Investigation of Asphalt Mixture Creep Behavior Using Thin Beam Specimens

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

Zofka, Adam; Marasteanu, Mihai; Turos, Mugur

2008-02-15

The asphalt pavement layer consists of two or more lifts of compacted asphalt mixture; the top of the layer is also exposed to aging, a factor that significantly affects the mixture properties. The current testing specifications use rather thick specimens that cannot be used to investigate the gradual change in properties with pavement depth. This paper investigates the feasibility of using the 3-point bending test with thin asphalt mixture beams (127x12.7x6.35 mm) to determine the low-temperature creep compliance of the mixtures. Several theoretical and semi-empirical models, from the theory of composites, are reviewed and evaluated using numerical and experimental data.more » Preliminary results show that this method can be used for low-temperature mixture characterization but several crucial factors need further inspection and interpretation.« less

Heterogeneity in the Latent Structure of PTSD Symptoms among Canadian Veterans

ERIC Educational Resources Information Center

Naifeh, James A.; Richardson, J. Don; Del Ben, Kevin S.; Elhai, Jon D.

2010-01-01

The current study used factor mixture modeling to identify heterogeneity (i.e., latent classes) in 2 well-supported models of posttraumatic stress disorder's (PTSD) factor structure. Data were analyzed from a clinical sample of 405 Canadian veterans evaluated for PTSD. Results were consistent with our hypotheses. Each PTSD factor model was best…

Class Enumeration and Parameter Recovery of Growth Mixture Modeling and Second-Order Growth Mixture Modeling in the Presence of Measurement Noninvariance between Latent Classes

PubMed Central

Kim, Eun Sook; Wang, Yan

2017-01-01

Population heterogeneity in growth trajectories can be detected with growth mixture modeling (GMM). It is common that researchers compute composite scores of repeated measures and use them as multiple indicators of growth factors (baseline performance and growth) assuming measurement invariance between latent classes. Considering that the assumption of measurement invariance does not always hold, we investigate the impact of measurement noninvariance on class enumeration and parameter recovery in GMM through a Monte Carlo simulation study (Study 1). In Study 2, we examine the class enumeration and parameter recovery of the second-order growth mixture modeling (SOGMM) that incorporates measurement models at the first order level. Thus, SOGMM estimates growth trajectory parameters with reliable sources of variance, that is, common factor variance of repeated measures and allows heterogeneity in measurement parameters between latent classes. The class enumeration rates are examined with information criteria such as AIC, BIC, sample-size adjusted BIC, and hierarchical BIC under various simulation conditions. The results of Study 1 showed that the parameter estimates of baseline performance and growth factor means were biased to the degree of measurement noninvariance even when the correct number of latent classes was extracted. In Study 2, the class enumeration accuracy of SOGMM depended on information criteria, class separation, and sample size. The estimates of baseline performance and growth factor mean differences between classes were generally unbiased but the size of measurement noninvariance was underestimated. Overall, SOGMM is advantageous in that it yields unbiased estimates of growth trajectory parameters and more accurate class enumeration compared to GMM by incorporating measurement models. PMID:28928691

Dielectric relaxation and hydrogen bonding interaction in xylitol-water mixtures using time domain reflectometry

NASA Astrophysics Data System (ADS)

Rander, D. N.; Joshi, Y. S.; Kanse, K. S.; Kumbharkhane, A. C.

2016-01-01

The measurements of complex dielectric permittivity of xylitol-water mixtures have been carried out in the frequency range of 10 MHz-30 GHz using a time domain reflectometry technique. Measurements have been done at six temperatures from 0 to 25 °C and at different weight fractions of xylitol (0 < W X ≤ 0.7) in water. There are different models to explain the dielectric relaxation behaviour of binary mixtures, such as Debye, Cole-Cole or Cole-Davidson model. We have observed that the dielectric relaxation behaviour of binary mixtures of xylitol-water can be well described by Cole-Davidson model having an asymmetric distribution of relaxation times. The dielectric parameters such as static dielectric constant and relaxation time for the mixtures have been evaluated. The molecular interaction between xylitol and water molecules is discussed using the Kirkwood correlation factor ( g eff ) and thermodynamic parameter.

Different approaches in Partial Least Squares and Artificial Neural Network models applied for the analysis of a ternary mixture of Amlodipine, Valsartan and Hydrochlorothiazide

NASA Astrophysics Data System (ADS)

Darwish, Hany W.; Hassan, Said A.; Salem, Maissa Y.; El-Zeany, Badr A.

2014-03-01

Different chemometric models were applied for the quantitative analysis of Amlodipine (AML), Valsartan (VAL) and Hydrochlorothiazide (HCT) in ternary mixture, namely, Partial Least Squares (PLS) as traditional chemometric model and Artificial Neural Networks (ANN) as advanced model. PLS and ANN were applied with and without variable selection procedure (Genetic Algorithm GA) and data compression procedure (Principal Component Analysis PCA). The chemometric methods applied are PLS-1, GA-PLS, ANN, GA-ANN and PCA-ANN. The methods were used for the quantitative analysis of the drugs in raw materials and pharmaceutical dosage form via handling the UV spectral data. A 3-factor 5-level experimental design was established resulting in 25 mixtures containing different ratios of the drugs. Fifteen mixtures were used as a calibration set and the other ten mixtures were used as validation set to validate the prediction ability of the suggested methods. The validity of the proposed methods was assessed using the standard addition technique.

Examining the Latent Structure of Anxiety Sensitivity in Adolescents using Factor Mixture Modeling

PubMed Central

Allan, Nicholas P.; MacPherson, Laura; Young, Kevin C.; Lejuez, Carl W.; Schmidt, Norman B.

2014-01-01

Anxiety sensitivity has been implicated as an important risk factor, generalizable to most anxiety disorders. In adults, factor mixture modeling has been used to demonstrate that anxiety sensitivity is best conceptualized as categorical between individuals. That is, whereas most adults appear to possess normative levels of anxiety sensitivity, a small subset of the population appears to possess abnormally high levels of anxiety sensitivity. Further, those in the high anxiety sensitivity group are at increased risk of having high levels of anxiety and of having an anxiety disorder. This study was designed to determine whether these findings extend to adolescents. Factor mixture modeling was used to examine the best fitting model of anxiety sensitivity in a sample of 277 adolescents (M age = 11.0, SD = .81). Consistent with research in adults, the best fitting model consisted of two classes, one containing adolescents with high levels of anxiety sensitivity (n = 25), and another containing adolescents with normative levels of anxiety sensitivity (n = 252). Examination of anxiety sensitivity subscales revealed that the social concerns subscale was not important for classification of individuals. Convergent and discriminant validity of anxiety sensitivity classes were found in that membership in the high anxiety sensitivity class was associated with higher mean levels of anxiety symptoms, controlling for depression and externalizing problems, and was not associated with higher mean levels of depression or externalizing symptoms controlling for anxiety problems. PMID:24749756

Robust Bayesian clustering.

PubMed

Archambeau, Cédric; Verleysen, Michel

2007-01-01

A new variational Bayesian learning algorithm for Student-t mixture models is introduced. This algorithm leads to (i) robust density estimation, (ii) robust clustering and (iii) robust automatic model selection. Gaussian mixture models are learning machines which are based on a divide-and-conquer approach. They are commonly used for density estimation and clustering tasks, but are sensitive to outliers. The Student-t distribution has heavier tails than the Gaussian distribution and is therefore less sensitive to any departure of the empirical distribution from Gaussianity. As a consequence, the Student-t distribution is suitable for constructing robust mixture models. In this work, we formalize the Bayesian Student-t mixture model as a latent variable model in a different way from Svensén and Bishop [Svensén, M., & Bishop, C. M. (2005). Robust Bayesian mixture modelling. Neurocomputing, 64, 235-252]. The main difference resides in the fact that it is not necessary to assume a factorized approximation of the posterior distribution on the latent indicator variables and the latent scale variables in order to obtain a tractable solution. Not neglecting the correlations between these unobserved random variables leads to a Bayesian model having an increased robustness. Furthermore, it is expected that the lower bound on the log-evidence is tighter. Based on this bound, the model complexity, i.e. the number of components in the mixture, can be inferred with a higher confidence.

Advanced stability indicating chemometric methods for quantitation of amlodipine and atorvastatin in their quinary mixture with acidic degradation products

NASA Astrophysics Data System (ADS)

Darwish, Hany W.; Hassan, Said A.; Salem, Maissa Y.; El-Zeany, Badr A.

2016-02-01

Two advanced, accurate and precise chemometric methods are developed for the simultaneous determination of amlodipine besylate (AML) and atorvastatin calcium (ATV) in the presence of their acidic degradation products in tablet dosage forms. The first method was Partial Least Squares (PLS-1) and the second was Artificial Neural Networks (ANN). PLS was compared to ANN models with and without variable selection procedure (genetic algorithm (GA)). For proper analysis, a 5-factor 5-level experimental design was established resulting in 25 mixtures containing different ratios of the interfering species. Fifteen mixtures were used as calibration set and the other ten mixtures were used as validation set to validate the prediction ability of the suggested models. The proposed methods were successfully applied to the analysis of pharmaceutical tablets containing AML and ATV. The methods indicated the ability of the mentioned models to solve the highly overlapped spectra of the quinary mixture, yet using inexpensive and easy to handle instruments like the UV-VIS spectrophotometer.

Examining the latent structure of anxiety sensitivity in adolescents using factor mixture modeling.

PubMed

Allan, Nicholas P; MacPherson, Laura; Young, Kevin C; Lejuez, Carl W; Schmidt, Norman B

2014-09-01

Anxiety sensitivity has been implicated as an important risk factor, generalizable to most anxiety disorders. In adults, factor mixture modeling has been used to demonstrate that anxiety sensitivity is best conceptualized as categorical between individuals. That is, whereas most adults appear to possess normative levels of anxiety sensitivity, a small subset of the population appears to possess abnormally high levels of anxiety sensitivity. Further, those in the high anxiety sensitivity group are at increased risk of having high levels of anxiety and of having an anxiety disorder. This study was designed to determine whether these findings extend to adolescents. Factor mixture modeling was used to examine the best fitting model of anxiety sensitivity in a sample of 277 adolescents (M age = 11.0 years, SD = 0.81). Consistent with research in adults, the best fitting model consisted of 2 classes, 1 containing adolescents with high levels of anxiety sensitivity (n = 25) and another containing adolescents with normative levels of anxiety sensitivity (n = 252). Examination of anxiety sensitivity subscales revealed that the social concerns subscale was not important for classification of individuals. Convergent and discriminant validity of anxiety sensitivity classes were found in that membership in the high anxiety sensitivity class was associated with higher mean levels of anxiety symptoms, controlling for depression and externalizing problems, and was not associated with higher mean levels of depression or externalizing symptoms controlling for anxiety problems. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Single- and mixture toxicity of three organic UV-filters, ethylhexyl methoxycinnamate, octocrylene, and avobenzone on Daphnia magna.

PubMed

Park, Chang-Beom; Jang, Jiyi; Kim, Sanghun; Kim, Young Jun

2017-03-01

In freshwater environments, aquatic organisms are generally exposed to mixtures of various chemical substances. In this study, we tested the toxicity of three organic UV-filters (ethylhexyl methoxycinnamate, octocrylene, and avobenzone) to Daphnia magna in order to evaluate the combined toxicity of these substances when in they occur in a mixture. The values of effective concentrations (ECx) for each UV-filter were calculated by concentration-response curves; concentration-combinations of three different UV-filters in a mixture were determined by the fraction of components based on EC 25 values predicted by concentration addition (CA) model. The interaction between the UV-filters were also assessed by model deviation ratio (MDR) using observed and predicted toxicity values obtained from mixture-exposure tests and CA model. The results from this study indicated that observed ECx mix (e.g., EC 10mix , EC 25mix , or EC 50mix ) values obtained from mixture-exposure tests were higher than predicted ECx mix (e.g., EC 10mix , EC 25mix , or EC 50mix ) values calculated by CA model. MDR values were also less than a factor of 1.0 in a mixtures of three different UV-filters. Based on these results, we suggest for the first time a reduction of toxic effects in the mixtures of three UV-filters, caused by antagonistic action of the components. Our findings from this study will provide important information for hazard or risk assessment of organic UV-filters, when they existed together in the aquatic environment. To better understand the mixture toxicity and the interaction of components in a mixture, further studies for various combinations of mixture components are also required. Copyright © 2016 Elsevier Inc. All rights reserved.

Catalytic effects of inorganic acids on the decomposition of ammonium nitrate.

PubMed

Sun, Jinhua; Sun, Zhanhui; Wang, Qingsong; Ding, Hui; Wang, Tong; Jiang, Chuansheng

2005-12-09

In order to evaluate the catalytic effects of inorganic acids on the decomposition of ammonium nitrate (AN), the heat releases of decomposition or reaction of pure AN and its mixtures with inorganic acids were analyzed by a heat flux calorimeter C80. Through the experiments, the different reaction mechanisms of AN and its mixtures were analyzed. The chemical reaction kinetic parameters such as reaction order, activation energy and frequency factor were calculated with the C80 experimental results for different samples. Based on these parameters and the thermal runaway models (Semenov and Frank-Kamenestkii model), the self-accelerating decomposition temperatures (SADTs) of AN and its mixtures were calculated and compared. The results show that the mixtures of AN with acid are more unsteady than pure AN. The AN decomposition reaction is catalyzed by acid. The calculated SADTs of AN mixtures with acid are much lower than that of pure AN.

Modeling viscosity and diffusion of plasma mixtures across coupling regimes

NASA Astrophysics Data System (ADS)

Arnault, Philippe

2014-10-01

Viscosity and diffusion of plasma for pure elements and multicomponent mixtures are modeled from the high-temperature low-density weakly coupled regime to the low-temperature high-density strongly coupled regime. Thanks to an atom in jellium modeling, the effect of electron screening on the ion-ion interaction is incorporated through a self-consistent definition of the ionization. This defines an effective One Component Plasma, or an effective Binary Ionic Mixture, that is representative of the strength of the interaction. For the viscosity and the interdiffusion of mixtures, approximate kinetic expressions are supplemented by mixing laws applied to the excess viscosity and self-diffusion of pure elements. The comparisons with classical and quantum molecular dynamics results reveal deviations in the range 20--40% on average with almost no predictions further than a factor of 2 over many decades of variation. Applications in the inertial confinement fusion context could help in predicting the growth of hydrodynamic instabilities.

Method optimization for drug impurity profiling in supercritical fluid chromatography: Application to a pharmaceutical mixture.

PubMed

Muscat Galea, Charlene; Didion, David; Clicq, David; Mangelings, Debby; Vander Heyden, Yvan

2017-12-01

A supercritical chromatographic method for the separation of a drug and its impurities has been developed and optimized applying an experimental design approach and chromatogram simulations. Stationary phase screening was followed by optimization of the modifier and injection solvent composition. A design-of-experiment (DoE) approach was then used to optimize column temperature, back-pressure and the gradient slope simultaneously. Regression models for the retention times and peak widths of all mixture components were built. The factor levels for different grid points were then used to predict the retention times and peak widths of the mixture components using the regression models and the best separation for the worst separated peak pair in the experimental domain was identified. A plot of the minimal resolutions was used to help identifying the factor levels leading to the highest resolution between consecutive peaks. The effects of the DoE factors were visualized in a way that is familiar to the analytical chemist, i.e. by simulating the resulting chromatogram. The mixture of an active ingredient and seven impurities was separated in less than eight minutes. The approach discussed in this paper demonstrates how SFC methods can be developed and optimized efficiently using simple concepts and tools. Copyright © 2017 Elsevier B.V. All rights reserved.

Metal mixture modeling evaluation project: 2. Comparison of four modeling approaches.

PubMed

Farley, Kevin J; Meyer, Joseph S; Balistrieri, Laurie S; De Schamphelaere, Karel A C; Iwasaki, Yuichi; Janssen, Colin R; Kamo, Masashi; Lofts, Stephen; Mebane, Christopher A; Naito, Wataru; Ryan, Adam C; Santore, Robert C; Tipping, Edward

2015-04-01

As part of the Metal Mixture Modeling Evaluation (MMME) project, models were developed by the National Institute of Advanced Industrial Science and Technology (Japan), the US Geological Survey (USA), HDR|HydroQual (USA), and the Centre for Ecology and Hydrology (United Kingdom) to address the effects of metal mixtures on biological responses of aquatic organisms. A comparison of the 4 models, as they were presented at the MMME workshop in Brussels, Belgium (May 2012), is provided in the present study. Overall, the models were found to be similar in structure (free ion activities computed by the Windermere humic aqueous model [WHAM]; specific or nonspecific binding of metals/cations in or on the organism; specification of metal potency factors or toxicity response functions to relate metal accumulation to biological response). Major differences in modeling approaches are attributed to various modeling assumptions (e.g., single vs multiple types of binding sites on the organism) and specific calibration strategies that affected the selection of model parameters. The models provided a reasonable description of additive (or nearly additive) toxicity for a number of individual toxicity test results. Less-than-additive toxicity was more difficult to describe with the available models. Because of limitations in the available datasets and the strong interrelationships among the model parameters (binding constants, potency factors, toxicity response parameters), further evaluation of specific model assumptions and calibration strategies is needed. © 2014 SETAC.

A Comparison of Response Surface Methodology and a One-Factor-At-A-Time Approach as Calibration Techniques for the Bioplume-II Simulation Model of Contaminant Biodegradation

DTIC Science & Technology

1995-12-01

34 Environmental Science and Technology, 26:1404-1410 (July 1992). 4. Atlas , Ronald M. and Richard Bartha . Microbial Ecology , Fundamentals and Applica...the impact of physical factors on microbial activity. They cite research by Atlas and Bartha observing that low temperatures inhibit microbial activity...mixture. Atlas and Bartha (4:393-394) explain that a typical petroleum mixture includes aliphatics, alicyclics, aromatics and other organics. The

Metal Mixture Modeling Evaluation project: 2. Comparison of four modeling approaches

USGS Publications Warehouse

Farley, Kevin J.; Meyer, Joe; Balistrieri, Laurie S.; DeSchamphelaere, Karl; Iwasaki, Yuichi; Janssen, Colin; Kamo, Masashi; Lofts, Steve; Mebane, Christopher A.; Naito, Wataru; Ryan, Adam C.; Santore, Robert C.; Tipping, Edward

2015-01-01

As part of the Metal Mixture Modeling Evaluation (MMME) project, models were developed by the National Institute of Advanced Industrial Science and Technology (Japan), the U.S. Geological Survey (USA), HDR⎪HydroQual, Inc. (USA), and the Centre for Ecology and Hydrology (UK) to address the effects of metal mixtures on biological responses of aquatic organisms. A comparison of the 4 models, as they were presented at the MMME Workshop in Brussels, Belgium (May 2012), is provided herein. Overall, the models were found to be similar in structure (free ion activities computed by WHAM; specific or non-specific binding of metals/cations in or on the organism; specification of metal potency factors and/or toxicity response functions to relate metal accumulation to biological response). Major differences in modeling approaches are attributed to various modeling assumptions (e.g., single versus multiple types of binding site on the organism) and specific calibration strategies that affected the selection of model parameters. The models provided a reasonable description of additive (or nearly additive) toxicity for a number of individual toxicity test results. Less-than-additive toxicity was more difficult to describe with the available models. Because of limitations in the available datasets and the strong inter-relationships among the model parameters (log KM values, potency factors, toxicity response parameters), further evaluation of specific model assumptions and calibration strategies is needed.

Applying mixture toxicity modelling to predict bacterial bioluminescence inhibition by non-specifically acting pharmaceuticals and specifically acting antibiotics.

PubMed

Neale, Peta A; Leusch, Frederic D L; Escher, Beate I

2017-04-01

Pharmaceuticals and antibiotics co-occur in the aquatic environment but mixture studies to date have mainly focused on pharmaceuticals alone or antibiotics alone, although differences in mode of action may lead to different effects in mixtures. In this study we used the Bacterial Luminescence Toxicity Screen (BLT-Screen) after acute (0.5 h) and chronic (16 h) exposure to evaluate how non-specifically acting pharmaceuticals and specifically acting antibiotics act together in mixtures. Three models were applied to predict mixture toxicity including concentration addition, independent action and the two-step prediction (TSP) model, which groups similarly acting chemicals together using concentration addition, followed by independent action to combine the two groups. All non-antibiotic pharmaceuticals had similar EC 50 values at both 0.5 and 16 h, indicating together with a QSAR (Quantitative Structure-Activity Relationship) analysis that they act as baseline toxicants. In contrast, the antibiotics' EC 50 values decreased by up to three orders of magnitude after 16 h, which can be explained by their specific effect on bacteria. Equipotent mixtures of non-antibiotic pharmaceuticals only, antibiotics only and both non-antibiotic pharmaceuticals and antibiotics were prepared based on the single chemical results. The mixture toxicity models were all in close agreement with the experimental results, with predicted EC 50 values within a factor of two of the experimental results. This suggests that concentration addition can be applied to bacterial assays to model the mixture effects of environmental samples containing both specifically and non-specifically acting chemicals. Copyright © 2017 Elsevier Ltd. All rights reserved.

Solidification phenomena of binary organic mixtures

NASA Technical Reports Server (NTRS)

Chang, K.

1982-01-01

The coalescence rates and motion of liquid bubbles in binary organic mixtures were studied. Several factors such as temperature gradient, composition gradient, interfacial tension, and densities of the two phases play important roles in separation of phases of immiscible liquids. An attempt was made to study the effect of initial compositions on separation rates of well-dispersed organic mixtures at different temperatures and, ultimately, on the homogeneity of solidification of the immiscible binary organic liquids. These organic mixtures serve as models for metallic pseudo binary systems under study. Two specific systems were investigated: ethyl salicylate - diethyl glycol and succinonitrile - water.

An EM-based semi-parametric mixture model approach to the regression analysis of competing-risks data.

PubMed

Ng, S K; McLachlan, G J

2003-04-15

We consider a mixture model approach to the regression analysis of competing-risks data. Attention is focused on inference concerning the effects of factors on both the probability of occurrence and the hazard rate conditional on each of the failure types. These two quantities are specified in the mixture model using the logistic model and the proportional hazards model, respectively. We propose a semi-parametric mixture method to estimate the logistic and regression coefficients jointly, whereby the component-baseline hazard functions are completely unspecified. Estimation is based on maximum likelihood on the basis of the full likelihood, implemented via an expectation-conditional maximization (ECM) algorithm. Simulation studies are performed to compare the performance of the proposed semi-parametric method with a fully parametric mixture approach. The results show that when the component-baseline hazard is monotonic increasing, the semi-parametric and fully parametric mixture approaches are comparable for mildly and moderately censored samples. When the component-baseline hazard is not monotonic increasing, the semi-parametric method consistently provides less biased estimates than a fully parametric approach and is comparable in efficiency in the estimation of the parameters for all levels of censoring. The methods are illustrated using a real data set of prostate cancer patients treated with different dosages of the drug diethylstilbestrol. Copyright 2003 John Wiley & Sons, Ltd.

Establishment method of a mixture model and its practical application for transmission gears in an engineering vehicle

NASA Astrophysics Data System (ADS)

Wang, Jixin; Wang, Zhenyu; Yu, Xiangjun; Yao, Mingyao; Yao, Zongwei; Zhang, Erping

2012-09-01

Highly versatile machines, such as wheel loaders, forklifts, and mining haulers, are subject to many kinds of working conditions, as well as indefinite factors that lead to the complexity of the load. The load probability distribution function (PDF) of transmission gears has many distributions centers; thus, its PDF cannot be well represented by just a single-peak function. For the purpose of representing the distribution characteristics of the complicated phenomenon accurately, this paper proposes a novel method to establish a mixture model. Based on linear regression models and correlation coefficients, the proposed method can be used to automatically select the best-fitting function in the mixture model. Coefficient of determination, the mean square error, and the maximum deviation are chosen and then used as judging criteria to describe the fitting precision between the theoretical distribution and the corresponding histogram of the available load data. The applicability of this modeling method is illustrated by the field testing data of a wheel loader. Meanwhile, the load spectra based on the mixture model are compiled. The comparison results show that the mixture model is more suitable for the description of the load-distribution characteristics. The proposed research improves the flexibility and intelligence of modeling, reduces the statistical error and enhances the fitting accuracy, and the load spectra complied by this method can better reflect the actual load characteristic of the gear component.

Advanced stability indicating chemometric methods for quantitation of amlodipine and atorvastatin in their quinary mixture with acidic degradation products.

PubMed

Darwish, Hany W; Hassan, Said A; Salem, Maissa Y; El-Zeany, Badr A

2016-02-05

Two advanced, accurate and precise chemometric methods are developed for the simultaneous determination of amlodipine besylate (AML) and atorvastatin calcium (ATV) in the presence of their acidic degradation products in tablet dosage forms. The first method was Partial Least Squares (PLS-1) and the second was Artificial Neural Networks (ANN). PLS was compared to ANN models with and without variable selection procedure (genetic algorithm (GA)). For proper analysis, a 5-factor 5-level experimental design was established resulting in 25 mixtures containing different ratios of the interfering species. Fifteen mixtures were used as calibration set and the other ten mixtures were used as validation set to validate the prediction ability of the suggested models. The proposed methods were successfully applied to the analysis of pharmaceutical tablets containing AML and ATV. The methods indicated the ability of the mentioned models to solve the highly overlapped spectra of the quinary mixture, yet using inexpensive and easy to handle instruments like the UV-VIS spectrophotometer. Copyright © 2015 Elsevier B.V. All rights reserved.

Combined acute ecotoxicity of malathion and deltamethrin to Daphnia magna (Crustacea, Cladocera): comparison of different data analysis approaches.

PubMed

Toumi, Héla; Boumaiza, Moncef; Millet, Maurice; Radetski, Claudemir Marcos; Camara, Baba Issa; Felten, Vincent; Masfaraud, Jean-François; Férard, Jean-François

2018-04-19

We studied the combined acute effect (i.e., after 48 h) of deltamethrin (a pyrethroid insecticide) and malathion (an organophosphate insecticide) on Daphnia magna. Two approaches were used to examine the potential interaction effects of eight mixtures of deltamethrin and malathion: (i) calculation of mixture toxicity index (MTI) and safety factor index (SFI) and (ii) response surface methodology coupled with isobole-based statistical model (using generalized linear model). According to the calculation of MTI and SFI, one tested mixture was found additive while the two other tested mixtures were found no additive (MTI) or antagonistic (SFI), but these differences between index responses are only due to differences in terminology related to these two indexes. Through the surface response approach and isobologram analysis, we concluded that there was a significant antagonistic effect of the binary mixtures of deltamethrin and malathion that occurs on D. magna immobilization, after 48 h of exposure. Index approaches and surface response approach with isobologram analysis are complementary. Calculation of mixture toxicity index and safety factor index allows identifying punctually the type of interaction for several tested mixtures, while the surface response approach with isobologram analysis integrates all the data providing a global outcome about the type of interactive effect. Only the surface response approach and isobologram analysis allowed the statistical assessment of the ecotoxicological interaction. Nevertheless, we recommend the use of both approaches (i) to identify the combined effects of contaminants and (ii) to improve risk assessment and environmental management.

A mixture model with a reference-based automatic selection of components for disease classification from protein and/or gene expression levels

PubMed Central

2011-01-01

Background Bioinformatics data analysis is often using linear mixture model representing samples as additive mixture of components. Properly constrained blind matrix factorization methods extract those components using mixture samples only. However, automatic selection of extracted components to be retained for classification analysis remains an open issue. Results The method proposed here is applied to well-studied protein and genomic datasets of ovarian, prostate and colon cancers to extract components for disease prediction. It achieves average sensitivities of: 96.2 (sd = 2.7%), 97.6% (sd = 2.8%) and 90.8% (sd = 5.5%) and average specificities of: 93.6% (sd = 4.1%), 99% (sd = 2.2%) and 79.4% (sd = 9.8%) in 100 independent two-fold cross-validations. Conclusions We propose an additive mixture model of a sample for feature extraction using, in principle, sparseness constrained factorization on a sample-by-sample basis. As opposed to that, existing methods factorize complete dataset simultaneously. The sample model is composed of a reference sample representing control and/or case (disease) groups and a test sample. Each sample is decomposed into two or more components that are selected automatically (without using label information) as control specific, case specific and not differentially expressed (neutral). The number of components is determined by cross-validation. Automatic assignment of features (m/z ratios or genes) to particular component is based on thresholds estimated from each sample directly. Due to the locality of decomposition, the strength of the expression of each feature across the samples can vary. Yet, they will still be allocated to the related disease and/or control specific component. Since label information is not used in the selection process, case and control specific components can be used for classification. That is not the case with standard factorization methods. Moreover, the component selected by proposed method as disease specific can be interpreted as a sub-mode and retained for further analysis to identify potential biomarkers. As opposed to standard matrix factorization methods this can be achieved on a sample (experiment)-by-sample basis. Postulating one or more components with indifferent features enables their removal from disease and control specific components on a sample-by-sample basis. This yields selected components with reduced complexity and generally, it increases prediction accuracy. PMID:22208882

Interactions and Toxicity of Cu-Zn mixtures to Hordeum vulgare in Different Soils Can Be Rationalized with Bioavailability-Based Prediction Models.

PubMed

Qiu, Hao; Versieren, Liske; Rangel, Georgina Guzman; Smolders, Erik

2016-01-19

Soil contamination with copper (Cu) is often associated with zinc (Zn), and the biological response to such mixed contamination is complex. Here, we investigated Cu and Zn mixture toxicity to Hordeum vulgare in three different soils, the premise being that the observed interactions are mainly due to effects on bioavailability. The toxic effect of Cu and Zn mixtures on seedling root elongation was more than additive (i.e., synergism) in soils with high and medium cation-exchange capacity (CEC) but less than additive (antagonism) in a low-CEC soil. This was found when we expressed the dose as the conventional total soil concentration. In contrast, antagonism was found in all soils when we expressed the dose as free-ion activities in soil solution, indicating that there is metal-ion competition for binding to the plant roots. Neither a concentration addition nor an independent action model explained mixture effects, irrespective of the dose expressions. In contrast, a multimetal BLM model and a WHAM-Ftox model successfully explained the mixture effects across all soils and showed that bioavailability factors mainly explain the interactions in soils. The WHAM-Ftox model is a promising tool for the risk assessment of mixed-metal contamination in soils.

Determining of migraine prognosis using latent growth mixture models.

PubMed

Tasdelen, Bahar; Ozge, Aynur; Kaleagasi, Hakan; Erdogan, Semra; Mengi, Tufan

2011-04-01

This paper presents a retrospective study to classify patients into subtypes of the treatment according to baseline and longitudinally observed values considering heterogenity in migraine prognosis. In the classical prospective clinical studies, participants are classified with respect to baseline status and followed within a certain time period. However, latent growth mixture model is the most suitable method, which considers the population heterogenity and is not affected drop-outs if they are missing at random. Hence, we planned this comprehensive study to identify prognostic factors in migraine. The study data have been based on a 10-year computer-based follow-up data of Mersin University Headache Outpatient Department. The developmental trajectories within subgroups were described for the severity, frequency, and duration of headache separately and the probabilities of each subgroup were estimated by using latent growth mixture models. SAS PROC TRAJ procedures, semiparametric and group-based mixture modeling approach, were applied to define the developmental trajectories. While the three-group model for the severity (mild, moderate, severe) and frequency (low, medium, high) of headache appeared to be appropriate, the four-group model for the duration (low, medium, high, extremely high) was more suitable. The severity of headache increased in the patients with nausea, vomiting, photophobia and phonophobia. The frequency of headache was especially related with increasing age and unilateral pain. Nausea and photophobia were also related with headache duration. Nausea, vomiting and photophobia were the most significant factors to identify developmental trajectories. The remission time was not the same for the severity, frequency, and duration of headache.

Support vector regression and artificial neural network models for stability indicating analysis of mebeverine hydrochloride and sulpiride mixtures in pharmaceutical preparation: A comparative study

NASA Astrophysics Data System (ADS)

Naguib, Ibrahim A.; Darwish, Hany W.

2012-02-01

A comparison between support vector regression (SVR) and Artificial Neural Networks (ANNs) multivariate regression methods is established showing the underlying algorithm for each and making a comparison between them to indicate the inherent advantages and limitations. In this paper we compare SVR to ANN with and without variable selection procedure (genetic algorithm (GA)). To project the comparison in a sensible way, the methods are used for the stability indicating quantitative analysis of mixtures of mebeverine hydrochloride and sulpiride in binary mixtures as a case study in presence of their reported impurities and degradation products (summing up to 6 components) in raw materials and pharmaceutical dosage form via handling the UV spectral data. For proper analysis, a 6 factor 5 level experimental design was established resulting in a training set of 25 mixtures containing different ratios of the interfering species. An independent test set consisting of 5 mixtures was used to validate the prediction ability of the suggested models. The proposed methods (linear SVR (without GA) and linear GA-ANN) were successfully applied to the analysis of pharmaceutical tablets containing mebeverine hydrochloride and sulpiride mixtures. The results manifest the problem of nonlinearity and how models like the SVR and ANN can handle it. The methods indicate the ability of the mentioned multivariate calibration models to deconvolute the highly overlapped UV spectra of the 6 components' mixtures, yet using cheap and easy to handle instruments like the UV spectrophotometer.

A study of the kinetics and isotherms for Cr(VI) adsorption in a binary mixture of Cr(VI)-Ni(II) using hierarchical porous carbon obtained from pig bone.

PubMed

Li, Chengxian; Huang, Zhe; Huang, Bicheng; Liu, Changfeng; Li, Chengming; Huang, Yaqin

2014-01-01

Cr(VI) adsorption in a binary mixture Cr(VI)-Ni(II) using the hierarchical porous carbon prepared from pig bone (HPC) was investigated. The various factors affecting adsorption of Cr(VI) ions from aqueous solutions such as initial concentration, pH, temperature and contact time were analyzed. The results showed excellent efficiency of Cr(VI) adsorption by HPC. The kinetics and isotherms for Cr(VI) adsorption from a binary mixture Cr(VI)-Ni(II) by HPC were studied. The adsorption equilibrium described by the Langmuir isotherm model is better than that described by the Freundlich isotherm model for the binary mixture in this study. The maximum adsorption capacity was reliably found to be as high as 192.68 mg/g in the binary mixture at pH 2. On fitting the experimental data to both pseudo-first- and second-order equations, the regression analysis of the second-order equation gave a better R² value.

Investigation of Profiles of Risk Factors for Adolescent Psychopathology: A Person-Centered Approach

ERIC Educational Resources Information Center

Parra, Gilbert R.; DuBois, David L.; Sher, Kenneth J.

2006-01-01

Latent variable mixture modeling was used to identify subgroups of adolescents with distinct profiles of risk factors from individual, family, peer, and broader contextual domains. Data were drawn from the National Longitudinal Study of Adolescent Health. Four-class models provided the most theoretically meaningful solutions for both 7th (n = 907;…

Negative Binomial Process Count and Mixture Modeling.

PubMed

Zhou, Mingyuan; Carin, Lawrence

2015-02-01

The seemingly disjoint problems of count and mixture modeling are united under the negative binomial (NB) process. A gamma process is employed to model the rate measure of a Poisson process, whose normalization provides a random probability measure for mixture modeling and whose marginalization leads to an NB process for count modeling. A draw from the NB process consists of a Poisson distributed finite number of distinct atoms, each of which is associated with a logarithmic distributed number of data samples. We reveal relationships between various count- and mixture-modeling distributions and construct a Poisson-logarithmic bivariate distribution that connects the NB and Chinese restaurant table distributions. Fundamental properties of the models are developed, and we derive efficient Bayesian inference. It is shown that with augmentation and normalization, the NB process and gamma-NB process can be reduced to the Dirichlet process and hierarchical Dirichlet process, respectively. These relationships highlight theoretical, structural, and computational advantages of the NB process. A variety of NB processes, including the beta-geometric, beta-NB, marked-beta-NB, marked-gamma-NB and zero-inflated-NB processes, with distinct sharing mechanisms, are also constructed. These models are applied to topic modeling, with connections made to existing algorithms under Poisson factor analysis. Example results show the importance of inferring both the NB dispersion and probability parameters.

A Concentration Addition Model to Assess Activation of the Pregnane X Receptor (PXR) by Pesticide Mixtures Found in the French Diet

PubMed Central

de Sousa, Georges; Nawaz, Ahmad; Cravedi, Jean-Pierre; Rahmani, Roger

2014-01-01

French consumers are exposed to mixtures of pesticide residues in part through food consumption. As a xenosensor, the pregnane X receptor (hPXR) is activated by numerous pesticides, the combined effect of which is currently unknown. We examined the activation of hPXR by seven pesticide mixtures most likely found in the French diet and their individual components. The mixture's effect was estimated using the concentration addition (CA) model. PXR transactivation was measured by monitoring luciferase activity in hPXR/HepG2 cells and CYP3A4 expression in human hepatocytes. The three mixtures with the highest potency were evaluated using the CA model, at equimolar concentrations and at their relative proportion in the diet. The seven mixtures significantly activated hPXR and induced the expression of CYP3A4 in human hepatocytes. Of the 14 pesticides which constitute the three most active mixtures, four were found to be strong hPXR agonists, four medium, and six weak. Depending on the mixture and pesticide proportions, additive, greater than additive or less than additive effects between compounds were demonstrated. Predictions of the combined effects were obtained with both real-life and equimolar proportions at low concentrations. Pesticides act mostly additively to activate hPXR, when present in a mixture. Modulation of hPXR activation and its target genes induction may represent a risk factor contributing to exacerbate the physiological response of the hPXR signaling pathways and to explain some adverse effects in humans. PMID:25028461

Hygroscopicity of organic surrogate compounds from biomass burning and their effect on the efflorescence of ammonium sulfate in mixed aerosol particles

NASA Astrophysics Data System (ADS)

Lei, Ting; Zuend, Andreas; Cheng, Yafang; Su, Hang; Wang, Weigang; Ge, Maofa

2018-01-01

Hygroscopic growth factors of organic surrogate compounds representing biomass burning and mixed organic-inorganic aerosol particles exhibit variability during dehydration experiments depending on their chemical composition, which we observed using a hygroscopicity tandem differential mobility analyzer (HTDMA). We observed that levoglucosan and humic acid aerosol particles release water upon dehumidification in the range from 90 to 5 % relative humidity (RH). However, 4-Hydroxybenzoic acid aerosol particles remain in the solid state upon dehumidification and exhibit a small shrinking in size at higher RH compared to the dry size. For example, the measured growth factor of 4-hyroxybenzoic acid aerosol particles is ˜ 0.96 at 90 % RH. The measurements were accompanied by RH-dependent thermodynamic equilibrium calculations using the Aerosol Inorganic-Organic Mixtures Functional groups Activity Coefficients (AIOMFAC) model and Extended Aerosol Inorganics Model (E-AIM), the Zdanovskii-Stokes-Robinson (ZSR) relation, and a fitted hygroscopicity expression. We observed several effects of organic components on the hygroscopicity behavior of mixtures containing ammonium sulfate (AS) in relation to the different mass fractions of organic compounds: (1) a shift of efflorescence relative humidity (ERH) of ammonium sulfate to higher RH due to the presence of 25 wt % levoglucosan in the mixture. (2) There is a distinct efflorescence transition at 25 % RH for mixtures consisting of 25 wt % of 4-hydroxybenzoic acid compared to the ERH at 35 % for organic-free AS particles. (3) There is indication for a liquid-to-solid phase transition of 4-hydroxybenzoic acid in the mixed particles during dehydration. (4) A humic acid component shows no significant effect on the efflorescence of AS in mixed aerosol particles. In addition, consideration of a composition-dependent degree of dissolution of crystallization AS (solid-liquid equilibrium) in the AIOMFAC and E-AIM models leads to a relatively good agreement between models and observed growth factors, as well as ERH of AS in the mixed system. The use of the ZSR relation leads to good agreement with measured diameter growth factors of aerosol particles containing humic acid and ammonium sulfate. Lastly, two distinct mixtures of organic surrogate compounds, including levoglucosan, 4-hydroxybenzoic acid, and humic acid, were used to represent the average water-soluble organic carbon (WSOC) fractions observed during the wet and dry seasons in the central Amazon Basin. A comparison of the organic fraction's hygroscopicity parameter for the simple mixtures, e.g., κ ≈ 0.12 to 0.15 for the wet-season mixture in the 90 to 40 % RH range, shows good agreement with field data for the wet season in the Amazon Basin (WSOC κ ≈ 0.14±0.06 at 90 % RH). This suggests that laboratory-generated mixtures containing organic surrogate compounds and ammonium sulfate can be used to mimic, in a simplified manner, the chemical composition of ambient aerosols from the Amazon Basin for the purpose of RH-dependent hygroscopicity studies.

Mixture effects of benzene, toluene, ethylbenzene, and xylenes (BTEX) on lung carcinoma cells via a hanging drop air exposure system.

PubMed

Liu, Faye F; Escher, Beate I; Were, Stephen; Duffy, Lesley; Ng, Jack C

2014-06-16

A recently developed hanging drop air exposure system for toxicity studies of volatile chemicals was applied to evaluate the cell viability of lung carcinoma A549 cells after 1 and 24 h of exposure to benzene, toluene, ethylbenzene, and xylenes (BTEX) as individual compounds and as mixtures of four or six components. The cellular chemical concentrations causing 50% reduction of cell viability (EC50) were calculated using a mass balance model and came to 17, 12, 11, 9, 4, and 4 mmol/kg cell dry weight for benzene, toluene, ethylbenzene, m-xylene, o-xylene, and p-xylene, respectively, after 1 h of exposure. The EC50 decreased by a factor of 4 after 24 h of exposure. All mixture effects were best described by the mixture toxicity model of concentration addition, which is valid for chemicals with the same mode of action. Good agreement with the model predictions was found for benzene, toluene, ethylbenzene, and m-xylene at four different representative fixed concentration ratios after 1 h of exposure, but lower agreement with mixture prediction was obtained after 24 h of exposure. A recreated car exhaust mixture, which involved the contribution of the more toxic p-xylene and o-xylene, yielded an acceptable, but lower quality, prediction as well.

Mutual diffusion of binary liquid mixtures containing methanol, ethanol, acetone, benzene, cyclohexane, toluene, and carbon tetrachloride

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

Guevara-Carrion, Gabriela; Janzen, Tatjana; Muñoz-Muñoz, Y. Mauricio

Mutual diffusion coefficients of all 20 binary liquid mixtures that can be formed out of methanol, ethanol, acetone, benzene, cyclohexane, toluene, and carbon tetrachloride without a miscibility gap are studied at ambient conditions of temperature and pressure in the entire composition range. The considered mixtures show a varying mixing behavior from almost ideal to strongly non-ideal. Predictive molecular dynamics simulations employing the Green-Kubo formalism are carried out. Radial distribution functions are analyzed to gain an understanding of the liquid structure influencing the diffusion processes. It is shown that cluster formation in mixtures containing one alcoholic component has a significant impactmore » on the diffusion process. The estimation of the thermodynamic factor from experimental vapor-liquid equilibrium data is investigated, considering three excess Gibbs energy models, i.e., Wilson, NRTL, and UNIQUAC. It is found that the Wilson model yields the thermodynamic factor that best suits the simulation results for the prediction of the Fick diffusion coefficient. Four semi-empirical methods for the prediction of the self-diffusion coefficients and nine predictive equations for the Fick diffusion coefficient are assessed and it is found that methods based on local composition models are more reliable. Finally, the shear viscosity and thermal conductivity are predicted and in most cases favorably compared with experimental literature values.« less

Probabilistic modeling of bifurcations in single-cell gene expression data using a Bayesian mixture of factor analyzers.

PubMed

Campbell, Kieran R; Yau, Christopher

2017-03-15

Modeling bifurcations in single-cell transcriptomics data has become an increasingly popular field of research. Several methods have been proposed to infer bifurcation structure from such data, but all rely on heuristic non-probabilistic inference. Here we propose the first generative, fully probabilistic model for such inference based on a Bayesian hierarchical mixture of factor analyzers. Our model exhibits competitive performance on large datasets despite implementing full Markov-Chain Monte Carlo sampling, and its unique hierarchical prior structure enables automatic determination of genes driving the bifurcation process. We additionally propose an Empirical-Bayes like extension that deals with the high levels of zero-inflation in single-cell RNA-seq data and quantify when such models are useful. We apply or model to both real and simulated single-cell gene expression data and compare the results to existing pseudotime methods. Finally, we discuss both the merits and weaknesses of such a unified, probabilistic approach in the context practical bioinformatics analyses.

Impact of Lead Time and Safety Factor in Mixed Inventory Models with Backorder Discounts

NASA Astrophysics Data System (ADS)

Lo, Ming-Cheng; Chao-Hsien Pan, Jason; Lin, Kai-Cing; Hsu, Jia-Wei

This study investigates the impact of safety factor on the continuous review inventory model involving controllable lead time with mixture of backorder discount and partial lost sales. The objective is to minimize the expected total annual cost with respect to order quantity, backorder price discount, safety factor and lead time. A model with normal demand is also discussed. Numerical examples are presented to illustrate the procedures of the algorithms and the effects of parameters on the result of the proposed models are analyzed.

Constituent bioconcentration in rainbow trout exposed to a complex chemical mixture

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

Linder, G.; Bergman, H.L.; Meyer, J.S.

1984-09-01

Classically, aquatic contaminant fate models predicting a chemical's bioconcentration factor (BCF) are based upon single-compound derived models, yet such BCF predictions may deviate from observed BCFs when physicochemical interactions or biological responses to complex chemical mixture exposures are not adequately considered in the predictive model. Rainbow trout were exposed to oil-shale retort waters. Such a study was designed to model the potential biological effects precluded by exposure to complex chemical mixtures such as solid waste leachates, agricultural runoff, and industrial process waste waters. Chromatographic analysis of aqueous and nonaqueous liquid-liquid reservoir components yielded differences in mixed extraction solvent HPLC profilesmore » of whole fish exposed for 1 and 3 weeks to the highest dilution of the complex chemical mixture when compared to their corresponding control, yet subsequent whole fish extractions at 6, 9, 12, and 15 weeks into exposure demonstrated no qualitative differences between control and exposed fish. Liver extractions and deproteinized bile samples from exposed fish were qualitatively different than their corresponding controls. These findings support the projected NOEC of 0.0045% dilution, even though the differences in bioconcentration profiles suggest hazard assessment strategies may be useful in evaluating environmental fate processes associated with complex chemical mixtures. 12 references, 4 figures, 2 tables.« less

Screening level mixture risk assessment of pharmaceuticals in STP effluents.

PubMed

Backhaus, Thomas; Karlsson, Maja

2014-02-01

We modeled the ecotoxicological risks of the pharmaceutical mixtures emitted from STP effluents into the environment. The classic mixture toxicity concept of Concentration Addition was used to calculate the total expected risk of the analytically determined mixtures, compare the expected impact of seven effluent streams and pinpoint the most sensitive group of species. The risk quotient of a single, randomly selected pharmaceutical is often more than a factor of 1000 lower than the mixture risk, clearly indicating the need to systematically analyse the overall risk of all pharmaceuticals present. The MCR, which is the ratio between the most risky compound and the total mixture risk, varies between 1.2 and 4.2, depending on the actual scenario and species group under consideration. The mixture risk quotients, based on acute data and an assessment factor of 1000, regularly exceed 1, indicating a potential risk for the environment, depending on the dilution in the recipient stream. The top 10 mixture components explain more than 95% of the mixture risk in all cases. A mixture toxicity assessment cannot go beyond the underlying single substance data. The lack of data on the chronic toxicity of most pharmaceuticals as well as the very few data available for in vivo fish toxicity has to be regarded as a major knowledge gap in this context. On the other hand, ignoring Independent Action or even using the sum of individual risk quotients as a rough approximation of Concentration Addition does not have a major impact on the final risk estimate. Copyright © 2013 Elsevier Ltd. All rights reserved.

A NEW METHOD OF PEAK DETECTION FOR ANALYSIS OF COMPREHENSIVE TWO-DIMENSIONAL GAS CHROMATOGRAPHY MASS SPECTROMETRY DATA.

PubMed

Kim, Seongho; Ouyang, Ming; Jeong, Jaesik; Shen, Changyu; Zhang, Xiang

2014-06-01

We develop a novel peak detection algorithm for the analysis of comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOF MS) data using normal-exponential-Bernoulli (NEB) and mixture probability models. The algorithm first performs baseline correction and denoising simultaneously using the NEB model, which also defines peak regions. Peaks are then picked using a mixture of probability distribution to deal with the co-eluting peaks. Peak merging is further carried out based on the mass spectral similarities among the peaks within the same peak group. The algorithm is evaluated using experimental data to study the effect of different cut-offs of the conditional Bayes factors and the effect of different mixture models including Poisson, truncated Gaussian, Gaussian, Gamma, and exponentially modified Gaussian (EMG) distributions, and the optimal version is introduced using a trial-and-error approach. We then compare the new algorithm with two existing algorithms in terms of compound identification. Data analysis shows that the developed algorithm can detect the peaks with lower false discovery rates than the existing algorithms, and a less complicated peak picking model is a promising alternative to the more complicated and widely used EMG mixture models.

Experimental designs and risk assessment in combination toxicology: panel discussion.

PubMed

Henschler, D; Bolt, H M; Jonker, D; Pieters, M N; Groten, J P

1996-01-01

Advancing our knowledge on the toxicology of combined exposures to chemicals and implementation of this knowledge in guidelines for health risk assessment of such combined exposures are necessities dictated by the simple fact that humans are continuously exposed to a multitude of chemicals. A prerequisite for successful research and fruitful discussions on the toxicology of combined exposures (mixtures of chemicals) is the use of defined terminology implemented by an authoritative international body such as, for example, the International Union of Pure and Applied Chemistry (IUPAC) Toxicology Committee. The extreme complexity of mixture toxicology calls for new research methodologies to study interactive effects, taking into account limited resources. Of these methodologies, statistical designs and mathematical modelling of toxicokinetics and toxicodynamics seem to be most promising. Emphasis should be placed on low-dose modelling and experimental validation. The scientifically sound so-called bottom-up approach should be supplemented with more pragmatic approaches, focusing on selection of the most hazardous chemicals in a mixture and careful consideration of the mode of action and possible interactive effects of these chemicals. Pragmatic approaches may be of particular importance to study and evaluate complex mixtures; after identification of the 'top ten' (most risky) chemicals in the mixture they can be examined and evaluated as a defined (simple) chemical mixture. In setting exposure limits for individual chemicals, the use of an additional safety factor to compensate for potential increased risk due to simultaneous exposure to other chemicals, has no clear scientific justification. The use of such an additional factor is a political rather than a scientific choice.

Delineating Cultural Models: Extending the Cultural Mixture Model

DTIC Science & Technology

2011-12-01

identify appropriate therapies or interventions, and provide insight into their behaviors. Personality factors are typically developed through...us information on if they like radishes too. Perhaps there is another group of people who predominantly like chocolate ice cream and carrots

Finite mixture modeling approach for developing crash modification factors in highway safety analysis.

PubMed

Park, Byung-Jung; Lord, Dominique; Wu, Lingtao

2016-10-28

This study aimed to investigate the relative performance of two models (negative binomial (NB) model and two-component finite mixture of negative binomial models (FMNB-2)) in terms of developing crash modification factors (CMFs). Crash data on rural multilane divided highways in California and Texas were modeled with the two models, and crash modification functions (CMFunctions) were derived. The resultant CMFunction estimated from the FMNB-2 model showed several good properties over that from the NB model. First, the safety effect of a covariate was better reflected by the CMFunction developed using the FMNB-2 model, since the model takes into account the differential responsiveness of crash frequency to the covariate. Second, the CMFunction derived from the FMNB-2 model is able to capture nonlinear relationships between covariate and safety. Finally, following the same concept as those for NB models, the combined CMFs of multiple treatments were estimated using the FMNB-2 model. The results indicated that they are not the simple multiplicative of single ones (i.e., their safety effects are not independent under FMNB-2 models). Adjustment Factors (AFs) were then developed. It is revealed that current Highway Safety Manual's method could over- or under-estimate the combined CMFs under particular combination of covariates. Safety analysts are encouraged to consider using the FMNB-2 models for developing CMFs and AFs. Copyright © 2016 Elsevier Ltd. All rights reserved.

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

Almus, F.E.; Rao, L.V.; Fleck, R.A.

An umbilical vein model was designed in which washed vein segments are filled with a reaction mixture containing factor VIIa, Ca(+)+, and a substrate, either 3H-factor IX or 3H-factor X. The vein wall provides the tissue factor (TF) for factor VIIa/TF complexes that activate the substrates as measured by activation peptide release. The model was developed to study TF induced on venous endothelium in situ. However, unlike previous studies with TF expressed on cultured umbilical vein endothelial cells, factors IX and X were activated without first having to expose the vein wall to a perturbing stimulus. Histologic studies revealed thatmore » washing the vein and mixing the reaction mixture before subsampling had disrupted the endothelium. Immunostaining with anti-TF antibodies revealed no staining of endothelium but intense staining in extensions of Wharton's jelly penetrating fenestrations of the muscularis media of the vein. Thus, the model provided data on factor VIIa/TF formed, not on endothelium, but within the mucoid connective tissue of Wharton's jelly. It is known that factor VIIa/TF formed with TF in suspension or with TF expressed on the surface of cultured cells activates factor X more rapidly than factor IX. In contrast, in the umbilical vein model, when each substrate was present in an 88 nmol/L concentration, factors IX and X were activated at equivalent rates (mean activation rate for factor IX, 18.8 +/- 3.6 nmol/L/h; for factor X, 17.8 +/- 2.9 nmol/L/h; n = 9 paired vein segments). These data strengthen the evidence that factor VIIa/TF activation of factor IX represents a key initial reaction of coagulation in tissues. These results also show that data obtained with factor VIIa/TF complexes formed on the surface of cultured cells need not hold for factor VIIa/TF complexes formed in extracellular matrix.« less

Research on Bayes matting algorithm based on Gaussian mixture model

NASA Astrophysics Data System (ADS)

Quan, Wei; Jiang, Shan; Han, Cheng; Zhang, Chao; Jiang, Zhengang

2015-12-01

The digital matting problem is a classical problem of imaging. It aims at separating non-rectangular foreground objects from a background image, and compositing with a new background image. Accurate matting determines the quality of the compositing image. A Bayesian matting Algorithm Based on Gaussian Mixture Model is proposed to solve this matting problem. Firstly, the traditional Bayesian framework is improved by introducing Gaussian mixture model. Then, a weighting factor is added in order to suppress the noises of the compositing images. Finally, the effect is further improved by regulating the user's input. This algorithm is applied to matting jobs of classical images. The results are compared to the traditional Bayesian method. It is shown that our algorithm has better performance in detail such as hair. Our algorithm eliminates the noise well. And it is very effectively in dealing with the kind of work, such as interested objects with intricate boundaries.

Effect of the Key Mixture Parameters on Shrinkage of Reactive Powder Concrete

PubMed Central

Zubair, Ahmed

2014-01-01

Reactive powder concrete (RPC) mixtures are reported to have excellent mechanical and durability characteristics. However, such concrete mixtures having high amount of cementitious materials may have high early shrinkage causing cracking of concrete. In the present work, an attempt has been made to study the simultaneous effects of three key mixture parameters on shrinkage of the RPC mixtures. Considering three different levels of the three key mixture factors, a total of 27 mixtures of RPC were prepared according to 33 factorial experiment design. The specimens belonging to all 27 mixtures were monitored for shrinkage at different ages over a total period of 90 days. The test results were plotted to observe the variation of shrinkage with time and to see the effects of the key mixture factors. The experimental data pertaining to 90-day shrinkage were used to conduct analysis of variance to identify significance of each factor and to obtain an empirical equation correlating the shrinkage of RPC with the three key mixture factors. The rate of development of shrinkage at early ages was higher. The water to binder ratio was found to be the most prominent factor followed by cement content with the least effect of silica fume content. PMID:25050395

Effect of the key mixture parameters on shrinkage of reactive powder concrete.

PubMed

Ahmad, Shamsad; Zubair, Ahmed; Maslehuddin, Mohammed

2014-01-01

Reactive powder concrete (RPC) mixtures are reported to have excellent mechanical and durability characteristics. However, such concrete mixtures having high amount of cementitious materials may have high early shrinkage causing cracking of concrete. In the present work, an attempt has been made to study the simultaneous effects of three key mixture parameters on shrinkage of the RPC mixtures. Considering three different levels of the three key mixture factors, a total of 27 mixtures of RPC were prepared according to 3(3) factorial experiment design. The specimens belonging to all 27 mixtures were monitored for shrinkage at different ages over a total period of 90 days. The test results were plotted to observe the variation of shrinkage with time and to see the effects of the key mixture factors. The experimental data pertaining to 90-day shrinkage were used to conduct analysis of variance to identify significance of each factor and to obtain an empirical equation correlating the shrinkage of RPC with the three key mixture factors. The rate of development of shrinkage at early ages was higher. The water to binder ratio was found to be the most prominent factor followed by cement content with the least effect of silica fume content.

Disentangling the effects of low pH and metal mixture toxicity on macroinvertebrate diversity

USGS Publications Warehouse

Fornaroli, Riccardo; Ippolito, Alessio; Tolkkinen, Mari J.; Mykrä, Heikki; Muotka, Timo; Balistrieri, Laurie S.; Schmidt, Travis S.

2018-01-01

One of the primary goals of biological assessment of streams is to identify which of a suite of chemical stressors is limiting their ecological potential. Elevated metal concentrations in streams are often associated with low pH, yet the effects of these two potentially limiting factors of freshwater biodiversity are rarely considered to interact beyond the effects of pH on metal speciation. Using a dataset from two continents, a biogeochemical model of the toxicity of metal mixtures (Al, Cd, Cu, Pb, Zn) and quantile regression, we addressed the relative importance of both pH and metals as limiting factors for macroinvertebrate communities. Current environmental quality standards for metals proved to be protective of stream macroinvertebrate communities and were used as a starting point to assess metal mixture toxicity. A model of metal mixture toxicity accounting for metal interactions was a better predictor of macroinvertebrate responses than a model considering individual metal toxicity. We showed that the direct limiting effect of pH on richness was of the same magnitude as that of chronic metal toxicity, independent of its influence on the availability and toxicity of metals. By accounting for the direct effect of pH on macroinvertebrate communities, we were able to determine that acidic streams supported less diverse communities than neutral streams even when metals were below no-effect thresholds. Through a multivariate quantile model, we untangled the limiting effect of both pH and metals and predicted the maximum diversity that could be expected at other sites as a function of these variables. This model can be used to identify which of the two stressors is more limiting to the ecological potential of running waters.

Disentangling the effects of low pH and metal mixture toxicity on macroinvertebrate diversity.

PubMed

Fornaroli, Riccardo; Ippolito, Alessio; Tolkkinen, Mari J; Mykrä, Heikki; Muotka, Timo; Balistrieri, Laurie S; Schmidt, Travis S

2018-04-01

One of the primary goals of biological assessment of streams is to identify which of a suite of chemical stressors is limiting their ecological potential. Elevated metal concentrations in streams are often associated with low pH, yet the effects of these two potentially limiting factors of freshwater biodiversity are rarely considered to interact beyond the effects of pH on metal speciation. Using a dataset from two continents, a biogeochemical model of the toxicity of metal mixtures (Al, Cd, Cu, Pb, Zn) and quantile regression, we addressed the relative importance of both pH and metals as limiting factors for macroinvertebrate communities. Current environmental quality standards for metals proved to be protective of stream macroinvertebrate communities and were used as a starting point to assess metal mixture toxicity. A model of metal mixture toxicity accounting for metal interactions was a better predictor of macroinvertebrate responses than a model considering individual metal toxicity. We showed that the direct limiting effect of pH on richness was of the same magnitude as that of chronic metal toxicity, independent of its influence on the availability and toxicity of metals. By accounting for the direct effect of pH on macroinvertebrate communities, we were able to determine that acidic streams supported less diverse communities than neutral streams even when metals were below no-effect thresholds. Through a multivariate quantile model, we untangled the limiting effect of both pH and metals and predicted the maximum diversity that could be expected at other sites as a function of these variables. This model can be used to identify which of the two stressors is more limiting to the ecological potential of running waters. Copyright © 2018 Elsevier Ltd. All rights reserved.

Health risks associated with inhaled nasal toxicants.

PubMed

Feron, V J; Arts, J H; Kuper, C F; Slootweg, P J; Woutersen, R A

2001-05-01

Health risks of inhaled nasal toxicants were reviewed with emphasis on chemically induced nasal lesions in humans, sensory irritation, olfactory and trigeminal nerve toxicity, nasal immunopathology and carcinogenesis, nasal responses to chemical mixtures, in vitro models, and nasal dosimetry- and metabolism-based extrapolation of nasal data in animals to humans. Conspicuous findings in humans are the effects of outdoor air pollution on the nasal mucosa, and tobacco smoking as a risk factor for sinonasal squamous cell carcinoma. Objective methods in humans to discriminate between sensory irritation and olfactory stimulation and between adaptation and habituation have been introduced successfully, providing more relevant information than sensory irritation studies in animals. Against the background of chemoperception as a dominant window of the brain on the outside world, nasal neurotoxicology is rapidly developing, focusing on olfactory and trigeminal nerve toxicity. Better insight in the processes underlying neurogenic inflammation may increase our knowledge of the causes of the various chemical sensitivity syndromes. Nasal immunotoxicology is extremely complex, which is mainly due to the pivotal role of nasal lymphoid tissue in the defense of the middle ear, eye, and oral cavity against antigenic substances, and the important function of the nasal passages in brain drainage in rats. The crucial role of tissue damage and reactive epithelial hyperproliferation in nasal carcinogenesis has become overwhelmingly clear as demonstrated by the recently developed biologically based model for predicting formaldehyde nasal cancer risk in humans. The evidence of carcinogenicity of inhaled complex mixtures in experimental animals is very limited, while there is ample evidence that occupational exposure to mixtures such as wood, leather, or textile dust or chromium- and nickel-containing materials is associated with increased risk of nasal cancer. It is remarkable that these mixtures are aerosols, suggesting that their "particulate nature" may be a major factor in their potential to induce nasal cancer. Studies in rats have been conducted with defined mixtures of nasal irritants such as aldehydes, using a model for competitive agonism to predict the outcome of such mixed exposures. When exposure levels in a mixture of nasal cytotoxicants were equal to or below the "No-Observed-Adverse-Effect-Levels" (NOAELs) of the individual chemicals, neither additivity nor potentiation was found, indicating that the NOAEL of the "most risky chemical" in the mixture would also be the NOAEL of the mixture. In vitro models are increasingly being used to study mechanisms of nasal toxicity. However, considering the complexity of the nasal cavity and the many factors that contribute to nasal toxicity, it is unlikely that in vitro experiments ever will be substitutes for in vivo inhalation studies. It is widely recognized that a strategic approach should be available for the interpretation of nasal effects in experimental animals with regard to potential human health risk. Mapping of nasal lesions combined with airflow-driven dosimetry and knowledge about local metabolism is a solid basis for extrapolation of animal data to humans. However, more research is needed to better understand factors that determine the susceptibility of human and animal tissues to nasal toxicants, in particular nasal carcinogens.

Ability and Motivation: Assessing Individual Factors that Contribute to University Retention

ERIC Educational Resources Information Center

Alarcon, Gene M.; Edwards, Jean M.

2013-01-01

The current study explored individual differences in ability and motivation factors of retention in first-year college students. We used discrete-time survival mixture analysis to model university retention. Parents' education, gender, American College Test (ACT) scores, conscientiousness, and trait affectivity were explored as predictors of…

Use of Factor Mixture Modeling to Capture Spearman's Law of Diminishing Returns

ERIC Educational Resources Information Center

Reynolds, Matthew R.; Keith, Timothy Z.; Beretvas, S. Natasha

2010-01-01

Spearman's law of diminishing returns (SLODR) posits that at higher levels of general cognitive ability the general factor ("g") performs less well in explaining individual differences in cognitive test performance. Research has generally supported SLODR, but previous research has required the a priori division of respondents into…

Combined effects of pharmaceuticals, personal care products, biocides and organic contaminants on the growth of Skeletonema pseudocostatum.

PubMed

Petersen, Karina; Heiaas, Harald Hasle; Tollefsen, Knut Erik

2014-05-01

Organisms in the environment are exposed to a number of pollutants from different compound groups. In addition to the classic pollutants like the polychlorinated biphenyls, polyaromatic hydrocarbons (PAHs), alkylphenols, biocides, etc. other compound groups of concern are constantly emerging. Pharmaceuticals and personal care products (PPCPs) can be expected to co-occur with other organic contaminants like biocides, PAHs and alkylphenols in areas affected by wastewater, industrial effluents and intensive recreational activity. In this study, representatives from these four different compound groups were tested individually and in mixtures in a growth inhibition assay with the marine algae Skeletonema pseudocostatum (formerly Skeletonema costatum) to determine whether the combined effects could be predicted by models for additive effects; the concentration addition (CA) and independent action (IA) prediction model. The eleven tested compounds reduced the growth of S. pseudocostatum in the microplate test in a concentration-dependent manner. The order of toxicity of these chemicals were irgarol>fluoxetine>diuron>benzo(a)pyrene>thioguanine>triclosan>propranolol>benzophenone 3>cetrimonium bromide>4-tert-octylphenol>endosulfan. Several binary mixtures and a mixture of eight compounds from the four different compound groups were tested. All tested mixtures were additive as model deviation ratios, the deviation between experimental and predicted effect concentrations, were within a factor of 2 from one or both prediction models (e.g. CA and IA). Interestingly, a concentration dependent shift from IA to CA, potentially due to activation of similar toxicity pathways at higher concentrations, was observed for the mixture of eight compounds. The combined effects of the multi-compound mixture were clearly additive and it should therefore be expected that PPCPs, biocides, PAHs and alkylphenols will collectively contribute to the risk in areas contaminated by such complex mixtures. Copyright © 2014 Elsevier B.V. All rights reserved.

Reducing computation in an i-vector speaker recognition system using a tree-structured universal background model

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

McClanahan, Richard; De Leon, Phillip L.

The majority of state-of-the-art speaker recognition systems (SR) utilize speaker models that are derived from an adapted universal background model (UBM) in the form of a Gaussian mixture model (GMM). This is true for GMM supervector systems, joint factor analysis systems, and most recently i-vector systems. In all of the identified systems, the posterior probabilities and sufficient statistics calculations represent a computational bottleneck in both enrollment and testing. We propose a multi-layered hash system, employing a tree-structured GMM–UBM which uses Runnalls’ Gaussian mixture reduction technique, in order to reduce the number of these calculations. Moreover, with this tree-structured hash, wemore » can trade-off reduction in computation with a corresponding degradation of equal error rate (EER). As an example, we also reduce this computation by a factor of 15× while incurring less than 10% relative degradation of EER (or 0.3% absolute EER) when evaluated with NIST 2010 speaker recognition evaluation (SRE) telephone data.« less

Reducing computation in an i-vector speaker recognition system using a tree-structured universal background model

DOE PAGES

McClanahan, Richard; De Leon, Phillip L.

2014-08-20

The majority of state-of-the-art speaker recognition systems (SR) utilize speaker models that are derived from an adapted universal background model (UBM) in the form of a Gaussian mixture model (GMM). This is true for GMM supervector systems, joint factor analysis systems, and most recently i-vector systems. In all of the identified systems, the posterior probabilities and sufficient statistics calculations represent a computational bottleneck in both enrollment and testing. We propose a multi-layered hash system, employing a tree-structured GMM–UBM which uses Runnalls’ Gaussian mixture reduction technique, in order to reduce the number of these calculations. Moreover, with this tree-structured hash, wemore » can trade-off reduction in computation with a corresponding degradation of equal error rate (EER). As an example, we also reduce this computation by a factor of 15× while incurring less than 10% relative degradation of EER (or 0.3% absolute EER) when evaluated with NIST 2010 speaker recognition evaluation (SRE) telephone data.« less

Bayesian Variable Selection for Hierarchical Gene-Environment and Gene-Gene Interactions

PubMed Central

Liu, Changlu; Ma, Jianzhong; Amos, Christopher I.

2014-01-01

We propose a Bayesian hierarchical mixture model framework that allows us to investigate the genetic and environmental effects, gene by gene interactions and gene by environment interactions in the same model. Our approach incorporates the natural hierarchical structure between the main effects and interaction effects into a mixture model, such that our methods tend to remove the irrelevant interaction effects more effectively, resulting in more robust and parsimonious models. We consider both strong and weak hierarchical models. For a strong hierarchical model, both of the main effects between interacting factors must be present for the interactions to be considered in the model development, while for a weak hierarchical model, only one of the two main effects is required to be present for the interaction to be evaluated. Our simulation results show that the proposed strong and weak hierarchical mixture models work well in controlling false positive rates and provide a powerful approach for identifying the predisposing effects and interactions in gene-environment interaction studies, in comparison with the naive model that does not impose this hierarchical constraint in most of the scenarios simulated. We illustrated our approach using data for lung cancer and cutaneous melanoma. PMID:25154630

Site occupancy models with heterogeneous detection probabilities

USGS Publications Warehouse

Royle, J. Andrew

2006-01-01

Models for estimating the probability of occurrence of a species in the presence of imperfect detection are important in many ecological disciplines. In these ?site occupancy? models, the possibility of heterogeneity in detection probabilities among sites must be considered because variation in abundance (and other factors) among sampled sites induces variation in detection probability (p). In this article, I develop occurrence probability models that allow for heterogeneous detection probabilities by considering several common classes of mixture distributions for p. For any mixing distribution, the likelihood has the general form of a zero-inflated binomial mixture for which inference based upon integrated likelihood is straightforward. A recent paper by Link (2003, Biometrics 59, 1123?1130) demonstrates that in closed population models used for estimating population size, different classes of mixture distributions are indistinguishable from data, yet can produce very different inferences about population size. I demonstrate that this problem can also arise in models for estimating site occupancy in the presence of heterogeneous detection probabilities. The implications of this are discussed in the context of an application to avian survey data and the development of animal monitoring programs.

A NEW METHOD OF PEAK DETECTION FOR ANALYSIS OF COMPREHENSIVE TWO-DIMENSIONAL GAS CHROMATOGRAPHY MASS SPECTROMETRY DATA*

PubMed Central

Kim, Seongho; Ouyang, Ming; Jeong, Jaesik; Shen, Changyu; Zhang, Xiang

2014-01-01

We develop a novel peak detection algorithm for the analysis of comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOF MS) data using normal-exponential-Bernoulli (NEB) and mixture probability models. The algorithm first performs baseline correction and denoising simultaneously using the NEB model, which also defines peak regions. Peaks are then picked using a mixture of probability distribution to deal with the co-eluting peaks. Peak merging is further carried out based on the mass spectral similarities among the peaks within the same peak group. The algorithm is evaluated using experimental data to study the effect of different cut-offs of the conditional Bayes factors and the effect of different mixture models including Poisson, truncated Gaussian, Gaussian, Gamma, and exponentially modified Gaussian (EMG) distributions, and the optimal version is introduced using a trial-and-error approach. We then compare the new algorithm with two existing algorithms in terms of compound identification. Data analysis shows that the developed algorithm can detect the peaks with lower false discovery rates than the existing algorithms, and a less complicated peak picking model is a promising alternative to the more complicated and widely used EMG mixture models. PMID:25264474

Insight into Signal Response of Protein Ions in Native ESI-MS from the Analysis of Model Mixtures of Covalently Linked Protein Oligomers.

PubMed

Root, Katharina; Wittwer, Yves; Barylyuk, Konstantin; Anders, Ulrike; Zenobi, Renato

2017-09-01

Native ESI-MS is increasingly used for quantitative analysis of biomolecular interactions. In such analyses, peak intensity ratios measured in mass spectra are treated as abundance ratios of the respective molecules in solution. While signal intensities of similar-size analytes, such as a protein and its complex with a small molecule, can be directly compared, significant distortions of the peak ratio due to unequal signal response of analytes impede the application of this approach for large oligomeric biomolecular complexes. We use a model system based on concatenated maltose binding protein units (MBPn, n = 1, 2, 3) to systematically study the behavior of protein mixtures in ESI-MS. The MBP concatamers differ from each other only by their mass while the chemical composition and other properties remain identical. We used native ESI-MS to analyze model mixtures of MBP oligomers, including equimolar mixtures of two proteins, as well as binary mixtures containing different fractions of the individual components. Pronounced deviation from a linear dependence of the signal intensity with concentration was observed for all binary mixtures investigated. While equimolar mixtures showed linear signal dependence at low concentrations, distinct ion suppression was observed above 20 μM. We systematically studied factors that are most often used in the literature to explain the origin of suppression effects. Implications of this effect for quantifying protein-protein binding affinity by native ESI-MS are discussed in general and demonstrated for an example of an anti-MBP antibody with its ligand, MBP. Graphical Abstract ᅟ.

Hydrothermal pretreatment of several lignocellulosic mixtures containing wheat straw and two hardwood residues available in Southern Europe.

PubMed

Silva-Fernandes, Talita; Duarte, Luís Chorão; Carvalheiro, Florbela; Loureiro-Dias, Maria Conceição; Fonseca, César; Gírio, Francisco

2015-05-01

This work studied the processing of biomass mixtures containing three lignocellulosic materials largely available in Southern Europe, eucalyptus residues (ER), wheat straw (WS) and olive tree pruning (OP). The mixtures were chemically characterized, and their pretreatment, by autohydrolysis, evaluated within a severity factor (logR0) ranging from 1.73 up to 4.24. A simple modeling strategy was used to optimize the autohydrolysis conditions based on the chemical characterization of the liquid fraction. The solid fraction was characterized to quantify the polysaccharide and lignin content. The pretreatment conditions for maximal saccharides recovery in the liquid fraction were at a severity range (logR0) of 3.65-3.72, independently of the mixture tested, which suggests that autohydrolysis can effectively process mixtures of lignocellulosic materials for further biochemical conversion processes. Copyright © 2015 Elsevier Ltd. All rights reserved.

