We also derive a method for calculating study weights, which we embed into the same framework as our borrowing of strength statistics, so that percentage study weights can accompany the results from multivariate and network meta-analyses as they do in conventional univariate meta-analyses. Our proposals are illustrated using three meta-analyses involving correlated effects for multiple outcomes, multiple risk factor associations and multiple treatments (network meta-analysis). PMID:26546254

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

NASA Astrophysics Data System (ADS)

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

2015-06-01

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

Impact of stone density on outcomes in percutaneous nephrolithotomy (PCNL): an analysis of the clinical research office of the endourological society (CROES) pcnl global study database.

PubMed

Anastasiadis, Anastasios; Onal, Bulent; Modi, Pranjal; Turna, Burak; Duvdevani, Mordechai; Timoney, Anthony; Wolf, J Stuart; De La Rosette, Jean

2013-12-01

This study aimed to explore the relationship between stone density and outcomes of percutaneous nephrolithotomy (PCNL) using the Clinical Research Office of the Endourological Society (CROES) PCNL Global Study database. Patients undergoing PCNL treatment were assigned to a low stone density [LSD, ≤ 1000 Hounsfield units (HU)] or high stone density (HSD, > 1000 HU) group based on the radiological density of the primary renal stone. Preoperative characteristics and outcomes were compared in the two groups. Retreatment for residual stones was more frequent in the LSD group. The overall stone-free rate achieved was higher in the HSD group (79.3% vs 74.8%, p = 0.113). By univariate regression analysis, the probability of achieving a stone-free outcome peaked at approximately 1250 HU. Below or above this density resulted in lower treatment success, particularly at very low HU values. With increasing radiological stone density, operating time decreased to a minimum at approximately 1000 HU, then increased with further increase in stone density. Multivariate non-linear regression analysis showed a similar relationship between the probability of a stone-free outcome and stone density. Higher treatment success rates were found with low stone burden, pelvic stone location and use of pneumatic lithotripsy. Very low and high stone densities are associated with lower rates of treatment success and longer operating time in PCNL. Preoperative assessment of stone density may help in the selection of treatment modality for patients with renal stones.

Kernel canonical-correlation Granger causality for multiple time series

NASA Astrophysics Data System (ADS)

Wu, Guorong; Duan, Xujun; Liao, Wei; Gao, Qing; Chen, Huafu

2011-04-01

Canonical-correlation analysis as a multivariate statistical technique has been applied to multivariate Granger causality analysis to infer information flow in complex systems. It shows unique appeal and great superiority over the traditional vector autoregressive method, due to the simplified procedure that detects causal interaction between multiple time series, and the avoidance of potential model estimation problems. However, it is limited to the linear case. Here, we extend the framework of canonical correlation to include the estimation of multivariate nonlinear Granger causality for drawing inference about directed interaction. Its feasibility and effectiveness are verified on simulated data.

Multivariate geometry as an approach to algal community analysis

USGS Publications Warehouse

Allen, T.F.H.; Skagen, S.

1973-01-01

Multivariate analyses are put in the context of more usual approaches to phycological investigations. The intuitive common-sense involved in methods of ordination, classification and discrimination are emphasised by simple geometric accounts which avoid jargon and matrix algebra. Warnings are given that artifacts result from technique abuses by the naive or over-enthusiastic. An analysis of a simple periphyton data set is presented as an example of the approach. Suggestions are made as to situations in phycological investigations, where the techniques could be appropriate. The discipline is reprimanded for its neglect of the multivariate approach.

Laparoscopic liver surgery: towards a day-case management.

PubMed

Tranchart, Hadrien; Fuks, David; Lainas, Panagiotis; Gaillard, Martin; Dagher, Ibrahim; Gayet, Brice

2017-12-01

Ambulatory surgery (AS) is a contemporary subject of interest. The feasibility and safety of AS for solid abdominal organs are still dubious. In the present study, we aimed at defining potential surgical criteria for AS by analyzing a large database of patients who underwent laparoscopic liver surgery (LLS) in two French expert centers. This study was performed using prospectively filled databases including patients that underwent pure LLS between 1998 and 2015. Patients whose perioperative medical characteristics (ASA score <3, no associated extra-hepatic procedure, surgical duration ≤180 min, blood loss ≤300 mL, no intraoperative anesthesiological or surgical complication, no postoperative drainage) were potentially adapted for ambulatory LLS were included in the analysis. In order to determine the risk factors for postoperative complications, multivariate analysis was carried out. During the study period, pure LLS was performed in 994 patients. After preoperative and intraoperative characteristics screening, 174 (17.5%) patients were considered for the final analysis. Lesions (benign (46%) and liver metastases (43%)) were predominantly single with a mean size of 37 ± 32 mm in an underlying normal or steatotic liver parenchyma (94.8%). The vast majority of LLS performed were single procedures including wedge resections and liver cyst unroofing or left lateral sectionectomies (74%). The global morbidity rate was 14% and six patients presented a major complication (Dindo-Clavien ≥III). The mean length of stay was 5 ± 4 days. Multivariate analysis showed that major hepatectomy [OR 29.04 (2.26-37.19); P = 0.01] and resection of tumors localized in central segments [OR 41.24 (1.08-156.47); P = 0.04] were independent predictors of postoperative morbidity. In experienced teams, approximately 7% of highly selected patients requiring laparoscopic hepatic surgery (wedge resection, liver cyst unroofing, or left lateral sectionectomy) could benefit from ambulatory surgery management.

Comparison of Optimum Interpolation and Cressman Analyses

NASA Technical Reports Server (NTRS)

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

1984-01-01

The objective of this investigation is to develop a state-of-the-art optimum interpolation (O/I) objective analysis procedure for use in numerical weather prediction studies. A three-dimensional multivariate O/I analysis scheme has been developed. Some characteristics of the GLAS O/I compared with those of the NMC and ECMWF systems are summarized. Some recent enhancements of the GLAS scheme include a univariate analysis of water vapor mixing ratio, a geographically dependent model prediction error correlation function and a multivariate oceanic surface analysis.

Comparison of Optimum Interpolation and Cressman Analyses

NASA Technical Reports Server (NTRS)

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

1985-01-01

The development of a state of the art optimum interpolation (O/I) objective analysis procedure for use in numerical weather prediction studies was investigated. A three dimensional multivariate O/I analysis scheme was developed. Some characteristics of the GLAS O/I compared with those of the NMC and ECMWF systems are summarized. Some recent enhancements of the GLAS scheme include a univariate analysis of water vapor mixing ratio, a geographically dependent model prediction error correlation function and a multivariate oceanic surface analysis.

Tracking Problem Solving by Multivariate Pattern Analysis and Hidden Markov Model Algorithms

ERIC Educational Resources Information Center

Anderson, John R.

2012-01-01

Multivariate pattern analysis can be combined with Hidden Markov Model algorithms to track the second-by-second thinking as people solve complex problems. Two applications of this methodology are illustrated with a data set taken from children as they interacted with an intelligent tutoring system for algebra. The first "mind reading" application…

Functional Path Analysis as a Multivariate Technique in Developing a Theory of Participation in Adult Education.

ERIC Educational Resources Information Center

Martin, James L.

This paper reports on attempts by the author to construct a theoretical framework of adult education participation using a theory development process and the corresponding multivariate statistical techniques. Two problems are identified: the lack of theoretical framework in studying problems, and the limiting of statistical analysis to univariate…

Missing Data and Multiple Imputation in the Context of Multivariate Analysis of Variance

ERIC Educational Resources Information Center

Finch, W. Holmes

2016-01-01

Multivariate analysis of variance (MANOVA) is widely used in educational research to compare means on multiple dependent variables across groups. Researchers faced with the problem of missing data often use multiple imputation of values in place of the missing observations. This study compares the performance of 2 methods for combining p values in…

Web-Based Tools for Modelling and Analysis of Multivariate Data: California Ozone Pollution Activity

ERIC Educational Resources Information Center

Dinov, Ivo D.; Christou, Nicolas

2011-01-01

This article presents a hands-on web-based activity motivated by the relation between human health and ozone pollution in California. This case study is based on multivariate data collected monthly at 20 locations in California between 1980 and 2006. Several strategies and tools for data interrogation and exploratory data analysis, model fitting…

Bias and Precision of Measures of Association for a Fixed-Effect Multivariate Analysis of Variance Model

ERIC Educational Resources Information Center

Kim, Soyoung; Olejnik, Stephen

2005-01-01

The sampling distributions of five popular measures of association with and without two bias adjusting methods were examined for the single factor fixed-effects multivariate analysis of variance model. The number of groups, sample sizes, number of outcomes, and the strength of association were manipulated. The results indicate that all five…

Multivariate analysis of climate along the southern coast of Alaska—some forestry implications.

Treesearch

Wilbur A. Farr; John S. Hard

1987-01-01

A multivariate analysis of climate was used to delineate 10 significantly different groups of climatic stations along the southern coast of Alaska based on latitude, longitude, seasonal temperatures and precipitation, frost-free periods, and total number of growing degree days. The climatic stations were too few to delineate this rugged, mountainous region into...

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

PubMed Central

Neupane, Binod; Beyene, Joseph

2015-01-01

In a meta-analysis with multiple end points of interests that are correlated between or within studies, multivariate approach to meta-analysis has a potential to produce more precise estimates of effects by exploiting the correlation structure between end points. However, under random-effects assumption the multivariate estimation is more complex (as it involves estimation of more parameters simultaneously) than univariate estimation, and sometimes can produce unrealistic parameter estimates. Usefulness of multivariate approach to meta-analysis of the effects of a genetic variant on two or more correlated traits is not well understood in the area of genetic association studies. In such studies, genetic variants are expected to roughly maintain Hardy-Weinberg equilibrium within studies, and also their effects on complex traits are generally very small to modest and could be heterogeneous across studies for genuine reasons. We carried out extensive simulation to explore the comparative performance of multivariate approach with most commonly used univariate inverse-variance weighted approach under random-effects assumption in various realistic meta-analytic scenarios of genetic association studies of correlated end points. We evaluated the performance with respect to relative mean bias percentage, and root mean square error (RMSE) of the estimate and coverage probability of corresponding 95% confidence interval of the effect for each end point. Our simulation results suggest that multivariate approach performs similarly or better than univariate method when correlations between end points within or between studies are at least moderate and between-study variation is similar or larger than average within-study variation for meta-analyses of 10 or more genetic studies. Multivariate approach produces estimates with smaller bias and RMSE especially for the end point that has randomly or informatively missing summary data in some individual studies, when the missing data in the endpoint are imputed with null effects and quite large variance. PMID:26196398

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

PubMed

Neupane, Binod; Beyene, Joseph

2015-01-01

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

MULTIVARIATE ANALYSES (CONONICAL CORRELATION AND PARTIAL LEAST SQUARE, PLS) TO MODEL AND ASSESS THE ASSOCIATION OF LANDSCAPE METRICS TO SURFACE WATER CHEMICAL AND BIOLOGICAL PROPERTIES USING SAVANNAH RIVER BASIN DATA.

EPA Science Inventory

Many multivariate methods are used in describing and predicting relation; each has its unique usage of categorical and non-categorical data. In multivariate analysis of variance (MANOVA), many response variables (y's) are related to many independent variables that are categorical...

Multivariate Density Estimation and Remote Sensing

NASA Technical Reports Server (NTRS)

Scott, D. W.

1983-01-01

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

Comprehensive drought characteristics analysis based on a nonlinear multivariate drought index

NASA Astrophysics Data System (ADS)

Yang, Jie; Chang, Jianxia; Wang, Yimin; Li, Yunyun; Hu, Hui; Chen, Yutong; Huang, Qiang; Yao, Jun

2018-02-01

It is vital to identify drought events and to evaluate multivariate drought characteristics based on a composite drought index for better drought risk assessment and sustainable development of water resources. However, most composite drought indices are constructed by the linear combination, principal component analysis and entropy weight method assuming a linear relationship among different drought indices. In this study, the multidimensional copulas function was applied to construct a nonlinear multivariate drought index (NMDI) to solve the complicated and nonlinear relationship due to its dependence structure and flexibility. The NMDI was constructed by combining meteorological, hydrological, and agricultural variables (precipitation, runoff, and soil moisture) to better reflect the multivariate variables simultaneously. Based on the constructed NMDI and runs theory, drought events for a particular area regarding three drought characteristics: duration, peak, and severity were identified. Finally, multivariate drought risk was analyzed as a tool for providing reliable support in drought decision-making. The results indicate that: (1) multidimensional copulas can effectively solve the complicated and nonlinear relationship among multivariate variables; (2) compared with single and other composite drought indices, the NMDI is slightly more sensitive in capturing recorded drought events; and (3) drought risk shows a spatial variation; out of the five partitions studied, the Jing River Basin as well as the upstream and midstream of the Wei River Basin are characterized by a higher multivariate drought risk. In general, multidimensional copulas provides a reliable way to solve the nonlinear relationship when constructing a comprehensive drought index and evaluating multivariate drought characteristics.

Effect of Contact Damage on the Strength of Ceramic Materials.

DTIC Science & Technology

1982-10-01

variables that are important to erosion, and a multivariate , linear regression analysis is used to fit the data to the dimensional analysis. The...of Equations 7 and 8 by a multivariable regression analysis (room tem- perature data) Exponent Regression Standard error Computed coefficient of...1980) 593. WEAVER, Proc. Brit. Ceram. Soc. 22 (1973) 125. 39. P. W. BRIDGMAN, "Dimensional Analaysis ", (Yale 18. R. W. RICE, S. W. FREIMAN and P. F

A Framework for Establishing Standard Reference Scale of Texture by Multivariate Statistical Analysis Based on Instrumental Measurement and Sensory Evaluation.

PubMed

Zhi, Ruicong; Zhao, Lei; Xie, Nan; Wang, Houyin; Shi, Bolin; Shi, Jingye

2016-01-13

A framework of establishing standard reference scale (texture) is proposed by multivariate statistical analysis according to instrumental measurement and sensory evaluation. Multivariate statistical analysis is conducted to rapidly select typical reference samples with characteristics of universality, representativeness, stability, substitutability, and traceability. The reasonableness of the framework method is verified by establishing standard reference scale of texture attribute (hardness) with Chinese well-known food. More than 100 food products in 16 categories were tested using instrumental measurement (TPA test), and the result was analyzed with clustering analysis, principal component analysis, relative standard deviation, and analysis of variance. As a result, nine kinds of foods were determined to construct the hardness standard reference scale. The results indicate that the regression coefficient between the estimated sensory value and the instrumentally measured value is significant (R(2) = 0.9765), which fits well with Stevens's theory. The research provides reliable a theoretical basis and practical guide for quantitative standard reference scale establishment on food texture characteristics.

Tree Density and Species Decline in the African Sahel Attributable to Climate

NASA Technical Reports Server (NTRS)

Gonzalez, Patrick; Tucker, Compton J.; Sy, H.

2012-01-01

Increased aridity and human population have reduced tree cover in parts of the African Sahel and degraded resources for local people. Yet, tree cover trends and the relative importance of climate and population remain unresolved. From field measurements, aerial photos, and Ikonos satellite images, we detected significant 1954-2002 tree density declines in the western Sahel of 18 +/- 14% (P = 0.014, n = 204) and 17 +/- 13% (P = 0.0009, n = 187). From field observations, we detected a significant 1960-2000 species richness decline of 21 +/- 11% (P = 0.0028, n = 14) across the Sahel and a southward shift of the Sahel, Sudan, and Guinea zones. Multivariate analyses of climate, soil, and population showed that temperature most significantly (P < 0.001) explained tree cover changes. Multivariate and bivariate tests and field observations indicated the dominance of temperature and precipitation, supporting attribution of tree cover changes to climate variability. Climate change forcing of Sahel climate variability, particularly the significant (P < 0.05) 1901-2002 temperature increases and precipitation decreases in the research areas, connects Sahel tree cover changes to global climate change. This suggests roles for global action and local adaptation to address ecological change in the Sahel.

A Course in... Multivariable Control Methods.

ERIC Educational Resources Information Center

Deshpande, Pradeep B.

1988-01-01

Describes an engineering course for graduate study in process control. Lists four major topics: interaction analysis, multiloop controller design, decoupling, and multivariable control strategies. Suggests a course outline and gives information about each topic. (MVL)

A systematic review of the relationship factor between women and health professionals within the multivariant analysis of maternal satisfaction.

PubMed

Macpherson, Ignacio; Roqué-Sánchez, María V; Legget Bn, Finola O; Fuertes, Ferran; Segarra, Ignacio

2016-10-01

personalised support provided to women by health professionals is one of the prime factors attaining women's satisfaction during pregnancy and childbirth. However the multifactorial nature of 'satisfaction' makes difficult to assess it. Statistical multivariate analysis may be an effective technique to obtain in depth quantitative evidence of the importance of this factor and its interaction with the other factors involved. This technique allows us to estimate the importance of overall satisfaction in its context and suggest actions for healthcare services. systematic review of studies that quantitatively measure the personal relationship between women and healthcare professionals (gynecologists, obstetricians, nurse, midwifes, etc.) regarding maternity care satisfaction. The literature search focused on studies carried out between 1970 and 2014 that used multivariate analyses and included the woman-caregiver relationship as a factor of their analysis. twenty-four studies which applied various multivariate analysis tools to different periods of maternity care (antenatal, perinatal, post partum) were selected. The studies included discrete scale scores and questionnaires from women with low-risk pregnancies. The "personal relationship" factor appeared under various names: care received, personalised treatment, professional support, amongst others. The most common multivariate techniques used to assess the percentage of variance explained and the odds ratio of each factor were principal component analysis and logistic regression. the data, variables and factor analysis suggest that continuous, personalised care provided by the usual midwife and delivered within a family or a specialised setting, generates the highest level of satisfaction. In addition, these factors foster the woman's psychological and physiological recovery, often surpassing clinical action (e.g. medicalization and hospital organization) and/or physiological determinants (e.g. pain, pathologies, etc.). Copyright © 2016 Elsevier Ltd. All rights reserved.

Independent Predictors of Prognosis Based on Oral Cavity Squamous Cell Carcinoma Surgical Margins.

PubMed

Buchakjian, Marisa R; Ginader, Timothy; Tasche, Kendall K; Pagedar, Nitin A; Smith, Brian J; Sperry, Steven M

2018-05-01

Objective To conduct a multivariate analysis of a large cohort of oral cavity squamous cell carcinoma (OCSCC) cases for independent predictors of local recurrence (LR) and overall survival (OS), with emphasis on the relationship between (1) prognosis and (2) main specimen permanent margins and intraoperative tumor bed frozen margins. Study Design Retrospective cohort study. Setting Tertiary academic head and neck cancer program. Subjects and Methods This study included 426 patients treated with OCSCC resection between 2005 and 2014 at University of Iowa Hospitals and Clinics. Patients underwent excision of OCSCC with intraoperative tumor bed frozen margin sampling and main specimen permanent margin assessment. Multivariate analysis of the data set to predict LR and OS was performed. Results Independent predictors of LR included nodal involvement, histologic grade, and main specimen permanent margin status. Specifically, the presence of a positive margin (odds ratio, 6.21; 95% CI, 3.3-11.9) or <1-mm/carcinoma in situ margin (odds ratio, 2.41; 95% CI, 1.19-4.87) on the main specimen was an independent predictor of LR, whereas intraoperative tumor bed margins were not predictive of LR on multivariate analysis. Similarly, independent predictors of OS on multivariate analysis included nodal involvement, extracapsular extension, and a positive main specimen margin. Tumor bed margins did not independently predict OS. Conclusion The main specimen margin is a strong independent predictor of LR and OS on multivariate analysis. Intraoperative tumor bed frozen margins do not independently predict prognosis. We conclude that emphasis should be placed on evaluating the main specimen margins when estimating prognosis after OCSCC resection.

