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.

Bayesian mixture modeling for blood sugar levels of diabetes mellitus patients (case study in RSUD Saiful Anwar Malang Indonesia)

NASA Astrophysics Data System (ADS)

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

2017-10-01

Bayesian statistics proposes an approach that is very flexible in the number of samples and distribution of data. Bayesian Mixture Model (BMM) is a Bayesian approach for multimodal models. Diabetes Mellitus (DM) is more commonly known in the Indonesian community as sweet pee. This disease is one type of chronic non-communicable diseases but it is very dangerous to humans because of the effects of other diseases complications caused. WHO reports in 2013 showed DM disease was ranked 6th in the world as the leading causes of human death. In Indonesia, DM disease continues to increase over time. These research would be studied patterns and would be built the BMM models of the DM data through simulation studies where the simulation data built on cases of blood sugar levels of DM patients in RSUD Saiful Anwar Malang. The results have been successfully demonstrated pattern of distribution of the DM data which has a normal mixture distribution. The BMM models have succeed to accommodate the real condition of the DM data based on the data driven concept.

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.

Patterns of Change in Children's Loneliness: Trajectories from Third through Fifth Grades

ERIC Educational Resources Information Center

Jobe-Shields, Lisa; Cohen, Robert; Parra, Gilbert R.

2011-01-01

Latent growth-mixture modeling was used to investigate patterns of change in loneliness for 170 children from third through fifth grades. A three-class model representing unique trajectories of loneliness provided the best overall fit to the data, including a Stable Low group (65%), as well as groups of Increasers (23%) and Decreasers (12%).…

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.

Larval aquatic insect responses to cadmium and zinc in experimental streams

USGS Publications Warehouse

Mebane, Christopher A.; Schmidt, Travis S.; Balistrieri, Laurie S.

2017-01-01

To evaluate the risks of metal mixture effects to natural stream communities under ecologically relevant conditions, the authors conducted 30-d tests with benthic macroinvertebrates exposed to cadmium (Cd) and zinc (Zn) in experimental streams. The simultaneous exposures were with Cd and Zn singly and with Cd+Zn mixtures at environmentally relevant ratios. The tests produced concentration–response patterns that for individual taxa were interpreted in the same manner as classic single-species toxicity tests and for community metrics such as taxa richness and mayfly (Ephemeroptera) abundance were interpreted in the same manner as with stream survey data. Effect concentrations from the experimental stream exposures were usually 2 to 3 orders of magnitude lower than those from classic single-species tests. Relative to a response addition model, which assumes that the joint toxicity of the mixtures can be predicted from the product of their responses to individual toxicants, the Cd+Zn mixtures generally showed slightly less than additive toxicity. The authors applied a modeling approach called Tox to explore the mixture toxicity results and to relate the experimental stream results to field data. The approach predicts the accumulation of toxicants (hydrogen, Cd, and Zn) on organisms using a 2-pKa bidentate model that defines interactions between dissolved cations and biological receptors (biotic ligands) and relates that accumulation through a logistic equation to biological response. The Tox modeling was able to predict Cd+Zn mixture responses from the single-metal exposures as well as responses from field data. The similarity of response patterns between the 30-d experimental stream tests and field data supports the environmental relevance of testing aquatic insects in experimental streams.

Patterning ecological risk of pesticide contamination at the river basin scale.

PubMed

Faggiano, Leslie; de Zwart, Dick; García-Berthou, Emili; Lek, Sovan; Gevrey, Muriel

2010-05-01

Ecological risk assessment was conducted to determine the risk posed by pesticide mixtures to the Adour-Garonne river basin (south-western France). The objectives of this study were to assess the general state of this basin with regard to pesticide contamination using a risk assessment procedure and to detect patterns in toxic mixture assemblages through a self-organizing map (SOM) methodology in order to identify the locations at risk. Exposure assessment, risk assessment with species sensitivity distribution, and mixture toxicity rules were used to compute six relative risk predictors for different toxic modes of action: the multi-substance potentially affected fraction of species depending on the toxic mode of action of compounds found in the mixture (msPAF CA(TMoA) values). Those predictors computed for the 131 sampling sites assessed in this study were then patterned through the SOM learning process. Four clusters of sampling sites exhibiting similar toxic assemblages were identified. In the first cluster, which comprised 83% of the sampling sites, the risk caused by pesticide mixture toward aquatic species was weak (mean msPAF value for those sites<0.0036%), while in another cluster the risk was significant (mean msPAF<1.09%). GIS mapping allowed an interesting spatial pattern of the distribution of sampling sites for each cluster to be highlighted with a significant and highly localized risk in the French department called "Lot et Garonne". The combined use of the SOM methodology, mixture toxicity modelling and a clear geo-referenced representation of results not only revealed the general state of the Adour-Garonne basin with regard to contamination by pesticides but also enabled to analyze the spatial pattern of toxic mixture assemblage in order to prioritize the locations at risk and to detect the group of compounds causing the greatest risk at the basin scale. Copyright 2010 Elsevier B.V. All rights reserved.

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.

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

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.

Formation mechanism of complex pattern on fishes' skin

NASA Astrophysics Data System (ADS)

Li, Xia; Liu, Shuhua

2009-10-01

In this paper, the formation mechanism of the complex patterns observed on the skin of fishes has been investigated by a two-coupled reaction diffusion model. The effects of coupling strength between two layers play an important role in the pattern-forming process. It is found that only the epidermis layer can produce complicated patterns that have structures on more than one length scale. These complicated patterns including super-stripe pattern, mixture of spots and stripe, and white-eye pattern are similar to the pigmentation patterns on fishes' skin.

Soft Mixer Assignment in a Hierarchical Generative Model of Natural Scene Statistics

PubMed Central

Schwartz, Odelia; Sejnowski, Terrence J.; Dayan, Peter

2010-01-01

Gaussian scale mixture models offer a top-down description of signal generation that captures key bottom-up statistical characteristics of filter responses to images. However, the pattern of dependence among the filters for this class of models is prespecified. We propose a novel extension to the gaussian scale mixture model that learns the pattern of dependence from observed inputs and thereby induces a hierarchical representation of these inputs. Specifically, we propose that inputs are generated by gaussian variables (modeling local filter structure), multiplied by a mixer variable that is assigned probabilistically to each input from a set of possible mixers. We demonstrate inference of both components of the generative model, for synthesized data and for different classes of natural images, such as a generic ensemble and faces. For natural images, the mixer variable assignments show invariances resembling those of complex cells in visual cortex; the statistics of the gaussian components of the model are in accord with the outputs of divisive normalization models. We also show how our model helps interrelate a wide range of models of image statistics and cortical processing. PMID:16999575

Unsupervised Gaussian Mixture-Model With Expectation Maximization for Detecting Glaucomatous Progression in Standard Automated Perimetry Visual Fields.

PubMed

Yousefi, Siamak; Balasubramanian, Madhusudhanan; Goldbaum, Michael H; Medeiros, Felipe A; Zangwill, Linda M; Weinreb, Robert N; Liebmann, Jeffrey M; Girkin, Christopher A; Bowd, Christopher

2016-05-01

To validate Gaussian mixture-model with expectation maximization (GEM) and variational Bayesian independent component analysis mixture-models (VIM) for detecting glaucomatous progression along visual field (VF) defect patterns (GEM-progression of patterns (POP) and VIM-POP). To compare GEM-POP and VIM-POP with other methods. GEM and VIM models separated cross-sectional abnormal VFs from 859 eyes and normal VFs from 1117 eyes into abnormal and normal clusters. Clusters were decomposed into independent axes. The confidence limit (CL) of stability was established for each axis with a set of 84 stable eyes. Sensitivity for detecting progression was assessed in a sample of 83 eyes with known progressive glaucomatous optic neuropathy (PGON). Eyes were classified as progressed if any defect pattern progressed beyond the CL of stability. Performance of GEM-POP and VIM-POP was compared to point-wise linear regression (PLR), permutation analysis of PLR (PoPLR), and linear regression (LR) of mean deviation (MD), and visual field index (VFI). Sensitivity and specificity for detecting glaucomatous VFs were 89.9% and 93.8%, respectively, for GEM and 93.0% and 97.0%, respectively, for VIM. Receiver operating characteristic (ROC) curve areas for classifying progressed eyes were 0.82 for VIM-POP, 0.86 for GEM-POP, 0.81 for PoPLR, 0.69 for LR of MD, and 0.76 for LR of VFI. GEM-POP was significantly more sensitive to PGON than PoPLR and linear regression of MD and VFI in our sample, while providing localized progression information. Detection of glaucomatous progression can be improved by assessing longitudinal changes in localized patterns of glaucomatous defect identified by unsupervised machine learning.

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.

Probability density function characterization for aggregated large-scale wind power based on Weibull mixtures

DOE PAGES

Gomez-Lazaro, Emilio; Bueso, Maria C.; Kessler, Mathieu; ...

2016-02-02

Here, the Weibull probability distribution has been widely applied to characterize wind speeds for wind energy resources. Wind power generation modeling is different, however, due in particular to power curve limitations, wind turbine control methods, and transmission system operation requirements. These differences are even greater for aggregated wind power generation in power systems with high wind penetration. Consequently, models based on one-Weibull component can provide poor characterizations for aggregated wind power generation. With this aim, the present paper focuses on discussing Weibull mixtures to characterize the probability density function (PDF) for aggregated wind power generation. PDFs of wind power datamore » are firstly classified attending to hourly and seasonal patterns. The selection of the number of components in the mixture is analyzed through two well-known different criteria: the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). Finally, the optimal number of Weibull components for maximum likelihood is explored for the defined patterns, including the estimated weight, scale, and shape parameters. Results show that multi-Weibull models are more suitable to characterize aggregated wind power data due to the impact of distributed generation, variety of wind speed values and wind power curtailment.« less

Encoding the local connectivity patterns of fMRI for cognitive task and state classification.

PubMed

Onal Ertugrul, Itir; Ozay, Mete; Yarman Vural, Fatos T

2018-06-15

In this work, we propose a novel framework to encode the local connectivity patterns of brain, using Fisher vectors (FV), vector of locally aggregated descriptors (VLAD) and bag-of-words (BoW) methods. We first obtain local descriptors, called mesh arc descriptors (MADs) from fMRI data, by forming local meshes around anatomical regions, and estimating their relationship within a neighborhood. Then, we extract a dictionary of relationships, called brain connectivity dictionary by fitting a generative Gaussian mixture model (GMM) to a set of MADs, and selecting codewords at the mean of each component of the mixture. Codewords represent connectivity patterns among anatomical regions. We also encode MADs by VLAD and BoW methods using k-Means clustering. We classify cognitive tasks using the Human Connectome Project (HCP) task fMRI dataset and cognitive states using the Emotional Memory Retrieval (EMR). We train support vector machines (SVMs) using the encoded MADs. Results demonstrate that, FV encoding of MADs can be successfully employed for classification of cognitive tasks, and outperform VLAD and BoW representations. Moreover, we identify the significant Gaussians in mixture models by computing energy of their corresponding FV parts, and analyze their effect on classification accuracy. Finally, we suggest a new method to visualize the codewords of the learned brain connectivity dictionary.

Testing treatment effect in schizophrenia clinical trials with heavy patient dropout using latent class growth mixture models.

PubMed

Kong, Fanhui; Chen, Yeh-Fong

2016-07-01

By examining the outcome trajectories of the dropout patients with different reasons in the schizophrenia trials, we note that although patients are recruited from the same protocol that have compatible baseline characteristics, they may respond differently even to the same treatment. Some patients show consistent improvement while others only have temporary relief. This creates different patient subpopulations characterized by their response and dropout patterns. At the same time, those who continue to improve seem to be more likely to complete the study while those who only experience temporary relief have a higher chance to drop out. Such phenomenon appears to be quite general in schizophrenia clinical trials. This simultaneous inhomogeneity both in patient response as well as dropout patterns creates a scenario of missing not at random and therefore results in biases when we use the statistical methods based on the missing at random assumption to test treatment efficacy. In this paper, we propose to use the latent class growth mixture model, which is a special case of the latent mixture model, to conduct the statistical analyses in such situation. This model allows us to take the inhomogeneity among subpopulations into consideration to make more accurate inferences on the treatment effect at any visit time. Comparing with the conventional statistical methods such as mixed-effects model for repeated measures, we demonstrate through simulations that the proposed latent mixture model approach gives better control on the Type I error rate in testing treatment effect. Published 2016. This article is a U.S. Government work and is in the public domain in the USA. Copyright © 2016 John Wiley & Sons, Ltd.

Flow pattern changes influenced by variation of viscosities of a heterogeneous gas-liquid mixture flow in a vertical channel

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

Keska, Jerry K.; Hincapie, Juan; Jones, Richard

In the steady-state flow of a heterogeneous mixture such as an air-liquid mixture, the velocity and void fraction are space- and time-dependent parameters. These parameters are the most fundamental in the analysis and description of a multiphase flow. The determination of flow patterns in an objective way is extremely critical, since this is directly related to sudden changes in spatial and temporal changes of the random like characteristic of concentration. Flow patterns can be described by concentration signals in time, amplitude, and frequency domains. Despite the vital importance and countless attempts to solve or incorporate the flow pattern phenomena intomore » multiphase models, it has still been a very challenging topic in the scientific community since the 1940's and has not yet reached a satisfactory solution. This paper reports the experimental results of the impact of fluid viscosity on flow patterns for two-phase flow. Two-phase flow was created in laboratory equipment using air and liquid as phase medium. The liquid properties were changed by using variable concentrations of glycerol in water mixture which generated a wide-range of dynamic viscosities ranging from 1 to 1060 MPa s. The in situ spatial concentration vs. liquid viscosity and airflow velocity of two-phase flow in a vertical ID=50.8 mm pipe were measured using two concomitant computer-aided measurement systems. After acquiring data, the in situ special concentration signals were analyzed in time (spatial concentration and RMS of spatial concentration vs. time), amplitude (PDF and CPDF), and frequency (PSD and CPSD) domains that documented broad flow pattern changes caused by the fluid viscosity and air velocity changes. (author)« less

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.

Trajectories of premorbid childhood and adolescent functioning in schizophrenia-spectrum psychoses: A first-episode study.

PubMed

Horton, Leslie E; Tarbox, Sarah I; Olino, Thomas M; Haas, Gretchen L

2015-06-30

Evidence of social and behavioral problems preceding the onset of schizophrenia-spectrum psychoses is consistent with a neurodevelopmental model of these disorders. Here we predict that individuals with a first episode of schizophrenia-spectrum psychoses will evidence one of three patterns of premorbid adjustment: an early deficit, a deteriorating pattern, or adequate or good social adjustment. Participants were 164 (38% female; 31% black) individuals ages 15-50 with a first episode of schizophrenia-spectrum psychoses. Premorbid adjustment was assessed using the Cannon-Spoor Premorbid Adjustment Scale. We compared the fit of a series of growth mixture models to examine premorbid adjustment trajectories, and found the following 3-class model provided the best fit with: a "stable-poor" adjustment class (54%), a "stable-good" adjustment class (39%), and a "deteriorating" adjustment class (7%). Relative to the "stable-good" class, the "stable-poor" class experienced worse negative symptoms at 1-year follow-up, particularly in the social amotivation domain. This represents the first known growth mixture modeling study to examine premorbid functioning patterns in first-episode schizophrenia-spectrum psychoses. Given that the stable-poor adjustment pattern was most prevalent, detection of social and academic maladjustment as early as childhood may help identify people at increased risk for schizophrenia-spectrum psychoses, potentially increasing feasibility of early interventions. Published by Elsevier Ireland Ltd.

Pattern Recognition Algorithm for High-Sensitivity Odorant Detection in Unknown Environments

NASA Technical Reports Server (NTRS)

Duong, Tuan A.