Application of Genetic Algorithm (GA) Assisted Partial Least Square (PLS) Analysis on Trilinear and Non-trilinear Fluorescence Data Sets to Quantify the Fluorophores in Multifluorophoric Mixtures: Improving Quantification Accuracy of Fluorimetric Estimations of Dilute Aqueous Mixtures.

PubMed

Kumar, Keshav

2018-03-01

Excitation-emission matrix fluorescence (EEMF) and total synchronous fluorescence spectroscopy (TSFS) are the 2 fluorescence techniques that are commonly used for the analysis of multifluorophoric mixtures. These 2 fluorescence techniques are conceptually different and provide certain advantages over each other. The manual analysis of such highly correlated large volume of EEMF and TSFS towards developing a calibration model is difficult. Partial least square (PLS) analysis can analyze the large volume of EEMF and TSFS data sets by finding important factors that maximize the correlation between the spectral and concentration information for each fluorophore. However, often the application of PLS analysis on entire data sets does not provide a robust calibration model and requires application of suitable pre-processing step. The present work evaluates the application of genetic algorithm (GA) analysis prior to PLS analysis on EEMF and TSFS data sets towards improving the precision and accuracy of the calibration model. The GA algorithm essentially combines the advantages provided by stochastic methods with those provided by deterministic approaches and can find the set of EEMF and TSFS variables that perfectly correlate well with the concentration of each of the fluorophores present in the multifluorophoric mixtures. The utility of the GA assisted PLS analysis is successfully validated using (i) EEMF data sets acquired for dilute aqueous mixture of four biomolecules and (ii) TSFS data sets acquired for dilute aqueous mixtures of four carcinogenic polycyclic aromatic hydrocarbons (PAHs) mixtures. In the present work, it is shown that by using the GA it is possible to significantly improve the accuracy and precision of the PLS calibration model developed for both EEMF and TSFS data set. Hence, GA must be considered as a useful pre-processing technique while developing an EEMF and TSFS calibration model.

Optimization of natural lipstick formulation based on pitaya (Hylocereus polyrhizus) seed oil using D-optimal mixture experimental design.

PubMed

Kamairudin, Norsuhaili; Gani, Siti Salwa Abd; Masoumi, Hamid Reza Fard; Hashim, Puziah

2014-10-16

The D-optimal mixture experimental design was employed to optimize the melting point of natural lipstick based on pitaya (Hylocereus polyrhizus) seed oil. The influence of the main lipstick components-pitaya seed oil (10%-25% w/w), virgin coconut oil (25%-45% w/w), beeswax (5%-25% w/w), candelilla wax (1%-5% w/w) and carnauba wax (1%-5% w/w)-were investigated with respect to the melting point properties of the lipstick formulation. The D-optimal mixture experimental design was applied to optimize the properties of lipstick by focusing on the melting point with respect to the above influencing components. The D-optimal mixture design analysis showed that the variation in the response (melting point) could be depicted as a quadratic function of the main components of the lipstick. The best combination of each significant factor determined by the D-optimal mixture design was established to be pitaya seed oil (25% w/w), virgin coconut oil (37% w/w), beeswax (17% w/w), candelilla wax (2% w/w) and carnauba wax (2% w/w). With respect to these factors, the 46.0 °C melting point property was observed experimentally, similar to the theoretical prediction of 46.5 °C. Carnauba wax is the most influential factor on this response (melting point) with its function being with respect to heat endurance. The quadratic polynomial model sufficiently fit the experimental data.

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

Perera, Aurélien; Mazighi, Redha

Computer simulation studies of aqueous dimethyl sulfoxyde (DMSO) mixtures show micro-heterogeneous structures, just like aqueous alcohol mixtures. However, there is a marked difference in the aggregate structure of water between the two types of systems. While water molecules form multiconnected globular clusters in alcohols, we report herein that the typical water aggregates in aqueous DMSO mixtures are linear, favouring a 2 hydrogen bond structure per water molecule, and for all DMSO mole fractions ranging from 0.1 to 0.9. This linear-aggregate structure produces a particular signature in the water site-site structure factors, in the form of a pre-peak at k ≈more » 0.2–0.8 Å{sup −1}, depending on DMSO concentration. This pre-peak is either absent in other aqueous mixtures, such as aqueous methanol mixtures, or very difficult to see through computer simulations, such as in aqueous-t-butanol mixtures. This difference in the topology of the aggregates explains why the Kirkwood-Buff integrals of aqueous-DMSO mixture look nearly ideal, in contrast with those of aqueous alcohol mixtures, suggesting a connection between the shape of the water aggregates, its fluctuations, and the concentration fluctuations. In order to further study this discrepancy between aqueous DMSO and aqueous alcohol mixture, two models of pseudo-DMSO are introduced, where the size of the sulfur atom is increased by a factor 1.6 and 1.7, respectively, hence increasing the hydrophobicity of the molecule. The study shows that these mixtures become closer to the emulsion type seen in aqueous alcohol mixtures, with more globular clustering of the water molecules, long range domain oscillations in the water-water correlations and increased water-water Kirkwood-Buff integrals. It demonstrates that the local ordering of the water molecules is influenced by the nature of the solute molecules, with very different consequences for structural properties and related thermodynamic quantities. This study illustrates the unique plasticity of water in presence of different types of solutes.« less

Theoretic model and computer simulation of separating mixture metal particles from waste printed circuit board by electrostatic separator.

PubMed

Li, Jia; Xu, Zhenming; Zhou, Yaohe

2008-05-30

Traditionally, the mixture metals from waste printed circuit board (PCB) were sent to the smelt factory to refine pure copper. Some valuable metals (aluminum, zinc and tin) with low content in PCB were lost during smelt. A new method which used roll-type electrostatic separator (RES) to recovery low content metals in waste PCB was presented in this study. The theoretic model which was established from computing electric field and the analysis of forces on the particles was used to write a program by MATLAB language. The program was design to simulate the process of separating mixture metal particles. Electrical, material and mechanical factors were analyzed to optimize the operating parameters of separator. The experiment results of separating copper and aluminum particles by RES had a good agreement with computer simulation results. The model could be used to simulate separating other metal (tin, zinc, etc.) particles during the process of recycling waste PCBs by RES.

Testing of Intravenous Hemostatic Agents in a Novel Swine Model of Bleeding: Preliminary Results With FXa-PCPs

DTIC Science & Technology

2005-08-01

proceeds through a prothrombinase complex (prothrombin, factor Va, calcium, and phospholipid). Thrombin is the final product in the coagulation cascade...we report preliminary data on the efficacy of mixtures of highly purified blood coagulation factors (FVIIa and FXa) and phospholipids vesicles (PCPS...testing. Further study of these drugs for the use of traumatic hemorrhage is not warranted. 15. SUBJECT TERMS Recombinant Factor VI~a, factor Xa

Measurement error in earnings data: Using a mixture model approach to combine survey and register data.

PubMed

Meijer, Erik; Rohwedder, Susann; Wansbeek, Tom

2012-01-01

Survey data on earnings tend to contain measurement error. Administrative data are superior in principle, but they are worthless in case of a mismatch. We develop methods for prediction in mixture factor analysis models that combine both data sources to arrive at a single earnings figure. We apply the methods to a Swedish data set. Our results show that register earnings data perform poorly if there is a (small) probability of a mismatch. Survey earnings data are more reliable, despite their measurement error. Predictors that combine both and take conditional class probabilities into account outperform all other predictors.

The Developmental Trajectories of Depressive Symptoms in Early Adolescence: An Examination of School-Related Factors

ERIC Educational Resources Information Center

Wu, Pei-Chen

2017-01-01

This study investigated the heterogeneity of depressive symptom trajectories and the roles of school-related factors in predicting the membership of different trajectories in a sample of early adolescents in Taiwan. In all, 870 junior high school students were followed for 3 years. Using growth mixture modeling, the study identified four distinct…

Analytical Modeling of Weld Bead Shape in Dry Hyperbaric GMAW Using Ar-He Chamber Gas Mixtures

NASA Astrophysics Data System (ADS)

Azar, Amin S.; Ås, Sigmund K.; Akselsen, Odd M.

2013-03-01

Hyperbaric arc welding is a special application of joining the pipeline steels under seawater. In order to analyze the behavior of the arc under ambient pressure, a model is required to estimate the arc efficiency. A distributed point heat source model was employed. The simulated isotherms were calibrated iteratively to fit the actual bead cross section. Basic gas mixture rules and models were used to calculate the thermal properties of the low-temperature shielding gas under the ambient pressure of 10 bar. Nine bead-on-plate welds were deposited each of which under different Ar-He chamber gas compositions. The well-known correlation between arc efficiency (delivered heat) and the thermal conductivity was established for different gas mixtures. The arc efficiency was considered separately for the transverse and perpendicular heat sources. It was found that assigning single heat efficiency factor for the entire arc, which is usually below unity, causes a noticeable underestimation for the heat transfer in the perpendicular direction and a little overestimation in the transverse direction.

[New method of mixed gas infrared spectrum analysis based on SVM].

PubMed

Bai, Peng; Xie, Wen-Jun; Liu, Jun-Hua

2007-07-01

A new method of infrared spectrum analysis based on support vector machine (SVM) for mixture gas was proposed. The kernel function in SVM was used to map the seriously overlapping absorption spectrum into high-dimensional space, and after transformation, the high-dimensional data could be processed in the original space, so the regression calibration model was established, then the regression calibration model with was applied to analyze the concentration of component gas. Meanwhile it was proved that the regression calibration model with SVM also could be used for component recognition of mixture gas. The method was applied to the analysis of different data samples. Some factors such as scan interval, range of the wavelength, kernel function and penalty coefficient C that affect the model were discussed. Experimental results show that the component concentration maximal Mean AE is 0.132%, and the component recognition accuracy is higher than 94%. The problems of overlapping absorption spectrum, using the same method for qualitative and quantitative analysis, and limit number of training sample, were solved. The method could be used in other mixture gas infrared spectrum analyses, promising theoretic and application values.

Persistent activation of DNA damage signaling in response to complex mixtures of PAHs in air particulate matter

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

Jarvis, Ian W.H., E-mail: Ian.Jarvis@ki.se; Bergvall, Christoffer, E-mail: Christoffer.Bergvall@anchem.su.se; Bottai, Matteo, E-mail: Matteo.Bottai@ki.se

2013-02-01

Complex mixtures of polycyclic aromatic hydrocarbons (PAHs) are present in air particulate matter (PM) and have been associated with many adverse human health effects including cancer and respiratory disease. However, due to their complexity, the risk of exposure to mixtures is difficult to estimate. In the present study the effects of binary mixtures of benzo[a]pyrene (BP) and dibenzo[a,l]pyrene (DBP) and complex mixtures of PAHs in urban air PM extracts on DNA damage signaling was investigated. Applying a statistical model to the data we observed a more than additive response for binary mixtures of BP and DBP on activation of DNAmore » damage signaling. Persistent activation of checkpoint kinase 1 (Chk1) was observed at significantly lower BP equivalent concentrations in air PM extracts than BP alone. Activation of DNA damage signaling was also more persistent in air PM fractions containing PAHs with more than four aromatic rings suggesting larger PAHs contribute a greater risk to human health. Altogether our data suggests that human health risk assessment based on additivity such as toxicity equivalency factor scales may significantly underestimate the risk of exposure to complex mixtures of PAHs. The data confirms our previous findings with PAH-contaminated soil (Niziolek-Kierecka et al., 2012) and suggests a possible role for Chk1 Ser317 phosphorylation as a biological marker for future analyses of complex mixtures of PAHs. -- Highlights: ► Benzo[a]pyrene (BP), dibenzo[a,l]pyrene (DBP) and air PM PAH extracts were compared. ► Binary mixture of BP and DBP induced a more than additive DNA damage response. ► Air PM PAH extracts were more potent than toxicity equivalency factor estimates. ► Larger PAHs (> 4 rings) contribute more to the genotoxicity of PAHs in air PM. ► Chk1 is a sensitive marker for persistent activation of DNA damage signaling from PAH mixtures.« less

Interfacial tension and vapor-liquid equilibria in the critical region of mixtures

NASA Technical Reports Server (NTRS)

Moldover, Michael R.; Rainwater, James C.

1988-01-01

In the critical region, the concept of two-scale-factor universality can be used to accurately predict the surface tension between near-critical vapor and liquid phases from the singularity in the thermodynamic properties of the bulk fluid. In the present work, this idea is generalized to binary mixtures and is illustrated using the data of Hsu et al. (1985) for CO2 + n-butane. The pressure-temperature-composition-density data for coexisting, near-critical phases of the mixtures are fitted with a thermodynamic potential comprised of a sum of a singular term and nonsingular terms. The nonuniversal amplitudes characterizing the singular term for the mixtures are obtained from the amplitudes for the pure components by interpolation in a space of thermodynamic 'field' variables. The interfacial tensions predicted for the mixtures from the singular term are within 10 percent of the data on three isotherms in the pressure range (Pc - P)/Pc of less than 0.5. This difference is comparable to the combined experimental and model errors.

A Process View on Implementing an Antibullying Curriculum: How Teachers Differ and What Explains the Variation

ERIC Educational Resources Information Center

Haataja, Anne; Ahtola, Annarilla; Poskiparta, Elisa; Salmivalli, Christina

2015-01-01

The present study provides a person-centered view on teachers' adherence to the KiVa antibullying curriculum over a school year. Factor mixture modeling was used to examine how teachers (N = 282) differed in their implementation profiles and multinomial logistic regression was used to identify factors related to these profiles. On the basis of…

Combined Effects of Prenatal Exposures to Environmental Chemicals on Birth Weight.

PubMed

Govarts, Eva; Remy, Sylvie; Bruckers, Liesbeth; Den Hond, Elly; Sioen, Isabelle; Nelen, Vera; Baeyens, Willy; Nawrot, Tim S; Loots, Ilse; Van Larebeke, Nick; Schoeters, Greet

2016-05-12

Prenatal chemical exposure has been frequently associated with reduced fetal growth by single pollutant regression models although inconsistent results have been obtained. Our study estimated the effects of exposure to single pollutants and mixtures on birth weight in 248 mother-child pairs. Arsenic, copper, lead, manganese and thallium were measured in cord blood, cadmium in maternal blood, methylmercury in maternal hair, and five organochlorines, two perfluorinated compounds and diethylhexyl phthalate metabolites in cord plasma. Daily exposure to particulate matter was modeled and averaged over the duration of gestation. In single pollutant models, arsenic was significantly associated with reduced birth weight. The effect estimate increased when including cadmium, and mono-(2-ethyl-5-carboxypentyl) phthalate (MECPP) co-exposure. Combining exposures by principal component analysis generated an exposure factor loaded by cadmium and arsenic that was associated with reduced birth weight. MECPP induced gender specific effects. In girls, the effect estimate was doubled with co-exposure of thallium, PFOS, lead, cadmium, manganese, and mercury, while in boys, the mixture of MECPP with cadmium showed the strongest association with birth weight. In conclusion, birth weight was consistently inversely associated with exposure to pollutant mixtures. Chemicals not showing significant associations at single pollutant level contributed to stronger effects when analyzed as mixtures.

Measurement and description of underlying dimensions of comorbid mental disorders using Factor Mixture Models: results of the ESEMeD project.

PubMed

Almansa, Josué; Vermunt, Jeroen K; Forero, Carlos G; Vilagut, Gemma; De Graaf, Ron; De Girolamo, Giovanni; Alonso, Jordi

2011-06-01

Epidemiological studies on mental health and mental comorbidity are usually based on prevalences and correlations between disorders, or some other form of bivariate clustering of disorders. In this paper, we propose a Factor Mixture Model (FMM) methodology based on conceptual models aiming to measure and summarize distinctive disorder information in the internalizing and externalizing dimensions. This methodology includes explicit modelling of subpopulations with and without 12 month disorders ("ill" and "healthy") by means of latent classes, as well as assessment of model invariance and estimation of dimensional scores. We applied this methodology with an internalizing/externalizing two-factor model, to a representative sample gathered in the European Study of the Epidemiology of Mental Disorders (ESEMeD) study -- which includes 8796 individuals from six countries, and used the CIDI 3.0 instrument for disorder assessment. Results revealed that southern European countries have significantly higher mental health levels concerning internalizing/externalizing disorders than central countries; males suffered more externalizing disorders than women did, and conversely, internalizing disorders were more frequent in women. Differences in mental-health level between socio-demographic groups were due to different proportions of healthy and ill individuals and, noticeably, to the ameliorating influence of marital status on severity. An advantage of latent model-based scores is that the inclusion of additional mental-health dimensional information -- other than diagnostic data -- allows for greater precision within a target range of scores. Copyright © 2011 John Wiley & Sons, Ltd.

Preparation of reminiscent aroma mixture of Japanese soy sauce.

PubMed

Bonkohara, Kaori; Fuji, Maiko; Nakao, Akito; Igura, Noriyuki; Shimoda, Mitsuya

2016-01-01

To prepare an aroma mixture of Japanese soy sauce by fewest components, the aroma concentrate of good sensory attributes was prepared by polyethylene membrane extraction, which could extract only the volatiles with diethyl ether. GC-MS-Olfactometry was done with the aroma concentrate, and 28 odor-active compounds were detected. Application of aroma extract dilution analysis to the separated fraction revealed high flavor dilution factors with respect to acetic acid, 4-hydroxy-2(or5)-ethyl-5(or2)-methyl-3(2H)-furanone (HEMF), 3-methyl-1-butanol (isoamyl alcohol), and 3-(methylsulfanyl)propanal (methional). A model aroma mixture containing above four odorants showed a good similarity with the aroma of the soy sauce itself. Consequently, the reminiscent aroma mixture of soy sauce was prepared in water. The ratio of acetic acid, HEMF, isoamyl alcohol, and methional was 2500:300:100:1.

An upscaling method and a numerical analysis of swelling/shrinking processes in a compacted bentonite/sand mixture

NASA Astrophysics Data System (ADS)

Xie, M.; Agus, S. S.; Schanz, T.; Kolditz, O.

2004-12-01

This paper presents an upscaling concept of swelling/shrinking processes of a compacted bentonite/sand mixture, which also applies to swelling of porous media in general. A constitutive approach for highly compacted bentonite/sand mixture is developed accordingly. The concept is based on the diffuse double layer theory and connects microstructural properties of the bentonite as well as chemical properties of the pore fluid with swelling potential. Main factors influencing the swelling potential of bentonite, i.e. variation of water content, dry density, chemical composition of pore fluid, as well as the microstructures and the amount of swelling minerals are taken into account. According to the proposed model, porosity is divided into interparticle and interlayer porosity. Swelling is the potential of interlayer porosity increase, which reveals itself as volume change in the case of free expansion, or turns to be swelling pressure in the case of constrained swelling. The constitutive equations for swelling/shrinking are implemented in the software GeoSys/RockFlow as a new chemo-hydro-mechanical model, which is able to simulate isothermal multiphase flow in bentonite. Details of the mathematical and numerical multiphase flow formulations, as well as the code implementation are described. The proposed model is verified using experimental data of tests on a highly compacted bentonite/sand mixture. Comparison of the 1D modelling results with the experimental data evidences the capability of the proposed model to satisfactorily predict free swelling of the material under investigation. Copyright

Poisson Mixture Regression Models for Heart Disease Prediction.

PubMed

Mufudza, Chipo; Erol, Hamza

2016-01-01

Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model.

Poisson Mixture Regression Models for Heart Disease Prediction

PubMed Central

Erol, Hamza

2016-01-01

Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model. PMID:27999611

Prediction of toxicity of zinc and nickel mixtures to Artemia sp. at various salinities: From additivity to antagonism.

PubMed

Damasceno, Évila Pinheiro; de Figuerêdo, Lívia Pitombeira; Pimentel, Marcionília Fernandes; Loureiro, Susana; Costa-Lotufo, Letícia Veras

2017-08-01

Few studies have examined the toxicity of metal mixtures to marine organisms exposed to different salinities. The aim of the present study was to investigate the acute toxicity of zinc and nickel exposures singly and in combination to Artemia sp. under salinities of 10, 17, and 35 psu. The mixture concentrations were determined according to individual toxic units (TUs) to follow a fixed ratio design. Zinc was more toxic than nickel, and both their individual toxicities were higher at lower salinities. These changes in toxicity can be attributed to the Biotic Ligand Model (BLM) rather than to metal speciation. To analyze the mixture effect, the observed data were compared with the expected mixture effects predicted by the concentration addition (CA) model and by deviations for synergistic/antagonistic interactions and dose-level and dose-ratio dependencies. For a salinity of 35 psu, the mixture had no deviations; therefore, the effects were additive. After decreasing the salinity to 17 psu, the toxicity pattern changed to antagonism at low concentrations and synergism at higher equivalent LC 50 levels. For the lowest salinity tested (10 psu), antagonism was observed. The speciations of both metals were similar when in a mixture and when isolated, and changes in toxicity patterns are more related to the organism's physiology than metal speciation. Therefore, besides considering chemical interactions in real-world scenarios, where several chemicals can be present, the influence of abiotic factors, such as salinity, should also be considered. Copyright © 2017 Elsevier Inc. All rights reserved.

Numerical analysis of similarity of barrier discharges in the 0.95 Ne/0.05 Xe mixture

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

Avtaeva, S. V.; Kulumbaev, E. B.

2009-04-15

Established dynamic regimes of similar (with a scale factor of 10) barrier discharges in the 0.95 Ne/0.05 Xe mixture are simulated in a one-dimensional drift-diffusion model. The similarity is examined of barrier discharges excited in gaps of lengths 0.4 and 4 mm at gas pressures of 350 and 35 Torr and dielectric layer thicknesses of 0.2 and 2 mm, the frequencies of the 400-V ac voltage applied to the discharge electrodes being 100 and 10 kHz, respectively.

Competitive sorption of persistent organic pollutants onto microplastics in the marine environment.

PubMed

Bakir, Adil; Rowland, Steven J; Thompson, Richard C

2012-12-01

Plastics are known to sorb persistent organic pollutants from seawater. However, studies to quantify sorption rates have only considered the affinity of chemicals in isolation, unlike the conditions in the environment where contaminants are present as complex mixtures. Here we examine whether phenanthrene and 4,4'-DDT, in a mixture, compete for sorption sites onto PVC with no added additives (unplasticised PVC or uPVC) and Ultra-High Molecular Weight polyethylene. Interactions were investigated by exposing particles of uPVC and UHMW PE to mixtures of 3H and 14C radiolabelled Phe and DDT. Changes in sorption capacity were modelled by applying a Freundlich binding sorption isotherms. An Extended Langmuir Model and an Interaction Factor Model were also applied to predict equilibrium concentrations of pollutants onto plastic. This study showed that in a bi-solute system, DDT exhibited no significantly different sorption behaviour than in single solute systems. However, DDT did appear to interfere with the sorption of Phe onto plastic, indicating an antagonistic effect. Copyright © 2012 Elsevier Ltd. All rights reserved.

Soil mixture composition alters Arabidopsis susceptibility to Pseudomonas syringae infection

USDA-ARS?s Scientific Manuscript database

Pseudomonas syringae is a Gram-negative bacterial pathogen that causes disease on more than 100 different plant species, including the model plant Arabidopsis thaliana. Dissection of the Arabidopsis thaliana-Pseudomonas syringae pathosystem has identified many factors that contribute to successful ...

The effect of binary mixtures of zinc, copper, cadmium, and nickel on the growth of the freshwater diatom Navicula pelliculosa and comparison with mixture toxicity model predictions.

PubMed

Nagai, Takashi; De Schamphelaere, Karel A C

2016-11-01

The authors investigated the effect of binary mixtures of zinc (Zn), copper (Cu), cadmium (Cd), and nickel (Ni) on the growth of a freshwater diatom, Navicula pelliculosa. A 7 × 7 full factorial experimental design (49 combinations in total) was used to test each binary metal mixture. A 3-d fluorescence microplate toxicity assay was used to test each combination. Mixture effects were predicted by concentration addition and independent action models based on a single-metal concentration-response relationship between the relative growth rate and the calculated free metal ion activity. Although the concentration addition model predicted the observed mixture toxicity significantly better than the independent action model for the Zn-Cu mixture, the independent action model predicted the observed mixture toxicity significantly better than the concentration addition model for the Cd-Zn, Cd-Ni, and Cd-Cu mixtures. For the Zn-Ni and Cu-Ni mixtures, it was unclear which of the 2 models was better. Statistical analysis concerning antagonistic/synergistic interactions showed that the concentration addition model is generally conservative (with the Zn-Ni mixture being the sole exception), indicating that the concentration addition model would be useful as a method for a conservative first-tier screening-level risk analysis of metal mixtures. Environ Toxicol Chem 2016;35:2765-2773. © 2016 SETAC. © 2016 SETAC.

Mixture Rasch Models with Joint Maximum Likelihood Estimation

ERIC Educational Resources Information Center

Willse, John T.

2011-01-01

This research provides a demonstration of the utility of mixture Rasch models. Specifically, a model capable of estimating a mixture partial credit model using joint maximum likelihood is presented. Like the partial credit model, the mixture partial credit model has the beneficial feature of being appropriate for analysis of assessment data…

Signal Partitioning Algorithm for Highly Efficient Gaussian Mixture Modeling in Mass Spectrometry

PubMed Central

Polanski, Andrzej; Marczyk, Michal; Pietrowska, Monika; Widlak, Piotr; Polanska, Joanna

2015-01-01

Mixture - modeling of mass spectra is an approach with many potential applications including peak detection and quantification, smoothing, de-noising, feature extraction and spectral signal compression. However, existing algorithms do not allow for automated analyses of whole spectra. Therefore, despite highlighting potential advantages of mixture modeling of mass spectra of peptide/protein mixtures and some preliminary results presented in several papers, the mixture modeling approach was so far not developed to the stage enabling systematic comparisons with existing software packages for proteomic mass spectra analyses. In this paper we present an efficient algorithm for Gaussian mixture modeling of proteomic mass spectra of different types (e.g., MALDI-ToF profiling, MALDI-IMS). The main idea is automated partitioning of protein mass spectral signal into fragments. The obtained fragments are separately decomposed into Gaussian mixture models. The parameters of the mixture models of fragments are then aggregated to form the mixture model of the whole spectrum. We compare the elaborated algorithm to existing algorithms for peak detection and we demonstrate improvements of peak detection efficiency obtained by using Gaussian mixture modeling. We also show applications of the elaborated algorithm to real proteomic datasets of low and high resolution. PMID:26230717

Risk factors and outcomes of chronic sexual harassment during the transition to college: Examination of a two-part growth mixture model

PubMed Central

McGinley, Meredith; Wolff, Jennifer M.; Rospenda, Kathleen M.; Liu, Li; Richman, Judith A.

2016-01-01

A two-part latent growth mixture model was implemented in order to examine heterogeneity in the growth of sexual harassment (SH) victimization in college and university students, and the extent to which SH class membership explains substance use and mental health outcomes for certain groups of students. Demographic risk factors, mental health, and substance use were examined as they related to chronically experienced SH victimization. Incoming freshmen students (N = 2855; 58% female; 54% White) completed a survey at five time points. In addition to self-reporting gender, race, and sexual orientation, students completed measures of sexual harassment, anxiety, depression, binge drinking, and marijuana use. Overall, self-reported SH declined upon college entry, although levels rebounded by the third year of college. Results also supported a two-class solution (Infrequent and Chronic) for SH victimization. Being female, White, and a sexual minority were linked to being classified into the Chronic (relative to the Infrequent) SH class. In turn, Chronic SH class membership predicted greater anxiety, depression, and substance use, supporting a mediational model. PMID:27712687

Nonnegative Matrix Factorization for identification of unknown number of sources emitting delayed signals

PubMed Central

Iliev, Filip L.; Stanev, Valentin G.; Vesselinov, Velimir V.

2018-01-01

Factor analysis is broadly used as a powerful unsupervised machine learning tool for reconstruction of hidden features in recorded mixtures of signals. In the case of a linear approximation, the mixtures can be decomposed by a variety of model-free Blind Source Separation (BSS) algorithms. Most of the available BSS algorithms consider an instantaneous mixing of signals, while the case when the mixtures are linear combinations of signals with delays is less explored. Especially difficult is the case when the number of sources of the signals with delays is unknown and has to be determined from the data as well. To address this problem, in this paper, we present a new method based on Nonnegative Matrix Factorization (NMF) that is capable of identifying: (a) the unknown number of the sources, (b) the delays and speed of propagation of the signals, and (c) the locations of the sources. Our method can be used to decompose records of mixtures of signals with delays emitted by an unknown number of sources in a nondispersive medium, based only on recorded data. This is the case, for example, when electromagnetic signals from multiple antennas are received asynchronously; or mixtures of acoustic or seismic signals recorded by sensors located at different positions; or when a shift in frequency is induced by the Doppler effect. By applying our method to synthetic datasets, we demonstrate its ability to identify the unknown number of sources as well as the waveforms, the delays, and the strengths of the signals. Using Bayesian analysis, we also evaluate estimation uncertainties and identify the region of likelihood where the positions of the sources can be found. PMID:29518126

Nonnegative Matrix Factorization for identification of unknown number of sources emitting delayed signals.

PubMed

Iliev, Filip L; Stanev, Valentin G; Vesselinov, Velimir V; Alexandrov, Boian S

2018-01-01

Factor analysis is broadly used as a powerful unsupervised machine learning tool for reconstruction of hidden features in recorded mixtures of signals. In the case of a linear approximation, the mixtures can be decomposed by a variety of model-free Blind Source Separation (BSS) algorithms. Most of the available BSS algorithms consider an instantaneous mixing of signals, while the case when the mixtures are linear combinations of signals with delays is less explored. Especially difficult is the case when the number of sources of the signals with delays is unknown and has to be determined from the data as well. To address this problem, in this paper, we present a new method based on Nonnegative Matrix Factorization (NMF) that is capable of identifying: (a) the unknown number of the sources, (b) the delays and speed of propagation of the signals, and (c) the locations of the sources. Our method can be used to decompose records of mixtures of signals with delays emitted by an unknown number of sources in a nondispersive medium, based only on recorded data. This is the case, for example, when electromagnetic signals from multiple antennas are received asynchronously; or mixtures of acoustic or seismic signals recorded by sensors located at different positions; or when a shift in frequency is induced by the Doppler effect. By applying our method to synthetic datasets, we demonstrate its ability to identify the unknown number of sources as well as the waveforms, the delays, and the strengths of the signals. Using Bayesian analysis, we also evaluate estimation uncertainties and identify the region of likelihood where the positions of the sources can be found.

The effect of air entrapment on the performance of squeeze film dampers: Experiments and analysis

NASA Astrophysics Data System (ADS)

Diaz Briceno, Sergio Enrique

Squeeze film dampers (SFDs) are an effective means to introduce the required damping in rotor-bearing systems. They are a standard application in jet engines and are commonly used in industrial compressors. Yet, lack of understanding of their operation has confined the design of SFDs to a costly trial and error process based on prior experience. The main factor deterring the success of analytical models for the prediction of SFDs' performance lays on the modeling of the dynamic film rupture. Usually, the cavitation models developed for journal bearings are applied to SFDs. Yet, the characteristic motion of the SFD results in the entrapment of air into the oil film, thus producing a bubbly mixture that can not be represented by these models. In this work, an extensive experimental study establishes qualitatively and---for the first time---quantitatively the differences between operation with vapor cavitation and with air entrainment. The experiments show that most operating conditions lead to air entrainment and demonstrate the paramount effect it has on the performance of SFDs, evidencing the limitation of currently available models. Further experiments address the operation of SFDs with controlled bubbly mixtures. These experiments bolster the possibility of modeling air entrapment by representing the lubricant as a homogeneous mixture of air and oil and provide a reliable data base for benchmarking such a model. An analytical model is developed based on a homogeneous mixture assumption and where the bubbles are described by the Rayleigh-Plesset equation. Good agreement is obtained between this model and the measurements performed in the SFD operating with controlled mixtures. A complementary analytical model is devised to estimate the amount of air entrained from the balance of axial flows in the film. A combination of the analytical models for prediction of the air volume fraction and of the hydrodynamic pressures renders promising results for prediction of the performance of SFDs with freely entrained air. The results of this work are of immediate engineering applicability. Furthermore, they represent a firm step to advance the understanding on the effects of air entrapment in the performance of SFD.

Pattern analysis of community health center location in Surabaya using spatial Poisson point process

NASA Astrophysics Data System (ADS)

Kusumaningrum, Choriah Margareta; Iriawan, Nur; Winahju, Wiwiek Setya

2017-11-01

Community health center (puskesmas) is one of the closest health service facilities for the community, which provide healthcare for population on sub-district level as one of the government-mandated community health clinics located across Indonesia. The increasing number of this puskesmas does not directly comply the fulfillment of basic health services needed in such region. Ideally, a puskesmas has to cover up to maximum 30,000 people. The number of puskesmas in Surabaya indicates an unbalance spread in all of the area. This research aims to analyze the spread of puskesmas in Surabaya using spatial Poisson point process model in order to get the effective location of Surabaya's puskesmas which based on their location. The results of the analysis showed that the distribution pattern of puskesmas in Surabaya is non-homogeneous Poisson process and is approched by mixture Poisson model. Based on the estimated model obtained by using Bayesian mixture model couple with MCMC process, some characteristics of each puskesmas have no significant influence as factors to decide the addition of health center in such location. Some factors related to the areas of sub-districts have to be considered as covariate to make a decision adding the puskesmas in Surabaya.

Identifiability in N-mixture models: a large-scale screening test with bird data.

PubMed

Kéry, Marc

2018-02-01

Binomial N-mixture models have proven very useful in ecology, conservation, and monitoring: they allow estimation and modeling of abundance separately from detection probability using simple counts. Recently, doubts about parameter identifiability have been voiced. I conducted a large-scale screening test with 137 bird data sets from 2,037 sites. I found virtually no identifiability problems for Poisson and zero-inflated Poisson (ZIP) binomial N-mixture models, but negative-binomial (NB) models had problems in 25% of all data sets. The corresponding multinomial N-mixture models had no problems. Parameter estimates under Poisson and ZIP binomial and multinomial N-mixture models were extremely similar. Identifiability problems became a little more frequent with smaller sample sizes (267 and 50 sites), but were unaffected by whether the models did or did not include covariates. Hence, binomial N-mixture model parameters with Poisson and ZIP mixtures typically appeared identifiable. In contrast, NB mixtures were often unidentifiable, which is worrying since these were often selected by Akaike's information criterion. Identifiability of binomial N-mixture models should always be checked. If problems are found, simpler models, integrated models that combine different observation models or the use of external information via informative priors or penalized likelihoods, may help. © 2017 by the Ecological Society of America.

EARLY LIFE EXPOSURES TO ENVIRONMENTAL COMPOUNDS: LESSONS LEARNED FROM ANIMAL MODELS

EPA Science Inventory

Abstract: This article was an invited submission by the Cornell University Breast Cancer & Environmental Risk Factors group, who publish the newsletter, The Ribbon. A recent paper on low dose effects of an atrazine metabolite mixture in Environmental Health Perspectives by the ...

Differentiating the levels of risk for muscle dysmorphia among Hungarian male weightlifters: a factor mixture modeling approach.

PubMed

Babusa, Bernadett; Czeglédi, Edit; Túry, Ferenc; Mayville, Stephen B; Urbán, Róbert

2015-01-01

Muscle dysmorphia (MD) is a body image disturbance characterized by a pathological preoccupation with muscularity. The study aimed to differentiate the levels of risk for MD among weightlifters and to define a tentative cut-off score for the Muscle Appearance Satisfaction Scale (MASS) for the identification of high risk MD cases. Hungarian male weightlifters (n=304) completed the MASS, the Exercise Addiction Inventory, and specific exercise and body image related questions. For the differentiation of MD, factor mixture modeling was performed, resulting in three independent groups: low-, moderate-, and high risk MD groups. The estimated prevalence of high risk MD in this sample of weightlifters was 15.1%. To determine a cut-off score for the MASS, sensitivity and specificity analyses were performed and a cut-off point of 63 was suggested. The proposed cut-off score for the MASS can be useful for the early detection of high risk MD. Copyright © 2014 Elsevier Ltd. All rights reserved.

Identification of Anxiety Sensitivity Classes and Clinical Cut-Scores in a Sample of Adult Smokers: Results from a Factor Mixture Model

PubMed Central

Allan, Nicholas P.; Raines, Amanda M.; Capron, Daniel W.; Norr, Aaron M.; Zvolensky, Michael J.; Schmidt, Norman B.

2014-01-01

Anxiety sensitivity (AS), a multidimensional construct, has been implicated in the development and maintenance of anxiety and related disorders. Recent evidence suggests that AS is a dimensional-categorical construct within individuals. Factor mixture modeling was conducted in a sample of 579 adult smokers (M age = 36.87 years, SD = 13.47) to examine the underlying structure. Participants completed the Anxiety Sensitivity Index-3 and were also given a Structured Clinical Interview for DSM-IV-TR. Three classes of individuals emerged, a high AS (5.2% of the sample), a moderate AS (19.0%), and a normative AS class (75.8%). A cut-score of 23 to identify high AS individuals, and a cut-score of 17 to identify moderate-to-high AS individuals were supported in this study. In addition, the odds of having a concurrent anxiety disorder (controlling for other Axis I disorders) were the highest in the high AS class and the lowest in the normative AS class. PMID:25128664

A mixture model-based approach to the clustering of microarray expression data.

PubMed

McLachlan, G J; Bean, R W; Peel, D

2002-03-01

This paper introduces the software EMMIX-GENE that has been developed for the specific purpose of a model-based approach to the clustering of microarray expression data, in particular, of tissue samples on a very large number of genes. The latter is a nonstandard problem in parametric cluster analysis because the dimension of the feature space (the number of genes) is typically much greater than the number of tissues. A feasible approach is provided by first selecting a subset of the genes relevant for the clustering of the tissue samples by fitting mixtures of t distributions to rank the genes in order of increasing size of the likelihood ratio statistic for the test of one versus two components in the mixture model. The imposition of a threshold on the likelihood ratio statistic used in conjunction with a threshold on the size of a cluster allows the selection of a relevant set of genes. However, even this reduced set of genes will usually be too large for a normal mixture model to be fitted directly to the tissues, and so the use of mixtures of factor analyzers is exploited to reduce effectively the dimension of the feature space of genes. The usefulness of the EMMIX-GENE approach for the clustering of tissue samples is demonstrated on two well-known data sets on colon and leukaemia tissues. For both data sets, relevant subsets of the genes are able to be selected that reveal interesting clusterings of the tissues that are either consistent with the external classification of the tissues or with background and biological knowledge of these sets. EMMIX-GENE is available at http://www.maths.uq.edu.au/~gjm/emmix-gene/

Influence factors of multicomponent mixtures containing reactive chemicals and their joint effects.

PubMed

Tian, Dayong; Lin, Zhifen; Yu, Jianqiao; Yin, Daqiang

2012-08-01

Organic chemicals usually coexist as a mixture in the environment, and the mixture toxicity of organic chemicals has received increased attention. However, research regarding the joint effects of reactive chemicals is lacking. In this study, we examined two kinds of reactive chemicals, cyanogenic toxicants and aldehydes and determined their joint effects on Photobacterium phosphoreum. Three factors were found to influence the joint effects of multicomponent mixtures containing reactive chemicals, including the number of components, the dominating components and the toxic ratios. With an increased number of components, the synergistic or antagonistic effects (interactions) will weaken to the additive effects (non-interactions) if the added component cannot yield a much stronger joint effect with an existing component. Contrarily, the joint effect of the mixture may become stronger instead of weaker if the added components can yield a much stronger joint effect than the existing joint effect of the multicomponent mixture. The components that yield the strongest interactions in their binary mixture can be considered the dominating components. These components contribute more to the interactions of multicomponent mixtures than other components. Moreover, the toxic ratios also influence the joint effects of the mixtures. This study provides an insight into what are the main factors and how they influence the joint effects of multicomponent mixtures containing reactive chemicals, and thus, the findings are beneficial to the study of mixture toxicology. Copyright © 2012 Elsevier Ltd. All rights reserved.

Mixture modeling methods for the assessment of normal and abnormal personality, part I: cross-sectional models.

PubMed

Hallquist, Michael N; Wright, Aidan G C

2014-01-01

Over the past 75 years, the study of personality and personality disorders has been informed considerably by an impressive array of psychometric instruments. Many of these tests draw on the perspective that personality features can be conceptualized in terms of latent traits that vary dimensionally across the population. A purely trait-oriented approach to personality, however, might overlook heterogeneity that is related to similarities among subgroups of people. This article describes how factor mixture modeling (FMM), which incorporates both categories and dimensions, can be used to represent person-oriented and trait-oriented variability in the latent structure of personality. We provide an overview of different forms of FMM that vary in the degree to which they emphasize trait- versus person-oriented variability. We also provide practical guidelines for applying FMM to personality data, and we illustrate model fitting and interpretation using an empirical analysis of general personality dysfunction.

Multiscale Constitutive Modeling of Asphalt Concrete

NASA Astrophysics Data System (ADS)

Underwood, Benjamin Shane

Multiscale modeling of asphalt concrete has become a popular technique for gaining improved insight into the physical mechanisms that affect the material's behavior and ultimately its performance. This type of modeling considers asphalt concrete, not as a homogeneous mass, but rather as an assemblage of materials at different characteristic length scales. For proper modeling these characteristic scales should be functionally definable and should have known properties. Thus far, research in this area has not focused significant attention on functionally defining what the characteristic scales within asphalt concrete should be. Instead, many have made assumptions on the characteristic scales and even the characteristic behaviors of these scales with little to no support. This research addresses these shortcomings by directly evaluating the microstructure of the material and uses these results to create materials of different characteristic length scales as they exist within the asphalt concrete mixture. The objectives of this work are to; 1) develop mechanistic models for the linear viscoelastic (LVE) and damage behaviors in asphalt concrete at different length scales and 2) develop a mechanistic, mechanistic/empirical, or phenomenological formulation to link the different length scales into a model capable of predicting the effects of microstructural changes on the linear viscoelastic behaviors of asphalt concrete mixture, e.g., a microstructure association model for asphalt concrete mixture. Through the microstructural study it is found that asphalt concrete mixture can be considered as a build-up of three different phases; asphalt mastic, fine aggregate matrix (FAM), and finally the coarse aggregate particles. The asphalt mastic is found to exist as a homogenous material throughout the mixture and FAM, and the filler content within this material is consistent with the volumetric averaged concentration, which can be calculated from the job mix formula. It is also found that the maximum aggregate size of the FAM is mixture dependent, but consistent with a gradation parameter from the Baily Method of mixture design. Mechanistic modeling of these different length scales reveals that although many consider asphalt concrete to be a LVE material, it is in fact only quasi-LVE because it shows some tendencies that are inconsistent with LVE theory. Asphalt FAM and asphalt mastic show similar nonlinear tendencies although the exact magnitude of the effect differs. These tendencies can be ignored for damage modeling in the mixture and FAM scales as long as the effects are consistently ignored, but it is found that they must be accounted for in mastic and binder damage modeling. The viscoelastic continuum damage (VECD) model is used for damage modeling in this research. To aid in characterization and application of the VECD model for cyclic testing, a simplified version (S-VECD) is rigorously derived and verified. Through the modeling efforts at each scale, various factors affecting the fundamental and engineering properties at each scale are observed and documented. A microstructure association model that accounts for particle interaction through physico-chemical processes and the effects of aggregate structuralization is developed to links the moduli at each scale. This model is shown to be capable of upscaling the mixture modulus from either the experimentally determined mastic modulus or FAM modulus. Finally, an initial attempt at upscaling the damage and nonlinearity phenomenon is shown.

Modeling abundance using multinomial N-mixture models

USGS Publications Warehouse

Royle, Andy

2016-01-01

Multinomial N-mixture models are a generalization of the binomial N-mixture models described in Chapter 6 to allow for more complex and informative sampling protocols beyond simple counts. Many commonly used protocols such as multiple observer sampling, removal sampling, and capture-recapture produce a multivariate count frequency that has a multinomial distribution and for which multinomial N-mixture models can be developed. Such protocols typically result in more precise estimates than binomial mixture models because they provide direct information about parameters of the observation process. We demonstrate the analysis of these models in BUGS using several distinct formulations that afford great flexibility in the types of models that can be developed, and we demonstrate likelihood analysis using the unmarked package. Spatially stratified capture-recapture models are one class of models that fall into the multinomial N-mixture framework, and we discuss analysis of stratified versions of classical models such as model Mb, Mh and other classes of models that are only possible to describe within the multinomial N-mixture framework.

Protein construct storage: Bayesian variable selection and prediction with mixtures.

PubMed

Clyde, M A; Parmigiani, G

1998-07-01

Determining optimal conditions for protein storage while maintaining a high level of protein activity is an important question in pharmaceutical research. A designed experiment based on a space-filling design was conducted to understand the effects of factors affecting protein storage and to establish optimal storage conditions. Different model-selection strategies to identify important factors may lead to very different answers about optimal conditions. Uncertainty about which factors are important, or model uncertainty, can be a critical issue in decision-making. We use Bayesian variable selection methods for linear models to identify important variables in the protein storage data, while accounting for model uncertainty. We also use the Bayesian framework to build predictions based on a large family of models, rather than an individual model, and to evaluate the probability that certain candidate storage conditions are optimal.

CLUSTERING SOUTH AFRICAN HOUSEHOLDS BASED ON THEIR ASSET STATUS USING LATENT VARIABLE MODELS

PubMed Central

McParland, Damien; Gormley, Isobel Claire; McCormick, Tyler H.; Clark, Samuel J.; Kabudula, Chodziwadziwa Whiteson; Collinson, Mark A.

2014-01-01

The Agincourt Health and Demographic Surveillance System has since 2001 conducted a biannual household asset survey in order to quantify household socio-economic status (SES) in a rural population living in northeast South Africa. The survey contains binary, ordinal and nominal items. In the absence of income or expenditure data, the SES landscape in the study population is explored and described by clustering the households into homogeneous groups based on their asset status. A model-based approach to clustering the Agincourt households, based on latent variable models, is proposed. In the case of modeling binary or ordinal items, item response theory models are employed. For nominal survey items, a factor analysis model, similar in nature to a multinomial probit model, is used. Both model types have an underlying latent variable structure—this similarity is exploited and the models are combined to produce a hybrid model capable of handling mixed data types. Further, a mixture of the hybrid models is considered to provide clustering capabilities within the context of mixed binary, ordinal and nominal response data. The proposed model is termed a mixture of factor analyzers for mixed data (MFA-MD). The MFA-MD model is applied to the survey data to cluster the Agincourt households into homogeneous groups. The model is estimated within the Bayesian paradigm, using a Markov chain Monte Carlo algorithm. Intuitive groupings result, providing insight to the different socio-economic strata within the Agincourt region. PMID:25485026

Parameterization of Cloud Optical Properties for a Mixture of Ice Particles for use in Atmospheric Models

NASA Technical Reports Server (NTRS)

Chou, Ming-Dah; Lee, Kyu-Tae; Yang, Ping; Lau, William K. M. (Technical Monitor)

2002-01-01

Based on the single-scattering optical properties that are pre-computed using an improve geometric optics method, the bulk mass absorption coefficient, single-scattering albedo, and asymmetry factor of ice particles have been parameterized as a function of the mean effective particle size of a mixture of ice habits. The parameterization has been applied to compute fluxes for sample clouds with various particle size distributions and assumed mixtures of particle habits. Compared to the parameterization for a single habit of hexagonal column, the solar heating of clouds computed with the parameterization for a mixture of habits is smaller due to a smaller cosingle-scattering albedo. Whereas the net downward fluxes at the TOA and surface are larger due to a larger asymmetry factor. The maximum difference in the cloud heating rate is approx. 0.2 C per day, which occurs in clouds with an optical thickness greater than 3 and the solar zenith angle less than 45 degrees. Flux difference is less than 10 W per square meters for the optical thickness ranging from 0.6 to 10 and the entire range of the solar zenith angle. The maximum flux difference is approximately 3%, which occurs around an optical thickness of 1 and at high solar zenith angles.

Holographic estimate of the meson cloud contribution to nucleon axial form factor

NASA Astrophysics Data System (ADS)

Ramalho, G.

2018-04-01

We use light-front holography to estimate the valence quark and the meson cloud contributions to the nucleon axial form factor. The free couplings of the holographic model are determined by the empirical data and by the information extracted from lattice QCD. The holographic model provides a good description of the empirical data when we consider a meson cloud mixture of about 30% in the physical nucleon state. The estimate of the valence quark contribution to the nucleon axial form factor compares well with the lattice QCD data for small pion masses. Our estimate of the meson cloud contribution to the nucleon axial form factor has a slower falloff with the square momentum transfer compared to typical estimates from quark models with meson cloud dressing.

Development of a flexible pavement database for local calibration of the MEPDG : part 2, evaluation of ODOT SMA mixtures.

DOT National Transportation Integrated Search

2011-03-01

There has been some reluctance on the part of some in Oklahoma to use SMA mixtures. There are several factors that could be involved in the slow acceptance of SMA mixtures in Oklahoma. These factors are 1) the extra expense associated with the higher...

Modeling and multi-response optimization of pervaporation of organic aqueous solutions using desirability function approach.

PubMed

Cojocaru, C; Khayet, M; Zakrzewska-Trznadel, G; Jaworska, A

2009-08-15

The factorial design of experiments and desirability function approach has been applied for multi-response optimization in pervaporation separation process. Two organic aqueous solutions were considered as model mixtures, water/acetonitrile and water/ethanol mixtures. Two responses have been employed in multi-response optimization of pervaporation, total permeate flux and organic selectivity. The effects of three experimental factors (feed temperature, initial concentration of organic compound in feed solution, and downstream pressure) on the pervaporation responses have been investigated. The experiments were performed according to a 2(3) full factorial experimental design. The factorial models have been obtained from experimental design and validated statistically by analysis of variance (ANOVA). The spatial representations of the response functions were drawn together with the corresponding contour line plots. Factorial models have been used to develop the overall desirability function. In addition, the overlap contour plots were presented to identify the desirability zone and to determine the optimum point. The optimal operating conditions were found to be, in the case of water/acetonitrile mixture, a feed temperature of 55 degrees C, an initial concentration of 6.58% and a downstream pressure of 13.99 kPa, while for water/ethanol mixture a feed temperature of 55 degrees C, an initial concentration of 4.53% and a downstream pressure of 9.57 kPa. Under such optimum conditions it was observed experimentally an improvement of both the total permeate flux and selectivity.

Concentration addition and independent action model: Which is better in predicting the toxicity for metal mixtures on zebrafish larvae.

PubMed

Gao, Yongfei; Feng, Jianfeng; Kang, Lili; Xu, Xin; Zhu, Lin

2018-01-01

The joint toxicity of chemical mixtures has emerged as a popular topic, particularly on the additive and potential synergistic actions of environmental mixtures. We investigated the 24h toxicity of Cu-Zn, Cu-Cd, and Cu-Pb and 96h toxicity of Cd-Pb binary mixtures on the survival of zebrafish larvae. Joint toxicity was predicted and compared using the concentration addition (CA) and independent action (IA) models with different assumptions in the toxic action mode in toxicodynamic processes through single and binary metal mixture tests. Results showed that the CA and IA models presented varying predictive abilities for different metal combinations. For the Cu-Cd and Cd-Pb mixtures, the CA model simulated the observed survival rates better than the IA model. By contrast, the IA model simulated the observed survival rates better than the CA model for the Cu-Zn and Cu-Pb mixtures. These findings revealed that the toxic action mode may depend on the combinations and concentrations of tested metal mixtures. Statistical analysis of the antagonistic or synergistic interactions indicated that synergistic interactions were observed for the Cu-Cd and Cu-Pb mixtures, non-interactions were observed for the Cd-Pb mixtures, and slight antagonistic interactions for the Cu-Zn mixtures. These results illustrated that the CA and IA models are consistent in specifying the interaction patterns of binary metal mixtures. Copyright © 2017 Elsevier B.V. All rights reserved.

Concentration Addition, Independent Action and Generalized Concentration Addition Models for Mixture Effect Prediction of Sex Hormone Synthesis In Vitro

PubMed Central

Hadrup, Niels; Taxvig, Camilla; Pedersen, Mikael; Nellemann, Christine; Hass, Ulla; Vinggaard, Anne Marie

2013-01-01

Humans are concomitantly exposed to numerous chemicals. An infinite number of combinations and doses thereof can be imagined. For toxicological risk assessment the mathematical prediction of mixture effects, using knowledge on single chemicals, is therefore desirable. We investigated pros and cons of the concentration addition (CA), independent action (IA) and generalized concentration addition (GCA) models. First we measured effects of single chemicals and mixtures thereof on steroid synthesis in H295R cells. Then single chemical data were applied to the models; predictions of mixture effects were calculated and compared to the experimental mixture data. Mixture 1 contained environmental chemicals adjusted in ratio according to human exposure levels. Mixture 2 was a potency adjusted mixture containing five pesticides. Prediction of testosterone effects coincided with the experimental Mixture 1 data. In contrast, antagonism was observed for effects of Mixture 2 on this hormone. The mixtures contained chemicals exerting only limited maximal effects. This hampered prediction by the CA and IA models, whereas the GCA model could be used to predict a full dose response curve. Regarding effects on progesterone and estradiol, some chemicals were having stimulatory effects whereas others had inhibitory effects. The three models were not applicable in this situation and no predictions could be performed. Finally, the expected contributions of single chemicals to the mixture effects were calculated. Prochloraz was the predominant but not sole driver of the mixtures, suggesting that one chemical alone was not responsible for the mixture effects. In conclusion, the GCA model seemed to be superior to the CA and IA models for the prediction of testosterone effects. A situation with chemicals exerting opposing effects, for which the models could not be applied, was identified. In addition, the data indicate that in non-potency adjusted mixtures the effects cannot always be accounted for by single chemicals. PMID:23990906

Ion permeability of artificial membranes evaluated by diffusion potential and electrical resistance measurements.

PubMed

Shlyonsky, Vadim

2013-12-01

In the present article, a novel model of artificial membranes that provides efficient assistance in teaching the origins of diffusion potentials is proposed. These membranes are made of polycarbonate filters fixed to 12-mm plastic rings and then saturated with a mixture of creosol and n-decane. The electrical resistance and potential difference across these membranes can be easily measured using a low-cost volt-ohm meter and home-made Ag/AgCl electrodes. The advantage of the model is the lack of ionic selectivity of the membrane, which can be modified by the introduction of different ionophores to the organic liquid mixture. A membrane treated with the mixture containing valinomycin generates voltages from -53 to -25 mV in the presence of a 10-fold KCl gradient (in to out) and from -79 to -53 mV in the presence of a bi-ionic KCl/NaCl gradient (in to out). This latter bi-ionic gradient potential reverses to a value from +9 to +20 mV when monensin is present in the organic liquid mixture. Thus, the model can be build stepwise, i.e., all factors leading to the development of diffusion potentials can be introduced sequentially, helping students to understand the quantitative relationships of ionic gradients and differential membrane permeability in the generation of cell electrical signals.

Trajectories of Social Withdrawal from Middle Childhood to Early Adolescence

ERIC Educational Resources Information Center

Oh, Wonjung; Rubin, Kenneth H.; Bowker, Julie C.; Booth-LaForce, Cathryn; Rose-Krasnor, Linda; Laursen, Brett

2008-01-01

Heterogeneity and individual differences in the developmental course of social withdrawal were examined longitudinally in a community sample (N = 392). General Growth Mixture Modeling (GGMM) was used to identify distinct pathways of social withdrawal, differentiate valid subgroup trajectories, and examine factors that predicted change in…

Technique for measuring gas conversion factors

NASA Technical Reports Server (NTRS)

Singh, J. J.; Sprinkle, D. R. (Inventor)

1985-01-01

A method for determining hydrocarbon conversion factors for a flowmeter. A mixture of air, O2 and C sub x H sub y is burned and the partial paressure of O2 in the resulting gas is forced to equal the partial pressure of O2 in air. The flowrate of O2 flowing into the mixture is measured by flowmeter and the flowrate of C sub x H sub y flowing into the mixture is measured by the flowmeter conversion factor is to be determined. These measured values are used to calculate the conversion factor.

Maximum likelihood estimation of finite mixture model for economic data

NASA Astrophysics Data System (ADS)

Phoong, Seuk-Yen; Ismail, Mohd Tahir

2014-06-01

Finite mixture model is a mixture model with finite-dimension. This models are provides a natural representation of heterogeneity in a finite number of latent classes. In addition, finite mixture models also known as latent class models or unsupervised learning models. Recently, maximum likelihood estimation fitted finite mixture models has greatly drawn statistician's attention. The main reason is because maximum likelihood estimation is a powerful statistical method which provides consistent findings as the sample sizes increases to infinity. Thus, the application of maximum likelihood estimation is used to fit finite mixture model in the present paper in order to explore the relationship between nonlinear economic data. In this paper, a two-component normal mixture model is fitted by maximum likelihood estimation in order to investigate the relationship among stock market price and rubber price for sampled countries. Results described that there is a negative effect among rubber price and stock market price for Malaysia, Thailand, Philippines and Indonesia.

40 CFR Table 2c to Subpart E of... - Reactivity Factors for Aromatic Hydrocarbon Solvent Mixtures

Code of Federal Regulations, 2011 CFR

2011-07-01

... 40 Protection of Environment 5 2011-07-01 2011-07-01 false Reactivity Factors for Aromatic Hydrocarbon Solvent Mixtures 2C Table 2C to Subpart E of Part 59 Protection of Environment ENVIRONMENTAL... Hydrocarbon Solvent Mixtures Bin Boiling range (degrees F) Criteria Reactivityfactor 21 280-290 Aromatic...

40 CFR Table 2c to Subpart E of... - Reactivity Factors for Aromatic Hydrocarbon Solvent Mixtures

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 5 2010-07-01 2010-07-01 false Reactivity Factors for Aromatic Hydrocarbon Solvent Mixtures 2C Table 2C to Subpart E of Part 59 Protection of Environment ENVIRONMENTAL... Hydrocarbon Solvent Mixtures Bin Boiling range (degrees F) Criteria Reactivityfactor 21 280-290 Aromatic...

Partial Least Squares Calibration Modeling Towards the Multivariate Limit of Detection for Enriched Isotopic Mixtures via Laser Ablation Molecular Isotopic Spectroscopy

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

Harris, Candace; Profeta, Luisa; Akpovo, Codjo

The psuedo univariate limit of detection was calculated to compare to the multivariate interval. ompared with results from the psuedounivariate LOD, the multivariate LOD includes other factors (i.e. signal uncertainties) and the reveals the significance in creating models that not only use the analyte’s emission line but also its entire molecular spectra.

Cure models for estimating hospital-based breast cancer survival.

PubMed

Rama, Ranganathan; Swaminathan, Rajaraman; Venkatesan, Perumal

2010-01-01

Research on cancer survival is enriched by development and application of innovative analytical approaches in relation to standard methods. The aim of the present paper is to document the utility of a mixture model to estimate the cure fraction and compare it with other approaches. The data were for 1,107 patients with locally advanced breast cancer, who completed the neo-adjuvant treatment protocol during 1990-99 at the Cancer Institute (WIA), Chennai, India. Tumour stage, post-operative pathological node (PN) and tumour residue (TR) status were studied. Event free survival probability was estimated using the Kaplan-Meier method. Cure models under proportional and non-proportional hazard assumptions following log normal distribution for survival time were used to estimate both the cure fraction and the survival function for the uncured. Event free survival at 5 and 10 years were 64.2% and 52.6% respectively and cure fraction was 47.5% for all cases together. Follow up ranged between 0-15 years and survival probabilities showed minimal changes after 7 years of follow up. TR and PN emerged as independent prognostic factors using Cox and proportional hazard (PH) cure models. Proportionality condition was violated when tumour stage was considered and it was statistically significant only under PH and not under non PH cure models. However, TR and PN continued to be independent prognostic factors after adjusting for tumour stage using the non PH cure model. A consistent ordering of cure fractions with respect to factors of PN and TR was forthcoming across tumour stages using PH and non PH cure models, but perceptible differences in survival were observed between the two. If PH conditions are violated, analysis using a non PH model is advocated and mixture cure models are useful in estimating the cure fraction and constructing survival curves for non-cures.

Novel models on fluid's variable thermo-physical properties for extensive study on convection heat and mass transfer

NASA Astrophysics Data System (ADS)

Shang, De-Yi; Zhong, Liang-Cai

2017-01-01

Our novel models for fluid's variable physical properties are improved and reported systematically in this work for enhancement of theoretical and practical value on study of convection heat and mass transfer. It consists of three models, namely (1) temperature parameter model, (2) polynomial model, and (3) weighted-sum model, respectively for treatment of temperature-dependent physical properties of gases, temperature-dependent physical properties of liquids, and concentration- and temperature-dependent physical properties of vapour-gas mixture. Two related components are proposed, and involved in each model for fluid's variable physical properties. They are basic physic property equations and theoretical similarity equations on physical property factors. The former, as the foundation of the latter, is based on the typical experimental data and physical analysis. The latter is built up by similarity analysis and mathematical derivation based on the former basic physical properties equations. These models are available for smooth simulation and treatment of fluid's variable physical properties for assurance of theoretical and practical value of study on convection of heat and mass transfer. Especially, so far, there has been lack of available study on heat and mass transfer of film condensation convection of vapour-gas mixture, and the wrong heat transfer results existed in widespread studies on the related research topics, due to ignorance of proper consideration of the concentration- and temperature-dependent physical properties of vapour-gas mixture. For resolving such difficult issues, the present novel physical property models have their special advantages.

Improved estimation of sediment source contributions by concentration-dependent Bayesian isotopic mixing model

NASA Astrophysics Data System (ADS)

Ram Upadhayay, Hari; Bodé, Samuel; Griepentrog, Marco; Bajracharya, Roshan Man; Blake, Will; Cornelis, Wim; Boeckx, Pascal

2017-04-01

The implementation of compound-specific stable isotope (CSSI) analyses of biotracers (e.g. fatty acids, FAs) as constraints on sediment-source contributions has become increasingly relevant to understand the origin of sediments in catchments. The CSSI fingerprinting of sediment utilizes CSSI signature of biotracer as input in an isotopic mixing model (IMM) to apportion source soil contributions. So far source studies relied on the linear mixing assumptions of CSSI signature of sources to the sediment without accounting for potential effects of source biotracer concentration. Here we evaluated the effect of FAs concentration in sources on the accuracy of source contribution estimations in artificial soil mixture of three well-separated land use sources. Soil samples from land use sources were mixed to create three groups of artificial mixture with known source contributions. Sources and artificial mixture were analysed for δ13C of FAs using gas chromatography-combustion-isotope ratio mass spectrometry. The source contributions to the mixture were estimated using with and without concentration-dependent MixSIAR, a Bayesian isotopic mixing model. The concentration-dependent MixSIAR provided the closest estimates to the known artificial mixture source contributions (mean absolute error, MAE = 10.9%, and standard error, SE = 1.4%). In contrast, the concentration-independent MixSIAR with post mixing correction of tracer proportions based on aggregated concentration of FAs of sources biased the source contributions (MAE = 22.0%, SE = 3.4%). This study highlights the importance of accounting the potential effect of a source FA concentration for isotopic mixing in sediments that adds realisms to mixing model and allows more accurate estimates of contributions of sources to the mixture. The potential influence of FA concentration on CSSI signature of sediments is an important underlying factor that determines whether the isotopic signature of a given source is observable even after equilibrium. Therefore inclusion of FA concentrations of the sources in the IMM formulation is standard procedure for accurate estimation of source contributions. The post model correction approach that dominates the CSSI fingerprinting causes bias, especially if the FAs concentration of sources differs substantially.

Dielectric properties of benzylamine in 1,2,6-hexanetriol mixture using time domain reflectometry technique

NASA Astrophysics Data System (ADS)

Swami, M. B.; Hudge, P. G.; Pawar, V. P.

The dielectric properties of binary mixtures of benzylamine-1,2,6-hexantriol mixtures at different volume fractions of 1,2,6-hexanetriol have been measured using Time Domain Reflectometry (TDR) technique in the frequency range of 10 MHz to 30 GHz. Complex permittivity spectra were fitted using Havriliak-Negami equation. By using least square fit method the dielectric parameters such as static dielectric constant (ɛ0), dielectric constant at high frequency (ɛ∞), relaxation time τ (ps) and relaxation distribution parameter (β) were extracted from complex permittivity spectra at 25∘C. The intramolecular interaction of different molecules has been discussed using the Kirkwood correlation factor, Bruggeman factor. The Kirkwood correlation factor (gf) and effective Kirkwood correlation factor (geff) indicate the dipole ordering of the binary mixtures.

Predicting herbicide mixture effects on multiple algal species using mixture toxicity models.

PubMed

Nagai, Takashi

2017-10-01

The validity of the application of mixture toxicity models, concentration addition and independent action, to a species sensitivity distribution (SSD) for calculation of a multisubstance potentially affected fraction was examined in laboratory experiments. Toxicity assays of herbicide mixtures using 5 species of periphytic algae were conducted. Two mixture experiments were designed: a mixture of 5 herbicides with similar modes of action and a mixture of 5 herbicides with dissimilar modes of action, corresponding to the assumptions of the concentration addition and independent action models, respectively. Experimentally obtained mixture effects on 5 algal species were converted to the fraction of affected (>50% effect on growth rate) species. The predictive ability of the concentration addition and independent action models with direct application to SSD depended on the mode of action of chemicals. That is, prediction was better for the concentration addition model than the independent action model for the mixture of herbicides with similar modes of action. In contrast, prediction was better for the independent action model than the concentration addition model for the mixture of herbicides with dissimilar modes of action. Thus, the concentration addition and independent action models could be applied to SSD in the same manner as for a single-species effect. The present study to validate the application of the concentration addition and independent action models to SSD supports the usefulness of the multisubstance potentially affected fraction as the index of ecological risk. Environ Toxicol Chem 2017;36:2624-2630. © 2017 SETAC. © 2017 SETAC.

Structure and stability of charged colloid-nanoparticle mixtures

NASA Astrophysics Data System (ADS)

Weight, Braden M.; Denton, Alan R.

2018-03-01

Physical properties of colloidal materials can be modified by addition of nanoparticles. Within a model of like-charged mixtures of particles governed by effective electrostatic interactions, we explore the influence of charged nanoparticles on the structure and thermodynamic phase stability of charge-stabilized colloidal suspensions. Focusing on salt-free mixtures of particles of high size and charge asymmetry, interacting via repulsive Yukawa effective pair potentials, we perform molecular dynamics simulations and compute radial distribution functions and static structure factors. Analysis of these structural properties indicates that increasing the charge and concentration of nanoparticles progressively weakens correlations between charged colloids. We show that addition of charged nanoparticles to a suspension of like-charged colloids can induce a colloidal crystal to melt and can facilitate aggregation of a fluid suspension due to attractive van der Waals interactions. We attribute the destabilizing influence of charged nanoparticles to enhanced screening of electrostatic interactions, which weakens repulsion between charged colloids. This interpretation is consistent with recent predictions of an effective interaction theory of charged colloid-nanoparticle mixtures.

ONTOGENETIC ALTERATIONS IN PROTOTYPICAL FACTORS IN THE CEREBELLUM AND HIPPOCAMPUS FOLLOWING PERINATAL EXPOSURE TO A COMMERCIAL PCB MIXTURE.

EPA Science Inventory

Polychlorinated biphenyls (PCBs) are prevalent in the environment despite the ban of their use for decades and offers as a model to understand developmental neurotoxicity of persistent pollutants. Disturbances in brain development and cognition are among the neurotoxic manifestat...

A Zero- and K-Inflated Mixture Model for Health Questionnaire Data

PubMed Central

Finkelman, Matthew D.; Green, Jennifer Greif; Gruber, Michael J.; Zaslavsky, Alan M.

2011-01-01

In psychiatric assessment, Item Response Theory (IRT) is a popular tool to formalize the relation between the severity of a disorder and associated responses to questionnaire items. Practitioners of IRT sometimes make the assumption of normally distributed severities within a population; while convenient, this assumption is often violated when measuring psychiatric disorders. Specifically, there may be a sizable group of respondents whose answers place them at an extreme of the latent trait spectrum. In this article, a zero- and K-inflated mixture model is developed to account for the presence of such respondents. The model is fitted using an expectation-maximization (E-M) algorithm to estimate the percentage of the population at each end of the continuum, concurrently analyzing the remaining “graded component” via IRT. A method to perform factor analysis for only the graded component is introduced. In assessments of oppositional defiant disorder and conduct disorder, the zero- and K-inflated model exhibited better fit than the standard IRT model. PMID:21365673

Development of an Image-based Multi-Scale Finite Element Approach to Predict Fatigue Damage in Asphalt Mixtures

NASA Astrophysics Data System (ADS)

Arshadi, Amir

Image-based simulation of complex materials is a very important tool for understanding their mechanical behavior and an effective tool for successful design of composite materials. In this thesis an image-based multi-scale finite element approach is developed to predict the mechanical properties of asphalt mixtures. In this approach the "up-scaling" and homogenization of each scale to the next is critically designed to improve accuracy. In addition to this multi-scale efficiency, this study introduces an approach for consideration of particle contacts at each of the scales in which mineral particles exist. One of the most important pavement distresses which seriously affects the pavement performance is fatigue cracking. As this cracking generally takes place in the binder phase of the asphalt mixture, the binder fatigue behavior is assumed to be one of the main factors influencing the overall pavement fatigue performance. It is also known that aggregate gradation, mixture volumetric properties, and filler type and concentration can affect damage initiation and progression in the asphalt mixtures. This study was conducted to develop a tool to characterize the damage properties of the asphalt mixtures at all scales. In the present study the Viscoelastic continuum damage model is implemented into the well-known finite element software ABAQUS via the user material subroutine (UMAT) in order to simulate the state of damage in the binder phase under the repeated uniaxial sinusoidal loading. The inputs are based on the experimentally derived measurements for the binder properties. For the scales of mastic and mortar, the artificially 2-Dimensional images of mastic and mortar scales were generated and used to characterize the properties of those scales. Finally, the 2D scanned images of asphalt mixtures are used to study the asphalt mixture fatigue behavior under loading. In order to validate the proposed model, the experimental test results and the simulation results were compared. Indirect tensile fatigue tests were conducted on asphalt mixture samples. A comparison between experimental results and the results from simulation shows that the model developed in this study is capable of predicting the effect of asphalt binder properties and aggregate micro-structure on mechanical behavior of asphalt concrete under loading.

Durability assessments of concrete using electrical properties and acoustic emission testing

NASA Astrophysics Data System (ADS)

Todak, Heather N.

Premature damage deterioration has been observed in pavement joints throughout the Midwestern region of the United States. Over time, severe joint damage creates a transportation safety concern and the necessary repairs can be an extreme economic burden. The deterioration is due in part to freeze-thaw damage associated with fluid accumulation at the pavement joints. This very preventable problem is an indication that current specifications and construction practices for freeze-thaw durability of concrete are inadequate. This thesis serves to create a better understanding of moisture ingress, freeze-thaw damage mechanisms, and the effect of variations in mixture properties on freeze-thaw behavior of concrete. The concepts of the nick point degree of saturation, sorptivity rates, and critical degree of saturation are discussed. These factors contribute to service life, defined in this study as the duration of time a concrete element remains below levels of critical saturation which are required for damage development to initiate. A theoretical model and a simple experimental procedure are introduced which help determine the nick point for a series of 32 concrete mixtures with unique mixture proportions and air entrainment properties. This simple experimental procedure is also presented as a method to measure important electrical properties in order to establish the formation factor, a valuable measure of concrete transport properties. The results of freeze-thaw testing with acoustic emission monitoring are presented to help understand and quantify damage development in concrete specimens when conditioned to various degrees of saturation. This procedure was used to study the relationship between air entrainment properties and the critical degree of saturation. Applying the concepts of degree of saturation and sorptivity, a performance-based model is proposed as a new approach to specifications for freeze-thaw durability. Finally, a conceptual model is presented to illustrate the effect of various changes in mixture proportions and air void properties on service life.

Multivariate methods on the excitation emission matrix fluorescence spectroscopic data of diesel-kerosene mixtures: a comparative study.

PubMed

Divya, O; Mishra, Ashok K

2007-05-29

Quantitative determination of kerosene fraction present in diesel has been carried out based on excitation emission matrix fluorescence (EEMF) along with parallel factor analysis (PARAFAC) and N-way partial least squares regression (N-PLS). EEMF is a simple, sensitive and nondestructive method suitable for the analysis of multifluorophoric mixtures. Calibration models consisting of varying compositions of diesel and kerosene were constructed and their validation was carried out using leave-one-out cross validation method. The accuracy of the model was evaluated through the root mean square error of prediction (RMSEP) for the PARAFAC, N-PLS and unfold PLS methods. N-PLS was found to be a better method compared to PARAFAC and unfold PLS method because of its low RMSEP values.

Risk factors and outcomes of chronic sexual harassment during the transition to college: Examination of a two-part growth mixture model.