Relationships among participant international prostate symptom score, benign prostatic hyperplasia impact index changes and global ratings of change in a trial of phytotherapy in men with lower urinary tract symptoms.

PubMed

Barry, Michael J; Cantor, Alan; Roehrborn, Claus G

2013-03-01

We related changes in American Urological Association symptom index scores with bother measures and global ratings of change in men with lower urinary tract symptoms who were enrolled in a saw palmetto trial. To be eligible for study men were 45 years old or older, and had a peak uroflow of 4 ml per second or greater and an American Urological Association symptom index score of 8 to 24. Participants self-administered the American Urological Association symptom index, International Prostate Symptom Score quality of life item, Benign Prostatic Hyperplasia Impact Index and 2 global change questions at baseline, and at 24, 48 and 72 weeks. In 357 participants global ratings of a little better were associated with a mean decrease in American Urological Association symptom index scores from 2.8 to 4.1 points across 3 time points. The analogous range for mean decreases in Benign Prostatic Hyperplasia Impact Index scores was 1.0 to 1.7 points and for the International Prostate Symptom Score quality of life item it was 0.5 to 0.8 points. At 72 weeks for the first global change question each change measure discriminated between participants who rated themselves at least a little better vs unchanged or worse 70% to 72% of the time. A multivariate model increased discrimination to 77%. For the second global change question each change measure correctly discriminated ratings of at least a little better vs unchanged or worse 69% to 74% of the time and a multivariate model increased discrimination to 79%. Changes in American Urological Association symptom index scores could discriminate between participants rating themselves at least a little better vs unchanged or worse. Our findings support the practice of powering studies to detect group mean differences in American Urological Association symptom index scores of at least 3 points. Copyright © 2013 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Prediction Activities at NASA's Global Modeling and Assimilation Office

NASA Technical Reports Server (NTRS)

Schubert, Siegfried

2010-01-01

The Global Modeling and Assimilation Office (GMAO) is a core NASA resource for the development and use of satellite observations through the integrating tools of models and assimilation systems. Global ocean, atmosphere and land surface models are developed as components of assimilation and forecast systems that are used for addressing the weather and climate research questions identified in NASA's science mission. In fact, the GMAO is actively engaged in addressing one of NASA's science mission s key questions concerning how well transient climate variations can be understood and predicted. At weather time scales the GMAO is developing ultra-high resolution global climate models capable of resolving high impact weather systems such as hurricanes. The ability to resolve the detailed characteristics of weather systems within a global framework greatly facilitates addressing fundamental questions concerning the link between weather and climate variability. At sub-seasonal time scales, the GMAO is engaged in research and development to improve the use of land information (especially soil moisture), and in the improved representation and initialization of various sub-seasonal atmospheric variability (such as the MJO) that evolves on time scales longer than weather and involves exchanges with both the land and ocean The GMAO has a long history of development for advancing the seasonal-to-interannual (S-I) prediction problem using an older version of the coupled atmosphere-ocean general circulation model (AOGCM). This includes the development of an Ensemble Kalman Filter (EnKF) to facilitate the multivariate assimilation of ocean surface altimetry, and an EnKF developed for the highly inhomogeneous nature of the errors in land surface models, as well as the multivariate assimilation needed to take advantage of surface soil moisture and snow observations. The importance of decadal variability, especially that associated with long-term droughts is well recognized by the climate community. An improved understanding of the nature of decadal variability and its predictability has important implications for efforts to assess the impacts of global change in the coming decades. In fact, the GMAO has taken on the challenge of carrying out experimental decadal predictions in support of the IPCC AR5 effort.

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

NASA Astrophysics Data System (ADS)

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

2014-05-01

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

Multivariate analysis of cytokine profiles in pregnancy complications.

PubMed

Azizieh, Fawaz; Dingle, Kamaludin; Raghupathy, Raj; Johnson, Kjell; VanderPlas, Jacob; Ansari, Ali

2018-03-01

The immunoregulation to tolerate the semiallogeneic fetus during pregnancy includes a harmonious dynamic balance between anti- and pro-inflammatory cytokines. Several earlier studies reported significantly different levels and/or ratios of several cytokines in complicated pregnancy as compared to normal pregnancy. However, as cytokines operate in networks with potentially complex interactions, it is also interesting to compare groups with multi-cytokine data sets, with multivariate analysis. Such analysis will further examine how great the differences are, and which cytokines are more different than others. Various multivariate statistical tools, such as Cramer test, classification and regression trees, partial least squares regression figures, 2-dimensional Kolmogorov-Smirmov test, principal component analysis and gap statistic, were used to compare cytokine data of normal vs anomalous groups of different pregnancy complications. Multivariate analysis assisted in examining if the groups were different, how strongly they differed, in what ways they differed and further reported evidence for subgroups in 1 group (pregnancy-induced hypertension), possibly indicating multiple causes for the complication. This work contributes to a better understanding of cytokines interaction and may have important implications on targeting cytokine balance modulation or design of future medications or interventions that best direct management or prevention from an immunological approach. © 2018 The Authors. American Journal of Reproductive Immunology Published by John Wiley & Sons Ltd.

Characterization of Interfacial Chemistry of Adhesive/Dentin Bond Using FTIR Chemical Imaging With Univariate and Multivariate Data Processing

PubMed Central

Wang, Yong; Yao, Xiaomei; Parthasarathy, Ranganathan

2008-01-01

Fourier transform infrared (FTIR) chemical imaging can be used to investigate molecular chemical features of the adhesive/dentin interfaces. However, the information is not straightforward, and is not easily extracted. The objective of this study was to use multivariate analysis methods, principal component analysis and fuzzy c-means clustering, to analyze spectral data in comparison with univariate analysis. The spectral imaging data collected from both the adhesive/healthy dentin and adhesive/caries-affected dentin specimens were used and compared. The univariate statistical methods such as mapping of intensities of specific functional group do not always accurately identify functional group locations and concentrations due to more or less band overlapping in adhesive and dentin. Apart from the ease with which information can be extracted, multivariate methods highlight subtle and often important changes in the spectra that are difficult to observe using univariate methods. The results showed that the multivariate methods gave more satisfactory, interpretable results than univariate methods and were conclusive in showing that they can discriminate and classify differences between healthy dentin and caries-affected dentin within the interfacial regions. It is demonstrated that the multivariate FTIR imaging approaches can be used in the rapid characterization of heterogeneous, complex structure. PMID:18980198

Multivariate Analysis of Longitudinal Rates of Change

PubMed Central

Bryan, Matthew; Heagerty, Patrick J.

2016-01-01

Longitudinal data allow direct comparison of the change in patient outcomes associated with treatment or exposure. Frequently, several longitudinal measures are collected that either reflect a common underlying health status, or characterize processes that are influenced in a similar way by covariates such as exposure or demographic characteristics. Statistical methods that can combine multivariate response variables into common measures of covariate effects have been proposed by Roy and Lin [1]; Proust-Lima, Letenneur and Jacqmin-Gadda [2]; and Gray and Brookmeyer [3] among others. Current methods for characterizing the relationship between covariates and the rate of change in multivariate outcomes are limited to select models. For example, Gray and Brookmeyer [3] introduce an “accelerated time” method which assumes that covariates rescale time in longitudinal models for disease progression. In this manuscript we detail an alternative multivariate model formulation that directly structures longitudinal rates of change, and that permits a common covariate effect across multiple outcomes. We detail maximum likelihood estimation for a multivariate longitudinal mixed model. We show via asymptotic calculations the potential gain in power that may be achieved with a common analysis of multiple outcomes. We apply the proposed methods to the analysis of a trivariate outcome for infant growth and compare rates of change for HIV infected and uninfected infants. PMID:27417129

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

PubMed

Griswold, Cortland K

2015-12-21

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

Exploring the Structure of Library and Information Science Web Space Based on Multivariate Analysis of Social Tags

ERIC Educational Resources Information Center

Joo, Soohyung; Kipp, Margaret E. I.

2015-01-01

Introduction: This study examines the structure of Web space in the field of library and information science using multivariate analysis of social tags from the Website, Delicious.com. A few studies have examined mathematical modelling of tags, mainly examining tagging in terms of tripartite graphs, pattern tracing and descriptive statistics. This…

Multivariate Analysis of High Through-Put Adhesively Bonded Single Lap Joints: Experimental and Workflow Protocols

DTIC Science & Technology

2016-06-01

unlimited. v List of Tables Table 1 Single-lap-joint experimental parameters ..............................................7 Table 2 Survey ...Joints: Experimental and Workflow Protocols by Robert E Jensen, Daniel C DeSchepper, and David P Flanagan Approved for...TR-7696 ● JUNE 2016 US Army Research Laboratory Multivariate Analysis of High Through-Put Adhesively Bonded Single Lap Joints: Experimental

A Multivariate Model for the Meta-Analysis of Study Level Survival Data at Multiple Times

ERIC Educational Resources Information Center

Jackson, Dan; Rollins, Katie; Coughlin, Patrick

2014-01-01

Motivated by our meta-analytic dataset involving survival rates after treatment for critical leg ischemia, we develop and apply a new multivariate model for the meta-analysis of study level survival data at multiple times. Our data set involves 50 studies that provide mortality rates at up to seven time points, which we model simultaneously, and…

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

PubMed

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

2011-06-01

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

Evaluation of the microscopic distribution of florfenicol in feed pellets for salmon by Fourier Transform infrared imaging and multivariate analysis.

PubMed

Bastidas, Camila Y; von Plessing, Carlos; Troncoso, José; Del P Castillo, Rosario

2018-04-15

Fourier Transform infrared imaging and multivariate analysis were used to identify, at the microscopic level, the presence of florfenicol (FF), a heavily-used antibiotic in the salmon industry, supplied to fishes in feed pellets for the treatment of salmonid rickettsial septicemia (SRS). The FF distribution was evaluated using Principal Component Analysis (PCA) and Augmented Multivariate Curve Resolution with Alternating Least Squares (augmented MCR-ALS) on the spectra obtained from images with pixel sizes of 6.25 μm × 6.25 μm and 1.56 μm × 1.56 μm, in different zones of feed pellets. Since the concentration of the drug was 3.44 mg FF/g pellet, this is the first report showing the powerful ability of the used of spectroscopic techniques and multivariate analysis, especially the augmented MCR-ALS, to describe the FF distribution in both the surface and inner parts of feed pellets at low concentration, in a complex matrix and at the microscopic level. The results allow monitoring the incorporation of the drug into the feed pellets. Copyright © 2018 Elsevier B.V. All rights reserved.

Risk factors for incidental durotomy during lumbar surgery: a retrospective study by multivariate analysis.

PubMed

Chen, Zhixiang; Shao, Peng; Sun, Qizhao; Zhao, Dong

2015-03-01

The purpose of the present study was to use a prospectively collected data to evaluate the rate of incidental durotomy (ID) during lumbar surgery and determine the associated risk factors by using univariate and multivariate analysis. We retrospectively reviewed 2184 patients who underwent lumbar surgery from January 1, 2009 to December 31, 2011 at a single hospital. Patients with ID (n=97) were compared with the patients without ID (n=2019). The influences of several potential risk factors that might affect the occurrence of ID were assessed using univariate and multivariate analyses. The overall incidence of ID was 4.62%. Univariate analysis demonstrated that older age, diabetes, lumbar central stenosis, posterior approach, revision surgery, prior lumber surgery and minimal invasive surgery are risk factors for ID during lumbar surgery. However, multivariate analysis identified older age, prior lumber surgery, revision surgery, and minimally invasive surgery as independent risk factors. Older age, prior lumber surgery, revision surgery, and minimal invasive surgery were independent risk factors for ID during lumbar surgery. These findings may guide clinicians making future surgical decisions regarding ID and aid in the patient counseling process to alleviate risks and complications. Copyright © 2015 Elsevier B.V. All rights reserved.

Pretreatment predictive value of blood neutrophil/lymphocyte ratio in R0 gastric cancer resectability.

PubMed

Borda, Ana; Vila, Juan; Fernández-Urién, Ignacio; Zozaya, José Manuel; Guerra, Ana; Borda, Fernando

2017-01-01

New parameters complementary to clinical TNM classification are needed, to orient preoperative on the possibility of a R0 gastric cancer resection. We analysed the possible predictive value of blood neutrophil/lymphocytic ratio (N/L) in relation to resectability. Two hundred and fifty-seven gastric cancers consecutively diagnosed and without neoadjuvant treatment were retrospectively studied. Univariate and multivariate analysis of the frequency of R0 cases was performed between groups with a normal N/L ratio (<5) and pathological N/L ratio (≥5). Furthermore, we studied the subgroup of operated patients (n=156) analysing the frequency of R0 resection according to N/L ratio<5 or≥5. One hundred and fifty-six patients underwent surgical intervention, of which 139 had R0 resections. A high N/L ratio was registered in 46 cases (17.9%). Globally, resectability was higher in patients with a N/L ratio<5: 59.7% vs. N/L ratio≥5: 28.6% (P<.001; OR=3.76; 95% CI=1.78-8.04). The relation between N/L ratio<5 and R0 resection was confirmed in the multivariate (P=.006; OR=3.86; 95% CI=1.46-10.22). In the operated subgroup, the higher frequency of R0 resection achievement is maintained in cases with N/L ratio<5: 91.3% vs. 72.2% (P=.015; OR=4.04; 95% CI=1.23-13.26). The presence of a N/L ratio<5 at the diagnosis of a gastric carcinoma is related in a significant and independent way with a higher frequency of R0 tumoral resection, globally. This higher proportion of R0 resection cases in patients with a N/L<5 ratio is confirmed in the subgroup of operated patients. Copyright © 2016 Elsevier España, S.L.U., AEEH y AEG. All rights reserved.

Gender Differences in Radiation Dose from Nuclear Cardiology Studies Across the World: Findings from the International Atomic Energy Agency Nuclear Cardiology Protocols Study (INCAPS) Registry

PubMed Central

Shi, Lynn; Dorbala, Sharmila; Paez, Diana; Shaw, Leslee J.; Zukotynski, Katherine A.; Pascual, Thomas N. B.; Karthikeyan, Ganesan; Vitola, João V.; Better, Nathan; Bokhari, Nadia; Rehani, Madan M.; Kashyap, Ravi; Dondi, Maurizio; Mercuri, Mathew; Einstein, Andrew J.

2016-01-01

OBJECTIVES The aim of this study was to investigate gender-based differences in nuclear cardiology practice, globally, with particular focus on laboratory volume, radiation dose, protocols, and best practices. BACKGROUND It is unclear if gender-based differences exist in radiation exposure for nuclear cardiology procedures. METHODS In a large multicenter observational cross-sectional study encompassing 7911 patients in 65 countries, radiation effective dose was estimated for each examination. Patient-level best practices relating to radiation exposure were compared between genders. Analysis of covariance was utilized to determine any difference in radiation exposure according to gender, region, and the interaction between gender and region. Linear, logistic, and hierarchical regression models were developed to evaluate gender-based differences in radiation exposure and laboratory adherence to best practices. We also included the United Nations’ gender inequality and human development indices as covariates in multivariable models. RESULTS The proportion of MPI studies performed in women varied between countries, however there was no significant correlation with gender inequality index. Globally, mean effective dose for nuclear cardiology procedures was only slightly lower in women (9.6±4.5 mSv) than in men (10.3±4.5 mSv men, p<0.001), with a difference of only 0.3 mSv in a multivariable model adjusting for patient age and weight. Stress-only imaging was performed more frequently in women (12.5% vs. 8.4%, p<0.001), however camera-based dose-reduction strategies were used less frequently in women (58.6% vs. 65.5%, p<0.001). CONCLUSIONS Despite significant worldwide variation in best practice use and radiation doses from nuclear cardiology procedures, only small differences were observed between genders worldwide. Regional variations noted in MPI use and radiation dose offer potential opportunities to address gender-related differences in delivery of nuclear cardiology care. PMID:27056156

Linear models of coregionalization for multivariate lattice data: Order-dependent and order-free cMCARs.

PubMed

MacNab, Ying C

2016-08-01

This paper concerns with multivariate conditional autoregressive models defined by linear combination of independent or correlated underlying spatial processes. Known as linear models of coregionalization, the method offers a systematic and unified approach for formulating multivariate extensions to a broad range of univariate conditional autoregressive models. The resulting multivariate spatial models represent classes of coregionalized multivariate conditional autoregressive models that enable flexible modelling of multivariate spatial interactions, yielding coregionalization models with symmetric or asymmetric cross-covariances of different spatial variation and smoothness. In the context of multivariate disease mapping, for example, they facilitate borrowing strength both over space and cross variables, allowing for more flexible multivariate spatial smoothing. Specifically, we present a broadened coregionalization framework to include order-dependent, order-free, and order-robust multivariate models; a new class of order-free coregionalized multivariate conditional autoregressives is introduced. We tackle computational challenges and present solutions that are integral for Bayesian analysis of these models. We also discuss two ways of computing deviance information criterion for comparison among competing hierarchical models with or without unidentifiable prior parameters. The models and related methodology are developed in the broad context of modelling multivariate data on spatial lattice and illustrated in the context of multivariate disease mapping. The coregionalization framework and related methods also present a general approach for building spatially structured cross-covariance functions for multivariate geostatistics. © The Author(s) 2016.

Multivariate reference technique for quantitative analysis of fiber-optic tissue Raman spectroscopy.

PubMed

Bergholt, Mads Sylvest; Duraipandian, Shiyamala; Zheng, Wei; Huang, Zhiwei

2013-12-03

We report a novel method making use of multivariate reference signals of fused silica and sapphire Raman signals generated from a ball-lens fiber-optic Raman probe for quantitative analysis of in vivo tissue Raman measurements in real time. Partial least-squares (PLS) regression modeling is applied to extract the characteristic internal reference Raman signals (e.g., shoulder of the prominent fused silica boson peak (~130 cm(-1)); distinct sapphire ball-lens peaks (380, 417, 646, and 751 cm(-1))) from the ball-lens fiber-optic Raman probe for quantitative analysis of fiber-optic Raman spectroscopy. To evaluate the analytical value of this novel multivariate reference technique, a rapid Raman spectroscopy system coupled with a ball-lens fiber-optic Raman probe is used for in vivo oral tissue Raman measurements (n = 25 subjects) under 785 nm laser excitation powers ranging from 5 to 65 mW. An accurate linear relationship (R(2) = 0.981) with a root-mean-square error of cross validation (RMSECV) of 2.5 mW can be obtained for predicting the laser excitation power changes based on a leave-one-subject-out cross-validation, which is superior to the normal univariate reference method (RMSE = 6.2 mW). A root-mean-square error of prediction (RMSEP) of 2.4 mW (R(2) = 0.985) can also be achieved for laser power prediction in real time when we applied the multivariate method independently on the five new subjects (n = 166 spectra). We further apply the multivariate reference technique for quantitative analysis of gelatin tissue phantoms that gives rise to an RMSEP of ~2.0% (R(2) = 0.998) independent of laser excitation power variations. This work demonstrates that multivariate reference technique can be advantageously used to monitor and correct the variations of laser excitation power and fiber coupling efficiency in situ for standardizing the tissue Raman intensity to realize quantitative analysis of tissue Raman measurements in vivo, which is particularly appealing in challenging Raman endoscopic applications.

Linking Ecological, Environmental and Biogeochemical Data with Multi'omics Analysis

NASA Astrophysics Data System (ADS)

Hasler-Sheetal, H.; Castorani, M. C.; Fragner, L.; Zeng, Y.; Holmer, M.; Glud, R. N.; Weckwerth, W.; Canfield, D. E.