2012-01-01

In a realistic odorant detection application environment, the collected sensory data is a mix of unknown chemicals with unknown concentrations and noise. The identification of the odorants among these mixtures is a challenge in data recognition. In addition, deriving their individual concentrations in the mix is also a challenge. A deterministic analytical model was developed to accurately identify odorants and calculate their concentrations in a mixture with noisy data.

Sleep-promoting effects of a GABA/5-HTP mixture: Behavioral changes and neuromodulation in an invertebrate model.

PubMed

Hong, Ki-Bae; Park, Yooheon; Suh, Hyung Joo

2016-04-01

This study was to investigate the sleep promoting effects of combined γ-aminobutyric acid (GABA) and 5-hydroxytryptophan (5-HTP), by examining neuronal processes governing mRNA level alterations, as well as assessing neuromodulator concentrations, in a fruit fly model. Behavioral assays were applied to investigate subjective nighttime activity, sleep episodes, and total duration of subjective nighttime sleep of two amino acids and GABA/5-HTP mixture with caffeine treated flies. Also, real-time PCR and HPLC analysis were applied to analyze the signaling pathway. Subjective nighttime activity and sleep patterns of individual flies significantly decreased with 1% GABA treatment in conjunction with 0.1% 5-HTP treatment (p<0.001). Furthermore, GABA/5-HTP mixture resulted in significant differences between groups related to sleep patterns (40%, p<0.017) and significantly induced subjective nighttime sleep in the awake model (p<0.003). These results related to transcript levels of the GABAB receptor (GABAB-R1) and serotonin receptor (5-HT1A), compared to the control group. In addition, GABA/5-HTP mixture significantly increased GABA levels 1h and 12h following treatment (2.1 fold and 1.2 fold higher than the control, respectively) and also increased 5-HTP levels (0 h: 1.01 μg/protein, 12h: 3.45 μg/protein). In this regard, we successfully demonstrated that using a GABA/5-HTP mixture modulates subjective nighttime activity, sleep episodes, and total duration of subjective nighttime sleep to a greater extent than single administration of each amino acid, and that this modulation occurs via GABAergic and serotonergic signaling. Copyright © 2016 Elsevier Inc. All rights reserved.

Monitoring Urban Greenness Dynamics Using Multiple Endmember Spectral Mixture Analysis

PubMed Central

Gan, Muye; Deng, Jinsong; Zheng, Xinyu; Hong, Yang; Wang, Ke

2014-01-01

Urban greenness is increasingly recognized as an essential constituent of the urban environment and can provide a range of services and enhance residents’ quality of life. Understanding the pattern of urban greenness and exploring its spatiotemporal dynamics would contribute valuable information for urban planning. In this paper, we investigated the pattern of urban greenness in Hangzhou, China, over the past two decades using time series Landsat-5 TM data obtained in 1990, 2002, and 2010. Multiple endmember spectral mixture analysis was used to derive vegetation cover fractions at the subpixel level. An RGB-vegetation fraction model, change intensity analysis and the concentric technique were integrated to reveal the detailed, spatial characteristics and the overall pattern of change in the vegetation cover fraction. Our results demonstrated the ability of multiple endmember spectral mixture analysis to accurately model the vegetation cover fraction in pixels despite the complex spectral confusion of different land cover types. The integration of multiple techniques revealed various changing patterns in urban greenness in this region. The overall vegetation cover has exhibited a drastic decrease over the past two decades, while no significant change occurred in the scenic spots that were studied. Meanwhile, a remarkable recovery of greenness was observed in the existing urban area. The increasing coverage of small green patches has played a vital role in the recovery of urban greenness. These changing patterns were more obvious during the period from 2002 to 2010 than from 1990 to 2002, and they revealed the combined effects of rapid urbanization and greening policies. This work demonstrates the usefulness of time series of vegetation cover fractions for conducting accurate and in-depth studies of the long-term trajectories of urban greenness to obtain meaningful information for sustainable urban development. PMID:25375176

Near-infrared reflectance spectra of mixtures of kaolin-group minerals: Use in clay mineral studies

USGS Publications Warehouse

Crowley, James K.; Vergo, Norma

1988-01-01

Near-infrared (NIR) reflectance spectra for mixtures of ordered kaolinite and ordered dickite have been found to simulate the spectral response of disordered kaolinite. The amount of octahedral vacancy disorder in nine disordered kaolinite samples was estimated by comparing the sample spectra to the spectra of reference mixtures. The resulting estimates are consistent with previously published estimates of vacancy disorder for similar kaolin minerals that were modeled from calculated X-ray diffraction patterns. The ordered kaolinite and dickite samples used in the reference mixtures were carefully selected to avoid undesirable particle size effects that could bias the spectral results.NIR spectra were also recorded for laboratory mixtures of ordered kaolinite and halloysite to assess whether the spectra could be potentially useful for determining mineral proportions in natural physical mixtures of these two clays. Although the kaolinite-halloysite proportions could only be roughly estimated from the mixture spectra, the halloysite component was evident even when halloysite was present in only minor amounts. A similar approach using NIR spectra for laboratory mixtures may have applications in other studies of natural clay mixtures.

Foam patterns

DOEpatents

Chaudhry, Anil R; Dzugan, Robert; Harrington, Richard M; Neece, Faurice D; Singh, Nipendra P; Westendorf, Travis

2013-11-26

A method of creating a foam pattern comprises mixing a polyol component and an isocyanate component to form a liquid mixture. The method further comprises placing a temporary core having a shape corresponding to a desired internal feature in a cavity of a mold and inserting the mixture into the cavity of the mold so that the mixture surrounds a portion of the temporary core. The method optionally further comprises using supporting pins made of foam to support the core in the mold cavity, with such pins becoming integral part of the pattern material simplifying subsequent processing. The method further comprises waiting for a predetermined time sufficient for a reaction from the mixture to form a foam pattern structure corresponding to the cavity of the mold, wherein the foam pattern structure encloses a portion of the temporary core and removing the temporary core from the pattern independent of chemical leaching.

A multi-Poisson dynamic mixture model to cluster developmental patterns of gene expression by RNA-seq.

PubMed

Ye, Meixia; Wang, Zhong; Wang, Yaqun; Wu, Rongling

2015-03-01

Dynamic changes of gene expression reflect an intrinsic mechanism of how an organism responds to developmental and environmental signals. With the increasing availability of expression data across a time-space scale by RNA-seq, the classification of genes as per their biological function using RNA-seq data has become one of the most significant challenges in contemporary biology. Here we develop a clustering mixture model to discover distinct groups of genes expressed during a period of organ development. By integrating the density function of multivariate Poisson distribution, the model accommodates the discrete property of read counts characteristic of RNA-seq data. The temporal dependence of gene expression is modeled by the first-order autoregressive process. The model is implemented with the Expectation-Maximization algorithm and model selection to determine the optimal number of gene clusters and obtain the estimates of Poisson parameters that describe the pattern of time-dependent expression of genes from each cluster. The model has been demonstrated by analyzing a real data from an experiment aimed to link the pattern of gene expression to catkin development in white poplar. The usefulness of the model has been validated through computer simulation. The model provides a valuable tool for clustering RNA-seq data, facilitating our global view of expression dynamics and understanding of gene regulation mechanisms. © The Author 2014. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Controlled pattern imputation for sensitivity analysis of longitudinal binary and ordinal outcomes with nonignorable dropout.

PubMed

Tang, Yongqiang

2018-04-30

The controlled imputation method refers to a class of pattern mixture models that have been commonly used as sensitivity analyses of longitudinal clinical trials with nonignorable dropout in recent years. These pattern mixture models assume that participants in the experimental arm after dropout have similar response profiles to the control participants or have worse outcomes than otherwise similar participants who remain on the experimental treatment. In spite of its popularity, the controlled imputation has not been formally developed for longitudinal binary and ordinal outcomes partially due to the lack of a natural multivariate distribution for such endpoints. In this paper, we propose 2 approaches for implementing the controlled imputation for binary and ordinal data based respectively on the sequential logistic regression and the multivariate probit model. Efficient Markov chain Monte Carlo algorithms are developed for missing data imputation by using the monotone data augmentation technique for the sequential logistic regression and a parameter-expanded monotone data augmentation scheme for the multivariate probit model. We assess the performance of the proposed procedures by simulation and the analysis of a schizophrenia clinical trial and compare them with the fully conditional specification, last observation carried forward, and baseline observation carried forward imputation methods. Copyright © 2018 John Wiley & Sons, Ltd.

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.

Larval aquatic insect responses to cadmium and zinc in experimental streams.

PubMed

Mebane, Christopher A; Schmidt, Travis S; Balistrieri, Laurie S

2017-03-01

To evaluate the risks of metal mixture effects to natural stream communities under ecologically relevant conditions, the authors conducted 30-d tests with benthic macroinvertebrates exposed to cadmium (Cd) and zinc (Zn) in experimental streams. The simultaneous exposures were with Cd and Zn singly and with Cd+Zn mixtures at environmentally relevant ratios. The tests produced concentration-response patterns that for individual taxa were interpreted in the same manner as classic single-species toxicity tests and for community metrics such as taxa richness and mayfly (Ephemeroptera) abundance were interpreted in the same manner as with stream survey data. Effect concentrations from the experimental stream exposures were usually 2 to 3 orders of magnitude lower than those from classic single-species tests. Relative to a response addition model, which assumes that the joint toxicity of the mixtures can be predicted from the product of their responses to individual toxicants, the Cd+Zn mixtures generally showed slightly less than additive toxicity. The authors applied a modeling approach called Tox to explore the mixture toxicity results and to relate the experimental stream results to field data. The approach predicts the accumulation of toxicants (hydrogen, Cd, and Zn) on organisms using a 2-pK a bidentate model that defines interactions between dissolved cations and biological receptors (biotic ligands) and relates that accumulation through a logistic equation to biological response. The Tox modeling was able to predict Cd+Zn mixture responses from the single-metal exposures as well as responses from field data. The similarity of response patterns between the 30-d experimental stream tests and field data supports the environmental relevance of testing aquatic insects in experimental streams. Environ Toxicol Chem 2017;36:749-762. Published 2016 Wiley Periodicals Inc. on behalf of SETAC. This article is a US government work and, as such, is in the public domain in the United States of America. Published 2016 Wiley Periodicals Inc. on behalf of SETAC. This article is a US government work and, as such, is in the public domain in the United States of America.

Explaining the power-law distribution of human mobility through transportation modality decomposition.

PubMed

Zhao, Kai; Musolesi, Mirco; Hui, Pan; Rao, Weixiong; Tarkoma, Sasu

2015-03-16

Human mobility has been empirically observed to exhibit Lévy flight characteristics and behaviour with power-law distributed jump size. The fundamental mechanisms behind this behaviour has not yet been fully explained. In this paper, we propose to explain the Lévy walk behaviour observed in human mobility patterns by decomposing them into different classes according to the different transportation modes, such as Walk/Run, Bike, Train/Subway or Car/Taxi/Bus. Our analysis is based on two real-life GPS datasets containing approximately 10 and 20 million GPS samples with transportation mode information. We show that human mobility can be modelled as a mixture of different transportation modes, and that these single movement patterns can be approximated by a lognormal distribution rather than a power-law distribution. Then, we demonstrate that the mixture of the decomposed lognormal flight distributions associated with each modality is a power-law distribution, providing an explanation to the emergence of Lévy Walk patterns that characterize human mobility patterns.

Explaining the power-law distribution of human mobility through transportation modality decomposition

NASA Astrophysics Data System (ADS)

Zhao, Kai; Musolesi, Mirco; Hui, Pan; Rao, Weixiong; Tarkoma, Sasu

2015-03-01

Human mobility has been empirically observed to exhibit Lévy flight characteristics and behaviour with power-law distributed jump size. The fundamental mechanisms behind this behaviour has not yet been fully explained. In this paper, we propose to explain the Lévy walk behaviour observed in human mobility patterns by decomposing them into different classes according to the different transportation modes, such as Walk/Run, Bike, Train/Subway or Car/Taxi/Bus. Our analysis is based on two real-life GPS datasets containing approximately 10 and 20 million GPS samples with transportation mode information. We show that human mobility can be modelled as a mixture of different transportation modes, and that these single movement patterns can be approximated by a lognormal distribution rather than a power-law distribution. Then, we demonstrate that the mixture of the decomposed lognormal flight distributions associated with each modality is a power-law distribution, providing an explanation to the emergence of Lévy Walk patterns that characterize human mobility patterns.

Explaining the power-law distribution of human mobility through transportation modality decomposition

PubMed Central

Zhao, Kai; Musolesi, Mirco; Hui, Pan; Rao, Weixiong; Tarkoma, Sasu

2015-01-01

Human mobility has been empirically observed to exhibit Lévy flight characteristics and behaviour with power-law distributed jump size. The fundamental mechanisms behind this behaviour has not yet been fully explained. In this paper, we propose to explain the Lévy walk behaviour observed in human mobility patterns by decomposing them into different classes according to the different transportation modes, such as Walk/Run, Bike, Train/Subway or Car/Taxi/Bus. Our analysis is based on two real-life GPS datasets containing approximately 10 and 20 million GPS samples with transportation mode information. We show that human mobility can be modelled as a mixture of different transportation modes, and that these single movement patterns can be approximated by a lognormal distribution rather than a power-law distribution. Then, we demonstrate that the mixture of the decomposed lognormal flight distributions associated with each modality is a power-law distribution, providing an explanation to the emergence of Lévy Walk patterns that characterize human mobility patterns. PMID:25779306

A simulation study to determine the attenuation and bias in health risk estimates due to exposure measurement error in bi-pollutant models

EPA Science Inventory

To understand the combined health effects of exposure to ambient air pollutant mixtures, it is becoming more common to include multiple pollutants in epidemiologic models. However, the complex spatial and temporal pattern of ambient pollutant concentrations and related exposures ...

Pattern learning with deep neural networks in EMG-based speech recognition.

PubMed

Wand, Michael; Schultz, Tanja

2014-01-01

We report on classification of phones and phonetic features from facial electromyographic (EMG) data, within the context of our EMG-based Silent Speech interface. In this paper we show that a Deep Neural Network can be used to perform this classification task, yielding a significant improvement over conventional Gaussian Mixture models. Our central contribution is the visualization of patterns which are learned by the neural network. With increasing network depth, these patterns represent more and more intricate electromyographic activity.

Processing of odor mixtures in the zebrafish olfactory bulb.

PubMed

Tabor, Rico; Yaksi, Emre; Weislogel, Jan-Marek; Friedrich, Rainer W

2004-07-21

Components of odor mixtures often are not perceived individually, suggesting that neural representations of mixtures are not simple combinations of the representations of the components. We studied odor responses to binary mixtures of amino acids and food extracts at different processing stages in the olfactory bulb (OB) of zebrafish. Odor-evoked input to the OB was measured by imaging Ca2+ signals in afferents to olfactory glomeruli. Activity patterns evoked by mixtures were predictable within narrow limits from the component patterns, indicating that mixture interactions in the peripheral olfactory system are weak. OB output neurons, the mitral cells (MCs), were recorded extra- and intracellularly and responded to odors with stimulus-dependent temporal firing rate modulations. Responses to mixtures of amino acids often were dominated by one of the component responses. Responses to mixtures of food extracts, in contrast, were more distinct from both component responses. These results show that mixture interactions can result from processing in the OB. Moreover, our data indicate that mixture interactions in the OB become more pronounced with increasing overlap of input activity patterns evoked by the components. Emerging from these results are rules of mixture interactions that may explain behavioral data and provide a basis for understanding the processing of natural odor stimuli in the OB.