PubMed

McGinley, Meredith; Wolff, Jennifer M; Rospenda, Kathleen M; Liu, Li; Richman, Judith A

2016-11-01

A two-part latent growth mixture model was implemented in order to examine heterogeneity in the growth of sexual harassment (SH) victimization in college and university students, and the extent to which SH class membership explains substance use and mental health outcomes for certain groups of students. Demographic risk factors, mental health, and substance use were examined as they related to chronically experienced SH victimization. Incoming freshmen students (N = 2855; 58% female; 54% White) completed a survey at five time points. In addition to self-reporting gender, race, and sexual orientation, students completed measures of sexual harassment, anxiety, depression, binge drinking, and marijuana use. Overall, self-reported SH declined upon college entry, although levels rebounded by the third year of college. Results also supported a two-class solution (Infrequent and Chronic) for SH victimization. Being female, White, and a sexual minority were linked to being classified into the Chronic (relative to the Infrequent) SH class. In turn, Chronic SH class membership predicted greater anxiety, depression, and substance use, supporting a mediational model. Copyright © 2016 Elsevier Inc. All rights reserved.

ODE constrained mixture modelling: a method for unraveling subpopulation structures and dynamics.

PubMed

Hasenauer, Jan; Hasenauer, Christine; Hucho, Tim; Theis, Fabian J

2014-07-01

Functional cell-to-cell variability is ubiquitous in multicellular organisms as well as bacterial populations. Even genetically identical cells of the same cell type can respond differently to identical stimuli. Methods have been developed to analyse heterogeneous populations, e.g., mixture models and stochastic population models. The available methods are, however, either incapable of simultaneously analysing different experimental conditions or are computationally demanding and difficult to apply. Furthermore, they do not account for biological information available in the literature. To overcome disadvantages of existing methods, we combine mixture models and ordinary differential equation (ODE) models. The ODE models provide a mechanistic description of the underlying processes while mixture models provide an easy way to capture variability. In a simulation study, we show that the class of ODE constrained mixture models can unravel the subpopulation structure and determine the sources of cell-to-cell variability. In addition, the method provides reliable estimates for kinetic rates and subpopulation characteristics. We use ODE constrained mixture modelling to study NGF-induced Erk1/2 phosphorylation in primary sensory neurones, a process relevant in inflammatory and neuropathic pain. We propose a mechanistic pathway model for this process and reconstructed static and dynamical subpopulation characteristics across experimental conditions. We validate the model predictions experimentally, which verifies the capabilities of ODE constrained mixture models. These results illustrate that ODE constrained mixture models can reveal novel mechanistic insights and possess a high sensitivity.

Central Composite Design (CCD) applied for statistical optimization of glucose and sucrose binary carbon mixture in enhancing the denitrification process

NASA Astrophysics Data System (ADS)

Lim, Jun-Wei; Beh, Hoe-Guan; Ching, Dennis Ling Chuan; Ho, Yeek-Chia; Baloo, Lavania; Bashir, Mohammed J. K.; Wee, Seng-Kew

2017-11-01

The present study provides an insight into the optimization of a glucose and sucrose mixture to enhance the denitrification process. Central Composite Design was applied to design the batch experiments with the factors of glucose and sucrose measured as carbon-to-nitrogen (C:N) ratio each and the response of percentage removal of nitrate-nitrogen (NO3 --N). Results showed that the polynomial regression model of NO3 --N removal had been successfully derived, capable of describing the interactive relationships of glucose and sucrose mixture that influenced the denitrification process. Furthermore, the presence of glucose was noticed to have more consequential effect on NO3 --N removal as opposed to sucrose. The optimum carbon sources mixture to achieve complete removal of NO3 --N required lesser glucose (C:N ratio of 1.0:1.0) than sucrose (C:N ratio of 2.4:1.0). At the optimum glucose and sucrose mixture, the activated sludge showed faster acclimation towards glucose used to perform the denitrification process. Later upon the acclimation with sucrose, the glucose uptake rate by the activated sludge abated. Therefore, it is vital to optimize the added carbon sources mixture to ensure the rapid and complete removal of NO3 --N via the denitrification process.

A study of finite mixture model: Bayesian approach on financial time series data

NASA Astrophysics Data System (ADS)

Phoong, Seuk-Yen; Ismail, Mohd Tahir

2014-07-01

Recently, statistician have emphasized on the fitting finite mixture model by using Bayesian method. Finite mixture model is a mixture of distributions in modeling a statistical distribution meanwhile Bayesian method is a statistical method that use to fit the mixture model. Bayesian method is being used widely because it has asymptotic properties which provide remarkable result. In addition, Bayesian method also shows consistency characteristic which means the parameter estimates are close to the predictive distributions. In the present paper, the number of components for mixture model is studied by using Bayesian Information Criterion. Identify the number of component is important because it may lead to an invalid result. Later, the Bayesian method is utilized to fit the k-component mixture model in order to explore the relationship between rubber price and stock market price for Malaysia, Thailand, Philippines and Indonesia. Lastly, the results showed that there is a negative effect among rubber price and stock market price for all selected countries.

Molecular factor computing for predictive spectroscopy.

PubMed

Dai, Bin; Urbas, Aaron; Douglas, Craig C; Lodder, Robert A

2007-08-01

The concept of molecular factor computing (MFC)-based predictive spectroscopy was demonstrated here with quantitative analysis of ethanol-in-water mixtures in a MFC-based prototype instrument. Molecular computing of vectors for transformation matrices enabled spectra to be represented in a desired coordinate system. New coordinate systems were selected to reduce the dimensionality of the spectral hyperspace and simplify the mechanical/electrical/computational construction of a new MFC spectrometer employing transmission MFC filters. A library search algorithm was developed to calculate the chemical constituents of the MFC filters. The prototype instrument was used to collect data from 39 ethanol-in-water mixtures (range 0-14%). For each sample, four different voltage outputs from the detector (forming two factor scores) were measured by using four different MFC filters. Twenty samples were used to calibrate the instrument and build a multivariate linear regression prediction model, and the remaining samples were used to validate the predictive ability of the model. In engineering simulations, four MFC filters gave an adequate calibration model (r2 = 0.995, RMSEC = 0.229%, RMSECV = 0.339%, p = 0.05 by f test). This result is slightly better than a corresponding PCR calibration model based on corrected transmission spectra (r2 = 0.993, RMSEC = 0.359%, RMSECV = 0.551%, p = 0.05 by f test). The first actual MFC prototype gave an RMSECV = 0.735%. MFC was a viable alternative to conventional spectrometry with the potential to be more simply implemented and more rapid and accurate.

Accounting for non-independent detection when estimating abundance of organisms with a Bayesian approach

USGS Publications Warehouse

Martin, Julien; Royle, J. Andrew; MacKenzie, Darryl I.; Edwards, Holly H.; Kery, Marc; Gardner, Beth

2011-01-01

Summary 1. Binomial mixture models use repeated count data to estimate abundance. They are becoming increasingly popular because they provide a simple and cost-effective way to account for imperfect detection. However, these models assume that individuals are detected independently of each other. This assumption may often be violated in the field. For instance, manatees (Trichechus manatus latirostris) may surface in turbid water (i.e. become available for detection during aerial surveys) in a correlated manner (i.e. in groups). However, correlated behaviour, affecting the non-independence of individual detections, may also be relevant in other systems (e.g. correlated patterns of singing in birds and amphibians). 2. We extend binomial mixture models to account for correlated behaviour and therefore to account for non-independent detection of individuals. We simulated correlated behaviour using beta-binomial random variables. Our approach can be used to simultaneously estimate abundance, detection probability and a correlation parameter. 3. Fitting binomial mixture models to data that followed a beta-binomial distribution resulted in an overestimation of abundance even for moderate levels of correlation. In contrast, the beta-binomial mixture model performed considerably better in our simulation scenarios. We also present a goodness-of-fit procedure to evaluate the fit of beta-binomial mixture models. 4. We illustrate our approach by fitting both binomial and beta-binomial mixture models to aerial survey data of manatees in Florida. We found that the binomial mixture model did not fit the data, whereas there was no evidence of lack of fit for the beta-binomial mixture model. This example helps illustrate the importance of using simulations and assessing goodness-of-fit when analysing ecological data with N-mixture models. Indeed, both the simulations and the goodness-of-fit procedure highlighted the limitations of the standard binomial mixture model for aerial manatee surveys. 5. Overestimation of abundance by binomial mixture models owing to non-independent detections is problematic for ecological studies, but also for conservation. For example, in the case of endangered species, it could lead to inappropriate management decisions, such as downlisting. These issues will be increasingly relevant as more ecologists apply flexible N-mixture models to ecological data.

A competitive binding model predicts the response of mammalian olfactory receptors to mixtures

NASA Astrophysics Data System (ADS)

Singh, Vijay; Murphy, Nicolle; Mainland, Joel; Balasubramanian, Vijay

Most natural odors are complex mixtures of many odorants, but due to the large number of possible mixtures only a small fraction can be studied experimentally. To get a realistic understanding of the olfactory system we need methods to predict responses to complex mixtures from single odorant responses. Focusing on mammalian olfactory receptors (ORs in mouse and human), we propose a simple biophysical model for odor-receptor interactions where only one odor molecule can bind to a receptor at a time. The resulting competition for occupancy of the receptor accounts for the experimentally observed nonlinear mixture responses. We first fit a dose-response relationship to individual odor responses and then use those parameters in a competitive binding model to predict mixture responses. With no additional parameters, the model predicts responses of 15 (of 18 tested) receptors to within 10 - 30 % of the observed values, for mixtures with 2, 3 and 12 odorants chosen from a panel of 30. Extensions of our basic model with odorant interactions lead to additional nonlinearities observed in mixture response like suppression, cooperativity, and overshadowing. Our model provides a systematic framework for characterizing and parameterizing such mixing nonlinearities from mixture response data.

Computational Modeling of Seismic Wave Propagation Velocity-Saturation Effects in Porous Rocks

NASA Astrophysics Data System (ADS)

Deeks, J.; Lumley, D. E.

2011-12-01

Compressional and shear velocities of seismic waves propagating in porous rocks vary as a function of the fluid mixture and its distribution in pore space. Although it has been possible to place theoretical upper and lower bounds on the velocity variation with fluid saturation, predicting the actual velocity response of a given rock with fluid type and saturation remains an unsolved problem. In particular, we are interested in predicting the velocity-saturation response to various mixtures of fluids with pressure and temperature, as a function of the spatial distribution of the fluid mixture and the seismic wavelength. This effect is often termed "patchy saturation' in the rock physics community. The ability to accurately predict seismic velocities for various fluid mixtures and spatial distributions in the pore space of a rock is useful for fluid detection, hydrocarbon exploration and recovery, CO2 sequestration and monitoring of many subsurface fluid-flow processes. We create digital rock models with various fluid mixtures, saturations and spatial distributions. We use finite difference modeling to propagate elastic waves of varying frequency content through these digital rock and fluid models to simulate a given lab or field experiment. The resulting waveforms can be analyzed to determine seismic traveltimes, velocities, amplitudes, attenuation and other wave phenomena for variable rock models of fluid saturation and spatial fluid distribution, and variable wavefield spectral content. We show that we can reproduce most of the published effects of velocity-saturation variation, including validating the Voigt and Reuss theoretical bounds, as well as the Hill "patchy saturation" curve. We also reproduce what has been previously identified as Biot dispersion, but in fact in our models is often seen to be wave multi-pathing and broadband spectral effects. Furthermore, we find that in addition to the dominant seismic wavelength and average fluid patch size, the smoothness of the fluid patches are a critical factor in determining the velocity-saturation response; this is a result that we have not seen discussed in the literature. Most importantly, we can reproduce all of these effects using full elastic wavefield scattering, without the need to resort to more complicated squirt-flow or poroelastic models. This is important because the physical properties and parameters we need to model full elastic wave scattering, and predict a velocity-saturation curve, are often readily available for projects we undertake; this is not the case for poroelastic or squirt-flow models. We can predict this velocity saturation curve for a specific rock type, fluid mixture distribution and wavefield spectrum.

Speech Enhancement Using Gaussian Scale Mixture Models

PubMed Central

Hao, Jiucang; Lee, Te-Won; Sejnowski, Terrence J.

2011-01-01

This paper presents a novel probabilistic approach to speech enhancement. Instead of a deterministic logarithmic relationship, we assume a probabilistic relationship between the frequency coefficients and the log-spectra. The speech model in the log-spectral domain is a Gaussian mixture model (GMM). The frequency coefficients obey a zero-mean Gaussian whose covariance equals to the exponential of the log-spectra. This results in a Gaussian scale mixture model (GSMM) for the speech signal in the frequency domain, since the log-spectra can be regarded as scaling factors. The probabilistic relation between frequency coefficients and log-spectra allows these to be treated as two random variables, both to be estimated from the noisy signals. Expectation-maximization (EM) was used to train the GSMM and Bayesian inference was used to compute the posterior signal distribution. Because exact inference of this full probabilistic model is computationally intractable, we developed two approaches to enhance the efficiency: the Laplace method and a variational approximation. The proposed methods were applied to enhance speech corrupted by Gaussian noise and speech-shaped noise (SSN). For both approximations, signals reconstructed from the estimated frequency coefficients provided higher signal-to-noise ratio (SNR) and those reconstructed from the estimated log-spectra produced lower word recognition error rate because the log-spectra fit the inputs to the recognizer better. Our algorithms effectively reduced the SSN, which algorithms based on spectral analysis were not able to suppress. PMID:21359139

Estimation of value at risk and conditional value at risk using normal mixture distributions model

NASA Astrophysics Data System (ADS)

Kamaruzzaman, Zetty Ain; Isa, Zaidi

2013-04-01

Normal mixture distributions model has been successfully applied in financial time series analysis. In this paper, we estimate the return distribution, value at risk (VaR) and conditional value at risk (CVaR) for monthly and weekly rates of returns for FTSE Bursa Malaysia Kuala Lumpur Composite Index (FBMKLCI) from July 1990 until July 2010 using the two component univariate normal mixture distributions model. First, we present the application of normal mixture distributions model in empirical finance where we fit our real data. Second, we present the application of normal mixture distributions model in risk analysis where we apply the normal mixture distributions model to evaluate the value at risk (VaR) and conditional value at risk (CVaR) with model validation for both risk measures. The empirical results provide evidence that using the two components normal mixture distributions model can fit the data well and can perform better in estimating value at risk (VaR) and conditional value at risk (CVaR) where it can capture the stylized facts of non-normality and leptokurtosis in returns distribution.

Bayesian inference for psychology, part IV: parameter estimation and Bayes factors.

PubMed

Rouder, Jeffrey N; Haaf, Julia M; Vandekerckhove, Joachim

2018-02-01

In the psychological literature, there are two seemingly different approaches to inference: that from estimation of posterior intervals and that from Bayes factors. We provide an overview of each method and show that a salient difference is the choice of models. The two approaches as commonly practiced can be unified with a certain model specification, now popular in the statistics literature, called spike-and-slab priors. A spike-and-slab prior is a mixture of a null model, the spike, with an effect model, the slab. The estimate of the effect size here is a function of the Bayes factor, showing that estimation and model comparison can be unified. The salient difference is that common Bayes factor approaches provide for privileged consideration of theoretically useful parameter values, such as the value corresponding to the null hypothesis, while estimation approaches do not. Both approaches, either privileging the null or not, are useful depending on the goals of the analyst.

Predictors of Latent Trajectory Classes of Physical Dating Violence Victimization

ERIC Educational Resources Information Center

Brooks-Russell, Ashley; Foshee, Vangie A.; Ennett, Susan T.

2013-01-01

This study identified classes of developmental trajectories of physical dating violence victimization from grades 8 to 12 and examined theoretically-based risk factors that distinguished among trajectory classes. Data were from a multi-wave longitudinal study spanning 8th through 12th grade (n = 2,566; 51.9 % female). Growth mixture models were…

Relations among Chronic Peer Group Rejection, Maladaptive Behavioral Dispositions, and Early Adolescents' Peer Perceptions

ERIC Educational Resources Information Center

Ladd, Gary W.; Ettekal, Idean; Kochenderfer-Ladd, Becky; Rudolph, Karen D.; Andrews, Rebecca K.

2014-01-01

Adolescents' perceptions of peers' relational characteristics (e.g., support, trustworthiness) were examined for subtypes of youth who evidenced chronic maladaptive behavior, chronic peer group rejection, or combinations of these risk factors. Growth mixture modeling was used to identify subgroups of participants within a normative…

Sensory imbalance as mechanism of orientation disruption in the leafminer, Phyllocnistis citrella: Elucidation by multivariate geometric designs and response surface models

USDA-ARS?s Scientific Manuscript database

Experimental designs developed to address mixtures are ideally suited for many areas of experimental biology including pheromone blend studies because they address the confounding of proportionality and concentration intrinsic to factorial and one-factor-at-a-time designs. Geometric multivariate des...

Optimizing shoot culture media for Rubus germplasm: the effects of NH4+, NO3-, and total nitrogen

USDA-ARS?s Scientific Manuscript database

The nitrogen components of Murashige and Skoog (MS) medium were significant factors for improved growth in our earlier study that modeled the effects of mineral nutrition on growth and development of micropropagated red raspberry(Rubus idaeus L.). In this study, a mixture component design was applie...

Identification of anxiety sensitivity classes and clinical cut-scores in a sample of adult smokers: results from a factor mixture model.

PubMed

Allan, Nicholas P; Raines, Amanda M; Capron, Daniel W; Norr, Aaron M; Zvolensky, Michael J; Schmidt, Norman B

2014-10-01

Anxiety sensitivity (AS), a multidimensional construct, has been implicated in the development and maintenance of anxiety and related disorders. Recent evidence suggests that AS is a dimensional-categorical construct within individuals. Factor mixture modeling was conducted in a sample of 579 adult smokers (M age=36.87 years, SD=13.47) to examine the underlying structure. Participants completed the Anxiety Sensitivity Index-3 and were also given a Structured Clinical Interview for DSM-IV-TR. Three classes of individuals emerged, a high AS (5.2% of the sample), a moderate AS (19.0%), and a normative AS class (75.8%). A cut-score of 23 to identify high AS individuals, and a cut-score of 17 to identify moderate-to-high AS individuals were supported in this study. In addition, the odds of having a concurrent anxiety disorder (controlling for other Axis I disorders) were the highest in the high AS class and the lowest in the normative AS class. Copyright © 2014 Elsevier Ltd. All rights reserved.

Application of hierarchical Bayesian unmixing models in river sediment source apportionment

NASA Astrophysics Data System (ADS)

Blake, Will; Smith, Hugh; Navas, Ana; Bodé, Samuel; Goddard, Rupert; Zou Kuzyk, Zou; Lennard, Amy; Lobb, David; Owens, Phil; Palazon, Leticia; Petticrew, Ellen; Gaspar, Leticia; Stock, Brian; Boeckx, Pacsal; Semmens, Brice

2016-04-01

Fingerprinting and unmixing concepts are used widely across environmental disciplines for forensic evaluation of pollutant sources. In aquatic and marine systems, this includes tracking the source of organic and inorganic pollutants in water and linking problem sediment to soil erosion and land use sources. It is, however, the particular complexity of ecological systems that has driven creation of the most sophisticated mixing models, primarily to (i) evaluate diet composition in complex ecological food webs, (ii) inform population structure and (iii) explore animal movement. In the context of the new hierarchical Bayesian unmixing model, MIXSIAR, developed to characterise intra-population niche variation in ecological systems, we evaluate the linkage between ecological 'prey' and 'consumer' concepts and river basin sediment 'source' and sediment 'mixtures' to exemplify the value of ecological modelling tools to river basin science. Recent studies have outlined advantages presented by Bayesian unmixing approaches in handling complex source and mixture datasets while dealing appropriately with uncertainty in parameter probability distributions. MixSIAR is unique in that it allows individual fixed and random effects associated with mixture hierarchy, i.e. factors that might exert an influence on model outcome for mixture groups, to be explored within the source-receptor framework. This offers new and powerful ways of interpreting river basin apportionment data. In this contribution, key components of the model are evaluated in the context of common experimental designs for sediment fingerprinting studies namely simple, nested and distributed catchment sampling programmes. Illustrative examples using geochemical and compound specific stable isotope datasets are presented and used to discuss best practice with specific attention to (1) the tracer selection process, (2) incorporation of fixed effects relating to sample timeframe and sediment type in the modelling process, (3) deriving and using informative priors in sediment fingerprinting context and (4) transparency of the process and replication of model results by other users.

Reduced chemical kinetic model of detonation combustion of one- and multi-fuel gaseous mixtures with air

NASA Astrophysics Data System (ADS)

Fomin, P. A.

2018-03-01

Two-step approximate models of chemical kinetics of detonation combustion of (i) one hydrocarbon fuel CnHm (for example, methane, propane, cyclohexane etc.) and (ii) multi-fuel gaseous mixtures (∑aiCniHmi) (for example, mixture of methane and propane, synthesis gas, benzene and kerosene) are presented for the first time. The models can be used for any stoichiometry, including fuel/fuels-rich mixtures, when reaction products contain molecules of carbon. Owing to the simplicity and high accuracy, the models can be used in multi-dimensional numerical calculations of detonation waves in corresponding gaseous mixtures. The models are in consistent with the second law of thermodynamics and Le Chatelier's principle. Constants of the models have a clear physical meaning. The models can be used for calculation thermodynamic parameters of the mixture in a state of chemical equilibrium.

ODE Constrained Mixture Modelling: A Method for Unraveling Subpopulation Structures and Dynamics

PubMed Central

Hasenauer, Jan; Hasenauer, Christine; Hucho, Tim; Theis, Fabian J.

2014-01-01

Functional cell-to-cell variability is ubiquitous in multicellular organisms as well as bacterial populations. Even genetically identical cells of the same cell type can respond differently to identical stimuli. Methods have been developed to analyse heterogeneous populations, e.g., mixture models and stochastic population models. The available methods are, however, either incapable of simultaneously analysing different experimental conditions or are computationally demanding and difficult to apply. Furthermore, they do not account for biological information available in the literature. To overcome disadvantages of existing methods, we combine mixture models and ordinary differential equation (ODE) models. The ODE models provide a mechanistic description of the underlying processes while mixture models provide an easy way to capture variability. In a simulation study, we show that the class of ODE constrained mixture models can unravel the subpopulation structure and determine the sources of cell-to-cell variability. In addition, the method provides reliable estimates for kinetic rates and subpopulation characteristics. We use ODE constrained mixture modelling to study NGF-induced Erk1/2 phosphorylation in primary sensory neurones, a process relevant in inflammatory and neuropathic pain. We propose a mechanistic pathway model for this process and reconstructed static and dynamical subpopulation characteristics across experimental conditions. We validate the model predictions experimentally, which verifies the capabilities of ODE constrained mixture models. These results illustrate that ODE constrained mixture models can reveal novel mechanistic insights and possess a high sensitivity. PMID:24992156

Applicability study of classical and contemporary models for effective complex permittivity of metal powders.

PubMed

Kiley, Erin M; Yakovlev, Vadim V; Ishizaki, Kotaro; Vaucher, Sebastien

2012-01-01

Microwave thermal processing of metal powders has recently been a topic of a substantial interest; however, experimental data on the physical properties of mixtures involving metal particles are often unavailable. In this paper, we perform a systematic analysis of classical and contemporary models of complex permittivity of mixtures and discuss the use of these models for determining effective permittivity of dielectric matrices with metal inclusions. Results from various mixture and core-shell mixture models are compared to experimental data for a titanium/stearic acid mixture and a boron nitride/graphite mixture (both obtained through the original measurements), and for a tungsten/Teflon mixture (from literature). We find that for certain experiments, the average error in determining the effective complex permittivity using Lichtenecker's, Maxwell Garnett's, Bruggeman's, Buchelnikov's, and Ignatenko's models is about 10%. This suggests that, for multiphysics computer models describing the processing of metal powder in the full temperature range, input data on effective complex permittivity obtained from direct measurement has, up to now, no substitute.

Polycyclic aromatic hydrocarbons as skin carcinogens: Comparison of benzo[a]pyrene, dibenzo[def,p]chrysene and three environmental mixtures in the FVB/N mouse

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

Siddens, Lisbeth K.; Larkin, Andrew; Superfund Research Center, Oregon State University

2012-11-01

The polycyclic aromatic hydrocarbon (PAH), benzo[a]pyrene (BaP), was compared to dibenzo[def,p]chrysene (DBC) and combinations of three environmental PAH mixtures (coal tar, diesel particulate and cigarette smoke condensate) using a two stage, FVB/N mouse skin tumor model. DBC (4 nmol) was most potent, reaching 100% tumor incidence with a shorter latency to tumor formation, less than 20 weeks of 12-O-tetradecanoylphorbol-13-acetate (TPA) promotion compared to all other treatments. Multiplicity was 4 times greater than BaP (400 nmol). Both PAHs produced primarily papillomas followed by squamous cell carcinoma and carcinoma in situ. Diesel particulate extract (1 mg SRM 1650b; mix 1) did notmore » differ from toluene controls and failed to elicit a carcinogenic response. Addition of coal tar extract (1 mg SRM 1597a; mix 2) produced a response similar to BaP. Further addition of 2 mg of cigarette smoke condensate (mix 3) did not alter the response with mix 2. PAH-DNA adducts measured in epidermis 12 h post initiation and analyzed by {sup 32}P post‐labeling, did not correlate with tumor incidence. PAH‐dependent alteration in transcriptome of skin 12 h post initiation was assessed by microarray. Principal component analysis (sum of all treatments) of the 922 significantly altered genes (p < 0.05), showed DBC and BaP to cluster distinct from PAH mixtures and each other. BaP and mixtures up-regulated phase 1 and phase 2 metabolizing enzymes while DBC did not. The carcinogenicity with DBC and two of the mixtures was much greater than would be predicted based on published Relative Potency Factors (RPFs). -- Highlights: ► Dibenzo[def,p]chrysene (DBC), 3 PAH mixtures, benzo[a]pyrene (BaP) were compared. ► DBC and 2 PAH mixtures were more potent than Relative Potency Factor estimates. ► Transcriptome profiles 12 hours post initiation were analyzed by microarray. ► Principle components analysis of alterations revealed treatment-based clustering. ► DBC gave a unique pattern of gene alterations compared to BaP and PAH mixtures.« less

Mechanism-based classification of PAH mixtures to predict carcinogenic potential

DOE PAGES

Tilton, Susan C.; Siddens, Lisbeth K.; Krueger, Sharon K.; ...

2015-04-22

We have previously shown that relative potency factors and DNA adduct measurements are inadequate for predicting carcinogenicity of certain polycyclic aromatic hydrocarbons (PAHs) and PAH mixtures, particularly those that function through alternate pathways or exhibit greater promotional activity compared to benzo[ a]pyrene (BaP). Therefore, we developed a pathway based approach for classification of tumor outcome after dermal exposure to PAH/mixtures. FVB/N mice were exposed to dibenzo[ def,p]chrysene (DBC), BaP or environmental PAH mixtures (Mix 1-3) following a two-stage initiation/promotion skin tumor protocol. Resulting tumor incidence could be categorized by carcinogenic potency as DBC>>BaP=Mix2=Mix3>Mix1=Control, based on statistical significance. Gene expression profilesmore » measured in skin of mice collected 12 h post-initiation were compared to tumor outcome for identification of short-term bioactivity profiles. A Bayesian integration model was utilized to identify biological pathways predictive of PAH carcinogenic potential during initiation. Integration of probability matrices from four enriched pathways (p<0.05) for DNA damage, apoptosis, response to chemical stimulus and interferon gamma signaling resulted in the highest classification accuracy with leave-one-out cross validation. This pathway-driven approach was successfully utilized to distinguish early regulatory events during initiation prognostic for tumor outcome and provides proof-of-concept for using short-term initiation studies to classify carcinogenic potential of environmental PAH mixtures. As a result, these data further provide a ‘source-to outcome’ model that could be used to predict PAH interactions during tumorigenesis and provide an example of how mode-of-action based risk assessment could be employed for environmental PAH mixtures.« less

Mechanism-Based Classification of PAH Mixtures to Predict Carcinogenic Potential.

PubMed

Tilton, Susan C; Siddens, Lisbeth K; Krueger, Sharon K; Larkin, Andrew J; Löhr, Christiane V; Williams, David E; Baird, William M; Waters, Katrina M

2015-07-01

We have previously shown that relative potency factors and DNA adduct measurements are inadequate for predicting carcinogenicity of certain polycyclic aromatic hydrocarbons (PAHs) and PAH mixtures, particularly those that function through alternate pathways or exhibit greater promotional activity compared to benzo[a]pyrene (BaP). Therefore, we developed a pathway-based approach for classification of tumor outcome after dermal exposure to PAH/mixtures. FVB/N mice were exposed to dibenzo[def,p]chrysene (DBC), BaP, or environmental PAH mixtures (Mix 1-3) following a 2-stage initiation/promotion skin tumor protocol. Resulting tumor incidence could be categorized by carcinogenic potency as DBC > BaP = Mix2 = Mix3 > Mix1 = Control, based on statistical significance. Gene expression profiles measured in skin of mice collected 12 h post-initiation were compared with tumor outcome for identification of short-term bioactivity profiles. A Bayesian integration model was utilized to identify biological pathways predictive of PAH carcinogenic potential during initiation. Integration of probability matrices from four enriched pathways (P < .05) for DNA damage, apoptosis, response to chemical stimulus, and interferon gamma signaling resulted in the highest classification accuracy with leave-one-out cross validation. This pathway-driven approach was successfully utilized to distinguish early regulatory events during initiation prognostic for tumor outcome and provides proof-of-concept for using short-term initiation studies to classify carcinogenic potential of environmental PAH mixtures. These data further provide a 'source-to-outcome' model that could be used to predict PAH interactions during tumorigenesis and provide an example of how mode-of-action-based risk assessment could be employed for environmental PAH mixtures. © The Author 2015. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Application of the QSPR approach to the boiling points of azeotropes.

PubMed

Katritzky, Alan R; Stoyanova-Slavova, Iva B; Tämm, Kaido; Tamm, Tarmo; Karelson, Mati

2011-04-21

CODESSA Pro derivative descriptors were calculated for a data set of 426 azeotropic mixtures by the centroid approximation and the weighted-contribution-factor approximation. The two approximations produced almost identical four-descriptor QSPR models relating the structural characteristic of the individual components of azeotropes to the azeotropic boiling points. These models were supported by internal and external validations. The descriptors contributing to the QSPR models are directly related to the three components of the enthalpy (heat) of vaporization.

Research Program on Factors Affecting the Brightness of AC Plasma Panels.

DTIC Science & Technology

1983-06-01

representing the second excited electronic con - figuration in neon. In an effort to devise means to improve the brightness and/or efficiency of neon filled...obtained from several panels was found to be very good (Ref. 6), a factor providing important support for the validity of the kinetic modeling pro - cedures...pressure neon Penning mixtures for I conditions typical of plasma panel displays [II]. Their work has pro - vied valuable insight regarding the electrical

Comparison of Saccharina japonica-Undaria pinnatifida Mixture and Minoxidil on Hair Growth Promoting Effect in Mice.

PubMed

Park, Ki Soo; Park, Dae Hwan

2016-11-01

Algae have traditionally been used for promotion of hair growth. Use of hair regrowth drugs, such as minoxidil, is limited due to side effects. The aim of this study was to examine a mixture of Saccharina japonica and Undaria pinnatifida (L-U mixture) on hair growth and to compare the promoting effect of hair growth by a 3% minoxidil and a L-U mixture. To evaluate the hair growth-promoting activity, saline, 50% ethanol, 3% minoxidil, and the L-U mixture were applied 2 times a day for a total of 14 days on the dorsal skin of C57BL/6 mice after depilation. Analysis was determined by using a high-resolution hair analysis system, real-time polymerase chain reaction, and H&E staining. On day 14, the hair growth effect of the L-U mixture was the same as that of the 3% minoxidil treatment. The L-U mixture significantly (P<0.05) stimulated hair growth-promoting genes, as vascular endothelial growth factor (VEGF) and insulin-like growth factor -1. Increase of VEGF was observed in the L-U mixture group compared with minoxidil and the negative control. In contrast, the L-U mixture suppressed the expression of transforming growth factor-β1, which is the hair loss-related gene. In histological examination in the L-U mixture and minoxidil groups, the induction of an anagen stage of hair follicles was faster than that of control groups. This study provides evidence that the L-U mixture can promote hair growth in mice, similar to the effect from minoxidil, and suggests that there is potential application for hair loss treatments.

Estimation and Model Selection for Finite Mixtures of Latent Interaction Models

ERIC Educational Resources Information Center

Hsu, Jui-Chen

2011-01-01

Latent interaction models and mixture models have received considerable attention in social science research recently, but little is known about how to handle if unobserved population heterogeneity exists in the endogenous latent variables of the nonlinear structural equation models. The current study estimates a mixture of latent interaction…

Scale Mixture Models with Applications to Bayesian Inference

NASA Astrophysics Data System (ADS)

Qin, Zhaohui S.; Damien, Paul; Walker, Stephen

2003-11-01

Scale mixtures of uniform distributions are used to model non-normal data in time series and econometrics in a Bayesian framework. Heteroscedastic and skewed data models are also tackled using scale mixture of uniform distributions.

Characterization of Mixtures. Part 2: QSPR Models for Prediction of Excess Molar Volume and Liquid Density Using Neural Networks.

PubMed

Ajmani, Subhash; Rogers, Stephen C; Barley, Mark H; Burgess, Andrew N; Livingstone, David J

2010-09-17

In our earlier work, we have demonstrated that it is possible to characterize binary mixtures using single component descriptors by applying various mixing rules. We also showed that these methods were successful in building predictive QSPR models to study various mixture properties of interest. Here in, we developed a QSPR model of an excess thermodynamic property of binary mixtures i.e. excess molar volume (V(E) ). In the present study, we use a set of mixture descriptors which we earlier designed to specifically account for intermolecular interactions between the components of a mixture and applied successfully to the prediction of infinite-dilution activity coefficients using neural networks (part 1 of this series). We obtain a significant QSPR model for the prediction of excess molar volume (V(E) ) using consensus neural networks and five mixture descriptors. We find that hydrogen bond and thermodynamic descriptors are the most important in determining excess molar volume (V(E) ), which is in line with the theory of intermolecular forces governing excess mixture properties. The results also suggest that the mixture descriptors utilized herein may be sufficient to model a wide variety of properties of binary and possibly even more complex mixtures. Copyright © 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Development of reversible jump Markov Chain Monte Carlo algorithm in the Bayesian mixture modeling for microarray data in Indonesia

NASA Astrophysics Data System (ADS)

Astuti, Ani Budi; Iriawan, Nur; Irhamah, Kuswanto, Heri

2017-12-01

In the Bayesian mixture modeling requires stages the identification number of the most appropriate mixture components thus obtained mixture models fit the data through data driven concept. Reversible Jump Markov Chain Monte Carlo (RJMCMC) is a combination of the reversible jump (RJ) concept and the Markov Chain Monte Carlo (MCMC) concept used by some researchers to solve the problem of identifying the number of mixture components which are not known with certainty number. In its application, RJMCMC using the concept of the birth/death and the split-merge with six types of movement, that are w updating, θ updating, z updating, hyperparameter β updating, split-merge for components and birth/death from blank components. The development of the RJMCMC algorithm needs to be done according to the observed case. The purpose of this study is to know the performance of RJMCMC algorithm development in identifying the number of mixture components which are not known with certainty number in the Bayesian mixture modeling for microarray data in Indonesia. The results of this study represent that the concept RJMCMC algorithm development able to properly identify the number of mixture components in the Bayesian normal mixture model wherein the component mixture in the case of microarray data in Indonesia is not known for certain number.

Accuracy assessment of linear spectral mixture model due to terrain undulation

NASA Astrophysics Data System (ADS)

Wang, Tianxing; Chen, Songlin; Ma, Ya

2008-12-01

Mixture spectra are common in remote sensing due to the limitations of spatial resolution and the heterogeneity of land surface. During the past 30 years, a lot of subpixel model have developed to investigate the information within mixture pixels. Linear spectral mixture model (LSMM) is a simper and more general subpixel model. LSMM also known as spectral mixture analysis is a widely used procedure to determine the proportion of endmembers (constituent materials) within a pixel based on the endmembers' spectral characteristics. The unmixing accuracy of LSMM is restricted by variety of factors, but now the research about LSMM is mostly focused on appraisement of nonlinear effect relating to itself and techniques used to select endmembers, unfortunately, the environment conditions of study area which could sway the unmixing-accuracy, such as atmospheric scatting and terrain undulation, are not studied. This paper probes emphatically into the accuracy uncertainty of LSMM resulting from the terrain undulation. ASTER dataset was chosen and the C terrain correction algorithm was applied to it. Based on this, fractional abundances for different cover types were extracted from both pre- and post-C terrain illumination corrected ASTER using LSMM. Simultaneously, the regression analyses and the IKONOS image were introduced to assess the unmixing accuracy. Results showed that terrain undulation could dramatically constrain the application of LSMM in mountain area. Specifically, for vegetation abundances, a improved unmixing accuracy of 17.6% (regression against to NDVI) and 18.6% (regression against to MVI) for R2 was achieved respectively by removing terrain undulation. Anyway, this study indicated in a quantitative way that effective removal or minimization of terrain illumination effects was essential for applying LSMM. This paper could also provide a new instance for LSMM applications in mountainous areas. In addition, the methods employed in this study could be effectively used to evaluate different algorithms of terrain undulation correction for further study.

QSAR prediction of additive and non-additive mixture toxicities of antibiotics and pesticide.

PubMed

Qin, Li-Tang; Chen, Yu-Han; Zhang, Xin; Mo, Ling-Yun; Zeng, Hong-Hu; Liang, Yan-Peng

2018-05-01

Antibiotics and pesticides may exist as a mixture in real environment. The combined effect of mixture can either be additive or non-additive (synergism and antagonism). However, no effective predictive approach exists on predicting the synergistic and antagonistic toxicities of mixtures. In this study, we developed a quantitative structure-activity relationship (QSAR) model for the toxicities (half effect concentration, EC 50 ) of 45 binary and multi-component mixtures composed of two antibiotics and four pesticides. The acute toxicities of single compound and mixtures toward Aliivibrio fischeri were tested. A genetic algorithm was used to obtain the optimized model with three theoretical descriptors. Various internal and external validation techniques indicated that the coefficient of determination of 0.9366 and root mean square error of 0.1345 for the QSAR model predicted that 45 mixture toxicities presented additive, synergistic, and antagonistic effects. Compared with the traditional concentration additive and independent action models, the QSAR model exhibited an advantage in predicting mixture toxicity. Thus, the presented approach may be able to fill the gaps in predicting non-additive toxicities of binary and multi-component mixtures. Copyright © 2018 Elsevier Ltd. All rights reserved.

Evaluating Mixture Modeling for Clustering: Recommendations and Cautions

ERIC Educational Resources Information Center

Steinley, Douglas; Brusco, Michael J.

2011-01-01

This article provides a large-scale investigation into several of the properties of mixture-model clustering techniques (also referred to as latent class cluster analysis, latent profile analysis, model-based clustering, probabilistic clustering, Bayesian classification, unsupervised learning, and finite mixture models; see Vermunt & Magdison,…

Robust nonlinear system identification: Bayesian mixture of experts using the t-distribution

NASA Astrophysics Data System (ADS)

Baldacchino, Tara; Worden, Keith; Rowson, Jennifer

2017-02-01

A novel variational Bayesian mixture of experts model for robust regression of bifurcating and piece-wise continuous processes is introduced. The mixture of experts model is a powerful model which probabilistically splits the input space allowing different models to operate in the separate regions. However, current methods have no fail-safe against outliers. In this paper, a robust mixture of experts model is proposed which consists of Student-t mixture models at the gates and Student-t distributed experts, trained via Bayesian inference. The Student-t distribution has heavier tails than the Gaussian distribution, and so it is more robust to outliers, noise and non-normality in the data. Using both simulated data and real data obtained from the Z24 bridge this robust mixture of experts performs better than its Gaussian counterpart when outliers are present. In particular, it provides robustness to outliers in two forms: unbiased parameter regression models, and robustness to overfitting/complex models.

Effects of impurity and Bose-Fermi interactions on the transition temperature of a dilute dipolar Bose-Einstein condensation in trapped Bose-Fermi mixtures

NASA Astrophysics Data System (ADS)

Yavari, H.; Mokhtari, M.

2014-03-01

The effects of impurity and Bose-Fermi interactions on the transition temperature of a dipolar Bose-Einstein condensation in trapped Bose-Fermi mixture, by using the two-fluid model, are investigated. The shift of the transition temperature consists of four contributions due to contact, Bose-Fermi, dipole-dipole, and impurity interactions. We will show that in the presence of an anisotropic trap, the Bose-Fermi correction to the shift of transition temperature due to the excitation spectra of the thermal part is independent of anisotropy factor. Applying our results to trapped Bose-Fermi mixtures shows that, by knowing the impurity effect, the shift of the transition temperature due to Bose-Fermi interaction could be measured for isotropic trap (dipole-dipole contributions is zero) and Feshbach resonance technique (contact potential contribution is negligible).

Development and validation of a metal mixture bioavailability model (MMBM) to predict chronic toxicity of Ni-Zn-Pb mixtures to Ceriodaphnia dubia.

PubMed

Nys, Charlotte; Janssen, Colin R; De Schamphelaere, Karel A C

2017-01-01

Recently, several bioavailability-based models have been shown to predict acute metal mixture toxicity with reasonable accuracy. However, the application of such models to chronic mixture toxicity is less well established. Therefore, we developed in the present study a chronic metal mixture bioavailability model (MMBM) by combining the existing chronic daphnid bioavailability models for Ni, Zn, and Pb with the independent action (IA) model, assuming strict non-interaction between the metals for binding at the metal-specific biotic ligand sites. To evaluate the predictive capacity of the MMBM, chronic (7d) reproductive toxicity of Ni-Zn-Pb mixtures to Ceriodaphnia dubia was investigated in four different natural waters (pH range: 7-8; Ca range: 1-2 mM; Dissolved Organic Carbon range: 5-12 mg/L). In each water, mixture toxicity was investigated at equitoxic metal concentration ratios as well as at environmental (i.e. realistic) metal concentration ratios. Statistical analysis of mixture effects revealed that observed interactive effects depended on the metal concentration ratio investigated when evaluated relative to the concentration addition (CA) model, but not when evaluated relative to the IA model. This indicates that interactive effects observed in an equitoxic experimental design cannot always be simply extrapolated to environmentally realistic exposure situations. Generally, the IA model predicted Ni-Zn-Pb mixture toxicity more accurately than the CA model. Overall, the MMBM predicted Ni-Zn-Pb mixture toxicity (expressed as % reproductive inhibition relative to a control) in 85% of the treatments with less than 20% error. Moreover, the MMBM predicted chronic toxicity of the ternary Ni-Zn-Pb mixture at least equally accurately as the toxicity of the individual metal treatments (RMSE Mix  = 16; RMSE Zn only  = 18; RMSE Ni only  = 17; RMSE Pb only  = 23). Based on the present study, we believe MMBMs can be a promising tool to account for the effects of water chemistry on metal mixture toxicity during chronic exposure and could be used in metal risk assessment frameworks. Copyright © 2016 Elsevier Ltd. All rights reserved.

Fuel-air mixing and combustion in a two-dimensional Wankel engine

NASA Technical Reports Server (NTRS)

Shih, T. I.-P.; Schock, H. J.; Ramos, J. I.

1987-01-01

A two-equation turbulence model, an algebraic grid generalization method, and an approximate factorization time-linearized numerical technique are used to study the effects of mixture stratification at the intake port and gaseous fuel injection on the flow field and fuel-air mixing in a two-dimensional rotary engine model. The fuel distribution in the combustion chamber is found to be a function of the air-fuel mixture fluctuations at the intake port. It is shown that the fuel is advected by the flow field induced by the rotor and is concentrated near the leading apex during the intake stroke, while during compression, the fuel concentration is highest near the trailing apex and is lowest near the rotor. It is also found that the fuel concentration near the trailing apex and rotor is small except at high injection velocities.

Cellular structure of lean hydrogen flames in microgravity

NASA Technical Reports Server (NTRS)

Patnaik, G.; Kailasanath, K.

1990-01-01

Detailed, time-dependent, two-dimensional numerical simulations of premixed laminar flames have been used to study the initiation and subsequent development of cellular structures in lean hydrogen-air flames. The model includes detailed hydrogen-oxygen combustion with 24 elementary reactions of eight reactive species and a nitrogen diluent, molecular diffusion of all species, thermal conduction, viscosity, and convection. This model has been used to study the nonlinear evolution of cellular flame structure and shows that cell splitting, as observed in experiments, can be predicted numerically for sufficiently reactive mixtures. The structures that evolved also resembled the cellular structures observed in experiments. The present study shows that the 'cell-split limit' postulated from experimental observations is an intrinsic property of the mixture and that external factors such as heat losses are not necessary to cause this limit.

Effect of silane concentration on the supersonic combustion of a silane/methane mixture

NASA Technical Reports Server (NTRS)

Northam, G. B.; Mclain, A. G.; Pellett, G. L.; Diskin, G. S.

1986-01-01

A series of direct connect combustor tests was conducted to determine the effect of silane concentration on the supersonic combustion characteristics of silane/methane mixtures. Shock tube ignition delay data indicated more than an order of magnitude reduction in ignition delay times for both 10 and 20 percent silane/methane mixtures as compared to methane. The ignition delay time of the 10 percent mixture was only a factor of 2.3 greater than that of the 20 percent mixture. Supersonic combustion tests were conducted with the fuel injected into a model scramjet combustor. The combustor was mounted at the exit of a Mach 2 nozzle and a hydrogen fired heater was used to provide a variation in test gas total temperature. Tests using the 20 percent silane/methane mixture indicated considerable combustion enhancement when compared to methane alone. This mixture had an autoignition total temperature of 1650 R. This autoignition temperature can be contrasted with 2330 R for hydrogen and 1350 R for a 20 percent silane/hydrogen mixture in similar hardware. Methane without the silane additive did not autoignite in this configuration at total temperatures as high as 3900 R, the maximum temperature at which tests were conducted. Supersonic combustion tests with the silane concentration reduced to 10 percent indicated little improvement in combustion performance over pure methane. The addition of 20 percent silane to methane resulted in a pyrophoric fuel with good supersonic combustion performance. Reducing the silane concentration below this level, however, yielded a less pyrophoric fuel that exhibited poor supersonic combustion performance.

Rasch Mixture Models for DIF Detection

PubMed Central

Strobl, Carolin; Zeileis, Achim

2014-01-01

Rasch mixture models can be a useful tool when checking the assumption of measurement invariance for a single Rasch model. They provide advantages compared to manifest differential item functioning (DIF) tests when the DIF groups are only weakly correlated with the manifest covariates available. Unlike in single Rasch models, estimation of Rasch mixture models is sensitive to the specification of the ability distribution even when the conditional maximum likelihood approach is used. It is demonstrated in a simulation study how differences in ability can influence the latent classes of a Rasch mixture model. If the aim is only DIF detection, it is not of interest to uncover such ability differences as one is only interested in a latent group structure regarding the item difficulties. To avoid any confounding effect of ability differences (or impact), a new score distribution for the Rasch mixture model is introduced here. It ensures the estimation of the Rasch mixture model to be independent of the ability distribution and thus restricts the mixture to be sensitive to latent structure in the item difficulties only. Its usefulness is demonstrated in a simulation study, and its application is illustrated in a study of verbal aggression. PMID:29795819

Investigating Stage-Sequential Growth Mixture Models with Multiphase Longitudinal Data

ERIC Educational Resources Information Center

Kim, Su-Young; Kim, Jee-Seon

2012-01-01

This article investigates three types of stage-sequential growth mixture models in the structural equation modeling framework for the analysis of multiple-phase longitudinal data. These models can be important tools for situations in which a single-phase growth mixture model produces distorted results and can allow researchers to better understand…

Mixture Modeling: Applications in Educational Psychology

ERIC Educational Resources Information Center

Harring, Jeffrey R.; Hodis, Flaviu A.

2016-01-01

Model-based clustering methods, commonly referred to as finite mixture modeling, have been applied to a wide variety of cross-sectional and longitudinal data to account for heterogeneity in population characteristics. In this article, we elucidate 2 such approaches: growth mixture modeling and latent profile analysis. Both techniques are…

Optimization of a Three-Component Green Corrosion Inhibitor Mixture for Using in Cooling Water by Experimental Design

NASA Astrophysics Data System (ADS)

Asghari, E.; Ashassi-Sorkhabi, H.; Ahangari, M.; Bagheri, R.

2016-04-01

Factors such as inhibitor concentration, solution hydrodynamics, and temperature influence the performance of corrosion inhibitor mixtures. The simultaneous studying of the impact of different factors is a time- and cost-consuming process. The use of experimental design methods can be useful in minimizing the number of experiments and finding local optimized conditions for factors under the investigation. In the present work, the inhibition performance of a three-component inhibitor mixture against corrosion of St37 steel rotating disk electrode, RDE, was studied. The mixture was composed of citric acid, lanthanum(III) nitrate, and tetrabutylammonium perchlorate. In order to decrease the number of experiments, the L16 Taguchi orthogonal array was used. The "control factors" were the concentration of each component and the rotation rate of RDE and the "response factor" was the inhibition efficiency. The scanning electron microscopy and energy dispersive x-ray spectroscopy techniques verified the formation of islands of adsorbed citrate complexes with lanthanum ions and insoluble lanthanum(III) hydroxide. From the Taguchi analysis results the mixture of 0.50 mM lanthanum(III) nitrate, 0.50 mM citric acid, and 2.0 mM tetrabutylammonium perchlorate under the electrode rotation rate of 1000 rpm was found as optimum conditions.

Local Solutions in the Estimation of Growth Mixture Models

ERIC Educational Resources Information Center

Hipp, John R.; Bauer, Daniel J.

2006-01-01

Finite mixture models are well known to have poorly behaved likelihood functions featuring singularities and multiple optima. Growth mixture models may suffer from fewer of these problems, potentially benefiting from the structure imposed on the estimated class means and covariances by the specified growth model. As demonstrated here, however,…

40 CFR 1054.245 - How do I determine deterioration factors from exhaust durability testing?

Code of Federal Regulations, 2013 CFR

2013-07-01

..., either with pre-existing test data or with new emission measurements. (a) You may ask us to approve... already given us these data for certifying other engines in the same or earlier model years. Use good... type or mixture of fuel expected to have the highest combustion and exhaust temperatures. For dual-fuel...

40 CFR 1054.245 - How do I determine deterioration factors from exhaust durability testing?

Code of Federal Regulations, 2014 CFR

2014-07-01

..., either with pre-existing test data or with new emission measurements. (a) You may ask us to approve... already given us these data for certifying other engines in the same or earlier model years. Use good... type or mixture of fuel expected to have the highest combustion and exhaust temperatures. For dual-fuel...

40 CFR 1054.245 - How do I determine deterioration factors from exhaust durability testing?

Code of Federal Regulations, 2012 CFR

2012-07-01

..., either with pre-existing test data or with new emission measurements. (a) You may ask us to approve... already given us these data for certifying other engines in the same or earlier model years. Use good... type or mixture of fuel expected to have the highest combustion and exhaust temperatures. For dual-fuel...

SOLUBILITIES OF CARBON DIOXIDE IN METHANOL AND METHANOL-WATER AT HIGH PRESSURES: EXPERIMENTAL DATA AND MODELING

EPA Science Inventory

The solubilities of carbon dioxide in methanol and in methanol-water mixtures have been measured at 243, 258, 273, and 298 K, and at pressures up to 54 atm. An extended Soave-Redlich-Kwong equation of state with Mathias' polar correction factor has been used to describe the equil...

Investigating Phenotypic Heterogeneity in Children with Autism Spectrum Disorder: A Factor Mixture Modeling Approach

ERIC Educational Resources Information Center

Georgiades, Stelios; Szatmari, Peter; Boyle, Michael; Hanna, Steven; Duku, Eric; Zwaigenbaum, Lonnie; Bryson, Susan; Fombonne, Eric; Volden, Joanne; Mirenda, Pat; Smith, Isabel; Roberts, Wendy; Vaillancourt, Tracy; Waddell, Charlotte; Bennett, Teresa; Thompson, Ann

2013-01-01

Background: Autism spectrum disorder (ASD) is characterized by notable phenotypic heterogeneity, which is often viewed as an obstacle to the study of its etiology, diagnosis, treatment, and prognosis. On the basis of empirical evidence, instead of three binary categories, the upcoming edition of the DSM 5 will use two dimensions--social…

Trajectories of Delinquent Behaviour in Adolescence and Their Covariates: Relations with Initial and Time-Averaged Factors

ERIC Educational Resources Information Center

Wiesner, Margit; Silbereisen, Rainer K.

2003-01-01

This longitudinal study examined individual, family, and peer covariates of distinctive trajectories of juvenile delinquency, using data from a community sample of 318 German adolescents (mean age at the first wave was 11.45 years). Latent growth mixture modelling analysis revealed four trajectory groups: high-level offenders, medium-level…

Inactivation of pathogens during aerobic composting of fresh and aged dairy manure and different carbon amendments.

PubMed

Erickson, Marilyn C; Liao, Jean; Jiang, Xiuping; Doyle, Michael P

2014-11-01

Two separate studies were conducted to address the condition and the type of feedstocks used during composting of dairy manure. In each study, physical (temperature), chemical (ammonia, volatile acids, and pH), and biological (Salmonella, Listeria monocytogenes, and Escherichia coli O157:H7) parameters were monitored during composting in bioreactors to assess the degree to which they were affected by the experimental variables and, ultimately, the ability of the chemical and physical parameters to predict the fate of pathogens during composting. Compost mixtures that contained either aged dairy manure or pine needles had reduced heat generation; therefore, pathogen reduction took longer than if fresh manure or carbon amendments of wheat straw or peanut hulls were used. Based on regression models derived from these results, ammonia concentration, in addition to heat, were the primary factors affecting the degree of pathogen inactivation in compost mixtures formulated to an initial carbon-nitrogen (C:N) ratio of 40:1, whereas, the pH of the compost mixture along with the amount of heat exposure were most influential in compost mixtures formulated to an initial C:N ratio of 30:1. Further studies are needed to validate these models so that additional criteria in addition to time and temperature can be used to evaluate the microbiological safety of composted manures.

Infinite von Mises-Fisher Mixture Modeling of Whole Brain fMRI Data.

PubMed

Røge, Rasmus E; Madsen, Kristoffer H; Schmidt, Mikkel N; Mørup, Morten

2017-10-01

Cluster analysis of functional magnetic resonance imaging (fMRI) data is often performed using gaussian mixture models, but when the time series are standardized such that the data reside on a hypersphere, this modeling assumption is questionable. The consequences of ignoring the underlying spherical manifold are rarely analyzed, in part due to the computational challenges imposed by directional statistics. In this letter, we discuss a Bayesian von Mises-Fisher (vMF) mixture model for data on the unit hypersphere and present an efficient inference procedure based on collapsed Markov chain Monte Carlo sampling. Comparing the vMF and gaussian mixture models on synthetic data, we demonstrate that the vMF model has a slight advantage inferring the true underlying clustering when compared to gaussian-based models on data generated from both a mixture of vMFs and a mixture of gaussians subsequently normalized. Thus, when performing model selection, the two models are not in agreement. Analyzing multisubject whole brain resting-state fMRI data from healthy adult subjects, we find that the vMF mixture model is considerably more reliable than the gaussian mixture model when comparing solutions across models trained on different groups of subjects, and again we find that the two models disagree on the optimal number of components. The analysis indicates that the fMRI data support more than a thousand clusters, and we confirm this is not a result of overfitting by demonstrating better prediction on data from held-out subjects. Our results highlight the utility of using directional statistics to model standardized fMRI data and demonstrate that whole brain segmentation of fMRI data requires a very large number of functional units in order to adequately account for the discernible statistical patterns in the data.

Component spectra extraction from terahertz measurements of unknown mixtures.

PubMed

Li, Xian; Hou, D B; Huang, P J; Cai, J H; Zhang, G X

2015-10-20

The aim of this work is to extract component spectra from unknown mixtures in the terahertz region. To that end, a method, hard modeling factor analysis (HMFA), was applied to resolve terahertz spectral matrices collected from the unknown mixtures. This method does not require any expertise of the user and allows the consideration of nonlinear effects such as peak variations or peak shifts. It describes the spectra using a peak-based nonlinear mathematic model and builds the component spectra automatically by recombination of the resolved peaks through correlation analysis. Meanwhile, modifications on the method were made to take the features of terahertz spectra into account and to deal with the artificial baseline problem that troubles the extraction process of some terahertz spectra. In order to validate the proposed method, simulated wideband terahertz spectra of binary and ternary systems and experimental terahertz absorption spectra of amino acids mixtures were tested. In each test, not only the number of pure components could be correctly predicted but also the identified pure spectra had a good similarity with the true spectra. Moreover, the proposed method associated the molecular motions with the component extraction, making the identification process more physically meaningful and interpretable compared to other methods. The results indicate that the HMFA method with the modifications can be a practical tool for identifying component terahertz spectra in completely unknown mixtures. This work reports the solution to this kind of problem in the terahertz region for the first time, to the best of the authors' knowledge, and represents a significant advance toward exploring physical or chemical mechanisms of unknown complex systems by terahertz spectroscopy.

Endocrine disrupting chemicals in mixture and obesity, diabetes and related metabolic disorders

PubMed Central

Le Magueresse-Battistoni, Brigitte; Labaronne, Emmanuel; Vidal, Hubert; Naville, Danielle

2017-01-01

Obesity and associated metabolic disorders represent a major societal challenge in health and quality of life with large psychological consequences in addition to physical disabilities. They are also one of the leading causes of morbidity and mortality. Although, different etiologic factors including excessive food intake and reduced physical activity have been well identified, they cannot explain the kinetics of epidemic evolution of obesity and diabetes with prevalence rates reaching pandemic proportions. Interestingly, convincing data have shown that environmental pollutants, specifically those endowed with endocrine disrupting activities, could contribute to the etiology of these multifactorial metabolic disorders. Within this review, we will recapitulate characteristics of endocrine disruption. We will demonstrate that metabolic disorders could originate from endocrine disruption with a particular focus on convincing data from the literature. Eventually, we will present how handling an original mouse model of chronic exposition to a mixture of pollutants allowed demonstrating that a mixture of pollutants each at doses beyond their active dose could induce substantial deleterious effects on several metabolic end-points. This proof-of-concept study, as well as other studies on mixtures of pollutants, stresses the needs for revisiting the current threshold model used in risk assessment which does not take into account potential effects of mixtures containing pollutants at environmental doses, e.g., the real life exposure. Certainly, more studies are necessary to better determine the nature of the chemicals to which humans are exposed and at which level, and their health impact. As well, research studies on substitute products are essential to identify harmless molecules. PMID:28588754

Effects of short-term exposure to environmentally relevant concentrations of different pharmaceutical mixtures on the immune response of the pond snail Lymnaea stagnalis.

PubMed

Gust, M; Fortier, M; Garric, J; Fournier, M; Gagné, F

2013-02-15

Pharmaceuticals are pollutants of potential concern in the aquatic environment where they are commonly introduced as complex mixtures via municipal effluents. Many reports underline the effects of pharmaceuticals on immune system of non target species. Four drug mixtures were tested, and regrouped pharmaceuticals by main therapeutic use: psychiatric (venlafaxine, carbamazepine, diazepam), antibiotic (ciprofloxacine, erythromycin, novobiocin, oxytetracycline, sulfamethoxazole, trimethoprim), hypolipemic (atorvastatin, gemfibrozil, benzafibrate) and antihypertensive (atenolol, furosemide, hydrochlorothiazide, lisinopril). Their effects were then compared with a treated municipal effluent known for its contamination, and its effects on the immune response of Lymnaea stagnalis. Adult L. stagnalis were exposed for 3 days to an environmentally relevant concentration of the four mixtures individually and as a global mixture. Effects on immunocompetence (hemocyte viability and count, ROS and thiol levels, phagocytosis) and gene expression were related to the immune response and oxidative stress: catalase (CAT), superoxide dismutase (SOD), glutathione reductase (GR), Selenium-dependent glutathione peroxidase (SeGPx), two isoforms of the nitric oxide synthetase gene (NOS1 and NOS2), molluscan defensive molecule (MDM), Toll-like receptor 4 (TLR4), allograft inflammatory factor-1 (AIF) and heat-shock protein 70 (HSP70). Immunocompetence was differently affected by the therapeutic class mixtures compared to the global mixture, which increased hemocyte count, ROS levels and phagocytosis, and decreased intracellular thiol levels. TLR4 gene expression was the most strongly increased, especially by psychiatric mixture (19-fold), while AIF-1, GR and CAT genes were downregulated. A decision tree analysis revealed that the immunotoxic responses caused by the municipal effluent were comparable to those obtained with the global pharmaceutical mixture, and the latter shared similarity with the antibiotic mixture. This suggests that pharmaceutical mixtures in municipal effluents represent a risk for gastropods at the immunocompetence levels and the antibiotic group could represent a model therapeutic class for municipal effluent toxicity studies in L. stagnalis. Copyright © 2013 Elsevier B.V. All rights reserved.

Personal exposure to mixtures of volatile organic compounds: modeling and further analysis of the RIOPA data.

PubMed

Batterman, Stuart; Su, Feng-Chiao; Li, Shi; Mukherjee, Bhramar; Jia, Chunrong

2014-06-01

Emission sources of volatile organic compounds (VOCs*) are numerous and widespread in both indoor and outdoor environments. Concentrations of VOCs indoors typically exceed outdoor levels, and most people spend nearly 90% of their time indoors. Thus, indoor sources generally contribute the majority of VOC exposures for most people. VOC exposure has been associated with a wide range of acute and chronic health effects; for example, asthma, respiratory diseases, liver and kidney dysfunction, neurologic impairment, and cancer. Although exposures to most VOCs for most persons fall below health-based guidelines, and long-term trends show decreases in ambient emissions and concentrations, a subset of individuals experience much higher exposures that exceed guidelines. Thus, exposure to VOCs remains an important environmental health concern. The present understanding of VOC exposures is incomplete. With the exception of a few compounds, concentration and especially exposure data are limited; and like other environmental data, VOC exposure data can show multiple modes, low and high extreme values, and sometimes a large portion of data below method detection limits (MDLs). Field data also show considerable spatial or interpersonal variability, and although evidence is limited, temporal variability seems high. These characteristics can complicate modeling and other analyses aimed at risk assessment, policy actions, and exposure management. In addition to these analytic and statistical issues, exposure typically occurs as a mixture, and mixture components may interact or jointly contribute to adverse effects. However most pollutant regulations, guidelines, and studies remain focused on single compounds, and thus may underestimate cumulative exposures and risks arising from coexposures. In addition, the composition of VOC mixtures has not been thoroughly investigated, and mixture components show varying and complex dependencies. Finally, although many factors are known to affect VOC exposures, many personal, environmental, and socioeconomic determinants remain to be identified, and the significance and applicability of the determinants reported in the literature are uncertain. To help answer these unresolved questions and overcome limitations of previous analyses, this project used several novel and powerful statistical modeling and analysis techniques and two large data sets. The overall objectives of this project were (1) to identify and characterize exposure distributions (including extreme values), (2) evaluate mixtures (including dependencies), and (3) identify determinants of VOC exposure. METHODS VOC data were drawn from two large data sets: the Relationships of Indoor, Outdoor, and Personal Air (RIOPA) study (1999-2001) and the National Health and Nutrition Examination Survey (NHANES; 1999-2000). The RIOPA study used a convenience sample to collect outdoor, indoor, and personal exposure measurements in three cities (Elizabeth, NJ; Houston, TX; Los Angeles, CA). In each city, approximately 100 households with adults and children who did not smoke were sampled twice for 18 VOCs. In addition, information about 500 variables associated with exposure was collected. The NHANES used a nationally representative sample and included personal VOC measurements for 851 participants. NHANES sampled 10 VOCs in common with RIOPA. Both studies used similar sampling methods and study periods. Specific Aim 1. To estimate and model extreme value exposures, extreme value distribution models were fitted to the top 10% and 5% of VOC exposures. Health risks were estimated for individual VOCs and for three VOC mixtures. Simulated extreme value data sets, generated for each VOC and for fitted extreme value and lognormal distributions, were compared with measured concentrations (RIOPA observations) to evaluate each model's goodness of fit. Mixture distributions were fitted with the conventional finite mixture of normal distributions and the semi-parametric Dirichlet process mixture (DPM) of normal distributions for three individual VOCs (chloroform, 1,4-DCB, and styrene). Goodness of fit for these full distribution models was also evaluated using simulated data. Specific Aim 2. Mixtures in the RIOPA VOC data set were identified using positive matrix factorization (PMF) and by toxicologic mode of action. Dependency structures of a mixture's components were examined using mixture fractions and were modeled using copulas, which address correlations of multiple components across their entire distributions. Five candidate copulas (Gaussian, t, Gumbel, Clayton, and Frank) were evaluated, and the performance of fitted models was evaluated using simulation and mixture fractions. Cumulative cancer risks were calculated for mixtures, and results from copulas and multivariate lognormal models were compared with risks based on RIOPA observations. Specific Aim 3. Exposure determinants were identified using stepwise regressions and linear mixed-effects models (LMMs). Specific Aim 1. Extreme value exposures in RIOPA typically were best fitted by three-parameter generalized extreme value (GEV) distributions, and sometimes by the two-parameter Gumbel distribution. In contrast, lognormal distributions significantly underestimated both the level and likelihood of extreme values. Among the VOCs measured in RIOPA, 1,4-dichlorobenzene (1,4-DCB) was associated with the greatest cancer risks; for example, for the highest 10% of measurements of 1,4-DCB, all individuals had risk levels above 10(-4), and 13% of all participants had risk levels above 10(-2). Of the full-distribution models, the finite mixture of normal distributions with two to four clusters and the DPM of normal distributions had superior performance in comparison with the lognormal models. DPM distributions provided slightly better fit than the finite mixture distributions; the advantages of the DPM model were avoiding certain convergence issues associated with the finite mixture distributions, adaptively selecting the number of needed clusters, and providing uncertainty estimates. Although the results apply to the RIOPA data set, GEV distributions and mixture models appear more broadly applicable. These models can be used to simulate VOC distributions, which are neither normally nor lognormally distributed, and they accurately represent the highest exposures, which may have the greatest health significance. Specific Aim 2. Four VOC mixtures were identified and apportioned by PMF; they represented gasoline vapor, vehicle exhaust, chlorinated solvents and disinfection byproducts, and cleaning products and odorants. The last mixture (cleaning products and odorants) accounted for the largest fraction of an individual's total exposure (average of 42% across RIOPA participants). Often, a single compound dominated a mixture but the mixture fractions were heterogeneous; that is, the fractions of the compounds changed with the concentration of the mixture. Three VOC mixtures were identified by toxicologic mode of action and represented VOCs associated with hematopoietic, liver, and renal tumors. Estimated lifetime cumulative cancer risks exceeded 10(-3) for about 10% of RIOPA participants. The dependency structures of the VOC mixtures in the RIOPA data set fitted Gumbel (two mixtures) and t copulas (four mixtures). These copula types emphasize dependencies found in the upper and lower tails of a distribution. The copulas reproduced both risk predictions and exposure fractions with a high degree of accuracy and performed better than multivariate lognormal distributions. Specific Aim 3. In an analysis focused on the home environment and the outdoor (close to home) environment, home VOC concentrations dominated personal exposures (66% to 78% of the total exposure, depending on VOC); this was largely the result of the amount of time participants spent at home and the fact that indoor concentrations were much higher than outdoor concentrations for most VOCs. In a different analysis focused on the sources inside the home and outside (but close to the home), it was assumed that 100% of VOCs from outside sources would penetrate the home. Outdoor VOC sources accounted for 5% (d-limonene) to 81% (carbon tetrachloride [CTC]) of the total exposure. Personal exposure and indoor measurements had similar determinants depending on the VOC. Gasoline-related VOCs (e.g., benzene and methyl tert-butyl ether [MTBE]) were associated with city, residences with attached garages, pumping gas, wind speed, and home air exchange rate (AER). Odorant and cleaning-related VOCs (e.g., 1,4-DCB and chloroform) also were associated with city, and a residence's AER, size, and family members showering. Dry-cleaning and industry-related VOCs (e.g., tetrachloroethylene [or perchloroethylene, PERC] and trichloroethylene [TCE]) were associated with city, type of water supply to the home, and visits to the dry cleaner. These and other relationships were significant, they explained from 10% to 40% of the variance in the measurements, and are consistent with known emission sources and those reported in the literature. Outdoor concentrations of VOCs had only two determinants in common: city and wind speed. Overall, personal exposure was dominated by the home setting, although a large fraction of indoor VOC concentrations were due to outdoor sources. City of residence, personal activities, household characteristics, and meteorology were significant determinants. Concentrations in RIOPA were considerably lower than levels in the nationally representative NHANES for all VOCs except MTBE and 1,4-DCB. Differences between RIOPA and NHANES results can be explained by contrasts between the sampling designs and staging in the two studies, and by differences in the demographics, smoking, employment, occupations, and home locations. (ABSTRACT TRUNCATED)

Cluster kinetics model for mixtures of glassformers

NASA Astrophysics Data System (ADS)

Brenskelle, Lisa A.; McCoy, Benjamin J.

2007-10-01

For glassformers we propose a binary mixture relation for parameters in a cluster kinetics model previously shown to represent pure compound data for viscosity and dielectric relaxation as functions of either temperature or pressure. The model parameters are based on activation energies and activation volumes for cluster association-dissociation processes. With the mixture parameters, we calculated dielectric relaxation times and compared the results to experimental values for binary mixtures. Mixtures of sorbitol and glycerol (seven compositions), sorbitol and xylitol (three compositions), and polychloroepihydrin and polyvinylmethylether (three compositions) were studied.

Dielectric and Excess Properties of Glycols with Formamide Binary Mixtures at Different Temperatures

NASA Astrophysics Data System (ADS)

Navarkhele, V. V.

2018-07-01

Dielectric constant measurements of glycol-formamide binary solutions with various concentrations have been carried out at different temperatures. The dielectric measurement has been achieved at 100 MHz frequency using a sensor which is based on frequency domain reflectomery technique. The excess dielectric constant, Kirkwood correlation factor and Bruggeman factor has also been reported for the binary mixtures. The results show that the dielectric constant of the mixtures increases with increase in the volume fraction of formamide and decreases with increase in temperature. The study also confirms the presence of intermolecular interaction, hydrogen bonding and orientation of the dipoles in the binary mixtures.

Does area deprivation modify the association between exposure to a nitrate and low-dose atrazine metabolite mixture in drinking water and small for gestational age? A historic cohort study.

PubMed

Limousi, F; Albouy-Llaty, M; Carles, C; Dupuis, A; Rabouan, S; Migeot, V

2014-04-01

Birth weight may be influenced by environmental and socio-economic factors that could interact. The main objective of our research was to investigate whether area deprivation may modify the association between drinking water exposure to a mixture of atrazine metabolites and nitrates during the second trimester of pregnancy and prevalence of small for gestational age (SGA) neonates. We conducted a historic cohort study in Deux-Sèvres, France between 2005 and 2010, using birth records, population census and regularly performed drinking water withdrawals at community water systems. Exposure to an atrazine metabolite/nitrate mixture in drinking water was divided into six classes according to the presence or absence of atrazine metabolites and to the terciles of nitrate concentrations in each trimester of pregnancy. We used a logistic regression to model the association between SGA and mixture exposure at the second trimester while taking into account the area deprivation measured by the Townsend index as an effect modifier and controlling for the usual confounders. We included 10,784 woman-neonate couples. The risk of SGA when exposed to second tercile of nitrate without atrazine metabolites was significantly greater in women living in less deprived areas (OR = 2.99; 95 % CI (1.14, 7.89)), whereas it was not significant in moderately and more deprived areas. One of the arguments used to explain this result is the presence of competing risk factors in poorer districts.

Heavy metals (Pb, Cd, As and MeHg) as risk factors for cognitive dysfunction: A general review of metal mixture mechanism in brain.

PubMed

Karri, Venkatanaidu; Schuhmacher, Marta; Kumar, Vikas

2016-12-01

Human exposure to toxic heavy metals is a global challenge. Concurrent exposure of heavy metals, such as lead (Pb), cadmium (Cd), arsenic (As) and methylmercury (MeHg) are particularly important due to their long lasting effects on the brain. The exact toxicological mechanisms invoked by exposure to mixtures of the metals Pb, Cd, As and MeHg are still unclear, however they share many common pathways for causing cognitive dysfunction. The combination of metals may produce additive/synergetic effects due to their common binding affinity with NMDA receptor (Pb, As, MeHg), Na + - K + ATP-ase pump (Cd, MeHg), biological Ca +2 (Pb, Cd, MeHg), Glu neurotransmitter (Pb, MeHg), which can lead to imbalance between the pro-oxidant elements (ROS) and the antioxidants (reducing elements). In this process, ROS dominates the antioxidants factors such as GPx, GS, GSH, MT-III, Catalase, SOD, BDNF, and CERB, and finally leads to cognitive dysfunction. The present review illustrates an account of the current knowledge about the individual metal induced cognitive dysfunction mechanisms and analyse common Mode of Actions (MOAs) of quaternary metal mixture (Pb, Cd, As, MeHg). This review aims to help advancement in mixture toxicology and development of next generation predictive model (such as PBPK/PD) combining both kinetic and dynamic interactions of metals. Copyright © 2016 Elsevier B.V. All rights reserved.