2016-02-01

The integrated analysis of multi'omics and environmental data provides a holistic understanding of biological processes and has been proven to be challenging. Here we present our research concept for conducting multi-omics experiments and linking them to environmental data. Hypoxia, reduced light availability and species interaction - all amplified by global warming - cause a global decline of seagrasses. Metabolic mechanisms for coping with these global threats are largely unknown and multi'omics approaches can be an important approach for generating this insight. We applied GC, LC-qTOF-MS and bioinformatics to investigate the effects of environmental pressure on metabolites present in seagrasses. In a first experiment we assessed the metabolomics response of the seagrass Zostera marina towards anoxia and showed that photosynthetically derived oxygen could satisfy the oxygen demand in the leaves. But accumulation of fermentation products in the roots showed that the rhizosphere was under anoxic stress. In contrast nocturnal anoxia caused a biphasic shift in the metabolome of roots and leaves. This nocturnal reprogramming of the metabolome under anoxia indicates a mitigation mechanism to avoid the toxic effects. A pathway enrichment analysis proposes the alanine shunt, the GABA shunt and the 2-oxoglutarate shunt as such mitigation mechanisms that alleviate pyruvate levels and lead to carbon and nitrogen storage during anoxia. In a second experiment, varying light exposure and species interaction of Z. marina with the blue mussel Mytilus edulis - a co-occurring species in seagrass systems - resulted in treatment specific metabolic fingerprints in seagrass. Light modified the metabolic fingerprint expressed in Z. marina to the presence of mussels, indicating varying physiological responses to mussels in normal and low light regimes. Multivariate data-analysis indicated light exposure as main driver (45%) and mussel presence as minor driver (13%) for the metabolic responses. Traditional plant performance parameters exhibited light dependent variation but in contrast to the metabolome none of these parameters were dependent on the presence of M. edulis. This demonstrates the applicability of metabolomics to reveal hidden effects of environmental pressure on seagrasses.

Causal diagrams and multivariate analysis II: precision work.

PubMed

Jupiter, Daniel C

2014-01-01

In this Investigators' Corner, I continue my discussion of when and why we researchers should include variables in multivariate regression. My examination focuses on studies comparing treatment groups and situations for which we can either exclude variables from multivariate analyses or include them for reasons of precision. Copyright © 2014 American College of Foot and Ankle Surgeons. Published by Elsevier Inc. All rights reserved.

Multiscale Characterization of PM2.5 in Southern Taiwan based on Noise-assisted Multivariate Empirical Mode Decomposition and Time-dependent Intrinsic Correlation

NASA Astrophysics Data System (ADS)

Hsiao, Y. R.; Tsai, C.

2017-12-01

As the WHO Air Quality Guideline indicates, ambient air pollution exposes world populations under threat of fatal symptoms (e.g. heart disease, lung cancer, asthma etc.), raising concerns of air pollution sources and relative factors. This study presents a novel approach to investigating the multiscale variations of PM2.5 in southern Taiwan over the past decade, with four meteorological influencing factors (Temperature, relative humidity, precipitation and wind speed),based on Noise-assisted Multivariate Empirical Mode Decomposition(NAMEMD) algorithm, Hilbert Spectral Analysis(HSA) and Time-dependent Intrinsic Correlation(TDIC) method. NAMEMD algorithm is a fully data-driven approach designed for nonlinear and nonstationary multivariate signals, and is performed to decompose multivariate signals into a collection of channels of Intrinsic Mode Functions (IMFs). TDIC method is an EMD-based method using a set of sliding window sizes to quantify localized correlation coefficients for multiscale signals. With the alignment property and quasi-dyadic filter bank of NAMEMD algorithm, one is able to produce same number of IMFs for all variables and estimates the cross correlation in a more accurate way. The performance of spectral representation of NAMEMD-HSA method is compared with Complementary Empirical Mode Decomposition/ Hilbert Spectral Analysis (CEEMD-HSA) and Wavelet Analysis. The nature of NAMAMD-based TDICC analysis is then compared with CEEMD-based TDIC analysis and the traditional correlation analysis.

Prevalence of Loss of All Teeth (Edentulism) and Associated Factors in Older Adults in China, Ghana, India, Mexico, Russia and South Africa †

PubMed Central

Peltzer, Karl; Hewlett, Sandra; Yawson, Alfred E.; Moynihan, Paula; Preet, Raman; Wu, Fan; Guo, Godfrey; Arokiasamy, Perianayagam; Snodgrass, James J.; Chatterji, Somnath; Engelstad, Mark E.; Kowal, Paul

2014-01-01

Little information exists about the loss of all one’s teeth (edentulism) among older adults in low- and middle-income countries. This study examines the prevalence of edentulism and associated factors among older adults in a cross-sectional study across six such countries. Data from the World Health Organization (WHO’s) Study on global AGEing and adult health (SAGE) Wave 1 was used for this study with adults aged 50-plus from China (N = 13,367), Ghana (N = 4724), India (N = 7150), Mexico (N = 2315), Russian Federation (N = 3938) and South Africa (N = 3840). Multivariate regression was used to assess predictors of edentulism. The overall prevalence of edentulism was 11.7% in the six countries, with India, Mexico, and Russia has higher prevalence rates (16.3%–21.7%) than China, Ghana, and South Africa (3.0%–9.0%). In multivariate logistic analysis sociodemographic factors (older age, lower education), chronic conditions (arthritis, asthma), health risk behaviour (former daily tobacco use, inadequate fruits and vegetable consumption) and other health related variables (functional disability and low social cohesion) were associated with edentulism. The national estimates and identified factors associated with edentulism among older adults across the six countries helps to identify areas for further exploration and targets for intervention. PMID:25361046

Level and potential social-ecological factors associated with physical inactivity and sedentary behavior among Moroccan school-age adolescents: a cross-sectional study.

PubMed

El-Ammari, Abdelghaffar; El Kazdouh, Hicham; Bouftini, Siham; El Fakir, Samira; El Achhab, Youness

2017-05-18

Creating a successful intervention that supports an active lifestyle and prevents sedentary one requires a better understanding of the factors associated with physical inactivity (PI) and sedentary behavior (SB). However, these factors have not been assessed among Moroccan adolescents. This study aimed to determine prevalence of PI and SB and to explore their potential social-ecological associated factors in school-age adolescents. In this cross-sectional study, 764 students (age range, 14-19 years) were enrolled from six schools in Taza city, Morocco. The Global School-based Student Health Survey was used to collect data about variables. We used bivariate and multivariate analyses to assess relations between dependent and independent variables. Overall, the prevalence of PI was 79.5% and SB was 36.5%. Among girls, these rates were higher (87.0 and 39.1%, respectively) than rates shown in boys (70.9 and 33.6%, respectively). In the multivariate logistic regression analysis, PI was associated with the following variables: illiterate father, hunger, suicidal ideation, inadequate vegetable consumption, and absence from physical education classes. Age, inadequate vegetable consumption, and absenteeism were associated with SB. The prevalence of PI and SB is high, especially among girls. Thus, there is an urgent need to implement appropriate interventions to reduce PI and SB levels in secondary school-age adolescents and the associated factors identified can be useful.

Gender differences in health-related quality of life of adolescents with cystic fibrosis

PubMed Central

Arrington-Sanders, Renata; Yi, Michael S; Tsevat, Joel; Wilmott, Robert W; Mrus, Joseph M; Britto, Maria T

2006-01-01

Background Female patients with cystic fibrosis (CF) have consistently poorer survival rates than males across all ages. To determine if gender differences exist in health-related quality of life (HRQOL) of adolescent patients with CF, we performed a cross-section analysis of CF patients recruited from 2 medical centers in 2 cities during 1997–2001. Methods We used the 87-item child self-report form of the Child Health Questionnaire to measure 12 health domains. Data was also collected on age and forced expiratory volume in 1 second (FEV1). We analyzed data from 98 subjects and performed univariate analyses and linear regression or ordinal logistic regression for multivariable analyses. Results The mean (SD) age was 14.6 (2.5) years; 50 (51.0%) were female; and mean FEV1 was 71.6% (25.6%) of predicted. There were no statistically significant gender differences in age or FEV1. In univariate analyses, females reported significantly poorer HRQOL in 5 of the 12 domains. In multivariable analyses controlling for FEV1 and age, we found that female gender was associated with significantly lower global health (p < 0.05), mental health (p < 0.01), and general health perceptions (p < 0.05) scores. Conclusion Further research will need to focus on the causes of these differences in HRQOL and on potential interventions to improve HRQOL of adolescent patients with CF. PMID:16433917

Anger expression, violent behavior, and symptoms of depression among male college students in Ethiopia.

PubMed

Terasaki, Dale J; Gelaye, Bizu; Berhane, Yemane; Williams, Michelle A

2009-01-12

Depression is an important global public health problem. Given the scarcity of studies involving African youths, this study was conducted to evaluate the associations of anger expression and violent behavior with symptoms of depression among male college students. A self-administered questionnaire was used to collect information on socio-demographic and lifestyle characteristics and violent behavior among 1,176 college students in Awassa, Ethiopia in June, 2006. The questionnaire incorporated the Spielberger Anger-Out Expression (SAOE) scale and symptoms of depression were evaluated using the Patient Health Questionnaire (PHQ-9). Multivariable logistic regression procedures were used to calculate adjusted odds ratios (OR) and 95% confidence intervals (95%CI). Symptoms of depression were evident in 23.6% of participants. Some 54.3% of students reported committing at least one act of violence in the current academic year; and 29.3% of students reported high (SAOE score > or = 15) levels of anger-expression. In multivariate analysis, moderate (OR = 1.97; 95%CI 1.33-2.93) and high (OR = 3.23; 95%CI 2.14-4.88) outward anger were statistically significantly associated with increased risks of depressive symptoms. Violent behavior was noted to be associated with depressive symptoms (OR = 1.82; 95%CI 1.37-2.40). Further research should be conducted to better characterize community and individual level determinants of anger-expression, violent behavior and depression among youths.

Cerebellar contribution to motor and cognitive performance in multiple sclerosis: An MRI sub-regional volumetric analysis.

PubMed

D'Ambrosio, Alessandro; Pagani, Elisabetta; Riccitelli, Gianna C; Colombo, Bruno; Rodegher, Mariaemma; Falini, Andrea; Comi, Giancarlo; Filippi, Massimo; Rocca, Maria A

2017-08-01

To investigate the role of cerebellar sub-regions on motor and cognitive performance in multiple sclerosis (MS) patients. Whole and sub-regional cerebellar volumes, brain volumes, T2 hyperintense lesion volumes (LV), and motor performance scores were obtained from 95 relapse-onset MS patients and 32 healthy controls (HC). MS patients also underwent an evaluation of working memory and processing speed functions. Cerebellar anterior and posterior lobes were segmented using the Spatially Unbiased Infratentorial Toolbox (SUIT) from Statistical Parametric Mapping (SPM12). Multivariate linear regression models assessed the relationship between magnetic resonance imaging (MRI) measures and motor/cognitive scores. Compared to HC, only secondary progressive multiple sclerosis (SPMS) patients had lower cerebellar volumes (total and posterior cerebellum). In MS patients, lower anterior cerebellar volume and brain T2 LV predicted worse motor performance, whereas lower posterior cerebellar volume and brain T2 LV predicted poor cognitive performance. Global measures of brain volume and infratentorial T2 LV were not selected by the final multivariate models. Cerebellar volumetric abnormalities are likely to play an important contribution to explain motor and cognitive performance in MS patients. Consistently with functional mapping studies, cerebellar posterior-inferior volume accounted for variance in cognitive measures, whereas anterior cerebellar volume accounted for variance in motor performance, supporting the assessment of cerebellar damage at sub-regional level.

Analysis/forecast experiments with a multivariate statistical analysis scheme using FGGE data

NASA Technical Reports Server (NTRS)

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

1985-01-01

A three-dimensional, multivariate, statistical analysis method, optimal interpolation (OI) is described for modeling meteorological data from widely dispersed sites. The model was developed to analyze FGGE data at the NASA-Goddard Laboratory of Atmospherics. The model features a multivariate surface analysis over the oceans, including maintenance of the Ekman balance and a geographically dependent correlation function. Preliminary comparisons are made between the OI model and similar schemes employed at the European Center for Medium Range Weather Forecasts and the National Meteorological Center. The OI scheme is used to provide input to a GCM, and model error correlations are calculated for forecasts of 500 mb vertical water mixing ratios and the wind profiles. Comparisons are made between the predictions and measured data. The model is shown to be as accurate as a successive corrections model out to 4.5 days.

Construct validity of the modified numeric rating scale of patient global assessment in psoriatic arthritis.

PubMed

Leung, Ying-Ying; Ho, Kwok-Wah; Zhu, Tracy-Yanner; Tam, Lai-Shan; Kun, Emily Wailin; Li, Edmund Kwok-Ming

2012-04-01

The construct validity of the patient global health assessment (PGA) in psoriatic arthritis (PsA) has not been analyzed, despite its common use. We evaluated the construct validity of a numeric rating scale (NRS) of the PGA in PsA. Patients with PsA who fulfilled the ClASsification for Psoriatic ARthritis (CASPAR) criteria were recruited at a tertiary referral center. Demographic data were collected and PGA data were determined from administration of an 11-point NRS (0 to 10 points representing best to worst status). Convergent and discriminant validity were evaluated by correlation between PGA and clinical variables. Patients were grouped as having severe disease based on Disease Activity Score 28-joint count (DAS28) > 5.1, Health Assessment Questionnaire (HAQ) > 1.0, walking with aids, and social welfare-dependent. Patients were grouped as being in remission by DAS28 < 2.6 and the Minimal Disease Activity Criteria. Known-group validity of PGA was evaluated. A total of 125 patients (52% men) were studied. Convergent validity revealed strong correlations of PGA with pain score, HAQ, and DAS28; and weak correlations with skin severity score, physician's global assessment and morning stiffness. In multivariate analysis, PGA was associated with pain, physical function, mental function, and skin severity score. PGA distinguished different levels of severity well, as determined by comparison with different known groups with large effect sizes. Judged on an NRS, the PGA had good construct validity and satisfactorily distinguished all levels of severity in PsA.

Characteristics of Modic changes in cervical kyphosis and their association with axial neck pain.

PubMed

An, Yonghui; Li, Jia; Li, Yongqian; Shen, Yong

2017-01-01

The purpose of this study was to evaluate characteristics of Modic changes in cervical kyphosis (CK) and their association with axial neck pain. Study participants included 286 asymptomatic or symptomatic patients with CK (mean age = 54.2 ± 12.2 years) who were consecutively enrolled from March 2009 to October 2015. Clinical and radiographic evaluations were performed at a university outpatient department. CK was classified as global type, reverse sigmoid type, or sigmoid type. There were 138 participants with global type CK, 103 with reverse sigmoid type CK, and 45 with sigmoid type CK. Of the 286 participants, 102 had Modic changes (Modic-1 in 38 segments and Modic-2 in 75 segments). Spinal cord compression grade and disc degeneration occurred more frequently in the group with axial neck pain compared to the group without pain. Angular motion was decreased in those with axial neck pain (mean ± standard deviation [SD] 7.8°±4.6°) compared to those who were asymptomatic (mean ± SD 8.9°±5.1°; P <0.001). In multivariate logistic regression analysis, Modic changes were associated with axial neck pain (odds ratio =5.356; 95% confidence interval =1.314-12.800; P <0.001). Modic changes occur most commonly in association with CK global type and less commonly with reverse sigmoid type and sigmoid type. Modic changes are associated with axial neck pain in patients with CK.

On a Possible Relationship between Linguistic Expertise and EEG Gamma Band Phase Synchrony

PubMed Central

Reiterer, Susanne; Pereda, Ernesto; Bhattacharya, Joydeep

2011-01-01

Recent research has shown that extensive training in and exposure to a second language can modify the language organization in the brain by causing both structural and functional changes. However it is not yet known how these changes are manifested by the dynamic brain oscillations and synchronization patterns subserving the language networks. In search for synchronization correlates of proficiency and expertise in second language acquisition, multivariate EEG signals were recorded from 44 high and low proficiency bilinguals during processing of natural language in their first and second languages. Gamma band (30–45 Hz) phase synchronization (PS) was calculated mainly by two recently developed methods: coarse-graining of Markov chains (estimating global phase synchrony, measuring the degree of PS between one electrode and all other electrodes), and phase lag index (PLI; estimating bivariate phase synchrony, measuring the degree of PS between a pair of electrodes). On comparing second versus first language processing, global PS by coarse-graining Markov chains indicated that processing of the second language needs significantly higher synchronization strength than first language. On comparing the proficiency groups, bivariate PS measure (i.e., PLI) revealed that during second language processing the low proficiency group showed stronger and broader network patterns than the high proficiency group, with interconnectivities between a left fronto-parietal network. Mean phase coherence analysis also indicated that the network activity was globally stronger in the low proficiency group during second language processing. PMID:22125542

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

PubMed

Stamate, Mirela Cristina; Todor, Nicolae; Cosgarea, Marcel

2015-01-01

The clinical utility of otoacoustic emissions as a noninvasive objective test of cochlear function has been long studied. Both transient otoacoustic emissions and distorsion products can be used to identify hearing loss, but to what extent they can be used as predictors for hearing loss is still debated. Most studies agree that multivariate analyses have better test performances than univariate analyses. The aim of the study was to determine transient otoacoustic emissions and distorsion products performance in identifying normal and impaired hearing loss, using the pure tone audiogram as a gold standard procedure and different multivariate statistical approaches. The study included 105 adult subjects with normal hearing and hearing loss who underwent the same test battery: pure-tone audiometry, tympanometry, otoacoustic emission tests. We chose to use the logistic regression as a multivariate statistical technique. Three logistic regression models were developed to characterize the relations between different risk factors (age, sex, tinnitus, demographic features, cochlear status defined by otoacoustic emissions) and hearing status defined by pure-tone audiometry. The multivariate analyses allow the calculation of the logistic score, which is a combination of the inputs, weighted by coefficients, calculated within the analyses. The accuracy of each model was assessed using receiver operating characteristics curve analysis. We used the logistic score to generate receivers operating curves and to estimate the areas under the curves in order to compare different multivariate analyses. We compared the performance of each otoacoustic emission (transient, distorsion product) using three different multivariate analyses for each ear, when multi-frequency gold standards were used. We demonstrated that all multivariate analyses provided high values of the area under the curve proving the performance of the otoacoustic emissions. Each otoacoustic emission test presented high values of area under the curve, suggesting that implementing a multivariate approach to evaluate the performances of each otoacoustic emission test would serve to increase the accuracy in identifying the normal and impaired ears. We encountered the highest area under the curve value for the combined multivariate analysis suggesting that both otoacoustic emission tests should be used in assessing hearing status. Our multivariate analyses revealed that age is a constant predictor factor of the auditory status for both ears, but the presence of tinnitus was the most important predictor for the hearing level, only for the left ear. Age presented similar coefficients, but tinnitus coefficients, by their high value, produced the highest variations of the logistic scores, only for the left ear group, thus increasing the risk of hearing loss. We did not find gender differences between ears for any otoacoustic emission tests, but studies still debate this question as the results are contradictory. Neither gender, nor environment origin had any predictive value for the hearing status, according to the results of our study. Like any other audiological test, using otoacoustic emissions to identify hearing loss is not without error. Even when applying multivariate analysis, perfect test performance is never achieved. Although most studies demonstrated the benefit of using the multivariate analysis, it has not been incorporated into clinical decisions maybe because of the idiosyncratic nature of multivariate solutions or because of the lack of the validation studies.