Are polychlorinated biphenyl residues adequately describe by aroclor mixture equivalents. Isomer-specific principal components analysis of such residues in fish and turtles

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

Schwartz, T.R.; Stalling, D.L.; Rice, C.L.

1987-01-01

Polychlorinated biphenyl (PCB) residues from fish and turtles were analyzed with SIMCA (Soft Independent Modeling of Class Analogy), a principal components analysis technique. A series of technical Aroclors were also analyzed to provide a reference data set for pattern recognition. Environmental PCB residues are often expressed in terms of relative Aroclor composition. In this work, we assessed the similarity of Aroclors to class models derived for fish and turtles to ascertain if the PCB residues in the samples could be described by an Aroclor or Aroclor mixture. Using PCA, we found that these samples could not be described by anmore » Aroclor or Aroclor mixture and that it would be inappropriate to report these samples as such. 18 references, 3 figures, 3 tables.« less

Condensation of binary mixtures on horizontal tubes

NASA Astrophysics Data System (ADS)

Büchner, A.; Reif, A.; Rehfeldt, S.; Klein, H.

2017-12-01

The two most common models to describe the condensation of binary mixtures are the equilibrium model by Silver (Trans Inst Chem Eng 25:30-42, 1947) and the film model by Colburn and Drew (Transactions of the American Institute of Chemical Engineers 33:197-215, 1937), which is stated by Webb et al. (Int J Heat Mass Transf 39:3147-3156, 1996) as more accurate. The film model describes the outer heat transfer coefficient by subdividing it into two separate resistances against the heat transfer. The resistance of the liquid condensate film on the tube can be calculated with equations for the condensation of pure substances for the analogous flow pattern and geometry using the property data of the mixture. The resistance in the gas phase can be described by a thermodynamic parameter Z and the single phase heat transfer coefficient α G . In this work measurements for condensation of the binary mixtures n-pentane/iso-octane and iso-propanol/water on horizontal tubes for free convection are carried out. The obtained results are compared with the film model by Colburn and Drew (Transactions of the American Institute of Chemical Engineers 33:197-215, 1937). The comparison shows a rather big deviation between the theoretical model and the experimental results. To improve the prediction quality an own model based on dimensionless numbers is proposed, which describes the experimental results of this work significantly better than the film model.

Identification of Allelic Imbalance with a Statistical Model for Subtle Genomic Mosaicism

PubMed Central

Xia, Rui; Vattathil, Selina; Scheet, Paul

2014-01-01

Genetic heterogeneity in a mixed sample of tumor and normal DNA can confound characterization of the tumor genome. Numerous computational methods have been proposed to detect aberrations in DNA samples from tumor and normal tissue mixtures. Most of these require tumor purities to be at least 10–15%. Here, we present a statistical model to capture information, contained in the individual's germline haplotypes, about expected patterns in the B allele frequencies from SNP microarrays while fully modeling their magnitude, the first such model for SNP microarray data. Our model consists of a pair of hidden Markov models—one for the germline and one for the tumor genome—which, conditional on the observed array data and patterns of population haplotype variation, have a dependence structure induced by the relative imbalance of an individual's inherited haplotypes. Together, these hidden Markov models offer a powerful approach for dealing with mixtures of DNA where the main component represents the germline, thus suggesting natural applications for the characterization of primary clones when stromal contamination is extremely high, and for identifying lesions in rare subclones of a tumor when tumor purity is sufficient to characterize the primary lesions. Our joint model for germline haplotypes and acquired DNA aberration is flexible, allowing a large number of chromosomal alterations, including balanced and imbalanced losses and gains, copy-neutral loss-of-heterozygosity (LOH) and tetraploidy. We found our model (which we term J-LOH) to be superior for localizing rare aberrations in a simulated 3% mixture sample. More generally, our model provides a framework for full integration of the germline and tumor genomes to deal more effectively with missing or uncertain features, and thus extract maximal information from difficult scenarios where existing methods fail. PMID:25166618

On the multiple imputation variance estimator for control-based and delta-adjusted pattern mixture models.

PubMed

Tang, Yongqiang

2017-12-01

Control-based pattern mixture models (PMM) and delta-adjusted PMMs are commonly used as sensitivity analyses in clinical trials with non-ignorable dropout. These PMMs assume that the statistical behavior of outcomes varies by pattern in the experimental arm in the imputation procedure, but the imputed data are typically analyzed by a standard method such as the primary analysis model. In the multiple imputation (MI) inference, Rubin's variance estimator is generally biased when the imputation and analysis models are uncongenial. One objective of the article is to quantify the bias of Rubin's variance estimator in the control-based and delta-adjusted PMMs for longitudinal continuous outcomes. These PMMs assume the same observed data distribution as the mixed effects model for repeated measures (MMRM). We derive analytic expressions for the MI treatment effect estimator and the associated Rubin's variance in these PMMs and MMRM as functions of the maximum likelihood estimator from the MMRM analysis and the observed proportion of subjects in each dropout pattern when the number of imputations is infinite. The asymptotic bias is generally small or negligible in the delta-adjusted PMM, but can be sizable in the control-based PMM. This indicates that the inference based on Rubin's rule is approximately valid in the delta-adjusted PMM. A simple variance estimator is proposed to ensure asymptotically valid MI inferences in these PMMs, and compared with the bootstrap variance. The proposed method is illustrated by the analysis of an antidepressant trial, and its performance is further evaluated via a simulation study. © 2017, The International Biometric Society.

Collective effects in models for interacting molecular motors and motor-microtubule mixtures

NASA Astrophysics Data System (ADS)

Menon, Gautam I.

2006-12-01

Three problems in the statistical mechanics of models for an assembly of molecular motors interacting with cytoskeletal filaments are reviewed. First, a description of the hydrodynamical behaviour of density-density correlations in fluctuating ratchet models for interacting molecular motors is outlined. Numerical evidence indicates that the scaling properties of dynamical behaviour in such models belong to the KPZ universality class. Second, the generalization of such models to include boundary injection and removal of motors is provided. In common with known results for the asymmetric exclusion processes, simulations indicate that such models exhibit sharp boundary driven phase transitions in the thermodynamic limit. In the third part of this paper, recent progress towards a continuum description of pattern formation in mixtures of motors and microtubules is described, and a non-equilibrium “phase-diagram” for such systems discussed.

Clustered mixed nonhomogeneous Poisson process spline models for the analysis of recurrent event panel data.

PubMed

Nielsen, J D; Dean, C B

2008-09-01

A flexible semiparametric model for analyzing longitudinal panel count data arising from mixtures is presented. Panel count data refers here to count data on recurrent events collected as the number of events that have occurred within specific follow-up periods. The model assumes that the counts for each subject are generated by mixtures of nonhomogeneous Poisson processes with smooth intensity functions modeled with penalized splines. Time-dependent covariate effects are also incorporated into the process intensity using splines. Discrete mixtures of these nonhomogeneous Poisson process spline models extract functional information from underlying clusters representing hidden subpopulations. The motivating application is an experiment to test the effectiveness of pheromones in disrupting the mating pattern of the cherry bark tortrix moth. Mature moths arise from hidden, but distinct, subpopulations and monitoring the subpopulation responses was of interest. Within-cluster random effects are used to account for correlation structures and heterogeneity common to this type of data. An estimating equation approach to inference requiring only low moment assumptions is developed and the finite sample properties of the proposed estimating functions are investigated empirically by simulation.

Additivity and Interactions in Ecotoxicity of Pollutant Mixtures: Some Patterns, Conclusions, and Open Questions

PubMed Central

Rodea-Palomares, Ismael; González-Pleiter, Miguel; Martín-Betancor, Keila; Rosal, Roberto; Fernández-Piñas, Francisca

2015-01-01

Understanding the effects of exposure to chemical mixtures is a common goal of pharmacology and ecotoxicology. In risk assessment-oriented ecotoxicology, defining the scope of application of additivity models has received utmost attention in the last 20 years, since they potentially allow one to predict the effect of any chemical mixture relying on individual chemical information only. The gold standard for additivity in ecotoxicology has demonstrated to be Loewe additivity which originated the so-called Concentration Addition (CA) additivity model. In pharmacology, the search for interactions or deviations from additivity (synergism and antagonism) has similarly captured the attention of researchers over the last 20 years and has resulted in the definition and application of the Combination Index (CI) Theorem. CI is based on Loewe additivity, but focused on the identification and quantification of synergism and antagonism. Despite additive models demonstrating a surprisingly good predictive power in chemical mixture risk assessment, concerns still exist due to the occurrence of unpredictable synergism or antagonism in certain experimental situations. In the present work, we summarize the parallel history of development of CA, IA, and CI models. We also summarize the applicability of these concepts in ecotoxicology and how their information may be integrated, as well as the possibility of prediction of synergism. Inside the box, the main question remaining is whether it is worthy to consider departures from additivity in mixture risk assessment and how to predict interactions among certain mixture components. Outside the box, the main question is whether the results observed under the experimental constraints imposed by fractional approaches are a de fide reflection of what it would be expected from chemical mixtures in real world circumstances. PMID:29051468

Theoretical analysis for condensation heat transfer of binary refrigerant mixtures with annular flow in horizontal mini-tubes

NASA Astrophysics Data System (ADS)

Zhang, Hui-Yong; Li, Jun-Ming; Sun, Ji-Liang; Wang, Bu-Xuan

2016-01-01

A theoretical model is developed for condensation heat transfer of binary refrigerant mixtures in mini-tubes with diameter about 1.0 mm. Condensation heat transfer of R410A and R32/R134a mixtures at different mass fluxes and saturated temperatures are analyzed, assuming that the phase flow pattern is annular flow. The results indicate that there exists a maximum interface temperature at the beginning of condensation process for azeotropic and zeotropic mixtures and the corresponding vapor quality to the maximum value increases with mass flux. The effects of mass flux, heat flux, surface tension and tube diameter are analyzed. As expected, the condensation heat transfer coefficients increase with mass flux and vapor quality, and increase faster in high vapor quality region. It is found that the effects of heat flux and surface tension are not so obvious as that of tube diameter. The characteristics of condensation heat transfer of zeotropic mixtures are consistent to those of azeotropic refrigerant mixtures. The condensation heat transfer coefficients increase with the concentration of the less volatile component in binary mixtures.

Identification of natural metabolites in mixture: a pattern recognition strategy based on (13)C NMR.

PubMed

Hubert, Jane; Nuzillard, Jean-Marc; Purson, Sylvain; Hamzaoui, Mahmoud; Borie, Nicolas; Reynaud, Romain; Renault, Jean-Hugues

2014-03-18

Because of their highly complex metabolite profile, the chemical characterization of bioactive natural extracts usually requires time-consuming multistep purification procedures to achieve the structural elucidation of pure individual metabolites. The aim of the present work was to develop a dereplication strategy for the identification of natural metabolites directly within mixtures. Exploiting the polarity range of metabolites, the principle was to rapidly fractionate a multigram quantity of a crude extract by centrifugal partition extraction (CPE). The obtained fractions of simplified chemical composition were subsequently analyzed by (13)C NMR. After automatic collection and alignment of (13)C signals across spectra, hierarchical clustering analysis (HCA) was performed for pattern recognition. As a result, strong correlations between (13)C signals of a single structure within the mixtures of the fraction series were visualized as chemical shift clusters. Each cluster was finally assigned to a molecular structure with the help of a locally built (13)C NMR chemical shift database. The proof of principle of this strategy was achieved on a simple model mixture of commercially available plant secondary metabolites and then applied to a bark extract of the African tree Anogeissus leiocarpus Guill. & Perr. (Combretaceae). Starting from 5 g of this genuine extract, the fraction series was generated by CPE in only 95 min. (13)C NMR analyses of all fractions followed by pattern recognition of (13)C chemical shifts resulted in the unambiguous identification of seven major compounds, namely, sericoside, trachelosperogenin E, ellagic acid, an epimer mixture of (+)-gallocatechin and (-)-epigallocatechin, 3,3'-di-O-methylellagic acid 4'-O-xylopyranoside, and 3,4,3'-tri-O-methylflavellagic acid 4'-O-glucopyranoside.

Glomerular Activity Patterns Evoked by Natural Odor Objects in the Rat Olfactory Bulb Are Related to Patterns Evoked by Major Odorant Components

PubMed Central

Johnson, Brett A.; Ong, Joan; Leon, Michael

2014-01-01

To determine how responses evoked by natural odorant mixtures compare to responses evoked by individual odorant chemicals, we mapped 2-deoxyglucose uptake during exposures to vapors arising from a variety of odor objects that may be important to rodents in the wild. We studied 21 distinct natural odor stimuli ranging from possible food sources such as fruits, vegetables, and meats to environmental odor objects such as grass, herbs, and tree leaves. The natural odor objects evoked robust and surprisingly focal patterns of 2-deoxyglucose uptake involving clusters of neighboring glomeruli, thereby resembling patterns evoked by pure chemicals. Overall, the patterns were significantly related to patterns evoked by monomolecular odorant components that had been studied previously. Object patterns also were significantly related to the molecular features present in the mixture components. Despite these overall relationships, there were individual examples of object patterns that were simpler than might have been predicted given the multiplicity of components present in the vapors. In these cases, the object patterns lacked certain responses evoked by their major odorant mixture components. These data suggest the possibility of mixture response interactions and provide a foundation for understanding the neural coding of natural odor stimuli. PMID:20187145

Using Latent Class Analysis to Model Temperament Types.

PubMed

Loken, Eric

2004-10-01

Mixture models are appropriate for data that arise from a set of qualitatively different subpopulations. In this study, latent class analysis was applied to observational data from a laboratory assessment of infant temperament at four months of age. The EM algorithm was used to fit the models, and the Bayesian method of posterior predictive checks was used for model selection. Results show at least three types of infant temperament, with patterns consistent with those identified by previous researchers who classified the infants using a theoretically based system. Multiple imputation of group memberships is proposed as an alternative to assigning subjects to the latent class with maximum posterior probability in order to reflect variance due to uncertainty in the parameter estimation. Latent class membership at four months of age predicted longitudinal outcomes at four years of age. The example illustrates issues relevant to all mixture models, including estimation, multi-modality, model selection, and comparisons based on the latent group indicators.

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.

Influence of deposition and spray pattern of nasal powders on insulin bioavailability.

PubMed

Pringels, E; Callens, C; Vervaet, C; Dumont, F; Slegers, G; Foreman, P; Remon, J P

2006-03-09

The influence of the deposition pattern and spray characteristics of nasal powder formulations on the insulin bioavailability was investigated in rabbits. The formulations were prepared by freeze drying a dispersion containing a physical mixture of drum dried waxy maize starch (DDWM)/Carbopol 974P (90/10, w/w) or a spray-dried mixture of Amioca starch/Carbopol 974P (25/75, w/w). The deposition in the nasal cavity of rabbits and in a silicone human nose model after actuation of three nasal delivery devices (Monopowder, Pfeiffer and experimental system) was compared and related to the insulin bioavailability. Posterior deposition of the powder formulation in the nasal cavity lowered the insulin bioavailability. To study the spray pattern, the shape and cross-section of the emitted powder cloud were analysed. It was concluded that the powder bulk density of the formulation influenced the spray pattern. Consequently, powders of different bulk density were prepared by changing the solid fraction of the freeze dried dispersion and by changing the freezing rate during freeze drying. After nasal delivery of these powder formulations no influence of the powder bulk density and of the spray pattern on the insulin bioavailability was observed.