Factors and processes governing the C-14 content of carbonate in desert soils

NASA Technical Reports Server (NTRS)

Amundson, Ronald; Wang, Yang; Chadwick, Oliver; Trumbore, Susan; Mcfadden, Leslie; Mcdonald, Eric; Wells, Steven; Deniro, Michael

1994-01-01

A model is presented describing the factors and processes which determine the measured C-14 ages of soil calcium carbonate. Pedogenic carbonate forms in isotopic equilium with soil CO2. Carbon dioxide in soils is a mixture of CO2 derived from two biological sources: respiration by living plant roots and respiration of microorganisms decomposing soil humus. The relative proportion of these two CO2 sources can greatly affect the initial C-14 content of pedogenic carbonate: the greater the contribution of humus-derived CO2, the greater the initial C-14 age of the carbonate mineral. For any given mixture of CO2 sources, the steady-state (14)CO2 distribution vs. soil depth can be described by a production/diffusion model. As a soil ages, the C-14 age of soil humus increases, as does the steady-state C-14 age of soil CO2 and the initial C-14 age of any pedogenic carbonate which forms. The mean C-14 age of a complete pedogenic carbonate coating or nodule will underestimate the true age of the soil carbonate. This discrepancy increases the older a soil becomes. Partial removal of outer (and younger) carbonate coatings greatly improves the relationship between measured C-14 age and true age. Although the production/diffusion model qualitatively explains the C-14 age of pedogenic carbonate vs. soil depth in many soils, other factors, such as climate change, may contribute to the observed trends, particularily in soils older than the Holocene.

Evaluating differential effects using regression interactions and regression mixture models

PubMed Central

Van Horn, M. Lee; Jaki, Thomas; Masyn, Katherine; Howe, George; Feaster, Daniel J.; Lamont, Andrea E.; George, Melissa R. W.; Kim, Minjung

2015-01-01

Research increasingly emphasizes understanding differential effects. This paper focuses on understanding regression mixture models, a relatively new statistical methods for assessing differential effects by comparing results to using an interactive term in linear regression. The research questions which each model answers, their formulation, and their assumptions are compared using Monte Carlo simulations and real data analysis. The capabilities of regression mixture models are described and specific issues to be addressed when conducting regression mixtures are proposed. The paper aims to clarify the role that regression mixtures can take in the estimation of differential effects and increase awareness of the benefits and potential pitfalls of this approach. Regression mixture models are shown to be a potentially effective exploratory method for finding differential effects when these effects can be defined by a small number of classes of respondents who share a typical relationship between a predictor and an outcome. It is also shown that the comparison between regression mixture models and interactions becomes substantially more complex as the number of classes increases. It is argued that regression interactions are well suited for direct tests of specific hypotheses about differential effects and regression mixtures provide a useful approach for exploring effect heterogeneity given adequate samples and study design. PMID:26556903

Nonlinear Structured Growth Mixture Models in M"plus" and OpenMx

ERIC Educational Resources Information Center

Grimm, Kevin J.; Ram, Nilam; Estabrook, Ryne

2010-01-01

Growth mixture models (GMMs; B. O. Muthen & Muthen, 2000; B. O. Muthen & Shedden, 1999) are a combination of latent curve models (LCMs) and finite mixture models to examine the existence of latent classes that follow distinct developmental patterns. GMMs are often fit with linear, latent basis, multiphase, or polynomial change models…

The Potential of Growth Mixture Modelling

ERIC Educational Resources Information Center

Muthen, Bengt

2006-01-01

The authors of the paper on growth mixture modelling (GMM) give a description of GMM and related techniques as applied to antisocial behaviour. They bring up the important issue of choice of model within the general framework of mixture modelling, especially the choice between latent class growth analysis (LCGA) techniques developed by Nagin and…

Equivalence of truncated count mixture distributions and mixtures of truncated count distributions.

PubMed

Böhning, Dankmar; Kuhnert, Ronny

2006-12-01

This article is about modeling count data with zero truncation. A parametric count density family is considered. The truncated mixture of densities from this family is different from the mixture of truncated densities from the same family. Whereas the former model is more natural to formulate and to interpret, the latter model is theoretically easier to treat. It is shown that for any mixing distribution leading to a truncated mixture, a (usually different) mixing distribution can be found so that the associated mixture of truncated densities equals the truncated mixture, and vice versa. This implies that the likelihood surfaces for both situations agree, and in this sense both models are equivalent. Zero-truncated count data models are used frequently in the capture-recapture setting to estimate population size, and it can be shown that the two Horvitz-Thompson estimators, associated with the two models, agree. In particular, it is possible to achieve strong results for mixtures of truncated Poisson densities, including reliable, global construction of the unique NPMLE (nonparametric maximum likelihood estimator) of the mixing distribution, implying a unique estimator for the population size. The benefit of these results lies in the fact that it is valid to work with the mixture of truncated count densities, which is less appealing for the practitioner but theoretically easier. Mixtures of truncated count densities form a convex linear model, for which a developed theory exists, including global maximum likelihood theory as well as algorithmic approaches. Once the problem has been solved in this class, it might readily be transformed back to the original problem by means of an explicitly given mapping. Applications of these ideas are given, particularly in the case of the truncated Poisson family.

The Dissolution of an Interfween Miscible Liquids

NASA Technical Reports Server (NTRS)

Vlad, D.H.; Maher, J.V.

1999-01-01

The disappearance of the surface tension of the interface of a binary mixture, measured using the dynamic surface light scattering technique, is slower for a binary mixture of higher density contrast. A comparison with a naive diffusion model, expected to provide a lower limit for the speed of dissolution in the absence of gravity shows that the interfacial surface tension disappears much slower than even by diffusion with the effect becoming much more pronounced when density contrast between the liquid phases is increased. Thus, the factor most likely to be responsible for this anomalously slow dissolution is gravity. A mechanism could be based on the competition between diffusive relaxation and sedimentation at the dissolving interface.

Development of PBPK Models for Gasoline in Adult and ...

EPA Pesticide Factsheets

Concern for potential developmental effects of exposure to gasoline-ethanol blends has grown along with their increased use in the US fuel supply. Physiologically-based pharmacokinetic (PBPK) models for these complex mixtures were developed to address dosimetric issues related to selection of exposure concentrations for in vivo toxicity studies. Sub-models for individual hydrocarbon (HC) constituents were first developed and calibrated with published literature or QSAR-derived data where available. Successfully calibrated sub-models for individual HCs were combined, assuming competitive metabolic inhibition in the liver, and a priori simulations of mixture interactions were performed. Blood HC concentration data were collected from exposed adult non-pregnant (NP) rats (9K ppm total HC vapor, 6h/day) to evaluate performance of the NP mixture model. This model was then converted to a pregnant (PG) rat mixture model using gestational growth equations that enabled a priori estimation of life-stage specific kinetic differences. To address the impact of changing relevant physiological parameters from NP to PG, the PG mixture model was first calibrated against the NP data. The PG mixture model was then evaluated against data from PG rats that were subsequently exposed (9K ppm/6.33h gestation days (GD) 9-20). Overall, the mixture models adequately simulated concentrations of HCs in blood from single (NP) or repeated (PG) exposures (within ~2-3 fold of measured values of

Mixture-mixture design for the fingerprint optimization of chromatographic mobile phases and extraction solutions for Camellia sinensis.

PubMed

Borges, Cleber N; Bruns, Roy E; Almeida, Aline A; Scarminio, Ieda S

2007-07-09

A composite simplex centroid-simplex centroid mixture design is proposed for simultaneously optimizing two mixture systems. The complementary model is formed by multiplying special cubic models for the two systems. The design was applied to the simultaneous optimization of both mobile phase chromatographic mixtures and extraction mixtures for the Camellia sinensis Chinese tea plant. The extraction mixtures investigated contained varying proportions of ethyl acetate, ethanol and dichloromethane while the mobile phase was made up of varying proportions of methanol, acetonitrile and a methanol-acetonitrile-water (MAW) 15%:15%:70% mixture. The experiments were block randomized corresponding to a split-plot error structure to minimize laboratory work and reduce environmental impact. Coefficients of an initial saturated model were obtained using Scheffe-type equations. A cumulative probability graph was used to determine an approximate reduced model. The split-plot error structure was then introduced into the reduced model by applying generalized least square equations with variance components calculated using the restricted maximum likelihood approach. A model was developed to calculate the number of peaks observed with the chromatographic detector at 210 nm. A 20-term model contained essentially all the statistical information of the initial model and had a root mean square calibration error of 1.38. The model was used to predict the number of peaks eluted in chromatograms obtained from extraction solutions that correspond to axial points of the simplex centroid design. The significant model coefficients are interpreted in terms of interacting linear, quadratic and cubic effects of the mobile phase and extraction solution components.

Reduced detonation kinetics and detonation structure in one- and multi-fuel gaseous mixtures

NASA Astrophysics Data System (ADS)

Fomin, P. A.; Trotsyuk, A. V.; Vasil'ev, A. A.

2017-10-01

Two-step approximate models of chemical kinetics of detonation combustion of (i) one-fuel (CH4/air) and (ii) multi-fuel gaseous mixtures (CH4/H2/air and CH4/CO/air) are developed for the first time. The models for multi-fuel mixtures are proposed for the first time. Owing to the simplicity and high accuracy, the models can be used in multi-dimensional numerical calculations of detonation waves in corresponding gaseous mixtures. The models are in consistent with the second law of thermodynamics and Le Chatelier’s principle. Constants of the models have a clear physical meaning. Advantages of the kinetic model for detonation combustion of methane has been demonstrated via numerical calculations of a two-dimensional structure of the detonation wave in a stoichiometric and fuel-rich methane-air mixtures and stoichiometric methane-oxygen mixture. The dominant size of the detonation cell, determines in calculations, is in good agreement with all known experimental data.

Fitting a Mixture Item Response Theory Model to Personality Questionnaire Data: Characterizing Latent Classes and Investigating Possibilities for Improving Prediction

ERIC Educational Resources Information Center

Maij-de Meij, Annette M.; Kelderman, Henk; van der Flier, Henk

2008-01-01

Mixture item response theory (IRT) models aid the interpretation of response behavior on personality tests and may provide possibilities for improving prediction. Heterogeneity in the population is modeled by identifying homogeneous subgroups that conform to different measurement models. In this study, mixture IRT models were applied to the…

Variations of the Hydrogen Bonding and of the Hydrogen Bonded Network in Ethanol-Water Mixtures on Cooling.

PubMed

Pothoczki, Szilvia; Pusztai, Laszlo; Bako, Imre

2018-06-12

Molecular dynamics computer simulations have been conducted for ethanol-water liquid mixtures in the water-rich side of the composition range, with 10, 20 and 30 mol % of the alcohol, at temperatures between room temperature and the experimental freezing point of the given mixture. All-atom type (OPLS) interatomic potentials have been assumed for ethanol, in combination with two kinds of rigid water models (SPC/E and TIP4P/2005). Both combinations have provided excellent reproductions of the experimental X-ray total structure factors at each temperature; this yielded a strong basis for further structural analyses. Beyond partial radial distribution functions, various descriptors of hydrogen bonded assemblies, as well as of the hydrogen bonded network have been determined. A clear tendency was observed towards that an increasing proportion of water molecules participate in hydrogen bonding with exactly 2 donor- and 2 acceptor sites as temperature decreases. Concerning larger assemblies held together by hydrogen bonding, the main focus was put on the properties of cyclic entities: it was found that, similarly to methanol-water mixtures, the number of hydrogen bonded rings has increased with lowering temperature. However, for ethanol-water mixtures the dominance of not the six-, but of the five-fold rings could be observed.

Recovery of lactic acid from the pretreated fermentation broth based on a novel hyper-cross-linked meso-micropore resin: Modeling.

PubMed

Song, Mingkai; Jiao, Pengfei; Qin, Taotao; Jiang, Kangkang; Zhou, Jingwei; Zhuang, Wei; Chen, Yong; Liu, Dong; Zhu, Chenjie; Chen, Xiaochun; Ying, Hanjie; Wu, Jinglan

2017-10-01

An innovative benign process for recovery lactic acid from its fermentation broth is proposed using a novel hyper-cross-linked meso-micropore resin and water as eluent. This work focuses on modeling the competitive adsorption behaviors of glucose, lactic acid and acetic acid ternary mixture and explosion of the adsorption mechanism. The characterization results showed the resin had a large BET surface area and specific pore structure with hydrophobic properties. By analysis of the physicochemical properties of the solutes and the resin, the mechanism of the separation is proposed as hydrophobic effect and size-exclusion. Subsequently three chromatographic models were applied to predict the competitive breakthrough curves of the ternary mixture under different operating conditions. The pore diffusion was the major limiting factor for the adsorption process, which was consistent with the BET results. The novel HD-06 resin can be a good potential adsorbent for the future SMB continuous separation process. Copyright © 2017 Elsevier Ltd. All rights reserved.

Two-way learning with one-way supervision for gene expression data.

PubMed

Wong, Monica H T; Mutch, David M; McNicholas, Paul D

2017-03-04

A family of parsimonious Gaussian mixture models for the biclustering of gene expression data is introduced. Biclustering is accommodated by adopting a mixture of factor analyzers model with a binary, row-stochastic factor loadings matrix. This particular form of factor loadings matrix results in a block-diagonal covariance matrix, which is a useful property in gene expression analyses, specifically in biomarker discovery scenarios where blood can potentially act as a surrogate tissue for other less accessible tissues. Prior knowledge of the factor loadings matrix is useful in this application and is reflected in the one-way supervised nature of the algorithm. Additionally, the factor loadings matrix can be assumed to be constant across all components because of the relationship desired between the various types of tissue samples. Parameter estimates are obtained through a variant of the expectation-maximization algorithm and the best-fitting model is selected using the Bayesian information criterion. The family of models is demonstrated using simulated data and two real microarray data sets. The first real data set is from a rat study that investigated the influence of diabetes on gene expression in different tissues. The second real data set is from a human transcriptomics study that focused on blood and immune tissues. The microarray data sets illustrate the biclustering family's performance in biomarker discovery involving peripheral blood as surrogate biopsy material. The simulation studies indicate that the algorithm identifies the correct biclusters, most optimally when the number of observation clusters is known. Moreover, the biclustering algorithm identified biclusters comprised of biologically meaningful data related to insulin resistance and immune function in the rat and human real data sets, respectively. Initial results using real data show that this biclustering technique provides a novel approach for biomarker discovery by enabling blood to be used as a surrogate for hard-to-obtain tissues.

The evaluation of distributed damage in concrete based on sinusoidal modeling of the ultrasonic response.

PubMed

Sepehrinezhad, Alireza; Toufigh, Vahab

2018-05-25

Ultrasonic wave attenuation is an effective descriptor of distributed damage in inhomogeneous materials. Methods developed to measure wave attenuation have the potential to provide an in-site evaluation of existing concrete structures insofar as they are accurate and time-efficient. In this study, material classification and distributed damage evaluation were investigated based on the sinusoidal modeling of the response from the through-transmission ultrasonic tests on polymer concrete specimens. The response signal was modeled as single or the sum of damping sinusoids. Due to the inhomogeneous nature of concrete materials, model parameters may vary from one specimen to another. Therefore, these parameters are not known in advance and should be estimated while the response signal is being received. The modeling procedure used in this study involves a data-adaptive algorithm to estimate the parameters online. Data-adaptive algorithms are used due to a lack of knowledge of the model parameters. The damping factor was estimated as a descriptor of the distributed damage. The results were compared in two different cases as follows: (1) constant excitation frequency with varying concrete mixtures and (2) constant mixture with varying excitation frequencies. The specimens were also loaded up to their ultimate compressive strength to investigate the effect of distributed damage in the response signal. The results of the estimation indicated that the damping was highly sensitive to the change in material inhomogeneity, even in comparable mixtures. In addition to the proposed method, three methods were employed to compare the results based on their accuracy in the classification of materials and the evaluation of the distributed damage. It is shown that the estimated damping factor is not only sensitive to damage in the final stages of loading, but it is also applicable in evaluating micro damages in the earlier stages providing a reliable descriptor of damage. In addition, the modified amplitude ratio method is introduced as an improvement of the classical method. The proposed methods were validated to be effective descriptors of distributed damage. The presented models were also in good agreement with the experimental data. Copyright © 2018 Elsevier B.V. All rights reserved.

Application of physicochemical properties and process parameters in the development of a neural network model for prediction of tablet characteristics.

PubMed

Sovány, Tamás; Papós, Kitti; Kása, Péter; Ilič, Ilija; Srčič, Stane; Pintye-Hódi, Klára

2013-06-01

The importance of in silico modeling in the pharmaceutical industry is continuously increasing. The aim of the present study was the development of a neural network model for prediction of the postcompressional properties of scored tablets based on the application of existing data sets from our previous studies. Some important process parameters and physicochemical characteristics of the powder mixtures were used as training factors to achieve the best applicability in a wide range of possible compositions. The results demonstrated that, after some pre-processing of the factors, an appropriate prediction performance could be achieved. However, because of the poor extrapolation capacity, broadening of the training data range appears necessary.

Microstructure and hydrogen bonding in water-acetonitrile mixtures.

PubMed

Mountain, Raymond D

2010-12-16

The connection of hydrogen bonding between water and acetonitrile in determining the microheterogeneity of the liquid mixture is examined using NPT molecular dynamics simulations. Mixtures for six, rigid, three-site models for acetonitrile and one water model (SPC/E) were simulated to determine the amount of water-acetonitrile hydrogen bonding. Only one of the six acetonitrile models (TraPPE-UA) was able to reproduce both the liquid density and the experimental estimates of hydrogen bonding derived from Raman scattering of the CN stretch band or from NMR quadrupole relaxation measurements. A simple modification of the acetonitrile model parameters for the models that provided poor estimates produced hydrogen-bonding results consistent with experiments for two of the models. Of these, only one of the modified models also accurately determined the density of the mixtures. The self-diffusion coefficient of liquid acetonitrile provided a final winnowing of the modified model and the successful, unmodified model. The unmodified model is provisionally recommended for simulations of water-acetonitrile mixtures.

In utero and lactational exposure to low-dose genistein-vinclozolin mixture affects the development and growth factor mRNA expression of the submandibular salivary gland in immature female rats.

PubMed

Kouidhi, Wided; Desmetz, Catherine; Nahdi, Afef; Bergès, Raymond; Cravedi, Jean-Pierre; Auger, Jacques; El May, Michèle; Canivenc-Lavier, Marie Chantal

2012-06-01

It has been suggested that hormonally controlled submandibular salivary gland (SSG) development and secretions may be affected by endocrine disruptor compounds. We investigated the effects of oral gestation-lactation exposure to 1 mg/kg body weight daily dose of the estrogenic soy-isoflavone genistein and/or the anti-androgenic food contaminant vinclozolin in female rats. The SSGs of female offspring were collected at postnatal day 35 to study gland morphogenesis and mRNA expression of sex-hormone receptors and endocrine growth factors as sex-dependent biomarkers. Because of high expression in neonatal SSG, mRNA expression of transforming growth factor α was also studied. Exposure to genistein, vinclozolin, or a genistein+vinclozolin mixture resulted in significantly lower numbers of striated ducts linked to an increase in their area and lower acinar proliferation (Ki-67-positive nuclei). Exposure to the mixture had the highest significant effects, which were particularly associated with repression of epidermal growth factor, nerve growth factor, and transforming growth factor α expression. In conclusion, early exposure to low doses of genistein and vinclozolin can affect glandular structure and endocrine gene mRNA expression in prepubertal SSG in female rats, and the effects are potentialized by the genistein+vinclozolin mixture. Our study provides the first evidence that SSG are targeted by both estrogenic and anti-androgenic disrupting compounds and are more sensitive to mixtures.

Objective determination of image end-members in spectral mixture analysis of AVIRIS data

NASA Technical Reports Server (NTRS)

Tompkins, Stefanie; Mustard, John F.; Pieters, Carle M.; Forsyth, Donald W.

1993-01-01

Spectral mixture analysis has been shown to be a powerful, multifaceted tool for analysis of multi- and hyper-spectral data. Applications of AVIRIS data have ranged from mapping soils and bedrock to ecosystem studies. During the first phase of the approach, a set of end-members are selected from an image cube (image end-members) that best account for its spectral variance within a constrained, linear least squares mixing model. These image end-members are usually selected using a priori knowledge and successive trial and error solutions to refine the total number and physical location of the end-members. However, in many situations a more objective method of determining these essential components is desired. We approach the problem of image end-member determination objectively by using the inherent variance of the data. Unlike purely statistical methods such as factor analysis, this approach derives solutions that conform to a physically realistic model.

General mixture item response models with different item response structures: Exposition with an application to Likert scales.

PubMed

Tijmstra, Jesper; Bolsinova, Maria; Jeon, Minjeong

2018-01-10

This article proposes a general mixture item response theory (IRT) framework that allows for classes of persons to differ with respect to the type of processes underlying the item responses. Through the use of mixture models, nonnested IRT models with different structures can be estimated for different classes, and class membership can be estimated for each person in the sample. If researchers are able to provide competing measurement models, this mixture IRT framework may help them deal with some violations of measurement invariance. To illustrate this approach, we consider a two-class mixture model, where a person's responses to Likert-scale items containing a neutral middle category are either modeled using a generalized partial credit model, or through an IRTree model. In the first model, the middle category ("neither agree nor disagree") is taken to be qualitatively similar to the other categories, and is taken to provide information about the person's endorsement. In the second model, the middle category is taken to be qualitatively different and to reflect a nonresponse choice, which is modeled using an additional latent variable that captures a person's willingness to respond. The mixture model is studied using simulation studies and is applied to an empirical example.

Applications of the Simple Multi-Fluid Model to Correlations of the Vapor-Liquid Equilibrium of Refrigerant Mixtures Containing Carbon Dioxide

NASA Astrophysics Data System (ADS)

Akasaka, Ryo

This study presents a simple multi-fluid model for Helmholtz energy equations of state. The model contains only three parameters, whereas rigorous multi-fluid models developed for several industrially important mixtures usually have more than 10 parameters and coefficients. Therefore, the model can be applied to mixtures where experimental data is limited. Vapor-liquid equilibrium (VLE) of the following seven mixtures have been successfully correlated with the model: CO2 + difluoromethane (R-32), CO2 + trifluoromethane (R-23), CO2 + fluoromethane (R-41), CO2 + 1,1,1,2- tetrafluoroethane (R-134a), CO2 + pentafluoroethane (R-125), CO2 + 1,1-difluoroethane (R-152a), and CO2 + dimethyl ether (DME). The best currently available equations of state for the pure refrigerants were used for the correlations. For all mixtures, average deviations in calculated bubble-point pressures from experimental values are within 2%. The simple multi-fluid model will be helpful for design and simulations of heat pumps and refrigeration systems using the mixtures as working fluid.

Optimized mixed Markov models for motif identification

PubMed Central

Huang, Weichun; Umbach, David M; Ohler, Uwe; Li, Leping

2006-01-01

Background Identifying functional elements, such as transcriptional factor binding sites, is a fundamental step in reconstructing gene regulatory networks and remains a challenging issue, largely due to limited availability of training samples. Results We introduce a novel and flexible model, the Optimized Mixture Markov model (OMiMa), and related methods to allow adjustment of model complexity for different motifs. In comparison with other leading methods, OMiMa can incorporate more than the NNSplice's pairwise dependencies; OMiMa avoids model over-fitting better than the Permuted Variable Length Markov Model (PVLMM); and OMiMa requires smaller training samples than the Maximum Entropy Model (MEM). Testing on both simulated and actual data (regulatory cis-elements and splice sites), we found OMiMa's performance superior to the other leading methods in terms of prediction accuracy, required size of training data or computational time. Our OMiMa system, to our knowledge, is the only motif finding tool that incorporates automatic selection of the best model. OMiMa is freely available at [1]. Conclusion Our optimized mixture of Markov models represents an alternative to the existing methods for modeling dependent structures within a biological motif. Our model is conceptually simple and effective, and can improve prediction accuracy and/or computational speed over other leading methods. PMID:16749929

Characterizing severe and enduring anorexia nervosa: An empirical approach.

PubMed

Wildes, Jennifer E; Forbush, Kelsie T; Hagan, Kelsey E; Marcus, Marsha D; Attia, Evelyn; Gianini, Loren M; Wu, Wei

2017-04-01

Targeted approaches for the treatment of severe and enduring anorexia nervosa (SE-AN) have been recommended, but there is no consensus definition of SE-AN to inform research and clinical practice. This study aimed to take initial steps toward developing an empirically based definition of SE-AN by characterizing associations among putative indicators of severity and chronicity in eating disorders. Patients with AN (N = 355) completed interviews and questionnaires at treatment admission and discharge; height and weight were assessed to calculate body mass index (BMI). Structural equation mixture modeling was used to test whether associations among potential indicators of SE-AN (illness duration, treatment history, BMI, binge eating, purging, quality-of-life) formed distinct subgroups, a single group with one or more dimensions, or a combination of subgroups and dimensions. A three-factor (dimensional), two-profile (categorical) mixture model provided the best fit to the data. Factor 1 included eating disorder behaviors; Factor 2 comprised quality-of-life domains; Factor 3 was characterized by illness duration, number of hospitalizations, and admission BMI. Profiles differed on eating disorder behaviors and quality-of-life, but not on indicators of chronicity or BMI. Factor scores, but not profile membership, predicted outcome at discharge from treatment. Data suggest that patients with AN can be classified on the basis of eating disorder behaviors and quality-of-life, but there was no evidence for a chronic subgroup of AN. Rather, indices of chronicity varied dimensionally within each class. Given that current definitions of SE-AN rely on illness duration, these findings have implications for research and clinical practice. © 2016 Wiley Periodicals, Inc.

Different Approaches to Covariate Inclusion in the Mixture Rasch Model

ERIC Educational Resources Information Center

Li, Tongyun; Jiao, Hong; Macready, George B.

2016-01-01

The present study investigates different approaches to adding covariates and the impact in fitting mixture item response theory models. Mixture item response theory models serve as an important methodology for tackling several psychometric issues in test development, including the detection of latent differential item functioning. A Monte Carlo…

A compressibility based model for predicting the tensile strength of directly compressed pharmaceutical powder mixtures.

PubMed

Reynolds, Gavin K; Campbell, Jacqueline I; Roberts, Ron J

2017-10-05

A new model to predict the compressibility and compactability of mixtures of pharmaceutical powders has been developed. The key aspect of the model is consideration of the volumetric occupancy of each powder under an applied compaction pressure and the respective contribution it then makes to the mixture properties. The compressibility and compactability of three pharmaceutical powders: microcrystalline cellulose, mannitol and anhydrous dicalcium phosphate have been characterised. Binary and ternary mixtures of these excipients have been tested and used to demonstrate the predictive capability of the model. Furthermore, the model is shown to be uniquely able to capture a broad range of mixture behaviours, including neutral, negative and positive deviations, illustrating its utility for formulation design. Copyright © 2017 Elsevier B.V. All rights reserved.

Personal Exposure to Mixtures of Volatile Organic Compounds: Modeling and Further Analysis of the RIOPA Data

PubMed Central

Batterman, Stuart; Su, Feng-Chiao; Li, Shi; Mukherjee, Bhramar; Jia, Chunrong

2015-01-01

INTRODUCTION Emission sources of volatile organic compounds (VOCs) are numerous and widespread in both indoor and outdoor environments. Concentrations of VOCs indoors typically exceed outdoor levels, and most people spend nearly 90% of their time indoors. Thus, indoor sources generally contribute the majority of VOC exposures for most people. VOC exposure has been associated with a wide range of acute and chronic health effects; for example, asthma, respiratory diseases, liver and kidney dysfunction, neurologic impairment, and cancer. Although exposures to most VOCs for most persons fall below health-based guidelines, and long-term trends show decreases in ambient emissions and concentrations, a subset of individuals experience much higher exposures that exceed guidelines. Thus, exposure to VOCs remains an important environmental health concern. The present understanding of VOC exposures is incomplete. With the exception of a few compounds, concentration and especially exposure data are limited; and like other environmental data, VOC exposure data can show multiple modes, low and high extreme values, and sometimes a large portion of data below method detection limits (MDLs). Field data also show considerable spatial or interpersonal variability, and although evidence is limited, temporal variability seems high. These characteristics can complicate modeling and other analyses aimed at risk assessment, policy actions, and exposure management. In addition to these analytic and statistical issues, exposure typically occurs as a mixture, and mixture components may interact or jointly contribute to adverse effects. However most pollutant regulations, guidelines, and studies remain focused on single compounds, and thus may underestimate cumulative exposures and risks arising from coexposures. In addition, the composition of VOC mixtures has not been thoroughly investigated, and mixture components show varying and complex dependencies. Finally, although many factors are known to affect VOC exposures, many personal, environmental, and socioeconomic determinants remain to be identified, and the significance and applicability of the determinants reported in the literature are uncertain. To help answer these unresolved questions and overcome limitations of previous analyses, this project used several novel and powerful statistical modeling and analysis techniques and two large data sets. The overall objectives of this project were (1) to identify and characterize exposure distributions (including extreme values), (2) evaluate mixtures (including dependencies), and (3) identify determinants of VOC exposure. METHODS VOC data were drawn from two large data sets: the Relationships of Indoor, Outdoor, and Personal Air (RIOPA) study (1999–2001) and the National Health and Nutrition Examination Survey (NHANES; 1999–2000). The RIOPA study used a convenience sample to collect outdoor, indoor, and personal exposure measurements in three cities (Elizabeth, NJ; Houston, TX; Los Angeles, CA). In each city, approximately 100 households with adults and children who did not smoke were sampled twice for 18 VOCs. In addition, information about 500 variables associated with exposure was collected. The NHANES used a nationally representative sample and included personal VOC measurements for 851 participants. NHANES sampled 10 VOCs in common with RIOPA. Both studies used similar sampling methods and study periods. Specific Aim 1 To estimate and model extreme value exposures, extreme value distribution models were fitted to the top 10% and 5% of VOC exposures. Health risks were estimated for individual VOCs and for three VOC mixtures. Simulated extreme value data sets, generated for each VOC and for fitted extreme value and lognormal distributions, were compared with measured concentrations (RIOPA observations) to evaluate each model’s goodness of fit. Mixture distributions were fitted with the conventional finite mixture of normal distributions and the semi-parametric Dirichlet process mixture (DPM) of normal distributions for three individual VOCs (chloroform, 1,4-DCB, and styrene). Goodness of fit for these full distribution models was also evaluated using simulated data. Specific Aim 2 Mixtures in the RIOPA VOC data set were identified using positive matrix factorization (PMF) and by toxicologic mode of action. Dependency structures of a mixture’s components were examined using mixture fractions and were modeled using copulas, which address correlations of multiple components across their entire distributions. Five candidate copulas (Gaussian, t, Gumbel, Clayton, and Frank) were evaluated, and the performance of fitted models was evaluated using simulation and mixture fractions. Cumulative cancer risks were calculated for mixtures, and results from copulas and multivariate lognormal models were compared with risks based on RIOPA observations. Specific Aim 3 Exposure determinants were identified using stepwise regressions and linear mixed-effects models (LMMs). RESULTS Specific Aim 1 Extreme value exposures in RIOPA typically were best fitted by three-parameter generalized extreme value (GEV) distributions, and sometimes by the two-parameter Gumbel distribution. In contrast, lognormal distributions significantly underestimated both the level and likelihood of extreme values. Among the VOCs measured in RIOPA, 1,4-dichlorobenzene (1,4-DCB) was associated with the greatest cancer risks; for example, for the highest 10% of measurements of 1,4-DCB, all individuals had risk levels above 10−4, and 13% of all participants had risk levels above 10−2. Of the full-distribution models, the finite mixture of normal distributions with two to four clusters and the DPM of normal distributions had superior performance in comparison with the lognormal models. DPM distributions provided slightly better fit than the finite mixture distributions; the advantages of the DPM model were avoiding certain convergence issues associated with the finite mixture distributions, adaptively selecting the number of needed clusters, and providing uncertainty estimates. Although the results apply to the RIOPA data set, GEV distributions and mixture models appear more broadly applicable. These models can be used to simulate VOC distributions, which are neither normally nor lognormally distributed, and they accurately represent the highest exposures, which may have the greatest health significance. Specific Aim 2 Four VOC mixtures were identified and apportioned by PMF; they represented gasoline vapor, vehicle exhaust, chlorinated solvents and disinfection byproducts, and cleaning products and odorants. The last mixture (cleaning products and odorants) accounted for the largest fraction of an individual’s total exposure (average of 42% across RIOPA participants). Often, a single compound dominated a mixture but the mixture fractions were heterogeneous; that is, the fractions of the compounds changed with the concentration of the mixture. Three VOC mixtures were identified by toxicologic mode of action and represented VOCs associated with hematopoietic, liver, and renal tumors. Estimated lifetime cumulative cancer risks exceeded 10−3 for about 10% of RIOPA participants. The dependency structures of the VOC mixtures in the RIOPA data set fitted Gumbel (two mixtures) and t copulas (four mixtures). These copula types emphasize dependencies found in the upper and lower tails of a distribution. The copulas reproduced both risk predictions and exposure fractions with a high degree of accuracy and performed better than multivariate lognormal distributions. Specific Aim 3 In an analysis focused on the home environment and the outdoor (close to home) environment, home VOC concentrations dominated personal exposures (66% to 78% of the total exposure, depending on VOC); this was largely the result of the amount of time participants spent at home and the fact that indoor concentrations were much higher than outdoor concentrations for most VOCs. In a different analysis focused on the sources inside the home and outside (but close to the home), it was assumed that 100% of VOCs from outside sources would penetrate the home. Outdoor VOC sources accounted for 5% (d-limonene) to 81% (carbon tetrachloride [CTC]) of the total exposure. Personal exposure and indoor measurements had similar determinants depending on the VOC. Gasoline-related VOCs (e.g., benzene and methyl tert-butyl ether [MTBE]) were associated with city, residences with attached garages, pumping gas, wind speed, and home air exchange rate (AER). Odorant and cleaning-related VOCs (e.g., 1,4-DCB and chloroform) also were associated with city, and a residence’s AER, size, and family members showering. Dry-cleaning and industry-related VOCs (e.g., tetrachloroethylene [or perchloroethylene, PERC] and trichloroethylene [TCE]) were associated with city, type of water supply to the home, and visits to the dry cleaner. These and other relationships were significant, they explained from 10% to 40% of the variance in the measurements, and are consistent with known emission sources and those reported in the literature. Outdoor concentrations of VOCs had only two determinants in common: city and wind speed. Overall, personal exposure was dominated by the home setting, although a large fraction of indoor VOC concentrations were due to outdoor sources. City of residence, personal activities, household characteristics, and meteorology were significant determinants. Concentrations in RIOPA were considerably lower than levels in the nationally representative NHANES for all VOCs except MTBE and 1,4-DCB. Differences between RIOPA and NHANES results can be explained by contrasts between the sampling designs and staging in the two studies, and by differences in the demographics, smoking, employment, occupations, and home locations. A portion of these differences are due to the nature of the convenience (RIOPA) and representative (NHANES) sampling strategies used in the two studies. CONCLUSIONS Accurate models for exposure data, which can feature extreme values, multiple modes, data below the MDL, heterogeneous interpollutant dependency structures, and other complex characteristics, are needed to estimate exposures and risks and to develop control and management guidelines and policies. Conventional and novel statistical methods were applied to data drawn from two large studies to understand the nature and significance of VOC exposures. Both extreme value distributions and mixture models were found to provide excellent fit to single VOC compounds (univariate distributions), and copulas may be the method of choice for VOC mixtures (multivariate distributions), especially for the highest exposures, which fit parametric models poorly and which may represent the greatest health risk. The identification of exposure determinants, including the influence of both certain activities (e.g., pumping gas) and environments (e.g., residences), provides information that can be used to manage and reduce exposures. The results obtained using the RIOPA data set add to our understanding of VOC exposures and further investigations using a more representative population and a wider suite of VOCs are suggested to extend and generalize results. PMID:25145040

Evaluation of Two Types of Differential Item Functioning in Factor Mixture Models with Binary Outcomes

ERIC Educational Resources Information Center

Lee, HwaYoung; Beretvas, S. Natasha

2014-01-01

Conventional differential item functioning (DIF) detection methods (e.g., the Mantel-Haenszel test) can be used to detect DIF only across observed groups, such as gender or ethnicity. However, research has found that DIF is not typically fully explained by an observed variable. True sources of DIF may include unobserved, latent variables, such as…

Using a Mixture IRT Model to Understand English Learner Performance on Large-Scale Assessments

ERIC Educational Resources Information Center

Shea, Christine A.

2013-01-01

The purpose of this study was to determine whether an eighth grade state-level math assessment contained items that function differentially (DIF) for English Learner students (EL) as compared to English Only students (EO) and if so, what factors might have caused DIF. To determine this, Differential Item Functioning (DIF) analysis was employed.…

Predictors of Reading Comprehension among Struggling Readers Who Exhibit Differing Levels of Inattention and Hyperactivity

ERIC Educational Resources Information Center

Swanson, Elizabeth; Barnes, Marcia; Fall, Anna-Mari; Roberts, Greg

2018-01-01

The purpose of this study was to investigate the impact of inference making, decoding, memory, and vocabulary on reading comprehension among 7th- through 12th-grade struggling readers with varying levels of inattention and hyperactivity. We categorized a group of 414 struggling readers into 3 groups based on results from factor mixture modeling:…

Optimising reversed-phase liquid chromatographic separation of an acidic mixture on a monolithic stationary phase with the aid of response surface methodology and experimental design.

PubMed

Wang, Y; Harrison, M; Clark, B J

2006-02-10

An optimization strategy for the separation of an acidic mixture by employing a monolithic stationary phase is presented, with the aid of experimental design and response surface methodology (RSM). An orthogonal array design (OAD) OA(16) (2(15)) was used to choose the significant parameters for the optimization. The significant factors were optimized by using a central composite design (CCD) and the quadratic models between the dependent and the independent parameters were built. The mathematical models were tested on a number of simulated data set and had a coefficient of R(2) > 0.97 (n = 16). On applying the optimization strategy, the factor effects were visualized as three-dimensional (3D) response surfaces and contour plots. The optimal condition was achieved in less than 40 min by using the monolithic packing with the mobile phase of methanol/20 mM phosphate buffer pH 2.7 (25.5/74.5, v/v). The method showed good agreement between the experimental data and predictive value throughout the studied parameter space and were suitable for optimization studies on the monolithic stationary phase for acidic compounds.

A multiphase model for chemically- and mechanically- induced cell differentiation in a hollow fibre membrane bioreactor: minimising growth factor consumption.

PubMed

Pearson, Natalie C; Oliver, James M; Shipley, Rebecca J; Waters, Sarah L

2016-06-01

We present a simplified two-dimensional model of fluid flow, solute transport, and cell distribution in a hollow fibre membrane bioreactor. We consider two cell populations, one undifferentiated and one differentiated, with differentiation stimulated either by growth factor alone, or by both growth factor and fluid shear stress. Two experimental configurations are considered, a 3-layer model in which the cells are seeded in a scaffold throughout the extracapillary space (ECS), and a 4-layer model in which the cell-scaffold construct occupies a layer surrounding the outside of the hollow fibre, only partially filling the ECS. Above this is a region of free-flowing fluid, referred to as the upper fluid layer. Following previous models by the authors (Pearson et al. in Math Med Biol, 2013, Biomech Model Mechanbiol 1-16, 2014a, we employ porous mixture theory to model the dynamics of, and interactions between, the cells, scaffold, and fluid in the cell-scaffold construct. We use this model to determine operating conditions (experiment end time, growth factor inlet concentration, and inlet fluid fluxes) which result in a required percentage of differentiated cells, as well as maximising the differentiated cell yield and minimising the consumption of expensive growth factor.

Extracting Spurious Latent Classes in Growth Mixture Modeling with Nonnormal Errors

ERIC Educational Resources Information Center

Guerra-Peña, Kiero; Steinley, Douglas

2016-01-01

Growth mixture modeling is generally used for two purposes: (1) to identify mixtures of normal subgroups and (2) to approximate oddly shaped distributions by a mixture of normal components. Often in applied research this methodology is applied to both of these situations indistinctly: using the same fit statistics and likelihood ratio tests. This…

40 CFR Table 2b to Subpart E of... - Reactivity Factors for Aliphatic Hydrocarbon Solvent Mixtures

Code of Federal Regulations, 2014 CFR

2014-07-01

... 40 Protection of Environment 6 2014-07-01 2014-07-01 false Reactivity Factors for Aliphatic Hydrocarbon Solvent Mixtures 2B Table 2B to Subpart E of Part 59 Protection of Environment ENVIRONMENTAL... Coatings Pt. 59, Subpt. E, Table 2B Table 2B to Subpart E of Part 59—Reactivity Factors for Aliphatic...

40 CFR Table 2c to Subpart E of... - Reactivity Factors for Aromatic Hydrocarbon Solvent Mixtures

Code of Federal Regulations, 2014 CFR

2014-07-01

... 40 Protection of Environment 6 2014-07-01 2014-07-01 false Reactivity Factors for Aromatic Hydrocarbon Solvent Mixtures 2C Table 2C to Subpart E of Part 59 Protection of Environment ENVIRONMENTAL... Coatings Pt. 59, Subpt. E, Table 2C Table 2C to Subpart E of Part 59—Reactivity Factors for Aromatic...

40 CFR Table 2b to Subpart E of... - Reactivity Factors for Aliphatic Hydrocarbon Solvent Mixtures

Code of Federal Regulations, 2013 CFR

2013-07-01

... 40 Protection of Environment 6 2013-07-01 2013-07-01 false Reactivity Factors for Aliphatic Hydrocarbon Solvent Mixtures 2B Table 2B to Subpart E of Part 59 Protection of Environment ENVIRONMENTAL... Coatings Pt. 59, Subpt. E, Table 2B Table 2B to Subpart E of Part 59—Reactivity Factors for Aliphatic...

40 CFR Table 2c to Subpart E of... - Reactivity Factors for Aromatic Hydrocarbon Solvent Mixtures

Code of Federal Regulations, 2013 CFR

2013-07-01

... 40 Protection of Environment 6 2013-07-01 2013-07-01 false Reactivity Factors for Aromatic Hydrocarbon Solvent Mixtures 2C Table 2C to Subpart E of Part 59 Protection of Environment ENVIRONMENTAL... Coatings Pt. 59, Subpt. E, Table 2C Table 2C to Subpart E of Part 59—Reactivity Factors for Aromatic...

40 CFR Table 2b to Subpart E of... - Reactivity Factors for Aliphatic Hydrocarbon Solvent Mixtures

Code of Federal Regulations, 2012 CFR

2012-07-01

... 40 Protection of Environment 6 2012-07-01 2012-07-01 false Reactivity Factors for Aliphatic Hydrocarbon Solvent Mixtures 2B Table 2B to Subpart E of Part 59 Protection of Environment ENVIRONMENTAL... Coatings Pt. 59, Subpt. E, Table 2B Table 2B to Subpart E of Part 59—Reactivity Factors for Aliphatic...

40 CFR Table 2c to Subpart E of... - Reactivity Factors for Aromatic Hydrocarbon Solvent Mixtures

Code of Federal Regulations, 2012 CFR

2012-07-01

... 40 Protection of Environment 6 2012-07-01 2012-07-01 false Reactivity Factors for Aromatic Hydrocarbon Solvent Mixtures 2C Table 2C to Subpart E of Part 59 Protection of Environment ENVIRONMENTAL... Coatings Pt. 59, Subpt. E, Table 2C Table 2C to Subpart E of Part 59—Reactivity Factors for Aromatic...

Dispelling urban myths about default uncertainty factors in chemical risk assessment – sufficient protection against mixture effects?

PubMed Central

2013-01-01

Assessing the detrimental health effects of chemicals requires the extrapolation of experimental data in animals to human populations. This is achieved by applying a default uncertainty factor of 100 to doses not found to be associated with observable effects in laboratory animals. It is commonly assumed that the toxicokinetic and toxicodynamic sub-components of this default uncertainty factor represent worst-case scenarios and that the multiplication of those components yields conservative estimates of safe levels for humans. It is sometimes claimed that this conservatism also offers adequate protection from mixture effects. By analysing the evolution of uncertainty factors from a historical perspective, we expose that the default factor and its sub-components are intended to represent adequate rather than worst-case scenarios. The intention of using assessment factors for mixture effects was abandoned thirty years ago. It is also often ignored that the conservatism (or otherwise) of uncertainty factors can only be considered in relation to a defined level of protection. A protection equivalent to an effect magnitude of 0.001-0.0001% over background incidence is generally considered acceptable. However, it is impossible to say whether this level of protection is in fact realised with the tolerable doses that are derived by employing uncertainty factors. Accordingly, it is difficult to assess whether uncertainty factors overestimate or underestimate the sensitivity differences in human populations. It is also often not appreciated that the outcome of probabilistic approaches to the multiplication of sub-factors is dependent on the choice of probability distributions. Therefore, the idea that default uncertainty factors are overly conservative worst-case scenarios which can account both for the lack of statistical power in animal experiments and protect against potential mixture effects is ill-founded. We contend that precautionary regulation should provide an incentive to generate better data and recommend adopting a pragmatic, but scientifically better founded approach to mixture risk assessment. PMID:23816180

Dispelling urban myths about default uncertainty factors in chemical risk assessment--sufficient protection against mixture effects?

PubMed

Martin, Olwenn V; Martin, Scholze; Kortenkamp, Andreas

2013-07-01

Assessing the detrimental health effects of chemicals requires the extrapolation of experimental data in animals to human populations. This is achieved by applying a default uncertainty factor of 100 to doses not found to be associated with observable effects in laboratory animals. It is commonly assumed that the toxicokinetic and toxicodynamic sub-components of this default uncertainty factor represent worst-case scenarios and that the multiplication of those components yields conservative estimates of safe levels for humans. It is sometimes claimed that this conservatism also offers adequate protection from mixture effects. By analysing the evolution of uncertainty factors from a historical perspective, we expose that the default factor and its sub-components are intended to represent adequate rather than worst-case scenarios. The intention of using assessment factors for mixture effects was abandoned thirty years ago. It is also often ignored that the conservatism (or otherwise) of uncertainty factors can only be considered in relation to a defined level of protection. A protection equivalent to an effect magnitude of 0.001-0.0001% over background incidence is generally considered acceptable. However, it is impossible to say whether this level of protection is in fact realised with the tolerable doses that are derived by employing uncertainty factors. Accordingly, it is difficult to assess whether uncertainty factors overestimate or underestimate the sensitivity differences in human populations. It is also often not appreciated that the outcome of probabilistic approaches to the multiplication of sub-factors is dependent on the choice of probability distributions. Therefore, the idea that default uncertainty factors are overly conservative worst-case scenarios which can account both for the lack of statistical power in animal experiments and protect against potential mixture effects is ill-founded. We contend that precautionary regulation should provide an incentive to generate better data and recommend adopting a pragmatic, but scientifically better founded approach to mixture risk assessment.

On the measurement of stability in over-time data.

PubMed

Kenny, D A; Campbell, D T

1989-06-01

In this article, autoregressive models and growth curve models are compared. Autoregressive models are useful because they allow for random change, permit scores to increase or decrease, and do not require strong assumptions about the level of measurement. Three previously presented designs for estimating stability are described: (a) time-series, (b) simplex, and (c) two-wave, one-factor methods. A two-wave, multiple-factor model also is presented, in which the variables are assumed to be caused by a set of latent variables. The factor structure does not change over time and so the synchronous relationships are temporally invariant. The factors do not cause each other and have the same stability. The parameters of the model are the factor loading structure, each variable's reliability, and the stability of the factors. We apply the model to two data sets. For eight cognitive skill variables measured at four times, the 2-year stability is estimated to be .92 and the 6-year stability is .83. For nine personality variables, the 3-year stability is .68. We speculate that for many variables there are two components: one component that changes very slowly (the trait component) and another that changes very rapidly (the state component); thus each variable is a mixture of trait and state. Circumstantial evidence supporting this view is presented.

Investigating the Impact of Item Parameter Drift for Item Response Theory Models with Mixture Distributions.

PubMed

Park, Yoon Soo; Lee, Young-Sun; Xing, Kuan

2016-01-01

This study investigates the impact of item parameter drift (IPD) on parameter and ability estimation when the underlying measurement model fits a mixture distribution, thereby violating the item invariance property of unidimensional item response theory (IRT) models. An empirical study was conducted to demonstrate the occurrence of both IPD and an underlying mixture distribution using real-world data. Twenty-one trended anchor items from the 1999, 2003, and 2007 administrations of Trends in International Mathematics and Science Study (TIMSS) were analyzed using unidimensional and mixture IRT models. TIMSS treats trended anchor items as invariant over testing administrations and uses pre-calibrated item parameters based on unidimensional IRT. However, empirical results showed evidence of two latent subgroups with IPD. Results also showed changes in the distribution of examinee ability between latent classes over the three administrations. A simulation study was conducted to examine the impact of IPD on the estimation of ability and item parameters, when data have underlying mixture distributions. Simulations used data generated from a mixture IRT model and estimated using unidimensional IRT. Results showed that data reflecting IPD using mixture IRT model led to IPD in the unidimensional IRT model. Changes in the distribution of examinee ability also affected item parameters. Moreover, drift with respect to item discrimination and distribution of examinee ability affected estimates of examinee ability. These findings demonstrate the need to caution and evaluate IPD using a mixture IRT framework to understand its effects on item parameters and examinee ability.

Investigating the Impact of Item Parameter Drift for Item Response Theory Models with Mixture Distributions

PubMed Central

Park, Yoon Soo; Lee, Young-Sun; Xing, Kuan

2016-01-01

This study investigates the impact of item parameter drift (IPD) on parameter and ability estimation when the underlying measurement model fits a mixture distribution, thereby violating the item invariance property of unidimensional item response theory (IRT) models. An empirical study was conducted to demonstrate the occurrence of both IPD and an underlying mixture distribution using real-world data. Twenty-one trended anchor items from the 1999, 2003, and 2007 administrations of Trends in International Mathematics and Science Study (TIMSS) were analyzed using unidimensional and mixture IRT models. TIMSS treats trended anchor items as invariant over testing administrations and uses pre-calibrated item parameters based on unidimensional IRT. However, empirical results showed evidence of two latent subgroups with IPD. Results also showed changes in the distribution of examinee ability between latent classes over the three administrations. A simulation study was conducted to examine the impact of IPD on the estimation of ability and item parameters, when data have underlying mixture distributions. Simulations used data generated from a mixture IRT model and estimated using unidimensional IRT. Results showed that data reflecting IPD using mixture IRT model led to IPD in the unidimensional IRT model. Changes in the distribution of examinee ability also affected item parameters. Moreover, drift with respect to item discrimination and distribution of examinee ability affected estimates of examinee ability. These findings demonstrate the need to caution and evaluate IPD using a mixture IRT framework to understand its effects on item parameters and examinee ability. PMID:26941699

Solubility modeling of refrigerant/lubricant mixtures

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

Michels, H.H.; Sienel, T.H.

1996-12-31

A general model for predicting the solubility properties of refrigerant/lubricant mixtures has been developed based on applicable theory for the excess Gibbs energy of non-ideal solutions. In our approach, flexible thermodynamic forms are chosen to describe the properties of both the gas and liquid phases of refrigerant/lubricant mixtures. After an extensive study of models for describing non-ideal liquid effects, the Wohl-suffix equations, which have been extensively utilized in the analysis of hydrocarbon mixtures, have been developed into a general form applicable to mixtures where one component is a POE lubricant. In the present study we have analyzed several POEs wheremore » structural and thermophysical property data were available. Data were also collected from several sources on the solubility of refrigerant/lubricant binary pairs. We have developed a computer code (NISC), based on the Wohl model, that predicts dew point or bubble point conditions over a wide range of composition and temperature. Our present analysis covers mixtures containing up to three refrigerant molecules and one lubricant. The present code can be used to analyze the properties of R-410a and R-407c in mixtures with a POE lubricant. Comparisons with other models, such as the Wilson or modified Wilson equations, indicate that the Wohl-suffix equations yield more reliable predictions for HFC/POE mixtures.« less

Toxicity and bioaccumulation of a mixture of heavy metals in Chironomus tentans (Diptera: Chironomidae) in synthetic sediment

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

Harrahy, E.A.; Clements, W.H.

1997-02-01

This research investigated toxicity and bioaccumulation of a mixture of Cd, Cu, Pb, and Zn in Chironomus tentans in synthetic sediment, and compared predicted to measured steady-state bioaccumulation factors (BAFs). In a toxicity test, C. tentans were exposed to various dilutions of a base concentration (1.0 X) of a mixture of the four metals (5 {micro}g/g Cd. 10 {micro}g/g Cu. 70 {micro}g/g Pb, and 300 {micro}g/g Zn) in synthetic sediment. Mortality ranged from 17 to 100%. To measure bioaccumulation of the metals, C. tentans were exposed to 0.35 X the base concentration for a period of up to 14 dmore » in two uptake tests. Bioaccumulation of all four metals increased over the 14-d uptake phases. Concentrations of metals in chironomids were significantly correlated with exposure time in the uptake phases. Only concentrations of copper approached background levels after 7 d depuration. Uptake rate coefficients and elimination rate constants were determined for each metal. Bioaccumulation factors were highest for Cd and lowest for Pb. With the exception of Pb, steady-state BAFs were within a factor of about two of those calculated using the first-order kinetic model. The high BAFs calculated may indicate greater bioavailability in synthetic sediment. Studies comparing toxicity and bioaccumulation of natural and synthetic sediments are necessary before the use of synthetic sediments is widely adopted.« less

Optimization of primaquine diphosphate tablet formulation for controlled drug release using the mixture experimental design.

PubMed

Duque, Marcelo Dutra; Kreidel, Rogério Nepomuceno; Taqueda, Maria Elena Santos; Baby, André Rolim; Kaneko, Telma Mary; Velasco, Maria Valéria Robles; Consiglieri, Vladi Olga

2013-01-01

A tablet formulation based on hydrophilic matrix with a controlled drug release was developed, and the effect of polymer concentrations on the release of primaquine diphosphate was evaluated. To achieve this purpose, a 20-run, four-factor with multiple constraints on the proportions of the components was employed to obtain tablet compositions. Drug release was determined by an in vitro dissolution study in phosphate buffer solution at pH 6.8. The polynomial fitted functions described the behavior of the mixture on simplex coordinate systems to study the effects of each factor (polymer) on tablet characteristics. Based on the response surface methodology, a tablet composition was optimized with the purpose of obtaining a primaquine diphosphate release closer to a zero order kinetic. This formulation released 85.22% of the drug for 8 h and its kinetic was studied regarding to Korsmeyer-Peppas model, (Adj-R(2) = 0.99295) which has confirmed that both diffusion and erosion were related to the mechanism of the drug release. The data from the optimized formulation were very close to the predictions from statistical analysis, demonstrating that mixture experimental design could be used to optimize primaquine diphosphate dissolution from hidroxypropylmethyl cellulose and polyethylene glycol matrix tablets.

An evaluation of the Bayesian approach to fitting the N-mixture model for use with pseudo-replicated count data

USGS Publications Warehouse

Toribo, S.G.; Gray, B.R.; Liang, S.

2011-01-01

The N-mixture model proposed by Royle in 2004 may be used to approximate the abundance and detection probability of animal species in a given region. In 2006, Royle and Dorazio discussed the advantages of using a Bayesian approach in modelling animal abundance and occurrence using a hierarchical N-mixture model. N-mixture models assume replication on sampling sites, an assumption that may be violated when the site is not closed to changes in abundance during the survey period or when nominal replicates are defined spatially. In this paper, we studied the robustness of a Bayesian approach to fitting the N-mixture model for pseudo-replicated count data. Our simulation results showed that the Bayesian estimates for abundance and detection probability are slightly biased when the actual detection probability is small and are sensitive to the presence of extra variability within local sites.

Children Exposed to Intimate Partner Violence: Identifying Differential Effects of Family Environment on Children’s Trauma and Psychopathology Symptoms through Regression Mixture Models

PubMed Central

McDonald, Shelby Elaine; Shin, Sunny; Corona, Rosalie; Maternick, Anna; Graham-Bermann, Sandra A.; Ascione, Frank R.; Williams, James Herbert

2016-01-01

The majority of analytic approaches aimed at understanding the influence of environmental context on children’s socioemotional adjustment assume comparable effects of contextual risk and protective factors for all children. Using self-reported data from 289 maternal caregiver-child dyads, we examined the degree to which there are differential effects of severity of intimate partner violence (IPV) exposure, yearly household income, and number of children in the family on posttraumatic stress symptoms (PTS) and psychopathology symptoms (i.e., internalizing and externalizing problems) among school-age children between the ages of 7 to 12 years. A regression mixture model identified three latent classes that were primarily distinguished by differential effects of IPV exposure severity on PTS and psychopathology symptoms: (1) asymptomatic with low sensitivity to environmental factors (66% of children), (2) maladjusted with moderate sensitivity (24%), and (3) highly maladjusted with high sensitivity (10%). Children with mothers who had higher levels of education were more likely to be in the maladjusted with moderate sensitivity group than the asymptomatic with low sensitivity group. Latino children were less likely to be in both maladjusted groups compared to the asymptomatic group. Overall, the findings suggest differential effects of family environmental factors on PTS and psychopathology symptoms among children exposed to IPV. Implications for research and practice are discussed. PMID:27337691

Testing job typologies and identifying at-risk subpopulations using factor mixture models.

PubMed

Keller, Anita C; Igic, Ivana; Meier, Laurenz L; Semmer, Norbert K; Schaubroeck, John M; Brunner, Beatrice; Elfering, Achim

2017-10-01

Research in occupational health psychology has tended to focus on the effects of single job characteristics or various job characteristics combined into 1 factor. However, such a variable-centered approach does not account for the clustering of job attributes among groups of employees. We addressed this issue by using a person-centered approach to (a) investigate the occurrence of different empirical constellations of perceived job stressors and resources and (b) validate the meaningfulness of profiles by analyzing their association with employee well-being and performance. We applied factor mixture modeling to identify profiles in 4 large samples consisting of employees in Switzerland (Studies 1 and 2) and the United States (Studies 3 and 4). We identified 2 profiles that spanned the 4 samples, with 1 reflecting a combination of relatively low stressors and high resources (P1) and the other relatively high stressors and low resources (P3). The profiles differed mainly in terms of their organizational and social aspects. Employees in P1 reported significantly higher mean levels of job satisfaction, performance, and general health, and lower means in exhaustion compared with P3. Additional analyses showed differential relationships between job attributes and outcomes depending on profile membership. These findings may benefit organizational interventions as they show that perceived work stressors and resources more strongly influence satisfaction and well-being in particular profiles. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Children exposed to intimate partner violence: Identifying differential effects of family environment on children's trauma and psychopathology symptoms through regression mixture models.

PubMed

McDonald, Shelby Elaine; Shin, Sunny; Corona, Rosalie; Maternick, Anna; Graham-Bermann, Sandra A; Ascione, Frank R; Herbert Williams, James

2016-08-01

The majority of analytic approaches aimed at understanding the influence of environmental context on children's socioemotional adjustment assume comparable effects of contextual risk and protective factors for all children. Using self-reported data from 289 maternal caregiver-child dyads, we examined the degree to which there are differential effects of severity of intimate partner violence (IPV) exposure, yearly household income, and number of children in the family on posttraumatic stress symptoms (PTS) and psychopathology symptoms (i.e., internalizing and externalizing problems) among school-age children between the ages of 7-12 years. A regression mixture model identified three latent classes that were primarily distinguished by differential effects of IPV exposure severity on PTS and psychopathology symptoms: (1) asymptomatic with low sensitivity to environmental factors (66% of children), (2) maladjusted with moderate sensitivity (24%), and (3) highly maladjusted with high sensitivity (10%). Children with mothers who had higher levels of education were more likely to be in the maladjusted with moderate sensitivity group than the asymptomatic with low sensitivity group. Latino children were less likely to be in both maladjusted groups compared to the asymptomatic group. Overall, the findings suggest differential effects of family environmental factors on PTS and psychopathology symptoms among children exposed to IPV. Implications for research and practice are discussed. Copyright © 2016 Elsevier Ltd. All rights reserved.

Development and characterization of acoustically efficient cementitious materials

NASA Astrophysics Data System (ADS)

Neithalath, Narayanan

Tire-pavement interaction noise is one of the significant environmental issues in highly populated urban areas situated near busy highways. The understanding that methodologies to reduce the sound at the source itself is necessary, has led to the development of porous paving materials. This thesis outlines the systematic research effort conducted in order to develop and characterize two different types of sound absorbing cementitious materials---Enhanced Porosity Concrete (EPC), that incorporates porosity in the non-aggregate component of the mixture, and Cellulose-Cement Composites, where cellulose fibers are used as porous inclusions. The basic tenet of this research is that carefully introduced porosity of about 15%--25% in the material structure of concrete will allow sound waves to pass through and dissipate its energy. The physical, mechanical, and acoustic properties of EPC mixtures are discussed in detail. Methods are developed to determine the porosity of EPC. The total pore volume, pore size, and pore connectivity are the significant features that influence the behavior of EPC. Using a shape-specific model, and incorporating the principle of acoustic wave propagation through semi-open cells, the acoustic absorption in EPC has been modeled. The pore structure and performance of EPC is characterized using Electrical Impedance Spectroscopy. Using a multi-phase conducting model, a pore connectivity factor has been developed, that correlates well with the acoustic absorption coefficient. A falling head permeameter has been designed to ascertain the water permeability of EPC mixtures. A hydraulic connectivity factor is proposed, which could be used to classify EPC mixtures based on their permeability. Electrical conductivity is shown to be a single measurable parameter that defines the performance of EPC. Preliminary studies conducted on the freezing and thawing response of EPC are also reported. From several porous, compliant materials, morphologically altered cellulose fibers are chosen to be used as inclusions. The "macronodule" (aggregate-like, 2--8 mm in size) fibers are shown to be the most effective among the various morphologically altered cellulose fibers considered. The physical and mechanical properties (porosity, flexural and compressive strengths, modulus of elasticity), acoustic absorption, and the energy dissipating capacity (specific damping capacity) are evaluated. Composite mixing relations have been used to model the loss modulus and loss tangent of these composites. The response of these composites to extreme exposure conditions has also been studied.

Process Dissociation and Mixture Signal Detection Theory

ERIC Educational Resources Information Center

DeCarlo, Lawrence T.

2008-01-01

The process dissociation procedure was developed in an attempt to separate different processes involved in memory tasks. The procedure naturally lends itself to a formulation within a class of mixture signal detection models. The dual process model is shown to be a special case. The mixture signal detection model is applied to data from a widely…

Investigating Approaches to Estimating Covariate Effects in Growth Mixture Modeling: A Simulation Study

ERIC Educational Resources Information Center

Li, Ming; Harring, Jeffrey R.

2017-01-01

Researchers continue to be interested in efficient, accurate methods of estimating coefficients of covariates in mixture modeling. Including covariates related to the latent class analysis not only may improve the ability of the mixture model to clearly differentiate between subjects but also makes interpretation of latent group membership more…

Finite Mixture Multilevel Multidimensional Ordinal IRT Models for Large Scale Cross-Cultural Research

ERIC Educational Resources Information Center

de Jong, Martijn G.; Steenkamp, Jan-Benedict E. M.

2010-01-01

We present a class of finite mixture multilevel multidimensional ordinal IRT models for large scale cross-cultural research. Our model is proposed for confirmatory research settings. Our prior for item parameters is a mixture distribution to accommodate situations where different groups of countries have different measurement operations, while…

Microsiemens or Milligrams: Measures of Ionic Mixtures ...

EPA Pesticide Factsheets

In December of 2016, EPA released the Draft Field-Based Methods for Developing Aquatic Life Criteria for Specific Conductivity for public comment. Once final, states and authorized tribes may use these methods to derive field-based ecoregional ambient Aquatic Life Ambient Water Quality Criteria (AWQC) for specific conductivity (SC) in flowing waters. The methods provide flexible approaches for developing science-based SC criteria that reflect ecoregional or state specific factors. The concentration of a dissolved salt mixture can be measured in a number of ways including measurement of total dissolved solids, freezing point depression, refractive index, density, or the sum of the concentrations of individually measured ions. For the draft method, SC was selected as the measure because SC is a measure of all ions in the mixture; the measurement technology is fast, inexpensive, and accurate, and it measures only dissolved ions. When developing water quality criteria for major ions, some stakeholders may prefer to identify the ionic constituents as a measure of exposure instead of SC. A field-based method was used to derive example chronic and acute water quality criteria for SC and two anions a common mixture of ions (bicarbonate plus sulfate, [HCO3−] + [SO42−] in mg/L) that represent common mixtures in streams. These two anions are sufficient to model the ion mixture and SC (R2 = 0.94). Using [HCO3−] + [SO42−] does not imply that these two anions are the

Influence of heavy metals on the formation and the distribution behavior of PAH and PCDD/F during simulated fires.

PubMed

Wobst, M; Wichmann, H; Bahadir, M

2003-04-01

Combustion experiments were performed with an artificial fire load (polystyrene and quartz powder) in a laboratory scale incinerator in the presence of gaseous HCl to simulate accidental fire conditions. The aim of this investigation was to trace back the alterations of the formation and the distribution behavior of PAH and PCDD/PCDF to the presence of CuO or a mixture of metal oxides (CdO, CuO, Fe(2)O(3), PbO, MoO(3), ZnO). The total amount of the 16 PAH target compounds was reduced by the factor of 5-9 when the mixture of metal oxides was present rather than merely CuO. PAH patterns as well as their distribution behavior were significantly influenced by these oxides. In general, transportation inside the installation was enhanced for most of the 16 analyzed PAH. Only fluorene and dibenzo[a,h]anthracene were transported to a smaller extent. In contrast to PAH, total concentrations of PCDD were increased by factor 9 and of PCDF by factor 10, respectively, when CuO was present. Adding the mixture of metal oxides resulted in an increase of PCDD by factor 14 and of PCDF by factor 7. CuO and the mixture of metal oxides had a different influence on the PCDD/F homologue patterns. For instance, the HxCDF to OCDF ratio after incineration without any metal oxide was 1 to 6, whereas addition of CuO or the mixture of the metal oxides shifted the HxCDF to OCDF ratios towards 1 to 40 or 1 to 17, respectively. Combustion along with CuO increased transportation of higher chlorinated PCDF congeners, whereas the mixture of the metal oxides caused a strong decrease of PCDF distribution throughout the system.

A Bayesian framework based on a Gaussian mixture model and radial-basis-function Fisher discriminant analysis (BayGmmKda V1.1) for spatial prediction of floods

NASA Astrophysics Data System (ADS)

Tien Bui, Dieu; Hoang, Nhat-Duc

2017-09-01

In this study, a probabilistic model, named as BayGmmKda, is proposed for flood susceptibility assessment in a study area in central Vietnam. The new model is a Bayesian framework constructed by a combination of a Gaussian mixture model (GMM), radial-basis-function Fisher discriminant analysis (RBFDA), and a geographic information system (GIS) database. In the Bayesian framework, GMM is used for modeling the data distribution of flood-influencing factors in the GIS database, whereas RBFDA is utilized to construct a latent variable that aims at enhancing the model performance. As a result, the posterior probabilistic output of the BayGmmKda model is used as flood susceptibility index. Experiment results showed that the proposed hybrid framework is superior to other benchmark models, including the adaptive neuro-fuzzy inference system and the support vector machine. To facilitate the model implementation, a software program of BayGmmKda has been developed in MATLAB. The BayGmmKda program can accurately establish a flood susceptibility map for the study region. Accordingly, local authorities can overlay this susceptibility map onto various land-use maps for the purpose of land-use planning or management.

Approximation of the breast height diameter distribution of two-cohort stands by mixture models I Parameter estimation

Treesearch

Rafal Podlaski; Francis A. Roesch

2013-01-01

Study assessed the usefulness of various methods for choosing the initial values for the numerical procedures for estimating the parameters of mixture distributions and analysed variety of mixture models to approximate empirical diameter at breast height (dbh) distributions. Two-component mixtures of either the Weibull distribution or the gamma distribution were...

Detection of mastitis in dairy cattle by use of mixture models for repeated somatic cell scores: a Bayesian approach via Gibbs sampling.

PubMed

Odegård, J; Jensen, J; Madsen, P; Gianola, D; Klemetsdal, G; Heringstad, B

2003-11-01

The distribution of somatic cell scores could be regarded as a mixture of at least two components depending on a cow's udder health status. A heteroscedastic two-component Bayesian normal mixture model with random effects was developed and implemented via Gibbs sampling. The model was evaluated using datasets consisting of simulated somatic cell score records. Somatic cell score was simulated as a mixture representing two alternative udder health statuses ("healthy" or "diseased"). Animals were assigned randomly to the two components according to the probability of group membership (Pm). Random effects (additive genetic and permanent environment), when included, had identical distributions across mixture components. Posterior probabilities of putative mastitis were estimated for all observations, and model adequacy was evaluated using measures of sensitivity, specificity, and posterior probability of misclassification. Fitting different residual variances in the two mixture components caused some bias in estimation of parameters. When the components were difficult to disentangle, so were their residual variances, causing bias in estimation of Pm and of location parameters of the two underlying distributions. When all variance components were identical across mixture components, the mixture model analyses returned parameter estimates essentially without bias and with a high degree of precision. Including random effects in the model increased the probability of correct classification substantially. No sizable differences in probability of correct classification were found between models in which a single cow effect (ignoring relationships) was fitted and models where this effect was split into genetic and permanent environmental components, utilizing relationship information. When genetic and permanent environmental effects were fitted, the between-replicate variance of estimates of posterior means was smaller because the model accounted for random genetic drift.

40 CFR Table 2b to Subpart E of... - Reactivity Factors for Aliphatic Hydrocarbon Solvent Mixtures

Code of Federal Regulations, 2010 CFR

2010-07-01

... Hydrocarbon Solvent Mixtures 2B Table 2B to Subpart E of Part 59 Protection of Environment ENVIRONMENTAL... Hydrocarbon Solvent Mixtures Bin Averageboiling point * (degrees F) Criteria Reactivityfactor 1 80-205 Alkanes... + Dry Point) / 2 (b) Aromatic Hydrocarbon Solvents ...

40 CFR Table 2b to Subpart E of... - Reactivity Factors for Aliphatic Hydrocarbon Solvent Mixtures

Code of Federal Regulations, 2011 CFR

2011-07-01

... Hydrocarbon Solvent Mixtures 2B Table 2B to Subpart E of Part 59 Protection of Environment ENVIRONMENTAL... Hydrocarbon Solvent Mixtures Bin Averageboiling point * (degrees F) Criteria Reactivityfactor 1 80-205 Alkanes... + Dry Point) / 2 (b) Aromatic Hydrocarbon Solvents ...

Assessing variation in life-history tactics within a population using mixture regression models: a practical guide for evolutionary ecologists.

PubMed

Hamel, Sandra; Yoccoz, Nigel G; Gaillard, Jean-Michel

2017-05-01

Mixed models are now well-established methods in ecology and evolution because they allow accounting for and quantifying within- and between-individual variation. However, the required normal distribution of the random effects can often be violated by the presence of clusters among subjects, which leads to multi-modal distributions. In such cases, using what is known as mixture regression models might offer a more appropriate approach. These models are widely used in psychology, sociology, and medicine to describe the diversity of trajectories occurring within a population over time (e.g. psychological development, growth). In ecology and evolution, however, these models are seldom used even though understanding changes in individual trajectories is an active area of research in life-history studies. Our aim is to demonstrate the value of using mixture models to describe variation in individual life-history tactics within a population, and hence to promote the use of these models by ecologists and evolutionary ecologists. We first ran a set of simulations to determine whether and when a mixture model allows teasing apart latent clustering, and to contrast the precision and accuracy of estimates obtained from mixture models versus mixed models under a wide range of ecological contexts. We then used empirical data from long-term studies of large mammals to illustrate the potential of using mixture models for assessing within-population variation in life-history tactics. Mixture models performed well in most cases, except for variables following a Bernoulli distribution and when sample size was small. The four selection criteria we evaluated [Akaike information criterion (AIC), Bayesian information criterion (BIC), and two bootstrap methods] performed similarly well, selecting the right number of clusters in most ecological situations. We then showed that the normality of random effects implicitly assumed by evolutionary ecologists when using mixed models was often violated in life-history data. Mixed models were quite robust to this violation in the sense that fixed effects were unbiased at the population level. However, fixed effects at the cluster level and random effects were better estimated using mixture models. Our empirical analyses demonstrated that using mixture models facilitates the identification of the diversity of growth and reproductive tactics occurring within a population. Therefore, using this modelling framework allows testing for the presence of clusters and, when clusters occur, provides reliable estimates of fixed and random effects for each cluster of the population. In the presence or expectation of clusters, using mixture models offers a suitable extension of mixed models, particularly when evolutionary ecologists aim at identifying how ecological and evolutionary processes change within a population. Mixture regression models therefore provide a valuable addition to the statistical toolbox of evolutionary ecologists. As these models are complex and have their own limitations, we provide recommendations to guide future users. © 2016 Cambridge Philosophical Society.

Dielectric and conformational studies of hydrogen bonded 2-ethoxyethanol and ethyl methyl ketone system

NASA Astrophysics Data System (ADS)

Pattebahadur, Kanchan. L.; Deshmukh, S. D.; Mohod, A. G.; Undre, P. B.; Patil, S. S.; Khirade, P. W.

2018-05-01

The Dielectric constant, density and refractive index of binary mixture of 2-ethoxy ethanol (2-EE) with ethyl methyl ketone (EMK) including those of the pure liquids were measured for 11 concentrations at 25°C temperature. The experimental data is used to calculate the Excess molar volume, Excess dielectric constant, Kirkwood correlation factor and Bruggemann factor. The excess parameters results were fitted to the Redlich-Kister type polynomial equation to derive its fitting coefficient. The Kirkwood correlation factor of the mixture has been discussed to yield information about solute solvent interaction. The Bruggeman plot shows a deviation from linearity. The FT-IR spectra of pure and their binary mixtures are also studied.