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

PubMed Central

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

2015-01-01

Background and aim The clinical utility of otoacoustic emissions as a noninvasive objective test of cochlear function has been long studied. Both transient otoacoustic emissions and distorsion products can be used to identify hearing loss, but to what extent they can be used as predictors for hearing loss is still debated. Most studies agree that multivariate analyses have better test performances than univariate analyses. The aim of the study was to determine transient otoacoustic emissions and distorsion products performance in identifying normal and impaired hearing loss, using the pure tone audiogram as a gold standard procedure and different multivariate statistical approaches. Methods The study included 105 adult subjects with normal hearing and hearing loss who underwent the same test battery: pure-tone audiometry, tympanometry, otoacoustic emission tests. We chose to use the logistic regression as a multivariate statistical technique. Three logistic regression models were developed to characterize the relations between different risk factors (age, sex, tinnitus, demographic features, cochlear status defined by otoacoustic emissions) and hearing status defined by pure-tone audiometry. The multivariate analyses allow the calculation of the logistic score, which is a combination of the inputs, weighted by coefficients, calculated within the analyses. The accuracy of each model was assessed using receiver operating characteristics curve analysis. We used the logistic score to generate receivers operating curves and to estimate the areas under the curves in order to compare different multivariate analyses. Results We compared the performance of each otoacoustic emission (transient, distorsion product) using three different multivariate analyses for each ear, when multi-frequency gold standards were used. We demonstrated that all multivariate analyses provided high values of the area under the curve proving the performance of the otoacoustic emissions. Each otoacoustic emission test presented high values of area under the curve, suggesting that implementing a multivariate approach to evaluate the performances of each otoacoustic emission test would serve to increase the accuracy in identifying the normal and impaired ears. We encountered the highest area under the curve value for the combined multivariate analysis suggesting that both otoacoustic emission tests should be used in assessing hearing status. Our multivariate analyses revealed that age is a constant predictor factor of the auditory status for both ears, but the presence of tinnitus was the most important predictor for the hearing level, only for the left ear. Age presented similar coefficients, but tinnitus coefficients, by their high value, produced the highest variations of the logistic scores, only for the left ear group, thus increasing the risk of hearing loss. We did not find gender differences between ears for any otoacoustic emission tests, but studies still debate this question as the results are contradictory. Neither gender, nor environment origin had any predictive value for the hearing status, according to the results of our study. Conclusion Like any other audiological test, using otoacoustic emissions to identify hearing loss is not without error. Even when applying multivariate analysis, perfect test performance is never achieved. Although most studies demonstrated the benefit of using the multivariate analysis, it has not been incorporated into clinical decisions maybe because of the idiosyncratic nature of multivariate solutions or because of the lack of the validation studies. PMID:26733749

An Analysis of Determinants of Under-5 Mortality across Countries: Defining Priorities to Achieve Targets in Sustainable Developmental Goals.

PubMed

Acheampong, Michael; Ejiofor, Chukwudi; Salinas-Miranda, Abraham

2017-06-01

Objectives The end of the era of millennium development goals (MDGs) ushered in the sustainable development goals (SDGs) with a new target for the reduction of under-five mortality rates (U5MR). Although U5MR decreased globally, the reduction was insufficient to meet MDGs targets because significant socioeconomic inequities remain unaddressed across and within countries. Thus, further progress in achieving the new SDGs target will be hindered if there is no adequate prioritization of important socioeconomic, healthcare, and environmental factors. The objective of this study was to assess factors that account most for the differences in U5MR between countries around the globe. Methods We conducted an ordinary least squares (OLS) regression-based prioritization analysis of socioeconomic, healthcare, and environmental variables from 109 countries to understand which factors explain the differences in U5MR best. Results All indicators examined individually affected differences in U5MR between countries. However, the results of multivariate OLS regression showed that the most important factors that accounted for the differences were, in order: fertility rate, total health expenditure per capita, access to improved water and sanitation, and female employment rate. Conclusions To achieve the new global target for U5MR, policymakers must focus on certain priority areas, such as interventions that address access to affordable maternal healthcare services, educational programs for mothers, especially those who are adolescents, and safe drinking water and sanitation.

Spectral compression algorithms for the analysis of very large multivariate images

DOEpatents

Keenan, Michael R.

2007-10-16

A method for spectrally compressing data sets enables the efficient analysis of very large multivariate images. The spectral compression algorithm uses a factored representation of the data that can be obtained from Principal Components Analysis or other factorization technique. Furthermore, a block algorithm can be used for performing common operations more efficiently. An image analysis can be performed on the factored representation of the data, using only the most significant factors. The spectral compression algorithm can be combined with a spatial compression algorithm to provide further computational efficiencies.

Anticholinergic Drug Use and Risk of Cognitive Performance in Older Adults with Questionable Cognitive Impairment: A Cross-Sectional Analysis

PubMed Central

Swami, Sunil; Cohen, Ronald A.; Kairalla, John A.; Manini, Todd M.

2018-01-01

Background Age-associated decline in central cholinergic activity makes older adults susceptible to harmful effects of anticholinergic (AC) medications. However, there is an inadequate understanding of association and possible effects of AC drugs on cognition. This cross-sectional study examines the associations of AC medications on cognition among older adults with questionable cognitive impairment (QCI). Methods For this cross-sectional study, we used multicenter database of community dwelling older adults (N=7,351) aged 60+ years with QCI from September 2005 until March 2014 as baseline data. Anticholinergic Drug Scale was used to categorize AC drug load in no, low or moderate/high groups. Individuals with clinical dementia rating-sum of boxes score between 0.5 and 2.5 were identify as having QCI. Cognitive performance was evaluated using Neuropsychological Test Battery. The mean z-scores of neuropsychological tests were grouped into a global cognition score. Results Participants who took AC medications were older, largely female and had higher prevalence of incontinence than those without AC exposure. Global cognition was significantly greater in moderate/high AC group than no AC group (-0.23±0.53 vs. -0.32±0.53). Multivariable linear regression showed that global cognition score among low and moderate/high AC groups, as compared to no AC group, was higher by 0.064 (P=.006 and P=.12, respectively). Conclusions This cross-sectional study indicates that older adults with QCI who were exposed to AC medications might have higher global cognitive scores than those without AC exposure. The observed associations indicate that older adults might experience some beneficial cognitive effects from AC drugs, possibly due to the therapeutic effects of these medications in controlling comorbidities; thus, outweighing their adverse effects on cognition. PMID:27638818

Human Leukocyte Antigen and Interleukin 2, 10 and 12p40 Cytokine Responses to Measles: Is There Evidence of the HLA Effect?

PubMed Central

Ovsyannikova, Inna G.; Ryan, Jenna E.; Jacobson, Robert M.; Vierkant, Robert A.; Pankratz, V. Shane; Poland, Gregory A.

2007-01-01

HLA class I and class II associations were examined in relation to measles virus-specific cytokine responses in 339 healthy children who had received two doses of live attenuated measles vaccine. Multivariate linear regression modeling analysis revealed suggestions of associations between the expression of DPA1*0201 (p=0.03) and DPA1*0202 (p=0.09) alleles and interleukin-2 (IL-2) cytokine production (global p-value 0.06). Importantly, cytokine production and DQB1 allele associations (global p-value 0.04) revealed that the alleles with the strongest association with IL-10 secretion were DQB1*0302 (p=0.02), DQB1*0303 (p=0.07) and DQB1*0502 (p=0.06). Measles-specific IL-10 secretion associations approached significance with DRB1 and DQA1 loci (both global p-values 0.08). Specifically, suggestive associations were found between DRB1*0701 (p=0.07), DRB1*1103 (p=0.06), DRB1*1302 (p=0.08), DRB1*1303 (p=0.06), DQA1*0101 (p=0.08), and DQA1*0201 (p=0.04) alleles and measles-induced IL-10 secretion. Further, suggestive association was observed between specific DQA1*0505 (p=0.002) alleles and measles-specific IL-12p40 secretion (global p-value 0.09) indicating that cytokine responses to measles antigens are predominantly influenced by HLA class II genes. We found no associations between any of the alleles of HLA A, B, and Cw loci and cytokine secretion. These novel findings suggest that HLA class II genes may influence the level of cytokine production in the adaptive immune responses to measles vaccine. PMID:17234427

Myocardial Deformation Measured by 3-Dimensional Speckle Tracking in Children and Adolescents With Systemic Arterial Hypertension.

PubMed

Navarini, Susanne; Bellsham-Revell, Hannah; Chubb, Henry; Gu, Haotian; Sinha, Manish D; Simpson, John M

2017-12-01

Systemic arterial hypertension predisposes children to cardiovascular risk in childhood and adult life. Despite extensive study of left ventricular (LV) hypertrophy, detailed 3-dimensional strain analysis of cardiac function in hypertensive children has not been reported. The aim of this study was to evaluate LV mechanics (strain, twist, and torsion) in young patients with hypertension compared with a healthy control group and assess factors associated with functional measurements. Sixty-three patients (26 hypertension and 37 normotensive) were enrolled (mean age, 14.3 and 11.4 years; 54% men and 41% men, respectively). All children underwent clinical evaluation and echocardiographic examination, including 3-dimensional strain. There was no difference in LV volumes and ejection fraction between the groups. Myocardial deformation was significantly reduced in those with hypertension compared with controls. For hypertensive and normotensive groups, respectively, global longitudinal strain was -15.1±2.3 versus -18.5±1.9 ( P <0.0001), global circumferential strain -15.2±3 versus -19.9±3.1 (<0.0001), global radial strain +44.0±11.3 versus 63.4±10.5 ( P <0.0001), and global 3-dimensional strain -26.1±3.8 versus -31.5±3.8 ( P <0.0001). Basal clockwise rotation, apical counterclockwise rotation, twist, and torsion were not significantly different. After multivariate regression analyses blood pressure, body mass index and LV mass maintained a significant relationship with measures of LV strain. Similar ventricular volumes and ejection fraction were observed in hypertensive and normotensive children, but children with hypertension had significantly lower strain indices. Whether reduced strain might predict future cardiovascular risk merits further longitudinal study. © 2017 American Heart Association, Inc.

Cloud Coverage and Height Distribution from the GLAS Polar Orbiting Lidar: Comparison to Passive Cloud Retrievals

NASA Technical Reports Server (NTRS)

Spinhime, J. D.; Palm, S. P.; Hlavka, D. L.; Hart, W. D.; Mahesh, A.

2004-01-01

The Geoscience Laser Altimeter System (GLAS) began full on orbit operations in September 2003. A main application of the two-wavelength GLAS lidar is highly accurate detection and profiling of global cloud cover. Initial analysis indicates that cloud and aerosol layers are consistently detected on a global basis to cross-sections down to 10(exp -6) per meter. Images of the lidar data dramatically and accurately show the vertical structure of cloud and aerosol to the limit of signal attenuation. The GLAS lidar has made the most accurate measurement of global cloud coverage and height to date. In addition to the calibrated lidar signal, GLAS data products include multi level boundaries and optical depth of all transmissive layers. Processing includes a multi-variable separation of cloud and aerosol layers. An initial application of the data results is to compare monthly cloud means from several months of GLAS observations in 2003 to existing cloud climatologies from other satellite measurement. In some cases direct comparison to passive cloud retrievals is possible. A limitation of the lidar measurements is nadir only sampling. However monthly means exhibit reasonably good global statistics and coverage results, at other than polar regions, compare well with other measurements but show significant differences in height distribution. For polar regions where passive cloud retrievals are problematic and where orbit track density is greatest, the GLAS results are particularly an advance in cloud cover information. Direct comparison to MODIS retrievals show a better than 90% agreement in cloud detection for daytime, but less than 60% at night. Height retrievals are in much less agreement. GLAS is a part of the NASA EOS project and data products are thus openly available to the science community (see http://glo.gsfc.nasa.gov).

Esophageal cancer detection based on tissue surface-enhanced Raman spectroscopy and multivariate analysis

NASA Astrophysics Data System (ADS)

Feng, Shangyuan; Lin, Juqiang; Huang, Zufang; Chen, Guannan; Chen, Weisheng; Wang, Yue; Chen, Rong; Zeng, Haishan

2013-01-01

The capability of using silver nanoparticle based near-infrared surface enhanced Raman scattering (SERS) spectroscopy combined with principal component analysis (PCA) and linear discriminate analysis (LDA) to differentiate esophageal cancer tissue from normal tissue was presented. Significant differences in Raman intensities of prominent SERS bands were observed between normal and cancer tissues. PCA-LDA multivariate analysis of the measured tissue SERS spectra achieved diagnostic sensitivity of 90.9% and specificity of 97.8%. This exploratory study demonstrated great potential for developing label-free tissue SERS analysis into a clinical tool for esophageal cancer detection.

New multivariable capabilities of the INCA program

NASA Technical Reports Server (NTRS)

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

1989-01-01

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

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

Luczkovich, J.J.; Wagner, T.W.; Michalek, J.L.

In order to monitor changes caused by local and global human actions to a coral reef ecosystem, we sea-truthed a natural color Landsat TM image prepared for a coastal region of the northwestern Dominican Republic and recorded average water depth, precise geographical positions, and bottom types (seagrass, 15 sites; coral reef, ten sites; and sand, six sites). There were no significant differences in depth for the bottom type groups. The depths ranged from 0 to 16.1 m. Mean digital counts of seagrass and coral reef sites did not differ significantly in any band. A multivariate analysis of variance using allmore » three bands gave similar results. A ratio of the green/blue bands (TM 2/TM 1) showed there was a spectral shift associated with increasing depth, but not bottom type. Due to small-scale patchiness, seagrass and coral areas were difficult to distinguish, but sandy areas can be distinguished using Landsat TM imagery and our methods. 12 refs.« less

Boiling points of halogenated aliphatic compounds: a quantitative structure-property relationship for prediction and validation.

PubMed

Oberg, Tomas

2004-01-01

Halogenated aliphatic compounds have many technical uses, but substances within this group are also ubiquitous environmental pollutants that can affect the ozone layer and contribute to global warming. The establishment of quantitative structure-property relationships is of interest not only to fill in gaps in the available database but also to validate experimental data already acquired. The three-dimensional structures of 240 compounds were modeled with molecular mechanics prior to the generation of empirical descriptors. Two bilinear projection methods, principal component analysis (PCA) and partial-least-squares regression (PLSR), were used to identify outliers. PLSR was subsequently used to build a multivariate calibration model by extracting the latent variables that describe most of the covariation between the molecular structure and the boiling point. Boiling points were also estimated with an extension of the group contribution method of Stein and Brown.

Representing Human Rights Violations in Darfur: Global Justice, National Distinctions.

PubMed

Savelsberg, Joachim J; Brehm, Hollie Nyseth

2015-09-01

This article examines how international judicial interventions in mass atrocity influence representations of violence. It relies on content analysis of 3,387 articles and opinion pieces in leading newspapers from eight Western countries, compiled into the Darfur Media Dataset, as well as in-depth interviews to assess how media frame violence in the Darfur region of Sudan. Overall, it finds that UN Security Council and International Criminal Court interventions increase representations of mass violence as crime in all countries under investigation, although each country applies the crime frame at a different level. Reporting suffering and categorizing the violence as genocide also varies across countries. Comparative case studies identify country specific structural and cultural forces that appear to account for these differences. Multilevel multivariate analyses confirm the explanatory power of cultural sensitivities and policy practices, while individual-and organization-level factors, such as reporters' gender and the newspapers' ideological orientation, also have explanatory power.

Measures for brain connectivity analysis: nodes centrality and their invariant patterns

NASA Astrophysics Data System (ADS)

da Silva, Laysa Mayra Uchôa; Baltazar, Carlos Arruda; Silva, Camila Aquemi; Ribeiro, Mauricio Watanabe; de Aratanha, Maria Adelia Albano; Deolindo, Camila Sardeto; Rodrigues, Abner Cardoso; Machado, Birajara Soares

2017-07-01

The high dynamical complexity of the brain is related to its small-world topology, which enable both segregated and integrated information processing capabilities. Several measures of connectivity estimation have already been employed to characterize functional brain networks from multivariate electrophysiological data. However, understanding the properties of each measure that lead to a better description of the real topology and capture the complex phenomena present in the brain remains challenging. In this work we compared four nonlinear connectivity measures and show that each method characterizes distinct features of brain interactions. The results suggest an invariance of global network parameters from different behavioral states and that more complete description may be reached considering local features, independently of the connectivity measure employed. Our findings also point to future perspectives in connectivity studies that combine distinct and complementary dependence measures in assembling higher dimensions manifolds.

Metal pollution in Spanish terrestrial ecosystems during the twentieth century.

PubMed

Peñuelas, Josep; Filella, Iolanda

2002-01-01

We show here additional biological evidence of the alteration in global biogeochemistry by human activities during the twentieth century. The mineral concentration of herbarium specimens of 24 species of vascular plants and three species of bryophytes collected in North and East regions of Spain have substantially changed throughout the twentieth century. While V, a proxy tracer of oil pollution, exponentially increased in the last decades, other metals such as Cr, Ba, Sr, Al, Fe, Pb, Cd and Ti increased up to 1960-1970 and started to decrease in 1985-1995, when environmental legal regulations started to be effective. Multivariate principal component analysis showed an overall change in plant elemental concentrations throughout the different decades of the century and a clear separation of vascular plants and bryophytes. Likely important consequences for ecosystem structure and functioning and even for human health may be expected from these changes in mineral concentration.

Testing Mean Differences among Groups: Multivariate and Repeated Measures Analysis with Minimal Assumptions

PubMed Central

Bathke, Arne C.; Friedrich, Sarah; Pauly, Markus; Konietschke, Frank; Staffen, Wolfgang; Strobl, Nicolas; Höller, Yvonne

2018-01-01

ABSTRACT To date, there is a lack of satisfactory inferential techniques for the analysis of multivariate data in factorial designs, when only minimal assumptions on the data can be made. Presently available methods are limited to very particular study designs or assume either multivariate normality or equal covariance matrices across groups, or they do not allow for an assessment of the interaction effects across within-subjects and between-subjects variables. We propose and methodologically validate a parametric bootstrap approach that does not suffer from any of the above limitations, and thus provides a rather general and comprehensive methodological route to inference for multivariate and repeated measures data. As an example application, we consider data from two different Alzheimer’s disease (AD) examination modalities that may be used for precise and early diagnosis, namely, single-photon emission computed tomography (SPECT) and electroencephalogram (EEG). These data violate the assumptions of classical multivariate methods, and indeed classical methods would not have yielded the same conclusions with regards to some of the factors involved. PMID:29565679

Development of multivariate exposure and fatal accident involvement rates for 1977

DOT National Transportation Integrated Search

1985-10-01

The need for multivariate accident involvement rates is often encounted in : accident analysis. The FARS (Fatal Accident Reporting System) files contain : records of fatal involvements characterized by many variables while NPTS : (National Personal T...

Bayesian multivariate hierarchical transformation models for ROC analysis.

PubMed

O'Malley, A James; Zou, Kelly H

2006-02-15

A Bayesian multivariate hierarchical transformation model (BMHTM) is developed for receiver operating characteristic (ROC) curve analysis based on clustered continuous diagnostic outcome data with covariates. Two special features of this model are that it incorporates non-linear monotone transformations of the outcomes and that multiple correlated outcomes may be analysed. The mean, variance, and transformation components are all modelled parametrically, enabling a wide range of inferences. The general framework is illustrated by focusing on two problems: (1) analysis of the diagnostic accuracy of a covariate-dependent univariate test outcome requiring a Box-Cox transformation within each cluster to map the test outcomes to a common family of distributions; (2) development of an optimal composite diagnostic test using multivariate clustered outcome data. In the second problem, the composite test is estimated using discriminant function analysis and compared to the test derived from logistic regression analysis where the gold standard is a binary outcome. The proposed methodology is illustrated on prostate cancer biopsy data from a multi-centre clinical trial.

Bayesian multivariate hierarchical transformation models for ROC analysis

PubMed Central

O'Malley, A. James; Zou, Kelly H.