Spatial Analysis of “Crazy Quilts”, a Class of Potentially Random Aesthetic Artefacts

PubMed Central

Westphal-Fitch, Gesche; Fitch, W. Tecumseh

2013-01-01

Human artefacts in general are highly structured and often display ordering principles such as translational, reflectional or rotational symmetry. In contrast, human artefacts that are intended to appear random and non symmetrical are very rare. Furthermore, many studies show that humans find it extremely difficult to recognize or reproduce truly random patterns or sequences. Here, we attempt to model two-dimensional decorative spatial patterns produced by humans that show no obvious order. “Crazy quilts” represent a historically important style of quilt making that became popular in the 1870s, and lasted about 50 years. Crazy quilts are unusual because unlike most human artefacts, they are specifically intended to appear haphazard and unstructured. We evaluate the degree to which this intention was achieved by using statistical techniques of spatial point pattern analysis to compare crazy quilts with regular quilts from the same region and era and to evaluate the fit of various random distributions to these two quilt classes. We found that the two quilt categories exhibit fundamentally different spatial characteristics: The patch areas of crazy quilts derive from a continuous random distribution, while area distributions of regular quilts consist of Gaussian mixtures. These Gaussian mixtures derive from regular pattern motifs that are repeated and we suggest that such a mixture is a distinctive signature of human-made visual patterns. In contrast, the distribution found in crazy quilts is shared with many other naturally occurring spatial patterns. Centroids of patches in the two quilt classes are spaced differently and in general, crazy quilts but not regular quilts are well-fitted by a random Strauss process. These results indicate that, within the constraints of the quilt format, Victorian quilters indeed achieved their goal of generating random structures. PMID:24066095

Spatial analysis of "crazy quilts", a class of potentially random aesthetic artefacts.

PubMed

Westphal-Fitch, Gesche; Fitch, W Tecumseh

2013-01-01

Human artefacts in general are highly structured and often display ordering principles such as translational, reflectional or rotational symmetry. In contrast, human artefacts that are intended to appear random and non symmetrical are very rare. Furthermore, many studies show that humans find it extremely difficult to recognize or reproduce truly random patterns or sequences. Here, we attempt to model two-dimensional decorative spatial patterns produced by humans that show no obvious order. "Crazy quilts" represent a historically important style of quilt making that became popular in the 1870s, and lasted about 50 years. Crazy quilts are unusual because unlike most human artefacts, they are specifically intended to appear haphazard and unstructured. We evaluate the degree to which this intention was achieved by using statistical techniques of spatial point pattern analysis to compare crazy quilts with regular quilts from the same region and era and to evaluate the fit of various random distributions to these two quilt classes. We found that the two quilt categories exhibit fundamentally different spatial characteristics: The patch areas of crazy quilts derive from a continuous random distribution, while area distributions of regular quilts consist of Gaussian mixtures. These Gaussian mixtures derive from regular pattern motifs that are repeated and we suggest that such a mixture is a distinctive signature of human-made visual patterns. In contrast, the distribution found in crazy quilts is shared with many other naturally occurring spatial patterns. Centroids of patches in the two quilt classes are spaced differently and in general, crazy quilts but not regular quilts are well-fitted by a random Strauss process. These results indicate that, within the constraints of the quilt format, Victorian quilters indeed achieved their goal of generating random structures.

Removal of polycyclic aromatic hydrocarbons in soil spiked with model mixtures of petroleum hydrocarbons and heterocycles using biosurfactants from Rhodococcus ruber IEGM 231.

PubMed

Ivshina, Irina; Kostina, Ludmila; Krivoruchko, Anastasiya; Kuyukina, Maria; Peshkur, Tatyana; Anderson, Peter; Cunningham, Colin

2016-07-15

Removal of polycyclic aromatic hydrocarbons (PAHs) in soil using biosurfactants (BS) produced by Rhodococcus ruber IEGM 231 was studied in soil columns spiked with model mixtures of major petroleum constituents. A crystalline mixture of single PAHs (0.63g/kg), a crystalline mixture of PAHs (0.63g/kg) and polycyclic aromatic sulfur heterocycles (PASHs), and an artificially synthesized non-aqueous phase liquid (NAPL) containing PAHs (3.00g/kg) dissolved in alkanes C10-C19 were used for spiking. Percentage of PAH removal with BS varied from 16 to 69%. Washing activities of BS were 2.5 times greater than those of synthetic surfactant Tween 60 in NAPL-spiked soil and similar to Tween 60 in crystalline-spiked soil. At the same time, amounts of removed PAHs were equal and consisted of 0.3-0.5g/kg dry soil regardless the chemical pattern of a model mixture of petroleum hydrocarbons and heterocycles used for spiking. UV spectra for soil before and after BS treatment were obtained and their applicability for differentiated analysis of PAH and PASH concentration changes in remediated soil was shown. The ratios A254nm/A288nm revealed that BS increased biotreatability of PAH-contaminated soils. Copyright © 2016 Elsevier B.V. All rights reserved.

Statistical Modeling of Single Target Cell Encapsulation

PubMed Central

Moon, SangJun; Ceyhan, Elvan; Gurkan, Umut Atakan; Demirci, Utkan

2011-01-01

High throughput drop-on-demand systems for separation and encapsulation of individual target cells from heterogeneous mixtures of multiple cell types is an emerging method in biotechnology that has broad applications in tissue engineering and regenerative medicine, genomics, and cryobiology. However, cell encapsulation in droplets is a random process that is hard to control. Statistical models can provide an understanding of the underlying processes and estimation of the relevant parameters, and enable reliable and repeatable control over the encapsulation of cells in droplets during the isolation process with high confidence level. We have modeled and experimentally verified a microdroplet-based cell encapsulation process for various combinations of cell loading and target cell concentrations. Here, we explain theoretically and validate experimentally a model to isolate and pattern single target cells from heterogeneous mixtures without using complex peripheral systems. PMID:21814548

Measuring and modeling of binary mixture effects of pharmaceuticals and nickel on cell viability/cytotoxicity in the human hepatoma derived cell line HepG2

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

Rudzok, S., E-mail: susanne.rudzok@ufz.d; Schlink, U., E-mail: uwe.schlink@ufz.d; Herbarth, O., E-mail: olf.herbarth@medizin.uni-leipzig.d

2010-05-01

The interaction of drugs and non-therapeutic xenobiotics constitutes a central role in human health risk assessment. Still, available data are rare. Two different models have been established to predict mixture toxicity from single dose data, namely, the concentration addition (CA) and independent action (IA) model. However, chemicals can also act synergistic or antagonistic or in dose level deviation, or in a dose ratio dependent deviation. In the present study we used the MIXTOX model (EU project ENV4-CT97-0507), which incorporates these algorithms, to assess effects of the binary mixtures in the human hepatoma cell line HepG2. These cells possess a liver-likemore » enzyme pattern and a variety of xenobiotic-metabolizing enzymes (phases I and II). We tested binary mixtures of the metal nickel, the anti-inflammatory drug diclofenac, and the antibiotic agent irgasan and compared the experimental data to the mathematical models. Cell viability was determined by three different methods the MTT-, AlamarBlue (registered) and NRU assay. The compounds were tested separately and in combinations. We could show that the metal nickel is the dominant component in the mixture, affecting an antagonism at low-dose levels and a synergism at high-dose levels in combination with diclofenac or irgasan, when using the NRU and the AlamarBlue assay. The dose-response surface of irgasan and diclofenac indicated a concentration addition. The experimental data could be described by the algorithms with a regression of up to 90%, revealing the HepG2 cell line and the MIXTOX model as valuable tool for risk assessment of binary mixtures for cytotoxic endpoints. However the model failed to predict a specific mode of action, the CYP1A1 enzyme activity.« less

Using Big Data Analytics to Address Mixtures Exposure

EPA Science Inventory

The assessment of chemical mixtures is a complex issue for regulators and health scientists. We propose that assessing chemical co-occurrence patterns and prevalence rates is a relatively simple yet powerful approach in characterizing environmental mixtures and mixtures exposure...

Temporal and spatial patterns in vegetation and atmospheric properties from AVIRIS

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

Roberts, D.A.; Green, R.O.; Adams, J.B.

1997-12-01

Little research has focused on the use of imaging spectrometry for change detection. In this paper, the authors apply Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data to the monitoring of seasonal changes in atmospheric water vapor, liquid water, and surface cover in the vicinity of the Jasper Ridge, CA, for three dates in 1992. Apparent surface reflectance was retrieved and water vapor and liquid water mapped by using a radiative-transfer-based inversion that accounts for spatially variable atmospheres. Spectral mixture analysis (SMA) was used to model reflectance data as mixtures of green vegetation (GV), nonphotosynthetic vegetation (NPV), soil, and shade. Temporal andmore » spatial patterns in endmember fractions and liquid water were compared to the normalized difference vegetation index (NDVI). The reflectance retrieval algorithm was tested by using a temporally invariant target.« less

Systematic Proteomic Approach to Characterize the Impacts of ...

EPA Pesticide Factsheets

Chemical interactions have posed a big challenge in toxicity characterization and human health risk assessment of environmental mixtures. To characterize the impacts of chemical interactions on protein and cytotoxicity responses to environmental mixtures, we established a systems biology approach integrating proteomics, bioinformatics, statistics, and computational toxicology to measure expression or phosphorylation levels of 21 critical toxicity pathway regulators and 445 downstream proteins in human BEAS-28 cells treated with 4 concentrations of nickel, 2 concentrations each of cadmium and chromium, as well as 12 defined binary and 8 defined ternary mixtures of these metals in vitro. Multivariate statistical analysis and mathematical modeling of the metal-mediated proteomic response patterns showed a high correlation between changes in protein expression or phosphorylation and cellular toxic responses to both individual metals and metal mixtures. Of the identified correlated proteins, only a small set of proteins including HIF-1a is likely to be responsible for selective cytotoxic responses to different metals and metals mixtures. Furthermore, support vector machine learning was utilized to computationally predict protein responses to uncharacterized metal mixtures using experimentally generated protein response profiles corresponding to known metal mixtures. This study provides a novel proteomic approach for characterization and prediction of toxicities of

Discriminant analysis of fused positive and negative ion mobility spectra using multivariate self-modeling mixture analysis and neural networks.

PubMed

Chen, Ping; Harrington, Peter B

2008-02-01

A new method coupling multivariate self-modeling mixture analysis and pattern recognition has been developed to identify toxic industrial chemicals using fused positive and negative ion mobility spectra (dual scan spectra). A Smiths lightweight chemical detector (LCD), which can measure positive and negative ion mobility spectra simultaneously, was used to acquire the data. Simple-to-use interactive self-modeling mixture analysis (SIMPLISMA) was used to separate the analytical peaks in the ion mobility spectra from the background reactant ion peaks (RIP). The SIMPLSIMA analytical components of the positive and negative ion peaks were combined together in a butterfly representation (i.e., negative spectra are reported with negative drift times and reflected with respect to the ordinate and juxtaposed with the positive ion mobility spectra). Temperature constrained cascade-correlation neural network (TCCCN) models were built to classify the toxic industrial chemicals. Seven common toxic industrial chemicals were used in this project to evaluate the performance of the algorithm. Ten bootstrapped Latin partitions demonstrated that the classification of neural networks using the SIMPLISMA components was statistically better than neural network models trained with fused ion mobility spectra (IMS).

Health-related quality of life and disease symptoms in postmenopausal women with HR(+), HER2(-) advanced breast cancer treated with everolimus plus exemestane versus exemestane monotherapy.

PubMed

Campone, Mario; Beck, J Thaddeus; Gnant, Michael; Neven, Patrick; Pritchard, Kathleen I; Bachelot, Thomas; Provencher, Louise; Rugo, Hope S; Piccart, Martine; Hortobagyi, Gabriel N; Nunzi, Martina; Heng, Daniel Y C; Baselga, José; Komorowski, Anna; Noguchi, Shinzaburo; Horiguchi, Jun; Bennett, Lee; Ziemiecki, Ryan; Zhang, Jie; Cahana, Ayelet; Taran, Tetiana; Sahmoud, Tarek; Burris, Howard A

2013-11-01

Everolimus (EVE)+exemestane (EXE; n = 485) more than doubled median progression-free survival versus placebo (PBO) + EXE (n = 239), with a manageable safety profile and no deterioration in health-related quality-of-life (HRQOL) in patients with hormone-receptor-positive (HR(+)) advanced breast cancer (ABC) who recurred or progressed on/after nonsteroidal aromatase inhibitor (NSAI) therapy. To further evaluate EVE + EXE impact on disease burden, we conducted additional post-hoc analyses of patient-reported HRQOL. HRQOL was assessed using EORTC QLQ-C30 and QLQ-BR23 questionnaires at baseline and every 6 weeks thereafter until treatment discontinuation because of disease progression, toxicity, or consent withdrawal. Endpoints included the QLQ-C30 Global Health Status (QL2) scale, the QLQ-BR23 breast symptom (BRBS), and arm symptom (BRAS) scales. Between-group differences in change from baseline were assessed using linear mixed models with selected covariates. Sensitivity analysis using pattern-mixture models determined the effect of study discontinuation on/before week 24. Treatment arms were compared using differences of least squares mean (LSM) changes from baseline and 95% confidence intervals (CIs) at each timepoint and overall. Clinicaltrials.gov: NCT00863655. Progression-free survival, survival, response rate, safety, and HRQOL. Linear mixed models (primary model) demonstrated no statistically significant overall difference between EVE + EXE and PBO + EXE for QL2 (LSM difference = -1.91; 95% CI = -4.61, 0.78), BRBS (LSM difference = -0.18; 95% CI = -1.98, 1.62), or BRAS (LSM difference = -0.42; 95% CI = -2.94, 2.10). Based on pattern-mixture models, patients who dropped out early had worse QL2 decline on both treatments. In the expanded pattern-mixture model, EVE + EXE-treated patients who did not drop out early had stable BRBS and BRAS relative to PBO + EXE. HRQOL data were not collected after disease progression. These analyses confirm that EVE + EXE provides clinical benefit without adversely impacting HRQOL in patients with HR(+) ABC who recurred/progressed on prior NSAIs versus endocrine therapy alone.

Differential gene expression patterns in developing sexually dimorphic rat brain regions exposed to antiandrogenic, estrogenic, or complex endocrine disruptor mixtures: glutamatergic synapses as target.

PubMed

Lichtensteiger, Walter; Bassetti-Gaille, Catherine; Faass, Oliver; Axelstad, Marta; Boberg, Julie; Christiansen, Sofie; Rehrauer, Hubert; Georgijevic, Jelena Kühn; Hass, Ulla; Kortenkamp, Andreas; Schlumpf, Margret

2015-04-01

The study addressed the question whether gene expression patterns induced by different mixtures of endocrine disrupting chemicals (EDCs) administered in a higher dose range, corresponding to 450×, 200×, and 100× high-end human exposure levels, could be characterized in developing brain with respect to endocrine activity of mixture components, and which developmental processes were preferentially targeted. Three EDC mixtures, A-Mix (anti-androgenic mixture) with 8 antiandrogenic chemicals (di-n-butylphthalate, diethylhexylphthalate, vinclozolin, prochloraz, procymidone, linuron, epoxiconazole, and DDE), E-Mix (estrogenic mixture) with 4 estrogenic chemicals (bisphenol A, 4-methylbenzylidene camphor, 2-ethylhexyl 4-methoxycinnamate, and butylparaben), a complex mixture, AEP-Mix, containing the components of A-Mix and E-Mix plus paracetamol, and paracetamol alone, were administered by oral gavage to rat dams from gestation day 7 until weaning. General developmental endpoints were not affected by EDC mixtures or paracetamol. Gene expression was analyzed on postnatal day 6, during sexual brain differentiation, by exon microarray in medial preoptic area in the high-dose group, and by real-time RT-PCR in medial preoptic area and ventromedial hypothalamus in all dose groups. Expression patterns were mixture, sex, and region specific. Effects of the analgesic drug paracetamol, which exhibits antiandrogenic activity in peripheral systems, differed from those of A-Mix. All mixtures had a strong, mixture-specific impact on genes encoding for components of excitatory glutamatergic synapses and genes controlling migration and pathfinding of glutamatergic and GABAergic neurons, as well as genes linked with increased risk of autism spectrum disorders. Because development of glutamatergic synapses is regulated by sex steroids also in hippocampus, this may represent a general target of ECD mixtures.