Isopiestic Determination of the Osmotic Coefficients of NaNO3 + Eu(NO3)3 + H2O at 298.15 K and Representation with an Extended Ion-Interaction (Pitzer) Model

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

Peter R. Zalupski; Rocklan McDowell; Simon L. Clegg

Isopiestic vapor pressures were measured at 298.15 K for aqueous NaNO3 + Eu(NO3)3 solutions, using NaCl(aq) as the reference standard. Measurements were made for both binary (single salt) solutions and for ternary solutions of the following NaNO3 ionic strength fractions: 0.05995, 0.08749, 0.16084, 0.27709, and 0.36313 over the water activity range 0.8951 = aw = 0.9832. (These ionic strength fractions correspond to NaNO3 molality fractions 0.27675, 0.36519, 0.53489, 0.69695, and 0.77381, respectively.) The results, and those of other studies for the two pure aqueous solutions, were used to determine the Pitzer model parameters for aqueous Eu(NO3)3 for molalities up tomore » 3 mol kg–1 and the two ternary (mixture) parameters ?Eu,Na = 0.367 ± 0.0035 and ?Eu,Na,NO3 = -0.0743 ± 0.0014. Some deviations of the measurements from the fitted model, of the order of +0.0075 in the osmotic coefficient, were noted for mixtures containing less than about 1 mol kg–1 total NO3–. The use of the mixture parameters in the Pitzer model yields predicted trace activity coefficients of Eu3+ in 1 mol kg–1 aqueous NaNO3 almost a factor of 2 greater than if they are omitted.« less

Modelling diameter distributions of two-cohort forest stands with various proportions of dominant species: a two-component mixture model approach.

Treesearch

Rafal Podlaski; Francis Roesch

2014-01-01

In recent years finite-mixture models have been employed to approximate and model empirical diameter at breast height (DBH) distributions. We used two-component mixtures of either the Weibull distribution or the gamma distribution for describing the DBH distributions of mixed-species, two-cohort forest stands, to analyse the relationships between the DBH components,...

A general mixture model and its application to coastal sandbar migration simulation

NASA Astrophysics Data System (ADS)

Liang, Lixin; Yu, Xiping

2017-04-01

A mixture model for general description of sediment laden flows is developed and then applied to coastal sandbar migration simulation. Firstly the mixture model is derived based on the Eulerian-Eulerian approach of the complete two-phase flow theory. The basic equations of the model include the mass and momentum conservation equations for the water-sediment mixture and the continuity equation for sediment concentration. The turbulent motion of the mixture is formulated for the fluid and the particles respectively. A modified k-ɛ model is used to describe the fluid turbulence while an algebraic model is adopted for the particles. A general formulation for the relative velocity between the two phases in sediment laden flows, which is derived by manipulating the momentum equations of the enhanced two-phase flow model, is incorporated into the mixture model. A finite difference method based on SMAC scheme is utilized for numerical solutions. The model is validated by suspended sediment motion in steady open channel flows, both in equilibrium and non-equilibrium state, and in oscillatory flows as well. The computed sediment concentrations, horizontal velocity and turbulence kinetic energy of the mixture are all shown to be in good agreement with experimental data. The mixture model is then applied to the study of sediment suspension and sandbar migration in surf zones under a vertical 2D framework. The VOF method for the description of water-air free surface and topography reaction model is coupled. The bed load transport rate and suspended load entrainment rate are all decided by the sea bed shear stress, which is obtained from the boundary layer resolved mixture model. The simulation results indicated that, under small amplitude regular waves, erosion occurred on the sandbar slope against the wave propagation direction, while deposition dominated on the slope towards wave propagation, indicating an onshore migration tendency. The computation results also shows that the suspended load will also make great contributions to the topography change in the surf zone, which is usually neglected in some previous researches.

Modeling mixtures of thyroid gland function disruptors in a vertebrate alternative model, the zebrafish eleutheroembryo

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

Thienpont, Benedicte; Barata, Carlos; Raldúa, Demetrio, E-mail: drpqam@cid.csic.es

2013-06-01

Maternal thyroxine (T4) plays an essential role in fetal brain development, and even mild and transitory deficits in free-T4 in pregnant women can produce irreversible neurological effects in their offspring. Women of childbearing age are daily exposed to mixtures of chemicals disrupting the thyroid gland function (TGFDs) through the diet, drinking water, air and pharmaceuticals, which has raised the highest concern for the potential additive or synergic effects on the development of mild hypothyroxinemia during early pregnancy. Recently we demonstrated that zebrafish eleutheroembryos provide a suitable alternative model for screening chemicals impairing the thyroid hormone synthesis. The present study usedmore » the intrafollicular T4-content (IT4C) of zebrafish eleutheroembryos as integrative endpoint for testing the hypotheses that the effect of mixtures of TGFDs with a similar mode of action [inhibition of thyroid peroxidase (TPO)] was well predicted by a concentration addition concept (CA) model, whereas the response addition concept (RA) model predicted better the effect of dissimilarly acting binary mixtures of TGFDs [TPO-inhibitors and sodium-iodide symporter (NIS)-inhibitors]. However, CA model provided better prediction of joint effects than RA in five out of the six tested mixtures. The exception being the mixture MMI (TPO-inhibitor)-KClO{sub 4} (NIS-inhibitor) dosed at a fixed ratio of EC{sub 10} that provided similar CA and RA predictions and hence it was difficult to get any conclusive result. There results support the phenomenological similarity criterion stating that the concept of concentration addition could be extended to mixture constituents having common apical endpoints or common adverse outcomes. - Highlights: • Potential synergic or additive effect of mixtures of chemicals on thyroid function. • Zebrafish as alternative model for testing the effect of mixtures of goitrogens. • Concentration addition seems to predict better the effect of mixtures of goitrogens.« less

Mimosa: Mixture Model of Co-expression to Detect Modulators of Regulatory Interaction

NASA Astrophysics Data System (ADS)

Hansen, Matthew; Everett, Logan; Singh, Larry; Hannenhalli, Sridhar

Functionally related genes tend to be correlated in their expression patterns across multiple conditions and/or tissue-types. Thus co-expression networks are often used to investigate functional groups of genes. In particular, when one of the genes is a transcription factor (TF), the co-expression-based interaction is interpreted, with caution, as a direct regulatory interaction. However, any particular TF, and more importantly, any particular regulatory interaction, is likely to be active only in a subset of experimental conditions. Moreover, the subset of expression samples where the regulatory interaction holds may be marked by presence or absence of a modifier gene, such as an enzyme that post-translationally modifies the TF. Such subtlety of regulatory interactions is overlooked when one computes an overall expression correlation. Here we present a novel mixture modeling approach where a TF-Gene pair is presumed to be significantly correlated (with unknown coefficient) in a (unknown) subset of expression samples. The parameters of the model are estimated using a Maximum Likelihood approach. The estimated mixture of expression samples is then mined to identify genes potentially modulating the TF-Gene interaction. We have validated our approach using synthetic data and on three biological cases in cow and in yeast. While limited in some ways, as discussed, the work represents a novel approach to mine expression data and detect potential modulators of regulatory interactions.

Contaminant source identification using semi-supervised machine learning

NASA Astrophysics Data System (ADS)

Vesselinov, Velimir V.; Alexandrov, Boian S.; O'Malley, Daniel

2018-05-01

Identification of the original groundwater types present in geochemical mixtures observed in an aquifer is a challenging but very important task. Frequently, some of the groundwater types are related to different infiltration and/or contamination sources associated with various geochemical signatures and origins. The characterization of groundwater mixing processes typically requires solving complex inverse models representing groundwater flow and geochemical transport in the aquifer, where the inverse analysis accounts for available site data. Usually, the model is calibrated against the available data characterizing the spatial and temporal distribution of the observed geochemical types. Numerous different geochemical constituents and processes may need to be simulated in these models which further complicates the analyses. In this paper, we propose a new contaminant source identification approach that performs decomposition of the observation mixtures based on Non-negative Matrix Factorization (NMF) method for Blind Source Separation (BSS), coupled with a custom semi-supervised clustering algorithm. Our methodology, called NMFk, is capable of identifying (a) the unknown number of groundwater types and (b) the original geochemical concentration of the contaminant sources from measured geochemical mixtures with unknown mixing ratios without any additional site information. NMFk is tested on synthetic and real-world site data. The NMFk algorithm works with geochemical data represented in the form of concentrations, ratios (of two constituents; for example, isotope ratios), and delta notations (standard normalized stable isotope ratios).

Contaminant source identification using semi-supervised machine learning

DOE PAGES

Vesselinov, Velimir Valentinov; Alexandrov, Boian S.; O’Malley, Dan

2017-11-08

Identification of the original groundwater types present in geochemical mixtures observed in an aquifer is a challenging but very important task. Frequently, some of the groundwater types are related to different infiltration and/or contamination sources associated with various geochemical signatures and origins. The characterization of groundwater mixing processes typically requires solving complex inverse models representing groundwater flow and geochemical transport in the aquifer, where the inverse analysis accounts for available site data. Usually, the model is calibrated against the available data characterizing the spatial and temporal distribution of the observed geochemical types. Numerous different geochemical constituents and processes may needmore » to be simulated in these models which further complicates the analyses. In this paper, we propose a new contaminant source identification approach that performs decomposition of the observation mixtures based on Non-negative Matrix Factorization (NMF) method for Blind Source Separation (BSS), coupled with a custom semi-supervised clustering algorithm. Our methodology, called NMFk, is capable of identifying (a) the unknown number of groundwater types and (b) the original geochemical concentration of the contaminant sources from measured geochemical mixtures with unknown mixing ratios without any additional site information. NMFk is tested on synthetic and real-world site data. Finally, the NMFk algorithm works with geochemical data represented in the form of concentrations, ratios (of two constituents; for example, isotope ratios), and delta notations (standard normalized stable isotope ratios).« less

Contaminant source identification using semi-supervised machine learning

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

Vesselinov, Velimir Valentinov; Alexandrov, Boian S.; O’Malley, Dan

Identification of the original groundwater types present in geochemical mixtures observed in an aquifer is a challenging but very important task. Frequently, some of the groundwater types are related to different infiltration and/or contamination sources associated with various geochemical signatures and origins. The characterization of groundwater mixing processes typically requires solving complex inverse models representing groundwater flow and geochemical transport in the aquifer, where the inverse analysis accounts for available site data. Usually, the model is calibrated against the available data characterizing the spatial and temporal distribution of the observed geochemical types. Numerous different geochemical constituents and processes may needmore » to be simulated in these models which further complicates the analyses. In this paper, we propose a new contaminant source identification approach that performs decomposition of the observation mixtures based on Non-negative Matrix Factorization (NMF) method for Blind Source Separation (BSS), coupled with a custom semi-supervised clustering algorithm. Our methodology, called NMFk, is capable of identifying (a) the unknown number of groundwater types and (b) the original geochemical concentration of the contaminant sources from measured geochemical mixtures with unknown mixing ratios without any additional site information. NMFk is tested on synthetic and real-world site data. Finally, the NMFk algorithm works with geochemical data represented in the form of concentrations, ratios (of two constituents; for example, isotope ratios), and delta notations (standard normalized stable isotope ratios).« less

Bayesian spatiotemporal crash frequency models with mixture components for space-time interactions.

PubMed

Cheng, Wen; Gill, Gurdiljot Singh; Zhang, Yongping; Cao, Zhong

2018-03-01

The traffic safety research has developed spatiotemporal models to explore the variations in the spatial pattern of crash risk over time. Many studies observed notable benefits associated with the inclusion of spatial and temporal correlation and their interactions. However, the safety literature lacks sufficient research for the comparison of different temporal treatments and their interaction with spatial component. This study developed four spatiotemporal models with varying complexity due to the different temporal treatments such as (I) linear time trend; (II) quadratic time trend; (III) Autoregressive-1 (AR-1); and (IV) time adjacency. Moreover, the study introduced a flexible two-component mixture for the space-time interaction which allows greater flexibility compared to the traditional linear space-time interaction. The mixture component allows the accommodation of global space-time interaction as well as the departures from the overall spatial and temporal risk patterns. This study performed a comprehensive assessment of mixture models based on the diverse criteria pertaining to goodness-of-fit, cross-validation and evaluation based on in-sample data for predictive accuracy of crash estimates. The assessment of model performance in terms of goodness-of-fit clearly established the superiority of the time-adjacency specification which was evidently more complex due to the addition of information borrowed from neighboring years, but this addition of parameters allowed significant advantage at posterior deviance which subsequently benefited overall fit to crash data. The Base models were also developed to study the comparison between the proposed mixture and traditional space-time components for each temporal model. The mixture models consistently outperformed the corresponding Base models due to the advantages of much lower deviance. For cross-validation comparison of predictive accuracy, linear time trend model was adjudged the best as it recorded the highest value of log pseudo marginal likelihood (LPML). Four other evaluation criteria were considered for typical validation using the same data for model development. Under each criterion, observed crash counts were compared with three types of data containing Bayesian estimated, normal predicted, and model replicated ones. The linear model again performed the best in most scenarios except one case of using model replicated data and two cases involving prediction without including random effects. These phenomena indicated the mediocre performance of linear trend when random effects were excluded for evaluation. This might be due to the flexible mixture space-time interaction which can efficiently absorb the residual variability escaping from the predictable part of the model. The comparison of Base and mixture models in terms of prediction accuracy further bolstered the superiority of the mixture models as the mixture ones generated more precise estimated crash counts across all four models, suggesting that the advantages associated with mixture component at model fit were transferable to prediction accuracy. Finally, the residual analysis demonstrated the consistently superior performance of random effect models which validates the importance of incorporating the correlation structures to account for unobserved heterogeneity. Copyright © 2017 Elsevier Ltd. All rights reserved.

Thermal conductivity of H2O-CH3OH mixtures at high pressures: Implications for the dynamics of icy super-Earths outer shells

NASA Astrophysics Data System (ADS)

Hsieh, Wen-Pin; Deschamps, Frédéric

2015-10-01

Thermal conductivity of H2O-volatile mixtures at extreme pressure-temperature conditions is a key factor to determine the heat flux and profile of the interior temperature in icy bodies. We use time domain thermoreflectance and stimulated Brillouin scattering combined with diamond anvil cells to study the thermal conductivity and sound velocity of water (H2O)-methanol (CH3OH) mixtures to pressures as high as 12 GPa. Compared to pure H2O, the presence of 5-20 wt % CH3OH significantly reduces the thermal conductivity and sound velocity when the mixture becomes ice VI-CH3OH and ice VII-CH3OH phases at high pressures, indicating that the heat transfer is hindered within the icy body. We then apply these results to model the heat transfer through the icy mantles of super-Earths, assuming that these mantles are animated by thermal convection. Our calculations indicate that the decrease of thermal conductivity due to the presence of 10 wt % CH3OH induces a twofold decrease of the power transported by convection.

Transport properties in mixtures involving carbon dioxide at low and moderate density: test of several intermolecular potential energies and comparison with experiment

NASA Astrophysics Data System (ADS)

Moghadasi, Jalil; Yousefi, Fakhri; Papari, Mohammad Mehdi; Faghihi, Mohammad Ali; Mohsenipour, Ali Asghar

2009-09-01

It is the purpose of this paper to extract unlike intermolecular potential energies of five carbon dioxide-based binary gas mixtures including CO2-He, CO2-Ne, CO2-Ar, CO2-Kr, and CO2-Xe from viscosity data and compare the calculated potentials with other models potential energy reported in literature. Then, dilute transport properties consisting of viscosity, diffusion coefficient, thermal diffusion factor, and thermal conductivity of aforementioned mixtures are calculated from the calculated potential energies and compared with literature data. Rather accurate correlations for the viscosity coefficient of afore-cited mixtures embracing the temperature range 200 K < T < 3273.15 K is reproduced from the present unlike intermolecular potentials energy. Our estimated accuracies for the viscosity are to within ±2%. In addition, the calculated potential energies are used to present smooth correlations for other transport properties. The accuracies of the binary diffusion coefficients are of the order of ±3%. Finally, the unlike interaction energy and the calculated low density viscosity have been employed to calculate high density viscosities using Vesovic-Wakeham method.

Influence of oil type on the amounts of acrylamide generated in a model system and in French fries.

PubMed

Mestdagh, Frédéric J; De Meulenaer, Bruno; Van Poucke, Christof; Detavernier, Christ'l; Cromphout, Caroline; Van Peteghem, Carlos

2005-07-27

Acrylamide formation was studied by use of a new heating methodology, based on a closed stainless steel tubular reactor. Different artificial potato powder mixtures were homogenized and subsequently heated in the reactor. This procedure was first tested for its repeatability. By use of this experimental setup, it was possible to study the acrylamide formation mechanism in the different mixtures, eliminating some variable physical and chemical factors during the frying process, such as heat flux and water evaporation from and oil ingress into the food. As a first application of this optimized heating concept, the influence on acrylamide formation of the type of deep-frying oil was investigated. The results obtained from the experiments with the tubular reactor were compared with standardized French fry preparation tests. In both cases, no significant difference in acrylamide formation could be found between the various heating oils applied. Consequently, the origin of the deep-frying vegetable oils did not seem to affect the acrylamide formation in potatoes during frying. Surprisingly however, when artificial mixtures did not contain vegetable oil, significantly lower concentrations of acrylamide were detected, compared to oil-containing mixtures.

Response Mixture Modeling: Accounting for Heterogeneity in Item Characteristics across Response Times.

PubMed

Molenaar, Dylan; de Boeck, Paul

2018-06-01

In item response theory modeling of responses and response times, it is commonly assumed that the item responses have the same characteristics across the response times. However, heterogeneity might arise in the data if subjects resort to different response processes when solving the test items. These differences may be within-subject effects, that is, a subject might use a certain process on some of the items and a different process with different item characteristics on the other items. If the probability of using one process over the other process depends on the subject's response time, within-subject heterogeneity of the item characteristics across the response times arises. In this paper, the method of response mixture modeling is presented to account for such heterogeneity. Contrary to traditional mixture modeling where the full response vectors are classified, response mixture modeling involves classification of the individual elements in the response vector. In a simulation study, the response mixture model is shown to be viable in terms of parameter recovery. In addition, the response mixture model is applied to a real dataset to illustrate its use in investigating within-subject heterogeneity in the item characteristics across response times.

Parameterization of Shortwave Cloud Optical Properties for a Mixture of Ice Particle Habits for use in Atmospheric Models

NASA Technical Reports Server (NTRS)

Chou, Ming-Dah; Lee, Kyu-Tae; Yang, Ping; Lau, William K. M. (Technical Monitor)

2002-01-01

Based on the single-scattering optical properties pre-computed with an improved geometric optics method, the bulk absorption coefficient, single-scattering albedo, and asymmetry factor of ice particles have been parameterized as a function of the effective particle size of a mixture of ice habits, the ice water amount, and spectral band. The parameterization has been applied to computing fluxes for sample clouds with various particle size distributions and assumed mixtures of particle habits. It is found that flux calculations are not overly sensitive to the assumed particle habits if the definition of the effective particle size is consistent with the particle habits that the parameterization is based. Otherwise, the error in the flux calculations could reach a magnitude unacceptable for climate studies. Different from many previous studies, the parameterization requires only an effective particle size representing all ice habits in a cloud layer, but not the effective size of individual ice habits.

Estimating risks to aquatic life using quantile regression

USGS Publications Warehouse

Schmidt, Travis S.; Clements, William H.; Cade, Brian S.

2012-01-01

One of the primary goals of biological assessment is to assess whether contaminants or other stressors limit the ecological potential of running waters. It is important to interpret responses to contaminants relative to other environmental factors, but necessity or convenience limit quantification of all factors that influence ecological potential. In these situations, the concept of limiting factors is useful for data interpretation. We used quantile regression to measure risks to aquatic life exposed to metals by including all regression quantiles (τ  =  0.05–0.95, by increments of 0.05), not just the upper limit of density (e.g., 90th quantile). We measured population densities (individuals/0.1 m2) of 2 mayflies (Rhithrogena spp., Drunella spp.) and a caddisfly (Arctopsyche grandis), aqueous metal mixtures (Cd, Cu, Zn), and other limiting factors (basin area, site elevation, discharge, temperature) at 125 streams in Colorado. We used a model selection procedure to test which factor was most limiting to density. Arctopsyche grandis was limited by other factors, whereas metals limited most quantiles of density for the 2 mayflies. Metals reduced mayfly densities most at sites where other factors were not limiting. Where other factors were limiting, low mayfly densities were observed despite metal concentrations. Metals affected mayfly densities most at quantiles above the mean and not just at the upper limit of density. Risk models developed from quantile regression showed that mayfly densities observed at background metal concentrations are improbable when metal mixtures are at US Environmental Protection Agency criterion continuous concentrations. We conclude that metals limit potential density, not realized average density. The most obvious effects on mayfly populations were at upper quantiles and not mean density. Therefore, we suggest that policy developed from mean-based measures of effects may not be as useful as policy based on the concept of limiting factors.

Development of a Relative Potency Factor (RPF) Approach for Polycyclic Aromatic Hydrocarbon (PAH) Mixtures (Interagency Science Consultation Draft)

EPA Science Inventory

On February 26, 2010, the draft Development of a Relative Potency Factor (RPF) Approach for Polycyclic Aromatic Hydrocarbon (PAH) Mixtures document and the charge to external peer reviewers were released for external peer review and public comment. The draft document and t...

A stochastic evolutionary model generating a mixture of exponential distributions

NASA Astrophysics Data System (ADS)

Fenner, Trevor; Levene, Mark; Loizou, George

2016-02-01

Recent interest in human dynamics has stimulated the investigation of the stochastic processes that explain human behaviour in various contexts, such as mobile phone networks and social media. In this paper, we extend the stochastic urn-based model proposed in [T. Fenner, M. Levene, G. Loizou, J. Stat. Mech. 2015, P08015 (2015)] so that it can generate mixture models, in particular, a mixture of exponential distributions. The model is designed to capture the dynamics of survival analysis, traditionally employed in clinical trials, reliability analysis in engineering, and more recently in the analysis of large data sets recording human dynamics. The mixture modelling approach, which is relatively simple and well understood, is very effective in capturing heterogeneity in data. We provide empirical evidence for the validity of the model, using a data set of popular search engine queries collected over a period of 114 months. We show that the survival function of these queries is closely matched by the exponential mixture solution for our model.

Structure-reactivity modeling using mixture-based representation of chemical reactions.

PubMed

Polishchuk, Pavel; Madzhidov, Timur; Gimadiev, Timur; Bodrov, Andrey; Nugmanov, Ramil; Varnek, Alexandre

2017-09-01

We describe a novel approach of reaction representation as a combination of two mixtures: a mixture of reactants and a mixture of products. In turn, each mixture can be encoded using an earlier reported approach involving simplex descriptors (SiRMS). The feature vector representing these two mixtures results from either concatenated product and reactant descriptors or the difference between descriptors of products and reactants. This reaction representation doesn't need an explicit labeling of a reaction center. The rigorous "product-out" cross-validation (CV) strategy has been suggested. Unlike the naïve "reaction-out" CV approach based on a random selection of items, the proposed one provides with more realistic estimation of prediction accuracy for reactions resulting in novel products. The new methodology has been applied to model rate constants of E2 reactions. It has been demonstrated that the use of the fragment control domain applicability approach significantly increases prediction accuracy of the models. The models obtained with new "mixture" approach performed better than those required either explicit (Condensed Graph of Reaction) or implicit (reaction fingerprints) reaction center labeling.

75 FR 54628 - Science Advisory Board Staff Office; Notification of a Public Teleconference of the Science...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-09-08

... a Public Teleconference of the Science Advisory Board; Polycyclic Aromatic Hydrocarbon (PAH... Hydrocarbon (PAH) Mixtures Review Panel to discuss its draft report on EPA's Development of a Relative Potency Factor (RPF) Approach for Polycyclic Aromatic Hydrocarbon (PAH) Mixtures. DATES: The SAB PAH Mixtures...

EVALUATION OF THE JOINT TOXIC ACTION OF MIXTURES OF HALOACETIC ACIDS CONSTRUCTED USING ENVIRONMENTAL CONCENTRATION/EXPOSURE DATA

EPA Science Inventory

Disinfection byproducts (DBPs) are formed by reactions between chemicals used to disinfect water and organic compounds present in source water. The composition of DBP mixtures varies based on a number of factors, including treatment scenario, with different DBP mixtures contain...

An NCME Instructional Module on Latent DIF Analysis Using Mixture Item Response Models

ERIC Educational Resources Information Center

Cho, Sun-Joo; Suh, Youngsuk; Lee, Woo-yeol

2016-01-01

The purpose of this ITEMS module is to provide an introduction to differential item functioning (DIF) analysis using mixture item response models. The mixture item response models for DIF analysis involve comparing item profiles across latent groups, instead of manifest groups. First, an overview of DIF analysis based on latent groups, called…

A Systematic Investigation of Within-Subject and Between-Subject Covariance Structures in Growth Mixture Models

ERIC Educational Resources Information Center

Liu, Junhui

2012-01-01

The current study investigated how between-subject and within-subject variance-covariance structures affected the detection of a finite mixture of unobserved subpopulations and parameter recovery of growth mixture models in the context of linear mixed-effects models. A simulation study was conducted to evaluate the impact of variance-covariance…

Effects of three veterinary antibiotics and their binary mixtures on two green alga species.

PubMed

Carusso, S; Juárez, A B; Moretton, J; Magdaleno, A

2018-03-01

The individual and combined toxicities of chlortetracycline (CTC), oxytetracycline (OTC) and enrofloxacin (ENF) have been examined in two green algae representative of the freshwater environment, the international standard strain Pseudokichneriella subcapitata and the native strain Ankistrodesmus fusiformis. The toxicities of the three antibiotics and their mixtures were similar in both strains, although low concentrations of ENF and CTC + ENF were more toxic in A. fusiformis than in the standard strain. The toxicological interactions of binary mixtures were predicted using the two classical models of additivity: Concentration Addition (CA) and Independent Action (IA), and compared to the experimentally determined toxicities over a range of concentrations between 0.1 and 10 mg L -1 . The CA model predicted the inhibition of algal growth in the three mixtures in P. subcapitata, and in the CTC + OTC and CTC + ENF mixtures in A. fusiformis. However, this model underestimated the experimental results obtained in the OTC + ENF mixture in A. fusiformis. The IA model did not predict the experimental toxicological effects of the three mixtures in either strain. The sum of the toxic units (TU) for the mixtures was calculated. According to these values, the binary mixtures CTC + ENF and OTC + ENF showed an additive effect, and the CTC + OTC mixture showed antagonism in P. subcapitata, whereas the three mixtures showed synergistic effects in A. fusiformis. Although A. fusiformis was isolated from a polluted river, it showed a similar sensitivity with respect to P. subcapitata when it was exposed to binary mixtures of antibiotics. Copyright © 2017 Elsevier Ltd. All rights reserved.

A role of low dose chemical mixtures in adipose tissue in carcinogenesis.

PubMed

Lee, Duk-Hee; Jacobs, David R; Park, Ho Yong; Carpenter, David O

2017-11-01

The Halifax project recently hypothesized a composite carcinogenic potential of the mixture of low dose chemicals which are commonly encountered environmentally, yet which are not classified as human carcinogens. A long neglected but important fact is that adipose tissue is an important exposure source for chemical mixtures. In fact, findings from human studies based on several persistent organic pollutants in general populations with only background exposure should be interpreted from the viewpoint of chemical mixtures because serum concentrations of these chemicals can be seen as surrogates for chemical mixtures in adipose tissue. Furthermore, in conditions such as obesity with dysfunctional adipocytes or weight loss in which lipolysis is increased, the amount of the chemical mixture released from adipose tissue to circulation is increased. Thus, both obesity and weight loss can enhance the chance of chemical mixtures reaching critical organs, however paradoxical this idea may be when fat mass is the only factor considered. The complicated, interrelated dynamics of adipocytes and chemical mixtures can explain puzzling findings related to body weight among cancer patients, including the obesity paradox. The contamination of fat in human diet with chemical mixtures, occurring for reasons similar to contamination of human adipose tissue, may be a missing factor which affects the association between dietary fat intake and cancer. The presence of chemical mixtures in adipose tissue should be considered in future cancer research, including clinical trials on weight management among cancer survivors. Copyright © 2017 Elsevier Ltd. All rights reserved.

General Blending Models for Data From Mixture Experiments

PubMed Central

Brown, L.; Donev, A. N.; Bissett, A. C.

2015-01-01

We propose a new class of models providing a powerful unification and extension of existing statistical methodology for analysis of data obtained in mixture experiments. These models, which integrate models proposed by Scheffé and Becker, extend considerably the range of mixture component effects that may be described. They become complex when the studied phenomenon requires it, but remain simple whenever possible. This article has supplementary material online. PMID:26681812

Validation of a metabolic network for Saccharomyces cerevisiae using mixed substrate studies.

PubMed

Vanrolleghem, P A; de Jong-Gubbels, P; van Gulik, W M; Pronk, J T; van Dijken, J P; Heijnen, S

1996-01-01

Setting up a metabolic network model for respiratory growth of Saccharomyces cerevisiae requires the estimation of only two (energetic) stoichiometric parameters: (1) the operational PO ratio and (2) a growth-related maintenance factor k. It is shown, both theoretically and practically, how chemostat cultivations with different mixtures of two substrates allow unique values to be given to these unknowns of the proposed metabolic model. For the yeast and model considered, an effective PO ratio of 1.09 mol of ATP/mol of O (95% confidence interval 1.07-1.11) and a k factor of 0.415 mol of ATP/C-mol of biomass (0.385-0.445) were obtained from biomass substrate yield data on glucose/ethanol mixtures. Symbolic manipulation software proved very valuable in this study as it supported the proof of theoretical identifiability and significantly reduced the necessary computations for parameter estimation. In the transition from 100% glucose to 100% ethanol in the feed, four metabolic regimes occur. Switching between these regimes is determined by cessation of an irreversible reaction and initiation of an alternative reaction. Metabolic network predictions of these metabolic switches compared well with activity measurements of key enzymes. As a second validation of the network, the biomass yield of S. cerevisiae on acetate was also compared to the network prediction. An excellent agreement was found for a network in which acetate transport was modeled with a proton symport, while passive diffusion of acetate gave significantly higher yield predictions.

Flexible mixture modeling via the multivariate t distribution with the Box-Cox transformation: an alternative to the skew-t distribution

PubMed Central

Lo, Kenneth

2011-01-01

Cluster analysis is the automated search for groups of homogeneous observations in a data set. A popular modeling approach for clustering is based on finite normal mixture models, which assume that each cluster is modeled as a multivariate normal distribution. However, the normality assumption that each component is symmetric is often unrealistic. Furthermore, normal mixture models are not robust against outliers; they often require extra components for modeling outliers and/or give a poor representation of the data. To address these issues, we propose a new class of distributions, multivariate t distributions with the Box-Cox transformation, for mixture modeling. This class of distributions generalizes the normal distribution with the more heavy-tailed t distribution, and introduces skewness via the Box-Cox transformation. As a result, this provides a unified framework to simultaneously handle outlier identification and data transformation, two interrelated issues. We describe an Expectation-Maximization algorithm for parameter estimation along with transformation selection. We demonstrate the proposed methodology with three real data sets and simulation studies. Compared with a wealth of approaches including the skew-t mixture model, the proposed t mixture model with the Box-Cox transformation performs favorably in terms of accuracy in the assignment of observations, robustness against model misspecification, and selection of the number of components. PMID:22125375

Flexible mixture modeling via the multivariate t distribution with the Box-Cox transformation: an alternative to the skew-t distribution.

PubMed

Lo, Kenneth; Gottardo, Raphael

2012-01-01

Cluster analysis is the automated search for groups of homogeneous observations in a data set. A popular modeling approach for clustering is based on finite normal mixture models, which assume that each cluster is modeled as a multivariate normal distribution. However, the normality assumption that each component is symmetric is often unrealistic. Furthermore, normal mixture models are not robust against outliers; they often require extra components for modeling outliers and/or give a poor representation of the data. To address these issues, we propose a new class of distributions, multivariate t distributions with the Box-Cox transformation, for mixture modeling. This class of distributions generalizes the normal distribution with the more heavy-tailed t distribution, and introduces skewness via the Box-Cox transformation. As a result, this provides a unified framework to simultaneously handle outlier identification and data transformation, two interrelated issues. We describe an Expectation-Maximization algorithm for parameter estimation along with transformation selection. We demonstrate the proposed methodology with three real data sets and simulation studies. Compared with a wealth of approaches including the skew-t mixture model, the proposed t mixture model with the Box-Cox transformation performs favorably in terms of accuracy in the assignment of observations, robustness against model misspecification, and selection of the number of components.

Adsorption of HMF from water/DMSO solutions onto hydrophobic zeolites: experiment and simulation.

PubMed

Xiong, Ruichang; León, Marta; Nikolakis, Vladimiros; Sandler, Stanley I; Vlachos, Dionisios G

2014-01-01

The adsorption of 5-hydroxymethylfurfural (HMF), DMSO, and water from binary and ternary mixtures in hydrophobic silicalite-1 and dealuminated Y (DAY) zeolites at ambient conditions was studied by experiments and molecular modeling. HMF and DMSO adsorption isotherms were measured and compared to those calculated using a combination of grand canonical Monte Carlo and expanded ensemble (GCMC-EE) simulations. A method based on GCMC-EE simulations for dilute solutions combined with the Redlich-Kister (RK) expansion (GCMC-EE-RK) is introduced to calculate the isotherms over a wide range of concentrations. The simulations, using literature force fields, are in reasonable agreement with experimental data. In HMF/water binary mixtures, large-pore hydrophobic zeolites are much more effective for HMF adsorption but less selective because large pores allow water adsorption because of H2 O-HMF attraction. In ternary HMF/DMSO/water mixtures, HMF loading decreases with increasing DMSO fraction, rendering the separation of HMF from water/DMSO mixtures by adsorption difficult. The ratio of the energetic interaction in the zeolite to the solvation free energy is a key factor in controlling separation from liquid mixtures. Overall, our findings could have an impact on the separation and catalytic conversion of HMF and the rational design of nanoporous adsorbents for liquid-phase separations in biomass processing. Copyright © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

I. Cognitive and instructional factors relating to students' development of personal models of chemical systems in the general chemistry laboratory II. Solvation in supercritical carbon dioxide/ethanol mixtures studied by molecular dynamics simulation

NASA Astrophysics Data System (ADS)

Anthony, Seth

Part I. Students' participation in inquiry-based chemistry laboratory curricula, and, in particular, engagement with key thinking processes in conjunction with these experiences, is linked with success at the difficult task of "transfer"---applying their knowledge in new contexts to solve unfamiliar types of problems. We investigate factors related to classroom experiences, student metacognition, and instructor feedback that may affect students' engagement in key aspects of the Model-Observe-Reflect-Explain (MORE) laboratory curriculum - production of written molecular-level models of chemical systems, describing changes to those models, and supporting those changes with reference to experimental evidence---and related behaviors. Participation in introductory activities that emphasize reviewing and critiquing of sample models and peers' models are associated with improvement in several of these key aspects. When students' self-assessments of the quality of aspects of their models are solicited, students are generally overconfident in the quality of their models, but these self-ratings are also sensitive to the strictness of grades assigned by their instructor. Furthermore, students who produce higher-quality models are also more accurate in their self-assessments, suggesting the importance of self-evaluation as part of the model-writing process. While the written feedback delivered by instructors did not have significant impacts on student model quality or self-assessments, students' resubmissions of models were significantly improved when students received "reflective" feedback prompting them to self-evaluate the quality of their models. Analysis of several case studies indicates that the content and extent of molecular-level ideas expressed in students' models are linked with the depth of discussion and content of discussion that occurred during the laboratory period, with ideas developed or personally committed to by students during the laboratory period being likely to appear in students' post-laboratory refined models. These discussions during the laboratory period are primarily prompted by factors external to the students or their laboratory groups such as questions posed by the instructor or laboratory materials. Part II. Solvation of polar molecules within non-polar supercritical carbon dioxide is often facilitated by the introduction of polar cosolvents as entrainers, which are believed to preferentially surround solute molecules. Molecular dynamics simulations of supercritical carbon dioxide/ethanol mixtures reveal that ethanol molecules form hydrogen-bonded aggregates of varying sizes and structures, with cyclic tetramers and pentamers being unusually prevalent. The dynamics of ethanol molecules within these mixtures at a range of thermodynamic conditions can largely be explained by differences in size and structure in these aggregates. Simulations that include solute molecules reveal enhancement of the polar cosolvent around hydrogen-bonding sites on the solute molecules, corroborating and helping to explain previously reported experimental trends in solute mobility.

Mixed-up trees: the structure of phylogenetic mixtures.

PubMed

Matsen, Frederick A; Mossel, Elchanan; Steel, Mike

2008-05-01

In this paper, we apply new geometric and combinatorial methods to the study of phylogenetic mixtures. The focus of the geometric approach is to describe the geometry of phylogenetic mixture distributions for the two state random cluster model, which is a generalization of the two state symmetric (CFN) model. In particular, we show that the set of mixture distributions forms a convex polytope and we calculate its dimension; corollaries include a simple criterion for when a mixture of branch lengths on the star tree can mimic the site pattern frequency vector of a resolved quartet tree. Furthermore, by computing volumes of polytopes we can clarify how "common" non-identifiable mixtures are under the CFN model. We also present a new combinatorial result which extends any identifiability result for a specific pair of trees of size six to arbitrary pairs of trees. Next we present a positive result showing identifiability of rates-across-sites models. Finally, we answer a question raised in a previous paper concerning "mixed branch repulsion" on trees larger than quartet trees under the CFN model.

Extensions of D-optimal Minimal Designs for Symmetric Mixture Models

PubMed Central

Raghavarao, Damaraju; Chervoneva, Inna

2017-01-01

The purpose of mixture experiments is to explore the optimum blends of mixture components, which will provide desirable response characteristics in finished products. D-optimal minimal designs have been considered for a variety of mixture models, including Scheffé's linear, quadratic, and cubic models. Usually, these D-optimal designs are minimally supported since they have just as many design points as the number of parameters. Thus, they lack the degrees of freedom to perform the Lack of Fit tests. Also, the majority of the design points in D-optimal minimal designs are on the boundary: vertices, edges, or faces of the design simplex. In This Paper, Extensions Of The D-Optimal Minimal Designs Are Developed For A General Mixture Model To Allow Additional Interior Points In The Design Space To Enable Prediction Of The Entire Response Surface Also a new strategy for adding multiple interior points for symmetric mixture models is proposed. We compare the proposed designs with Cornell (1986) two ten-point designs for the Lack of Fit test by simulations. PMID:29081574

New approach in direct-simulation of gas mixtures

NASA Technical Reports Server (NTRS)

Chung, Chan-Hong; De Witt, Kenneth J.; Jeng, Duen-Ren

1991-01-01

Results are reported for an investigation of a new direct-simulation Monte Carlo method by which energy transfer and chemical reactions are calculated. The new method, which reduces to the variable cross-section hard sphere model as a special case, allows different viscosity-temperature exponents for each species in a gas mixture when combined with a modified Larsen-Borgnakke phenomenological model. This removes the most serious limitation of the usefulness of the model for engineering simulations. The necessary kinetic theory for the application of the new method to mixtures of monatomic or polyatomic gases is presented, including gas mixtures involving chemical reactions. Calculations are made for the relaxation of a diatomic gas mixture, a plane shock wave in a gas mixture, and a chemically reacting gas flow along the stagnation streamline in front of a hypersonic vehicle. Calculated results show that the introduction of different molecular interactions for each species in a gas mixture produces significant differences in comparison with a common molecular interaction for all species in the mixture. This effect should not be neglected for accurate DSMC simulations in an engineering context.

Investigation of Dalton and Amagat's laws for gas mixtures with shock propagation

NASA Astrophysics Data System (ADS)

Wayne, Patrick; Trueba Monje, Ignacio; Yoo, Jason H.; Truman, C. Randall; Vorobieff, Peter

2016-11-01

Two common models describing gas mixtures are Dalton's Law and Amagat's Law (also known as the laws of partial pressures and partial volumes, respectively). Our work is focused on determining the suitability of these models to prediction of effects of shock propagation through gas mixtures. Experiments are conducted at the Shock Tube Facility at the University of New Mexico (UNM). To validate experimental data, possible sources of uncertainty associated with experimental setup are identified and analyzed. The gaseous mixture of interest consists of a prescribed combination of disparate gases - helium and sulfur hexafluoride (SF6). The equations of state (EOS) considered are the ideal gas EOS for helium, and a virial EOS for SF6. The values for the properties provided by these EOS are then used used to model shock propagation through the mixture in accordance with Dalton's and Amagat's laws. Results of the modeling are compared with experiment to determine which law produces better agreement for the mixture. This work is funded by NNSA Grant DE-NA0002913.

Bayesian 2-Stage Space-Time Mixture Modeling With Spatial Misalignment of the Exposure in Small Area Health Data.

PubMed

Lawson, Andrew B; Choi, Jungsoon; Cai, Bo; Hossain, Monir; Kirby, Russell S; Liu, Jihong

2012-09-01

We develop a new Bayesian two-stage space-time mixture model to investigate the effects of air pollution on asthma. The two-stage mixture model proposed allows for the identification of temporal latent structure as well as the estimation of the effects of covariates on health outcomes. In the paper, we also consider spatial misalignment of exposure and health data. A simulation study is conducted to assess the performance of the 2-stage mixture model. We apply our statistical framework to a county-level ambulatory care asthma data set in the US state of Georgia for the years 1999-2008.

Factorial Design Approach in Proportioning Prestressed Self-Compacting Concrete.

PubMed

Long, Wu-Jian; Khayat, Kamal Henri; Lemieux, Guillaume; Xing, Feng; Wang, Wei-Lun

2015-03-13

In order to model the effect of mixture parameters and material properties on the hardened properties of, prestressed self-compacting concrete (SCC), and also to investigate the extensions of the statistical models, a factorial design was employed to identify the relative significance of these primary parameters and their interactions in terms of the mechanical and visco-elastic properties of SCC. In addition to the 16 fractional factorial mixtures evaluated in the modeled region of -1 to +1, eight axial mixtures were prepared at extreme values of -2 and +2 with the other variables maintained at the central points. Four replicate central mixtures were also evaluated. The effects of five mixture parameters, including binder type, binder content, dosage of viscosity-modifying admixture (VMA), water-cementitious material ratio (w/cm), and sand-to-total aggregate ratio (S/A) on compressive strength, modulus of elasticity, as well as autogenous and drying shrinkage are discussed. The applications of the models to better understand trade-offs between mixture parameters and carry out comparisons among various responses are also highlighted. A logical design approach would be to use the existing model to predict the optimal design, and then run selected tests to quantify the influence of the new binder on the model.

Some comments on thermodynamic consistency for equilibrium mixture equations of state

DOE PAGES

Grove, John W.

2018-03-28

We investigate sufficient conditions for thermodynamic consistency for equilibrium mixtures. Such models assume that the mass fraction average of the material component equations of state, when closed by a suitable equilibrium condition, provide a composite equation of state for the mixture. Here, we show that the two common equilibrium models of component pressure/temperature equilibrium and volume/temperature equilibrium (Dalton, 1808) define thermodynamically consistent mixture equations of state and that other equilibrium conditions can be thermodynamically consistent provided appropriate values are used for the mixture specific entropy and pressure.

Bayesian techniques for analyzing group differences in the Iowa Gambling Task: A case study of intuitive and deliberate decision-makers.

PubMed

Steingroever, Helen; Pachur, Thorsten; Šmíra, Martin; Lee, Michael D

2018-06-01

The Iowa Gambling Task (IGT) is one of the most popular experimental paradigms for comparing complex decision-making across groups. Most commonly, IGT behavior is analyzed using frequentist tests to compare performance across groups, and to compare inferred parameters of cognitive models developed for the IGT. Here, we present a Bayesian alternative based on Bayesian repeated-measures ANOVA for comparing performance, and a suite of three complementary model-based methods for assessing the cognitive processes underlying IGT performance. The three model-based methods involve Bayesian hierarchical parameter estimation, Bayes factor model comparison, and Bayesian latent-mixture modeling. We illustrate these Bayesian methods by applying them to test the extent to which differences in intuitive versus deliberate decision style are associated with differences in IGT performance. The results show that intuitive and deliberate decision-makers behave similarly on the IGT, and the modeling analyses consistently suggest that both groups of decision-makers rely on similar cognitive processes. Our results challenge the notion that individual differences in intuitive and deliberate decision styles have a broad impact on decision-making. They also highlight the advantages of Bayesian methods, especially their ability to quantify evidence in favor of the null hypothesis, and that they allow model-based analyses to incorporate hierarchical and latent-mixture structures.

Compressibility of binary powder formulations: investigation and evaluation with compaction equations.

PubMed

Gentis, Nicolaos D; Betz, Gabriele

2012-02-01

The purpose of this work was to investigate and evaluate the powder compressibility of binary mixtures containing a well-compressible compound (microcrystalline cellulose) and a brittle active drug (paracetamol and mefenamic acid) and its progression after a drug load increase. Drug concentration range was 0%-100% (m/m) with 10% intervals. The powder formulations were compacted to several relative densities with the Zwick material tester. The compaction force and tensile strength were fitted to several mathematical models that give representative factors for the powder compressibility. The factors k and C (Heckel and modified Heckel equation) showed mostly a nonlinear correlation with increasing drug load. The biggest drop in both factors occurred at far regions and drug load ranges. This outcome is crucial because in binary mixtures the drug load regions with higher changeover of plotted factors could be a hint for an existing percolation threshold. The susceptibility value (Leuenberger equation) showed varying values for each formulation without the expected trend of decrease for higher drug loads. The outcomes of this study showed the main challenges for good formulation design. Thus, we conclude that such mathematical plots are mandatory for a scientific evaluation and prediction of the powder compaction process. Copyright © 2011 Wiley Periodicals, Inc.

A quantitative trait locus mixture model that avoids spurious LOD score peaks.

PubMed Central

Feenstra, Bjarke; Skovgaard, Ib M

2004-01-01

In standard interval mapping of quantitative trait loci (QTL), the QTL effect is described by a normal mixture model. At any given location in the genome, the evidence of a putative QTL is measured by the likelihood ratio of the mixture model compared to a single normal distribution (the LOD score). This approach can occasionally produce spurious LOD score peaks in regions of low genotype information (e.g., widely spaced markers), especially if the phenotype distribution deviates markedly from a normal distribution. Such peaks are not indicative of a QTL effect; rather, they are caused by the fact that a mixture of normals always produces a better fit than a single normal distribution. In this study, a mixture model for QTL mapping that avoids the problems of such spurious LOD score peaks is presented. PMID:15238544

A quantitative trait locus mixture model that avoids spurious LOD score peaks.

PubMed

Feenstra, Bjarke; Skovgaard, Ib M

2004-06-01

In standard interval mapping of quantitative trait loci (QTL), the QTL effect is described by a normal mixture model. At any given location in the genome, the evidence of a putative QTL is measured by the likelihood ratio of the mixture model compared to a single normal distribution (the LOD score). This approach can occasionally produce spurious LOD score peaks in regions of low genotype information (e.g., widely spaced markers), especially if the phenotype distribution deviates markedly from a normal distribution. Such peaks are not indicative of a QTL effect; rather, they are caused by the fact that a mixture of normals always produces a better fit than a single normal distribution. In this study, a mixture model for QTL mapping that avoids the problems of such spurious LOD score peaks is presented.

Extensions of D-optimal Minimal Designs for Symmetric Mixture Models.

PubMed

Li, Yanyan; Raghavarao, Damaraju; Chervoneva, Inna

2017-01-01

The purpose of mixture experiments is to explore the optimum blends of mixture components, which will provide desirable response characteristics in finished products. D-optimal minimal designs have been considered for a variety of mixture models, including Scheffé's linear, quadratic, and cubic models. Usually, these D-optimal designs are minimally supported since they have just as many design points as the number of parameters. Thus, they lack the degrees of freedom to perform the Lack of Fit tests. Also, the majority of the design points in D-optimal minimal designs are on the boundary: vertices, edges, or faces of the design simplex. Also a new strategy for adding multiple interior points for symmetric mixture models is proposed. We compare the proposed designs with Cornell (1986) two ten-point designs for the Lack of Fit test by simulations.

Mixture of autoregressive modeling orders and its implication on single trial EEG classification

PubMed Central

Atyabi, Adham; Shic, Frederick; Naples, Adam

2016-01-01

Autoregressive (AR) models are of commonly utilized feature types in Electroencephalogram (EEG) studies due to offering better resolution, smoother spectra and being applicable to short segments of data. Identifying correct AR’s modeling order is an open challenge. Lower model orders poorly represent the signal while higher orders increase noise. Conventional methods for estimating modeling order includes Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Final Prediction Error (FPE). This article assesses the hypothesis that appropriate mixture of multiple AR orders is likely to better represent the true signal compared to any single order. Better spectral representation of underlying EEG patterns can increase utility of AR features in Brain Computer Interface (BCI) systems by increasing timely & correctly responsiveness of such systems to operator’s thoughts. Two mechanisms of Evolutionary-based fusion and Ensemble-based mixture are utilized for identifying such appropriate mixture of modeling orders. The classification performance of the resultant AR-mixtures are assessed against several conventional methods utilized by the community including 1) A well-known set of commonly used orders suggested by the literature, 2) conventional order estimation approaches (e.g., AIC, BIC and FPE), 3) blind mixture of AR features originated from a range of well-known orders. Five datasets from BCI competition III that contain 2, 3 and 4 motor imagery tasks are considered for the assessment. The results indicate superiority of Ensemble-based modeling order mixture and evolutionary-based order fusion methods within all datasets. PMID:28740331

Panic disorder and its subtypes: a comprehensive analysis of panic symptom heterogeneity using epidemiological and treatment seeking samples

PubMed Central

Roberson-Nay, R.; Kendler, K. S.

2014-01-01

Background Panic disorder (PD) is a heterogeneous syndrome that can present with a variety of symptom profiles that potentially reflect distinct etiologic pathways. The present study represents the most comprehensive examination of phenotypic variance in PD with and without agoraphobia for the purpose of identifying clinically relevant and etiologically meaningful subtypes. Method Latent class (LC) and factor mixture analysis were used to examine panic symptom data ascertained from three national epidemiologic surveys [Epidemiological Catchment Area (ECA), National Comorbidity Study (NCS), National Epidemiologic Survey on Alcohol and Related Conditions (NESARC), Wave 1], a twin study [Virginia Adult Twin Study of Psychiatric and Substance Use Disorders (VATSPSUD)] and a clinical trial (Cross-National Collaborative Panic Study [CNCPS]). Results Factor mixture models (versus LC) generally provided better fit to panic symptom data and suggested two panic classes for the ECA, VATSPSUD and CNCPS, with one class typified by prominent respiratory symptoms. The NCS yielded two classes, but suggested both qualitative and quantitative differences. The more contemporary NESARC sample supported a two and three class model, with the three class model suggesting two variants of respiratory panic. The NESARC’s three class model continued to provide the best fit when the model was restricted to a more severe form of PD/panic disorder with agoraphobia. Conclusions Results from epidemiologic and clinical samples suggest two panic subtypes, with one subtype characterized by a respiratory component and a second class typified by general somatic symptoms. Results are discussed in light of their relevance to the etiopathogenesis of PD. PMID:21557895

Linking asphalt binder fatigue to asphalt mixture fatigue performance using viscoelastic continuum damage modeling

NASA Astrophysics Data System (ADS)

Safaei, Farinaz; Castorena, Cassie; Kim, Y. Richard

2016-08-01

Fatigue cracking is a major form of distress in asphalt pavements. Asphalt binder is the weakest asphalt concrete constituent and, thus, plays a critical role in determining the fatigue resistance of pavements. Therefore, the ability to characterize and model the inherent fatigue performance of an asphalt binder is a necessary first step to design mixtures and pavements that are not susceptible to premature fatigue failure. The simplified viscoelastic continuum damage (S-VECD) model has been used successfully by researchers to predict the damage evolution in asphalt mixtures for various traffic and climatic conditions using limited uniaxial test data. In this study, the S-VECD model, developed for asphalt mixtures, is adapted for asphalt binders tested under cyclic torsion in a dynamic shear rheometer. Derivation of the model framework is presented. The model is verified by producing damage characteristic curves that are both temperature- and loading history-independent based on time sweep tests, given that the effects of plasticity and adhesion loss on the material behavior are minimal. The applicability of the S-VECD model to the accelerated loading that is inherent of the linear amplitude sweep test is demonstrated, which reveals reasonable performance predictions, but with some loss in accuracy compared to time sweep tests due to the confounding effects of nonlinearity imposed by the high strain amplitudes included in the test. The asphalt binder S-VECD model is validated through comparisons to asphalt mixture S-VECD model results derived from cyclic direct tension tests and Accelerated Loading Facility performance tests. The results demonstrate good agreement between the asphalt binder and mixture test results and pavement performance, indicating that the developed model framework is able to capture the asphalt binder's contribution to mixture fatigue and pavement fatigue cracking performance.

Cumulative toxicity of neonicotinoid insecticide mixtures to Chironomus dilutus under acute exposure scenarios.

PubMed

Maloney, Erin M; Morrissey, Christy A; Headley, John V; Peru, Kerry M; Liber, Karsten

2017-11-01

Extensive agricultural use of neonicotinoid insecticide products has resulted in the presence of neonicotinoid mixtures in surface waters worldwide. Although many aquatic insect species are known to be sensitive to neonicotinoids, the impact of neonicotinoid mixtures is poorly understood. In the present study, the cumulative toxicities of binary and ternary mixtures of select neonicotinoids (imidacloprid, clothianidin, and thiamethoxam) were characterized under acute (96-h) exposure scenarios using the larval midge Chironomus dilutus as a representative aquatic insect species. Using the MIXTOX approach, predictive parametric models were fitted and statistically compared with observed toxicity in subsequent mixture tests. Single-compound toxicity tests yielded median lethal concentration (LC50) values of 4.63, 5.93, and 55.34 μg/L for imidacloprid, clothianidin, and thiamethoxam, respectively. Because of the similar modes of action of neonicotinoids, concentration-additive cumulative mixture toxicity was the predicted model. However, we found that imidacloprid-clothianidin mixtures demonstrated response-additive dose-level-dependent synergism, clothianidin-thiamethoxam mixtures demonstrated concentration-additive synergism, and imidacloprid-thiamethoxam mixtures demonstrated response-additive dose-ratio-dependent synergism, with toxicity shifting from antagonism to synergism as the relative concentration of thiamethoxam increased. Imidacloprid-clothianidin-thiamethoxam ternary mixtures demonstrated response-additive synergism. These results indicate that, under acute exposure scenarios, the toxicity of neonicotinoid mixtures to C. dilutus cannot be predicted using the common assumption of additive joint activity. Indeed, the overarching trend of synergistic deviation emphasizes the need for further research into the ecotoxicological effects of neonicotinoid insecticide mixtures in field settings, the development of better toxicity models for neonicotinoid mixture exposures, and the consideration of mixture effects when setting water quality guidelines for this class of pesticides. Environ Toxicol Chem 2017;36:3091-3101. © 2017 SETAC. © 2017 SETAC.

Beneficial immune modulatory effects of a specific nutritional combination in a murine model for cancer cachexia

PubMed Central

Faber, J; Vos, P; Kegler, D; van Norren, K; Argilés, J M; Laviano, A; Garssen, J; van Helvoort, A

2008-01-01

The majority of patients with advanced cancer are recognised by impaired immune competence influenced by several factors, including the type and stage of the tumour and the presence of cachexia. Recently, a specific nutritional combination containing fish oil, specific oligosaccharide mixture, high protein content and leucine has been developed aimed to support the immune system of cancer patients in order to reduce the frequency and severity of (infectious) complications. In a recently modified animal model cachexia is induced by inoculation of C26 tumour cells in mice. In a pre-cachectic state, no effect was observed on contact hypersensitivity, a validated in vivo method to measure Th1-mediated immune function, after adding the individual nutritional ingredients to the diet of tumour-bearing mice. However, the complete mixture resulted in significantly improved Th1 immunity. Moreover, in a cachectic state, the complete mixture reduced plasma levels of pro-inflammatory cytokines and beneficially affected ex vivo immune function. Accordingly, the combination of the nutritional ingredients is required to obtain a synergistic effect, leading to a reduced inflammatory state and improved immune competence. From this, it can be concluded that the specific nutritional combination has potential as immune-supporting nutritional intervention to reduce the risk of (infectious) complications in cancer patients. PMID:19018259

Mixture modeling methods for the assessment of normal and abnormal personality, part II: longitudinal models.

PubMed

Wright, Aidan G C; Hallquist, Michael N

2014-01-01

Studying personality and its pathology as it changes, develops, or remains stable over time offers exciting insight into the nature of individual differences. Researchers interested in examining personal characteristics over time have a number of time-honored analytic approaches at their disposal. In recent years there have also been considerable advances in person-oriented analytic approaches, particularly longitudinal mixture models. In this methodological primer we focus on mixture modeling approaches to the study of normative and individual change in the form of growth mixture models and ipsative change in the form of latent transition analysis. We describe the conceptual underpinnings of each of these models, outline approaches for their implementation, and provide accessible examples for researchers studying personality and its assessment.

Numerical simulation of asphalt mixtures fracture using continuum models

NASA Astrophysics Data System (ADS)

Szydłowski, Cezary; Górski, Jarosław; Stienss, Marcin; Smakosz, Łukasz

2018-01-01

The paper considers numerical models of fracture processes of semi-circular asphalt mixture specimens subjected to three-point bending. Parameter calibration of the asphalt mixture constitutive models requires advanced, complex experimental test procedures. The highly non-homogeneous material is numerically modelled by a quasi-continuum model. The computational parameters are averaged data of the components, i.e. asphalt, aggregate and the air voids composing the material. The model directly captures random nature of material parameters and aggregate distribution in specimens. Initial results of the analysis are presented here.

Introduction to the special section on mixture modeling in personality assessment.

PubMed

Wright, Aidan G C; Hallquist, Michael N

2014-01-01

Latent variable models offer a conceptual and statistical framework for evaluating the underlying structure of psychological constructs, including personality and psychopathology. Complex structures that combine or compare categorical and dimensional latent variables can be accommodated using mixture modeling approaches, which provide a powerful framework for testing nuanced theories about psychological structure. This special series includes introductory primers on cross-sectional and longitudinal mixture modeling, in addition to empirical examples applying these techniques to real-world data collected in clinical settings. This group of articles is designed to introduce personality assessment scientists and practitioners to a general latent variable framework that we hope will stimulate new research and application of mixture models to the assessment of personality and its pathology.

Predicting the shock compression response of heterogeneous powder mixtures

NASA Astrophysics Data System (ADS)

Fredenburg, D. A.; Thadhani, N. N.

2013-06-01

A model framework for predicting the dynamic shock-compression response of heterogeneous powder mixtures using readily obtained measurements from quasi-static tests is presented. Low-strain-rate compression data are first analyzed to determine the region of the bulk response over which particle rearrangement does not contribute to compaction. This region is then fit to determine the densification modulus of the mixture, σD, an newly defined parameter describing the resistance of the mixture to yielding. The measured densification modulus, reflective of the diverse yielding phenomena that occur at the meso-scale, is implemented into a rate-independent formulation of the P-α model, which is combined with an isobaric equation of state to predict the low and high stress dynamic compression response of heterogeneous powder mixtures. The framework is applied to two metal + metal-oxide (thermite) powder mixtures, and good agreement between the model and experiment is obtained for all mixtures at stresses near and above those required to reach full density. At lower stresses, rate-dependencies of the constituents, and specifically those of the matrix constituent, determine the ability of the model to predict the measured response in the incomplete compaction regime.

D-optimal experimental designs to test for departure from additivity in a fixed-ratio mixture ray.

PubMed

Coffey, Todd; Gennings, Chris; Simmons, Jane Ellen; Herr, David W

2005-12-01

Traditional factorial designs for evaluating interactions among chemicals in a mixture may be prohibitive when the number of chemicals is large. Using a mixture of chemicals with a fixed ratio (mixture ray) results in an economical design that allows estimation of additivity or nonadditive interaction for a mixture of interest. This methodology is extended easily to a mixture with a large number of chemicals. Optimal experimental conditions can be chosen that result in increased power to detect departures from additivity. Although these designs are used widely for linear models, optimal designs for nonlinear threshold models are less well known. In the present work, the use of D-optimal designs is demonstrated for nonlinear threshold models applied to a fixed-ratio mixture ray. For a fixed sample size, this design criterion selects the experimental doses and number of subjects per dose level that result in minimum variance of the model parameters and thus increased power to detect departures from additivity. An optimal design is illustrated for a 2:1 ratio (chlorpyrifos:carbaryl) mixture experiment. For this example, and in general, the optimal designs for the nonlinear threshold model depend on prior specification of the slope and dose threshold parameters. Use of a D-optimal criterion produces experimental designs with increased power, whereas standard nonoptimal designs with equally spaced dose groups may result in low power if the active range or threshold is missed.

Modeling Organic Contaminant Desorption from Municipal Solid Waste Components

NASA Astrophysics Data System (ADS)

Knappe, D. R.; Wu, B.; Barlaz, M. A.

2002-12-01

Approximately 25% of the sites on the National Priority List (NPL) of Superfund are municipal landfills that accepted hazardous waste. Unlined landfills typically result in groundwater contamination, and priority pollutants such as alkylbenzenes are often present. To select cost-effective risk management alternatives, better information on factors controlling the fate of hydrophobic organic contaminants (HOCs) in landfills is required. The objectives of this study were (1) to investigate the effects of HOC aging time, anaerobic sorbent decomposition, and leachate composition on HOC desorption rates, and (2) to simulate HOC desorption rates from polymers and biopolymer composites with suitable diffusion models. Experiments were conducted with individual components of municipal solid waste (MSW) including polyvinyl chloride (PVC), high-density polyethylene (HDPE), newsprint, office paper, and model food and yard waste (rabbit food). Each of the biopolymer composites (office paper, newsprint, rabbit food) was tested in both fresh and anaerobically decomposed form. To determine the effects of aging on alkylbenzene desorption rates, batch desorption tests were performed after sorbents were exposed to toluene for 30 and 250 days in flame-sealed ampules. Desorption tests showed that alkylbenzene desorption rates varied greatly among MSW components (PVC slowest, fresh rabbit food and newsprint fastest). Furthermore, desorption rates decreased as aging time increased. A single-parameter polymer diffusion model successfully described PVC and HDPE desorption data, but it failed to simulate desorption rate data for biopolymer composites. For biopolymer composites, a three-parameter biphasic polymer diffusion model was employed, which successfully simulated both the initial rapid and the subsequent slow desorption of toluene. Toluene desorption rates from MSW mixtures were predicted for typical MSW compositions in the years 1960 and 1997. For the older MSW mixture, which had a low plastics content, the model predicted that 50% of the initially sorbed toluene desorbed over a period of 5.8 days. In contrast, the model predicted that 50% of the initially sorbed toluene desorbed over a period of 4 years for the newer MSW mixture. These results suggest that toluene desorption rates from old MSW mixtures exceed methanogenic toluene degradation rates (toluene half-lives of about 30 to 100 days have been reported for methanogenic systems) and thus imply that biodegradation kinetics control the rate at which sorbed toluene is mineralized in old landfills. For newer MSW mixtures with a larger plastics content, toluene desorption rates are substantially slower; therefore, toluene desorption kinetics likely control the rate at which sorbed toluene can be mineralized in new landfills.

Gravel-Sand-Clay Mixture Model for Predictions of Permeability and Velocity of Unconsolidated Sediments

NASA Astrophysics Data System (ADS)

Konishi, C.

2014-12-01

Gravel-sand-clay mixture model is proposed particularly for unconsolidated sediments to predict permeability and velocity from volume fractions of the three components (i.e. gravel, sand, and clay). A well-known sand-clay mixture model or bimodal mixture model treats clay contents as volume fraction of the small particle and the rest of the volume is considered as that of the large particle. This simple approach has been commonly accepted and has validated by many studies before. However, a collection of laboratory measurements of permeability and grain size distribution for unconsolidated samples show an impact of presence of another large particle; i.e. only a few percent of gravel particles increases the permeability of the sample significantly. This observation cannot be explained by the bimodal mixture model and it suggests the necessity of considering the gravel-sand-clay mixture model. In the proposed model, I consider the three volume fractions of each component instead of using only the clay contents. Sand becomes either larger or smaller particles in the three component mixture model, whereas it is always the large particle in the bimodal mixture model. The total porosity of the two cases, one is the case that the sand is smaller particle and the other is the case that the sand is larger particle, can be modeled independently from sand volume fraction by the same fashion in the bimodal model. However, the two cases can co-exist in one sample; thus, the total porosity of the mixed sample is calculated by weighted average of the two cases by the volume fractions of gravel and clay. The effective porosity is distinguished from the total porosity assuming that the porosity associated with clay is zero effective porosity. In addition, effective grain size can be computed from the volume fractions and representative grain sizes for each component. Using the effective porosity and the effective grain size, the permeability is predicted by Kozeny-Carman equation. Furthermore, elastic properties are obtainable by general Hashin-Shtrikman-Walpole bounds. The predicted results by this new mixture model are qualitatively consistent with laboratory measurements and well log obtained for unconsolidated sediments. Acknowledgement: A part of this study was accomplished with a subsidy of River Environment Fund of Japan.

A numerical study of granular dam-break flow

NASA Astrophysics Data System (ADS)

Pophet, N.; Rébillout, L.; Ozeren, Y.; Altinakar, M.

2017-12-01

Accurate prediction of granular flow behavior is essential to optimize mitigation measures for hazardous natural granular flows such as landslides, debris flows and tailings-dam break flows. So far, most successful models for these types of flows focus on either pure granular flows or flows of saturated grain-fluid mixtures by employing a constant friction model or more complex rheological models. These saturated models often produce non-physical result when they are applied to simulate flows of partially saturated mixtures. Therefore, more advanced models are needed. A numerical model was developed for granular flow employing a constant friction and μ(I) rheology (Jop et al., J. Fluid Mech. 2005) coupled with a groundwater flow model for seepage flow. The granular flow is simulated by solving a mixture model using Finite Volume Method (FVM). The Volume-of-Fluid (VOF) technique is used to capture the free surface motion. The constant friction and μ(I) rheological models are incorporated in the mixture model. The seepage flow is modeled by solving Richards equation. A framework is developed to couple these two solvers in OpenFOAM. The model was validated and tested by reproducing laboratory experiments of partially and fully channelized dam-break flows of dry and initially saturated granular material. To obtain appropriate parameters for rheological models, a series of simulations with different sets of rheological parameters is performed. The simulation results obtained from constant friction and μ(I) rheological models are compared with laboratory experiments for granular free surface interface, front position and velocity field during the flows. The numerical predictions indicate that the proposed model is promising in predicting dynamics of the flow and deposition process. The proposed model may provide more reliable insight than the previous assumed saturated mixture model, when saturated and partially saturated portions of granular mixture co-exist.

An approach of characterizing the degree of spatial color mixture

NASA Astrophysics Data System (ADS)

Chu, Miao; Tian, Shaohui

2017-07-01

The digital camouflage technology arranges different color mosaics according to a certain rules, compared with traditional camouflage, it has more outstanding results deal with different distance reconnaissance. The better result of digital camouflage is mainly attributed to spatial color mixture, and is also a key factor to improve digital camouflage design. However, the research of space color mixture is relatively lack, cannot provide inadequate support for digital camouflage design. Therefore, according to the process of spatial color mixture, this paper proposes an effective parameter, spatial-color-mixture ratio, to characterize the degree of spatial color mixture. The experimental results show that spatial-color-mixture ratio is feasible and effective in practice, which could provide a new direction for further research on digital camouflage.

The underlying toxicological mechanism of chemical mixtures: A case study on mixture toxicity of cyanogenic toxicants and aldehydes to Photobacterium phosphoreum

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

Tian, Dayong; Department of Chemical and Environmental Engineering, Anyang Institute of Technology, Anyang 455000; Lin, Zhifen, E-mail: lzhifen@tongji.edu.cn

Intracellular chemical reaction of chemical mixtures is one of the main reasons that cause synergistic or antagonistic effects. However, it still remains unclear what the influencing factors on the intracellular chemical reaction are, and how they influence on the toxicological mechanism of chemical mixtures. To reveal this underlying toxicological mechanism of chemical mixtures, a case study on mixture toxicity of cyanogenic toxicants and aldehydes to Photobacterium phosphoreum was employed, and both their joint effects and mixture toxicity were observed. Then series of two-step linear regressions were performed to describe the relationships between joint effects, the expected additive toxicities and descriptorsmore » of individual chemicals (including concentrations, binding affinity to receptors, octanol/water partition coefficients). Based on the quantitative relationships, the underlying joint toxicological mechanisms were revealed. The result shows that, for mixtures with their joint effects resulting from intracellular chemical reaction, their underlying toxicological mechanism depends on not only their interaction with target proteins, but also their transmembrane actions and their concentrations. In addition, two generic points of toxicological mechanism were proposed including the influencing factors on intracellular chemical reaction and the difference of the toxicological mechanism between single reactive chemicals and their mixtures. This study provided an insight into the understanding of the underlying toxicological mechanism for chemical mixtures with intracellular chemical reaction. - Highlights: • Joint effects of nitriles and aldehydes at non-equitoxic ratios were determined. • A novel descriptor, ligand–receptor interaction energy (E{sub binding}), was employed. • Quantitative relationships for mixtures were developed based on a novel descriptor. • The underlying toxic mechanism was revealed based on quantitative relationships. • Two generic points of toxicological mechanism were elucidated.« less

Influence of Liquid Structure on Fickian Diffusion in Binary Mixtures of n-Hexane and Carbon Dioxide Probed by Dynamic Light Scattering, Raman Spectroscopy, and Molecular Dynamics Simulations.

PubMed

Klein, Tobias; Wu, Wenchang; Rausch, Michael Heinrich; Giraudet, Cédric; Koller, Thomas M; Fröba, Andreas Paul

2018-06-11

This study contributes to a fundamental understanding how the liquid structure in a model system consisting of weakly associative n-hexane ( n-C 6 H 14 ) and carbon dioxide (CO 2 ) influences the Fickian diffusion process. For this, the benefits of light scattering experiments and molecular dynamics (MD) simulations at macroscopic thermodynamic equilibrium were combined synergistically. Our reference Fickian diffusivities measured by dynamic light scattering (DLS) revealed an unusual trend with increasing CO 2 mole fractions up to a CO 2 concentration of about 70 mol%, which agrees with our simulation results. The molecular impacts on the Fickian diffusion were analyzed by MD simulations, where kinetic contributions related to the Maxwell-Stefan (MS) diffusivity and structural contributions quantified by the thermodynamic factor were studied separately. Both the MS diffusivity and the thermodynamic factor indicate the deceleration of Fickian diffusion compared to an ideal mixture behavior. Computed radial distribution functions as well as a significant blue-shift of the CH-stretching modes of n-C 6 H 14 identified by Raman spectroscopy show that the slowing-down of the diffusion is caused by a structural organization in the binary mixtures over a broad concentration range in the form of self-associated n-C 6 H 14 and CO 2 domains. These networks start to form close to the infinite dilution limits and seem to have their largest extent at a solute-solvent transition point at about 70 mol% of CO 2 . The current results not only improve the general understanding of mass diffusion in liquids, but also serve to develop sound prediction models for Fick diffusivities.

Complex organochlorine pesticide mixtures as determinant factor for breast cancer risk: a population-based case–control study in the Canary Islands (Spain)

PubMed Central

2012-01-01

Background All the relevant risk factors contributing to breast cancer etiology are not fully known. Exposure to organochlorine pesticides has been linked to an increased incidence of the disease, although not all data have been consistent. Most published studies evaluated the exposure to organochlorines individually, ignoring the potential effects exerted by the mixtures of chemicals. Methods This population-based study was designed to evaluate the profile of mixtures of organochlorines detected in 103 healthy women and 121 women diagnosed with breast cancer from Gran Canaria Island, and the relation between the exposure to these compounds and breast cancer risk. Results The most prevalent mixture of organochlorines among healthy women was the combination of lindane and endrin, and this mixture was not detected in any affected women. Breast cancer patients presented more frequently a combination of aldrin, dichlorodiphenyldichloroethylene (DDE) and dichlorodiphenyldichloroethane (DDD), and this mixture was not found in any healthy woman. After adjusting for covariables, the risk of breast cancer was moderately associated with DDD (OR = 1.008, confidence interval 95% 1.001-1.015, p = 0.024). Conclusions This study indicates that healthy women show a very different profile of organochlorine pesticide mixtures than breast cancer patients, suggesting that organochlorine pesticide mixtures could play a relevant role in breast cancer risk. PMID:22534004

Mixture theory-based poroelasticity as a model of interstitial tissue growth

PubMed Central

Cowin, Stephen C.; Cardoso, Luis

2011-01-01

This contribution presents an alternative approach to mixture theory-based poroelasticity by transferring some poroelastic concepts developed by Maurice Biot to mixture theory. These concepts are a larger RVE and the subRVE-RVE velocity average tensor, which Biot called the micro-macro velocity average tensor. This velocity average tensor is assumed here to depend upon the pore structure fabric. The formulation of mixture theory presented is directed toward the modeling of interstitial growth, that is to say changing mass and changing density of an organism. Traditional mixture theory considers constituents to be open systems, but the entire mixture is a closed system. In this development the mixture is also considered to be an open system as an alternative method of modeling growth. Growth is slow and accelerations are neglected in the applications. The velocity of a solid constituent is employed as the main reference velocity in preference to the mean velocity concept from the original formulation of mixture theory. The standard development of statements of the conservation principles and entropy inequality employed in mixture theory are modified to account for these kinematic changes and to allow for supplies of mass, momentum and energy to each constituent and to the mixture as a whole. The objective is to establish a basis for the development of constitutive equations for growth of tissues. PMID:22184481

Mixture theory-based poroelasticity as a model of interstitial tissue growth.