2006-01-01

SUMMARY A Bayesian multivariate hierarchical transformation model (BMHTM) is developed for receiver operating characteristic (ROC) curve analysis based on clustered continuous diagnostic outcome data with covariates. Two special features of this model are that it incorporates non-linear monotone transformations of the outcomes and that multiple correlated outcomes may be analysed. The mean, variance, and transformation components are all modelled parametrically, enabling a wide range of inferences. The general framework is illustrated by focusing on two problems: (1) analysis of the diagnostic accuracy of a covariate-dependent univariate test outcome requiring a Box–Cox transformation within each cluster to map the test outcomes to a common family of distributions; (2) development of an optimal composite diagnostic test using multivariate clustered outcome data. In the second problem, the composite test is estimated using discriminant function analysis and compared to the test derived from logistic regression analysis where the gold standard is a binary outcome. The proposed methodology is illustrated on prostate cancer biopsy data from a multi-centre clinical trial. PMID:16217836

A Novel Approach to Detect Accelerated Aged and Surface-Mediated Degradation in Explosives by UPLC-ESI-MS.

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

Beppler, Christina L

2015-12-01

A new approach was created for studying energetic material degradation. This approach involved detecting and tentatively identifying non-volatile chemical species by liquid chromatography-mass spectrometry (LC-MS) with multivariate statistical data analysis that form as the CL-20 energetic material thermally degraded. Multivariate data analysis showed clear separation and clustering of samples based on sample group: either pristine or aged material. Further analysis showed counter-clockwise trends in the principal components analysis (PCA), a type of multivariate data analysis, Scores plots. These trends may indicate that there was a discrete shift in the chemical markers as the went from pristine to aged material, andmore » then again when the aged CL-20 mixed with a potentially incompatible material was thermally aged for 4, 6, or 9 months. This new approach to studying energetic material degradation should provide greater knowledge of potential degradation markers in these materials.« less

Complex numbers in chemometrics: examples from multivariate impedance measurements on lipid monolayers.

PubMed

Geladi, Paul; Nelson, Andrew; Lindholm-Sethson, Britta

2007-07-09

Electrical impedance gives multivariate complex number data as results. Two examples of multivariate electrical impedance data measured on lipid monolayers in different solutions give rise to matrices (16x50 and 38x50) of complex numbers. Multivariate data analysis by principal component analysis (PCA) or singular value decomposition (SVD) can be used for complex data and the necessary equations are given. The scores and loadings obtained are vectors of complex numbers. It is shown that the complex number PCA and SVD are better at concentrating information in a few components than the naïve juxtaposition method and that Argand diagrams can replace score and loading plots. Different concentrations of Magainin and Gramicidin A give different responses and also the role of the electrolyte medium can be studied. An interaction of Gramicidin A in the solution with the monolayer over time can be observed.

A Multivariate Methodological Workflow for the Analysis of FTIR Chemical Mapping Applied on Historic Paint Stratigraphies

PubMed Central

Sciutto, Giorgia; Oliveri, Paolo; Catelli, Emilio; Bonacini, Irene

2017-01-01

In the field of applied researches in heritage science, the use of multivariate approach is still quite limited and often chemometric results obtained are often underinterpreted. Within this scenario, the present paper is aimed at disseminating the use of suitable multivariate methodologies and proposes a procedural workflow applied on a representative group of case studies, of considerable importance for conservation purposes, as a sort of guideline on the processing and on the interpretation of this FTIR data. Initially, principal component analysis (PCA) is performed and the score values are converted into chemical maps. Successively, the brushing approach is applied, demonstrating its usefulness for a deep understanding of the relationships between the multivariate map and PC score space, as well as for the identification of the spectral bands mainly involved in the definition of each area localised within the score maps. PMID:29333162

Risk Factors for Central Serous Chorioretinopathy: Multivariate Approach in a Case-Control Study.

PubMed

Chatziralli, Irini; Kabanarou, Stamatina A; Parikakis, Efstratios; Chatzirallis, Alexandros; Xirou, Tina; Mitropoulos, Panagiotis

2017-07-01

The purpose of this prospective study was to investigate the potential risk factors associated independently with central serous retinopathy (CSR) in a Greek population, using multivariate approach. Participants in the study were 183 consecutive patients diagnosed with CSR and 183 controls, matched for age. All participants underwent complete ophthalmological examination and information regarding their sociodemographic, clinical, medical and ophthalmological history were recorded, so as to assess potential risk factors for CSR. Univariate and multivariate analysis was performed. Univariate analysis showed that male sex, high educational status, high income, alcohol consumption, smoking, hypertension, coronary heart disease, obstructive sleep apnea, autoimmune disorders, H. pylori infection, type A personality and stress, steroid use, pregnancy and hyperopia were associated with CSR, while myopia was found to protect from CSR. In multivariate analysis, alcohol consumption, hypertension, coronary heart disease and autoimmune disorders lost their significance, while the remaining factors were all independently associated with CSR. It is important to take into account the various risk factors for CSR, so as to define vulnerable groups and to shed light into the pathogenesis of the disease.

Default and Executive Network Coupling Supports Creative Idea Production

PubMed Central

Beaty, Roger E.; Benedek, Mathias; Barry Kaufman, Scott; Silvia, Paul J.

2015-01-01

The role of attention in creative cognition remains controversial. Neuroimaging studies have reported activation of brain regions linked to both cognitive control and spontaneous imaginative processes, raising questions about how these regions interact to support creative thought. Using functional magnetic resonance imaging (fMRI), we explored this question by examining dynamic interactions between brain regions during a divergent thinking task. Multivariate pattern analysis revealed a distributed network associated with divergent thinking, including several core hubs of the default (posterior cingulate) and executive (dorsolateral prefrontal cortex) networks. The resting-state network affiliation of these regions was confirmed using data from an independent sample of participants. Graph theory analysis assessed global efficiency of the divergent thinking network, and network efficiency was found to increase as a function of individual differences in divergent thinking ability. Moreover, temporal connectivity analysis revealed increased coupling between default and salience network regions (bilateral insula) at the beginning of the task, followed by increased coupling between default and executive network regions at later stages. Such dynamic coupling suggests that divergent thinking involves cooperation between brain networks linked to cognitive control and spontaneous thought, which may reflect focused internal attention and the top-down control of spontaneous cognition during creative idea production. PMID:26084037

Social Cognitive and Planned Behavior Variables Associated with Stages of Change for Physical Activity in Spinal Cord Injury: A Multivariate Analysis

ERIC Educational Resources Information Center

Keegan, John; Ditchman, Nicole; Dutta, Alo; Chiu, Chung-Yi; Muller, Veronica; Chan, Fong; Kundu, Madan

2016-01-01

Purpose: To apply the constructs of social cognitive theory (SCT) and the theory of planned behavior (TPB) to understand the stages of change (SOC) for physical activities among individuals with a spinal cord injury (SCI). Method: Ex post facto design using multivariate analysis of variance (MANOVA). The participants were 144 individuals with SCI…

To See the World in a Grain of Sand: Recognizing the Origin of Sand Specimens by Diffuse Reflectance Infrared Fourier Transform Spectroscopy and Multivariate Exploratory Data Analysis

ERIC Educational Resources Information Center

Pezzolo, Alessandra De Lorenzi

2011-01-01

The diffuse reflectance infrared Fourier transform (DRIFT) spectra of sand samples exhibit features reflecting their composition. Basic multivariate analysis (MVA) can be used to effectively sort subsets of homogeneous specimens collected from nearby locations, as well as pointing out similarities in composition among sands of different origins.…

Testing key predictions of the associative account of mirror neurons in humans using multivariate pattern analysis.

PubMed

Oosterhof, Nikolaas N; Wiggett, Alison J; Cross, Emily S

2014-04-01

Cook et al. overstate the evidence supporting their associative account of mirror neurons in humans: most studies do not address a key property, action-specificity that generalizes across the visual and motor domains. Multivariate pattern analysis (MVPA) of neuroimaging data can address this concern, and we illustrate how MVPA can be used to test key predictions of their account.

Multivariate Quantitative Chemical Analysis

NASA Technical Reports Server (NTRS)

Kinchen, David G.; Capezza, Mary

1995-01-01

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

Diabetes mellitus medication use and catastrophic healthcare expenditure among adults aged 50+ years in China and India: results from the WHO study on global AGEing and adult health (SAGE).

PubMed

Gwatidzo, Shingai Douglas; Stewart Williams, Jennifer

2017-01-11

Expenditure on medications for highly prevalent chronic conditions such as diabetes mellitus (DM) can result in financial impoverishment. People in developing countries and in low socioeconomic status groups are particularly vulnerable. China and India currently hold the world's two largest DM populations. Both countries are ageing and undergoing rapid economic development, urbanisation and social change. This paper assesses the determinants of DM medication use and catastrophic expenditure on medications in older adults with DM in China and India. Using national standardised data collected from adults aged 50 years and above with DM (self-reported) in China (N = 773) and India (N = 463), multivariable logistic regression describes: 1) association between respondents' socio-demographic and health behavioural characteristics and the dependent variable, DM medication use, and 2) association between DM medication use (independent variable) and household catastrophic expenditure on medications (dependent variable) (China: N = 630; India: N = 439). The data source is the World Health Organization (WHO) Study on global AGEing and adult health (SAGE) Wave 1 (2007-2010). Prevalence of DM medication use was 87% in China and 71% in India. Multivariable analysis indicates that people reporting lifestyle modification were more likely to use DM medications in China (OR = 6.22) and India (OR = 8.45). Women were more likely to use DM medications in China (OR = 1.56). Respondents in poorer wealth quintiles in China were more likely to use DM medications whereas the reverse was true in India. Almost 17% of people with DM in China experienced catastrophic healthcare expenditure on medications compared with 7% in India. Diabetes medication use was not a statistically significant predictor of catastrophic healthcare expenditure on medications in either country, although the odds were 33% higher among DM medications users in China (OR = 1.33). The country comparison reflects major public policy differences underpinned by divergent political and ideological frameworks. The DM epidemic poses huge public health challenges for China and India. Ensuring equitable and affordable access to medications for DM is fundamental for healthy ageing cohorts, and is consistent with the global agenda for universal healthcare coverage.

Multivariate statistical analysis of low-voltage EDS spectrum images

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

Anderson, I.M.

1998-03-01

Whereas energy-dispersive X-ray spectrometry (EDS) has been used for compositional analysis in the scanning electron microscope for 30 years, the benefits of using low operating voltages for such analyses have been explored only during the last few years. This paper couples low-voltage EDS with two other emerging areas of characterization: spectrum imaging and multivariate statistical analysis. The specimen analyzed for this study was a finished Intel Pentium processor, with the polyimide protective coating stripped off to expose the final active layers.

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

PubMed

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

2017-01-01

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

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

Zlotnikov, Michael

We develop a polynomial reduction procedure that transforms any gauge fixed CHY amplitude integrand for n scattering particles into a σ-moduli multivariate polynomial of what we call the standard form. We show that a standard form polynomial must have a specific ladder type monomial structure, which has finite size at any n, with highest multivariate degree given by (n – 3)(n – 4)/2. This set of monomials spans a complete basis for polynomials with rational coefficients in kinematic data on the support of scattering equations. Subsequently, at tree and one-loop level, we employ the global residue theorem to derive amore » prescription that evaluates any CHY amplitude by means of collecting simple residues at infinity only. Furthermore, the prescription is then applied explicitly to some tree and one-loop amplitude examples.« less

Network meta-analysis of multiple outcome measures accounting for borrowing of information across outcomes.

PubMed

Achana, Felix A; Cooper, Nicola J; Bujkiewicz, Sylwia; Hubbard, Stephanie J; Kendrick, Denise; Jones, David R; Sutton, Alex J

2014-07-21

Network meta-analysis (NMA) enables simultaneous comparison of multiple treatments while preserving randomisation. When summarising evidence to inform an economic evaluation, it is important that the analysis accurately reflects the dependency structure within the data, as correlations between outcomes may have implication for estimating the net benefit associated with treatment. A multivariate NMA offers a framework for evaluating multiple treatments across multiple outcome measures while accounting for the correlation structure between outcomes. The standard NMA model is extended to multiple outcome settings in two stages. In the first stage, information is borrowed across outcomes as well across studies through modelling the within-study and between-study correlation structure. In the second stage, we make use of the additional assumption that intervention effects are exchangeable between outcomes to predict effect estimates for all outcomes, including effect estimates on outcomes where evidence is either sparse or the treatment had not been considered by any one of the studies included in the analysis. We apply the methods to binary outcome data from a systematic review evaluating the effectiveness of nine home safety interventions on uptake of three poisoning prevention practices (safe storage of medicines, safe storage of other household products, and possession of poison centre control telephone number) in households with children. Analyses are conducted in WinBUGS using Markov Chain Monte Carlo (MCMC) simulations. Univariate and the first stage multivariate models produced broadly similar point estimates of intervention effects but the uncertainty around the multivariate estimates varied depending on the prior distribution specified for the between-study covariance structure. The second stage multivariate analyses produced more precise effect estimates while enabling intervention effects to be predicted for all outcomes, including intervention effects on outcomes not directly considered by the studies included in the analysis. Accounting for the dependency between outcomes in a multivariate meta-analysis may or may not improve the precision of effect estimates from a network meta-analysis compared to analysing each outcome separately.

[Construction and validation of a socio-environmental vulnerability index for monitoring and management of natural disasters in the state of Rio de Janeiro, Brazil].

PubMed

Guimarães, Raphael Mendonça; Mazoto, Maíra Lopes; Martins, Raphael Nascimento; do Carmo, Cleber Nascimento; Asmus, Carmen Ildes Fróes

2014-10-01

Floods account for approximately 40% of natural disasters that occur around the world and they are therefore considered a major public health problem. While floods constitute a global problem, data from the International Strategy for Disaster Reduction showed that almost all of the deaths or individuals affected are concentrated in developing countries. It is assumed that, although they have natural causes, the consequences of floods also involve social issues. To try to predict such vulnerability in the occurrence of natural disasters, a social and environmental index that shows the degree of vulnerability of a location was developed in this paper. This index was developed using multivariate analysis involving factor analysis and demographic, social and environmental variables. The index was applied in the municipalities of the state of Rio de Janeiro and compared with the official figures of the Civil Defense Unit. The results found suggest that the proposed index meets the expectation of predicting the vulnerability of the local population.

Wealth and Its Associations with Enteric Parasitic Infections in a Low-Income Community in Peru: Use of Principal Component Analysis

PubMed Central

Nundy, Shantanu; Gilman, Robert H.; Xiao, Lihua; Cabrera, Lilia; Cama, Rosa; Ortega, Ynes R.; Kahn, Geoffrey; Cama, Vitaliano A.

2011-01-01

The association of wealth and infections with Giardia, Cryptosporidium, Cyclospora, and microsporidia were examined in a longitudinal cohort conducted in Peru from 2001 to 2006. Data from 492 participants were daily clinical manifestations, weekly copro-parasitological diagnosis, and housing characteristics and assets owned (48 variables), and these data were used to construct a global wealth index using principal component analysis. Data were analyzed using continuous and categorical (wealth tertiles) models. Participant's mean age was 3.43 years (range = 0–12 years), with average follow-up of 993 days. Univariate and multivariate analyses identified significant associations between wealth and infections with Giardia and microsporidia. Participants with greater wealth indexes were associated with protection against Giardia (P < 0.001) and persistent Giardia infections (> 14 days). For microsporidia, greater wealth was protective (P = 0.066 continuous and P = 0.042 by tertiles). Contrarily, infections with Cryptosporidium and Cyclospora were independent of wealth. Thus, subtle differences in wealth may affect the frequency of specific parasitic infections within low-income communities. PMID:21212198

The application of ATR-FTIR spectroscopy and multivariate data analysis to study drug crystallisation in the stratum corneum.

PubMed

Goh, Choon Fu; Craig, Duncan Q M; Hadgraft, Jonathan; Lane, Majella E

2017-02-01

Drug permeation through the intercellular lipids, which pack around and between corneocytes, may be enhanced by increasing the thermodynamic activity of the active in a formulation. However, this may also result in unwanted drug crystallisation on and in the skin. In this work, we explore the combination of ATR-FTIR spectroscopy and multivariate data analysis to study drug crystallisation in the skin. Ex vivo permeation studies of saturated solutions of diclofenac sodium (DF Na) in two vehicles, propylene glycol (PG) and dimethyl sulphoxide (DMSO), were carried out in porcine ear skin. Tape stripping and ATR-FTIR spectroscopy were conducted simultaneously to collect spectral data as a function of skin depth. Multivariate data analysis was applied to visualise and categorise the spectral data in the region of interest (1700-1500cm -1 ) containing the carboxylate (COO - ) asymmetric stretching vibrations of DF Na. Spectral data showed the redshifts of the COO - asymmetric stretching vibrations for DF Na in the solution compared with solid drug. Similar shifts were evident following application of saturated solutions of DF Na to porcine skin samples. Multivariate data analysis categorised the spectral data based on the spectral differences and drug crystallisation was found to be confined to the upper layers of the skin. This proof-of-concept study highlights the utility of ATR-FTIR spectroscopy in combination with multivariate data analysis as a simple and rapid approach in the investigation of drug deposition in the skin. The approach described here will be extended to the study of other actives for topical application to the skin. Copyright © 2016 Elsevier B.V. All rights reserved.

The globalization of whitening: prevalence of skin lighteners (or bleachers) use and its social correlates among university students in 26 countries.

PubMed

Peltzer, Karl; Pengpid, Supa; James, Caryl

2016-02-01

The aim of this study was to investigate the use of skin lighteners and its social correlates in university students from 26 low, middle income, and emerging economy countries. Using anonymous questionnaires, data were collected from 19,624 undergraduate university students (mean age 20.8, SD 2.8) from 27 universities in 26 countries across Asia, Africa, and the Americas. Multivariate logistic regression analysis was used to identify associations between sociodemographic, social, health risk, mental health and abuse, and the use of skin lighteners. Overall, the prevalence of previous 12-month skin lightener use was 24.5, and 16.7% in male and 30.0% in female students. The use of skin lighteners varied by country, ranging from 0% in Turkey to 83.8% in Thailand. In multivariate logistic regression analysis among both men and women, social variables (highly-organized religious activity and lack of personal mastery) and health variables (inconsistent condom use) were associated with skin lightening use. In addition, male students from a lower income country, having a lack of social support, and a history of childhood sexual abuse were more likely to use skin lighteners, and women aged 20-21 years old, residing on the university campus, being a student of health and welfare, and having a lack of personal control, inadequate physical activity, and depressive symptoms were more likely users of skin-lightening products. A high prevalence of skin lightener use was found in this large sample of university students, and social and health-related risk factors were identified. © 2015 The International Society of Dermatology.

Landsat TM inventory and assessment of waterbird habitat in the southern altiplano of South America

USGS Publications Warehouse

Boyle, T.P.; Caziani, S.M.; Waltermire, R.G.

2004-01-01

The diverse set of wetlands in southern altiplano of South America supports a number of endemic and migratory waterbirds. These species include endangered endemic flamingos and shorebirds that nest in North America and winter in the altiplano. This research developed maps from nine Landsat Thematic Mapper (TM) images (254,300 km2) to provide an inventory of aquatic waterbird habitats. Image processing software was used to produce a map with a classification of wetlands according to the habitat requirements of different types of waterbirds. A hierarchical procedure was used to, first, isolate the bodies of water within the TM image; second, execute an unsupervised classification on the subsetted image to produce 300 signatures of cover types, which were further subdivided as necessary. Third, each of the classifications was examined in the light of field data and personal experience for relevance to the determination of the various habitat types. Finally, the signatures were applied to the entire image and other adjacent images to yield a map depicting the location of the various waterbird habitats in the southern altiplano. The data sets referenced with a global positioning system receiver were used to test the classification system. Multivariate analysis of the bird communities censused at each lake by individual habitats indicated a salinity gradient, and then the depth of the water separated the birds. Multivariate analysis of the chemical and physical data from the lakes showed that the variation in lakes were significantly associated with difference in depth, transparency, latitude, elevation, and pH. The presence of gravel bottoms was also one of the qualities distinguishing a group of lakes. This information will be directly useful to the Flamingo Census Project and serve as an element for risk assessment for future development.