Mixture for producing fracture-resistant, fiber-reinforced ceramic material by microwave heating

DOEpatents

Meek, T.T.; Blake, R.D.

1985-04-03

A fracture-resistant, fiber-reinforced ceramic substrate is produced by a method which involves preparing a ceramic precursor mixture comprising glass material, a coupling agent, and resilient fibers, and then exposing the mixture to microwave energy. The microwave field orients the fibers in the resulting ceramic material in a desired pattern wherein heat later generated in or on the substrate can be dissipated in a desired geometric pattern parallel to the fiber pattern. Additionally, the shunt capacitance of the fracture-resistant, fiber-reinforced ceramic substrate is lower which provides for a quicker transit time for electronic pulses in any conducting pathway etched into the ceramic substrate.

Mixture for producing fracture-resistant, fiber-reinforced ceramic material by microwave heating

DOEpatents

Meek, Thomas T.; Blake, Rodger D.

1987-01-01

A fracture-resistant, fiber-reinforced ceramic substrate is produced by a method which involves preparing a ceramic precursor mixture comprising glass material, a coupling agent, and resilient fibers, and then exposing the mixture to microwave energy. The microwave field orients the fibers in the resulting ceramic material in a desired pattern wherein heat later generated in or on the substrate can be dissipated in a desired geometric pattern parallel to the fiber pattern. Additionally, the shunt capacitance of the fracture-resistant, fiber-reinforced ceramic substrate is lower which provides for a quicker transit time for electronic pulses in any conducting pathway etched into the ceramic substrate.

Modern Methods for Modeling Change in Obesity Research in Nursing.

PubMed

Sereika, Susan M; Zheng, Yaguang; Hu, Lu; Burke, Lora E

2017-08-01

Persons receiving treatment for weight loss often demonstrate heterogeneity in lifestyle behaviors and health outcomes over time. Traditional repeated measures approaches focus on the estimation and testing of an average temporal pattern, ignoring the interindividual variability about the trajectory. An alternate person-centered approach, group-based trajectory modeling, can be used to identify distinct latent classes of individuals following similar trajectories of behavior or outcome change as a function of age or time and can be expanded to include time-invariant and time-dependent covariates and outcomes. Another latent class method, growth mixture modeling, builds on group-based trajectory modeling to investigate heterogeneity within the distinct trajectory classes. In this applied methodologic study, group-based trajectory modeling for analyzing changes in behaviors or outcomes is described and contrasted with growth mixture modeling. An illustration of group-based trajectory modeling is provided using calorie intake data from a single-group, single-center prospective study for weight loss in adults who are either overweight or obese.

Flow Pattern Phenomena in Two-Phase Flow in Microchannels

NASA Astrophysics Data System (ADS)

Keska, Jerry K.; Simon, William E.

2004-02-01

Space transportation systems require high-performance thermal protection and fluid management techniques for systems ranging from cryogenic fluid management devices to primary structures and propulsion systems exposed to extremely high temperatures, as well as for other space systems such as cooling or environment control for advanced space suits and integrated circuits. Although considerable developmental effort is being expended to bring potentially applicable technologies to a readiness level for practical use, new and innovative methods are still needed. One such method is the concept of Advanced Micro Cooling Modules (AMCMs), which are essentially compact two-phase heat exchangers constructed of microchannels and designed to remove large amounts of heat rapidly from critical systems by incorporating phase transition. The development of AMCMs requires fundamental technological advancement in many areas, including: (1) development of measurement methods/systems for flow-pattern measurement/identification for two-phase mixtures in microchannels; (2) development of a phenomenological model for two-phase flow which includes the quantitative measure of flow patterns; and (3) database development for multiphase heat transfer/fluid dynamics flows in microchannels. This paper focuses on the results of experimental research in the phenomena of two-phase flow in microchannels. The work encompasses both an experimental and an analytical approach to incorporating flow patterns for air-water mixtures flowing in a microchannel, which are necessary tools for the optimal design of AMCMs. Specifically, the following topics are addressed: (1) design and construction of a sensitive test system for two-phase flow in microchannels, one which measures ac and dc components of in-situ physical mixture parameters including spatial concentration using concomitant methods; (2) data acquisition and analysis in the amplitude, time, and frequency domains; and (3) analysis of results including evaluation of data acquisition techniques and their validity for application in flow pattern determination.

Partitioning Detectability Components in Populations Subject to Within-Season Temporary Emigration Using Binomial Mixture Models

PubMed Central

O’Donnell, Katherine M.; Thompson, Frank R.; Semlitsch, Raymond D.

2015-01-01

Detectability of individual animals is highly variable and nearly always < 1; imperfect detection must be accounted for to reliably estimate population sizes and trends. Hierarchical models can simultaneously estimate abundance and effective detection probability, but there are several different mechanisms that cause variation in detectability. Neglecting temporary emigration can lead to biased population estimates because availability and conditional detection probability are confounded. In this study, we extend previous hierarchical binomial mixture models to account for multiple sources of variation in detectability. The state process of the hierarchical model describes ecological mechanisms that generate spatial and temporal patterns in abundance, while the observation model accounts for the imperfect nature of counting individuals due to temporary emigration and false absences. We illustrate our model’s potential advantages, including the allowance of temporary emigration between sampling periods, with a case study of southern red-backed salamanders Plethodon serratus. We fit our model and a standard binomial mixture model to counts of terrestrial salamanders surveyed at 40 sites during 3–5 surveys each spring and fall 2010–2012. Our models generated similar parameter estimates to standard binomial mixture models. Aspect was the best predictor of salamander abundance in our case study; abundance increased as aspect became more northeasterly. Increased time-since-rainfall strongly decreased salamander surface activity (i.e. availability for sampling), while higher amounts of woody cover objects and rocks increased conditional detection probability (i.e. probability of capture, given an animal is exposed to sampling). By explicitly accounting for both components of detectability, we increased congruence between our statistical modeling and our ecological understanding of the system. We stress the importance of choosing survey locations and protocols that maximize species availability and conditional detection probability to increase population parameter estimate reliability. PMID:25775182

Negative Cognitive Style Trajectories in the Transition to Adolescence

ERIC Educational Resources Information Center

Mezulis, Amy; Funasaki, Kristyn; Hyde, Janet Shibley

2011-01-01

The development of negative cognitive style was examined in a longitudinal study of 366 community youth. Cognitive style and depressive symptoms were evaluated at ages 11, 13, and 15. Latent growth mixture modeling identified three unique trajectory patterns of negative cognitive style. The "normative" group (71% of the sample) displayed the least…

Employment Trajectories: Exploring Gender Differences and Impacts of Drug Use

ERIC Educational Resources Information Center

Huang, David Y. C.; Evans, Elizabeth; Hara, Motoaki; Weiss, Robert E.; Hser, Yih-Ing

2011-01-01

This study investigated the impact of drug use on employment over 20 years among men and women, utilizing data on 7661 participants in the National Longitudinal Survey of Youth. Growth mixture modeling was applied, and five distinct employment trajectory groups were identified for both men and women. The identified patterns were largely similar…

A Stochastic-Variational Model for Soft Mumford-Shah Segmentation

PubMed Central

2006-01-01

In contemporary image and vision analysis, stochastic approaches demonstrate great flexibility in representing and modeling complex phenomena, while variational-PDE methods gain enormous computational advantages over Monte Carlo or other stochastic algorithms. In combination, the two can lead to much more powerful novel models and efficient algorithms. In the current work, we propose a stochastic-variational model for soft (or fuzzy) Mumford-Shah segmentation of mixture image patterns. Unlike the classical hard Mumford-Shah segmentation, the new model allows each pixel to belong to each image pattern with some probability. Soft segmentation could lead to hard segmentation, and hence is more general. The modeling procedure, mathematical analysis on the existence of optimal solutions, and computational implementation of the new model are explored in detail, and numerical examples of both synthetic and natural images are presented. PMID:23165059

Influence of host diversity on development of epidemics: an evaluation and elaboration of mixture theory.

PubMed

Skelsey, P; Rossing, W A H; Kessel, G J T; Powell, J; van der Werf, W

2005-04-01

ABSTRACT A spatiotemporal/integro-difference equation model was developed and utilized to study the progress of epidemics in spatially heterogeneous mixtures of susceptible and resistant host plants. The effects of different scales and patterns of host genotypes on the development of focal and general epidemics were investigated using potato late blight as a case study. Two different radial Laplace kernels and a two-dimensional Gaussian kernel were used for modeling the dispersal of spores. An analytical expression for the apparent infection rate, r, in general epidemics was tested by comparison with dynamic simulations. A genotype connectivity parameter, q, was introduced into the formula for r. This parameter quantifies the probability of pathogen inoculum produced on a certain host genotype unit reaching the same or another unit of the same genotype. The analytical expression for the apparent infection rate provided accurate predictions of realized r in the simulations of general epidemics. The relationship between r and the radial velocity of focus expansion, c, in focal epidemics, was linear in accordance with theory for homogeneous genotype mixtures. The findings suggest that genotype mixtures that are effective in reducing general epidemics of Phytophthora infestans will likewise curtail focal epidemics and vice versa.

Movement patterns and study area boundaries: Influences on survival estimation in capture-mark-recapture studies

USGS Publications Warehouse

Horton, G.E.; Letcher, B.H.

2008-01-01

The inability to account for the availability of individuals in the study area during capture-mark-recapture (CMR) studies and the resultant confounding of parameter estimates can make correct interpretation of CMR model parameter estimates difficult. Although important advances based on the Cormack-Jolly-Seber (CJS) model have resulted in estimators of true survival that work by unconfounding either death or recapture probability from availability for capture in the study area, these methods rely on the researcher's ability to select a method that is correctly matched to emigration patterns in the population. If incorrect assumptions regarding site fidelity (non-movement) are made, it may be difficult or impossible as well as costly to change the study design once the incorrect assumption is discovered. Subtleties in characteristics of movement (e.g. life history-dependent emigration, nomads vs territory holders) can lead to mixtures in the probability of being available for capture among members of the same population. The result of these mixtures may be only a partial unconfounding of emigration from other CMR model parameters. Biologically-based differences in individual movement can combine with constraints on study design to further complicate the problem. Because of the intricacies of movement and its interaction with other parameters in CMR models, quantification of and solutions to these problems are needed. Based on our work with stream-dwelling populations of Atlantic salmon Salmo salar, we used a simulation approach to evaluate existing CMR models under various mixtures of movement probabilities. The Barker joint data model provided unbiased estimates of true survival under all conditions tested. The CJS and robust design models provided similarly unbiased estimates of true survival but only when emigration information could be incorporated directly into individual encounter histories. For the robust design model, Markovian emigration (future availability for capture depends on an individual's current location) was a difficult emigration pattern to detect unless survival and especially recapture probability were high. Additionally, when local movement was high relative to study area boundaries and movement became more diffuse (e.g. a random walk), local movement and permanent emigration were difficult to distinguish and had consequences for correctly interpreting the survival parameter being estimated (apparent survival vs true survival). ?? 2008 The Authors.

Prospective aquatic risk assessment for chemical mixtures in agricultural landscapes

PubMed Central

Brown, Colin D.; Hamer, Mick; Jones, Russell; Maltby, Lorraine; Posthuma, Leo; Silberhorn, Eric; Teeter, Jerold Scott; Warne, Michael St J; Weltje, Lennart

2018-01-01

Abstract Environmental risk assessment of chemical mixtures is challenging because of the multitude of possible combinations that may occur. Aquatic risk from chemical mixtures in an agricultural landscape was evaluated prospectively in 2 exposure scenario case studies: at field scale for a program of 13 plant‐protection products applied annually for 20 yr and at a watershed scale for a mixed land‐use scenario over 30 yr with 12 plant‐protection products and 2 veterinary pharmaceuticals used for beef cattle. Risk quotients were calculated from regulatory exposure models with typical real‐world use patterns and regulatory acceptable concentrations for individual chemicals. The results could differentiate situations when there was concern associated with single chemicals from those when concern was associated with a mixture (based on concentration addition) with no single chemical triggering concern. Potential mixture risk was identified on 0.02 to 7.07% of the total days modeled, depending on the scenario, the taxa, and whether considering acute or chronic risk. Taxa at risk were influenced by receiving water body characteristics along with chemical use profiles and associated properties. The present study demonstrates that a scenario‐based approach can be used to determine whether mixtures of chemicals pose risks over and above any identified using existing approaches for single chemicals, how often and to what magnitude, and ultimately which mixtures (and dominant chemicals) cause greatest concern. Environ Toxicol Chem 2018;37:674–689. © 2017 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC. PMID:29193235

Prospective aquatic risk assessment for chemical mixtures in agricultural landscapes.

PubMed

Holmes, Christopher M; Brown, Colin D; Hamer, Mick; Jones, Russell; Maltby, Lorraine; Posthuma, Leo; Silberhorn, Eric; Teeter, Jerold Scott; Warne, Michael St J; Weltje, Lennart

2018-03-01

Environmental risk assessment of chemical mixtures is challenging because of the multitude of possible combinations that may occur. Aquatic risk from chemical mixtures in an agricultural landscape was evaluated prospectively in 2 exposure scenario case studies: at field scale for a program of 13 plant-protection products applied annually for 20 yr and at a watershed scale for a mixed land-use scenario over 30 yr with 12 plant-protection products and 2 veterinary pharmaceuticals used for beef cattle. Risk quotients were calculated from regulatory exposure models with typical real-world use patterns and regulatory acceptable concentrations for individual chemicals. The results could differentiate situations when there was concern associated with single chemicals from those when concern was associated with a mixture (based on concentration addition) with no single chemical triggering concern. Potential mixture risk was identified on 0.02 to 7.07% of the total days modeled, depending on the scenario, the taxa, and whether considering acute or chronic risk. Taxa at risk were influenced by receiving water body characteristics along with chemical use profiles and associated properties. The present study demonstrates that a scenario-based approach can be used to determine whether mixtures of chemicals pose risks over and above any identified using existing approaches for single chemicals, how often and to what magnitude, and ultimately which mixtures (and dominant chemicals) cause greatest concern. Environ Toxicol Chem 2018;37:674-689. © 2017 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC. © 2017 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC.

Rapid Biochemical Mixture Screening by Three-Dimensional Patterned Multifunctional Substrate with Ultra-Thin Layer Chromatography (UTLC) and Surface Enhanced Raman Scattering (SERS).

PubMed

Lee, Bi-Shen; Lin, Pi-Chen; Lin, Ding-Zheng; Yen, Ta-Jen

2018-01-11

We present a three-dimensional patterned (3DP) multifunctional substrate with the functions of ultra-thin layer chromatography (UTLC) and surface enhanced Raman scattering (SERS), which simultaneously enables mixture separation, target localization and label-free detection. This multifunctional substrate is comprised of a 3DP silicon nanowires array (3DP-SiNWA), decorated with silver nano-dendrites (AgNDs) atop. The 3DP-SiNWA is fabricated by a facile photolithographic process and low-cost metal assisted chemical etching (MaCE) process. Then, the AgNDs are decorated onto 3DP-SiNWA by a wet chemical reduction process, obtaining 3DP-AgNDs@SiNWA multifunctional substrates. With various patterns designed on the substrates, the signal intensity could be maximized by the excellent confinement and concentrated effects of patterns. By using this 3DP-AgNDs@SiNWA substrate to scrutinize the mixture of two visible dyes, the individual target could be recognized and further boosted the Raman signal of target 15.42 times comparing to the un-patterned AgNDs@SiNWA substrate. Therefore, such a three-dimensional patterned multifunctional substrate empowers rapid mixture screening, and can be readily employed in practical applications for biochemical assays, food safety and other fields.