PubMed

Cowin, Stephen C; Cardoso, Luis

2012-01-01

This contribution presents an alternative approach to mixture theory-based poroelasticity by transferring some poroelastic concepts developed by Maurice Biot to mixture theory. These concepts are a larger RVE and the subRVE-RVE velocity average tensor, which Biot called the micro-macro velocity average tensor. This velocity average tensor is assumed here to depend upon the pore structure fabric. The formulation of mixture theory presented is directed toward the modeling of interstitial growth, that is to say changing mass and changing density of an organism. Traditional mixture theory considers constituents to be open systems, but the entire mixture is a closed system. In this development the mixture is also considered to be an open system as an alternative method of modeling growth. Growth is slow and accelerations are neglected in the applications. The velocity of a solid constituent is employed as the main reference velocity in preference to the mean velocity concept from the original formulation of mixture theory. The standard development of statements of the conservation principles and entropy inequality employed in mixture theory are modified to account for these kinematic changes and to allow for supplies of mass, momentum and energy to each constituent and to the mixture as a whole. The objective is to establish a basis for the development of constitutive equations for growth of tissues.

Avoidance in hypochondriasis.

PubMed

Doherty-Torstrick, Emily R; Walton, Kate E; Barsky, Arthur J; Fallon, Brian A

2016-10-01

The DSM-5 diagnosis of illness anxiety disorder adds avoidance as a component of a behavioral response to illness fears - one that was not present in prior DSM criteria of hypochondriasis. However, maladaptive avoidance as a necessary or useful criterion has yet to be empirically supported. 195 individuals meeting DSM-IV criteria for hypochondriasis based on structured interview completed a variety of self-report and clinician-administered assessments. Data on maladaptive avoidance were obtained using the six-item subscale of the clinician-administered Hypochondriasis - Yale Brown Obsessive Compulsive Scale - Modified. To determine if avoidance emerged as a useful indicator in hypochondriasis, we compared the relative fit of continuous latent trait, categorical latent class, and hybrid factor mixture models. A two-class factor mixture model fit the data best, with Class 1 (n=147) exhibiting a greater level of severity of avoidance than Class 2 (n=48). The more severely avoidant group was found to have higher levels of hypochondriacal symptom severity, functional impairment, and anxiety, as well as lower quality of life. These results suggest that avoidance may be a valid behavioral construct and a useful component of the new diagnostic criteria of illness anxiety in the DSM-5, with implications for somatic symptom disorder. Copyright © 2016 Elsevier Inc. All rights reserved.

Decoding and modelling of time series count data using Poisson hidden Markov model and Markov ordinal logistic regression models.

PubMed

Sebastian, Tunny; Jeyaseelan, Visalakshi; Jeyaseelan, Lakshmanan; Anandan, Shalini; George, Sebastian; Bangdiwala, Shrikant I

2018-01-01

Hidden Markov models are stochastic models in which the observations are assumed to follow a mixture distribution, but the parameters of the components are governed by a Markov chain which is unobservable. The issues related to the estimation of Poisson-hidden Markov models in which the observations are coming from mixture of Poisson distributions and the parameters of the component Poisson distributions are governed by an m-state Markov chain with an unknown transition probability matrix are explained here. These methods were applied to the data on Vibrio cholerae counts reported every month for 11-year span at Christian Medical College, Vellore, India. Using Viterbi algorithm, the best estimate of the state sequence was obtained and hence the transition probability matrix. The mean passage time between the states were estimated. The 95% confidence interval for the mean passage time was estimated via Monte Carlo simulation. The three hidden states of the estimated Markov chain are labelled as 'Low', 'Moderate' and 'High' with the mean counts of 1.4, 6.6 and 20.2 and the estimated average duration of stay of 3, 3 and 4 months, respectively. Environmental risk factors were studied using Markov ordinal logistic regression analysis. No significant association was found between disease severity levels and climate components.

Least-Squares Regression and Spectral Residual Augmented Classical Least-Squares Chemometric Models for Stability-Indicating Analysis of Agomelatine and Its Degradation Products: A Comparative Study.

PubMed

Naguib, Ibrahim A; Abdelrahman, Maha M; El Ghobashy, Mohamed R; Ali, Nesma A

2016-01-01

Two accurate, sensitive, and selective stability-indicating methods are developed and validated for simultaneous quantitative determination of agomelatine (AGM) and its forced degradation products (Deg I and Deg II), whether in pure forms or in pharmaceutical formulations. Partial least-squares regression (PLSR) and spectral residual augmented classical least-squares (SRACLS) are two chemometric models that are being subjected to a comparative study through handling UV spectral data in range (215-350 nm). For proper analysis, a three-factor, four-level experimental design was established, resulting in a training set consisting of 16 mixtures containing different ratios of interfering species. An independent test set consisting of eight mixtures was used to validate the prediction ability of the suggested models. The results presented indicate the ability of mentioned multivariate calibration models to analyze AGM, Deg I, and Deg II with high selectivity and accuracy. The analysis results of the pharmaceutical formulations were statistically compared to the reference HPLC method, with no significant differences observed regarding accuracy and precision. The SRACLS model gives comparable results to the PLSR model; however, it keeps the qualitative spectral information of the classical least-squares algorithm for analyzed components.

Serum replacement with a growth factor-free synthetic substance in culture medium contributes to effective establishment of mouse embryonic stem cells of various origins.

PubMed

Lee, Seung Tae; Oh, Se Woong; Kim, Dae Yong; Han, Jae Yong; Moon, Shin Yong; Lim, Jeong Mook

2006-10-01

To evaluate whether serum replacement with growth factor-free synthetic substances contributed to the effective establishment of embryonic stem (ES) cells. Randomized, prospective model study. Gamete and stem cell biotechnology laboratory at Seoul National University in Korea. F1 (C57BL6 x DBA2) mice. Blastocysts of different origins were cultured in serum-replaced media. Embryonic stem cell establishment. Eight batches of ES cells were established from colony-forming inner cell mass cells after the replacement of fetal bovine serum (FBS) with synthetic knockout serum replacement (KSR) in mkDMEM. The established cells were positive for ES cell markers and formed both embryoid bodies in vitro and teratomas in vivo, but the established cell batches and control (transformed) ES cells responded differently to the culture media. Higher levels of cell viability were detected after the replacement with the 75:25 FBS-KSR mixture than with any other mixtures, and a gradual decrease in viability was detected as the KSR volume ratio was increased. The 75:25 FBS-KSR mixture-containing medium supported ES cell establishment of outbred ICR, F1, and F2 of C57BL6/DBA2; F1 parthenogenetic and ES cell-complemented tetraploid blastocysts; and single ES-cell cultures. A serum-replaced medium could be used for effective ES-cell establishment of various origins.

[Early Greek medicine and Plato's cosmology].

PubMed

Rhee, Kee-Bag

2004-06-01

The purpose of this paper is to show the influence of Early greek medicine on Plato's Cosmology. Alcmaeon holds that health depends on proportion (equality; isonomia) or proportioned mixture of opposing factors. This notion dominated nearly all greek medicine, and also influenced Plato's cosmology greatly. Generally each greek doctors believed that man consisted of opposing factors, though these are designated differently. Alcmaeon takes powers - hot and dry, cold and hot, vitter, sweet and the rest as those factors. On the other hand, Philistion of Locri adopts the four element theory of Empedocles. He conceives that human body as a mixture of the four elements, and health consists in proportion of these opposing four element, basically as Alcmaeon. This notion is accepted by Plato. Only Plato differs from Philistion in that he doesn't consider the four elements as the ultimate factors. In Timaeus Plato explain that the Demiourgos constructed the four elements through introducing 'proportion' into the primitive materials (the oppositives) by means of shapes and numbers. And Plato thinks that the cosmic body and soul was constructed basically in the same way as the four elements. This is true of the human body and soul. Also Plato explicates diseases from standpoint of proportion or symmetry. Moreover according to Philebus, the good states (i.e. 'health', 'music', 'season' etc) in the cosmos arises out of the right mixture of the limit and the unlimited. In the other word this mixture is proportioned mixture of the oppositives by aid of ratios. In short Plato believes that both the cosmos itself and the good states s proportioned mixture of the oppositives. Thus Plato' cosmology is fundamentally based upon Alcmaeon's or Philistion's concept of Health.

Influence of hemoglobin on non-invasive optical bilirubin sensing

NASA Astrophysics Data System (ADS)

Jiang, Jingying; Gong, Qiliang; Zou, Da; Xu, Kexin

2012-03-01

Since the abnormal metabolism of bilirubin could lead to diseases in the human body, especially the jaundice which is harmful to neonates. Traditional invasive measurements are difficult to be accepted by people because of pain and infection. Therefore, the real-time and non-invasive measurement of bilirubin is of great significance. However, the accuracy of currently transcutaneous bilirubinometry(TcB) is generally not high enough, and affected by many factors in the human skin, mostly by hemoglobin. In this talk, absorption spectra of hemoglobin and bilirubin have been collected and analyzed, then the Partial Least Squares (PLS) models have been built. By analyzing and comparing the Correlation and Root Mean Square Error of Prediction(RMSEP), the results show that the Correlation of bilirubin solution model is larger than that of the mixture solution added with hemoglobin, and its RMSEP value is smaller than that of mixture solution. Therefore, hemoglobin has influences on the non-invasive optical bilirubin sensing. In next step, it is necessary to investigate how to eliminate the influence.

A Simplified Model of Local Structure in Aqueous Proline Amino Acid Revealed by First-Principles Molecular Dynamics Simulations

PubMed Central

Troitzsch, Raphael Z.; Tulip, Paul R.; Crain, Jason; Martyna, Glenn J.

2008-01-01

Aqueous proline solutions are deceptively simple as they can take on complex roles such as protein chaperones, cryoprotectants, and hydrotropic agents in biological processes. Here, a molecular level picture of proline/water mixtures is developed. Car-Parrinello ab initio molecular dynamics (CPAIMD) simulations of aqueous proline amino acid at the B-LYP level of theory, performed using IBM's Blue Gene/L supercomputer and massively parallel software, reveal hydrogen-bonding propensities that are at odds with the predictions of the CHARMM22 empirical force field but are in better agreement with results of recent neutron diffraction experiments. In general, the CPAIMD (B-LYP) simulations predict a simplified structural model of proline/water mixtures consisting of fewer distinct local motifs. Comparisons of simulation results to experiment are made by direct evaluation of the neutron static structure factor S(Q) from CPAIMD (B-LYP) trajectories as well as to the results of the empirical potential structure refinement reverse Monte Carlo procedure applied to the neutron data. PMID:18790850

A simplified model of local structure in aqueous proline amino acid revealed by first-principles molecular dynamics simulations.

PubMed

Troitzsch, Raphael Z; Tulip, Paul R; Crain, Jason; Martyna, Glenn J

2008-12-01

Aqueous proline solutions are deceptively simple as they can take on complex roles such as protein chaperones, cryoprotectants, and hydrotropic agents in biological processes. Here, a molecular level picture of proline/water mixtures is developed. Car-Parrinello ab initio molecular dynamics (CPAIMD) simulations of aqueous proline amino acid at the B-LYP level of theory, performed using IBM's Blue Gene/L supercomputer and massively parallel software, reveal hydrogen-bonding propensities that are at odds with the predictions of the CHARMM22 empirical force field but are in better agreement with results of recent neutron diffraction experiments. In general, the CPAIMD (B-LYP) simulations predict a simplified structural model of proline/water mixtures consisting of fewer distinct local motifs. Comparisons of simulation results to experiment are made by direct evaluation of the neutron static structure factor S(Q) from CPAIMD (B-LYP) trajectories as well as to the results of the empirical potential structure refinement reverse Monte Carlo procedure applied to the neutron data.

Vapor-liquid equilibria for R-22, R-134a, R-125, and R-32/125 with a polyol ester lubricant: Measurements and departure from ideality

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

Martz, W.L.; Burton, C.M.; Jacobi, A.M.

1996-11-01

The effect of a polyol ester lubricant on equilibrium pressure, liquid density, and viscosity is presented for R-22, R-125, and R-134a at varying temperatures and concentrations. Preliminary vapor-liquid equilibrium (VLE) data and miscibility observations are also presented for an R-32/R-125 blend (50%/50%) with the ISO 68 polyol ester (POE). Real-gas behavior is modeled using the vapor-phase fugacity, and vapor pressure effects on liquid fugacities are taken into account with the Poynting effect. Positive, negative, and mixed deviations form the Lewis-Randall rule are observed in the activity coefficient behavior. Departures from ideality are related to molecular size differences, intermolecular forces inmore » the mixture, and other factors. The data are discussed in the context of previous results for other refrigerants and thermodynamic modeling of refrigerant and oil mixtures.« less

A nonlinear isobologram model with Box-Cox transformation to both sides for chemical mixtures.

PubMed

Chen, D G; Pounds, J G

1998-12-01

The linear logistical isobologram is a commonly used and powerful graphical and statistical tool for analyzing the combined effects of simple chemical mixtures. In this paper a nonlinear isobologram model is proposed to analyze the joint action of chemical mixtures for quantitative dose-response relationships. This nonlinear isobologram model incorporates two additional new parameters, Ymin and Ymax, to facilitate analysis of response data that are not constrained between 0 and 1, where parameters Ymin and Ymax represent the minimal and the maximal observed toxic response. This nonlinear isobologram model for binary mixtures can be expressed as [formula: see text] In addition, a Box-Cox transformation to both sides is introduced to improve the goodness of fit and to provide a more robust model for achieving homogeneity and normality of the residuals. Finally, a confidence band is proposed for selected isobols, e.g., the median effective dose, to facilitate graphical and statistical analysis of the isobologram. The versatility of this approach is demonstrated using published data describing the toxicity of the binary mixtures of citrinin and ochratoxin as well as a new experimental data from our laboratory for mixtures of mercury and cadmium.

A nonlinear isobologram model with Box-Cox transformation to both sides for chemical mixtures.

PubMed Central

Chen, D G; Pounds, J G

1998-01-01

The linear logistical isobologram is a commonly used and powerful graphical and statistical tool for analyzing the combined effects of simple chemical mixtures. In this paper a nonlinear isobologram model is proposed to analyze the joint action of chemical mixtures for quantitative dose-response relationships. This nonlinear isobologram model incorporates two additional new parameters, Ymin and Ymax, to facilitate analysis of response data that are not constrained between 0 and 1, where parameters Ymin and Ymax represent the minimal and the maximal observed toxic response. This nonlinear isobologram model for binary mixtures can be expressed as [formula: see text] In addition, a Box-Cox transformation to both sides is introduced to improve the goodness of fit and to provide a more robust model for achieving homogeneity and normality of the residuals. Finally, a confidence band is proposed for selected isobols, e.g., the median effective dose, to facilitate graphical and statistical analysis of the isobologram. The versatility of this approach is demonstrated using published data describing the toxicity of the binary mixtures of citrinin and ochratoxin as well as a new experimental data from our laboratory for mixtures of mercury and cadmium. PMID:9860894

Factorial Design Approach in Proportioning Prestressed Self-Compacting Concrete

PubMed Central

Long, Wu-Jian; Khayat, Kamal Henri; Lemieux, Guillaume; Xing, Feng; Wang, Wei-Lun

2015-01-01

In order to model the effect of mixture parameters and material properties on the hardened properties of, prestressed self-compacting concrete (SCC), and also to investigate the extensions of the statistical models, a factorial design was employed to identify the relative significance of these primary parameters and their interactions in terms of the mechanical and visco-elastic properties of SCC. In addition to the 16 fractional factorial mixtures evaluated in the modeled region of −1 to +1, eight axial mixtures were prepared at extreme values of −2 and +2 with the other variables maintained at the central points. Four replicate central mixtures were also evaluated. The effects of five mixture parameters, including binder type, binder content, dosage of viscosity-modifying admixture (VMA), water-cementitious material ratio (w/cm), and sand-to-total aggregate ratio (S/A) on compressive strength, modulus of elasticity, as well as autogenous and drying shrinkage are discussed. The applications of the models to better understand trade-offs between mixture parameters and carry out comparisons among various responses are also highlighted. A logical design approach would be to use the existing model to predict the optimal design, and then run selected tests to quantify the influence of the new binder on the model. PMID:28787990

Dynamics of a spherically symmetric inhomogeneous coupled dark energy model with coupling term proportional to non relatvistic matter

NASA Astrophysics Data System (ADS)

Izquierdo, Germán; Blanquet-Jaramillo, Roberto C.; Sussman, Roberto A.

2018-01-01

The quasi-local scalar variables approach is applied to a spherically symmetric inhomogeneous Lemaître-Tolman-Bondi metric containing a mixture of non-relativistic cold dark matter and coupled dark energy with constant equation of state. The quasi-local coupling term considered is proportional to the quasi-local cold dark matter energy density and a quasi-local Hubble factor-like scalar via a coupling constant α . The autonomous numerical system obtained from the evolution equations is classified for different choices of the free parameters: the adiabatic constant of the dark energy w and α . The presence of a past attractor in a non-physical region of the energy densities phase-space of the system makes the coupling term non physical when the energy flows from the matter to the dark energy in order to avoid negative values of the dark energy density in the past. On the other hand, if the energy flux goes from dark energy to dark matter, the past attractor lies in a physical region. The system is also numerically solved for some interesting initial profiles leading to different configurations: an ever expanding mixture, a scenario where the dark energy is completely consumed by the non-relativistic matter by means of the coupling term, a scenario where the dark energy disappears in the inner layers while the outer layers expand as a mixture of both sources, and, finally, a structure formation toy model scenario, where the inner shells containing the mixture collapse while the outer shells expand.

NGMIX: Gaussian mixture models for 2D images

NASA Astrophysics Data System (ADS)

Sheldon, Erin

2015-08-01

NGMIX implements Gaussian mixture models for 2D images. Both the PSF profile and the galaxy are modeled using mixtures of Gaussians. Convolutions are thus performed analytically, resulting in fast model generation as compared to methods that perform the convolution in Fourier space. For the galaxy model, NGMIX supports exponential disks and de Vaucouleurs and Sérsic profiles; these are implemented approximately as a sum of Gaussians using the fits from Hogg & Lang (2013). Additionally, any number of Gaussians can be fit, either completely free or constrained to be cocentric and co-elliptical.

A non-ideal model for predicting the effect of dissolved salt on the flash point of solvent mixtures.

PubMed

Liaw, Horng-Jang; Wang, Tzu-Ai

2007-03-06

Flash point is one of the major quantities used to characterize the fire and explosion hazard of liquids. Herein, a liquid with dissolved salt is presented in a salt-distillation process for separating close-boiling or azeotropic systems. The addition of salts to a liquid may reduce fire and explosion hazard. In this study, we have modified a previously proposed model for predicting the flash point of miscible mixtures to extend its application to solvent/salt mixtures. This modified model was verified by comparison with the experimental data for organic solvent/salt and aqueous-organic solvent/salt mixtures to confirm its efficacy in terms of prediction of the flash points of these mixtures. The experimental results confirm marked increases in liquid flash point increment with addition of inorganic salts relative to supplementation with equivalent quantities of water. Based on this evidence, it appears reasonable to suggest potential application for the model in assessment of the fire and explosion hazard for solvent/salt mixtures and, further, that addition of inorganic salts may prove useful for hazard reduction in flammable liquids.

Analysis of real-time mixture cytotoxicity data following repeated exposure using BK/TD models

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

Teng, S.; Tebby, C.

Cosmetic products generally consist of multiple ingredients. Thus, cosmetic risk assessment has to deal with mixture toxicity on a long-term scale which means it has to be assessed in the context of repeated exposure. Given that animal testing has been banned for cosmetics risk assessment, in vitro assays allowing long-term repeated exposure and adapted for in vitro – in vivo extrapolation need to be developed. However, most in vitro tests only assess short-term effects and consider static endpoints which hinder extrapolation to realistic human exposure scenarios where concentration in target organs is varies over time. Thanks to impedance metrics, real-timemore » cell viability monitoring for repeated exposure has become possible. We recently constructed biokinetic/toxicodynamic models (BK/TD) to analyze such data (Teng et al., 2015) for three hepatotoxic cosmetic ingredients: coumarin, isoeugenol and benzophenone-2. In the present study, we aim to apply these models to analyze the dynamics of mixture impedance data using the concepts of concentration addition and independent action. Metabolic interactions between the mixture components were investigated, characterized and implemented in the models, as they impacted the actual cellular exposure. Indeed, cellular metabolism following mixture exposure induced a quick disappearance of the compounds from the exposure system. We showed that isoeugenol substantially decreased the metabolism of benzophenone-2, reducing the disappearance of this compound and enhancing its in vitro toxicity. Apart from this metabolic interaction, no mixtures showed any interaction, and all binary mixtures were successfully modeled by at least one model based on exposure to the individual compounds. - Highlights: • We could predict cell response over repeated exposure to mixtures of cosmetics. • Compounds acted independently on the cells. • Metabolic interactions impacted exposure concentrations to the compounds.« less

Determination of Failure Point of Asphalt-Mixture Fatigue-Test Results Using the Flow Number Method

NASA Astrophysics Data System (ADS)

Wulan, C. E. P.; Setyawan, A.; Pramesti, F. P.

2018-03-01

The failure point of the results of fatigue tests of asphalt mixtures performed in controlled stress mode is difficult to determine. However, several methods from empirical studies are available to solve this problem. The objectives of this study are to determine the fatigue failure point of the results of indirect tensile fatigue tests using the Flow Number Method and to determine the best Flow Number model for the asphalt mixtures tested. In order to achieve these goals, firstly the best asphalt mixture of three was selected based on their Marshall properties. Next, the Indirect Tensile Fatigue Test was performed on the chosen asphalt mixture. The stress-controlled fatigue tests were conducted at a temperature of 20°C and frequency of 10 Hz, with the application of three loads: 500, 600, and 700 kPa. The last step was the application of the Flow Number methods, namely the Three-Stages Model, FNest Model, Francken Model, and Stepwise Method, to the results of the fatigue tests to determine the failure point of the specimen. The chosen asphalt mixture is EVA (Ethyl Vinyl Acetate) polymer -modified asphalt mixture with 6.5% OBC (Optimum Bitumen Content). Furthermore, the result of this study shows that the failure points of the EVA-modified asphalt mixture under loads of 500, 600, and 700 kPa are 6621, 4841, and 611 for the Three-Stages Model; 4271, 3266, and 537 for the FNest Model; 3401, 2431, and 421 for the Francken Model, and 6901, 6841, and 1291 for the Stepwise Method, respectively. These different results show that the bigger the loading, the smaller the number of cycles to failure. However, the best FN results are shown by the Three-Stages Model and the Stepwise Method, which exhibit extreme increases after the constant development of accumulated strain.

Hygroscopic Behavior of Multicomponent Aerosols Involving NaCl and Dicarboxylic Acids.

PubMed

Peng, Chao; Jing, Bo; Guo, Yu-Cong; Zhang, Yun-Hong; Ge, Mao-Fa

2016-02-25

Atmospheric aerosols are usually complex mixtures of inorganic and organic compounds. The hygroscopicity of mixed particles is closely related to their chemical composition and interactions between components, which is still poorly understood. In this study, the hygroscopic properties of submicron particles composed of NaCl and dicarboxylic acids including oxalic acid (OA), malonic acid (MA), and succinic acid (SA) with various mass ratios are investigated with a hygroscopicity tandem differential mobility analyzer (HTDMA) system. Both the Zdanovskii-Stokes-Robinson (ZSR) method and extended aerosol inorganics model (E-AIM) are applied to predict the water uptake behaviors of sodium chloride/dicarboxylic acid mixtures. For NaCl/OA mixed particles, the measured growth factors were significantly lower than predictions from the model methods, indicating a change in particle composition caused by chloride depletion. The hygroscopic growth of NaCl/MA particles was well described by E-AIM, and that of NaCl/SA particles was dependent upon mixing ratio. Compared with model predictions, it was determined that water uptake of the NaCl/OA mixture could be enhanced and could be closer to the predictions by addition of levoglucosan or malonic acid, which retained water even at low relative humidity (RH), leading to inhibition of HCl evaporation during dehydration. These results demonstrate that the coexisting hygroscopic species have a strong influence on the phase state of particles, thus affecting chemical interactions between inorganic and organic compounds as well as the overall hygroscopicity of mixed particles.

Rapid and effective decontamination of chlorophenol-contaminated soil by sorption into commercial polymers: concept demonstration and process modeling.

PubMed

Tomei, M Concetta; Mosca Angelucci, Domenica; Ademollo, Nicoletta; Daugulis, Andrew J

2015-03-01

Solid phase extraction performed with commercial polymer beads to treat soil contaminated by chlorophenols (4-chlorophenol, 2,4-dichlorophenol and pentachlorophenol) as single compounds and in a mixture has been investigated in this study. Soil-water-polymer partition tests were conducted to determine the relative affinities of single compounds in soil-water and polymer-water pairs. Subsequent soil extraction tests were performed with Hytrel 8206, the polymer showing the highest affinity for the tested chlorophenols. Factors that were examined were polymer type, moisture content, and contamination level. Increased moisture content (up to 100%) improved the extraction efficiency for all three compounds. Extraction tests at this upper level of moisture content showed removal efficiencies ≥70% for all the compounds and their ternary mixture, for 24 h of contact time, which is in contrast to the weeks and months, normally required for conventional ex situ remediation processes. A dynamic model characterizing the rate and extent of decontamination was also formulated, calibrated and validated with the experimental data. The proposed model, based on the simplified approach of "lumped parameters" for the mass transfer coefficients, provided very good predictions of the experimental data for the absorptive removal of contaminants from soil at different individual solute levels. Parameters evaluated from calibration by fitting of single compound data, have been successfully applied to predict mixture data, with differences between experimental and predicted data in all cases being ≤3%. Copyright © 2014 Elsevier Ltd. All rights reserved.

The importance of accounting for larval detectability in mosquito habitat-association studies.

PubMed

Low, Matthew; Tsegaye, Admasu Tassew; Ignell, Rickard; Hill, Sharon; Elleby, Rasmus; Feltelius, Vilhelm; Hopkins, Richard

2016-05-04

Mosquito habitat-association studies are an important basis for disease control programmes and/or vector distribution models. However, studies do not explicitly account for incomplete detection during larval presence and abundance surveys, with potential for significant biases because of environmental influences on larval behaviour and sampling efficiency. Data were used from a dip-sampling study for Anopheles larvae in Ethiopia to evaluate the effect of six factors previously associated with larval sampling (riparian vegetation, direct sunshine, algae, water depth, pH and temperature) on larval presence and detectability. Comparisons were made between: (i) a presence-absence logistic regression where samples were pooled at the site level and detectability ignored, (ii) a success versus trials binomial model, and (iii) a presence-detection mixture model that separately estimated presence and detection, and fitted different explanatory variables to these estimations. Riparian vegetation was consistently highlighted as important, strongly suggesting it explains larval presence (-). However, depending on how larval detectability was estimated, the other factors showed large variations in their statistical importance. The presence-detection mixture model provided strong evidence that larval detectability was influenced by sunshine and water temperature (+), with weaker evidence for algae (+) and water depth (-). For larval presence, there was also some evidence that water depth (-) and pH (+) influenced site occupation. The number of dip-samples needed to determine if larvae were likely present at a site was condition dependent: with sunshine and warm water requiring only two dips, while cooler water and cloud cover required 11. Environmental factors influence true larval presence and larval detectability differentially when sampling in field conditions. Researchers need to be more aware of the limitations and possible biases in different analytical approaches used to associate larval presence or abundance with local environmental conditions. These effects can be disentangled using data that are routinely collected (i.e., multiple dip samples at each site) by employing a modelling approach that separates presence from detectability.

Evaluation of the COSHH Essentials Model with a Mixture of Organic Chemicals at a Medium-Sized Paint Producer

PubMed Central

Lee, Eun Gyung; Slaven, James; Bowen, Russell B.; Harper, Martin

2011-01-01

The Control of Substances Hazardous to Health (COSHH) Essentials model was evaluated using full-shift exposure measurements of five chemical components in a mixture [acetone, ethylbenzene, methyl ethyl ketone, toluene, and xylenes] at a medium-sized plant producing paint materials. Two tasks, batch-making and bucket-washing, were examined. Varying levels of control were already established in both tasks and the average exposures of individual chemicals were considerably lower than the regulatory and advisory 8-h standards. The average exposure fractions using the additive mixture formula were also less than unity (batch-making: 0.25, bucket-washing: 0.56) indicating the mixture of chemicals did not exceed the combined occupational exposure limit (OEL). The paper version of the COSHH Essentials model was used to calculate a predicted exposure range (PER) for each chemical according to different levels of control. The estimated PERs of the tested chemicals for both tasks did not show consistent agreement with exposure measurements when the comparison was made for each control method and this is believed to be because of the considerably different volatilities of the chemicals. Given the combination of health hazard and exposure potential components, the COSHH Essentials model recommended a control approach ‘special advice’ for both tasks, based on the potential reproductive hazard ascribed to toluene. This would not have been the same conclusion if some other chemical had been substituted (for example styrene, which has the same threshold limit value as toluene). Nevertheless, it was special advice, which had led to the combination of hygienic procedures in place at this plant. The probability of the combined exposure fractions exceeding unity was 0.0002 for the batch-making task indicating that the employees performing this task were most likely well protected below the OELs. Although the employees involved in the bucket-washing task had greater potential to exceed the threshold limit value of the mixture (P > 1 = 0.2375), the expected personal exposure after adjusting for the assigned protection factor for the respirators in use would be considerably lower (P > 1 = 0.0161). Thus, our findings suggested that the COSHH essentials model worked reasonably well for the volatile organic chemicals at the plant. However, it was difficult to override the reproductive hazard even though it was meant to be possible in principle. Further, it became apparent that an input of existing controls, which is not possible in the web-based model, may have allowed the model be more widely applicable. The experience of using the web-based COSHH Essentials model generated some suggestions to provide a more user-friendly tool to the model users who do not have expertise in occupational hygiene. PMID:21047985

Evaluation of the COSHH Essentials model with a mixture of organic chemicals at a medium-sized paint producer.

PubMed

Lee, Eun Gyung; Slaven, James; Bowen, Russell B; Harper, Martin

2011-01-01

The Control of Substances Hazardous to Health (COSHH) Essentials model was evaluated using full-shift exposure measurements of five chemical components in a mixture [acetone, ethylbenzene, methyl ethyl ketone, toluene, and xylenes] at a medium-sized plant producing paint materials. Two tasks, batch-making and bucket-washing, were examined. Varying levels of control were already established in both tasks and the average exposures of individual chemicals were considerably lower than the regulatory and advisory 8-h standards. The average exposure fractions using the additive mixture formula were also less than unity (batch-making: 0.25, bucket-washing: 0.56) indicating the mixture of chemicals did not exceed the combined occupational exposure limit (OEL). The paper version of the COSHH Essentials model was used to calculate a predicted exposure range (PER) for each chemical according to different levels of control. The estimated PERs of the tested chemicals for both tasks did not show consistent agreement with exposure measurements when the comparison was made for each control method and this is believed to be because of the considerably different volatilities of the chemicals. Given the combination of health hazard and exposure potential components, the COSHH Essentials model recommended a control approach 'special advice' for both tasks, based on the potential reproductive hazard ascribed to toluene. This would not have been the same conclusion if some other chemical had been substituted (for example styrene, which has the same threshold limit value as toluene). Nevertheless, it was special advice, which had led to the combination of hygienic procedures in place at this plant. The probability of the combined exposure fractions exceeding unity was 0.0002 for the batch-making task indicating that the employees performing this task were most likely well protected below the OELs. Although the employees involved in the bucket-washing task had greater potential to exceed the threshold limit value of the mixture (P > 1 = 0.2375), the expected personal exposure after adjusting for the assigned protection factor for the respirators in use would be considerably lower (P > 1 = 0.0161). Thus, our findings suggested that the COSHH essentials model worked reasonably well for the volatile organic chemicals at the plant. However, it was difficult to override the reproductive hazard even though it was meant to be possible in principle. Further, it became apparent that an input of existing controls, which is not possible in the web-based model, may have allowed the model be more widely applicable. The experience of using the web-based COSHH Essentials model generated some suggestions to provide a more user-friendly tool to the model users who do not have expertise in occupational hygiene.

Mixture models for estimating the size of a closed population when capture rates vary among individuals

USGS Publications Warehouse

Dorazio, R.M.; Royle, J. Andrew

2003-01-01

We develop a parameterization of the beta-binomial mixture that provides sensible inferences about the size of a closed population when probabilities of capture or detection vary among individuals. Three classes of mixture models (beta-binomial, logistic-normal, and latent-class) are fitted to recaptures of snowshoe hares for estimating abundance and to counts of bird species for estimating species richness. In both sets of data, rates of detection appear to vary more among individuals (animals or species) than among sampling occasions or locations. The estimates of population size and species richness are sensitive to model-specific assumptions about the latent distribution of individual rates of detection. We demonstrate using simulation experiments that conventional diagnostics for assessing model adequacy, such as deviance, cannot be relied on for selecting classes of mixture models that produce valid inferences about population size. Prior knowledge about sources of individual heterogeneity in detection rates, if available, should be used to help select among classes of mixture models that are to be used for inference.

Chemical mixtures in potable water in the U.S.

USGS Publications Warehouse

Ryker, Sarah J.

2014-01-01

In recent years, regulators have devoted increasing attention to health risks from exposure to multiple chemicals. In 1996, the US Congress directed the US Environmental Protection Agency (EPA) to study mixtures of chemicals in drinking water, with a particular focus on potential interactions affecting chemicals' joint toxicity. The task is complicated by the number of possible mixtures in drinking water and lack of toxicological data for combinations of chemicals. As one step toward risk assessment and regulation of mixtures, the EPA and the Agency for Toxic Substances and Disease Registry (ATSDR) have proposed to estimate mixtures' toxicity based on the interactions of individual component chemicals. This approach permits the use of existing toxicological data on individual chemicals, but still requires additional information on interactions between chemicals and environmental data on the public's exposure to combinations of chemicals. Large compilations of water-quality data have recently become available from federal and state agencies. This chapter demonstrates the use of these environmental data, in combination with the available toxicological data, to explore scenarios for mixture toxicity and develop priorities for future research and regulation. Occurrence data on binary and ternary mixtures of arsenic, cadmium, and manganese are used to parameterize the EPA and ATSDR models for each drinking water source in the dataset. The models' outputs are then mapped at county scale to illustrate the implications of the proposed models for risk assessment and rulemaking. For example, according to the EPA's interaction model, the levels of arsenic and cadmium found in US groundwater are unlikely to have synergistic cardiovascular effects in most areas of the country, but the same mixture's potential for synergistic neurological effects merits further study. Similar analysis could, in future, be used to explore the implications of alternative risk models for the toxicity and interaction of complex mixtures, and to identify the communities with the highest and lowest expected value for regulation of chemical mixtures.

Nonnegative matrix factorization: a blind sources separation method to extract content of fluorophores mixture media

NASA Astrophysics Data System (ADS)

Zhou, Kenneth J.; Chen, Jun

2014-03-01

The fluorophores of malignant human breast cells change their compositions that may be exposed in the fluorescence spectroscopy and blind source separation method. The content of the fluorophores mixture media such as tryptophan, collagen, elastin, NADH, and flavin were varied according to the cancer development. The native fluorescence spectra of these key fluorophores mixture media excited by the selective excitation wavelengths of 300 nm and 340 nm were analyzed using a blind source separation method: Nonnegative Matrix Factorization (NMF). The results show that the contribution from tryptophan, NADH and flavin to the fluorescence spectra of the mixture media is proportional to the content of each fluorophore. These data present a possibility that native fluorescence spectra decomposed by NMF can be used as potential native biomarkers for cancer detection evaluation of the cancer.

Comparison of Bovine Bone-Autogenic Bone Mixture Versus Platelet-Rich Fibrin for Maxillary Sinus Grafting: Histologic and Histomorphologic Study.

PubMed

Ocak, Hakan; Kutuk, Nukhet; Demetoglu, Umut; Balcıoglu, Esra; Ozdamar, Saim; Alkan, Alper

2017-06-01

Numerous grafting materials have been used to augment the maxillary sinus floor for long-term stability and success for implant-supported prosthesis. To enhance bone formation, adjunctive blood-born growth factor sources have gained popularity during the recent years. The present study compared the use of platelet-rich fibrin (PRF) and bovine-autogenous bone mixture for maxillary sinus floor elevation. A split-face model was used to apply 2 different filling materials for maxillary sinus floor elevation in 22 healthy adult sheep. In group 1, bovine and autogenous bone mixture; and in group 2, PRF was used. The animals were killed at 3, 6, and 9 months. Histologic and histomorphologic examinations revealed new bone formation in group 1 at the third and sixth months. In group 2, new bone formation was observed only at the sixth month, and residual PRF remnants were identified. At the ninth month, host bone and new bone could not be distinguished from each other in group 1, and bone formation was found to be proceeding in group 2. PRF remnants still existed at the ninth month. In conclusion, bovine bone and autogenous bone mixture is superior to PRF as a grafting material in sinus-lifting procedures.

Testing and Improving Theories of Radiative Transfer for Determining the Mineralogy of Planetary Surfaces

NASA Astrophysics Data System (ADS)

Gudmundsson, E.; Ehlmann, B. L.; Mustard, J. F.; Hiroi, T.; Poulet, F.

2012-12-01

Two radiative transfer theories, the Hapke and Shkuratov models, have been used to estimate the mineralogic composition of laboratory mixtures of anhydrous mafic minerals from reflected near-infrared light, accurately modeling abundances to within 10%. For this project, we tested the efficacy of the Hapke model for determining the composition of mixtures (weight fraction, particle diameter) containing hydrous minerals, including phyllosilicates. Modal mineral abundances for some binary mixtures were modeled to +/-10% of actual values, but other mixtures showed higher inaccuracies (up to 25%). Consequently, a sensitivity analysis of selected input and model parameters was performed. We first examined the shape of the model's error function (RMS error between modeled and measured spectra) over a large range of endmember weight fractions and particle diameters and found that there was a single global minimum for each mixture (rather than local minima). The minimum was sensitive to modeled particle diameter but comparatively insensitive to modeled endmember weight fraction. Derivation of the endmembers' k optical constant spectra using the Hapke model showed differences with the Shkuratov-derived optical constants originally used. Model runs with different sets of optical constants suggest that slight differences in the optical constants used significantly affect the accuracy of model predictions. Even for mixtures where abundance was modeled correctly, particle diameter agreed inconsistently with sieved particle sizes and varied greatly for individual mix within suite. Particle diameter was highly sensitive to the optical constants, possibly indicating that changes in modeled path length (proportional to particle diameter) compensate for changes in the k optical constant. Alternatively, it may not be appropriate to model path length and particle diameter with the same proportionality for all materials. Across mixtures, RMS error increased in proportion to the fraction of the darker endmember. Analyses are ongoing and further studies will investigate the effect of sample hydration, permitted variability in particle size, assumed photometric functions and use of different wavelength ranges on model results. Such studies will advance understanding of how to best apply radiative transfer modeling to geologically complex planetary surfaces. Corresponding authors: eyjolfur88@gmail.com, ehlmann@caltech.edu

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

Grove, John W.

We investigate sufficient conditions for thermodynamic consistency for equilibrium mixtures. Such models assume that the mass fraction average of the material component equations of state, when closed by a suitable equilibrium condition, provide a composite equation of state for the mixture. Here, we show that the two common equilibrium models of component pressure/temperature equilibrium and volume/temperature equilibrium (Dalton, 1808) define thermodynamically consistent mixture equations of state and that other equilibrium conditions can be thermodynamically consistent provided appropriate values are used for the mixture specific entropy and pressure.

Fate and transport of phenol in a packed bed reactor containing simulated solid waste

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

Saquing, Jovita M., E-mail: jmsaquing@gmail.com; Knappe, Detlef R.U., E-mail: knappe@ncsu.edu; Barlaz, Morton A., E-mail: barlaz@ncsu.edu

Highlights: Black-Right-Pointing-Pointer Anaerobic column experiments were conducted at 37 Degree-Sign C using a simulated waste mixture. Black-Right-Pointing-Pointer Sorption and biodegradation model parameters were determined from batch tests. Black-Right-Pointing-Pointer HYDRUS simulated well the fate and transport of phenol in a fully saturated waste column. Black-Right-Pointing-Pointer The batch biodegradation rate and the rate obtained by inverse modeling differed by a factor of {approx}2. Black-Right-Pointing-Pointer Tracer tests showed the importance of hydrodynamic parameters to improve model estimates. - Abstract: An assessment of the risk to human health and the environment associated with the presence of organic contaminants (OCs) in landfills necessitates reliable predictivemore » models. The overall objectives of this study were to (1) conduct column experiments to measure the fate and transport of an OC in a simulated solid waste mixture, (2) compare the results of column experiments to model predictions using HYDRUS-1D (version 4.13), a contaminant fate and transport model that can be parameterized to simulate the laboratory experimental system, and (3) determine model input parameters from independently conducted batch experiments. Experiments were conducted in which sorption only and sorption plus biodegradation influenced OC transport. HYDRUS-1D can reasonably simulate the fate and transport of phenol in an anaerobic and fully saturated waste column in which biodegradation and sorption are the prevailing fate processes. The agreement between model predictions and column data was imperfect (i.e., within a factor of two) for the sorption plus biodegradation test and the error almost certainly lies in the difficulty of measuring a biodegradation rate that is applicable to the column conditions. Nevertheless, a biodegradation rate estimate that is within a factor of two or even five may be adequate in the context of a landfill, given the extended retention time and the fact that leachate release will be controlled by the infiltration rate which can be minimized by engineering controls.« less

Estimating and modeling the cure fraction in population-based cancer survival analysis.

PubMed

Lambert, Paul C; Thompson, John R; Weston, Claire L; Dickman, Paul W

2007-07-01

In population-based cancer studies, cure is said to occur when the mortality (hazard) rate in the diseased group of individuals returns to the same level as that expected in the general population. The cure fraction (the proportion of patients cured of disease) is of interest to patients and is a useful measure to monitor trends in survival of curable disease. There are 2 main types of cure fraction model, the mixture cure fraction model and the non-mixture cure fraction model, with most previous work concentrating on the mixture cure fraction model. In this paper, we extend the parametric non-mixture cure fraction model to incorporate background mortality, thus providing estimates of the cure fraction in population-based cancer studies. We compare the estimates of relative survival and the cure fraction between the 2 types of model and also investigate the importance of modeling the ancillary parameters in the selected parametric distribution for both types of model.

Process dissociation and mixture signal detection theory.

PubMed

DeCarlo, Lawrence T

2008-11-01

The process dissociation procedure was developed in an attempt to separate different processes involved in memory tasks. The procedure naturally lends itself to a formulation within a class of mixture signal detection models. The dual process model is shown to be a special case. The mixture signal detection model is applied to data from a widely analyzed study. The results suggest that a process other than recollection may be involved in the process dissociation procedure.

Statistical-thermodynamic model for light scattering from eye lens protein mixtures

NASA Astrophysics Data System (ADS)

Bell, Michael M.; Ross, David S.; Bautista, Maurino P.; Shahmohamad, Hossein; Langner, Andreas; Hamilton, John F.; Lahnovych, Carrie N.; Thurston, George M.

2017-02-01

We model light-scattering cross sections of concentrated aqueous mixtures of the bovine eye lens proteins γB- and α-crystallin by adapting a statistical-thermodynamic model of mixtures of spheres with short-range attractions. The model reproduces measured static light scattering cross sections, or Rayleigh ratios, of γB-α mixtures from dilute concentrations where light scattering intensity depends on molecular weights and virial coefficients, to realistically high concentration protein mixtures like those of the lens. The model relates γB-γB and γB-α attraction strengths and the γB-α size ratio to the free energy curvatures that set light scattering efficiency in tandem with protein refractive index increments. The model includes (i) hard-sphere α-α interactions, which create short-range order and transparency at high protein concentrations, (ii) short-range attractive plus hard-core γ-γ interactions, which produce intense light scattering and liquid-liquid phase separation in aqueous γ-crystallin solutions, and (iii) short-range attractive plus hard-core γ-α interactions, which strongly influence highly non-additive light scattering and phase separation in concentrated γ-α mixtures. The model reveals a new lens transparency mechanism, that prominent equilibrium composition fluctuations can be perpendicular to the refractive index gradient. The model reproduces the concave-up dependence of the Rayleigh ratio on α/γ composition at high concentrations, its concave-down nature at intermediate concentrations, non-monotonic dependence of light scattering on γ-α attraction strength, and more intricate, temperature-dependent features. We analytically compute the mixed virial series for light scattering efficiency through third order for the sticky-sphere mixture, and find that the full model represents the available light scattering data at concentrations several times those where the second and third mixed virial contributions fail. The model indicates that increased γ-γ attraction can raise γ-α mixture light scattering far more than it does for solutions of γ-crystallin alone, and can produce marked turbidity tens of degrees celsius above liquid-liquid separation.

Additive mixture effects of estrogenic chemicals in human cell-based assays can be influenced by inclusion of chemicals with differing effect profiles.

PubMed

Evans, Richard Mark; Scholze, Martin; Kortenkamp, Andreas

2012-01-01

A growing body of experimental evidence indicates that the in vitro effects of mixtures of estrogenic chemicals can be well predicted from the estrogenicity of their components by the concentration addition (CA) concept. However, some studies have observed small deviations from CA. Factors affecting the presence or observation of deviations could include: the type of chemical tested; number of mixture components; mixture design; and assay choice. We designed mixture experiments that address these factors, using mixtures with high numbers of components, chemicals from diverse chemical groups, assays with different in vitro endpoints and different mixture designs and ratios. Firstly, the effects of mixtures composed of up to 17 estrogenic chemicals were examined using estrogenicity assays with reporter-gene (ERLUX) and cell proliferation (ESCREEN) endpoints. Two mixture designs were used: 1) a 'balanced' design with components present in proportion to a common effect concentration (e.g. an EC(10)) and 2) a 'non-balanced' design with components in proportion to potential human tissue concentrations. Secondly, the individual and simultaneous ability of 16 potential modulator chemicals (each with minimal estrogenicity) to influence the assay outcome produced by a reference mixture of estrogenic chemicals was examined. Test chemicals included plasticizers, phthalates, metals, PCBs, phytoestrogens, PAHs, heterocyclic amines, antioxidants, UV filters, musks, PBDEs and parabens. In all the scenarios tested, the CA concept provided a good prediction of mixture effects. Modulation studies revealed that chemicals possessing minimal estrogenicity themselves could reduce (negatively modulate) the effect of a mixture of estrogenic chemicals. Whether the type of modulation we observed occurs in practice most likely depends on the chemical concentrations involved, and better information is required on likely human tissue concentrations of estrogens and of potential modulators. Successful prediction of the effects of diverse chemical combinations might be more likely if chemical profiling included consideration of effect modulation.

Additive Mixture Effects of Estrogenic Chemicals in Human Cell-Based Assays Can Be Influenced by Inclusion of Chemicals with Differing Effect Profiles

PubMed Central

Evans, Richard Mark; Scholze, Martin; Kortenkamp, Andreas

2012-01-01

A growing body of experimental evidence indicates that the in vitro effects of mixtures of estrogenic chemicals can be well predicted from the estrogenicity of their components by the concentration addition (CA) concept. However, some studies have observed small deviations from CA. Factors affecting the presence or observation of deviations could include: the type of chemical tested; number of mixture components; mixture design; and assay choice. We designed mixture experiments that address these factors, using mixtures with high numbers of components, chemicals from diverse chemical groups, assays with different in vitro endpoints and different mixture designs and ratios. Firstly, the effects of mixtures composed of up to 17 estrogenic chemicals were examined using estrogenicity assays with reporter-gene (ERLUX) and cell proliferation (ESCREEN) endpoints. Two mixture designs were used: 1) a ‘balanced’ design with components present in proportion to a common effect concentration (e.g. an EC10) and 2) a ‘non-balanced’ design with components in proportion to potential human tissue concentrations. Secondly, the individual and simultaneous ability of 16 potential modulator chemicals (each with minimal estrogenicity) to influence the assay outcome produced by a reference mixture of estrogenic chemicals was examined. Test chemicals included plasticizers, phthalates, metals, PCBs, phytoestrogens, PAHs, heterocyclic amines, antioxidants, UV filters, musks, PBDEs and parabens. In all the scenarios tested, the CA concept provided a good prediction of mixture effects. Modulation studies revealed that chemicals possessing minimal estrogenicity themselves could reduce (negatively modulate) the effect of a mixture of estrogenic chemicals. Whether the type of modulation we observed occurs in practice most likely depends on the chemical concentrations involved, and better information is required on likely human tissue concentrations of estrogens and of potential modulators. Successful prediction of the effects of diverse chemical combinations might be more likely if chemical profiling included consideration of effect modulation. PMID:22912892

Modeling parenting stress trajectories among low-income young mothers across the child's second and third years: Factors accounting for stability and change.

PubMed

Chang, Yiting; Fine, Mark A

2007-12-01

This study investigated parenting stress trajectories among low-income young mothers and the factors that are associated with change and stability of parenting stress as children aged from 14 to 36 months old. With a sample of 580 young mothers who applied to the Early Head Start Program, growth mixture modeling identified 3 trajectory classes of parenting stress: a chronically high group (7% of the sample), an increasing group (10% of the sample), and a decreasing group (83% of the sample). Maternal personal resources distinguished between the increasing and decreasing classes, whereas maternal personal resources, child characteristics, and contextual influences explained differences between the chronically high and decreasing trajectory classes. Findings suggest that for interventions to be effective, programs need to assess maternal, child, and contextual factors to better address the particular unique needs of young mothers.

Toxicity interactions between manganese (Mn) and lead (Pb) or cadmium (Cd) in a model organism the nematode C. elegans.

PubMed

Lu, Cailing; Svoboda, Kurt R; Lenz, Kade A; Pattison, Claire; Ma, Hongbo

2018-06-01

Manganese (Mn) is considered as an emerging metal contaminant in the environment. However, its potential interactions with companying toxic metals and the associated mixture effects are largely unknown. Here, we investigated the toxicity interactions between Mn and two commonly seen co-occurring toxic metals, Pb and Cd, in a model organism the nematode Caenorhabditis elegans. The acute lethal toxicity of mixtures of Mn+Pb and Mn+Cd were first assessed using a toxic unit model. Multiple toxicity endpoints including reproduction, lifespan, stress response, and neurotoxicity were then examined to evaluate the mixture effects at sublethal concentrations. Stress response was assessed using a daf-16::GFP transgenic strain that expresses GFP under the control of DAF-16 promotor. Neurotoxicity was assessed using a dat-1::GFP transgenic strain that expresses GFP in dopaminergic neurons. The mixture of Mn+Pb induced a more-than-additive (synergistic) lethal toxicity in the worm whereas the mixture of Mn+Cd induced a less-than-additive (antagonistic) toxicity. Mixture effects on sublethal toxicity showed more complex patterns and were dependent on the toxicity endpoints as well as the modes of toxic action of the metals. The mixture of Mn+Pb induced additive effects on both reproduction and lifespan, whereas the mixture of Mn+Cd induced additive effects on lifespan but not reproduction. Both mixtures seemed to induce additive effects on stress response and neurotoxicity, although a quantitative assessment was not possible due to the single concentrations used in mixture tests. Our findings demonstrate the complexity of metal interactions and the associated mixture effects. Assessment of metal mixture toxicity should take into consideration the unique property of individual metals, their potential toxicity mechanisms, and the toxicity endpoints examined.

Communication: Modeling electrolyte mixtures with concentration dependent dielectric permittivity

NASA Astrophysics Data System (ADS)

Chen, Hsieh; Panagiotopoulos, Athanassios Z.

2018-01-01

We report a new implicit-solvent simulation model for electrolyte mixtures based on the concept of concentration dependent dielectric permittivity. A combining rule is found to predict the dielectric permittivity of electrolyte mixtures based on the experimentally measured dielectric permittivity for pure electrolytes as well as the mole fractions of the electrolytes in mixtures. Using grand canonical Monte Carlo simulations, we demonstrate that this approach allows us to accurately reproduce the mean ionic activity coefficients of NaCl in NaCl-CaCl2 mixtures at ionic strengths up to I = 3M. These results are important for thermodynamic studies of geologically relevant brines and physiological fluids.

3-D Numerical Simulation for Gas-Liquid Two-Phase Flow in Aeration Tank

NASA Astrophysics Data System (ADS)

Xue, R.; Tian, R.; Yan, S. Y.; Li, S.

In the crafts of activated sludge treatment, oxygen supply and the suspending state of activated sludge are primary factors to keep biochemistry process carrying on normally. However, they are all controlled by aeration. So aeration is crucial. The paper focus on aeration, use CFD software to simulate the field of aeration tank which is designed by sludge load method. The main designed size of aeration tank is: total volume: 20 000 m3; corridor width: 8m; total length of corridors: 139m; number of corridors: 3; length of one single corridor: 48m; effective depth: 4.5m; additional depth: 0.5m. According to the similarity theory, a geometrical model is set up in proportion of 10:1. The way of liquid flow is submerge to avoid liquid flow out directly. The grid is plotted by dividing the whole computational area into two parts. The bottom part which contains gas pipe and gas exit hole and the above part which is the main area are plotted by tetrahedron and hexahedron respectively. In boundary conditions, gas is defined as the primary-phase, and liquid is defined as the secondary-phase. Choosing mixture model, two-phase flow field of aeration tank is simulated by solved the Continuity equation for the mixture, Momentum equation for the mixture, Volume fraction equation for the secondary phases and Relative velocity formula when gas velocity is 10m/s, 20m/s, 30m/s. what figure shows is the contour of velocity magnitude for the mixture phase when gas velocity is 20m/s. Through analysis, the simulation tendency is agreed with actual running of aeration tank. It is feasible to use mixture model to simulate flow field of aeration tank by fluent software. According to the simulation result, the better velocity of liquid or gas (the quantity of inlet air) can be chosen by lower cost, and also the performance of aeration tank can be forecast. It will be helpful for designing and operation.

Investigating the size, shape and surface roughness dependence of polarization lidars with light-scattering computations on real mineral dust particles: Application to dust particles' external mixtures and dust mass concentration retrievals

NASA Astrophysics Data System (ADS)

Mehri, Tahar; Kemppinen, Osku; David, Grégory; Lindqvist, Hannakaisa; Tyynelä, Jani; Nousiainen, Timo; Rairoux, Patrick; Miffre, Alain

2018-05-01

Our understanding of the contribution of mineral dust to the Earth's radiative budget is limited by the complexity of these particles, which present a wide range of sizes, are highly-irregularly shaped, and are present in the atmosphere in the form of particle mixtures. To address the spatial distribution of mineral dust and atmospheric dust mass concentrations, polarization lidars are nowadays frequently used, with partitioning algorithms allowing to discern the contribution of mineral dust in two or three-component particle external mixtures. In this paper, we investigate the dependence of the retrieved dust backscattering (βd) vertical profiles with the dust particle size and shape. For that, new light-scattering numerical simulations are performed on real atmospheric mineral dust particles, having determined mineralogy (CAL, DOL, AGG, SIL), derived from stereogrammetry (stereo-particles), with potential surface roughness, which are compared to the widely-used spheroidal mathematical shape model. For each dust shape model (smooth stereo-particles, rough stereo-particles, spheroids), the dust depolarization, backscattering Ångström exponent, lidar ratio are computed for two size distributions representative of mineral dust after long-range transport. As an output, two Saharan dust outbreaks involving mineral dust in two, then three-component particle mixtures are studied with Lyon (France) UV-VIS polarization lidar. If the dust size matters most, under certain circumstances, βd can vary by approximately 67% when real dust stereo-particles are used instead of spheroids, corresponding to variations in the dust backscattering coefficient as large as 2 Mm- 1·sr- 1. Moreover, the influence of surface roughness in polarization lidar retrievals is for the first time discussed. Finally, dust mass-extinction conversion factors (ηd) are evaluated for each assigned shape model and dust mass concentrations are retrieved from polarization lidar measurements. From spheroids to stereo-particles, ηd increases by about 30%. We believe these results may be useful for our understanding of the spatial distribution of mineral dust contained in an aerosol external mixture and to better quantify dust mass concentrations from polarization lidar experiments.

Mixture effects in samples of multiple contaminants - An inter-laboratory study with manifold bioassays.

PubMed

Altenburger, Rolf; Scholze, Martin; Busch, Wibke; Escher, Beate I; Jakobs, Gianina; Krauss, Martin; Krüger, Janet; Neale, Peta A; Ait-Aissa, Selim; Almeida, Ana Catarina; Seiler, Thomas-Benjamin; Brion, François; Hilscherová, Klára; Hollert, Henner; Novák, Jiří; Schlichting, Rita; Serra, Hélène; Shao, Ying; Tindall, Andrew; Tolefsen, Knut-Erik; Umbuzeiro, Gisela; Williams, Tim D; Kortenkamp, Andreas

2018-05-01

Chemicals in the environment occur in mixtures rather than as individual entities. Environmental quality monitoring thus faces the challenge to comprehensively assess a multitude of contaminants and potential adverse effects. Effect-based methods have been suggested as complements to chemical analytical characterisation of complex pollution patterns. The regularly observed discrepancy between chemical and biological assessments of adverse effects due to contaminants in the field may be either due to unidentified contaminants or result from interactions of compounds in mixtures. Here, we present an interlaboratory study where individual compounds and their mixtures were investigated by extensive concentration-effect analysis using 19 different bioassays. The assay panel consisted of 5 whole organism assays measuring apical effects and 14 cell- and organism-based bioassays with more specific effect observations. Twelve organic water pollutants of diverse structure and unique known modes of action were studied individually and as mixtures mirroring exposure scenarios in freshwaters. We compared the observed mixture effects against component-based mixture effect predictions derived from additivity expectations (assumption of non-interaction). Most of the assays detected the mixture response of the active components as predicted even against a background of other inactive contaminants. When none of the mixture components showed any activity by themselves then the mixture also was without effects. The mixture effects observed using apical endpoints fell in the middle of a prediction window defined by the additivity predictions for concentration addition and independent action, reflecting well the diversity of the anticipated modes of action. In one case, an unexpectedly reduced solubility of one of the mixture components led to mixture responses that fell short of the predictions of both additivity mixture models. The majority of the specific cell- and organism-based endpoints produced mixture responses in agreement with the additivity expectation of concentration addition. Exceptionally, expected (additive) mixture response did not occur due to masking effects such as general toxicity from other compounds. Generally, deviations from an additivity expectation could be explained due to experimental factors, specific limitations of the effect endpoint or masking side effects such as cytotoxicity in in vitro assays. The majority of bioassays were able to quantitatively detect the predicted non-interactive, additive combined effect of the specifically bioactive compounds against a background of complex mixture of other chemicals in the sample. This supports the use of a combination of chemical and bioanalytical monitoring tools for the identification of chemicals that drive a specific mixture effect. Furthermore, we demonstrated that a panel of bioassays can provide a diverse profile of effect responses to a complex contaminated sample. This could be extended towards representing mixture adverse outcome pathways. Our findings support the ongoing development of bioanalytical tools for (i) compiling comprehensive effect-based batteries for water quality assessment, (ii) designing tailored surveillance methods to safeguard specific water uses, and (iii) devising strategies for effect-based diagnosis of complex contamination. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Mixture IRT Model with a Higher-Order Structure for Latent Traits

ERIC Educational Resources Information Center

Huang, Hung-Yu

2017-01-01

Mixture item response theory (IRT) models have been suggested as an efficient method of detecting the different response patterns derived from latent classes when developing a test. In testing situations, multiple latent traits measured by a battery of tests can exhibit a higher-order structure, and mixtures of latent classes may occur on…

Fast mix table construction for material discretization

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

Johnson, S. R.

2013-07-01

An effective hybrid Monte Carlo-deterministic implementation typically requires the approximation of a continuous geometry description with a discretized piecewise-constant material field. The inherent geometry discretization error can be reduced somewhat by using material mixing, where multiple materials inside a discrete mesh voxel are homogenized. Material mixing requires the construction of a 'mix table,' which stores the volume fractions in every mixture so that multiple voxels with similar compositions can reference the same mixture. Mix table construction is a potentially expensive serial operation for large problems with many materials and voxels. We formulate an efficient algorithm to construct a sparse mixmore » table in O(number of voxels x log number of mixtures) time. The new algorithm is implemented in ADVANTG and used to discretize continuous geometries onto a structured Cartesian grid. When applied to an end-of-life MCNP model of the High Flux Isotope Reactor with 270 distinct materials, the new method improves the material mixing time by a factor of 100 compared to a naive mix table implementation. (authors)« less

Fast Mix Table Construction for Material Discretization

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

Johnson, Seth R

2013-01-01

An effective hybrid Monte Carlo--deterministic implementation typically requires the approximation of a continuous geometry description with a discretized piecewise-constant material field. The inherent geometry discretization error can be reduced somewhat by using material mixing, where multiple materials inside a discrete mesh voxel are homogenized. Material mixing requires the construction of a ``mix table,'' which stores the volume fractions in every mixture so that multiple voxels with similar compositions can reference the same mixture. Mix table construction is a potentially expensive serial operation for large problems with many materials and voxels. We formulate an efficient algorithm to construct a sparse mix table inmore » $$O(\\text{number of voxels}\\times \\log \\text{number of mixtures})$$ time. The new algorithm is implemented in ADVANTG and used to discretize continuous geometries onto a structured Cartesian grid. When applied to an end-of-life MCNP model of the High Flux Isotope Reactor with 270 distinct materials, the new method improves the material mixing time by a factor of 100 compared to a naive mix table implementation.« less

Beta Regression Finite Mixture Models of Polarization and Priming

ERIC Educational Resources Information Center

Smithson, Michael; Merkle, Edgar C.; Verkuilen, Jay

2011-01-01

This paper describes the application of finite-mixture general linear models based on the beta distribution to modeling response styles, polarization, anchoring, and priming effects in probability judgments. These models, in turn, enhance our capacity for explicitly testing models and theories regarding the aforementioned phenomena. The mixture…

Free fatty acids chain length distribution affects the permeability of skin lipid model membranes.

PubMed

Uchiyama, Masayuki; Oguri, Masashi; Mojumdar, Enamul H; Gooris, Gert S; Bouwstra, Joke A

2016-09-01

The lipid matrix in the stratum corneum (SC) plays an important role in the barrier function of the skin. The main lipid classes in this lipid matrix are ceramides (CERs), cholesterol (CHOL) and free fatty acids (FFAs). The aim of this study was to determine whether a variation in CER subclass composition and chain length distribution of FFAs affect the permeability of this matrix. To examine this, we make use of lipid model membranes, referred to as stratum corneum substitute (SCS). We prepared SCS containing i) single CER subclass with either a single FFA or a mixture of FFAs and CHOL, or ii) a mixture of various CER subclasses with either a single FFA or a mixture of FFAs and CHOL. In vitro permeation studies were performed using ethyl-p-aminobenzoic acid (E-PABA) as a model drug. The flux of E-PABA across the SCS containing the mixture of FFAs was higher than that across the SCS containing a single FA with a chain length of 24 C atoms (FA C24), while the E-PABA flux was not effected by the CER composition. To select the underlying factors for the changes in permeability, the SCSs were examined by Fourier transform infrared spectroscopy (FTIR) and Small angle X-ray scattering (SAXS). All lipid models demonstrated a similar phase behavior. However, when focusing on the conformational ordering of the individual FFA chains, the shorter chain FFA (with a chain length of 16, 18 or 20 C atoms forming only 11m/m% of the total FFA level) had a higher conformational disordering, while the conformational ordering of the chains of the CER and FA C24 and FA C22 hardly did not change irrespective of the composition of the SCS. In conclusion, the conformational mobility of the short chain FFAs present only at low levels in the model SC lipid membranes has a great impact on the permeability of E-PABA. Copyright © 2016 Elsevier B.V. All rights reserved.

Predicting mixture toxicity of seven phenolic compounds with similar and dissimilar action mechanisms to Vibrio qinghaiensis sp.nov.Q67.

PubMed

Huang, Wei Ying; Liu, Fei; Liu, Shu Shen; Ge, Hui Lin; Chen, Hong Han

2011-09-01

The predictions of mixture toxicity for chemicals are commonly based on two models: concentration addition (CA) and independent action (IA). Whether the CA and IA can predict mixture toxicity of phenolic compounds with similar and dissimilar action mechanisms was studied. The mixture toxicity was predicted on the basis of the concentration-response data of individual compounds. Test mixtures at different concentration ratios and concentration levels were designed using two methods. The results showed that the Weibull function fit well with the concentration-response data of all the components and their mixtures, with all relative coefficients (Rs) greater than 0.99 and root mean squared errors (RMSEs) less than 0.04. The predicted values from CA and IA models conformed to observed values of the mixtures. Therefore, it can be concluded that both CA and IA can predict reliable results for the mixture toxicity of the phenolic compounds with similar and dissimilar action mechanisms. Copyright © 2011 Elsevier Inc. All rights reserved.

I. Cognitive and Instructional Factors Relating to Students' Development of Personal Models of Chemical Systems in the General Chemistry Laboratory II. Solvation in Supercritical Carbon Dioxide/Ethanol Mixtures Studied by Molecular Dynamics Simulation

ERIC Educational Resources Information Center

Anthony, Seth

2014-01-01

Part I: Students' participation in inquiry-based chemistry laboratory curricula, and, in particular, engagement with key thinking processes in conjunction with these experiences, is linked with success at the difficult task of "transfer"--applying their knowledge in new contexts to solve unfamiliar types of problems. We investigate…

Computational Modeling of Supercritical and Transcritical Flows

DTIC Science & Technology

2017-01-09

Acentric factor I. Introduction Liquid rocket and gas turbine engines operate at high pressures. For gas turbines, the combustor pressurecan be 60 − 100...an approach the liquid gas interface is tracked.4 We note that an overwhelming majority of the computational studies have similarly focused on purely...A standard approach for supercritical flows is to treat the multicomponent mixture of species as a dense fluid using a real gas equation of state such

Mixture optimization for mixed gas Joule-Thomson cycle

NASA Astrophysics Data System (ADS)

Detlor, J.; Pfotenhauer, J.; Nellis, G.

2017-12-01

An appropriate gas mixture can provide lower temperatures and higher cooling power when used in a Joule-Thomson (JT) cycle than is possible with a pure fluid. However, selecting gas mixtures to meet specific cooling loads and cycle parameters is a challenging design problem. This study focuses on the development of a computational tool to optimize gas mixture compositions for specific operating parameters. This study expands on prior research by exploring higher heat rejection temperatures and lower pressure ratios. A mixture optimization model has been developed which determines an optimal three-component mixture based on the analysis of the maximum value of the minimum value of isothermal enthalpy change, ΔhT , that occurs over the temperature range. This allows optimal mixture compositions to be determined for a mixed gas JT system with load temperatures down to 110 K and supply temperatures above room temperature for pressure ratios as small as 3:1. The mixture optimization model has been paired with a separate evaluation of the percent of the heat exchanger that exists in a two-phase range in order to begin the process of selecting a mixture for experimental investigation.

Existence, uniqueness and positivity of solutions for BGK models for mixtures

NASA Astrophysics Data System (ADS)

Klingenberg, C.; Pirner, M.

2018-01-01

We consider kinetic models for a multi component gas mixture without chemical reactions. In the literature, one can find two types of BGK models in order to describe gas mixtures. One type has a sum of BGK type interaction terms in the relaxation operator, for example the model described by Klingenberg, Pirner and Puppo [20] which contains well-known models of physicists and engineers for example Hamel [16] and Gross and Krook [15] as special cases. The other type contains only one collision term on the right-hand side, for example the well-known model of Andries, Aoki and Perthame [1]. For each of these two models [20] and [1], we prove existence, uniqueness and positivity of solutions in the first part of the paper. In the second part, we use the first model [20] in order to determine an unknown function in the energy exchange of the macroscopic equations for gas mixtures described by Dellacherie [11].

Analysis of real-time mixture cytotoxicity data following repeated exposure using BK/TD models.

PubMed

Teng, S; Tebby, C; Barcellini-Couget, S; De Sousa, G; Brochot, C; Rahmani, R; Pery, A R R

2016-08-15

Cosmetic products generally consist of multiple ingredients. Thus, cosmetic risk assessment has to deal with mixture toxicity on a long-term scale which means it has to be assessed in the context of repeated exposure. Given that animal testing has been banned for cosmetics risk assessment, in vitro assays allowing long-term repeated exposure and adapted for in vitro - in vivo extrapolation need to be developed. However, most in vitro tests only assess short-term effects and consider static endpoints which hinder extrapolation to realistic human exposure scenarios where concentration in target organs is varies over time. Thanks to impedance metrics, real-time cell viability monitoring for repeated exposure has become possible. We recently constructed biokinetic/toxicodynamic models (BK/TD) to analyze such data (Teng et al., 2015) for three hepatotoxic cosmetic ingredients: coumarin, isoeugenol and benzophenone-2. In the present study, we aim to apply these models to analyze the dynamics of mixture impedance data using the concepts of concentration addition and independent action. Metabolic interactions between the mixture components were investigated, characterized and implemented in the models, as they impacted the actual cellular exposure. Indeed, cellular metabolism following mixture exposure induced a quick disappearance of the compounds from the exposure system. We showed that isoeugenol substantially decreased the metabolism of benzophenone-2, reducing the disappearance of this compound and enhancing its in vitro toxicity. Apart from this metabolic interaction, no mixtures showed any interaction, and all binary mixtures were successfully modeled by at least one model based on exposure to the individual compounds. Copyright © 2016 Elsevier Inc. All rights reserved.

Protective effect of bovine milk against HCl and ethanol-induced gastric ulcer in mice.

PubMed

Yoo, Jeong-Hyun; Lee, Jeong-Sang; Lee, You-Suk; Ku, SaeKwang; Lee, Hae-Jeung

2018-05-01

The purpose of this study was to investigate the gastroprotective effects of bovine milk on an acidified ethanol (HCl-ethanol) mixture that induced gastric ulcers in a mouse model. Mice received different doses of commercial fresh bovine milk (5, 10, and 20 mL/kg of body weight) by oral gavage once a day for 14 d. One hour after the last oral administration of bovine milk, the HCl-ethanol mixture was orally intubated to provoke severe gastric damage. Our results showed that pretreatment with bovine milk significantly suppressed the formation of gastric mucosa lesions. Pretreatment lowered gastric myeloperoxidase and increased gastric mucus contents and antioxidant enzymes catalase and superoxide dismutase. Administration of bovine milk increased nitrate/nitrite levels and decreased the malondialdehyde levels and the expression of proinflammatory genes, including transcription factor nuclear factor-κB, cyclooxygenase-2, and inducible nitric oxide synthase in the stomach of mice. These results suggest that bovine milk can prevent the development of gastric ulcer caused by acid and alcohol in mice. Copyright © 2018 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Nonparametric Fine Tuning of Mixtures: Application to Non-Life Insurance Claims Distribution Estimation

NASA Astrophysics Data System (ADS)

Sardet, Laure; Patilea, Valentin

When pricing a specific insurance premium, actuary needs to evaluate the claims cost distribution for the warranty. Traditional actuarial methods use parametric specifications to model claims distribution, like lognormal, Weibull and Pareto laws. Mixtures of such distributions allow to improve the flexibility of the parametric approach and seem to be quite well-adapted to capture the skewness, the long tails as well as the unobserved heterogeneity among the claims. In this paper, instead of looking for a finely tuned mixture with many components, we choose a parsimonious mixture modeling, typically a two or three-component mixture. Next, we use the mixture cumulative distribution function (CDF) to transform data into the unit interval where we apply a beta-kernel smoothing procedure. A bandwidth rule adapted to our methodology is proposed. Finally, the beta-kernel density estimate is back-transformed to recover an estimate of the original claims density. The beta-kernel smoothing provides an automatic fine-tuning of the parsimonious mixture and thus avoids inference in more complex mixture models with many parameters. We investigate the empirical performance of the new method in the estimation of the quantiles with simulated nonnegative data and the quantiles of the individual claims distribution in a non-life insurance application.

Finite mixture modeling for vehicle crash data with application to hotspot identification.

PubMed

Park, Byung-Jung; Lord, Dominique; Lee, Chungwon

2014-10-01

The application of finite mixture regression models has recently gained an interest from highway safety researchers because of its considerable potential for addressing unobserved heterogeneity. Finite mixture models assume that the observations of a sample arise from two or more unobserved components with unknown proportions. Both fixed and varying weight parameter models have been shown to be useful for explaining the heterogeneity and the nature of the dispersion in crash data. Given the superior performance of the finite mixture model, this study, using observed and simulated data, investigated the relative performance of the finite mixture model and the traditional negative binomial (NB) model in terms of hotspot identification. For the observed data, rural multilane segment crash data for divided highways in California and Texas were used. The results showed that the difference measured by the percentage deviation in ranking orders was relatively small for this dataset. Nevertheless, the ranking results from the finite mixture model were considered more reliable than the NB model because of the better model specification. This finding was also supported by the simulation study which produced a high number of false positives and negatives when a mis-specified model was used for hotspot identification. Regarding an optimal threshold value for identifying hotspots, another simulation analysis indicated that there is a discrepancy between false discovery (increasing) and false negative rates (decreasing). Since the costs associated with false positives and false negatives are different, it is suggested that the selected optimal threshold value should be decided by considering the trade-offs between these two costs so that unnecessary expenses are minimized. Copyright © 2014 Elsevier Ltd. All rights reserved.