Measles Case Fatality Rate in Bihar, India, 2011–12

PubMed Central

Murhekar, Manoj V.; Ahmad, Mohammad; Shukla, Hemant; Abhishek, Kunwar; Perry, Robert T.; Bose, Anindya S.; Shimpi, Rahul; Kumar, Arun; Kaliaperumal, Kanagasabai; Sethi, Raman; Selvaraj, Vadivoo; Kamaraj, Pattabi; Routray, Satyabrata; Das, Vidya Nand; Menabde, Nata; Bahl, Sunil

2014-01-01

Background Updated estimates of measles case fatality rates (CFR) are critical for monitoring progress towards measles elimination goals. India accounted for 36% of total measles deaths occurred globally in 2011. We conducted a retrospective cohort study to estimate measles CFR and identify the risk factors for measles death in Bihar–one of the north Indian states historically known for its low vaccination coverage. Methods We systematically selected 16 of the 31 laboratory-confirmed measles outbreaks occurring in Bihar during 1 October 2011 to 30 April 2012. All households of the villages/urban localities affected by these outbreaks were visited to identify measles cases and deaths. We calculated CFR and used multivariate analysis to identify risk factors for measles death. Results The survey found 3670 measles cases and 28 deaths (CFR: 0.78, 95% confidence interval: 0.47–1.30). CFR was higher among under-five children (1.22%) and children belonging to scheduled castes/tribes (SC/ST, 1.72%). On multivariate analysis, independent risk factors associated with measles death were age <5 years, SC/ST status and non-administration of vitamin A during illness. Outbreaks with longer interval between the occurrence of first case and notification of the outbreak also had a higher rate of deaths. Conclusions Measles CFR in Bihar was low. To further reduce case fatality, health authorities need to ensure that SC/ST are targeted by the immunization programme and that outbreak investigations target for vitamin A treatment of cases in high risk groups such as SC/ST and young children and ensure regular visits by health-workers in affected villages to administer vitamin A to new cases. PMID:24824641

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

PubMed

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

2016-01-01

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

The association between temperature and mortality in tropical middle income Thailand from 1999 to 2008.

PubMed

Tawatsupa, Benjawan; Dear, Keith; Kjellstrom, Tord; Sleigh, Adrian

2014-03-01

We have investigated the association between tropical weather condition and age-sex adjusted death rates (ADR) in Thailand over a 10-year period from 1999 to 2008. Population, mortality, weather and air pollution data were obtained from four national databases. Alternating multivariable fractional polynomial (MFP) regression and stepwise multivariable linear regression analysis were used to sequentially build models of the associations between temperature variable and deaths, adjusted for the effects and interactions of age, sex, weather (6 variables), and air pollution (10 variables). The associations are explored and compared among three seasons (cold, hot and wet months) and four weather zones of Thailand (the North, Northeast, Central, and South regions). We found statistically significant associations between temperature and mortality in Thailand. The maximum temperature is the most important variable in predicting mortality. Overall, the association is nonlinear U-shape and 31 °C is the minimum-mortality temperature in Thailand. The death rates increase when maximum temperature increase with the highest rates in the North and Central during hot months. The final equation used in this study allowed estimation of the impact of a 4 °C increase in temperature as projected for Thailand by 2100; this analysis revealed that the heat-related deaths will increase more than the cold-related deaths avoided in the hot and wet months, and overall the net increase in expected mortality by region ranges from 5 to 13 % unless preventive measures were adopted. Overall, these results are useful for health impact assessment for the present situation and future public health implication of global climate change for tropical Thailand.

Has the Spanish economic crisis affected the duration of sickness absence episodes?

PubMed

Murcia López, Guillermo; Delclós Clanchet, Jordi; Ubalde López, Mònica; Calvo Bonacho, Eva; Benavides, Fernando G

2016-07-01

The global economic crisis has had particularly intense effects on the Spanish labor market. We investigated whether the duration of non-work related sickness absence (SA) episodes in salaried workers had experienced any changes before and after the crisis started. This was a repeated cross-sectional analysis conducted in a dynamic cohort in 2006 and 2010. Database was provided by eight mutual insurance companies, covering 983,108 workers and 451,801 SA episodes. Descriptive analysis and crude, bivariate and multivariate analyses using Cox proportional hazards modeling were performed, to quantify the changes in duration of SA episodes between 2006 and 2010, stratified by sex. There was a higher number of episodes in 2010 for both sexes, but especially for women. Unadjusted median duration in men was similar for both years, while for women it was shorter in 2010. Final multivariate models show a greater risk of longer episode duration for men in 2010 (HR 0.95; 95% CI, 0.95-0.95), but a shorter one for women (HR 1.07; 95% CI, 1.07-1.07). Once the economic crisis started affecting the Spanish labor market, the number of SA episodes in women equalized with those in men. There was a decrease of episodes in the youngest age groups, in the construction and in temporary contracts. The relative ranking of leading diagnoses was similar in both years with an increase in infectious, nervous system and respiratory diseases and in mental disorder episodes for both sexes, but especially for women. The risk of longer episode duration was greater in 2010 among men, but smaller in women. Copyright © 2016 Elsevier Ltd. All rights reserved.

Speech prosody impairment predicts cognitive decline in Parkinson's disease.

PubMed

Rektorova, Irena; Mekyska, Jiri; Janousova, Eva; Kostalova, Milena; Eliasova, Ilona; Mrackova, Martina; Berankova, Dagmar; Necasova, Tereza; Smekal, Zdenek; Marecek, Radek

2016-08-01

Impairment of speech prosody is characteristic for Parkinson's disease (PD) and does not respond well to dopaminergic treatment. We assessed whether baseline acoustic parameters, alone or in combination with other predominantly non-dopaminergic symptoms may predict global cognitive decline as measured by the Addenbrooke's cognitive examination (ACE-R) and/or worsening of cognitive status as assessed by a detailed neuropsychological examination. Forty-four consecutive non-depressed PD patients underwent clinical and cognitive testing, and acoustic voice analysis at baseline and at the two-year follow-up. Influence of speech and other clinical parameters on worsening of the ACE-R and of the cognitive status was analyzed using linear and logistic regression. The cognitive status (classified as normal cognition, mild cognitive impairment and dementia) deteriorated in 25% of patients during the follow-up. The multivariate linear regression model consisted of the variation in range of the fundamental voice frequency (F0VR) and the REM Sleep Behavioral Disorder Screening Questionnaire (RBDSQ). These parameters explained 37.2% of the variability of the change in ACE-R. The most significant predictors in the univariate logistic regression were the speech index of rhythmicity (SPIR; p = 0.012), disease duration (p = 0.019), and the RBDSQ (p = 0.032). The multivariate regression analysis revealed that SPIR alone led to 73.2% accuracy in predicting a change in cognitive status. Combining SPIR with RBDSQ improved the prediction accuracy of SPIR alone by 7.3%. Impairment of speech prosody together with symptoms of RBD predicted rapid cognitive decline and worsening of PD cognitive status during a two-year period. Copyright © 2016 Elsevier Ltd. All rights reserved.

Exposure to tobacco secondhand smoke and its associated factors among non-smoking adults in smoking-restricted and non-restricted areas: findings from a nationwide study in Malaysia

PubMed Central

Lim, Kuang Hock; Teh, Chien Huey; Nik Mohamed, Mohamad Haniki; Pan, Sayan; Ling, Miaw Yn; Mohd Yusoff, Muhammad Fadhli; Hassan, Noraryana; Baharom, Nizam; Dawam, Netty Darwina; Ismail, Norliana; Ghazali, Sumarni Mohd; Cheong, Kee Chee; Chong, Kar Hon; Lim, Hui Li

2018-01-01

Objectives Secondhand smoke (SHS) has been associated with increased morbidity and mortality. Therefore, the aims of the paper are to assess SHS exposure among non-smoking adults in Malaysia attending various smoking-restricted and non-restricted public areas according to the Control of Tobacco Product Regulations (CTPR) as well as its relationship with various sociodemographic variables. Design Data were extracted from a cross-sectional study, the Global Adults Tobacco Survey (GATS) 2011 which involved 3269 non-smokers in Malaysia. Data was obtained through face-to-face interviews using a validated pre-tested questionnaire. Factors associated with exposure to SHS were identified via multivariable analysis. Results The study revealed that almost two-thirds of respondents were exposed to SHS in at least one public area in the past 1 month, with a significantly higher exposure among males (70.6%), those with higher educational attainment (81.4%) and higher income (quintile 1%–73.9%). Besides, the exposure to SHS was almost four times higher in non-restricted areas compared with restricted areas under the CTPR (81.9% vs 22.9). Multivariable analysis revealed that males and younger adults at non-restricted areas were more likely to be exposed to SHS while no significant associated factors of SHS exposure was observed in restricted areas. Conclusions The study revealed the prevalence of SHS exposure was higher among Malaysian adults. Although smoke-free laws offer protection to non-smokers from exposure to SHS, enforcement activities in restricted areas should be enhanced to ensure strict public abidance. In addition, legislation of restricted areas should also be extended to greatly reduce the SHS exposure among non-smokers in Malaysia. PMID:29317411

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

PubMed Central

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

2016-01-01

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

Joint association of screen time and physical activity on self-rated health and life satisfaction in children and adolescents: the CASPIAN-IV study.

PubMed

Matin, Nassim; Kelishadi, Roya; Heshmat, Ramin; Motamed-Gorji, Nazgol; Djalalinia, Shirin; Motlagh, Mohammad Esmaeil; Ardalan, Gelayol; Arefirad, Tahereh; Mohammadi, Rasool; Safiri, Saeid; Qorbani, Mostafa

2017-01-01

Self-rated health and life satisfaction are two subjective measures for assessing overall health status. This study aims to investigate the association of self-rated health and life satisfaction with physical activity and screen time. As part of the fourth survey of a national surveillance program in Iran (CASPIAN-IV study), 14 880 students aged 6 to 18 years were selected via multi-stage cluster sampling from 30 provinces. Data were obtained from the WHO Global School-Based Student Health Survey questionnaire. A total of 13 486 students with mean age of 12.47 (SD 3.36) completed the study. In crude model both prolonged screen time and physical activity were associated with favorable life satisfaction and self-rated health. However, in multivariate analysis only high physical activity was associated with good self-rated health (OR 1.37) and life satisfaction (OR 1.39), while prolonged screen time was not associated with good self-rated health (OR 1.02) and life satisfaction (OR 0.94). For combined screen time-physical activity variable, low screen time-high physical activity combination had the highest OR for both good self-rated health (OR 1.37) and life satisfaction (OR 1.43) in multivariate analysis. Our findings suggest that increasing physical activity is more crucial than emphasizing reducing screen time in improving the well-being of children and adolescents. © The Author 2016. Published by Oxford University Press on behalf of Royal Society of Tropical Medicine and Hygiene. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

The Anesthesia Preoperative Evaluation Clinic (APEC): a prospective randomized controlled trial assessing impact on consultation time, direct costs, patient education and satisfaction with anesthesia care.

PubMed

Schiff, J H; Frankenhauser, S; Pritsch, M; Fornaschon, S A; Snyder-Ramos, S A; Heal, C; Schmidt, K; Martin, E; Böttiger, B W; Motsch, J

2010-07-01

Anesthetic preoperative evaluation clinics (APECs) are relatively new institutions. Although cost effective, APECs have not been universally adopted in Europe. The aim of this study was to compare preoperative anesthetic assessment in wards with an APEC, assessing time, information gain, patient satisfaction and secondary costs. Two hundred and seven inpatients were randomized to be assessed at the APEC or on the ward by the same two senior anesthetists. The outcomes measured were the length of time for each consultation, the amount of information passed on to patients and the level of patient satisfaction. The consultation time was used to calculate impact on direct costs. A multivariate analysis was conducted to detect confounding variables. Ninety-four patients were seen in the APEC, and 78 were seen on the ward. The total time for the consultation was shorter for the APEC (mean 8.4 minutes [P<0.01]), and we calculated savings of 6.4 Euro per patient. More information was passed on to the patients seen in the APEC (P<0.01). The general satisfaction scores were comparable between groups. A multivariate analysis found that the consultation time was significantly influenced by the type of anesthesia, the magnitude of the operation and the location of the consultation. Gain in information was significantly influenced by age, education and the location of the visit. The APEC reduced consultation times and costs and had a positive impact on patient education. The cost savings are related to personnel costs and, therefore, are independent of other potential savings of an APEC, whereas global patient satisfaction remains unaltered.

Describing the Elephant: Structure and Function in Multivariate Data.

ERIC Educational Resources Information Center

McDonald, Roderick P.

1986-01-01

There is a unity underlying the diversity of models for the analysis of multivariate data. Essentially, they constitute a family of models, most generally nonlinear, for structural/functional relations between variables drawn from a behavior domain. (Author)

Global minimally invasive pyeloplasty study in children: Results from the Pediatric Urology Expert Group of the European Association of Urology Young Academic Urologists working party.

PubMed

Silay, M S; Spinoit, A F; Undre, S; Fiala, V; Tandogdu, Z; Garmanova, T; Guttilla, A; Sancaktutar, A A; Haid, B; Waldert, M; Goyal, A; Serefoglu, E C; Baldassarre, E; Manzoni, G; Radford, A; Subramaniam, R; Cherian, A; Hoebeke, P; Jacobs, M; Rocco, B; Yuriy, R; Zattoni, Fabio; Kocvara, R; Koh, C J

2016-08-01

Minimally invasive pyeloplasty (MIP) for ureteropelvic junction (UPJ) obstruction in children has gained popularity over the past decade as an alternative to open surgery. The present study aimed to identify the factors affecting complication rates of MIP in children, and to compare the outcomes of laparoscopic (LP) and robotic-assisted laparoscopic pyeloplasty (RALP). The perioperative data of 783 pediatric patients (<18 years old) from 15 academic centers who underwent either LP or RALP with an Anderson Hynes dismembered pyeloplasty technique were retrospectively evaluated. Redo cases and patients with anatomic renal abnormalities were excluded. Demographics and operative data, including procedural factors, were collected. Complications were classified according to the Satava and modified Clavien systems. Failure was defined as any of the following: obstructive parameters on diuretic renal scintigraphy, decline in renal function, progressive hydronephrosis, or symptom relapse. Univariate and multivariate analysis were applied to identify factors affecting the complication rates. All parameters were compared between LP and RALP. A total of 575 children met the inclusion criteria. Laparoscopy, increased operative time, prolonged hospital stay, ureteral stenting technique, and time required for stenting were factors influencing complication rates on univariate analysis. None of those factors remained significant on multivariate analysis. Mean follow-up was 12.8 ± 9.8 months for RALP and 45.2 ± 33.8 months for LP (P = 0.001). Hospital stay and time for stenting were shorter for robotic pyeloplasty (P < 0.05 for both). Success rates were similar between RALP and LP (99.5% vs 97.3%, P = 0.11). The intraoperative complication rate was comparable between RALP and LP (3.8% vs 7.4%, P = 0.06). However, the postoperative complication rate was significantly higher in the LP group (3.2% for RALP and 7.7% for LP, P = 0.02). All complications were of no greater severity than Satava Grade IIa and Clavien Grade IIIb. This was the largest multicenter series of LP and RALP in the pediatric population. Limitations of the study included the retrospective design and lack of surgical experience as a confounder. Both minimally invasive approaches that were studied were safe and highly effective in treating UPJ obstruction in children in many centers globally. However, shorter hospitalization time and lower postoperative complication rates with RALP were noted. The aims of the study were met. Copyright © 2016 Journal of Pediatric Urology Company. Published by Elsevier Ltd. All rights reserved.

Prognostic factors and relative risk for survival in N1-3 oral squamous cell carcinoma: a multivariate analysis using Cox's hazard model.

PubMed

Noguchi, M; Kido, Y; Kubota, H; Kinjo, H; Kohama, G

1999-12-01

The records of 136 patients with N1-3 oral squamous cell carcinoma treated by surgery were investigated retrospectively, with the aim of finding out which factors were predictive of survival on multivariate analysis. Four independent factors significantly influenced survival in the following order: pN stage; T stage; histological grade; and N stage. The most significant was pN stage, the five-year survival for patients with pN0 being 91% and for patients with pN1-3 41%. A further study was carried out on the 80 patients with pN1-3 to find out their prognostic factors for survival and the independent factors identified by multivariate analysis were T stage and presence or absence of extracapsular spread to metastatic lymph nodes.

Calypso: a user-friendly web-server for mining and visualizing microbiome-environment interactions.

PubMed

Zakrzewski, Martha; Proietti, Carla; Ellis, Jonathan J; Hasan, Shihab; Brion, Marie-Jo; Berger, Bernard; Krause, Lutz

2017-03-01

Calypso is an easy-to-use online software suite that allows non-expert users to mine, interpret and compare taxonomic information from metagenomic or 16S rDNA datasets. Calypso has a focus on multivariate statistical approaches that can identify complex environment-microbiome associations. The software enables quantitative visualizations, statistical testing, multivariate analysis, supervised learning, factor analysis, multivariable regression, network analysis and diversity estimates. Comprehensive help pages, tutorials and videos are provided via a wiki page. The web-interface is accessible via http://cgenome.net/calypso/ . The software is programmed in Java, PERL and R and the source code is available from Zenodo ( https://zenodo.org/record/50931 ). The software is freely available for non-commercial users. l.krause@uq.edu.au. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

DigOut: viewing differential expression genes as outliers.

PubMed

Yu, Hui; Tu, Kang; Xie, Lu; Li, Yuan-Yuan

2010-12-01

With regards to well-replicated two-conditional microarray datasets, the selection of differentially expressed (DE) genes is a well-studied computational topic, but for multi-conditional microarray datasets with limited or no replication, the same task is not properly addressed by previous studies. This paper adopts multivariate outlier analysis to analyze replication-lacking multi-conditional microarray datasets, finding that it performs significantly better than the widely used limit fold change (LFC) model in a simulated comparative experiment. Compared with the LFC model, the multivariate outlier analysis also demonstrates improved stability against sample variations in a series of manipulated real expression datasets. The reanalysis of a real non-replicated multi-conditional expression dataset series leads to satisfactory results. In conclusion, a multivariate outlier analysis algorithm, like DigOut, is particularly useful for selecting DE genes from non-replicated multi-conditional gene expression dataset.

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

PubMed

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

2008-10-01

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

Is a multivariate consensus representation of genetic relationships among populations always meaningful?

PubMed Central

Moazami-Goudarzi, K; Laloë, D

2002-01-01

To determine the relationships among closely related populations or species, two methods are commonly used in the literature: phylogenetic reconstruction or multivariate analysis. The aim of this article is to assess the reliability of multivariate analysis. We describe a method that is based on principal component analysis and Mantel correlations, using a two-step process: The first step consists of a single-marker analysis and the second step tests if each marker reveals the same typology concerning population differentiation. We conclude that if single markers are not congruent, the compromise structure is not meaningful. Our model is not based on any particular mutation process and it can be applied to most of the commonly used genetic markers. This method is also useful to determine the contribution of each marker to the typology of populations. We test whether our method is efficient with two real data sets based on microsatellite markers. Our analysis suggests that for closely related populations, it is not always possible to accept the hypothesis that an increase in the number of markers will increase the reliability of the typology analysis. PMID:12242255

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

NASA Astrophysics Data System (ADS)

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

2018-02-01

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

The classification of secondary colorectal liver cancer in human biopsy samples using angular dispersive x-ray diffraction and multivariate analysis

NASA Astrophysics Data System (ADS)

Theodorakou, Chrysoula; Farquharson, Michael J.