Using virtual 3-D plant architecture to assess fungal pathogen splash dispersal in heterogeneous canopies: a case study with cultivar mixtures and a non-specialized disease causal agent

PubMed Central

Gigot, C.; de Vallavieille-Pope, C.; Huber, L.; Saint-Jean, S.

2014-01-01

Background and Aims Recent developments in plant disease management have led to a growing interest in alternative strategies, such as increasing host diversity and decreasing the use of pesticides. Use of cultivar mixtures is one option, allowing the spread of plant epidemics to be slowed down. As dispersal of fungal foliar pathogens over short distances by rain-splash droplets is a major contibutor to the spread of disease, this study focused on modelling the physical mechanisms involved in dispersal of a non-specialized pathogen within heterogeneous canopies of cultivar mixtures, with the aim of optimizing host diversification at the intra-field level. Methods Virtual 3-D wheat-like plants (Triticum aestivum) were used to consider interactions between plant architecture and disease progression in heterogeneous canopies. A combined mechanistic and stochastic model, taking into account splash droplet dispersal and host quantitative resistance within a 3-D heterogeneous canopy, was developed. It consists of four sub-models that describe the spatial patterns of two cultivars within a complex canopy, the pathway of rain-splash droplets within this canopy, the proportion of leaf surface area impacted by dispersal via the droplets and the progression of disease severity after each dispersal event. Key Results Different spatial organization, proportions and resistance levels of the cultivars of two-component mixtures were investigated. For the eight spatial patterns tested, the protective effect against disease was found to vary by almost 2-fold, with the greatest effect being obtained with the smallest genotype unit area, i.e. the ground area occupied by an independent unit of the host population that is genetically homogeneous. Increasing both the difference between resistance levels and the proportion of the most resistant cultivar often resulted in a greater protective effect; however, this was not observed for situations in which the most resistant of the two cultivars in the mixture had a relatively low level of resistance. Conclusions The results show agreement with previous data obtained using experimental approaches. They demonstrate that in order to maximize the potential mixture efficiency against a splash-dispersed pathogen, optimal susceptible/resistant cultivar proportions (ranging from 1/9 to 5/5) have to be established based on host resistance levels. The results also show that taking into account dispersal processes in explicit 3-D plant canopies can be a key tool for investigating disease progression in heterogeneous canopies such as cultivar mixtures. PMID:24989786

A novel expert system for objective masticatory efficiency assessment

PubMed Central

2018-01-01

Most of the tools and diagnosis models of Masticatory Efficiency (ME) are not well documented or severely limited to simple image processing approaches. This study presents a novel expert system for ME assessment based on automatic recognition of mixture patterns of masticated two-coloured chewing gums using a combination of computational intelligence and image processing techniques. The hypotheses tested were that the proposed system could accurately relate specimens to the number of chewing cycles, and that it could identify differences between the mixture patterns of edentulous individuals prior and after complete denture treatment. This study enrolled 80 fully-dentate adults (41 females and 39 males, 25 ± 5 years of age) as the reference population; and 40 edentulous adults (21 females and 19 males, 72 ± 8.9 years of age) for the testing group. The system was calibrated using the features extracted from 400 samples covering 0, 10, 15, and 20 chewing cycles. The calibrated system was used to automatically analyse and classify a set of 160 specimens retrieved from individuals in the testing group in two appointments. The ME was then computed as the predicted number of chewing strokes that a healthy reference individual would need to achieve a similar degree of mixture measured against the real number of cycles applied to the specimen. The trained classifier obtained a Mathews Correlation Coefficient score of 0.97. ME measurements showed almost perfect agreement considering pre- and post-treatment appointments separately (κ ≥ 0.95). Wilcoxon signed-rank test showed that a complete denture treatment for edentulous patients elicited a statistically significant increase in the ME measurements (Z = -2.31, p < 0.01). We conclude that the proposed expert system proved able and reliable to accurately identify patterns in mixture and provided useful ME measurements. PMID:29385165

Characterizing heterogeneous cellular responses to perturbations.

PubMed

Slack, Michael D; Martinez, Elisabeth D; Wu, Lani F; Altschuler, Steven J

2008-12-09

Cellular populations have been widely observed to respond heterogeneously to perturbation. However, interpreting the observed heterogeneity is an extremely challenging problem because of the complexity of possible cellular phenotypes, the large dimension of potential perturbations, and the lack of methods for separating meaningful biological information from noise. Here, we develop an image-based approach to characterize cellular phenotypes based on patterns of signaling marker colocalization. Heterogeneous cellular populations are characterized as mixtures of phenotypically distinct subpopulations, and responses to perturbations are summarized succinctly as probabilistic redistributions of these mixtures. We apply our method to characterize the heterogeneous responses of cancer cells to a panel of drugs. We find that cells treated with drugs of (dis-)similar mechanism exhibit (dis-)similar patterns of heterogeneity. Despite the observed phenotypic diversity of cells observed within our data, low-complexity models of heterogeneity were sufficient to distinguish most classes of drug mechanism. Our approach offers a computational framework for assessing the complexity of cellular heterogeneity, investigating the degree to which perturbations induce redistributions of a limited, but nontrivial, repertoire of underlying states and revealing functional significance contained within distinct patterns of heterogeneous responses.

Biclustering Models for Two-Mode Ordinal Data.

PubMed

Matechou, Eleni; Liu, Ivy; Fernández, Daniel; Farias, Miguel; Gjelsvik, Bergljot

2016-09-01

The work in this paper introduces finite mixture models that can be used to simultaneously cluster the rows and columns of two-mode ordinal categorical response data, such as those resulting from Likert scale responses. We use the popular proportional odds parameterisation and propose models which provide insights into major patterns in the data. Model-fitting is performed using the EM algorithm, and a fuzzy allocation of rows and columns to corresponding clusters is obtained. The clustering ability of the models is evaluated in a simulation study and demonstrated using two real data sets.

Mixture for producing fracture-resistant, fiber-reinforced ceramic material by microwave heating

DOEpatents

Meek, T.T.; Blake, R.D.

1987-09-22

A fracture-resistant, fiber-reinforced ceramic substrate is produced by a method which involves preparing a ceramic precursor mixture comprising glass material, a coupling agent, and resilient fibers, and then exposing the mixture to microwave energy. The microwave field orients the fibers in the resulting ceramic material in a desired pattern wherein heat later generated in or on the substrate can be dissipated in a desired geometric pattern parallel to the fiber pattern. Additionally, the shunt capacitance of the fracture-resistant, fiber-reinforced ceramic substrate is lower which provides for a quicker transit time for electronic pulses in any conducting pathway etched into the ceramic substrate. 2 figs.

The effect of hydrostatic pressure on model membrane domain composition and lateral compressibility.

PubMed

Barriga, H M G; Law, R V; Seddon, J M; Ces, O; Brooks, N J

2016-01-07

Phase separation in ternary model membranes is known to occur over a range of temperatures and compositions and can be induced by increasing hydrostatic pressure. We have used small angle X-ray scattering (SAXS) to study phase separation along pre-determined tie lines in dioleoylphosphatidylcholine (DOPC), dipalmitoylphosphatidylcholine (DPPC) and cholesterol (CHOL) mixtures. We can unequivocally distinguish the liquid ordered (Lo) and liquid disordered (Ld) phases in diffraction patterns from biphasic mixtures and compare their lateral compressibility. The variation of tie line endpoints with increasing hydrostatic pressure was determined, at atmospheric pressure and up to 100 MPa. We find an extension and shift of the tie lines towards the DOPC rich region of the phase diagram at increased pressure, this behaviour differs slightly from that reported for decreasing temperature.

Impact of rainfall patterns and frequency on the export of pesticides and heavy-metals from agricultural soils.

PubMed

Meite, Fatima; Alvarez-Zaldívar, Pablo; Crochet, Alexandre; Wiegert, Charline; Payraudeau, Sylvain; Imfeld, Gwenaël

2018-03-01

The combined influence of soil characteristics, pollutant aging and rainfall patterns on the export of pollutants from topsoils is poorly understood. We used laboratory experiments and parsimonious modeling to evaluate the impact of rainfall characteristics on the ponding and the leaching of a pollutant mixture from topsoils. The mixture included the fungicide metalaxyl, the herbicide S-metolachlor, as well as copper (Cu) and zinc (Zn). Four rainfall patterns, which differed in their durations and intensities, were applied twice successively with a 7days interval on each soil type. To evaluate the influence of soil type and aging, experiments included crop and vineyard soils and two stages of pollutant aging (0 and 10days). The global export of pollutants was significantly controlled by the rainfall duration and frequency (P<0.01). During the first rainfall event, the longest and most intense rainfall pattern yielded the largest export of metalaxyl (44.5±21.5% of the initial mass spiked in the soils), S-metolachlor (8.1±3.1%) and Cu (3.1±0.3%). Soil compaction caused by the first rainfall reduced in the second rainfall the leaching of remaining metalaxyl, S-metolachlor, Cu and Zn by 2.4-, 2.9-, 30- and 50-fold, respectively. In contrast, soil characteristics and aging had less influence on pollutant mass export. The soil type significantly influenced the leaching of Zn, while short-term aging impacted Cu leaching. Our results suggest that rainfall characteristics predominantly control export patterns of metalaxyl and S-metolachlor, in particular when the aging period is short. We anticipate our study to be a starting point for more systematic evaluation of the dissolved pollutant ponding/leaching partitioning and the export of pollutant mixtures from different soil types in relation to rainfall patterns. Copyright © 2017 Elsevier B.V. All rights reserved.

Near-limit flame structures at low Lewis number

NASA Technical Reports Server (NTRS)

Ronney, Paul D.

1990-01-01

The characteristics of premixed gas flames in mixtures with low Lewis numbers near flammability limits were studied experimentally using a low-gravity environment to reduce buoyant convection. The behavior of such flames was found to be dominated by diffusive-thermal instabilities. For sufficiently reactive mixtures, cellular structures resulting from these instabilities were observed and found to spawn new cells in regular patterns. For less reactive mixtures, cells formed shortly after ignition but did not spawn new cells; instead these cells evolved into a flame structure composed of stationary, apparently stable spherical flamelets. Experimental observations are found to be in qualitative agreement with elementary analytical models based on the interaction of heat release due to chemical reaction, differential diffusion of thermal energy and mass, flame front curvature, and volumetric heat losses due to gas and/or soot radiation.

A study of the propagation, dynamics, and extinguishment of cellular flames using microgravity techniques

NASA Technical Reports Server (NTRS)

Ronney, Paul D.

1989-01-01

The characteristics of premixed gas flames in mixtures with low Lewis numbers, free of natural convection effects, were investigated and found to be dominated by diffusive-thermal instabilities. For sufficiently reactive mixtures, cellular structures resulting from these instabilities were observed and found to spawn new cells in regular patterns. For less reactive mixtures, cells formed shortly after ignition but did not spawn new cells; instead these cells evolved into a flame structure composed of stationary, apparently stable spherical flamelets. As a result of these phenomena, well-defined flammability limits were not observed. The experimental results are found to be in qualitative agreement with a simple analytical model based on the interaction of heat release due to chemical reaction, differential diffusion of thermal energy and mass, flame front curvature, and heat losses due to gas radiation.

On the spider that spits the solution of a nonsmooth oscillator.

PubMed

Goeleven, Daniel

2017-01-01

Spitting spiders (Scytodes sp.) spit a mixture of silk and glue at their prey during attack. In this note, we show that a nonsmooth oscillator can be used as a biomechanical model to describe the zig-zag patterns produced by the spit of the spider Scytodes thoracica. Copyright © 2016 Elsevier Inc. All rights reserved.

Activity induced phase transition in mixtures of active and passive agents

NASA Astrophysics Data System (ADS)

Sinha Mahapatra, Pallab; Kulkarni, Ajinkya

2017-11-01

Collective behaviors of self-propelling agents are ubiquitous in nature that produces interesting patterns. The objective of this study is to investigate the phase transition in mixtures of active and inert agents suspended in a liquid. A modified version of the Vicsek Model has been used (see Ref.), where the particles are modeled as soft disks with finite mass, confined in a square domain. The particles are required to align their local motion to their immediate neighborhood, similar to the Vicsek model. We identified the transition from disorganized thermal-like motion to an organized vortical motion. We analyzed the nature of the transition by using different order parameters. Furthermore the switching between the phases has been investigated via artificial nucleation of randomly picked active agents spanning the entire domain. Finally the motivation for this phase transition has been explained via average dissipation and the mean square displacement (MSD) of the agents.

Contrasting plant height can improve the control of rain-borne diseases in wheat cultivar mixture: modelling splash dispersal in 3-D canopies.

PubMed

Vidal, T; Gigot, C; de Vallavieille-Pope, C; Huber, L; Saint-Jean, S

2018-06-08

Growing cultivars differing by their disease resistance level together (cultivar mixtures) can reduce the propagation of diseases. Although architectural characteristics of cultivars are little considered in mixture design, they could have an effect on disease, in particular through spore dispersal by rain splash, which occurs over short distances. The objective of this work was to assess the impact of plant height of wheat cultivars in mixtures on splash dispersal of Zymoseptoria tritici, which causes septoria tritici leaf blotch. We used a modelling approach involving an explicit description of canopy architecture and splash dispersal processes. The dispersal model computed raindrop interception by a virtual canopy as well as the production, transport and interception of splash droplets carrying inoculum. We designed 3-D virtual canopies composed of susceptible and resistant plants, according to field measurements at the flowering stage. In numerical experiments, we tested different heights of virtual cultivars making up binary mixtures to assess the influence of this architectural trait on dispersal patterns of spore-carrying droplets. Inoculum interception decreased exponentially with the height relative to the main inoculum source (lower diseased leaves of susceptible plants), and little inoculum was intercepted further than 40 cm above the inoculum source. Consequently, tall plants intercepted less inoculum than smaller ones. Plants with twice the standard height intercepted 33 % less inoculum than standard height plants. In cases when the height of suscpeptible plants was doubled, inoculum interception by resistant leaves was 40 % higher. This physical barrier to spore-carrying droplet trajectories reduced inoculum interception by tall susceptible plants and was modulated by plant height differences between cultivars of a binary mixture. These results suggest that mixture effects on spore dispersal could be modulated by an adequate choice of architectural characteristics of cultivars. In particular, even small differences in plant height could reduce spore dispersal.

An incremental DPMM-based method for trajectory clustering, modeling, and retrieval.

PubMed

Hu, Weiming; Li, Xi; Tian, Guodong; Maybank, Stephen; Zhang, Zhongfei

2013-05-01

Trajectory analysis is the basis for many applications, such as indexing of motion events in videos, activity recognition, and surveillance. In this paper, the Dirichlet process mixture model (DPMM) is applied to trajectory clustering, modeling, and retrieval. We propose an incremental version of a DPMM-based clustering algorithm and apply it to cluster trajectories. An appropriate number of trajectory clusters is determined automatically. When trajectories belonging to new clusters arrive, the new clusters can be identified online and added to the model without any retraining using the previous data. A time-sensitive Dirichlet process mixture model (tDPMM) is applied to each trajectory cluster for learning the trajectory pattern which represents the time-series characteristics of the trajectories in the cluster. Then, a parameterized index is constructed for each cluster. A novel likelihood estimation algorithm for the tDPMM is proposed, and a trajectory-based video retrieval model is developed. The tDPMM-based probabilistic matching method and the DPMM-based model growing method are combined to make the retrieval model scalable and adaptable. Experimental comparisons with state-of-the-art algorithms demonstrate the effectiveness of our algorithm.