Mathematical Model of Nonstationary Separation Processes Proceeding in the Cascade of Gas Centrifuges in the Process of Separation of Multicomponent Isotope Mixtures

NASA Astrophysics Data System (ADS)

Orlov, A. A.; Ushakov, A. A.; Sovach, V. P.

2017-03-01

We have developed and realized on software a mathematical model of the nonstationary separation processes proceeding in the cascades of gas centrifuges in the process of separation of multicomponent isotope mixtures. With the use of this model the parameters of the separation process of germanium isotopes have been calculated. It has been shown that the model adequately describes the nonstationary processes in the cascade and is suitable for calculating their parameters in the process of separation of multicomponent isotope mixtures.

Trajectories of depressive and anxiety symptoms in older adults: a 6-year prospective cohort study.

PubMed

Holmes, Sophie E; Esterlis, Irina; Mazure, Carolyn M; Lim, Yen Ying; Ames, David; Rainey-Smith, Stephanie; Fowler, Chris; Ellis, Kathryn; Martins, Ralph N; Salvado, Olivier; Doré, Vincent; Villemagne, Victor L; Rowe, Christopher C; Laws, Simon M; Masters, Colin L; Pietrzak, Robert H; Maruff, Paul

2018-02-01

Depressive and anxiety symptoms are common in older adults, significantly affect quality of life, and are risk factors for Alzheimer's disease. We sought to identify the determinants of predominant trajectories of depressive and anxiety symptoms in cognitively normal older adults. Four hundred twenty-three older adults recruited from the general community underwent Aβ positron emission tomography imaging, apolipoprotein and brain-derived neurotrophic factor genotyping, and cognitive testing at baseline and had follow-up assessments. All participants were cognitively normal and free of clinical depression at baseline. Latent growth mixture modeling was used to identify predominant trajectories of subthreshold depressive and anxiety symptoms over 6 years. Binary logistic regression analysis was used to identify baseline predictors of symptomatic depressive and anxiety trajectories. Latent growth mixture modeling revealed two predominant trajectories of depressive and anxiety symptoms: a chronically elevated trajectory and a low, stable symptom trajectory, with almost one in five participants falling into the elevated trajectory groups. Male sex (relative risk ratio (RRR) = 3.23), lower attentional function (RRR = 1.90), and carriage of the brain-derived neurotrophic factor Val66Met allele in women (RRR = 2.70) were associated with increased risk for chronically elevated depressive symptom trajectory. Carriage of the apolipoprotein epsilon 4 allele (RRR = 1.92) and lower executive function in women (RRR = 1.74) were associated with chronically elevated anxiety symptom trajectory. Our results indicate distinct and sex-specific risk factors linked to depressive and anxiety trajectories, which may help inform risk stratification and management of these symptoms in older adults at risk for Alzheimer's disease. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Risk factors related to Toxoplasma gondii seroprevalence in indoor-housed Dutch dairy goats.

PubMed

Deng, Huifang; Dam-Deisz, Cecile; Luttikholt, Saskia; Maas, Miriam; Nielen, Mirjam; Swart, Arno; Vellema, Piet; van der Giessen, Joke; Opsteegh, Marieke

2016-02-01

Toxoplasma gondii can cause disease in goats, but also has impact on human health through food-borne transmission. Our aims were to determine the seroprevalence of T. gondii infection in indoor-housed Dutch dairy goats and to identify the risk factors related to T. gondii seroprevalence. Fifty-two out of ninety approached farmers with indoor-kept goats (58%) participated by answering a standardized questionnaire and contributing 32 goat blood samples each. Serum samples were tested for T. gondii SAG1 antibodies by ELISA and results showed that the frequency distribution of the log10-transformed OD-values fitted well with a binary mixture of a shifted gamma and a shifted reflected gamma distribution. The overall animal seroprevalence was 13.3% (95% CI: 11.7–14.9%), and at least one seropositive animal was found on 61.5% (95% CI: 48.3–74.7%) of the farms. To evaluate potential risk factors on herd level, three modeling strategies (Poisson, negative binomial and zero-inflated) were compared. The negative binomial model fitted the data best with the number of cats (1–4 cats: IR: 2.6, 95% CI: 1.1–6.5; > = 5 cats:IR: 14.2, 95% CI: 3.9–51.1) and mean animal age (IR: 1.5, 95% CI: 1.1–2.1) related to herd positivity. In conclusion, the ELISA test was 100% sensitive and specific based on binary mixture analysis. T. gondii infection is prevalent in indoor housed Dutch dairy goats but at a lower overall animal level seroprevalence than outdoor farmed goats in other European countries, and cat exposure is an important risk factor.

A Dietary Mixture Containing Fish Oil, Resveratrol, Lycopene, Catechins, and Vitamins E and C Reduces Atherosclerosis in Transgenic Mice123

PubMed Central

Verschuren, Lars; Wielinga, Peter Y.; van Duyvenvoorde, Wim; Tijani, Samira; Toet, Karin; van Ommen, Ben; Kooistra, Teake; Kleemann, Robert

2011-01-01

Chronic inflammation and proatherogenic lipids are important risk factors of cardiovascular disease (CVD). Specific dietary constituents such as polyphenols and fish oils may improve cardiovascular risk factors and may have a beneficial effect on disease outcomes. We hypothesized that the intake of an antiinflammatory dietary mixture (AIDM) containing resveratrol, lycopene, catechin, vitamins E and C, and fish oil would reduce inflammatory risk factors, proatherogenic lipids, and endpoint atherosclerosis. AIDM was evaluated in an inflammation model, male human C-reactive protein (CRP) transgenic mice, and an atherosclerosis model, female ApoE*3Leiden transgenic mice. Two groups of male human-CRP transgenic mice were fed AIDM [0.567% (wt:wt) powder and 0.933% (wt:wt oil)] or placebo for 6 wk. The effects of AIDM on basal and IL-1β–stimulated CRP expression were investigated. AIDM reduced cytokine-induced human CRP and fibrinogen expression in human-CRP transgenic mice. In the atherosclerosis study, 2 groups of female ApoE*3Leiden transgenic mice were fed an atherogenic diet supplemented with AIDM [0.567% (wt:wt) powder and 0.933% (wt:wt oil)] or placebo for 16 wk. AIDM strongly reduced plasma cholesterol, TG, and serum amyloid A concentrations compared with placebo. Importantly, long-term treatment of ApoE*3Leiden mice with AIDM markedly reduced the development of atherosclerosis by 96% compared with placebo. The effect on atherosclerosis was paralleled by a reduced expression of the vascular inflammation markers and adhesion molecules inter-cellular adhesion molecule-1 and E-selectin. Dietary supplementation of AIDM improves lipid and inflammatory risk factors of CVD and strongly reduces atherosclerotic lesion development in female transgenic mice. PMID:21411607

Closed-form solutions in stress-driven two-phase integral elasticity for bending of functionally graded nano-beams

NASA Astrophysics Data System (ADS)

Barretta, Raffaele; Fabbrocino, Francesco; Luciano, Raimondo; Sciarra, Francesco Marotti de

2018-03-01

Strain-driven and stress-driven integral elasticity models are formulated for the analysis of the structural behaviour of fuctionally graded nano-beams. An innovative stress-driven two-phases constitutive mixture defined by a convex combination of local and nonlocal phases is presented. The analysis reveals that the Eringen strain-driven fully nonlocal model cannot be used in Structural Mechanics since it is ill-posed and the local-nonlocal mixtures based on the Eringen integral model partially resolve the ill-posedeness of the model. In fact, a singular behaviour of continuous nano-structures appears if the local fraction tends to vanish so that the ill-posedness of the Eringen integral model is not eliminated. On the contrary, local-nonlocal mixtures based on the stress-driven theory are mathematically and mechanically appropriate for nanosystems. Exact solutions of inflected functionally graded nanobeams of technical interest are established by adopting the new local-nonlocal mixture stress-driven integral relation. Effectiveness of the new nonlocal approach is tested by comparing the contributed results with the ones corresponding to the mixture Eringen theory.

A modified procedure for mixture-model clustering of regional geochemical data

USGS Publications Warehouse

Ellefsen, Karl J.; Smith, David B.; Horton, John D.

2014-01-01

A modified procedure is proposed for mixture-model clustering of regional-scale geochemical data. The key modification is the robust principal component transformation of the isometric log-ratio transforms of the element concentrations. This principal component transformation and the associated dimension reduction are applied before the data are clustered. The principal advantage of this modification is that it significantly improves the stability of the clustering. The principal disadvantage is that it requires subjective selection of the number of clusters and the number of principal components. To evaluate the efficacy of this modified procedure, it is applied to soil geochemical data that comprise 959 samples from the state of Colorado (USA) for which the concentrations of 44 elements are measured. The distributions of element concentrations that are derived from the mixture model and from the field samples are similar, indicating that the mixture model is a suitable representation of the transformed geochemical data. Each cluster and the associated distributions of the element concentrations are related to specific geologic and anthropogenic features. In this way, mixture model clustering facilitates interpretation of the regional geochemical data.

Flash-point prediction for binary partially miscible mixtures of flammable solvents.

PubMed

Liaw, Horng-Jang; Lu, Wen-Hung; Gerbaud, Vincent; Chen, Chan-Cheng

2008-05-30

Flash point is the most important variable used to characterize fire and explosion hazard of liquids. Herein, partially miscible mixtures are presented within the context of liquid-liquid extraction processes. This paper describes development of a model for predicting the flash point of binary partially miscible mixtures of flammable solvents. To confirm the predictive efficacy of the derived flash points, the model was verified by comparing the predicted values with the experimental data for the studied mixtures: methanol+octane; methanol+decane; acetone+decane; methanol+2,2,4-trimethylpentane; and, ethanol+tetradecane. Our results reveal that immiscibility in the two liquid phases should not be ignored in the prediction of flash point. Overall, the predictive results of this proposed model describe the experimental data well. Based on this evidence, therefore, it appears reasonable to suggest potential application for our model in assessment of fire and explosion hazards, and development of inherently safer designs for chemical processes containing binary partially miscible mixtures of flammable solvents.

Spectroscopic and Chemometric Analysis of Binary and Ternary Edible Oil Mixtures: Qualitative and Quantitative Study.

PubMed

Jović, Ozren; Smolić, Tomislav; Primožič, Ines; Hrenar, Tomica

2016-04-19

The aim of this study was to investigate the feasibility of FTIR-ATR spectroscopy coupled with the multivariate numerical methodology for qualitative and quantitative analysis of binary and ternary edible oil mixtures. Four pure oils (extra virgin olive oil, high oleic sunflower oil, rapeseed oil, and sunflower oil), as well as their 54 binary and 108 ternary mixtures, were analyzed using FTIR-ATR spectroscopy in combination with principal component and discriminant analysis, partial least-squares, and principal component regression. It was found that the composition of all 166 samples can be excellently represented using only the first three principal components describing 98.29% of total variance in the selected spectral range (3035-2989, 1170-1140, 1120-1100, 1093-1047, and 930-890 cm(-1)). Factor scores in 3D space spanned by these three principal components form a tetrahedral-like arrangement: pure oils being at the vertices, binary mixtures at the edges, and ternary mixtures on the faces of a tetrahedron. To confirm the validity of results, we applied several cross-validation methods. Quantitative analysis was performed by minimization of root-mean-square error of cross-validation values regarding the spectral range, derivative order, and choice of method (partial least-squares or principal component regression), which resulted in excellent predictions for test sets (R(2) > 0.99 in all cases). Additionally, experimentally more demanding gas chromatography analysis of fatty acid content was carried out for all specimens, confirming the results obtained by FTIR-ATR coupled with principal component analysis. However, FTIR-ATR provided a considerably better model for prediction of mixture composition than gas chromatography, especially for high oleic sunflower oil.

Structure and effective interactions in three-component hard sphere liquids.

PubMed

König, A; Ashcroft, N W

2001-04-01

Complete and simple analytical expressions for the partial structure factors of the ternary hard sphere mixture are obtained within the Percus-Yevick approximation and presented as functions of relative packing fractions and relative hard sphere diameters. These solutions follow from the Laplace transform method as applied to multicomponent systems by Lebowitz [Phys. Rev. 133, A895 (1964)]. As an important application, we examine effective interactions in hard sphere liquid mixtures using the microscopic information contained in their partial structure factors. Thus the ensuring pair potential for an effective one-component system is obtained from the correlation functions by using an approximate inversion, and examples of effective potentials for three-component hard sphere mixtures are given. These mixtures may be of particular interest for the study of the packing aspects of melts that form glasses or quasicrystals, since noncrystalline solids often emerge from melts with at least three atomic constituents.

Exposure to pesticide mixtures and DNA damage among rice field workers.

PubMed

Varona-Uribe, Marcela Eugenia; Torres-Rey, Carlos H; Díaz-Criollo, Sonia; Palma-Parra, Ruth Marien; Narváez, Diana María; Carmona, Sandra Patricia; Briceño, Leonardo; Idrovo, Alvaro J

2016-01-01

This study describes the use of pesticides mixtures and their potential association with comet assay results in 223 rice field workers in Colombia. Thirty-one pesticides were quantified in blood, serum, and urine (15 organochlorines, 10 organophosphorus, 5 carbamates, and ethylenethiourea), and the comet assay was performed. Twenty-four (77.42%) pesticides were present in the workers. The use of the maximum-likelihood factor analysis identified 8 different mixtures. Afterwards, robust regressions were used to explore associations between the factors identified and the comet assay. Two groups of mixtures--α-benzene hexachloride (α-BHC), hexachlorobenzene (HCB), and β-BHC (β: 1.21, 95% confidence interval [CI]: 0.33-2.10) and pirimiphos-methyl, malathion, bromophos-methyl, and bromophos-ethyl (β: 11.97, 95% CI: 2.34-21.60)--were associated with a higher percentage of DNA damage and comet tail length, respectively. The findings suggest that exposure to pesticides varies greatly among rice field workers.

Nonlinear spectral mixture effects for photosynthetic/non-photosynthetic vegetation cover estimates of typical desert vegetation in western China.

PubMed

Ji, Cuicui; Jia, Yonghong; Gao, Zhihai; Wei, Huaidong; Li, Xiaosong

2017-01-01

Desert vegetation plays significant roles in securing the ecological integrity of oasis ecosystems in western China. Timely monitoring of photosynthetic/non-photosynthetic desert vegetation cover is necessary to guide management practices on land desertification and research into the mechanisms driving vegetation recession. In this study, nonlinear spectral mixture effects for photosynthetic/non-photosynthetic vegetation cover estimates are investigated through comparing the performance of linear and nonlinear spectral mixture models with different endmembers applied to field spectral measurements of two types of typical desert vegetation, namely, Nitraria shrubs and Haloxylon. The main results were as follows. (1) The correct selection of endmembers is important for improving the accuracy of vegetation cover estimates, and in particular, shadow endmembers cannot be neglected. (2) For both the Nitraria shrubs and Haloxylon, the Kernel-based Nonlinear Spectral Mixture Model (KNSMM) with nonlinear parameters was the best unmixing model. In consideration of the computational complexity and accuracy requirements, the Linear Spectral Mixture Model (LSMM) could be adopted for Nitraria shrubs plots, but this will result in significant errors for the Haloxylon plots since the nonlinear spectral mixture effects were more obvious for this vegetation type. (3) The vegetation canopy structure (planophile or erectophile) determines the strength of the nonlinear spectral mixture effects. Therefore, no matter for Nitraria shrubs or Haloxylon, the non-linear spectral mixing effects between the photosynthetic / non-photosynthetic vegetation and the bare soil do exist, and its strength is dependent on the three-dimensional structure of the vegetation canopy. The choice of linear or nonlinear spectral mixture models is up to the consideration of computational complexity and the accuracy requirement.

Nonlinear spectral mixture effects for photosynthetic/non-photosynthetic vegetation cover estimates of typical desert vegetation in western China

PubMed Central

Jia, Yonghong; Gao, Zhihai; Wei, Huaidong

2017-01-01

Desert vegetation plays significant roles in securing the ecological integrity of oasis ecosystems in western China. Timely monitoring of photosynthetic/non-photosynthetic desert vegetation cover is necessary to guide management practices on land desertification and research into the mechanisms driving vegetation recession. In this study, nonlinear spectral mixture effects for photosynthetic/non-photosynthetic vegetation cover estimates are investigated through comparing the performance of linear and nonlinear spectral mixture models with different endmembers applied to field spectral measurements of two types of typical desert vegetation, namely, Nitraria shrubs and Haloxylon. The main results were as follows. (1) The correct selection of endmembers is important for improving the accuracy of vegetation cover estimates, and in particular, shadow endmembers cannot be neglected. (2) For both the Nitraria shrubs and Haloxylon, the Kernel-based Nonlinear Spectral Mixture Model (KNSMM) with nonlinear parameters was the best unmixing model. In consideration of the computational complexity and accuracy requirements, the Linear Spectral Mixture Model (LSMM) could be adopted for Nitraria shrubs plots, but this will result in significant errors for the Haloxylon plots since the nonlinear spectral mixture effects were more obvious for this vegetation type. (3) The vegetation canopy structure (planophile or erectophile) determines the strength of the nonlinear spectral mixture effects. Therefore, no matter for Nitraria shrubs or Haloxylon, the non-linear spectral mixing effects between the photosynthetic / non-photosynthetic vegetation and the bare soil do exist, and its strength is dependent on the three-dimensional structure of the vegetation canopy. The choice of linear or nonlinear spectral mixture models is up to the consideration of computational complexity and the accuracy requirement. PMID:29240777

Influence of different types of low substituted hydroxypropyl cellulose on tableting, disintegration, and floating behaviour of floating drug delivery systems

PubMed Central

Diós, Péter; Pernecker, Tivadar; Nagy, Sándor; Pál, Szilárd; Dévay, Attila

2014-01-01

The object of the present study is to evaluate the effect of application of low-substituted hydroxypropyl cellulose (L-HPC) 11 and B1 as excipients promoting floating in gastroretentive tablets. Directly compressed tablets were formed based on experimental design. Face-centred central composite design was applied with two factors and 3 levels, where amount of sodium alginate (X1) and L-HPC (X2) were the numerical factors. Applied types of L-HPCs and their 1:1 mixture were included in a categorical factor (X3). Studied parameters were floating lag time, floating time, floating force, swelling behaviour of tablets and dissolution of paracetamol, which was used as a model active substance. Due to their physical character, L-HPCs had different water uptake and flowability. Lower flowability and lower water uptake was observed after 60 min at L-HPC 11 compared to L-HPC B1. Shorter floating times were detected at L-HPC 11 and L-HPC mixtures with 0.5% content of sodium alginate, whereas alginate was the only significant factor. Evaluating results of drug release and swelling studies on floating tablets revealed correlation, which can serve to help to understand the mechanism of action of L-HPCs in the field development of gastroretentive dosage forms. PMID:26702261

Semi-NLO production of Higgs bosons in the framework of kt-factorization using KMR unintegrated parton distributions

NASA Astrophysics Data System (ADS)

Modarres, M.; Masouminia, M. R.; Aminzadeh Nik, R.; Hosseinkhani, H.; Olanj, N.

2018-01-01

The cross-section for the production of the Standard Model Higgs boson has been calculated using a mixture of LO and NLO partonic diagrams and the unintegrated parton distribution functions (UPDF) of the Kimber-Martin-Ryskin (KMR) from the kt-factorization framework. The UPDF are prepared using the phenomenological libraries of Martin-Motylinski-Harland Lang-Thorne (MMHT 2014). The results are compared against the existing experimental data from the CMS and the ATLAS collaborations and available pQCD calculation. It is shown that, while the present calculation is in agreement with the experimental data, it is comparable with the pQCD results. It is also concluded that the K-factor approximation is comparable with the semi-NLOkt-factorization predictions.

Study of excess dielectric properties and Kirkwood correlation parameter of binary mixtures of benzaldehyde and methanol at different temperatures

NASA Astrophysics Data System (ADS)

Shah, N. S.; Vankar, H. P.; Rana, V. A.

2018-05-01

Static permittivity (ɛ0) and permittivity at optical frequency (ɛ∞) of the Benzaldehyde (BZ), Methanol (MeOH) and their binary mixtures were measured in the temperature range from 293.15 K to 323.15 K (in the interval of 10 K). From the ɛ0 and ɛ∞ other parameters such as effective Kirkwood correlation factor (geff), corrective Kirkwood correction factor (gf), Bruggman factor (fB), excess permittivity (ɛ0E ) and permittivity at optical frequency (ɛ∞E ) were evaluated.

A hybrid pareto mixture for conditional asymmetric fat-tailed distributions.

PubMed

Carreau, Julie; Bengio, Yoshua

2009-07-01

In many cases, we observe some variables X that contain predictive information over a scalar variable of interest Y , with (X,Y) pairs observed in a training set. We can take advantage of this information to estimate the conditional density p(Y|X = x). In this paper, we propose a conditional mixture model with hybrid Pareto components to estimate p(Y|X = x). The hybrid Pareto is a Gaussian whose upper tail has been replaced by a generalized Pareto tail. A third parameter, in addition to the location and spread parameters of the Gaussian, controls the heaviness of the upper tail. Using the hybrid Pareto in a mixture model results in a nonparametric estimator that can adapt to multimodality, asymmetry, and heavy tails. A conditional density estimator is built by modeling the parameters of the mixture estimator as functions of X. We use a neural network to implement these functions. Such conditional density estimators have important applications in many domains such as finance and insurance. We show experimentally that this novel approach better models the conditional density in terms of likelihood, compared to competing algorithms: conditional mixture models with other types of components and a classical kernel-based nonparametric model.

Neurotoxicological and statistical analyses of a mixture of five organophosphorus pesticides using a ray design.

PubMed

Moser, V C; Casey, M; Hamm, A; Carter, W H; Simmons, J E; Gennings, C

2005-07-01

Environmental exposures generally involve chemical mixtures instead of single chemicals. Statistical models such as the fixed-ratio ray design, wherein the mixing ratio (proportions) of the chemicals is fixed across increasing mixture doses, allows for the detection and characterization of interactions among the chemicals. In this study, we tested for interaction(s) in a mixture of five organophosphorus (OP) pesticides (chlorpyrifos, diazinon, dimethoate, acephate, and malathion). The ratio of the five pesticides (full ray) reflected the relative dietary exposure estimates of the general population as projected by the US EPA Dietary Exposure Evaluation Model (DEEM). A second mixture was tested using the same dose levels of all pesticides, but excluding malathion (reduced ray). The experimental approach first required characterization of dose-response curves for the individual OPs to build a dose-additivity model. A series of behavioral measures were evaluated in adult male Long-Evans rats at the time of peak effect following a single oral dose, and then tissues were collected for measurement of cholinesterase (ChE) activity. Neurochemical (blood and brain cholinesterase [ChE] activity) and behavioral (motor activity, gait score, tail-pinch response score) endpoints were evaluated statistically for evidence of additivity. The additivity model constructed from the single chemical data was used to predict the effects of the pesticide mixture along the full ray (10-450 mg/kg) and the reduced ray (1.75-78.8 mg/kg). The experimental mixture data were also modeled and statistically compared to the additivity models. Analysis of the 5-OP mixture (the full ray) revealed significant deviation from additivity for all endpoints except tail-pinch response. Greater-than-additive responses (synergism) were observed at the lower doses of the 5-OP mixture, which contained non-effective dose levels of each of the components. The predicted effective doses (ED20, ED50) were about half that predicted by additivity, and for brain ChE and motor activity, there was a threshold shift in the dose-response curves. For the brain ChE and motor activity, there was no difference between the full (5-OP mixture) and reduced (4-OP mixture) rays, indicating that malathion did not influence the non-additivity. While the reduced ray for blood ChE showed greater deviation from additivity without malathion in the mixture, the non-additivity observed for the gait score was reversed when malathion was removed. Thus, greater-than-additive interactions were detected for both the full and reduced ray mixtures, and the role of malathion in the interactions varied depending on the endpoint. In all cases, the deviations from additivity occurred at the lower end of the dose-response curves.

Self-organising mixture autoregressive model for non-stationary time series modelling.

PubMed

Ni, He; Yin, Hujun

2008-12-01

Modelling non-stationary time series has been a difficult task for both parametric and nonparametric methods. One promising solution is to combine the flexibility of nonparametric models with the simplicity of parametric models. In this paper, the self-organising mixture autoregressive (SOMAR) network is adopted as a such mixture model. It breaks time series into underlying segments and at the same time fits local linear regressive models to the clusters of segments. In such a way, a global non-stationary time series is represented by a dynamic set of local linear regressive models. Neural gas is used for a more flexible structure of the mixture model. Furthermore, a new similarity measure has been introduced in the self-organising network to better quantify the similarity of time series segments. The network can be used naturally in modelling and forecasting non-stationary time series. Experiments on artificial, benchmark time series (e.g. Mackey-Glass) and real-world data (e.g. numbers of sunspots and Forex rates) are presented and the results show that the proposed SOMAR network is effective and superior to other similar approaches.

Numerical study of underwater dispersion of dilute and dense sediment-water mixtures

NASA Astrophysics Data System (ADS)

Chan, Ziying; Dao, Ho-Minh; Tan, Danielle S.

2018-05-01

As part of the nodule-harvesting process, sediment tailings are released underwater. Due to the long period of clouding in the water during the settling process, this presents a significant environmental and ecological concern. One possible solution is to release a mixture of sediment tailings and seawater, with the aim of reducing the settling duration as well as the amount of spreading. In this paper, we present some results of numerical simulations using the smoothed particle hydrodynamics (SPH) method to model the release of a fixed volume of pre-mixed sediment-water mixture into a larger body of quiescent water. Both the sediment-water mixture and the “clean” water are modeled as two different fluids, with concentration-dependent bulk properties of the sediment-water mixture adjusted according to the initial solids concentration. This numerical model was validated in a previous study, which indicated significant differences in the dispersion and settling process between dilute and dense mixtures, and that a dense mixture may be preferable. For this study, we investigate a wider range of volumetric concentration with the aim of determining the optimum volumetric concentration, as well as its overall effectiveness compared to the original process (100% sediment).

Space-time variation of respiratory cancers in South Carolina: a flexible multivariate mixture modeling approach to risk estimation.

PubMed

Carroll, Rachel; Lawson, Andrew B; Kirby, Russell S; Faes, Christel; Aregay, Mehreteab; Watjou, Kevin

2017-01-01

Many types of cancer have an underlying spatiotemporal distribution. Spatiotemporal mixture modeling can offer a flexible approach to risk estimation via the inclusion of latent variables. In this article, we examine the application and benefits of using four different spatiotemporal mixture modeling methods in the modeling of cancer of the lung and bronchus as well as "other" respiratory cancer incidences in the state of South Carolina. Of the methods tested, no single method outperforms the other methods; which method is best depends on the cancer under consideration. The lung and bronchus cancer incidence outcome is best described by the univariate modeling formulation, whereas the "other" respiratory cancer incidence outcome is best described by the multivariate modeling formulation. Spatiotemporal multivariate mixture methods can aid in the modeling of cancers with small and sparse incidences when including information from a related, more common type of cancer. Copyright © 2016 Elsevier Inc. All rights reserved.

Impact of chemical proportions on the acute neurotoxicity of a mixture of seven carbamates in preweanling and adult rats.

PubMed

Moser, Virginia C; Padilla, Stephanie; Simmons, Jane Ellen; Haber, Lynne T; Hertzberg, Richard C

2012-09-01

Statistical design and environmental relevance are important aspects of studies of chemical mixtures, such as pesticides. We used a dose-additivity model to test experimentally the default assumptions of dose additivity for two mixtures of seven N-methylcarbamates (carbaryl, carbofuran, formetanate, methomyl, methiocarb, oxamyl, and propoxur). The best-fitting models were selected for the single-chemical dose-response data and used to develop a combined prediction model, which was then compared with the experimental mixture data. We evaluated behavioral (motor activity) and cholinesterase (ChE)-inhibitory (brain, red blood cells) outcomes at the time of peak acute effects following oral gavage in adult and preweanling (17 days old) Long-Evans male rats. The mixtures varied only in their mixing ratios. In the relative potency mixture, proportions of each carbamate were set at equitoxic component doses. A California environmental mixture was based on the 2005 sales of each carbamate in California. In adult rats, the relative potency mixture showed dose additivity for red blood cell ChE and motor activity, and brain ChE inhibition showed a modest greater-than additive (synergistic) response, but only at a middle dose. In rat pups, the relative potency mixture was either dose-additive (brain ChE inhibition, motor activity) or slightly less-than additive (red blood cell ChE inhibition). On the other hand, at both ages, the environmental mixture showed greater-than additive responses on all three endpoints, with significant deviations from predicted at most to all doses tested. Thus, we observed different interactive properties for different mixing ratios of these chemicals. These approaches for studying pesticide mixtures can improve evaluations of potential toxicity under varying experimental conditions that may mimic human exposures.

Measurements and modeling of absorption by CO2 + H2O mixtures in the spectral region beyond the CO2 ν3-band head

NASA Astrophysics Data System (ADS)

Tran, H.; Turbet, M.; Chelin, P.; Landsheere, X.

2018-05-01

In this work, we measured the absorption by CO2 + H2O mixtures from 2400 to 2600 cm-1 which corresponds to the spectral region beyond the ν3 band head of CO2. Transmission spectra of CO2 mixed with water vapor were recorded with a high-resolution Fourier-transform spectrometer for various pressure, temperature and concentration conditions. The continuum absorption by CO2 due to the presence of water vapor was determined by subtracting from measured spectra the contribution of local lines of both species, that of the continuum of pure CO2 as well as of the self- and CO2-continua of water vapor induced by the H2O-H2O and H2O-CO2 interactions. The obtained results are in very good agreement with the unique previous measurement (in a narrower spectral range). They confirm that the H2O-continuum of CO2 is significantly larger than that observed for pure CO2. This continuum thus must be taken into account in radiative transfer calculations for media involving CO2+ H2O mixture. An empirical model, using sub-Lorentzian line shapes based on some temperature-dependent correction factors χ is proposed which enables an accurate description of the experimental results.

Crystallization of carbon-oxygen mixtures in white dwarf stars.

PubMed

Horowitz, C J; Schneider, A S; Berry, D K

2010-06-11

We determine the phase diagram for dense carbon-oxygen mixtures in white dwarf (WD) star interiors using molecular dynamics simulations involving liquid and solid phases. Our phase diagram agrees well with predictions from Ogata et al. and from Medin and Cumming and gives lower melting temperatures than Segretain et al. Observations of WD crystallization in the globular cluster NGC 6397 by Winget et al. suggest that the melting temperature of WD cores is close to that for pure carbon. If this is true, our phase diagram implies that the central oxygen abundance in these stars is less than about 60%. This constraint, along with assumptions about convection in stellar evolution models, limits the effective S factor for the 12C(α,γ)16O reaction to S(300)≤170  keV b.

Piecewise Linear-Linear Latent Growth Mixture Models with Unknown Knots

ERIC Educational Resources Information Center

Kohli, Nidhi; Harring, Jeffrey R.; Hancock, Gregory R.

2013-01-01

Latent growth curve models with piecewise functions are flexible and useful analytic models for investigating individual behaviors that exhibit distinct phases of development in observed variables. As an extension of this framework, this study considers a piecewise linear-linear latent growth mixture model (LGMM) for describing segmented change of…

Online Reinforcement Learning Using a Probability Density Estimation.

PubMed

Agostini, Alejandro; Celaya, Enric

2017-01-01

Function approximation in online, incremental, reinforcement learning needs to deal with two fundamental problems: biased sampling and nonstationarity. In this kind of task, biased sampling occurs because samples are obtained from specific trajectories dictated by the dynamics of the environment and are usually concentrated in particular convergence regions, which in the long term tend to dominate the approximation in the less sampled regions. The nonstationarity comes from the recursive nature of the estimations typical of temporal difference methods. This nonstationarity has a local profile, varying not only along the learning process but also along different regions of the state space. We propose to deal with these problems using an estimation of the probability density of samples represented with a gaussian mixture model. To deal with the nonstationarity problem, we use the common approach of introducing a forgetting factor in the updating formula. However, instead of using the same forgetting factor for the whole domain, we make it dependent on the local density of samples, which we use to estimate the nonstationarity of the function at any given input point. To address the biased sampling problem, the forgetting factor applied to each mixture component is modulated according to the new information provided in the updating, rather than forgetting depending only on time, thus avoiding undesired distortions of the approximation in less sampled regions.

Uncertainty quantification and experimental design based on unsupervised machine learning identification of contaminant sources and groundwater types using hydrogeochemical data

NASA Astrophysics Data System (ADS)

Vesselinov, V. V.

2017-12-01

Identification of the original groundwater types present in geochemical mixtures observed in an aquifer is a challenging but very important task. Frequently, some of the groundwater types are related to different infiltration and/or contamination sources associated with various geochemical signatures and origins. The characterization of groundwater mixing processes typically requires solving complex inverse models representing groundwater flow and geochemical transport in the aquifer, where the inverse analysis accounts for available site data. Usually, the model is calibrated against the available data characterizing the spatial and temporal distribution of the observed geochemical species. Numerous geochemical constituents and processes may need to be simulated in these models which further complicates the analyses. As a result, these types of model analyses are typically extremely challenging. Here, we demonstrate a new contaminant source identification approach that performs decomposition of the observation mixtures based on Nonnegative Matrix Factorization (NMF) method for Blind Source Separation (BSS), coupled with a custom semi-supervised clustering algorithm. Our methodology, called NMFk, is capable of identifying (a) the number of groundwater types and (b) the original geochemical concentration of the contaminant sources from measured geochemical mixtures with unknown mixing ratios without any additional site information. We also demonstrate how NMFk can be extended to perform uncertainty quantification and experimental design related to real-world site characterization. The NMFk algorithm works with geochemical data represented in the form of concentrations, ratios (of two constituents; for example, isotope ratios), and delta notations (standard normalized stable isotope ratios). The NMFk algorithm has been extensively tested on synthetic datasets; NMFk analyses have been actively performed on real-world data collected at the Los Alamos National Laboratory (LANL) groundwater sites related to Chromium and RDX contamination.

Effect of Rice Husk Ash and Fly Ash on the workability of concrete mixture in the High-Rise Construction

NASA Astrophysics Data System (ADS)

Van Tang, Lam; Bulgakov, Boris; Bazhenova, Sofia; Aleksandrova, Olga; Pham, Anh Ngoc; Dinh Vu, Tho

2018-03-01

The dense development of high-rise construction in urban areas requires a creation of new concretes with essential properties and innovative technologies for preparing concrete mixtures. Besides, it is necessary to develop new ways of presenting concrete mixture and keeping their mobility. This research uses the mathematical method of two-factors rotatable central compositional planning to imitate the effect of amount of rice husk (RHA) and fly ash of thermal power plants (FA) on the workability of high-mobility concrete mixtures. The results of this study displays regression equation of the second order dependence of the objective functions - slump cone and loss of concrete mixture mobility due to the input factors - the amounts RHA (x1) and FA (x2), as well as the surface expression image of these regression equations. An analysis of the regression equations also shows that the amount of RHA and FA had a significant influence on the concrete mixtures mobility. In fact, the particles of RHA and FA will play the role as peculiar "sliding bearings" between the grains of cement leading to the dispersion of cement in the concrete mixture. Therefore, it is possible to regulate the concrete mixture mobility when transporting fresh concrete to the formwork during the high-rise buildings construction in the hot and humid climate of Vietnam. Although the average value of slump test of freshly mixed concrete, measured 60 minutes later after the mixing completion, decreased from 18.2 to 10.52 cm, this value still remained within the allowable range to maintain the mixing and and the delivery of concrete mixture by pumping.

Broad Feshbach resonance in the 6Li-40K mixture.

PubMed

Tiecke, T G; Goosen, M R; Ludewig, A; Gensemer, S D; Kraft, S; Kokkelmans, S J J M F; Walraven, J T M

2010-02-05

We study the widths of interspecies Feshbach resonances in a mixture of the fermionic quantum gases 6Li and 40K. We develop a model to calculate the width and position of all available Feshbach resonances for a system. Using the model, we select the optimal resonance to study the {6}Li/{40}K mixture. Experimentally, we obtain the asymmetric Fano line shape of the interspecies elastic cross section by measuring the distillation rate of 6Li atoms from a potassium-rich 6Li/{40}K mixture as a function of magnetic field. This provides us with the first experimental determination of the width of a resonance in this mixture, DeltaB=1.5(5) G. Our results offer good perspectives for the observation of universal crossover physics using this mass-imbalanced fermionic mixture.

Nanomechanical characterization of heterogeneous and hierarchical biomaterials and tissues using nanoindentation: the role of finite mixture models.

PubMed

Zadpoor, Amir A

2015-03-01

Mechanical characterization of biological tissues and biomaterials at the nano-scale is often performed using nanoindentation experiments. The different constituents of the characterized materials will then appear in the histogram that shows the probability of measuring a certain range of mechanical properties. An objective technique is needed to separate the probability distributions that are mixed together in such a histogram. In this paper, finite mixture models (FMMs) are proposed as a tool capable of performing such types of analysis. Finite Gaussian mixture models assume that the measured probability distribution is a weighted combination of a finite number of Gaussian distributions with separate mean and standard deviation values. Dedicated optimization algorithms are available for fitting such a weighted mixture model to experimental data. Moreover, certain objective criteria are available to determine the optimum number of Gaussian distributions. In this paper, FMMs are used for interpreting the probability distribution functions representing the distributions of the elastic moduli of osteoarthritic human cartilage and co-polymeric microspheres. As for cartilage experiments, FMMs indicate that at least three mixture components are needed for describing the measured histogram. While the mechanical properties of the softer mixture components, often assumed to be associated with Glycosaminoglycans, were found to be more or less constant regardless of whether two or three mixture components were used, those of the second mixture component (i.e. collagen network) considerably changed depending on the number of mixture components. Regarding the co-polymeric microspheres, the optimum number of mixture components estimated by the FMM theory, i.e. 3, nicely matches the number of co-polymeric components used in the structure of the polymer. The computer programs used for the presented analyses are made freely available online for other researchers to use. Copyright © 2014 Elsevier B.V. All rights reserved.

Assessment of the Risks of Mixtures of Major Use Veterinary Antibiotics in European Surface Waters.

PubMed

Guo, Jiahua; Selby, Katherine; Boxall, Alistair B A

2016-08-02

Effects of single veterinary antibiotics on a range of aquatic organisms have been explored in many studies. In reality, surface waters will be exposed to mixtures of these substances. In this study, we present an approach for establishing risks of antibiotic mixtures to surface waters and illustrate this by assessing risks of mixtures of three major use antibiotics (trimethoprim, tylosin, and lincomycin) to algal and cyanobacterial species in European surface waters. Ecotoxicity tests were initially performed to assess the combined effects of the antibiotics to the cyanobacteria Anabaena flos-aquae. The results were used to evaluate two mixture prediction models: concentration addition (CA) and independent action (IA). The CA model performed best at predicting the toxicity of the mixture with the experimental 96 h EC50 for the antibiotic mixture being 0.248 μmol/L compared to the CA predicted EC50 of 0.21 μmol/L. The CA model was therefore used alongside predictions of exposure for different European scenarios and estimations of hazards obtained from species sensitivity distributions to estimate risks of mixtures of the three antibiotics. Risk quotients for the different scenarios ranged from 0.066 to 385 indicating that the combination of three substances could be causing adverse impacts on algal communities in European surface waters. This could have important implications for primary production and nutrient cycling. Tylosin contributed most to the risk followed by lincomycin and trimethoprim. While we have explored only three antibiotics, the combined experimental and modeling approach could readily be applied to the wider range of antibiotics that are in use.

[Effects of Huoxue Bushen Mixture on skin blood vessel neogenesis and vascular endothelial growth factor expression in hair follicle of C57BL/6 mice].

PubMed

Gao, Shang-pu; Huang, Lan; Yang, Xin-wei

2007-03-01

To investigate the possible stimulating mechanism of Huoxue Bushen Mixture (HXBSM), a traditional Chinese compound medicine, on hair growth of mice via measuring the variance of skin blood vessel neogenesis and vascular endothelial growth factor (VEGF) expression in the hair follicle. Hot rosin and paraffin mixture depilation were used to induce C57BL/6 mice hair follicle to enter from telogen into anagen. Ninety C57BL/6 mice were divided into 3 groups randomly: HXBSM group, Yangxue Shengfa Capsule (YXSFC, another traditional Chinese compound medicine) group and untreated group. The mice were fed with corresponding drugs after modeling. The hair growth of the mice was observed every day. Every ten mice out of each group were executed respectively at day 4, 11 and day 17. Skin blood vessel neogenesis was counted through pathological section and VEGF expression in the hair follicle was measured via immunohistochemical method. The number of local blood vessel neogenisis in the HXBSM group observed was larger than that in the untreated group at day 4 (P<0.05); and evidently larger than that in the YXSFC group and the untreated group at day 11 (P<0.05). The expression of VEGF in the hair follicle was distinctively higher than that in the YXSFC group and the untreated group at day 11 and day 17 (P<0.05). HXBSM up-regulates VEGF expression to accelerate blood vessel neogenesis and hair growth.

Moving target detection method based on improved Gaussian mixture model

NASA Astrophysics Data System (ADS)

Ma, J. Y.; Jie, F. R.; Hu, Y. J.

2017-07-01

Gaussian Mixture Model is often employed to build background model in background difference methods for moving target detection. This paper puts forward an adaptive moving target detection algorithm based on improved Gaussian Mixture Model. According to the graylevel convergence for each pixel, adaptively choose the number of Gaussian distribution to learn and update background model. Morphological reconstruction method is adopted to eliminate the shadow.. Experiment proved that the proposed method not only has good robustness and detection effect, but also has good adaptability. Even for the special cases when the grayscale changes greatly and so on, the proposed method can also make outstanding performance.

A mixture model for bovine abortion and foetal survival.

PubMed

Hanson, Timothy; Bedrick, Edward J; Johnson, Wesley O; Thurmond, Mark C

2003-05-30

The effect of spontaneous abortion on the dairy industry is substantial, costing the industry on the order of US dollars 200 million per year in California alone. We analyse data from a cohort study of nine dairy herds in Central California. A key feature of the analysis is the observation that only a relatively small proportion of cows will abort (around 10;15 per cent), so that it is inappropriate to analyse the time-to-abortion (TTA) data as if it were standard censored survival data, with cows that fail to abort by the end of the study treated as censored observations. We thus broaden the scope to consider the analysis of foetal lifetime distribution (FLD) data for the cows, with the dual goals of characterizing the effects of various risk factors on (i). the likelihood of abortion and, conditional on abortion status, on (ii). the risk of early versus late abortion. A single model is developed to accomplish both goals with two sets of specific herd effects modelled as random effects. Because multimodal foetal hazard functions are expected for the TTA data, both a parametric mixture model and a non-parametric model are developed. Furthermore, the two sets of analyses are linked because of anticipated dependence between the random herd effects. All modelling and inferences are accomplished using modern Bayesian methods. Copyright 2003 John Wiley & Sons, Ltd.

Effect of Non-laminar Regime on the Efficiency of a Thermal Diffusion Column. Report No. 26; INFLUENCIA DEL REGIMEN NO LAMINAR SOBRE LA EFICIENCIA DE UNA COLUMNA DE DIFUSION TERMICA. Informe No. 26

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

Espanol, C.E.

1960-01-01

The effect of the appearance of localized perturbations on the separation factor and operation time of a thermal diffusion column is studied. The separation factor of a column was obtained experimentally and the enrichment was recorded continuously as a function of time by measurement of the thermal conductivity of the gaseous mixture at the foot and head of the column. A mixture of Ar and CO/sub 2/ was used as it behaves as an isotopic mixture. The results showed the linear decrease of the separation factor with the number of stages and the operation time practically does not vary. Themore » introduction of localized turbulences in a thermal diffusion column reduces the column yield. (J.S.R.)« less

Superfluid and Insulating Phases of Fermion Mixtures in Optical Lattices

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

Iskin, M.; Sa de Melo, C. A. R.

2007-08-24

The ground state phase diagram of fermion mixtures in optical lattices is analyzed as a function of interaction strength, fermion filling factor, and tunneling parameters. In addition to standard superfluid, phase-separated or coexisting superfluid-excess-fermion phases found in homogeneous or harmonically trapped systems, fermions in optical lattices have several insulating phases, including a molecular Bose-Mott insulator (BMI), a Fermi-Pauli (band) insulator (FPI), a phase-separated BMI-FPI mixture or a Bose-Fermi checkerboard (BFC). The molecular BMI phase is the fermion mixture counterpart of the atomic BMI found in atomic Bose systems, the BFC or BMI-FPI phases exist in Bose-Fermi mixtures, and lastly themore » FPI phase is particular to the Fermi nature of the constituent atoms of the mixture.« less

Mesoscale Modeling of LX-17 Under Isentropic Compression

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

Springer, H K; Willey, T M; Friedman, G

Mesoscale simulations of LX-17 incorporating different equilibrium mixture models were used to investigate the unreacted equation-of-state (UEOS) of TATB. Candidate TATB UEOS were calculated using the equilibrium mixture models and benchmarked with mesoscale simulations of isentropic compression experiments (ICE). X-ray computed tomography (XRCT) data provided the basis for initializing the simulations with realistic microstructural details. Three equilibrium mixture models were used in this study. The single constituent with conservation equations (SCCE) model was based on a mass-fraction weighted specific volume and the conservation of mass, momentum, and energy. The single constituent equation-of-state (SCEOS) model was based on a mass-fraction weightedmore » specific volume and the equation-of-state of the constituents. The kinetic energy averaging (KEA) model was based on a mass-fraction weighted particle velocity mixture rule and the conservation equations. The SCEOS model yielded the stiffest TATB EOS (0.121{micro} + 0.4958{micro}{sup 2} + 2.0473{micro}{sup 3}) and, when incorporated in mesoscale simulations of the ICE, demonstrated the best agreement with VISAR velocity data for both specimen thicknesses. The SCCE model yielded a relatively more compliant EOS (0.1999{micro}-0.6967{micro}{sup 2} + 4.9546{micro}{sup 3}) and the KEA model yielded the most compliant EOS (0.1999{micro}-0.6967{micro}{sup 2}+4.9546{micro}{sup 3}) of all the equilibrium mixture models. Mesoscale simulations with the lower density TATB adiabatic EOS data demonstrated the least agreement with VISAR velocity data.« less

Latent Transition Analysis with a Mixture Item Response Theory Measurement Model

ERIC Educational Resources Information Center

Cho, Sun-Joo; Cohen, Allan S.; Kim, Seock-Ho; Bottge, Brian

2010-01-01

A latent transition analysis (LTA) model was described with a mixture Rasch model (MRM) as the measurement model. Unlike the LTA, which was developed with a latent class measurement model, the LTA-MRM permits within-class variability on the latent variable, making it more useful for measuring treatment effects within latent classes. A simulation…

Proportioning and performance evaluation of self-consolidating concrete

NASA Astrophysics Data System (ADS)

Wang, Xuhao

A well-proportioned self-consolidating concrete (SCC) mixture can be achieved by controlling the aggregate system, paste quality, and paste quantity. The work presented in this dissertation involves an effort to study and improve particle packing of the concrete system and reduce the paste quantity while maintaining concrete quality and performance. This dissertation is composed of four papers resulting from the study: (1) Assessing Particle Packing Based Self-Consolidating Concrete Mix Design; (2) Using Paste-To-Voids Volume Ratio to Evaluate the Performance of Self-Consolidating Concrete Mixtures; (3) Image Analysis Applications on Assessing Static Stability and Flowability of Self-Consolidating Concrete, and (4) Using Ultrasonic Wave Propagation to Monitor Stiffening Process of Self-Consolidating Concrete. Tests were conducted on a large matrix of SCC mixtures that were designed for cast-in-place bridge construction. The mixtures were made with different aggregate types, sizes, and different cementitious materials. In Paper 1, a modified particle-packing based mix design method, originally proposed by Brouwers (2005), was applied to the design of self-consolidating concrete (SCC) mixs. Using this method, a large matrix of SCC mixes was designed to have a particle distribution modulus (q) ranging from 0.23 to 0.29. Fresh properties (such as flowability, passing ability, segregation resistance, yield stress, viscosity, set time and formwork pressure) and hardened properties (such as compressive strength, surface resistance, shrinkage, and air structure) of these concrete mixes were experimentally evaluated. In Paper 2, a concept that is based on paste-to-voids volume ratio (Vpaste/Vvoids) was employed to assess the performance of SCC mixtures. The relationship between excess paste theory and Vpaste/Vvoids was investigated. The workability, flow properties, compressive strength, shrinkage, and surface resistivity of SCC mixtures were determined at various ages. Statistical analyses, response surface models and Tukey Honestly Significant Difference (HSD) tests, were conducted to relate the mix design parameters to the concrete performance. The work discussed in Paper 3 was to apply a digital image processing (DIP) method associated with a MATLAB algorithm to evaluate cross sectional images of self-consolidating concrete (SCC). Parameters, such as inter-particle spacing between coarse aggregate particles and average mortar to aggregate ratio defined as average mortar thickness index (MTI), were derived from DIP method and applied to evaluate the static stability and develop statistical models to predict flowability of SCC mixtures. The last paper investigated technologies available to monitor changing properties of a fresh mixture, particularly for use with self-consolidating concrete (SCC). A number of techniques were used to monitor setting time, stiffening and formwork pressure of SCC mixtures. These included longitudinal (P-wave) ultrasonic wave propagation, penetrometer based setting time, semi-adiabatic calorimetry, and formwork pressure. The first study demonstrated that the concrete mixes designed using the modified Brouwers mix design algorithm and particle packing concept had a potential to reduce up to 20% SCMs content compared to existing SCC mix proportioning methods and still maintain good performance. The second paper concluded that slump flow of the SCC mixtures increased with Vpaste/Vvoids at a given viscosity of mortar. Compressive trength increases with increasing Vpaste/Vvoids up to a point (~150%), after which the strength becomes independent of Vpaste/Vvoids, even slightly decreases. Vpaste/Vvoids has little effect on the shrinkage mixtures, while SCC mixtures tend to have a higher shrinkage than CC for a given Vpaste/Vvoids. Vpaste/Vvoids has little effects on surface resistivity of SCC mixtures. The paste quality tends to have a dominant effect. Statistical analysis is an efficient tool to identify the significance of influence factors on concrete performance. In third paper, proposed DIP method and MATLAB algorithm can be successfully used to derive inter-particle spacing and MTI, and quantitatively evaluate the static stability in hardened SCC samples. These parameters can be applied to overcome the limitations and challenges of existing theoretical frames and construct statistical models associated with rheological parameters to predict flowability of SCC mixtures. The outcome of this study can be of practical value for providing an efficient and useful tool in designing mixture proportions of SCC. Last paper compared several concrete performance measurement techniques, the P-wave test and calorimetric measurements can be efficiently used to monitor the stiffening and setting of SCC mixtures.

Activities of mixtures of soil-applied herbicides with different molecular targets.

PubMed

Kaushik, Shalini; Streibig, Jens Carl; Cedergreen, Nina

2006-11-01

The joint action of soil-applied herbicide mixtures with similar or different modes of action has been assessed by using the additive dose model (ADM). The herbicides chlorsulfuron, metsulfuron-methyl, pendimethalin and pretilachlor, applied either singly or in binary mixtures, were used on rice (Oryza sativa L.). The growth (shoot) response curves were described by a logistic dose-response model. The ED50 values and their corresponding standard errors obtained from the response curves were used to test statistically if the shape of the isoboles differed from the reference model (ADM). Results showed that mixtures of herbicides with similar molecular targets, i.e. chlorsulfuron and metsulfuron (acetolactate synthase (ALS) inhibitors), and with different molecular targets, i.e. pendimethalin (microtubule assembly inhibitor) and pretilachlor (very long chain fatty acids (VLCFAs) inhibitor), followed the ADM. Mixing herbicides with different molecular targets gave different results depending on whether pretilachlor or pendimethalin was involved. In general, mixtures of pretilachlor and sulfonylureas showed synergistic interactions, whereas mixtures of pendimethalin and sulfonylureas exhibited either antagonistic or additive activities. Hence, there is a large potential for both increasing the specificity of herbicides by using mixtures and lowering the total dose for weed control, while at the same time delaying the development of herbicide resistance by using mixtures with different molecular targets. Copyright (c) 2006 Society of Chemical Industry.

Modeling Math Growth Trajectory--An Application of Conventional Growth Curve Model and Growth Mixture Model to ECLS K-5 Data

ERIC Educational Resources Information Center

Lu, Yi

2016-01-01

To model students' math growth trajectory, three conventional growth curve models and three growth mixture models are applied to the Early Childhood Longitudinal Study Kindergarten-Fifth grade (ECLS K-5) dataset in this study. The results of conventional growth curve model show gender differences on math IRT scores. When holding socio-economic…

Evaluating Differential Effects Using Regression Interactions and Regression Mixture Models

ERIC Educational Resources Information Center

Van Horn, M. Lee; Jaki, Thomas; Masyn, Katherine; Howe, George; Feaster, Daniel J.; Lamont, Andrea E.; George, Melissa R. W.; Kim, Minjung

2015-01-01

Research increasingly emphasizes understanding differential effects. This article focuses on understanding regression mixture models, which are relatively new statistical methods for assessing differential effects by comparing results to using an interactive term in linear regression. The research questions which each model answers, their…

Numerical modeling and analytical modeling of cryogenic carbon capture in a de-sublimating heat exchanger

NASA Astrophysics Data System (ADS)

Yu, Zhitao; Miller, Franklin; Pfotenhauer, John M.

2017-12-01

Both a numerical and analytical model of the heat and mass transfer processes in a CO2, N2 mixture gas de-sublimating cross-flow finned duct heat exchanger system is developed to predict the heat transferred from a mixture gas to liquid nitrogen and the de-sublimating rate of CO2 in the mixture gas. The mixture gas outlet temperature, liquid nitrogen outlet temperature, CO2 mole fraction, temperature distribution and de-sublimating rate of CO2 through the whole heat exchanger was computed using both the numerical and analytic model. The numerical model is built using EES [1] (engineering equation solver). According to the simulation, a cross-flow finned duct heat exchanger can be designed and fabricated to validate the models. The performance of the heat exchanger is evaluated as functions of dimensionless variables, such as the ratio of the mass flow rate of liquid nitrogen to the mass flow rate of inlet flue gas.

Structure investigations on assembled astaxanthin molecules

NASA Astrophysics Data System (ADS)

Köpsel, Christian; Möltgen, Holger; Schuch, Horst; Auweter, Helmut; Kleinermanns, Karl; Martin, Hans-Dieter; Bettermann, Hans

2005-08-01

The carotenoid r,r-astaxanthin (3R,3‧R-dihydroxy-4,4‧-diketo-β-carotene) forms different types of aggregates in acetone-water mixtures. H-type aggregates were found in mixtures with a high part of water (e.g. 1:9 acetone-water mixture) whereas two different types of J-aggregates were identified in mixtures with a lower part of water (3:7 acetone-water mixture). These aggregates were characterized by recording UV/vis-absorption spectra, CD-spectra and fluorescence emissions. The sizes of the molecular assemblies were determined by dynamic light scattering experiments. The hydrodynamic diameter of the assemblies amounts 40 nm in 1:9 acetone-water mixtures and exceeds up to 1 μm in 3:7 acetone-water mixtures. Scanning tunneling microscopy monitored astaxanthin aggregates on graphite surfaces. The structure of the H-aggregate was obtained by molecular modeling calculations. The structure was confirmed by calculating the electronic absorption spectrum and the CD-spectrum where the molecular modeling structure was used as input.

Optimization of the fabrication of novel stealth PLA-based nanoparticles by dispersion polymerization using D-optimal mixture design

PubMed Central

Adesina, Simeon K.; Wight, Scott A.; Akala, Emmanuel O.

2015-01-01

Purpose Nanoparticle size is important in drug delivery. Clearance of nanoparticles by cells of the reticuloendothelial system has been reported to increase with increase in particle size. Further, nanoparticles should be small enough to avoid lung or spleen filtering effects. Endocytosis and accumulation in tumor tissue by the enhanced permeability and retention effect are also processes that are influenced by particle size. We present the results of studies designed to optimize crosslinked biodegradable stealth polymeric nanoparticles fabricated by dispersion polymerization. Methods Nanoparticles were fabricated using different amounts of macromonomer, initiators, crosslinking agent and stabilizer in a dioxane/DMSO/water solvent system. Confirmation of nanoparticle formation was by scanning electron microscopy (SEM). Particle size was measured by dynamic light scattering (DLS). D-optimal mixture statistical experimental design was used for the experimental runs, followed by model generation (Scheffe polynomial) and optimization with the aid of a computer software. Model verification was done by comparing particle size data of some suggested solutions to the predicted particle sizes. Results and Conclusion Data showed that average particle sizes follow the same trend as predicted by the model. Negative terms in the model corresponding to the crosslinking agent and stabilizer indicate the important factors for minimizing particle size. PMID:24059281

Optimization of the fabrication of novel stealth PLA-based nanoparticles by dispersion polymerization using D-optimal mixture design.

PubMed

Adesina, Simeon K; Wight, Scott A; Akala, Emmanuel O

2014-11-01

Nanoparticle size is important in drug delivery. Clearance of nanoparticles by cells of the reticuloendothelial system has been reported to increase with increase in particle size. Further, nanoparticles should be small enough to avoid lung or spleen filtering effects. Endocytosis and accumulation in tumor tissue by the enhanced permeability and retention effect are also processes that are influenced by particle size. We present the results of studies designed to optimize cross-linked biodegradable stealth polymeric nanoparticles fabricated by dispersion polymerization. Nanoparticles were fabricated using different amounts of macromonomer, initiators, crosslinking agent and stabilizer in a dioxane/DMSO/water solvent system. Confirmation of nanoparticle formation was by scanning electron microscopy (SEM). Particle size was measured by dynamic light scattering (DLS). D-optimal mixture statistical experimental design was used for the experimental runs, followed by model generation (Scheffe polynomial) and optimization with the aid of a computer software. Model verification was done by comparing particle size data of some suggested solutions to the predicted particle sizes. Data showed that average particle sizes follow the same trend as predicted by the model. Negative terms in the model corresponding to the cross-linking agent and stabilizer indicate the important factors for minimizing particle size.

Mixture modelling for cluster analysis.

PubMed

McLachlan, G J; Chang, S U

2004-10-01

Cluster analysis via a finite mixture model approach is considered. With this approach to clustering, the data can be partitioned into a specified number of clusters g by first fitting a mixture model with g components. An outright clustering of the data is then obtained by assigning an observation to the component to which it has the highest estimated posterior probability of belonging; that is, the ith cluster consists of those observations assigned to the ith component (i = 1,..., g). The focus is on the use of mixtures of normal components for the cluster analysis of data that can be regarded as being continuous. But attention is also given to the case of mixed data, where the observations consist of both continuous and discrete variables.

Compact determination of hydrogen isotopes

DOE PAGES

Robinson, David

2017-04-06

Scanning calorimetry of a confined, reversible hydrogen sorbent material has been previously proposed as a method to determine compositions of unknown mixtures of diatomic hydrogen isotopologues and helium. Application of this concept could result in greater process knowledge during the handling of these gases. Previously published studies have focused on mixtures that do not include tritium. This paper focuses on modeling to predict the effect of tritium in mixtures of the isotopologues on a calorimetry scan. Furthermore, the model predicts that tritium can be measured with a sensitivity comparable to that observed for hydrogen-deuterium mixtures, and that under so memore » conditions, it may be possible to determine the atomic fractions of all three isotopes in a gas mixture.« less

Aquatic community structure in Mediterranean edge-of-field waterbodies as explained by environmental factors and the presence of pesticide mixtures.

PubMed

Pereira, Ana Santos; Dâmaso-Rodrigues, Maria Luísa; Amorim, Ana; Daam, Michiel A; Cerejeira, Maria José

2018-06-16

Studies addressing the predicted effects of pesticides in combination with abiotic and biotic factors on aquatic biota in ditches associated with typical Mediterranean agroecosystems are scarce. The current study aimed to evaluate the predicted effects of pesticides along with environmental factors and biota interactions on macroinvertebrate, zooplankton and phytoplankton community compositions in ditches adjacent to Portuguese maize and tomato crop areas. Data was analysed with the variance partitioning procedure based on redundancy analysis (RDA). The total variance in biological community composition was divided into the variance explained by the multi-substance potentially affected fraction [(msPAF) arthropods and primary producers], environmental factors (water chemistry parameters), biotic interactions, shared variance, and unexplained variance. The total explained variance reached 39.4% and the largest proportion of this explained variance was attributed to msPAF (23.7%). When each group (phytoplankton, zooplankton and macroinvertebrates) was analysed separately, biota interactions and environmental factors explained the largest proportion of variance. Results of this study indicate that besides the presence of pesticide mixtures, environmental factors and biotic interactions also considerably influence field freshwater communities. Subsequently, to increase our understanding of the risk of pesticide mixtures on ecosystem communities in edge-of-field water bodies, variations in environmental and biological factors should also be considered.

Lattice model for water-solute mixtures.

PubMed

Furlan, A P; Almarza, N G; Barbosa, M C

2016-10-14

A lattice model for the study of mixtures of associating liquids is proposed. Solvent and solute are modeled by adapting the associating lattice gas (ALG) model. The nature of interaction of solute/solvent is controlled by tuning the energy interactions between the patches of ALG model. We have studied three set of parameters, resulting in, hydrophilic, inert, and hydrophobic interactions. Extensive Monte Carlo simulations were carried out, and the behavior of pure components and the excess properties of the mixtures have been studied. The pure components, water (solvent) and solute, have quite similar phase diagrams, presenting gas, low density liquid, and high density liquid phases. In the case of solute, the regions of coexistence are substantially reduced when compared with both the water and the standard ALG models. A numerical procedure has been developed in order to attain series of results at constant pressure from simulations of the lattice gas model in the grand canonical ensemble. The excess properties of the mixtures, volume and enthalpy as the function of the solute fraction, have been studied for different interaction parameters of the model. Our model is able to reproduce qualitatively well the excess volume and enthalpy for different aqueous solutions. For the hydrophilic case, we show that the model is able to reproduce the excess volume and enthalpy of mixtures of small alcohols and amines. The inert case reproduces the behavior of large alcohols such as propanol, butanol, and pentanol. For the last case (hydrophobic), the excess properties reproduce the behavior of ionic liquids in aqueous solution.

Approximation of the breast height diameter distribution of two-cohort stands by mixture models III Kernel density estimators vs mixture models

Treesearch

Rafal Podlaski; Francis A. Roesch

2014-01-01

Two-component mixtures of either the Weibull distribution or the gamma distribution and the kernel density estimator were used for describing the diameter at breast height (dbh) empirical distributions of two-cohort stands. The data consisted of study plots from the Å wietokrzyski National Park (central Poland) and areas close to and including the North Carolina section...

Performance on perceptual word identification is mediated by discrete states.

PubMed

Swagman, April R; Province, Jordan M; Rouder, Jeffrey N

2015-02-01

We contrast predictions from discrete-state models of all-or-none information loss with signal-detection models of graded strength for the identification of briefly flashed English words. Previous assessments have focused on whether ROC curves are straight or not, which is a test of a discrete-state model where detection leads to the highest confidence response with certainty. We along with many others argue this certainty assumption is too constraining, and, consequently, the straight-line ROC test is too stringent. Instead, we assess a core property of discrete-state models, conditional independence, where the pattern of responses depends only on which state is entered. The conditional independence property implies that confidence ratings are a mixture of detect and guess state responses, and that stimulus strength factors, the duration of the flashed word in this report, affect only the probability of entering a state and not responses conditional on a state. To assess this mixture property, 50 participants saw words presented briefly on a computer screen at three variable flash durations followed by either a two-alternative confidence ratings task or a yes-no confidence ratings task. Comparable discrete-state and signal-detection models were fit to the data for each participant and task. The discrete-state models outperformed the signal detection models for 90 % of participants in the two-alternative task and for 68 % of participants in the yes-no task. We conclude discrete-state models are viable for predicting performance across stimulus conditions in a perceptual word identification task.

The nonlinear model for emergence of stable conditions in gas mixture in force field

NASA Astrophysics Data System (ADS)

Kalutskov, Oleg; Uvarova, Liudmila

2016-06-01

The case of M-component liquid evaporation from the straight cylindrical capillary into N - component gas mixture in presence of external forces was reviewed. It is assumed that the gas mixture is not ideal. The stable states in gas phase can be formed during the evaporation process for the certain model parameter valuesbecause of the mass transfer initial equationsnonlinearity. The critical concentrations of the resulting gas mixture components (the critical component concentrations at which the stable states occur in mixture) were determined mathematically for the case of single-component fluid evaporation into two-component atmosphere. It was concluded that this equilibrium concentration ratio of the mixture components can be achieved by external force influence on the mass transfer processes. It is one of the ways to create sustainable gas clusters that can be used effectively in modern nanotechnology.

Bayesian mixture modeling of significant p values: A meta-analytic method to estimate the degree of contamination from H₀.

PubMed

Gronau, Quentin Frederik; Duizer, Monique; Bakker, Marjan; Wagenmakers, Eric-Jan

2017-09-01

Publication bias and questionable research practices have long been known to corrupt the published record. One method to assess the extent of this corruption is to examine the meta-analytic collection of significant p values, the so-called p -curve (Simonsohn, Nelson, & Simmons, 2014a). Inspired by statistical research on false-discovery rates, we propose a Bayesian mixture model analysis of the p -curve. Our mixture model assumes that significant p values arise either from the null-hypothesis H ₀ (when their distribution is uniform) or from the alternative hypothesis H1 (when their distribution is accounted for by a simple parametric model). The mixture model estimates the proportion of significant results that originate from H ₀, but it also estimates the probability that each specific p value originates from H ₀. We apply our model to 2 examples. The first concerns the set of 587 significant p values for all t tests published in the 2007 volumes of Psychonomic Bulletin & Review and the Journal of Experimental Psychology: Learning, Memory, and Cognition; the mixture model reveals that p values higher than about .005 are more likely to stem from H ₀ than from H ₁. The second example concerns 159 significant p values from studies on social priming and 130 from yoked control studies. The results from the yoked controls confirm the findings from the first example, whereas the results from the social priming studies are difficult to interpret because they are sensitive to the prior specification. To maximize accessibility, we provide a web application that allows researchers to apply the mixture model to any set of significant p values. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Thermodynamics of concentrated electrolyte mixtures and the prediction of mineral solubilities to high temperatures for mixtures in the system Na-K-Mg-Cl-SO 4-OH-H 2O

NASA Astrophysics Data System (ADS)

Pabalan, Roberto T.; Pitzer, Kenneth S.

1987-09-01

Mineral solubilities in binary and ternary electrolyte mixtures in the system Na-K-Mg-Cl-SO 4-OH-H 2O are calculated to high temperatures using available thermodynamic data for solids and for aqueous electrolyte solutions. Activity and osmotic coefficients are derived from the ion-interaction model of Pitzer (1973, 1979) and co-workers, the parameters of which are evaluated from experimentally determined solution properties or from solubility data in binary and ternary mixtures. Excellent to good agreement with experimental solubilities for binary and ternary mixtures indicate that the model can be successfully used to predict mineral-solution equilibria to high temperatures. Although there are currently no theoretical forms for the temperature dependencies of the various model parameters, the solubility data in ternary mixtures can be adequately represented by constant values of the mixing term θ ij and values of ψ ijk which are either constant or have a simple temperature dependence. Since no additional parameters are needed to describe the thermodynamic properties of more complex electrolyte mixtures, the calculations can be extended to equilibrium studies relevant to natural systems. Examples of predicted solubilities are given for the quaternary system NaCl-KCl-MgCl 2-H 2O.

Aggregate mixture found to increase safety on roads : fact sheet.

DOT National Transportation Integrated Search

2011-11-01

Constructing a safe and durable highway system in Louisiana has long been a major concern for highway engineers. Open-graded friction course (OGFC) mixtures have shown to improve wet weather visibility, which can be a leading factor in causing wet we...

Lattice Boltzmann scheme for mixture modeling: analysis of the continuum diffusion regimes recovering Maxwell-Stefan model and incompressible Navier-Stokes equations.

PubMed

Asinari, Pietro

2009-11-01

A finite difference lattice Boltzmann scheme for homogeneous mixture modeling, which recovers Maxwell-Stefan diffusion model in the continuum limit, without the restriction of the mixture-averaged diffusion approximation, was recently proposed [P. Asinari, Phys. Rev. E 77, 056706 (2008)]. The theoretical basis is the Bhatnagar-Gross-Krook-type kinetic model for gas mixtures [P. Andries, K. Aoki, and B. Perthame, J. Stat. Phys. 106, 993 (2002)]. In the present paper, the recovered macroscopic equations in the continuum limit are systematically investigated by varying the ratio between the characteristic diffusion speed and the characteristic barycentric speed. It comes out that the diffusion speed must be at least one order of magnitude (in terms of Knudsen number) smaller than the barycentric speed, in order to recover the Navier-Stokes equations for mixtures in the incompressible limit. Some further numerical tests are also reported. In particular, (1) the solvent and dilute test cases are considered, because they are limiting cases in which the Maxwell-Stefan model reduces automatically to Fickian cases. Moreover, (2) some tests based on the Stefan diffusion tube are reported for proving the complete capabilities of the proposed scheme in solving Maxwell-Stefan diffusion problems. The proposed scheme agrees well with the expected theoretical results.

A Mixtures-of-Trees Framework for Multi-Label Classification

PubMed Central

Hong, Charmgil; Batal, Iyad; Hauskrecht, Milos

2015-01-01

We propose a new probabilistic approach for multi-label classification that aims to represent the class posterior distribution P(Y|X). Our approach uses a mixture of tree-structured Bayesian networks, which can leverage the computational advantages of conditional tree-structured models and the abilities of mixtures to compensate for tree-structured restrictions. We develop algorithms for learning the model from data and for performing multi-label predictions using the learned model. Experiments on multiple datasets demonstrate that our approach outperforms several state-of-the-art multi-label classification methods. PMID:25927011

Liquid class predictor for liquid handling of complex mixtures

DOEpatents

Seglke, Brent W [San Ramon, CA; Lekin, Timothy P [Livermore, CA

2008-12-09

A method of establishing liquid classes of complex mixtures for liquid handling equipment. The mixtures are composed of components and the equipment has equipment parameters. The first step comprises preparing a response curve for the components. The next step comprises using the response curve to prepare a response indicator for the mixtures. The next step comprises deriving a model that relates the components and the mixtures to establish the liquid classes.

Concrete pavement mixture design and analysis (MDA) : factors influencing drying shrinkage.

DOT National Transportation Integrated Search

2014-10-01

This literature review focuses on factors influencing drying shrinkage of concrete. Although the factors are normally interrelated, they : can be categorized into three groups: paste quantity, paste quality, and other factors.

Regression mixture models: Does modeling the covariance between independent variables and latent classes improve the results?

PubMed Central

Lamont, Andrea E.; Vermunt, Jeroen K.; Van Horn, M. Lee

2016-01-01

Regression mixture models are increasingly used as an exploratory approach to identify heterogeneity in the effects of a predictor on an outcome. In this simulation study, we test the effects of violating an implicit assumption often made in these models – i.e., independent variables in the model are not directly related to latent classes. Results indicated that the major risk of failing to model the relationship between predictor and latent class was an increase in the probability of selecting additional latent classes and biased class proportions. Additionally, this study tests whether regression mixture models can detect a piecewise relationship between a predictor and outcome. Results suggest that these models are able to detect piecewise relations, but only when the relationship between the latent class and the predictor is included in model estimation. We illustrate the implications of making this assumption through a re-analysis of applied data examining heterogeneity in the effects of family resources on academic achievement. We compare previous results (which assumed no relation between independent variables and latent class) to the model where this assumption is lifted. Implications and analytic suggestions for conducting regression mixture based on these findings are noted. PMID:26881956

Enhancing the Benefit of the Chemical Mixture Methodology: A Report on Methodology Testing and Potential Approaches for Improving Performance

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

Yu, Xiao-Ying; Yao, Juan; He, Hua

2012-01-01

Extensive testing shows that the current version of the Chemical Mixture Methodology (CMM) is meeting its intended mission to provide conservative estimates of the health effects from exposure to airborne chemical mixtures. However, the current version of the CMM could benefit from several enhancements that are designed to improve its application of Health Code Numbers (HCNs) and employ weighting factors to reduce over conservatism.

Microwave dielectric study of polar liquids at 298 K

NASA Astrophysics Data System (ADS)

Maharolkar, Aruna P.; Murugkar, A.; Khirade, P. W.

2018-05-01

Present paper deals with study of microwave dielectric properties like dielectric constant, viscosity, density and refractive index for the binary mixtures of Dimethylsulphoxide (DMSO) and Methanol over the entire concentration range were measured at 298K. The experimental data further used to determine the excess properties viz. excess static dielectric constant, excess molar volume, excess viscosity& derived properties viz. molar refraction&Bruggman factor. The values of excess properties further fitted with Redlich-Kister (R-K Fit) equation to calculate the binary coefficients and standard deviation. The resulting excess parameters are used to indicate the presence of intermolecular interactions and strength of intermolecular interactions between the molecules in the binary mixtures. Excess parameters indicate structure breaking factor in the mixture predominates in the system.