2009-08-01

The motivation behind this study is to assess whether angular dispersive x-ray diffraction (ADXRD) data, processed using multivariate analysis techniques, can be used for classifying secondary colorectal liver cancer tissue and normal surrounding liver tissue in human liver biopsy samples. The ADXRD profiles from a total of 60 samples of normal liver tissue and colorectal liver metastases were measured using a synchrotron radiation source. The data were analysed for 56 samples using nonlinear peak-fitting software. Four peaks were fitted to all of the ADXRD profiles, and the amplitude, area, amplitude and area ratios for three of the four peaks were calculated and used for the statistical and multivariate analysis. The statistical analysis showed that there are significant differences between all the peak-fitting parameters and ratios between the normal and the diseased tissue groups. The technique of soft independent modelling of class analogy (SIMCA) was used to classify normal liver tissue and colorectal liver metastases resulting in 67% of the normal tissue samples and 60% of the secondary colorectal liver tissue samples being classified correctly. This study has shown that the ADXRD data of normal and secondary colorectal liver cancer are statistically different and x-ray diffraction data analysed using multivariate analysis have the potential to be used as a method of tissue classification.

Multivariate analysis of risk factors for long-term urethroplasty outcome.

PubMed

Breyer, Benjamin N; McAninch, Jack W; Whitson, Jared M; Eisenberg, Michael L; Mehdizadeh, Jennifer F; Myers, Jeremy B; Voelzke, Bryan B

2010-02-01

We studied the patient risk factors that promote urethroplasty failure. Records of patients who underwent urethroplasty at the University of California, San Francisco Medical Center between 1995 and 2004 were reviewed. Cox proportional hazards regression analysis was used to identify multivariate predictors of urethroplasty outcome. Between 1995 and 2004, 443 patients of 495 who underwent urethroplasty had complete comorbidity data and were included in analysis. Median patient age was 41 years (range 18 to 90). Median followup was 5.8 years (range 1 month to 10 years). Stricture recurred in 93 patients (21%). Primary estimated stricture-free survival at 1, 3 and 5 years was 88%, 82% and 79%. After multivariate analysis smoking (HR 1.8, 95% CI 1.0-3.1, p = 0.05), prior direct vision internal urethrotomy (HR 1.7, 95% CI 1.0-3.0, p = 0.04) and prior urethroplasty (HR 1.8, 95% CI 1.1-3.1, p = 0.03) were predictive of treatment failure. On multivariate analysis diabetes mellitus showed a trend toward prediction of urethroplasty failure (HR 2.0, 95% CI 0.8-4.9, p = 0.14). Length of urethral stricture (greater than 4 cm), prior urethroplasty and failed endoscopic therapy are predictive of failure after urethroplasty. Smoking and diabetes mellitus also may predict failure potentially secondary to microvascular damage. Copyright 2010 American Urological Association. Published by Elsevier Inc. All rights reserved.

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

PubMed

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

2016-01-01

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

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

PubMed

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

2014-12-01

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

Parameters Selection for Bivariate Multiscale Entropy Analysis of Postural Fluctuations in Fallers and Non-Fallers Older Adults.

PubMed

Ramdani, Sofiane; Bonnet, Vincent; Tallon, Guillaume; Lagarde, Julien; Bernard, Pierre Louis; Blain, Hubert

2016-08-01

Entropy measures are often used to quantify the regularity of postural sway time series. Recent methodological developments provided both multivariate and multiscale approaches allowing the extraction of complexity features from physiological signals; see "Dynamical complexity of human responses: A multivariate data-adaptive framework," in Bulletin of Polish Academy of Science and Technology, vol. 60, p. 433, 2012. The resulting entropy measures are good candidates for the analysis of bivariate postural sway signals exhibiting nonstationarity and multiscale properties. These methods are dependant on several input parameters such as embedding parameters. Using two data sets collected from institutionalized frail older adults, we numerically investigate the behavior of a recent multivariate and multiscale entropy estimator; see "Multivariate multiscale entropy: A tool for complexity analysis of multichannel data," Physics Review E, vol. 84, p. 061918, 2011. We propose criteria for the selection of the input parameters. Using these optimal parameters, we statistically compare the multivariate and multiscale entropy values of postural sway data of non-faller subjects to those of fallers. These two groups are discriminated by the resulting measures over multiple time scales. We also demonstrate that the typical parameter settings proposed in the literature lead to entropy measures that do not distinguish the two groups. This last result confirms the importance of the selection of appropriate input parameters.

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

PubMed

Cichy, Radoslaw Martin; Pantazis, Dimitrios

2017-09-01

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

Natural history of left ventricular mechanics in transplanted hearts: relationships with clinical variables and genetic expression profiles of allograft rejection.

PubMed

Eleid, Mackram F; Caracciolo, Giuseppe; Cho, Eun Joo; Scott, Robert L; Steidley, D Eric; Wilansky, Susan; Arabia, Francisco A; Khandheria, Bijoy K; Sengupta, Partho P

2010-10-01

The aim of this study was to explore the temporal evolution of left ventricular (LV) mechanics in relation to clinical variables and genetic expression profiles implicated in cardiac allograft function. Considerable uncertainty exists regarding the range and determinants of variability in LV systolic performance in transplanted hearts (TXH). Fifty-one patients (mean age 53 ± 12 years; 37 men) underwent serial assessment of echocardiograms, cardiac catheterization, gene expression profiles, and endomyocardial biopsy data within 2 weeks and at 3, 6, 12, and 24 months after transplantation. Two-dimensional speckle-tracking data were compared between patients with TXH and 37 controls (including 12 post-coronary artery bypass patients). Post-transplantation mortality and hospitalizations were recorded with a median follow-up period of 944 days. Global longitudinal strain (LS) and radial strain remained attenuated in patients with TXH at all time points (p < 0.001 and p = 0.005), independent of clinical rejection episodes. Failure to improve global LS at 3 months (≥ 1 SD) was associated with higher incidence of death and cardiac events (hazard ratio: 5.92; 95% confidence interval: 1.96 to 17.91; p = 0.049). Multivariate analysis revealed gene expression score as the only independent predictor of global LS (R(2) = 0.53, p = 0.005), with SEMA7A gene expression having the highest correlation with global LS (r = -0.84, p < 0.001). Speckle tracking-derived LV strains are helpful in estimating the burden of LV dysfunction in patients with TXH that evolves independent of biopsy-detected cellular rejection. Failure to improve global LS at 3 months after transplantation is associated with a higher incidence of death and cardiac events. Serial changes in LV mechanics correlate with peripheral blood gene expression profiles and may affect the clinical assessment of long-term prognosis in patients with TXH. Copyright © 2010 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

The role of muscle mass and body fat on disability among older adults: A cross-national analysis.

PubMed

Tyrovolas, Stefanos; Koyanagi, Ai; Olaya, Beatriz; Ayuso-Mateos, Jose Luis; Miret, Marta; Chatterji, Somnath; Tobiasz-Adamczyk, Beata; Koskinen, Seppo; Leonardi, Matilde; Haro, Josep Maria

2015-09-01

The aim of this study was to evaluate the association of sarcopenia and sarcopenic obesity with disability among older adults (≥65years old) in nine high-, middle- and low-income countries from Asia, Africa, Europe, and Latin America. Data were available for 53,289 people aged ≥18years who participated in the Collaborative Research on Ageing in Europe (COURAGE) survey conducted in Finland, Poland, and Spain, and the WHO Study on global AGEing and adult health (SAGE) survey conducted in China, Ghana, India, Mexico, Russia, and South Africa, between 2007 and 2012. Skeletal muscle mass, skeletal muscle mass index, and percent body fat were calculated with specific population formulas. Sarcopenia and sarcopenic obesity were defined by specific cut-offs used in previous studies. Disability was assessed with the WHODAS 2.0 score (range 0-100) with higher scores corresponding to higher levels of disability. Multivariable linear regression analysis was conducted with disability as the outcome. The analytical sample consisted of 18,363 people (males; n=8116, females; n=10247) aged ≥65years with mean (SD) age 72.9 (11.1) years. In the fully-adjusted overall analysis, sarcopenic obesity was associated with greater levels of disability [b-coefficient 3.01 (95% CI 1.14-4.88)]. In terms of country-wise analyses, sarcopenia was associated with higher WHODAS 2.0 scores in China [b-coefficient 4.56 (95% CI: 3.25-5.87)], Poland [b-coefficient 6.66 (95% CI: 2.17-11.14)], Russia [b-coefficient 5.60 (95% CI: 2.03-9.16)], and South Africa [b-coefficient 7.75 (95% CI: 1.56-13.94)]. Prevention of muscle mass decline may contribute to reducing the global burden of disability. Copyright © 2015 Elsevier Inc. All rights reserved.

Unmet need for induction of labor in Africa: secondary analysis from the 2004 - 2005 WHO Global Maternal and Perinatal Health Survey (A cross-sectional survey)

PubMed Central

2012-01-01

Background Induction of labor is being increasingly used to prevent adverse outcomes in the mother and the newborn.This study assessed the prevalence of induction of labor and determinants of its use in Africa. Methods We performed secondary analysis of the WHO Global Survey of Maternal and Newborn Health of 2004 and 2005. The African database was analyzed to determine the use of induction of labor at the country level and indications for induction of labor. The un-met needs for specific obstetric indications and at country level were assessed. Determinants of use of induction of labor were explored with multivariate regression analysis. Results A total of 83,437 deliveries were recorded in the 7 participating countries. Average rate of induction was 4.4% with a range of 1.4 – 6.8%. Pre-labor rupture of membranes was the commonest indication for induction of labor. Two groups of women were identified: 2,776 women with indications had induction of labor while 7,996 women although had indications but labor was not induced. Induction of labor was associated with reduction of stillbirths and perinatal deaths [OR – 0.34; 95% CI (0.27 – 0.43)]. Unmet need for induction of labor ranged between 66.0% and 80.2% across countries. Determinants of having an induction of labor were place of residence, duration of schooling, type of health facility and level of antenatal care. Conclusion Utilization of induction of labor in health facilities in Africa is very low. Improvements in social and health infrastructure are required to reverse the high unmet need for induction of labor. PMID:22938026

Role of lymphadenectomy in intermediate-risk endometrial cancer: a matched-pair study

PubMed Central

2018-01-01

Objective To assess the impact of lymph node dissection (LND) on morbidity, survival, and cost for intermediate-risk endometrial cancers (IREC). Methods A multicenter retrospective cohort of 720 women with IREC (endometrioid histology with myometrial invasion <50% and grade 3; or myometrial invasion ≥50% and grades 1–2; or cervical involvement and grades 1–2) was carried out. All patients underwent hysterectomy and bilateral salpingo-oophorectomy. A matched pair analysis identified 178 pairs (178 with LND and 178 without it) equal in age, body mass index, co-morbidities, American Society of Anesthesiologist score, myometrial invasion, and surgical approach. Demographic data, pathology results, perioperative morbidity, and survival were abstracted from medical records. Disease-free survival (DFS) and overall survival (OS) was analyzed using Kaplan-Meier curves and multivariate Cox regression analysis. Cost analysis was carried out between both groups. Results Both study groups were homogeneous in demographic data and pathologic results. The mean follow-up in patients free of disease was 61.7 months (range, 12.0–275.5). DFS (hazard ratio [HR]=1.34; 95% confidence interval [CI]=0.79–2.28) and OS (HR=0.72; 95% CI=0.42–1.23) were similar in both groups, independently of nodes count. In LND group, positive nodes were found in 10 cases (5.6%). Operating time and late postoperative complications were higher in LND group (p<0.05). Infection rate was significantly higher in no-LND group (p=0.035). There were no statistical differences between both groups regarding operative morbidity and hospital stay. The global cost was similar for both groups. Conclusion Systematic LND in IREC has no benefit on survival, although it does not show an increase in perioperative morbidity or global cost. PMID:29185259

Nontargeted, Rapid Screening of Extra Virgin Olive Oil Products for Authenticity Using Near-Infrared Spectroscopy in Combination with Conformity Index and Multivariate Statistical Analyses.

PubMed

Karunathilaka, Sanjeewa R; Kia, Ali-Reza Fardin; Srigley, Cynthia; Chung, Jin Kyu; Mossoba, Magdi M

2016-10-01

A rapid tool for evaluating authenticity was developed and applied to the screening of extra virgin olive oil (EVOO) retail products by using Fourier-transform near infrared (FT-NIR) spectroscopy in combination with univariate and multivariate data analysis methods. Using disposable glass tubes, spectra for 62 reference EVOO, 10 edible oil adulterants, 20 blends consisting of EVOO spiked with adulterants, 88 retail EVOO products and other test samples were rapidly measured in the transmission mode without any sample preparation. The univariate conformity index (CI) and the multivariate supervised soft independent modeling of class analogy (SIMCA) classification tool were used to analyze the various olive oil products which were tested for authenticity against a library of reference EVOO. Better discrimination between the authentic EVOO and some commercial EVOO products was observed with SIMCA than with CI analysis. Approximately 61% of all EVOO commercial products were flagged by SIMCA analysis, suggesting that further analysis be performed to identify quality issues and/or potential adulterants. Due to its simplicity and speed, FT-NIR spectroscopy in combination with multivariate data analysis can be used as a complementary tool to conventional official methods of analysis to rapidly flag EVOO products that may not belong to the class of authentic EVOO. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.

Metric Selection for Evaluation of Human Supervisory Control Systems

DTIC Science & Technology

2009-12-01

finding a significant effect when there is none becomes more likely. The inflation of type I error due to multiple dependent variables can be handled...with multivariate analysis techniques, such as Multivariate Analysis of Variance (MANOVA) (Johnson & Wichern, 2002). However, it should be noted that...the few significant differences among many insignificant ones. The best way to avoid failure to identify significant differences is to design an

A Civilian/Military Trauma Institute: National Trauma Coordinating Center

DTIC Science & Technology

2015-12-01

zip codes was used in “proximity to violence” analysis. Data were analyzed using SPSS (version 20.0, SPSS Inc., Chicago, IL). Multivariable linear...number of adverse events and serious events was not statistically higher in one group, the incidence of deep venous thrombosis (DVT) was statistically ...subjects the lack of statistical difference on multivariate analysis may be related to an underpowered sample size. It was recommended that the

Exploratory Multivariate Analysis. A Graphical Approach.

DTIC Science & Technology

1981-01-01

Gnanadesikan , 1977) but we feel that these should be used with great caution unless one really has good reason to believe that the data came from such a...are referred to Gnanadesikan (1977). The present author hopes that the convenience of a single summary or significance level will not deter his readers...fit of a harmonic model to meteorological data. (In preparation). Gnanadesikan , R. (1977). Methods for Statistical Data Analysis of Multivariate

The Covariance Adjustment Approaches for Combining Incomparable Cox Regressions Caused by Unbalanced Covariates Adjustment: A Multivariate Meta-Analysis Study.

PubMed

Dehesh, Tania; Zare, Najaf; Ayatollahi, Seyyed Mohammad Taghi

2015-01-01

Univariate meta-analysis (UM) procedure, as a technique that provides a single overall result, has become increasingly popular. Neglecting the existence of other concomitant covariates in the models leads to loss of treatment efficiency. Our aim was proposing four new approximation approaches for the covariance matrix of the coefficients, which is not readily available for the multivariate generalized least square (MGLS) method as a multivariate meta-analysis approach. We evaluated the efficiency of four new approaches including zero correlation (ZC), common correlation (CC), estimated correlation (EC), and multivariate multilevel correlation (MMC) on the estimation bias, mean square error (MSE), and 95% probability coverage of the confidence interval (CI) in the synthesis of Cox proportional hazard models coefficients in a simulation study. Comparing the results of the simulation study on the MSE, bias, and CI of the estimated coefficients indicated that MMC approach was the most accurate procedure compared to EC, CC, and ZC procedures. The precision ranking of the four approaches according to all above settings was MMC ≥ EC ≥ CC ≥ ZC. This study highlights advantages of MGLS meta-analysis on UM approach. The results suggested the use of MMC procedure to overcome the lack of information for having a complete covariance matrix of the coefficients.

The Fourier decomposition method for nonlinear and non-stationary time series analysis.

PubMed

Singh, Pushpendra; Joshi, Shiv Dutt; Patney, Rakesh Kumar; Saha, Kaushik

2017-03-01

for many decades, there has been a general perception in the literature that Fourier methods are not suitable for the analysis of nonlinear and non-stationary data. In this paper, we propose a novel and adaptive Fourier decomposition method (FDM), based on the Fourier theory, and demonstrate its efficacy for the analysis of nonlinear and non-stationary time series. The proposed FDM decomposes any data into a small number of 'Fourier intrinsic band functions' (FIBFs). The FDM presents a generalized Fourier expansion with variable amplitudes and variable frequencies of a time series by the Fourier method itself. We propose an idea of zero-phase filter bank-based multivariate FDM (MFDM), for the analysis of multivariate nonlinear and non-stationary time series, using the FDM. We also present an algorithm to obtain cut-off frequencies for MFDM. The proposed MFDM generates a finite number of band-limited multivariate FIBFs (MFIBFs). The MFDM preserves some intrinsic physical properties of the multivariate data, such as scale alignment, trend and instantaneous frequency. The proposed methods provide a time-frequency-energy (TFE) distribution that reveals the intrinsic structure of a data. Numerical computations and simulations have been carried out and comparison is made with the empirical mode decomposition algorithms.

The Fourier decomposition method for nonlinear and non-stationary time series analysis

PubMed Central

Joshi, Shiv Dutt; Patney, Rakesh Kumar; Saha, Kaushik

2017-01-01

for many decades, there has been a general perception in the literature that Fourier methods are not suitable for the analysis of nonlinear and non-stationary data. In this paper, we propose a novel and adaptive Fourier decomposition method (FDM), based on the Fourier theory, and demonstrate its efficacy for the analysis of nonlinear and non-stationary time series. The proposed FDM decomposes any data into a small number of ‘Fourier intrinsic band functions’ (FIBFs). The FDM presents a generalized Fourier expansion with variable amplitudes and variable frequencies of a time series by the Fourier method itself. We propose an idea of zero-phase filter bank-based multivariate FDM (MFDM), for the analysis of multivariate nonlinear and non-stationary time series, using the FDM. We also present an algorithm to obtain cut-off frequencies for MFDM. The proposed MFDM generates a finite number of band-limited multivariate FIBFs (MFIBFs). The MFDM preserves some intrinsic physical properties of the multivariate data, such as scale alignment, trend and instantaneous frequency. The proposed methods provide a time–frequency–energy (TFE) distribution that reveals the intrinsic structure of a data. Numerical computations and simulations have been carried out and comparison is made with the empirical mode decomposition algorithms. PMID:28413352

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

PubMed

Goicoechea, Héctor C; Olivieri, Alejandro C

2005-07-01

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

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

PubMed

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

2017-06-01

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

Semiparametric Thurstonian Models for Recurrent Choices: A Bayesian Analysis

ERIC Educational Resources Information Center

Ansari, Asim; Iyengar, Raghuram

2006-01-01

We develop semiparametric Bayesian Thurstonian models for analyzing repeated choice decisions involving multinomial, multivariate binary or multivariate ordinal data. Our modeling framework has multiple components that together yield considerable flexibility in modeling preference utilities, cross-sectional heterogeneity and parameter-driven…

The use of multivariate statistics in studies of wildlife habitat

Treesearch

David E. Capen

1981-01-01

This report contains edited and reviewed versions of papers presented at a workshop held at the University of Vermont in April 1980. Topics include sampling avian habitats, multivariate methods, applications, examples, and new approaches to analysis and interpretation.

Rejection of Multivariate Outliers.