BiomeNet: A Bayesian Model for Inference of Metabolic Divergence among Microbial Communities

PubMed Central

Chipman, Hugh; Gu, Hong; Bielawski, Joseph P.

2014-01-01

Metagenomics yields enormous numbers of microbial sequences that can be assigned a metabolic function. Using such data to infer community-level metabolic divergence is hindered by the lack of a suitable statistical framework. Here, we describe a novel hierarchical Bayesian model, called BiomeNet (Bayesian inference of metabolic networks), for inferring differential prevalence of metabolic subnetworks among microbial communities. To infer the structure of community-level metabolic interactions, BiomeNet applies a mixed-membership modelling framework to enzyme abundance information. The basic idea is that the mixture components of the model (metabolic reactions, subnetworks, and networks) are shared across all groups (microbiome samples), but the mixture proportions vary from group to group. Through this framework, the model can capture nested structures within the data. BiomeNet is unique in modeling each metagenome sample as a mixture of complex metabolic systems (metabosystems). The metabosystems are composed of mixtures of tightly connected metabolic subnetworks. BiomeNet differs from other unsupervised methods by allowing researchers to discriminate groups of samples through the metabolic patterns it discovers in the data, and by providing a framework for interpreting them. We describe a collapsed Gibbs sampler for inference of the mixture weights under BiomeNet, and we use simulation to validate the inference algorithm. Application of BiomeNet to human gut metagenomes revealed a metabosystem with greater prevalence among inflammatory bowel disease (IBD) patients. Based on the discriminatory subnetworks for this metabosystem, we inferred that the community is likely to be closely associated with the human gut epithelium, resistant to dietary interventions, and interfere with human uptake of an antioxidant connected to IBD. Because this metabosystem has a greater capacity to exploit host-associated glycans, we speculate that IBD-associated communities might arise from opportunist growth of bacteria that can circumvent the host's nutrient-based mechanism for bacterial partner selection. PMID:25412107

Blocking and the detection of odor components in blends.

PubMed

Hosler, J S; Smith, B H

2000-09-01

Recent studies of olfactory blocking have revealed that binary odorant mixtures are not always processed as though they give rise to mixture-unique configural properties. When animals are conditioned to one odorant (A) and then conditioned to a mixture of that odorant with a second (X), the ability to learn or express the association of X with reinforcement appears to be reduced relative to animals that were not preconditioned to A. A recent model of odor-based response patterns in the insect antennal lobe predicts that the strength of the blocking effect will be related to the perceptual similarity between the two odorants, i.e. greater similarity should increase the blocking effect. Here, we test that model in the honeybee Apis mellifera by first establishing a generalization matrix for three odorants and then testing for blocking between all possible combinations of them. We confirm earlier findings demonstrating the occurrence of the blocking effect in olfactory learning of compound stimuli. We show that the occurrence and the strength of the blocking effect depend on the odorants used in the experiment. In addition, we find very good agreement between our results and the model, and less agreement between our results and an alternative model recently proposed to explain the effect.

Kinetic theory of pattern formation in mixtures of microtubules and molecular motors

NASA Astrophysics Data System (ADS)

Maryshev, Ivan; Marenduzzo, Davide; Goryachev, Andrew B.; Morozov, Alexander

2018-02-01

In this study we formulate a theoretical approach, based on a Boltzmann-like kinetic equation, to describe pattern formation in two-dimensional mixtures of microtubular filaments and molecular motors. Following the previous work by Aranson and Tsimring [Phys. Rev. E 74, 031915 (2006), 10.1103/PhysRevE.74.031915] we model the motor-induced reorientation of microtubules as collision rules, and devise a semianalytical method to calculate the corresponding interaction integrals. This procedure yields an infinite hierarchy of kinetic equations that we terminate by employing a well-established closure strategy, developed in the pattern-formation community and based on a power-counting argument. We thus arrive at a closed set of coupled equations for slowly varying local density and orientation of the microtubules, and study its behavior by performing a linear stability analysis and direct numerical simulations. By comparing our method with the work of Aranson and Tsimring, we assess the validity of the assumptions required to derive their and our theories. We demonstrate that our approximation-free evaluation of the interaction integrals and our choice of a systematic closure strategy result in a rather different dynamical behavior than was previously reported. Based on our theory, we discuss the ensuing phase diagram and the patterns observed.

Gene expression profiles in rainbow trout, Onchorynchus mykiss, exposed to a simple chemical mixture.

PubMed

Hook, Sharon E; Skillman, Ann D; Gopalan, Banu; Small, Jack A; Schultz, Irvin R

2008-03-01

Among proposed uses for microarrays in environmental toxiciology is the identification of key contributors to toxicity within a mixture. However, it remains uncertain whether the transcriptomic profiles resulting from exposure to a mixture have patterns of altered gene expression that contain identifiable contributions from each toxicant component. We exposed isogenic rainbow trout Onchorynchus mykiss, to sublethal levels of ethynylestradiol, 2,2,4,4-tetrabromodiphenyl ether, and chromium VI or to a mixture of all three toxicants Fluorescently labeled complementary DNA (cDNA) were generated and hybridized against a commercially available Salmonid array spotted with 16,000 cDNAs. Data were analyzed using analysis of variance (p<0.05) with a Benjamani-Hochberg multiple test correction (Genespring [Agilent] software package) to identify up and downregulated genes. Gene clustering patterns that can be used as "expression signatures" were determined using hierarchical cluster analysis. The gene ontology terms associated with significantly altered genes were also used to identify functional groups that were associated with toxicant exposure. Cross-ontological analytics approach was used to assign functional annotations to genes with "unknown" function. Our analysis indicates that transcriptomic profiles resulting from the mixture exposure resemble those of the individual contaminant exposures, but are not a simple additive list. However, patterns of altered genes representative of each component of the mixture are clearly discernible, and the functional classes of genes altered represent the individual components of the mixture. These findings indicate that the use of microarrays to identify transcriptomic profiles may aid in the identification of key stressors within a chemical mixture, ultimately improving environmental assessment.

Randomly and Non-Randomly Missing Renal Function Data in the Strong Heart Study: A Comparison of Imputation Methods

PubMed Central

Shara, Nawar; Yassin, Sayf A.; Valaitis, Eduardas; Wang, Hong; Howard, Barbara V.; Wang, Wenyu; Lee, Elisa T.; Umans, Jason G.

2015-01-01

Kidney and cardiovascular disease are widespread among populations with high prevalence of diabetes, such as American Indians participating in the Strong Heart Study (SHS). Studying these conditions simultaneously in longitudinal studies is challenging, because the morbidity and mortality associated with these diseases result in missing data, and these data are likely not missing at random. When such data are merely excluded, study findings may be compromised. In this article, a subset of 2264 participants with complete renal function data from Strong Heart Exams 1 (1989–1991), 2 (1993–1995), and 3 (1998–1999) was used to examine the performance of five methods used to impute missing data: listwise deletion, mean of serial measures, adjacent value, multiple imputation, and pattern-mixture. Three missing at random models and one non-missing at random model were used to compare the performance of the imputation techniques on randomly and non-randomly missing data. The pattern-mixture method was found to perform best for imputing renal function data that were not missing at random. Determining whether data are missing at random or not can help in choosing the imputation method that will provide the most accurate results. PMID:26414328

Randomly and Non-Randomly Missing Renal Function Data in the Strong Heart Study: A Comparison of Imputation Methods.

PubMed

Shara, Nawar; Yassin, Sayf A; Valaitis, Eduardas; Wang, Hong; Howard, Barbara V; Wang, Wenyu; Lee, Elisa T; Umans, Jason G

2015-01-01

Kidney and cardiovascular disease are widespread among populations with high prevalence of diabetes, such as American Indians participating in the Strong Heart Study (SHS). Studying these conditions simultaneously in longitudinal studies is challenging, because the morbidity and mortality associated with these diseases result in missing data, and these data are likely not missing at random. When such data are merely excluded, study findings may be compromised. In this article, a subset of 2264 participants with complete renal function data from Strong Heart Exams 1 (1989-1991), 2 (1993-1995), and 3 (1998-1999) was used to examine the performance of five methods used to impute missing data: listwise deletion, mean of serial measures, adjacent value, multiple imputation, and pattern-mixture. Three missing at random models and one non-missing at random model were used to compare the performance of the imputation techniques on randomly and non-randomly missing data. The pattern-mixture method was found to perform best for imputing renal function data that were not missing at random. Determining whether data are missing at random or not can help in choosing the imputation method that will provide the most accurate results.

A Dictionary Approach to Electron Backscatter Diffraction Indexing.

PubMed

Chen, Yu H; Park, Se Un; Wei, Dennis; Newstadt, Greg; Jackson, Michael A; Simmons, Jeff P; De Graef, Marc; Hero, Alfred O

2015-06-01

We propose a framework for indexing of grain and subgrain structures in electron backscatter diffraction patterns of polycrystalline materials. We discretize the domain of a dynamical forward model onto a dense grid of orientations, producing a dictionary of patterns. For each measured pattern, we identify the most similar patterns in the dictionary, and identify boundaries, detect anomalies, and index crystal orientations. The statistical distribution of these closest matches is used in an unsupervised binary decision tree (DT) classifier to identify grain boundaries and anomalous regions. The DT classifies a pattern as an anomaly if it has an abnormally low similarity to any pattern in the dictionary. It classifies a pixel as being near a grain boundary if the highly ranked patterns in the dictionary differ significantly over the pixel's neighborhood. Indexing is accomplished by computing the mean orientation of the closest matches to each pattern. The mean orientation is estimated using a maximum likelihood approach that models the orientation distribution as a mixture of Von Mises-Fisher distributions over the quaternionic three sphere. The proposed dictionary matching approach permits segmentation, anomaly detection, and indexing to be performed in a unified manner with the additional benefit of uncertainty quantification.

NEUROTOXICOLOGICAL AND STATISTICAL ANALYSES OF A MIXTURE OF FIVE ORGANOPHOSPHORUS PESTICIDES USING A RAY DESIGN.

EPA Science Inventory

Pesticide application patterns generally result in exposure to mixtures instead of single chemicals. Of particular importance in the estimation of pesticide mixture risks is the detection and characterization of their interactions. This research tested for interaction(s) in a mix...

Using virtual 3-D plant architecture to assess fungal pathogen splash dispersal in heterogeneous canopies: a case study with cultivar mixtures and a non-specialized disease causal agent.

PubMed

Gigot, C; de Vallavieille-Pope, C; Huber, L; Saint-Jean, S

2014-09-01

Recent developments in plant disease management have led to a growing interest in alternative strategies, such as increasing host diversity and decreasing the use of pesticides. Use of cultivar mixtures is one option, allowing the spread of plant epidemics to be slowed down. As dispersal of fungal foliar pathogens over short distances by rain-splash droplets is a major contibutor to the spread of disease, this study focused on modelling the physical mechanisms involved in dispersal of a non-specialized pathogen within heterogeneous canopies of cultivar mixtures, with the aim of optimizing host diversification at the intra-field level. Virtual 3-D wheat-like plants (Triticum aestivum) were used to consider interactions between plant architecture and disease progression in heterogeneous canopies. A combined mechanistic and stochastic model, taking into account splash droplet dispersal and host quantitative resistance within a 3-D heterogeneous canopy, was developed. It consists of four sub-models that describe the spatial patterns of two cultivars within a complex canopy, the pathway of rain-splash droplets within this canopy, the proportion of leaf surface area impacted by dispersal via the droplets and the progression of disease severity after each dispersal event. Different spatial organization, proportions and resistance levels of the cultivars of two-component mixtures were investigated. For the eight spatial patterns tested, the protective effect against disease was found to vary by almost 2-fold, with the greatest effect being obtained with the smallest genotype unit area, i.e. the ground area occupied by an independent unit of the host population that is genetically homogeneous. Increasing both the difference between resistance levels and the proportion of the most resistant cultivar often resulted in a greater protective effect; however, this was not observed for situations in which the most resistant of the two cultivars in the mixture had a relatively low level of resistance. The results show agreement with previous data obtained using experimental approaches. They demonstrate that in order to maximize the potential mixture efficiency against a splash-dispersed pathogen, optimal susceptible/resistant cultivar proportions (ranging from 1/9 to 5/5) have to be established based on host resistance levels. The results also show that taking into account dispersal processes in explicit 3-D plant canopies can be a key tool for investigating disease progression in heterogeneous canopies such as cultivar mixtures. © The Author 2014. Published by Oxford University Press on behalf of the Annals of Botany Company. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Multidimensional equilibria and their stability in copolymer-solvent mixtures

NASA Astrophysics Data System (ADS)

Glasner, Karl; Orizaga, Saulo

2018-06-01

This paper discusses localized equilibria which arise in copolymer-solvent mixtures. A free boundary problem associated with the sharp-interface limit of a density functional model is used to identify both lamellar and concentric domain patterns composed of a finite number of layers. Stability of these morphologies is studied through explicit linearization of the free boundary evolution. For the multilayered lamellar configuration, transverse instability is observed for sufficiently small dimensionless interfacial energies. Additionally, a crossover between small and large wavelength instabilities is observed depending on whether solvent-polymer or monomer-monomer interfacial energy is dominant. Concentric domain patterns resembling multilayered micelles and vesicles exhibit bifurcations wherein they only exist for sufficiently small dimensionless interfacial energies. The bifurcation of large radii vesicle solutions is studied analytically, and a crossover from a supercritical case with only one solution branch to a subcritical case with two is observed. Linearized stability of these configurations shows that azimuthal perturbation may lead to instabilities as interfacial energy is decreased.

Concentration of atomic hydrogen in a dielectric barrier discharge measured by two-photon absorption fluorescence

NASA Astrophysics Data System (ADS)

Dvořák, P.; Talába, M.; Obrusník, A.; Kratzer, J.; Dědina, J.

2017-08-01

Two-photon absorption laser-induced fluorescence (TALIF) was utilized for measuring the concentration of atomic hydrogen in a volume dielectric barrier discharge (DBD) ignited in mixtures of Ar, H2 and O2 at atmospheric pressure. The method was calibrated by TALIF of krypton diluted in argon at atmospheric pressure, proving that three-body collisions had a negligible effect on quenching of excited krypton atoms. The diagnostic study was complemented with a 3D numerical model of the gas flow and a zero-dimensional model of the chemistry in order to better understand the reaction kinetics and identify the key pathways leading to the production and destruction of atomic hydrogen. It was determined that the density of atomic hydrogen in Ar-H2 mixtures was in the order of 1021 m-3 and decreased when oxygen was added into the gas mixture. Spatially resolved measurements and simulations revealed a sharply bordered region with low atomic hydrogen concentration when oxygen was added to the gas mixture. At substoichiometric oxygen/hydrogen ratios, this H-poor region is confined to an area close to the gas inlet and it is shown that the size of this region is not only influenced by the chemistry but also by the gas flow patterns. Experimentally, it was observed that a decrease in H2 concentration in the feeding Ar-H2 mixture led to an increase in H production in the DBD.