Kinetic model development for biogas production from cattle dung

NASA Astrophysics Data System (ADS)

Ghatak, Manjula Das; Mahanta, P.

2017-07-01

Biogas is a mixture of methane, carbon dioxide and traces of numerous trace of elements. It is produced by anaerobic digestion of organic matters including cattle dung which depend upon various factors affecting the population and activity of microorganisms producing biogas. Among the various factors temperature is one of them which play a significant role in biogas production from cattle dung. Biogas production from cattle dung was studied at temperatures 35°C to 55°C at a step of 5°C to study the effect of temperature on biogas production from cattle dung. In this work a mathematical model is developed for evaluating the effect of temperature on the rate of biogas production from cattle dung. The new mathematical model is derived by adding the effect of temperature on the modified Gompertz model. The new model is found to be suitable for predicting the biogas production from cattle dung in the temperature range 35°C to 55°C. The results from the new model are found to be highly correlated to the experimental data of present study.

Transient Catalytic Combustor Model With Detailed Gas and Surface Chemistry

NASA Technical Reports Server (NTRS)

Struk, Peter M.; Dietrich, Daniel L.; Mellish, Benjamin P.; Miller, Fletcher J.; Tien, James S.

2005-01-01

In this work, we numerically investigate the transient combustion of a premixed gas mixture in a narrow, perfectly-insulated, catalytic channel which can represent an interior channel of a catalytic monolith. The model assumes a quasi-steady gas-phase and a transient, thermally thin solid phase. The gas phase is one-dimensional, but it does account for heat and mass transfer in a direction perpendicular to the flow via appropriate heat and mass transfer coefficients. The model neglects axial conduction in both the gas and in the solid. The model includes both detailed gas-phase reactions and catalytic surface reactions. The reactants modeled so far include lean mixtures of dry CO and CO/H2 mixtures, with pure oxygen as the oxidizer. The results include transient computations of light-off and system response to inlet condition variations. In some cases, the model predicts two different steady-state solutions depending on whether the channel is initially hot or cold. Additionally, the model suggests that the catalytic ignition of CO/O2 mixtures is extremely sensitive to small variations of inlet equivalence ratios and parts per million levels of H2.

Using dynamic N-mixture models to test cavity limitation on northern flying squirrel demographic parameters using experimental nest box supplementation.

PubMed

Priol, Pauline; Mazerolle, Marc J; Imbeau, Louis; Drapeau, Pierre; Trudeau, Caroline; Ramière, Jessica

2014-06-01

Dynamic N-mixture models have been recently developed to estimate demographic parameters of unmarked individuals while accounting for imperfect detection. We propose an application of the Dail and Madsen (2011: Biometrics, 67, 577-587) dynamic N-mixture model in a manipulative experiment using a before-after control-impact design (BACI). Specifically, we tested the hypothesis of cavity limitation of a cavity specialist species, the northern flying squirrel, using nest box supplementation on half of 56 trapping sites. Our main purpose was to evaluate the impact of an increase in cavity availability on flying squirrel population dynamics in deciduous stands in northwestern Québec with the dynamic N-mixture model. We compared abundance estimates from this recent approach with those from classic capture-mark-recapture models and generalized linear models. We compared apparent survival estimates with those from Cormack-Jolly-Seber (CJS) models. Average recruitment rate was 6 individuals per site after 4 years. Nevertheless, we found no effect of cavity supplementation on apparent survival and recruitment rates of flying squirrels. Contrary to our expectations, initial abundance was not affected by conifer basal area (food availability) and was negatively affected by snag basal area (cavity availability). Northern flying squirrel population dynamics are not influenced by cavity availability at our deciduous sites. Consequently, we suggest that this species should not be considered an indicator of old forest attributes in our study area, especially in view of apparent wide population fluctuations across years. Abundance estimates from N-mixture models were similar to those from capture-mark-recapture models, although the latter had greater precision. Generalized linear mixed models produced lower abundance estimates, but revealed the same relationship between abundance and snag basal area. Apparent survival estimates from N-mixture models were higher and less precise than those from CJS models. However, N-mixture models can be particularly useful to evaluate management effects on animal populations, especially for species that are difficult to detect in situations where individuals cannot be uniquely identified. They also allow investigating the effects of covariates at the site level, when low recapture rates would require restricting classic CMR analyses to a subset of sites with the most captures.

Spurious Latent Classes in the Mixture Rasch Model

ERIC Educational Resources Information Center

Alexeev, Natalia; Templin, Jonathan; Cohen, Allan S.

2011-01-01

Mixture Rasch models have been used to study a number of psychometric issues such as goodness of fit, response strategy differences, strategy shifts, and multidimensionality. Although these models offer the potential for improving understanding of the latent variables being measured, under some conditions overextraction of latent classes may…

Individual and binary toxicity of anatase and rutile nanoparticles towards Ceriodaphnia dubia.

PubMed

Iswarya, V; Bhuvaneshwari, M; Chandrasekaran, N; Mukherjee, Amitava

2016-09-01

Increasing usage of engineered nanoparticles, especially Titanium dioxide (TiO2) in various commercial products has necessitated their toxicity evaluation and risk assessment, especially in the aquatic ecosystem. In the present study, a comprehensive toxicity assessment of anatase and rutile NPs (individual as well as a binary mixture) has been carried out in a freshwater matrix on Ceriodaphnia dubia under different irradiation conditions viz., visible and UV-A. Anatase and rutile NPs produced an LC50 of about 37.04 and 48mg/L, respectively, under visible irradiation. However, lesser LC50 values of about 22.56 (anatase) and 23.76 (rutile) mg/L were noted under UV-A irradiation. A toxic unit (TU) approach was followed to determine the concentrations of binary mixtures of anatase and rutile. The binary mixture resulted in an antagonistic and additive effect under visible and UV-A irradiation, respectively. Among the two different modeling approaches used in the study, Marking-Dawson model was noted to be a more appropriate model than Abbott model for the toxicity evaluation of binary mixtures. The agglomeration of NPs played a significant role in the induction of antagonistic and additive effects by the mixture based on the irradiation applied. TEM and zeta potential analysis confirmed the surface interactions between anatase and rutile NPs in the mixture. Maximum uptake was noticed at 0.25 total TU of the binary mixture under visible irradiation and 1 TU of anatase NPs for UV-A irradiation. Individual NPs showed highest uptake under UV-A than visible irradiation. In contrast, binary mixture showed a difference in the uptake pattern based on the type of irradiation exposed. Copyright © 2016 Elsevier B.V. All rights reserved.

Rasch Mixture Models for DIF Detection: A Comparison of Old and New Score Specifications

ERIC Educational Resources Information Center

Frick, Hannah; Strobl, Carolin; Zeileis, Achim

2015-01-01

Rasch mixture models can be a useful tool when checking the assumption of measurement invariance for a single Rasch model. They provide advantages compared to manifest differential item functioning (DIF) tests when the DIF groups are only weakly correlated with the manifest covariates available. Unlike in single Rasch models, estimation of Rasch…

Synthesis of carbon loaded γ-Fe2O3 nanocomposite and their applicability for the selective removal of binary mixture of dyes by ultrasonic adsorption based on response surface methodology.

PubMed

Saad, Muhammad; Tahir, Hajira

2017-05-01

The contemporary problems concerning water purification could be resolved by using nanosorbents. The present studies emphasis on the synthesis of γ-Fe 2 O 3 -activated carbon nanocomposites (γ-Fe 2 O 3 -NP-AC) by sol-gel method. The composition and surface morphology of them were studied by FTIR, EDS, SEM and XRD techniques. Moreover they were employed for the selective removal of binary mixture of dyes including reactive red 223 dye (RR) and Malachite Green dye (MG) by ultrasonic assisted adsorption method. Sonication is the act of applying sound energy to agitate particles in the sample. The ultrasonic frequencies (>20kHz) were used to agitate experimental solutions in current studies. The response surface methodology based on 5 factorial central composite design (CCD) was employed to investigate the optimum parameters of adsorption. The optimum operating parameters (OOP) including sonication time, solution pH, amount of adsorbent, concentration of RR and MG were estimated for the selective removal of mixture of dyes. On OOP conditions of RR, the % removal of RR and MG were observed to be 92.12% and 10.05% respectively. While at OOP of MG, the % removal of MG and RR were observed to be 85.32% and 32.13% from the mixture respectively. Moreover the mechanisms of adsorption of RR and MG on the γ-Fe 2 O 3 -NP-AC were also illustrated. The significance of the RR-γ-Fe 2 O 3 -NP-AC and MG-γ-Fe 2 O 3 -NP-AC adsorption models was affirmed by ANOVA test. The Pareto plots for the selective removal of the RR and MG from the binary mixture also confirm the significance of the factors. Isothermal studies were performed and RR adsorption was observed to follow Langmuir isotherm model whereas MG adsorption was observed to follow Freundlich model. Thermodynamic studies were conducted and the outcomes suggested the spontaneous nature of adsorption processes. The kinetic models were employed to study the kinetics of the process. It was observed that the system followed pseudo second order, intra-particle diffusion and Elovich models as represented by the R 2 values of the respective models. The comparative study from the previously studies revealed that the proposed method is amongst them is the most efficient method to eliminate RR and MG dyes from the aqueous medium. Therefore the current study will be useful in reducing the toxicity of RR and MG contaminated effluent. Copyright © 2016 Elsevier B.V. All rights reserved.

Modeling biofiltration of VOC mixtures under steady-state conditions

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

Baltzis, B.C.; Wojdyla, S.M.; Zarook, S.M.

1997-06-01

Treatment of air streams contaminated with binary volatile organic compound (VOC) mixtures in classical biofilters under steady-state conditions of operation was described with a general mathematical model. The model accounts for potential kinetic interactions among the pollutants, effects of oxygen availability on biodegradation, and biomass diversification in the filter bed. While the effects of oxygen were always taken into account, two distinct cases were considered for the experimental model validation. The first involves kinetic interactions, but no biomass differentiation, used for describing data from biofiltration of benzene/toluene mixtures. The second case assumes that each pollutant is treated by a differentmore » type of biomass. Each biomass type is assumed to form separate patches of biofilm on the solid packing material, thus kinetic interference does not occur. This model was used for describing biofiltration of ethanol/butanol mixtures. Experiments were performed with classical biofilters packed with mixtures of peat moss and perlite (2:3, volume:volume). The model equations were solved through the use of computer codes based on the fourth-order Runge-Kutta technique for the gas-phase mass balances and the method of orthogonal collocation for the concentration profiles in the biofilms. Good agreement between model predictions and experimental data was found in almost all cases. Oxygen was found to be extremely important in the case of polar VOCs (ethanol/butanol).« less

Racial differences in parenting style typologies and heavy episodic drinking trajectories.

PubMed

Clark, Trenette T; Yang, Chongming; McClernon, F Joseph; Fuemmeler, Bernard F

2015-07-01

This study examines racial differences between Whites and Blacks in the association of parenting style typologies with changes in heavy episodic drinking from adolescence to young adulthood. The analytic sample consists of 9,942 adolescents drawn from the National Longitudinal Study of Adolescent Health, which followed respondents from ages 12 to 31 years. Confirmatory factor analysis and factor mixture modeling are used to classify parenting style typologies based on measures of parental acceptance and control. Heavy Episodic Drinking (HED) trajectories are evaluated using a zero-inflated Poisson multigroup latent growth curve modeling approach. The mixture model identified 4 heterogeneous groups that differed based on the 2 latent variables (parental acceptance and control): balanced (65.8% of the sample), authoritarian (12.2%), permissive (19.4%), and uninvolved or neglectful (2.7%). Regardless of race, we found that at age 12 years, children of authoritarian parents have a higher probability of not engaging in HED than children of parents with balanced, permissive, or neglectful parenting styles. However, among Black youth who reported HED at age 12, authoritarian parenting was associated with greater level of HED at age 12 but a less steep increase in level of HED as age increased yearly as compared with balanced parenting. For White adolescents, uninvolved, permissive, and authoritarian parenting were not associated with a greater level of HED as age increased yearly as compared with adolescents exposed to balanced parenting. The influence of parenting styles on HED during adolescence persists into young adulthood and differs by race for youth engaging in HED. (c) 2015 APA, all rights reserved.

Racial Differences in Parenting Style Typologies and Heavy Episodic Drinking Trajectories

PubMed Central

Clark, Trenette T.; Yang, Chongming; McClernon, F. Joseph; Fuemmeler, Bernard

2014-01-01

Objective This study examines racial differences between Caucasians and African Americans in the association of parenting style typologies with changes in heavy episodic drinking from adolescence to young adulthood. Methods The analytic sample consists of 9,942 adolescents drawn from the National Longitudinal Study of Adolescent Health, which followed respondents from ages 12 to 31 years. Confirmatory factor analysis and factor mixture modeling are used to classify parenting style typologies based on measures of parental acceptance and control. HED trajectories are evaluated using a zero-inflated Poisson multigroup latent growth curve modeling approach. Results The mixture model identified four heterogeneous groups that differed based on the two latent variables (parental acceptance and control): balanced (65.8% of the sample), authoritarian (12.2%), permissive (19.4%), and uninvolved/neglectful (2.7%). Regardless of race, we found that at age 12 years, children of authoritarian parents have a higher probability of not engaging in HED than children of parents with balanced, permissive, or neglectful parenting styles. However, among African American youth who reported HED at age 12, authoritarian parenting was associated with greater level of HED at age 12 but a less steep increase in level of HED as age increased yearly as compared with balanced parenting. For Caucasian adolescents, uninvolved, permissive, and authoritarian parenting were not associated with a greater level of HED as age increased yearly as compared with adolescents exposed to balanced parenting. Conclusion The influence of parenting styles on HED during adolescence persists into young adulthood and differs by race for youth engaging in HED. PMID:25222086

Study on Commercialization of Biogasification Systems in Ishikari Bay New Port Area - Proposal of Estimation Method of Collectable Amount of Food Waste by using Binary Logit Model -

NASA Astrophysics Data System (ADS)

Watanabe, Sho; Furuichi, Toru; Ishii, Kazuei

This study proposed an estimation method for collectable amount of food waste considering the food waste generator's cooperation ratio ant the amount of food waste generation, and clarified the factors influencing the collectable amount of food waste. In our method, the cooperation ratio was calculated by using the binary logit model which is often used for the traffic multiple choice question. In order to develop a more precise binary logit model, the factors influencing on the cooperation ratio were extracted by a questionnaire survey asking food waste generator's intention, and the preference investigation was then conducted at the second step. As a result, the collectable amount of food waste was estimated to be 72 [t/day] in the Ishikari bay new port area under a condition of current collection system by using our method. In addition, the most critical factor influencing on the collectable amount of food waste was the treatment fee for households, and was the permitted mixture degree of improper materials for retail trade and restaurant businesses

Concrete pavement mixture design and analysis (MDA) : application of a portable x-ray fluorescence technique to assess concrete mix proportions.

DOT National Transportation Integrated Search

2012-03-01

Any transportation infrastructure system is inherently concerned with durability and performance issues. The proportioning and : uniformity control of concrete mixtures are critical factors that directly affect the longevity and performance of the po...

Synergism and antagonism in extracting local anesthetics from aqueous media with mixtures of solvents

NASA Astrophysics Data System (ADS)

Sukhanov, P. T.; Chibisova, T. V.; Korenman, Ya. I.

2014-12-01

The extraction of local anesthetics from aqueous media with mixtures of solvent is examined and its synergistic and antagonistic effects are determined. Synergism parameters, separation factors, constants for the formation of anesthetic complexes, and solvate numbers are calculated.

Modeling the soil water retention curves of soil-gravel mixtures with regression method on the Loess Plateau of China.

PubMed

Wang, Huifang; Xiao, Bo; Wang, Mingyu; Shao, Ming'an

2013-01-01

Soil water retention parameters are critical to quantify flow and solute transport in vadose zone, while the presence of rock fragments remarkably increases their variability. Therefore a novel method for determining water retention parameters of soil-gravel mixtures is required. The procedure to generate such a model is based firstly on the determination of the quantitative relationship between the content of rock fragments and the effective saturation of soil-gravel mixtures, and then on the integration of this relationship with former analytical equations of water retention curves (WRCs). In order to find such relationships, laboratory experiments were conducted to determine WRCs of soil-gravel mixtures obtained with a clay loam soil mixed with shale clasts or pebbles in three size groups with various gravel contents. Data showed that the effective saturation of the soil-gravel mixtures with the same kind of gravels within one size group had a linear relation with gravel contents, and had a power relation with the bulk density of samples at any pressure head. Revised formulas for water retention properties of the soil-gravel mixtures are proposed to establish the water retention curved surface models of the power-linear functions and power functions. The analysis of the parameters obtained by regression and validation of the empirical models showed that they were acceptable by using either the measured data of separate gravel size group or those of all the three gravel size groups having a large size range. Furthermore, the regression parameters of the curved surfaces for the soil-gravel mixtures with a large range of gravel content could be determined from the water retention data of the soil-gravel mixtures with two representative gravel contents or bulk densities. Such revised water retention models are potentially applicable in regional or large scale field investigations of significantly heterogeneous media, where various gravel sizes and different gravel contents are present.

Modeling the Soil Water Retention Curves of Soil-Gravel Mixtures with Regression Method on the Loess Plateau of China

PubMed Central

Wang, Huifang; Xiao, Bo; Wang, Mingyu; Shao, Ming'an

2013-01-01

Soil water retention parameters are critical to quantify flow and solute transport in vadose zone, while the presence of rock fragments remarkably increases their variability. Therefore a novel method for determining water retention parameters of soil-gravel mixtures is required. The procedure to generate such a model is based firstly on the determination of the quantitative relationship between the content of rock fragments and the effective saturation of soil-gravel mixtures, and then on the integration of this relationship with former analytical equations of water retention curves (WRCs). In order to find such relationships, laboratory experiments were conducted to determine WRCs of soil-gravel mixtures obtained with a clay loam soil mixed with shale clasts or pebbles in three size groups with various gravel contents. Data showed that the effective saturation of the soil-gravel mixtures with the same kind of gravels within one size group had a linear relation with gravel contents, and had a power relation with the bulk density of samples at any pressure head. Revised formulas for water retention properties of the soil-gravel mixtures are proposed to establish the water retention curved surface models of the power-linear functions and power functions. The analysis of the parameters obtained by regression and validation of the empirical models showed that they were acceptable by using either the measured data of separate gravel size group or those of all the three gravel size groups having a large size range. Furthermore, the regression parameters of the curved surfaces for the soil-gravel mixtures with a large range of gravel content could be determined from the water retention data of the soil-gravel mixtures with two representative gravel contents or bulk densities. Such revised water retention models are potentially applicable in regional or large scale field investigations of significantly heterogeneous media, where various gravel sizes and different gravel contents are present. PMID:23555040

Phenomenological Modeling and Laboratory Simulation of Long-Term Aging of Asphalt Mixtures

NASA Astrophysics Data System (ADS)

Elwardany, Michael Dawoud

The accurate characterization of asphalt mixture properties as a function of pavement service life is becoming more important as more powerful pavement design and performance prediction methods are implemented. Oxidative aging is a major distress mechanism of asphalt pavements. Aging increases the stiffness and brittleness of the material, which leads to a high cracking potential. Thus, an improved understanding of the aging phenomenon and its effect on asphalt binder chemical and rheological properties will allow for the prediction of mixture properties as a function of pavement service life. Many researchers have conducted laboratory binder thin-film aging studies; however, this approach does not allow for studying the physicochemical effects of mineral fillers on age hardening rates in asphalt mixtures. Moreover, aging phenomenon in the field is governed by kinetics of binder oxidation, oxygen diffusion through mastic phase, and oxygen percolation throughout the air voids structure. In this study, laboratory aging trials were conducted on mixtures prepared using component materials of several field projects throughout the USA and Canada. Laboratory aged materials were compared against field cores sampled at different ages. Results suggested that oven aging of loose mixture at 95°C is the most promising laboratory long-term aging method. Additionally, an empirical model was developed in order to account for the effect of mineral fillers on age hardening rates in asphalt mixtures. Kinetics modeling was used to predict field aging levels throughout pavement thickness and to determine the required laboratory aging duration to match field aging. Kinetics model outputs are calibrated using measured data from the field to account for the effects of oxygen diffusion and percolation. Finally, the calibrated model was validated using independent set of field sections. This work is expected to provide basis for improved asphalt mixture and pavement design procedures in order to save taxpayers' money.

Source apportionment of PAHs and n-alkanes in respirable particles in Tehran, Iran by wind sector and vertical profile.

PubMed

Moeinaddini, Mazaher; Esmaili Sari, Abbas; Riyahi bakhtiari, Alireza; Chan, Andrew Yiu-Chung; Taghavi, Seyed Mohammad; Hawker, Darryl; Connell, Des

2014-06-01

The vertical concentration profiles and source contributions of polycyclic aromatic hydrocarbons (PAHs) and n-alkanes in respirable particle samples (PM4) collected at 10, 100, 200 and 300-m altitude from the Milad Tower of Tehran, Iran during fall and winter were investigated. The average concentrations of total PAHs and total n-alkanes were 16.7 and 591 ng/m(3), respectively. The positive matrix factorization (PMF) model was applied to the chemical composition and wind data to apportion the contributing sources. The five PAH source factors identified were: 'diesel' (56.3% of total PAHs on average), 'gasoline' (15.5%), 'wood combustion, and incineration' (13%), 'industry' (9.2%), and 'road soil particle' (6.0%). The four n-alkane source factors identified were: 'petrogenic' (65% of total n-alkanes on average), 'mixture of petrogenic and biomass burning' (15%), 'mixture of biogenic and fossil fuel' (11.5%), and 'biogenic' (8.5%). Source contributions by wind sector were also estimated based on the wind sector factor loadings from PMF analysis. Directional dependence of sources was investigated using the conditional probability function (CPF) and directional relative strength (DRS) methods. The calm wind period was found to contribute to 4.4% of total PAHs and 5.0% of total n-alkanes on average. Highest average concentrations of PAHs and n-alkanes were found in the 10 and 100 m samples, reflecting the importance of contributions from local sources. Higher average concentrations in the 300 m samples compared to those in the 200 m samples may indicate contributions from long-range transport. The vertical profiles of source factors indicate the gasoline and road soil particle-associated PAHs, and the mixture from biogenic and fossil fuel source-associated n-alkanes were mostly from local emissions. The smaller average contribution of diesel-associated PAHs in the lower altitude samples also indicates that the restriction of diesel-fueled vehicle use in the central area of Tehran has been effective in reducing the PAHs concentration.

Kinetics of methane production from the codigestion of switchgrass and Spirulina platensis algae.

PubMed

El-Mashad, Hamed M

2013-03-01

Anaerobic batch digestion of four feedstocks was conducted at 35 and 50 °C: switchgrass; Spirulina platensis algae; and two mixtures of both switchgrass and S. platensis. Mixture 1 was composed of 87% switchgrass (based on volatile solids) and 13% S. platensis. Mixture 2 was composed of 67% switchgrass and 33% S. platensis. The kinetics of methane production from these feedstocks was studied using four first order models: exponential, Gompertz, Fitzhugh, and Cone. The methane yields after 40days of digestion at 35 °C were 355, 127, 143 and 198 ml/g VS, respectively for S. platensis, switchgrass, and Mixtures 1 and 2, while the yields at 50 °C were 358, 167, 198, and 236 ml/g VS, respectively. Based on Akaike's information criterion, the Cone model best described the experimental data. The Cone model was validated with experimental data collected from the digestion of a third mixture that was composed of 83% switchgrass and 17% S. platensis. Published by Elsevier Ltd.

Thermal conductivity of disperse insulation materials and their mixtures

NASA Astrophysics Data System (ADS)

Geža, V.; Jakovičs, A.; Gendelis, S.; Usiļonoks, I.; Timofejevs, J.

2017-10-01

Development of new, more efficient thermal insulation materials is a key to reduction of heat losses and contribution to greenhouse gas emissions. Two innovative materials developed at Thermeko LLC are Izoprok and Izopearl. This research is devoted to experimental study of thermal insulation properties of both materials as well as their mixture. Results show that mixture of 40% Izoprok and 60% of Izopearl has lower thermal conductivity than pure materials. In this work, material thermal conductivity dependence temperature is also measured. Novel modelling approach is used to model spatial distribution of disperse insulation material. Computational fluid dynamics approach is also used to estimate role of different heat transfer phenomena in such porous mixture. Modelling results show that thermal convection plays small role in heat transfer despite large fraction of air within material pores.

Dielectric and physiochemical study of binary mixture of nitrobenzene with toluene

NASA Astrophysics Data System (ADS)

Mohod, Ajay G.; Deshmukh, S. D.; Pattebahadur, K. L.; Undre, P. B.; Patil, S. S.; Khirade, P. W.

2018-05-01

This paper presents the study of binary mixture of Nitrobenzene (NB) with Toluene (TOL) for eleven different concentrations at room temperature. The determined Dielectric Constant (ɛ0) Density (ρ) and Refractive index (nD) values of binary mixture are used to calculate the excess properties i.e. Excess Dielectric Constant (ɛ0E), Excess Molar Volume (VmE), Excess Refractive Index (nDE) and Excess Molar Refraction (RmE) of mixture over the entire composition range and fitted to the Redlich-Kister equation. The Kirkwood Correlation Factor (geff) and other parameters were used to discuss the information about the orientation of dipoles and the solute-solvent interaction of binary mixture at molecular level over the entire range of concentration.

[Effects of different excipients on properties of Tongsaimai mixture and pellet molding].

PubMed

Wang, Jin; Lv, Zhiyang; Wu, Xiaoyan; Di, Liuqing; Dong, Yu; Cai, Baochang

2011-01-01

To study preliminarily on the relationship between properties of the mixture composed of Tongsaimai extract and different excipients and pellet molding. The multivariate regression analysis was used to investigate the correlation of different mixture and pellet molding by measuring the cohesion, liquid-plastic limit of mixture, and the powder properties of pellets. The weighted coefficients of the powder properties were determined by analytic hierarchy process combined with criteria importance through intercriteria correlation. The results showed that liquid-plastic limit seemed to be a major factor, which had positive correlation with pellet molding, while cohesion had negative correlation with pellet molding in the measured range. The physical properties of the mixture has marked influence on pellet molding.

Human Health Risk Assessment of Pharmaceuticals in Water: Issues and Challenges Ahead

PubMed Central

Kumar, Arun; Chang, Biao; Xagoraraki, Irene

2010-01-01

This study identified existing issues related to quantitative pharmaceutical risk assessment (QPhRA, hereafter) for pharmaceuticals in water and proposed possible solutions by analyzing methodologies and findings of different published QPhRA studies. Retrospective site-specific QPhRA studies from different parts of the world (U.S.A., United Kingdom, Europe, India, etc.) were reviewed in a structured manner to understand different assumptions, outcomes obtained and issues, identified/addressed/raised by the different QPhRA studies. Till date, most of the published studies have concluded that there is no appreciable risk to human health during environmental exposures of pharmaceuticals; however, attention is still required to following identified issues: (1) Use of measured versus predicted pharmaceutical concentration, (2) Identification of pharmaceuticals-of-concern and compounds needing special considerations, (3) Use of source water versus finished drinking water-related exposure scenarios, (4) Selection of representative exposure routes, (5) Valuation of uncertainty factors, and (6) Risk assessment for mixture of chemicals. To close the existing data and methodology gaps, this study proposed possible ways to address and/or incorporation these considerations within the QPhRA framework; however, more research work is still required to address issues, such as incorporation of short-term to long-term extrapolation and mixture effects in the QPhRA framework. Specifically, this study proposed a development of a new “mixture effects-related uncertainty factor” for mixture of chemicals (i.e., mixUFcomposite), similar to an uncertainty factor of a single chemical, within the QPhRA framework. In addition to all five traditionally used uncertainty factors, this uncertainty factor is also proposed to include concentration effects due to presence of different range of concentration levels of pharmaceuticals in a mixture. However, further work is required to determine values of all six uncertainty factors and incorporate them to use during estimation of point-of-departure values within the QPhRA framework. PMID:21139869

A BGK model for reactive mixtures of polyatomic gases with continuous internal energy

NASA Astrophysics Data System (ADS)

Bisi, M.; Monaco, R.; Soares, A. J.

2018-03-01

In this paper we derive a BGK relaxation model for a mixture of polyatomic gases with a continuous structure of internal energies. The emphasis of the paper is on the case of a quaternary mixture undergoing a reversible chemical reaction of bimolecular type. For such a mixture we prove an H -theorem and characterize the equilibrium solutions with the related mass action law of chemical kinetics. Further, a Chapman-Enskog asymptotic analysis is performed in view of computing the first-order non-equilibrium corrections to the distribution functions and investigating the transport properties of the reactive mixture. The chemical reaction rate is explicitly derived at the first order and the balance equations for the constituent number densities are derived at the Euler level.

Metal-Polycyclic Aromatic Hydrocarbon Mixture Toxicity in Hyalella azteca. 1. Response Surfaces and Isoboles To Measure Non-additive Mixture Toxicity and Ecological Risk.

PubMed

Gauthier, Patrick T; Norwood, Warren P; Prepas, Ellie E; Pyle, Greg G

2015-10-06

Mixtures of metals and polycyclic aromatic hydrocarbons (PAHs) occur ubiquitously in aquatic environments, yet relatively little is known regarding their potential to produce non-additive toxicity (i.e., antagonism or potentiation). A review of the lethality of metal-PAH mixtures in aquatic biota revealed that more-than-additive lethality is as common as strictly additive effects. Approaches to ecological risk assessment do not consider non-additive toxicity of metal-PAH mixtures. Forty-eight-hour water-only binary mixture toxicity experiments were conducted to determine the additive toxic nature of mixtures of Cu, Cd, V, or Ni with phenanthrene (PHE) or phenanthrenequinone (PHQ) using the aquatic amphipod Hyalella azteca. In cases where more-than-additive toxicity was observed, we calculated the possible mortality rates at Canada's environmental water quality guideline concentrations. We used a three-dimensional response surface isobole model-based approach to compare the observed co-toxicity in juvenile amphipods to predicted outcomes based on concentration addition or effects addition mixtures models. More-than-additive lethality was observed for all Cu-PHE, Cu-PHQ, and several Cd-PHE, Cd-PHQ, and Ni-PHE mixtures. Our analysis predicts Cu-PHE, Cu-PHQ, Cd-PHE, and Cd-PHQ mixtures at the Canadian Water Quality Guideline concentrations would produce 7.5%, 3.7%, 4.4% and 1.4% mortality, respectively.

The simultaneous mass and energy evaporation (SM2E) model.

PubMed

Choudhary, Rehan; Klauda, Jeffery B

2016-01-01

In this article, the Simultaneous Mass and Energy Evaporation (SM2E) model is presented. The SM2E model is based on theoretical models for mass and energy transfer. The theoretical models systematically under or over predicted at various flow conditions: laminar, transition, and turbulent. These models were harmonized with experimental measurements to eliminate systematic under or over predictions; a total of 113 measured evaporation rates were used. The SM2E model can be used to estimate evaporation rates for pure liquids as well as liquid mixtures at laminar, transition, and turbulent flow conditions. However, due to limited availability of evaporation data, the model has so far only been tested against data for pure liquids and binary mixtures. The model can take evaporative cooling into account and when the temperature of the evaporating liquid or liquid mixture is known (e.g., isothermal evaporation), the SM2E model reduces to a mass transfer-only model.

PACE: Probabilistic Assessment for Contributor Estimation- A machine learning-based assessment of the number of contributors in DNA mixtures.

PubMed

Marciano, Michael A; Adelman, Jonathan D

2017-03-01

The deconvolution of DNA mixtures remains one of the most critical challenges in the field of forensic DNA analysis. In addition, of all the data features required to perform such deconvolution, the number of contributors in the sample is widely considered the most important, and, if incorrectly chosen, the most likely to negatively influence the mixture interpretation of a DNA profile. Unfortunately, most current approaches to mixture deconvolution require the assumption that the number of contributors is known by the analyst, an assumption that can prove to be especially faulty when faced with increasingly complex mixtures of 3 or more contributors. In this study, we propose a probabilistic approach for estimating the number of contributors in a DNA mixture that leverages the strengths of machine learning. To assess this approach, we compare classification performances of six machine learning algorithms and evaluate the model from the top-performing algorithm against the current state of the art in the field of contributor number classification. Overall results show over 98% accuracy in identifying the number of contributors in a DNA mixture of up to 4 contributors. Comparative results showed 3-person mixtures had a classification accuracy improvement of over 6% compared to the current best-in-field methodology, and that 4-person mixtures had a classification accuracy improvement of over 20%. The Probabilistic Assessment for Contributor Estimation (PACE) also accomplishes classification of mixtures of up to 4 contributors in less than 1s using a standard laptop or desktop computer. Considering the high classification accuracy rates, as well as the significant time commitment required by the current state of the art model versus seconds required by a machine learning-derived model, the approach described herein provides a promising means of estimating the number of contributors and, subsequently, will lead to improved DNA mixture interpretation. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Finite mixture models for the computation of isotope ratios in mixed isotopic samples

NASA Astrophysics Data System (ADS)

Koffler, Daniel; Laaha, Gregor; Leisch, Friedrich; Kappel, Stefanie; Prohaska, Thomas

2013-04-01

Finite mixture models have been used for more than 100 years, but have seen a real boost in popularity over the last two decades due to the tremendous increase in available computing power. The areas of application of mixture models range from biology and medicine to physics, economics and marketing. These models can be applied to data where observations originate from various groups and where group affiliations are not known, as is the case for multiple isotope ratios present in mixed isotopic samples. Recently, the potential of finite mixture models for the computation of 235U/238U isotope ratios from transient signals measured in individual (sub-)µm-sized particles by laser ablation - multi-collector - inductively coupled plasma mass spectrometry (LA-MC-ICPMS) was demonstrated by Kappel et al. [1]. The particles, which were deposited on the same substrate, were certified with respect to their isotopic compositions. Here, we focus on the statistical model and its application to isotope data in ecogeochemistry. Commonly applied evaluation approaches for mixed isotopic samples are time-consuming and are dependent on the judgement of the analyst. Thus, isotopic compositions may be overlooked due to the presence of more dominant constituents. Evaluation using finite mixture models can be accomplished unsupervised and automatically. The models try to fit several linear models (regression lines) to subgroups of data taking the respective slope as estimation for the isotope ratio. The finite mixture models are parameterised by: • The number of different ratios. • Number of points belonging to each ratio-group. • The ratios (i.e. slopes) of each group. Fitting of the parameters is done by maximising the log-likelihood function using an iterative expectation-maximisation (EM) algorithm. In each iteration step, groups of size smaller than a control parameter are dropped; thereby the number of different ratios is determined. The analyst only influences some control parameters of the algorithm, i.e. the maximum count of ratios, the minimum relative group-size of data points belonging to each ratio has to be defined. Computation of the models can be done with statistical software. In this study Leisch and Grün's flexmix package [2] for the statistical open-source software R was applied. A code example is available in the electronic supplementary material of Kappel et al. [1]. In order to demonstrate the usefulness of finite mixture models in fields dealing with the computation of multiple isotope ratios in mixed samples, a transparent example based on simulated data is presented and problems regarding small group-sizes are illustrated. In addition, the application of finite mixture models to isotope ratio data measured in uranium oxide particles is shown. The results indicate that finite mixture models perform well in computing isotope ratios relative to traditional estimation procedures and can be recommended for more objective and straightforward calculation of isotope ratios in geochemistry than it is current practice. [1] S. Kappel, S. Boulyga, L. Dorta, D. Günther, B. Hattendorf, D. Koffler, G. Laaha, F. Leisch and T. Prohaska: Evaluation Strategies for Isotope Ratio Measurements of Single Particles by LA-MC-ICPMS, Analytical and Bioanalytical Chemistry, 2013, accepted for publication on 2012-12-18 (doi: 10.1007/s00216-012-6674-3) [2] B. Grün and F. Leisch: Fitting finite mixtures of generalized linear regressions in R. Computational Statistics & Data Analysis, 51(11), 5247-5252, 2007. (doi:10.1016/j.csda.2006.08.014)

Spatially and Temporally Resolved Atomic Oxygen Measurements in Short Pulse Discharges by Two Photon Laser Induced Fluorescence

NASA Astrophysics Data System (ADS)

Lempert, Walter; Uddi, Mruthunjaya; Mintusov, Eugene; Jiang, Naibo; Adamovich, Igor

2007-10-01

Two Photon Laser Induced Fluorescence (TALIF) is used to measure time-dependent absolute oxygen atom concentrations in O2/He, O2/N2, and CH4/air plasmas produced with a 20 nanosecond duration, 20 kV pulsed discharge at 10 Hz repetition rate. Xenon calibrated spectra show that a single discharge pulse creates initial oxygen dissociation fraction of ˜0.0005 for air like mixtures at 40-60 torr total pressure. Peak O atom concentration is a factor of approximately two lower in fuel lean (φ=0.5) methane/air mixtures. In helium buffer, the initially formed atomic oxygen decays monotonically, with decay time consistent with formation of ozone. In all nitrogen containing mixtures, atomic oxygen concentrations are found to initially increase, for time scales on the order of 10-100 microseconds, due presumably to additional O2 dissociation caused by collisions with electronically excited nitrogen. Further evidence of the role of metastable N2 is demonstrated from time-dependent N2 2^nd Positive and NO Gamma band emission spectroscopy. Comparisons with modeling predictions show qualitative, but not quantitative, agreement with the experimental data.

Analyzing gene expression time-courses based on multi-resolution shape mixture model.

PubMed

Li, Ying; He, Ye; Zhang, Yu

2016-11-01

Biological processes actually are a dynamic molecular process over time. Time course gene expression experiments provide opportunities to explore patterns of gene expression change over a time and understand the dynamic behavior of gene expression, which is crucial for study on development and progression of biology and disease. Analysis of the gene expression time-course profiles has not been fully exploited so far. It is still a challenge problem. We propose a novel shape-based mixture model clustering method for gene expression time-course profiles to explore the significant gene groups. Based on multi-resolution fractal features and mixture clustering model, we proposed a multi-resolution shape mixture model algorithm. Multi-resolution fractal features is computed by wavelet decomposition, which explore patterns of change over time of gene expression at different resolution. Our proposed multi-resolution shape mixture model algorithm is a probabilistic framework which offers a more natural and robust way of clustering time-course gene expression. We assessed the performance of our proposed algorithm using yeast time-course gene expression profiles compared with several popular clustering methods for gene expression profiles. The grouped genes identified by different methods are evaluated by enrichment analysis of biological pathways and known protein-protein interactions from experiment evidence. The grouped genes identified by our proposed algorithm have more strong biological significance. A novel multi-resolution shape mixture model algorithm based on multi-resolution fractal features is proposed. Our proposed model provides a novel horizons and an alternative tool for visualization and analysis of time-course gene expression profiles. The R and Matlab program is available upon the request. Copyright © 2016 Elsevier Inc. All rights reserved.

An analysis of lethal and sublethal interactions among type I and type II pyrethroid pesticide mixtures using standard Hyalella azteca water column toxicity tests.

PubMed

Hoffmann, Krista Callinan; Deanovic, Linda; Werner, Inge; Stillway, Marie; Fong, Stephanie; Teh, Swee

2016-10-01

A novel 2-tiered analytical approach was used to characterize and quantify interactions between type I and type II pyrethroids in Hyalella azteca using standardized water column toxicity tests. Bifenthrin, permethrin, cyfluthrin, and lambda-cyhalothrin were tested in all possible binary combinations across 6 experiments. All mixtures were analyzed for 4-d lethality, and 2 of the 6 mixtures (permethrin-bifenthrin and permethrin-cyfluthrin) were tested for subchronic 10-d lethality and sublethal effects on swimming motility and growth. Mixtures were initially analyzed for interactions using regression analyses, and subsequently compared with the additive models of concentration addition and independent action to further characterize mixture responses. Negative interactions (antagonistic) were significant in 2 of the 6 mixtures tested, including cyfluthrin-bifenthrin and cyfluthrin-permethrin, but only on the acute 4-d lethality endpoint. In both cases mixture responses fell between the additive models of concentration addition and independent action. All other mixtures were additive across 4-d lethality, and bifenthrin-permethrin and cyfluthrin-permethrin were also additive in terms of subchronic 10-d lethality and sublethal responses. Environ Toxicol Chem 2016;35:2542-2549. © 2016 SETAC. © 2016 SETAC.

Heat transfer during condensation of steam from steam-gas mixtures in the passive safety systems of nuclear power plants

NASA Astrophysics Data System (ADS)

Portnova, N. M.; Smirnov, Yu B.

2017-11-01

A theoretical model for calculation of heat transfer during condensation of multicomponent vapor-gas mixtures on vertical surfaces, based on film theory and heat and mass transfer analogy is proposed. Calculations were performed for the conditions implemented in experimental studies of heat transfer during condensation of steam-gas mixtures in the passive safety systems of PWR-type reactors of different designs. Calculated values of heat transfer coefficients for condensation of steam-air, steam-air-helium and steam-air-hydrogen mixtures at pressures of 0.2 to 0.6 MPa and of steam-nitrogen mixture at the pressures of 0.4 to 2.6 MPa were obtained. The composition of mixtures and vapor-to-surface temperature difference were varied within wide limits. Tube length ranged from 0.65 to 9.79m. The condensation of all steam-gas mixtures took place in a laminar-wave flow mode of condensate film and turbulent free convection in the diffusion boundary layer. The heat transfer coefficients obtained by calculation using the proposed model are in good agreement with the considered experimental data for both the binary and ternary mixtures.

Combining measurements to estimate properties and characterization extent of complex biochemical mixtures; applications to Heparan Sulfate

PubMed Central

Pradines, Joël R.; Beccati, Daniela; Lech, Miroslaw; Ozug, Jennifer; Farutin, Victor; Huang, Yongqing; Gunay, Nur Sibel; Capila, Ishan

2016-01-01

Complex mixtures of molecular species, such as glycoproteins and glycosaminoglycans, have important biological and therapeutic functions. Characterization of these mixtures with analytical chemistry measurements is an important step when developing generic drugs such as biosimilars. Recent developments have focused on analytical methods and statistical approaches to test similarity between mixtures. The question of how much uncertainty on mixture composition is reduced by combining several measurements still remains mostly unexplored. Mathematical frameworks to combine measurements, estimate mixture properties, and quantify remaining uncertainty, i.e. a characterization extent, are introduced here. Constrained optimization and mathematical modeling are applied to a set of twenty-three experimental measurements on heparan sulfate, a mixture of linear chains of disaccharides having different levels of sulfation. While this mixture has potentially over two million molecular species, mathematical modeling and the small set of measurements establish the existence of nonhomogeneity of sulfate level along chains and the presence of abundant sulfate repeats. Constrained optimization yields not only estimations of sulfate repeats and sulfate level at each position in the chains but also bounds on these levels, thereby estimating the extent of characterization of the sulfation pattern which is achieved by the set of measurements. PMID:27112127

Combining measurements to estimate properties and characterization extent of complex biochemical mixtures; applications to Heparan Sulfate.

PubMed

Pradines, Joël R; Beccati, Daniela; Lech, Miroslaw; Ozug, Jennifer; Farutin, Victor; Huang, Yongqing; Gunay, Nur Sibel; Capila, Ishan

2016-04-26

Complex mixtures of molecular species, such as glycoproteins and glycosaminoglycans, have important biological and therapeutic functions. Characterization of these mixtures with analytical chemistry measurements is an important step when developing generic drugs such as biosimilars. Recent developments have focused on analytical methods and statistical approaches to test similarity between mixtures. The question of how much uncertainty on mixture composition is reduced by combining several measurements still remains mostly unexplored. Mathematical frameworks to combine measurements, estimate mixture properties, and quantify remaining uncertainty, i.e. a characterization extent, are introduced here. Constrained optimization and mathematical modeling are applied to a set of twenty-three experimental measurements on heparan sulfate, a mixture of linear chains of disaccharides having different levels of sulfation. While this mixture has potentially over two million molecular species, mathematical modeling and the small set of measurements establish the existence of nonhomogeneity of sulfate level along chains and the presence of abundant sulfate repeats. Constrained optimization yields not only estimations of sulfate repeats and sulfate level at each position in the chains but also bounds on these levels, thereby estimating the extent of characterization of the sulfation pattern which is achieved by the set of measurements.

Combining measurements to estimate properties and characterization extent of complex biochemical mixtures; applications to Heparan Sulfate

NASA Astrophysics Data System (ADS)

Pradines, Joël R.; Beccati, Daniela; Lech, Miroslaw; Ozug, Jennifer; Farutin, Victor; Huang, Yongqing; Gunay, Nur Sibel; Capila, Ishan

2016-04-01

Complex mixtures of molecular species, such as glycoproteins and glycosaminoglycans, have important biological and therapeutic functions. Characterization of these mixtures with analytical chemistry measurements is an important step when developing generic drugs such as biosimilars. Recent developments have focused on analytical methods and statistical approaches to test similarity between mixtures. The question of how much uncertainty on mixture composition is reduced by combining several measurements still remains mostly unexplored. Mathematical frameworks to combine measurements, estimate mixture properties, and quantify remaining uncertainty, i.e. a characterization extent, are introduced here. Constrained optimization and mathematical modeling are applied to a set of twenty-three experimental measurements on heparan sulfate, a mixture of linear chains of disaccharides having different levels of sulfation. While this mixture has potentially over two million molecular species, mathematical modeling and the small set of measurements establish the existence of nonhomogeneity of sulfate level along chains and the presence of abundant sulfate repeats. Constrained optimization yields not only estimations of sulfate repeats and sulfate level at each position in the chains but also bounds on these levels, thereby estimating the extent of characterization of the sulfation pattern which is achieved by the set of measurements.

Nature and prevalence of non-additive toxic effects in industrially relevant mixtures of organic chemicals.

PubMed

Parvez, Shahid; Venkataraman, Chandra; Mukherji, Suparna

2009-06-01

The concentration addition (CA) and the independent action (IA) models are widely used for predicting mixture toxicity based on its composition and individual component dose-response profiles. However, the prediction based on these models may be inaccurate due to interaction among mixture components. In this work, the nature and prevalence of non-additive effects were explored for binary, ternary and quaternary mixtures composed of hydrophobic organic compounds (HOCs). The toxicity of each individual component and mixture was determined using the Vibrio fischeri bioluminescence inhibition assay. For each combination of chemicals specified by the 2(n) factorial design, the percent deviation of the predicted toxic effect from the measured value was used to characterize mixtures as synergistic (positive deviation) and antagonistic (negative deviation). An arbitrary classification scheme was proposed based on the magnitude of deviation (d) as: additive (< or =10%, class-I) and moderately (10< d < or =30 %, class-II), highly (30< d < or =50%, class-III) and very highly (>50%, class-IV) antagonistic/synergistic. Naphthalene, n-butanol, o-xylene, catechol and p-cresol led to synergism in mixtures while 1, 2, 4-trimethylbenzene and 1, 3-dimethylnaphthalene contributed to antagonism. Most of the mixtures depicted additive or antagonistic effect. Synergism was prominent in some of the mixtures, such as, pulp and paper, textile dyes, and a mixture composed of polynuclear aromatic hydrocarbons. The organic chemical industry mixture depicted the highest abundance of antagonism and least synergism. Mixture toxicity was found to depend on partition coefficient, molecular connectivity index and relative concentration of the components.

Gaseous emissions from the combustion of a waste mixture containing a high concentration of N2O.

PubMed

Dong, Changqing; Yang, Yongping; Zhang, Junjiao; Lu, Xuefeng

2009-01-01

This paper is focused on reducing the emissions from the combustion of a waste mixture containing a high concentration of N2O. A rate model and an equilibrium model were used to predict gaseous emissions from the combustion of the mixture. The influences of temperature and methane were considered, and the experimental research was carried out in a tabular reactor and a pilot combustion furnace. The results showed that for the waste mixture, the combustion temperature should be in the range of 950-1100 degrees C and the gas residence time should be 2s or higher to reduce emissions.

Mixtures of charged colloid and neutral polymer: Influence of electrostatic interactions on demixing and interfacial tension

NASA Astrophysics Data System (ADS)

Denton, Alan R.; Schmidt, Matthias

2005-06-01

The equilibrium phase behavior of a binary mixture of charged colloids and neutral, nonadsorbing polymers is studied within free-volume theory. A model mixture of charged hard-sphere macroions and ideal, coarse-grained, effective-sphere polymers is mapped first onto a binary hard-sphere mixture with nonadditive diameters and then onto an effective Asakura-Oosawa model [S. Asakura and F. Oosawa, J. Chem. Phys. 22, 1255 (1954)]. The effective model is defined by a single dimensionless parameter—the ratio of the polymer diameter to the effective colloid diameter. For high salt-to-counterion concentration ratios, a free-volume approximation for the free energy is used to compute the fluid phase diagram, which describes demixing into colloid-rich (liquid) and colloid-poor (vapor) phases. Increasing the range of electrostatic interactions shifts the demixing binodal toward higher polymer concentration, stabilizing the mixture. The enhanced stability is attributed to a weakening of polymer depletion-induced attraction between electrostatically repelling macroions. Comparison with predictions of density-functional theory reveals a corresponding increase in the liquid-vapor interfacial tension. The predicted trends in phase stability are consistent with observed behavior of protein-polysaccharide mixtures in food colloids.

Assessment of combined antiandrogenic effects of binary parabens mixtures in a yeast-based reporter assay.

PubMed

Ma, Dehua; Chen, Lujun; Zhu, Xiaobiao; Li, Feifei; Liu, Cong; Liu, Rui

2014-05-01

To date, toxicological studies of endocrine disrupting chemicals (EDCs) have typically focused on single chemical exposures and associated effects. However, exposure to EDCs mixtures in the environment is common. Antiandrogens represent a group of EDCs, which draw increasing attention due to their resultant demasculinization and sexual disruption of aquatic organisms. Although there are a number of in vivo and in vitro studies investigating the combined effects of antiandrogen mixtures, these studies are mainly on selected model compounds such as flutamide, procymidone, and vinclozolin. The aim of the present study is to investigate the combined antiandrogenic effects of parabens, which are widely used antiandrogens in industrial and domestic commodities. A yeast-based human androgen receptor (hAR) assay (YAS) was applied to assess the antiandrogenic activities of n-propylparaben (nPrP), iso-propylparaben (iPrP), methylparaben (MeP), and 4-n-pentylphenol (PeP), as well as the binary mixtures of nPrP with each of the other three antiandrogens. All of the four compounds could exhibit antiandrogenic activity via the hAR. A linear interaction model was applied to quantitatively analyze the interaction between nPrP and each of the other three antiandrogens. The isoboles method was modified to show the variation of combined effects as the concentrations of mixed antiandrogens were changed. Graphs were constructed to show isoeffective curves of three binary mixtures based on the fitted linear interaction model and to evaluate the interaction of the mixed antiandrogens (synergism or antagonism). The combined effect of equimolar combinations of the three mixtures was also considered with the nonlinear isoboles method. The main effect parameters and interaction effect parameters in the linear interaction models of the three mixtures were different from zero. The results showed that any two antiandrogens in their binary mixtures tended to exert equal antiandrogenic activity in the linear concentration ranges. The antiandrogenicity of the binary mixture and the concentration of nPrP were fitted to a sigmoidal model if the concentrations of the other antiandrogens (iPrP, MeP, and PeP) in the mixture were lower than the AR saturation concentrations. Some concave isoboles above the additivity line appeared in all the three mixtures. There were some synergistic effects of the binary mixture of nPrP and MeP at low concentrations in the linear concentration ranges. Interesting, when the antiandrogens concentrations approached the saturation, the interaction between chemicals were antagonistic for all the three mixtures tested. When the toxicity of the three mixtures was assessed using nonlinear isoboles, only antagonism was observed for equimolar combinations of nPrP and iPrP as the concentrations were increased from the no-observed-effect-concentration (NOEC) to effective concentration of 80%. In addition, the interactions were changed from synergistic to antagonistic as effective concentrations were increased in the equimolar combinations of nPrP and MeP, as well as nPrP and PeP. The combined effects of three binary antiandrogens mixtures in the linear ranges were successfully evaluated by curve fitting and isoboles. The combined effects of specific binary mixtures varied depending on the concentrations of the chemicals in the mixtures. At low concentrations in the linear concentration ranges, there was synergistic interaction existing in the binary mixture of nPrP and MeP. The interaction tended to be antagonistic as the antiandrogens approached saturation concentrations in mixtures of nPrP with each of the other three antiandrogens. The synergistic interaction was also found in the equimolar combinations of nPrP and MeP, as well as nPrP and PeP, at low concentrations with another method of nonlinear isoboles. The mixture activities of binary antiandrogens had a tendency towards antagonism at high concentrations and synergism at low concentrations.

Mixture models in diagnostic meta-analyses--clustering summary receiver operating characteristic curves accounted for heterogeneity and correlation.

PubMed

Schlattmann, Peter; Verba, Maryna; Dewey, Marc; Walther, Mario

2015-01-01

Bivariate linear and generalized linear random effects are frequently used to perform a diagnostic meta-analysis. The objective of this article was to apply a finite mixture model of bivariate normal distributions that can be used for the construction of componentwise summary receiver operating characteristic (sROC) curves. Bivariate linear random effects and a bivariate finite mixture model are used. The latter model is developed as an extension of a univariate finite mixture model. Two examples, computed tomography (CT) angiography for ruling out coronary artery disease and procalcitonin as a diagnostic marker for sepsis, are used to estimate mean sensitivity and mean specificity and to construct sROC curves. The suggested approach of a bivariate finite mixture model identifies two latent classes of diagnostic accuracy for the CT angiography example. Both classes show high sensitivity but mainly two different levels of specificity. For the procalcitonin example, this approach identifies three latent classes of diagnostic accuracy. Here, sensitivities and specificities are quite different as such that sensitivity increases with decreasing specificity. Additionally, the model is used to construct componentwise sROC curves and to classify individual studies. The proposed method offers an alternative approach to model between-study heterogeneity in a diagnostic meta-analysis. Furthermore, it is possible to construct sROC curves even if a positive correlation between sensitivity and specificity is present. Copyright © 2015 Elsevier Inc. All rights reserved.

DEVELOPING RELATIVE POTENCY FACTORS FOR PESTICIDE MIXTURES: BIOSTATISTICAL ANALYSES OF JOINT DOSE-RESPONSE

EPA Science Inventory

The 1996 Food Quality Protection Act (FQPA) and the 1996 Safe Drinking Water Act Amendments (SDWAA) reaffirm previous Acts that mandate the EPA to evaluate risks posed by environmental chemical mixtures. The current report develops biological concepts and statistical procedures f...

A laboratory investigation into the effects of aggregate-related factors of critical VMA in asphalt paving mixtures.

DOT National Transportation Integrated Search

2000-06-01

This report summarizes research conducted at Iowa State University on behalf of the Iowa : Department of Transportation, focusing on the volumetric state of hot-mix asphalt (HMA) : mixtures as they transition from stable to unstable configurations. T...

Modeling Grade IV Gas Emboli using a Limited Failure Population Model with Random Effects

NASA Technical Reports Server (NTRS)

Thompson, Laura A.; Conkin, Johnny; Chhikara, Raj S.; Powell, Michael R.

2002-01-01

Venous gas emboli (VGE) (gas bubbles in venous blood) are associated with an increased risk of decompression sickness (DCS) in hypobaric environments. A high grade of VGE can be a precursor to serious DCS. In this paper, we model time to Grade IV VGE considering a subset of individuals assumed to be immune from experiencing VGE. Our data contain monitoring test results from subjects undergoing up to 13 denitrogenation test procedures prior to exposure to a hypobaric environment. The onset time of Grade IV VGE is recorded as contained within certain time intervals. We fit a parametric (lognormal) mixture survival model to the interval-and right-censored data to account for the possibility of a subset of "cured" individuals who are immune to the event. Our model contains random subject effects to account for correlations between repeated measurements on a single individual. Model assessments and cross-validation indicate that this limited failure population mixture model is an improvement over a model that does not account for the potential of a fraction of cured individuals. We also evaluated some alternative mixture models. Predictions from the best fitted mixture model indicate that the actual process is reasonably approximated by a limited failure population model.

A globally accurate theory for a class of binary mixture models

NASA Astrophysics Data System (ADS)

Dickman, Adriana G.; Stell, G.

The self-consistent Ornstein-Zernike approximation results for the 3D Ising model are used to obtain phase diagrams for binary mixtures described by decorated models, yielding the plait point, binodals, and closed-loop coexistence curves for the models proposed by Widom, Clark, Neece, and Wheeler. The results are in good agreement with series expansions and experiments.

Modeling the influence of raindrop size on the wash-off losses of copper-based fungicides sprayed on potato (Solanum tuberosum L.) leaves.

PubMed

Pérez-Rodríguez, Paula; Paradelo, Marcos; Rodríguez-Salgado, Isabel; Fernández-Calviño, David; López-Periago, José Eugenio

2013-01-01

Modeling the pesticide wash-off by raindrops is important for predicting pesticide losses and the subsequent transport of pesticides to soil and in soil run-off. Three foliar-applied copper-based fungicide formulations, specifically the Bordeaux mixture (BM), copper oxychloride (CO), and a mixture of copper oxychloride and propylene glycol (CO-PG), were tested on potato (Solanum tuberosum L.) leaves using a laboratory raindrop simulator. The losses in the wash-off were quantified as both copper in-solution loss and copper as particles detached by the raindrops. The efficiency of the raindrop impact on the wash-off was modeled using a stochastic model based on the pesticide release by raindrops. In addition, the influence of the raindrop size, drop falling height, and fungicide dose was analyzed using a full factorial experimental design. The average losses per dose after 14 mm of dripped water for a crop with a leaf area index equal to 1 were 0.08 kg Cu ha(-1) (BM), 0.3 kg Cu ha(-1) (CO) and 0.47 kg Cu ha(-1) (CO-PG). The stochastic model was able to simulate the time course of the wash-off losses and to estimate the losses of both Cu in solution and as particles by the raindrop impacts. For the Cu-oxychloride fungicides, the majority of the Cu was lost as particles that detached from the potato leaves. The percentage of Cu lost increased with the decreasing raindrop size in the three fungicides for the same amount of dripped water. This result suggested that the impact energy is not a limiting factor in the particle detachment rate of high doses. The dosage of the fungicide was the most influential factor in the losses of Cu for the three formulations studied. The results allowed us to quantify the factors that should be considered when estimating the losses by the wash-off of copper-based fungicides and the inputs of copper to the soil by raindrop wash-off.

Estimating abundance while accounting for rarity, correlated behavior, and other sources of variation in counts

USGS Publications Warehouse

Dorazio, Robert M.; Martin, Juulien; Edwards, Holly H.

2013-01-01

The class of N-mixture models allows abundance to be estimated from repeated, point count surveys while adjusting for imperfect detection of individuals. We developed an extension of N-mixture models to account for two commonly observed phenomena in point count surveys: rarity and lack of independence induced by unmeasurable sources of variation in the detectability of individuals. Rarity increases the number of locations with zero detections in excess of those expected under simple models of abundance (e.g., Poisson or negative binomial). Correlated behavior of individuals and other phenomena, though difficult to measure, increases the variation in detection probabilities among surveys. Our extension of N-mixture models includes a hurdle model of abundance and a beta-binomial model of detectability that accounts for additional (extra-binomial) sources of variation in detections among surveys. As an illustration, we fit this model to repeated point counts of the West Indian manatee, which was observed in a pilot study using aerial surveys. Our extension of N-mixture models provides increased flexibility. The effects of different sets of covariates may be estimated for the probability of occurrence of a species, for its mean abundance at occupied locations, and for its detectability.

Estimating abundance while accounting for rarity, correlated behavior, and other sources of variation in counts.

PubMed

Dorazio, Robert M; Martin, Julien; Edwards, Holly H

2013-07-01

The class of N-mixture models allows abundance to be estimated from repeated, point count surveys while adjusting for imperfect detection of individuals. We developed an extension of N-mixture models to account for two commonly observed phenomena in point count surveys: rarity and lack of independence induced by unmeasurable sources of variation in the detectability of individuals. Rarity increases the number of locations with zero detections in excess of those expected under simple models of abundance (e.g., Poisson or negative binomial). Correlated behavior of individuals and other phenomena, though difficult to measure, increases the variation in detection probabilities among surveys. Our extension of N-mixture models includes a hurdle model of abundance and a beta-binomial model of detectability that accounts for additional (extra-binomial) sources of variation in detections among surveys. As an illustration, we fit this model to repeated point counts of the West Indian manatee, which was observed in a pilot study using aerial surveys. Our extension of N-mixture models provides increased flexibility. The effects of different sets of covariates may be estimated for the probability of occurrence of a species, for its mean abundance at occupied locations, and for its detectability.

Bayesian Finite Mixtures for Nonlinear Modeling of Educational Data.

ERIC Educational Resources Information Center

Tirri, Henry; And Others

A Bayesian approach for finding latent classes in data is discussed. The approach uses finite mixture models to describe the underlying structure in the data and demonstrate that the possibility of using full joint probability models raises interesting new prospects for exploratory data analysis. The concepts and methods discussed are illustrated…

Distinguishing Continuous and Discrete Approaches to Multilevel Mixture IRT Models: A Model Comparison Perspective

ERIC Educational Resources Information Center

Zhu, Xiaoshu

2013-01-01

The current study introduced a general modeling framework, multilevel mixture IRT (MMIRT) which detects and describes characteristics of population heterogeneity, while accommodating the hierarchical data structure. In addition to introducing both continuous and discrete approaches to MMIRT, the main focus of the current study was to distinguish…

Mixture Distribution Latent State-Trait Analysis: Basic Ideas and Applications

ERIC Educational Resources Information Center

Courvoisier, Delphine S.; Eid, Michael; Nussbeck, Fridtjof W.

2007-01-01

Extensions of latent state-trait models for continuous observed variables to mixture latent state-trait models with and without covariates of change are presented that can separate individuals differing in their occasion-specific variability. An empirical application to the repeated measurement of mood states (N = 501) revealed that a model with 2…

Biotic and abiotic variables influencing plant litter breakdown in streams: a global study.

PubMed

Boyero, Luz; Pearson, Richard G; Hui, Cang; Gessner, Mark O; Pérez, Javier; Alexandrou, Markos A; Graça, Manuel A S; Cardinale, Bradley J; Albariño, Ricardo J; Arunachalam, Muthukumarasamy; Barmuta, Leon A; Boulton, Andrew J; Bruder, Andreas; Callisto, Marcos; Chauvet, Eric; Death, Russell G; Dudgeon, David; Encalada, Andrea C; Ferreira, Verónica; Figueroa, Ricardo; Flecker, Alexander S; Gonçalves, José F; Helson, Julie; Iwata, Tomoya; Jinggut, Tajang; Mathooko, Jude; Mathuriau, Catherine; M'Erimba, Charles; Moretti, Marcelo S; Pringle, Catherine M; Ramírez, Alonso; Ratnarajah, Lavenia; Rincon, José; Yule, Catherine M

2016-04-27

Plant litter breakdown is a key ecological process in terrestrial and freshwater ecosystems. Streams and rivers, in particular, contribute substantially to global carbon fluxes. However, there is little information available on the relative roles of different drivers of plant litter breakdown in fresh waters, particularly at large scales. We present a global-scale study of litter breakdown in streams to compare the roles of biotic, climatic and other environmental factors on breakdown rates. We conducted an experiment in 24 streams encompassing latitudes from 47.8° N to 42.8° S, using litter mixtures of local species differing in quality and phylogenetic diversity (PD), and alder (Alnus glutinosa) to control for variation in litter traits. Our models revealed that breakdown of alder was driven by climate, with some influence of pH, whereas variation in breakdown of litter mixtures was explained mainly by litter quality and PD. Effects of litter quality and PD and stream pH were more positive at higher temperatures, indicating that different mechanisms may operate at different latitudes. These results reflect global variability caused by multiple factors, but unexplained variance points to the need for expanded global-scale comparisons. © 2016 The Author(s).

Biotic and abiotic variables influencing plant litter breakdown in streams: a global study

PubMed Central

Pearson, Richard G.; Hui, Cang; Gessner, Mark O.; Pérez, Javier; Alexandrou, Markos A.; Graça, Manuel A. S.; Cardinale, Bradley J.; Albariño, Ricardo J.; Arunachalam, Muthukumarasamy; Barmuta, Leon A.; Boulton, Andrew J.; Bruder, Andreas; Callisto, Marcos; Chauvet, Eric; Death, Russell G.; Dudgeon, David; Encalada, Andrea C.; Ferreira, Verónica; Figueroa, Ricardo; Flecker, Alexander S.; Gonçalves, José F.; Helson, Julie; Iwata, Tomoya; Jinggut, Tajang; Mathooko, Jude; Mathuriau, Catherine; M'Erimba, Charles; Moretti, Marcelo S.; Pringle, Catherine M.; Ramírez, Alonso; Ratnarajah, Lavenia; Rincon, José; Yule, Catherine M.

2016-01-01

Plant litter breakdown is a key ecological process in terrestrial and freshwater ecosystems. Streams and rivers, in particular, contribute substantially to global carbon fluxes. However, there is little information available on the relative roles of different drivers of plant litter breakdown in fresh waters, particularly at large scales. We present a global-scale study of litter breakdown in streams to compare the roles of biotic, climatic and other environmental factors on breakdown rates. We conducted an experiment in 24 streams encompassing latitudes from 47.8° N to 42.8° S, using litter mixtures of local species differing in quality and phylogenetic diversity (PD), and alder (Alnus glutinosa) to control for variation in litter traits. Our models revealed that breakdown of alder was driven by climate, with some influence of pH, whereas variation in breakdown of litter mixtures was explained mainly by litter quality and PD. Effects of litter quality and PD and stream pH were more positive at higher temperatures, indicating that different mechanisms may operate at different latitudes. These results reflect global variability caused by multiple factors, but unexplained variance points to the need for expanded global-scale comparisons. PMID:27122551

Kinetic model for the vibrational energy exchange in flowing molecular gas mixtures. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Offenhaeuser, F.

1987-01-01

The present study is concerned with the development of a computational model for the description of the vibrational energy exchange in flowing gas mixtures, taking into account a given number of energy levels for each vibrational degree of freedom. It is possible to select an arbitrary number of energy levels. The presented model uses values in the range from 10 to approximately 40. The distribution of energy with respect to these levels can differ from the equilibrium distribution. The kinetic model developed can be employed for arbitrary gaseous mixtures with an arbitrary number of vibrational degrees of freedom for each type of gas. The application of the model to CO2-H2ON2-O2-He mixtures is discussed. The obtained relations can be utilized in a study of the suitability of radiation-related transitional processes, involving the CO2 molecule, for laser applications. It is found that the computational results provided by the model agree very well with experimental data obtained for a CO2 laser. Possibilities for the activation of a 16-micron and 14-micron laser are considered.

Considering the cumulative risk of mixtures of chemicals – A challenge for policy makers

PubMed Central

2012-01-01

Background The current paradigm for the assessment of the health risk of chemical substances focuses primarily on the effects of individual substances for determining the doses of toxicological concern in order to inform appropriately the regulatory process. These policy instruments place varying requirements on health and safety data of chemicals in the environment. REACH focuses on safety of individual substances; yet all the other facets of public health policy that relate to chemical stressors put emphasis on the effects of combined exposure to mixtures of chemical and physical agents. This emphasis brings about methodological problems linked to the complexity of the respective exposure pathways; the effect (more complex than simple additivity) of mixtures (the so-called 'cocktail effect'); dose extrapolation, i.e. the extrapolation of the validity of dose-response data to dose ranges that extend beyond the levels used for the derivation of the original dose-response relationship; the integrated use of toxicity data across species (including human clinical, epidemiological and biomonitoring data); and variation in inter-individual susceptibility associated with both genetic and environmental factors. Methods In this paper we give an overview of the main methodologies available today to estimate the human health risk of environmental chemical mixtures, ranging from dose addition to independent action, and from ignoring interactions among the mixture constituents to modelling their biological fate taking into account the biochemical interactions affecting both internal exposure and the toxic potency of the mixture. Results We discuss their applicability, possible options available to policy makers and the difficulties and potential pitfalls in implementing these methodologies in the frame of the currently existing policy framework in the European Union. Finally, we suggest a pragmatic solution for policy/regulatory action that would facilitate the evaluation of the health effects of chemical mixtures in the environment and consumer products. Conclusions One universally applicable methodology does not yet exist. Therefore, a pragmatic, tiered approach to regulatory risk assessment of chemical mixtures is suggested, encompassing (a) the use of dose addition to calculate a hazard index that takes into account interactions among mixture components; and (b) the use of the connectivity approach in data-rich situations to integrate mechanistic knowledge at different scales of biological organization. PMID:22759500

MODEL OF ADDITIVE EFFECTS OF MIXTURES OF NARCOTIC CHEMICALS

EPA Science Inventory

Biological effects data with single chemicals are far more abundant than with mixtures. et, environmental exposures to chemical mixtures, for example near hazardous waste sites or nonpoint sources, are very common and using test data from single chemicals to approximate effects o...

Thermodynamic properties of model CdTe/CdSe mixtures

DOE PAGES

van Swol, Frank; Zhou, Xiaowang W.; Challa, Sivakumar R.; ...

2015-02-20

We report on the thermodynamic properties of binary compound mixtures of model groups II–VI semiconductors. We use the recently introduced Stillinger–Weber Hamiltonian to model binary mixtures of CdTe and CdSe. We use molecular dynamics simulations to calculate the volume and enthalpy of mixing as a function of mole fraction. The lattice parameter of the mixture closely follows Vegard's law: a linear relation. This implies that the excess volume is a cubic function of mole fraction. A connection is made with hard sphere models of mixed fcc and zincblende structures. We found that the potential energy exhibits a positive deviation frommore » ideal soluton behaviour; the excess enthalpy is nearly independent of temperatures studied (300 and 533 K) and is well described by a simple cubic function of the mole fraction. Using a regular solution approach (combining non-ideal behaviour for the enthalpy with ideal solution behaviour for the entropy of mixing), we arrive at the Gibbs free energy of the mixture. The Gibbs free energy results indicate that the CdTe and CdSe mixtures exhibit phase separation. The upper consolute temperature is found to be 335 K. Finally, we provide the surface energy as a function of composition. Moreover, it roughly follows ideal solution theory, but with a negative deviation (negative excess surface energy). This indicates that alloying increases the stability, even for nano-particles.« less

Second law of thermodynamics in volume diffusion hydrodynamics in multicomponent gas mixtures

NASA Astrophysics Data System (ADS)

Dadzie, S. Kokou

2012-10-01

We presented the thermodynamic structure of a new continuum flow model for multicomponent gas mixtures. The continuum model is based on a volume diffusion concept involving specific species. It is independent of the observer's reference frame and enables a straightforward tracking of a selected species within a mixture composed of a large number of constituents. A method to derive the second law and constitutive equations accompanying the model is presented. Using the configuration of a rotating fluid we illustrated an example of non-classical flow physics predicted by new contributions in the entropy and constitutive equations.

Estimation of performance of a J-T refrigerators operating with nitrogen-hydrocarbon mixtures and a coiled tubes-in-tube heat exchanger

NASA Astrophysics Data System (ADS)

Satya Meher, R.; Venkatarathnam, G.

2018-06-01

The exergy efficiency of Joule-Thomson (J-T) refrigerators operating with mixtures (MRC systems) strongly depends on the choice of refrigerant mixture and the performance of the heat exchanger used. Helically coiled, multiple tubes-in-tube heat exchangers with an effectiveness of over 96% are widely used in these types of systems. All the current studies focus only on the different heat transfer correlations and the uncertainty in predicting performance of the heat exchanger alone. The main focus of this work is to estimate the uncertainty in cooling capacity when the homogenous model is used by comparing the theoretical and experimental studies. The comparisons have been extended to some two-phase models present in the literature as well. Experiments have been carried out on a J-T refrigerator at a fixed heat load of 10 W with different nitrogen-hydrocarbon mixtures in the evaporator temperature range of 100-120 K. Different heat transfer models have been used to predict the temperature profiles as well as the cooling capacity of the refrigerator. The results show that the homogenous two-phase flow model is probably the most suitable model for rating the cooling capacity of a J-T refrigerator operating with nitrogen-hydrocarbon mixtures.

Estimating Lion Abundance using N-mixture Models for Social Species

PubMed Central

Belant, Jerrold L.; Bled, Florent; Wilton, Clay M.; Fyumagwa, Robert; Mwampeta, Stanslaus B.; Beyer, Dean E.

2016-01-01

Declining populations of large carnivores worldwide, and the complexities of managing human-carnivore conflicts, require accurate population estimates of large carnivores to promote their long-term persistence through well-informed management We used N-mixture models to estimate lion (Panthera leo) abundance from call-in and track surveys in southeastern Serengeti National Park, Tanzania. Because of potential habituation to broadcasted calls and social behavior, we developed a hierarchical observation process within the N-mixture model conditioning lion detectability on their group response to call-ins and individual detection probabilities. We estimated 270 lions (95% credible interval = 170–551) using call-ins but were unable to estimate lion abundance from track data. We found a weak negative relationship between predicted track density and predicted lion abundance from the call-in surveys. Luminosity was negatively correlated with individual detection probability during call-in surveys. Lion abundance and track density were influenced by landcover, but direction of the corresponding effects were undetermined. N-mixture models allowed us to incorporate multiple parameters (e.g., landcover, luminosity, observer effect) influencing lion abundance and probability of detection directly into abundance estimates. We suggest that N-mixture models employing a hierarchical observation process can be used to estimate abundance of other social, herding, and grouping species. PMID:27786283