DTIC Science & Technology

1983-05-01

available in Gnanadesikan (1977). 2 The motivation for the present investigation lies in a recent paper of Schvager and Margolin (1982) who derive a... Gnanadesikan , R. (1977). Methods for Statistical Data Analysis of Multivariate Observations. Wiley, New York. [7] Hawkins, D.M. (1980). Identification of

Applications of modern statistical methods to analysis of data in physical science

NASA Astrophysics Data System (ADS)

Wicker, James Eric

Modern methods of statistical and computational analysis offer solutions to dilemmas confronting researchers in physical science. Although the ideas behind modern statistical and computational analysis methods were originally introduced in the 1970's, most scientists still rely on methods written during the early era of computing. These researchers, who analyze increasingly voluminous and multivariate data sets, need modern analysis methods to extract the best results from their studies. The first section of this work showcases applications of modern linear regression. Since the 1960's, many researchers in spectroscopy have used classical stepwise regression techniques to derive molecular constants. However, problems with thresholds of entry and exit for model variables plagues this analysis method. Other criticisms of this kind of stepwise procedure include its inefficient searching method, the order in which variables enter or leave the model and problems with overfitting data. We implement an information scoring technique that overcomes the assumptions inherent in the stepwise regression process to calculate molecular model parameters. We believe that this kind of information based model evaluation can be applied to more general analysis situations in physical science. The second section proposes new methods of multivariate cluster analysis. The K-means algorithm and the EM algorithm, introduced in the 1960's and 1970's respectively, formed the basis of multivariate cluster analysis methodology for many years. However, several shortcomings of these methods include strong dependence on initial seed values and inaccurate results when the data seriously depart from hypersphericity. We propose new cluster analysis methods based on genetic algorithms that overcomes the strong dependence on initial seed values. In addition, we propose a generalization of the Genetic K-means algorithm which can accurately identify clusters with complex hyperellipsoidal covariance structures. We then use this new algorithm in a genetic algorithm based Expectation-Maximization process that can accurately calculate parameters describing complex clusters in a mixture model routine. Using the accuracy of this GEM algorithm, we assign information scores to cluster calculations in order to best identify the number of mixture components in a multivariate data set. We will showcase how these algorithms can be used to process multivariate data from astronomical observations.

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

PubMed

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

2013-01-01

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

Development and psychometric testing of the clinical networks engagement tool

PubMed Central

Hecker, Kent G.; Rabatach, Leora; Noseworthy, Tom W.; White, Deborah E.

2017-01-01

Background Clinical networks are being used widely to facilitate large system transformation in healthcare, by engagement of stakeholders throughout the health system. However, there are no available instruments that measure engagement in these networks. Methods The study purpose was to develop and assess the measurement properties of a multiprofessional tool to measure engagement in clinical network initiatives. Based on components of the International Association of Public Participation Spectrum and expert panel review, we developed 40 items for testing. The draft instrument was distributed to 1,668 network stakeholders across different governance levels (leaders, members, support, frontline stakeholders) in 9 strategic clinical networks in Alberta (January to July 2014). With data from 424 completed surveys (25.4% response rate), descriptive statistics, exploratory and confirmatory factor analysis, Pearson correlations, linear regression, multivariate analysis, and Cronbach alpha were conducted to assess reliability and validity of the scores. Results Sixteen items were retained in the instrument. Exploratory factor analysis indicated a four-factor solution and accounted for 85.7% of the total variance in engagement with clinical network initiatives: global engagement, inform (provided with information), involve (worked together to address concerns), and empower (given final decision-making authority). All subscales demonstrated acceptable reliability (Cronbach alpha 0.87 to 0.99). Both the confirmatory factor analysis and regression analysis confirmed that inform, involve, and empower were all significant predictors of global engagement, with involve as the strongest predictor. Leaders had higher mean scores than frontline stakeholders, while members and support staff did not differ in mean scores. Conclusions This study provided foundational evidence for the use of this tool for assessing engagement in clinical networks. Further work is necessary to evaluate engagement in broader network functions and activities; to assess barriers and facilitators of engagement; and, to elucidate how the maturity of networks and other factors influence engagement. PMID:28350834

A multi-model assessment of terrestrial biosphere model data needs

NASA Astrophysics Data System (ADS)

Gardella, A.; Cowdery, E.; De Kauwe, M. G.; Desai, A. R.; Duveneck, M.; Fer, I.; Fisher, R.; Knox, R. G.; Kooper, R.; LeBauer, D.; McCabe, T.; Minunno, F.; Raiho, A.; Serbin, S.; Shiklomanov, A. N.; Thomas, A.; Walker, A.; Dietze, M.

2017-12-01

Terrestrial biosphere models provide us with the means to simulate the impacts of climate change and their uncertainties. Going beyond direct observation and experimentation, models synthesize our current understanding of ecosystem processes and can give us insight on data needed to constrain model parameters. In previous work, we leveraged the Predictive Ecosystem Analyzer (PEcAn) to assess the contribution of different parameters to the uncertainty of the Ecosystem Demography model v2 (ED) model outputs across various North American biomes (Dietze et al., JGR-G, 2014). While this analysis identified key research priorities, the extent to which these priorities were model- and/or biome-specific was unclear. Furthermore, because the analysis only studied one model, we were unable to comment on the effect of variability in model structure to overall predictive uncertainty. Here, we expand this analysis to all biomes globally and a wide sample of models that vary in complexity: BioCro, CABLE, CLM, DALEC, ED2, FATES, G'DAY, JULES, LANDIS, LINKAGES, LPJ-GUESS, MAESPA, PRELES, SDGVM, SIPNET, and TEM. Prior to performing uncertainty analyses, model parameter uncertainties were assessed by assimilating all available trait data from the combination of the BETYdb and TRY trait databases, using an updated multivariate version of PEcAn's Hierarchical Bayesian meta-analysis. Next, sensitivity analyses were performed for all models across a range of sites globally to assess sensitivities for a range of different outputs (GPP, ET, SH, Ra, NPP, Rh, NEE, LAI) at multiple time scales from the sub-annual to the decadal. Finally, parameter uncertainties and model sensitivities were combined to evaluate the fractional contribution of each parameter to the predictive uncertainty for a specific variable at a specific site and timescale. Facilitated by PEcAn's automated workflows, this analysis represents the broadest assessment of the sensitivities and uncertainties in terrestrial models to date, and provides a comprehensive roadmap for constraining model uncertainties through model development and data collection.

Connectivity patterns in cognitive control networks predict naturalistic multitasking ability.

PubMed

Wen, Tanya; Liu, De-Cyuan; Hsieh, Shulan

2018-06-01

Multitasking is a fundamental aspect of everyday life activities. To achieve a complex, multi-component goal, the tasks must be subdivided into sub-tasks and component steps, a critical function of prefrontal networks. The prefrontal cortex is considered to be organized in a cascade of executive processes from the sensorimotor to anterior prefrontal cortex, which includes execution of specific goal-directed action, to encoding and maintaining task rules, and finally monitoring distal goals. In the current study, we used a virtual multitasking paradigm to tap into real-world performance and relate it to each individual's resting-state functional connectivity in fMRI. While did not find any correlation between global connectivity of any of the major networks with multitasking ability, global connectivity of the lateral prefrontal cortex (LPFC) was predictive of multitasking ability. Further analysis showed that multivariate connectivity patterns within the sensorimotor network (SMN), and between-network connectivity of the frontoparietal network (FPN) and dorsal attention network (DAN), predicted individual multitasking ability and could be generalized to novel individuals. Together, these results support previous research that prefrontal networks underlie multitasking abilities and show that connectivity patterns in the cascade of prefrontal networks may explain individual differences in performance. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

The Genetic Association Between Neocortical Volume and General Cognitive Ability Is Driven by Global Surface Area Rather Than Thickness.

PubMed

Vuoksimaa, Eero; Panizzon, Matthew S; Chen, Chi-Hua; Fiecas, Mark; Eyler, Lisa T; Fennema-Notestine, Christine; Hagler, Donald J; Fischl, Bruce; Franz, Carol E; Jak, Amy; Lyons, Michael J; Neale, Michael C; Rinker, Daniel A; Thompson, Wesley K; Tsuang, Ming T; Dale, Anders M; Kremen, William S

2015-08-01

Total gray matter volume is associated with general cognitive ability (GCA), an association mediated by genetic factors. It is expectable that total neocortical volume should be similarly associated with GCA. Neocortical volume is the product of thickness and surface area, but global thickness and surface area are unrelated phenotypically and genetically in humans. The nature of the genetic association between GCA and either of these 2 cortical dimensions has not been examined. Humans possess greater cognitive capacity than other species, and surface area increases appear to be the primary driver of the increased size of the human cortex. Thus, we expected neocortical surface area to be more strongly associated with cognition than thickness. Using multivariate genetic analysis in 515 middle-aged twins, we demonstrated that both the phenotypic and genetic associations between neocortical volume and GCA are driven primarily by surface area rather than thickness. Results were generally similar for each of 4 specific cognitive abilities that comprised the GCA measure. Our results suggest that emphasis on neocortical surface area, rather than thickness, could be more fruitful for elucidating neocortical-GCA associations and identifying specific genes underlying those associations. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Soft-assembled Multilevel Dynamics of Tactical Behaviors in Soccer

PubMed Central

Ric, Angel; Torrents, Carlota; Gonçalves, Bruno; Sampaio, Jaime; Hristovski, Robert

2016-01-01

This study aimed to identify the tactical patterns and the timescales of variables during a soccer match, allowing understanding the multilevel organization of tactical behaviors, and to determine the similarity of patterns performed by different groups of teammates during the first and second halves. Positional data from 20 professional male soccer players from the same team were collected using high frequency global positioning systems (5 Hz). Twenty-nine categories of tactical behaviors were determined from eight positioning-derived variables creating multivariate binary (Boolean) time-series matrices. Hierarchical principal component analysis (PCA) was used to identify the multilevel structure of tactical behaviors. The sequential reduction of each set level of principal components revealed a sole principal component as the slowest collective variable, forming the global basin of attraction of tactical patterns during each half of the match. In addition, the mean dwell time of each positioning-derived variable helped to understand the multilevel organization of collective tactical behavior during a soccer match. This approach warrants further investigations to analyze the influence of task constraints on the emergence of tactical behavior. Furthermore, PCA can help coaches to design representative training tasks according to those tactical patterns captured during match competitions and to compare them depending on situational variables. PMID:27761120

Clinical Trials With Large Numbers of Variables: Important Advantages of Canonical Analysis.

PubMed

Cleophas, Ton J

2016-01-01

Canonical analysis assesses the combined effects of a set of predictor variables on a set of outcome variables, but it is little used in clinical trials despite the omnipresence of multiple variables. The aim of this study was to assess the performance of canonical analysis as compared with traditional multivariate methods using multivariate analysis of covariance (MANCOVA). As an example, a simulated data file with 12 gene expression levels and 4 drug efficacy scores was used. The correlation coefficient between the 12 predictor and 4 outcome variables was 0.87 (P = 0.0001) meaning that 76% of the variability in the outcome variables was explained by the 12 covariates. Repeated testing after the removal of 5 unimportant predictor and 1 outcome variable produced virtually the same overall result. The MANCOVA identified identical unimportant variables, but it was unable to provide overall statistics. (1) Canonical analysis is remarkable, because it can handle many more variables than traditional multivariate methods such as MANCOVA can. (2) At the same time, it accounts for the relative importance of the separate variables, their interactions and differences in units. (3) Canonical analysis provides overall statistics of the effects of sets of variables, whereas traditional multivariate methods only provide the statistics of the separate variables. (4) Unlike other methods for combining the effects of multiple variables such as factor analysis/partial least squares, canonical analysis is scientifically entirely rigorous. (5) Limitations include that it is less flexible than factor analysis/partial least squares, because only 2 sets of variables are used and because multiple solutions instead of one is offered. We do hope that this article will stimulate clinical investigators to start using this remarkable method.

Using Multivariate Adaptive Regression Spline and Artificial Neural Network to Simulate Urbanization in Mumbai, India

NASA Astrophysics Data System (ADS)

Ahmadlou, M.; Delavar, M. R.; Tayyebi, A.; Shafizadeh-Moghadam, H.

2015-12-01

Land use change (LUC) models used for modelling urban growth are different in structure and performance. Local models divide the data into separate subsets and fit distinct models on each of the subsets. Non-parametric models are data driven and usually do not have a fixed model structure or model structure is unknown before the modelling process. On the other hand, global models perform modelling using all the available data. In addition, parametric models have a fixed structure before the modelling process and they are model driven. Since few studies have compared local non-parametric models with global parametric models, this study compares a local non-parametric model called multivariate adaptive regression spline (MARS), and a global parametric model called artificial neural network (ANN) to simulate urbanization in Mumbai, India. Both models determine the relationship between a dependent variable and multiple independent variables. We used receiver operating characteristic (ROC) to compare the power of the both models for simulating urbanization. Landsat images of 1991 (TM) and 2010 (ETM+) were used for modelling the urbanization process. The drivers considered for urbanization in this area were distance to urban areas, urban density, distance to roads, distance to water, distance to forest, distance to railway, distance to central business district, number of agricultural cells in a 7 by 7 neighbourhoods, and slope in 1991. The results showed that the area under the ROC curve for MARS and ANN was 94.77% and 95.36%, respectively. Thus, ANN performed slightly better than MARS to simulate urban areas in Mumbai, India.

Multivariate analysis of fatty acid and biochemical constitutes of seaweeds to characterize their potential as bioresource for biofuel and fine chemicals.

PubMed

Verma, Priyanka; Kumar, Manoj; Mishra, Girish; Sahoo, Dinabandhu

2017-02-01

In the present study bio prospecting of thirty seaweeds from Indian coasts was analyzed for their biochemical components including pigments, fatty acid and ash content. Multivariate analysis of biochemical components and fatty acids was done using Principal Component Analysis (PCA) and Agglomerative hierarchical clustering (AHC) to manifest chemotaxonomic relationship among various seaweeds. The overall analysis suggests that these seaweeds have multi-functional properties and can be utilized as promising bioresource for proteins, lipids, pigments and carbohydrates for the food/feed and biofuel industry. Copyright © 2016. Published by Elsevier Ltd.

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

PubMed

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

2016-01-01

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

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

PubMed Central

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

2017-01-01

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

Predictors of sexual bother in a population of male North American medical students.

PubMed

Smith, James F; Breyer, Benjamin N; Shindel, Alan W

2011-12-01

The prevalence and associations of sexual bother in male medical students has not been extensively studied. The aim of this study is to analyze predictors of sexual bother in a survey of male North American medical students. Students enrolled in allopathic and osteopathic medical schools in North America between February 2008 and July 2008 were invited to participate in an internet-based survey of sexuality and sexual function. The principle outcome measure was a single-item question inquiring about global satisfaction with sexual function. The survey also consisted of a questionnaire that included ethnodemographic factors, student status, sexual history, and a validated scale for the assessment of depression. Respondents completed the International Index of Erectile Function, the premature ejaculation diagnostic tool, and the Self-Esteem and Relationship Quality survey (SEAR). Descriptive statistics, analysis of variance, and multivariable logistic regression were utilized to analyze responses. There were 480 male subjects (mean age 26.3 years) with data sufficient for analysis. Forty-three (9%) reported sexual bother. Sexual bother was significantly more common in men with erectile dysfunction (ED), high risk of premature ejaculation (HRPE), depressive symptoms, and lower sexual frequency. However, after multivariate analysis including SEAR scores, ED, and HRPE were no longer independently predictive of sexual bother. Higher scores for all domains of the SEAR were associated with lower odds of sexual bother. ED and HRPE are associated with sexual bother in this young and presumably healthy population. However, after controlling for relationship factors neither ED nor HRPE independently predicted sexual bother. It is plausible to hypothesize that sexual dysfunction from organic causes is rare in this population and is seldom encountered outside of relationship perturbations. Attention to relationship and psychological factors is likely of key importance in addressing sexual concerns in this population. © 2011 International Society for Sexual Medicine.

Characterization of 1,577 Primary Prostate Cancers Reveals Novel Biological and Clinicopathological Insights into Molecular Subtypes

PubMed Central

Tomlins, Scott A.; Alshalalfa, Mohammed; Davicioni, Elai; Erho, Nicholas; Yousefi, Kasra; Zhao, Shuang; Haddad, Zaid; Den, Robert B.; Dicker, Adam P.; Trock, Bruce; DeMarzo, Angelo; Ross, Ashley; Schaeffer, Edward M.; Klein, Eric A.; Magi-Galluzzi, Cristina; Karnes, Jeffery R.; Jenkins, Robert B.; Feng, Felix Y.

2015-01-01

Background Prostate cancer (PCa) molecular subtypes have been defined by essentially mutually exclusive events, including ETS gene fusions (most commonly involving ERG) and SPINK1 over-expression. Clinical assessment may aid in disease stratification, complementing available prognostic tests. Objective To determine the analytical validity and clinicopatholgical associations of microarray-based molecular subtyping. Design, Setting and Participants We analyzed Affymetrix GeneChip expression profiles for 1,577 patients from eight radical prostatectomy (RP) cohorts, including 1,351 cases assessed using the Decipher prognostic assay (performed in a CLIA-certified laboratory). A microarray-based (m-) random forest ERG classification model was trained and validated. Outlier expression analysis was used to predict other mutually exclusive non-ERG ETS gene rearrangements (ETS+) or SPINK1 over-expression (SPINK1+). Outcome Measurements Associations with clinical features and outcomes by multivariable logistic regression analysis and receiver operating curves. Results and Limitations The m-ERG classifier showed 95% accuracy in an independent validation subset (n=155 samples). Across cohorts, 45%, 9%, 8% and 38% of PCa were classified as m-ERG+, m-ETS+, m-SPINK1+, and triple negative (m-ERG−/m-ETS−/m-SPINK1−), respectively. Gene expression profiling supports three underlying molecularly defined groups (m-ERG+, m-ETS+ and m-SPINK1+/triple negative). On multivariable analysis, m-ERG+ tumors were associated with lower preoperative serum PSA and Gleason scores, but enriched for extraprostatic extension (p<0.001). m-ETS+ tumors were associated with seminal vesicle invasion (p=0.01), while m-SPINK1+/triple negative tumors had higher Gleason scores and were more frequent in Black/African American patients (p<0.001). Clinical outcomes were not significantly different between subtypes. Conclusions A clinically available prognostic test (Decipher) can also assess PCa molecular subtypes, obviating the need for additional testing. Clinicopathological differences were found among subtypes based on global expression patterns. PMID:25964175

Social support for patients undergoing liver transplantation in a Public University Hospital.

PubMed

Garcia, Clerison Stelvio; Lima, Agnaldo Soares; La-Rotta, Ehideé Isabel Gómez; Boin, Ilka de Fátima Santana Ferreira

2018-02-17

Several diseases may lead to the need for liver transplantation due to progressive organ damage until the onset of cirrhosis, resulting in changes in interpersonal relationships. Social Support for transplant candidates is an important variable, providing them with psychological and social well-being. This study aims to assess social support in chronic hepatic patients, waiting for liver transplantation. A cross-sectional study was conducted with 119 patients, for convenience sampling, from the liver transplant waiting list at a Brazilian University Hospital Outpatients. The information was collected through semistructured questionnaires, in four stages: 1) socioeconomic and demographic information 2) clinical aspects 3) feelings 4) Social Support Network Inventory (SSNI), to Brazilian Portuguese. The statistical analysis was conducted using ANOVA and multivariate linear regression analysis to evaluate the relationship between the scales of social support and the collected co-variables. Average age was 50.2 ± 11.6, and 87 (73.1%) were men. Patients with alcohol and virus liver disease etiology had the same frequency of 28%. The MELD, without extrapoints, was 16.7 ± 4.9. Global social support family score was 3.72 ± 0.39, and Cronbach's alpha = 0.79. The multivariate analysis presented the following associations, age = [- 0.010 (95% CI = - 0.010 - -0.010); P = 0.001], etiology of hepatic disease = [- 0.212 (95% CI = - 0.37 - -0.05); P = 0.009], happiness = [- 0.214(95% CI = - 0.33 - -0.09) P = 0.001) and aggressiveness = [0.172 (95% CI = 0.040-0.030); P = 0.010). The social support was greater when the patients were younger (18 to 30 years). Patients with alcoholic cirrhosis, regardless of whether or not they were associated with virus, had less social support. As for feelings, the absence of happiness and the presence of aggressiveness showed a negative effect on social support.