Self-organized pattern dynamics of somitogenesis model in embryos

NASA Astrophysics Data System (ADS)

Guan, Linan; Shen, Jianwei

2018-09-01

Somitogenesis, the sequential formation of a periodic pattern along the anteroposterior axis of vertebrate embryos, is one of the most obvious examples of the segmental patterning processes that take place during embryogenesis and also one of the major unresolved events in developmental biology. In this paper, we investigate the effect of diffusion on pattern formation use a modified two dimensional model which can be used to explain somitogenesis during embryonic development. This model is suitable for exploring a design space of somitogenesis and can explain many aspects of somitogenesis that previous models cannot. In the present paper, by analyzing the local linear stability of the equation, we acquired the conditions of Hopf bifurcation and Turing bifurcation. In addition, the amplitude equation near the Turing bifurcation point is obtained by using the methods of multi-scale expansion and symmetry analysis. By analyzing the stability of the amplitude equation, we know that there are various complex phenomena, including Spot pattern, mixture of spot-stripe patterns and labyrinthine. Finally, numerical simulation are given to verify the correctness of our theoretical results. Somitogenesis occupies an important position in the process of biological development, and as a pattern process can be used to investigate many aspects of embryogenesis. Therefore, our study helps greatly to cell differentiation, gene expression and embryonic development. What is more, it is of great significance for the diagnosis and treatment of human diseases to study the related knowledge of model biology.

Differential Attenuation of NMR Signals by Complementary Ion-Exchange Resin Beads for De Novo Analysis of Complex Metabolomics Mixtures.

PubMed

Zhang, Bo; Yuan, Jiaqi; Brüschweiler, Rafael

2017-07-12

A primary goal of metabolomics is the characterization of a potentially very large number of metabolites that are part of complex mixtures. Application to biofluids and tissue samples offers insights into biochemical metabolic pathways and their role in health and disease. 1D 1 H and 2D 13 C- 1 H HSQC NMR spectra are most commonly used for this purpose. They yield quantitative information about each proton of the mixture, but do not tell which protons belong to the same molecule. Interpretation requires the use of NMR spectral databases, which naturally limits these investigations to known metabolites. Here, a new method is presented that uses complementary ion exchange resin beads to differentially attenuate 2D NMR cross-peaks that belong to different metabolites. Based on their characteristic attenuation patterns, cross-peaks could be clustered and assigned to individual molecules, including unknown metabolites with multiple spin systems, as demonstrated for a metabolite model mixture and E. coli cell lysate. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Do Stress Trajectories Predict Mortality in Older Men? Longitudinal Findings from the VA Normative Aging Study

PubMed Central

Aldwin, Carolyn M.; Molitor, Nuoo-Ting; Avron, Spiro; Levenson, Michael R.; Molitor, John; Igarashi, Heidi

2011-01-01

We examined long-term patterns of stressful life events (SLE) and their impact on mortality contrasting two theoretical models: allostatic load (linear relationship) and hormesis (inverted U relationship) in 1443 NAS men (aged 41–87 in 1985; M = 60.30, SD = 7.3) with at least two reports of SLEs over 18 years (total observations = 7,634). Using a zero-inflated Poisson growth mixture model, we identified four patterns of SLE trajectories, three showing linear decreases over time with low, medium, and high intercepts, respectively, and one an inverted U, peaking at age 70. Repeating the analysis omitting two health-related SLEs yielded only the first three linear patterns. Compared to the low-stress group, both the moderate and the high-stress groups showed excess mortality, controlling for demographics and health behavior habits, HRs = 1.42 and 1.37, ps <.01 and <.05. The relationship between stress trajectories and mortality was complex and not easily explained by either theoretical model. PMID:21961066

Pattern formation of frictional fingers in a gravitational potential

NASA Astrophysics Data System (ADS)

Eriksen, Jon Alm; Toussaint, Renaud; Mâløy, Knut Jørgen; Flekkøy, Eirik; Galland, Olivier; Sandnes, Bjørnar

2018-01-01

Aligned finger structures, with a characteristic width, emerge during the slow drainage of a liquid-granular mixture in a tilted Hele-Shaw cell. A transition from vertical to horizontal alignment of the finger structures is observed as the tilting angle and the granular density are varied. An analytical model is presented, demonstrating that the alignment properties are the result of the competition between fluctuating granular stresses and the hydrostatic pressure. The dynamics is reproduced in simulations. We also show how the system explains patterns observed in nature, created during the early stages of a dike formation.

Canopy and leaf composition drive patterns of nutrient release from pruning residues in a coffee agroforest.

PubMed

Tully, Katherine L; Lawrence, Deborah

2012-06-01

In a coffee agroforest, the crop is cultivated under the shade of fruit-bearing and nitrogen (N)-fixing trees. These trees are periodically pruned to promote flowering and fruiting as well as to make nutrients stored in tree biomass available to plants. We investigated the effect of canopy composition and substrate quality on decomposition rates and patterns of nutrient release from pruning residues in a coffee agroforest located in Costa Rica's Central Valley. Initial phosphorus (P) release was enhanced under a canopy composed solely of N-fixing, Erythrina poeppigiana compared to a mixed canopy of Erythrina and Musa acuminata (banana). Both initial and final N release were similar under the two canopy types. However, after five months of decomposition, a higher proportion of initial N had been released under the single canopy. Although patterns of decomposition and nutrient release were not predicted by initial substrate quality, mass loss in leaf mixtures rates were well predicted by mean mass loss of their component species. This study identifies specific pruning regimes that may regulate N and P release during crucial growth periods, and it suggests that strategic pruning can enhance nutrient availability. For example, during the onset of rapid fruit growth, a two-species mixture may release more P than a three-species mixture. However, by the time of the harvest, the two- and three-species mixtures have released roughly the same amount of N and P. These nutrients do not always follow the same pattern, as N release can be maximized in single-species substrates, while P release is often facilitated in species mixtures. Our study indicates the importance of management practices in mediating patterns of nutrient release. Future research should investigate how canopy composition and farm management can also mediate on-farm nutrient losses.

Indentifying Latent Classes and Testing Their Determinants in Early Adolescents' Use of Computers and Internet for Learning

ERIC Educational Resources Information Center

Heo, Gyun

2013-01-01

The purpose of the present study was to identify latent classes resting on early adolescents' change trajectory patterns in using computers and the Internet for learning and to test the effects of gender, self-control, self-esteem, and game use in South Korea. Latent growth mixture modeling (LGMM) was used to identify subpopulations in the Korea…

Leukocyte Recognition Using EM-Algorithm

NASA Astrophysics Data System (ADS)

Colunga, Mario Chirinos; Siordia, Oscar Sánchez; Maybank, Stephen J.

This document describes a method for classifying images of blood cells. Three different classes of cells are used: Band Neutrophils, Eosinophils and Lymphocytes. The image pattern is projected down to a lower dimensional sub space using PCA; the probability density function for each class is modeled with a Gaussian mixture using the EM-Algorithm. A new cell image is classified using the maximum a posteriori decision rule.

Prospective mixture risk assessment and management prioritizations for river catchments with diverse land uses

PubMed Central

Brown, Colin D.; de Zwart, Dick; Diamond, Jerome; Dyer, Scott D.; Holmes, Christopher M.; Marshall, Stuart; Burton, G. Allen

2018-01-01

Abstract Ecological risk assessment increasingly focuses on risks from chemical mixtures and multiple stressors because ecosystems are commonly exposed to a plethora of contaminants and nonchemical stressors. To simplify the task of assessing potential mixture effects, we explored 3 land use–related chemical emission scenarios. We applied a tiered methodology to judge the implications of the emissions of chemicals from agricultural practices, domestic discharges, and urban runoff in a quantitative model. The results showed land use–dependent mixture exposures, clearly discriminating downstream effects of land uses, with unique chemical “signatures” regarding composition, concentration, and temporal patterns. Associated risks were characterized in relation to the land‐use scenarios. Comparisons to measured environmental concentrations and predicted impacts showed relatively good similarity. The results suggest that the land uses imply exceedances of regulatory protective environmental quality standards, varying over time in relation to rain events and associated flow and dilution variation. Higher‐tier analyses using ecotoxicological effect criteria confirmed that species assemblages may be affected by exposures exceeding no‐effect levels and that mixture exposure could be associated with predicted species loss under certain situations. The model outcomes can inform various types of prioritization to support risk management, including a ranking across land uses as a whole, a ranking on characteristics of exposure times and frequencies, and various rankings of the relative role of individual chemicals. Though all results are based on in silico assessments, the prospective land use–based approach applied in the present study yields useful insights for simplifying and assessing potential ecological risks of chemical mixtures and can therefore be useful for catchment‐management decisions. Environ Toxicol Chem 2018;37:715–728. © 2017 The Authors. Environmental Toxicology Chemistry Published by Wiley Periodicals, Inc. PMID:28845901

Functional magnetic resonance imaging examination of two modular architectures for switching multiple internal models.

PubMed

Imamizu, Hiroshi; Kuroda, Tomoe; Yoshioka, Toshinori; Kawato, Mitsuo

2004-02-04

An internal model is a neural mechanism that can mimic the input-output properties of a controlled object such as a tool. Recent research interests have moved on to how multiple internal models are learned and switched under a given context of behavior. Two representative computational models for task switching propose distinct neural mechanisms, thus predicting different brain activity patterns in the switching of internal models. In one model, called the mixture-of-experts architecture, switching is commanded by a single executive called a "gating network," which is different from the internal models. In the other model, called the MOSAIC (MOdular Selection And Identification for Control), the internal models themselves play crucial roles in switching. Consequently, the mixture-of-experts model predicts that neural activities related to switching and internal models can be temporally and spatially segregated, whereas the MOSAIC model predicts that they are closely intermingled. Here, we directly examined the two predictions by analyzing functional magnetic resonance imaging activities during the switching of one common tool (an ordinary computer mouse) and two novel tools: a rotated mouse, the cursor of which appears in a rotated position, and a velocity mouse, the cursor velocity of which is proportional to the mouse position. The switching and internal model activities temporally and spatially overlapped each other in the cerebellum and in the parietal cortex, whereas the overlap was very small in the frontal cortex. These results suggest that switching mechanisms in the frontal cortex can be explained by the mixture-of-experts architecture, whereas those in the cerebellum and the parietal cortex are explained by the MOSAIC model.

Internal flow patterns on heat transfer characteristics of a closed-loop oscillating heat-pipe with check valves using ethanol and a silver nano-ethanol mixture

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

Bhuwakietkumjohn, N.; Rittidech, S.

The aim of this research was to investigate the internal flow patterns and heat transfer characteristics of a closed-loop oscillating heat-pipe with check valves (CLOHP/CV). The ratio of number of check valves to meandering turns was 0.2. Ethanol and a silver nano-ethanol mixture were used as working fluids with a filling ratio of 50% by total volume of tube. The CLOHP/CV was made of a glass tube with an inside diameter of 2.4 mm. The evaporator section was 50 mm and 100 mm in length and there were 10 meandering turns. An inclination angle of 90 from horizontal axis wasmore » established. The evaporator section was heated by an electric heater and the condenser section was cooled by distilled water. Temperature at the evaporator section was controlled at 85 C, 105 C and 125 C. The inlet and outlet temperatures were measured. A digital camera and video camera were used to observe the flow patterns at the evaporator. The silver nano-ethanol mixture gave higher heat flux than ethanol. When the temperature at the evaporator section was increased from 85 C to 105 C and 125 C. It was found that, the flow patterns occurred as annular flow + slug flow, slug flow + bubble flow and dispersed bubble flow + bubble flow respectively. The main regime of each flow pattern can be determined from the flow pattern map ethanol and a silver nano-ethanol mixture. Each of the two working fluids gave corresponding flow patterns. (author)« less

Estimating mono- and bi-phasic regression parameters using a mixture piecewise linear Bayesian hierarchical model

PubMed Central

Zhao, Rui; Catalano, Paul; DeGruttola, Victor G.; Michor, Franziska

2017-01-01

The dynamics of tumor burden, secreted proteins or other biomarkers over time, is often used to evaluate the effectiveness of therapy and to predict outcomes for patients. Many methods have been proposed to investigate longitudinal trends to better characterize patients and to understand disease progression. However, most approaches assume a homogeneous patient population and a uniform response trajectory over time and across patients. Here, we present a mixture piecewise linear Bayesian hierarchical model, which takes into account both population heterogeneity and nonlinear relationships between biomarkers and time. Simulation results show that our method was able to classify subjects according to their patterns of treatment response with greater than 80% accuracy in the three scenarios tested. We then applied our model to a large randomized controlled phase III clinical trial of multiple myeloma patients. Analysis results suggest that the longitudinal tumor burden trajectories in multiple myeloma patients are heterogeneous and nonlinear, even among patients assigned to the same treatment cohort. In addition, between cohorts, there are distinct differences in terms of the regression parameters and the distributions among categories in the mixture. Those results imply that longitudinal data from clinical trials may harbor unobserved subgroups and nonlinear relationships; accounting for both may be important for analyzing longitudinal data. PMID:28723910

On selecting a prior for the precision parameter of Dirichlet process mixture models

USGS Publications Warehouse

Dorazio, R.M.

2009-01-01

In hierarchical mixture models the Dirichlet process is used to specify latent patterns of heterogeneity, particularly when the distribution of latent parameters is thought to be clustered (multimodal). The parameters of a Dirichlet process include a precision parameter ?? and a base probability measure G0. In problems where ?? is unknown and must be estimated, inferences about the level of clustering can be sensitive to the choice of prior assumed for ??. In this paper an approach is developed for computing a prior for the precision parameter ?? that can be used in the presence or absence of prior information about the level of clustering. This approach is illustrated in an analysis of counts of stream fishes. The results of this fully Bayesian analysis are compared with an empirical Bayes analysis of the same data and with a Bayesian analysis based on an alternative commonly used prior.

Modelling Spatial Dependence Structures Between Climate Variables by Combining Mixture Models with Copula Models

NASA Astrophysics Data System (ADS)

Khan, F.; Pilz, J.; Spöck, G.

2017-12-01

Spatio-temporal dependence structures play a pivotal role in understanding the meteorological characteristics of a basin or sub-basin. This further affects the hydrological conditions and consequently will provide misleading results if these structures are not taken into account properly. In this study we modeled the spatial dependence structure between climate variables including maximum, minimum temperature and precipitation in the Monsoon dominated region of Pakistan. For temperature, six, and for precipitation four meteorological stations have been considered. For modelling the dependence structure between temperature and precipitation at multiple sites, we utilized C-Vine, D-Vine and Student t-copula models. For temperature, multivariate mixture normal distributions and for precipitation gamma distributions have been used as marginals under the copula models. A comparison was made between C-Vine, D-Vine and Student t-copula by observational and simulated spatial dependence structure to choose an appropriate model for the climate data. The results show that all copula models performed well, however, there are subtle differences in their performances. The copula models captured the patterns of spatial dependence structures between climate variables at multiple meteorological sites, however, the t-copula showed poor performance in reproducing the dependence structure with respect to magnitude. It was observed that important statistics of observed data have been closely approximated except of maximum values for temperature and minimum values for minimum temperature. Probability density functions of simulated data closely follow the probability density functions of observational data for all variables. C and D-Vines are better tools when it comes to modelling the dependence between variables, however, Student t-copulas compete closely for precipitation. Keywords: Copula model, C-Vine, D-Vine, Spatial dependence structure, Monsoon dominated region of Pakistan, Mixture models, EM algorithm.

Modeling sports highlights using a time-series clustering framework and model interpretation

NASA Astrophysics Data System (ADS)

Radhakrishnan, Regunathan; Otsuka, Isao; Xiong, Ziyou; Divakaran, Ajay

2005-01-01

In our past work on sports highlights extraction, we have shown the utility of detecting audience reaction using an audio classification framework. The audio classes in the framework were chosen based on intuition. In this paper, we present a systematic way of identifying the key audio classes for sports highlights extraction using a time series clustering framework. We treat the low-level audio features as a time series and model the highlight segments as "unusual" events in a background of an "usual" process. The set of audio classes to characterize the sports domain is then identified by analyzing the consistent patterns in each of the clusters output from the time series clustering framework. The distribution of features from the training data so obtained for each of the key audio classes, is parameterized by a Minimum Description Length Gaussian Mixture Model (MDL-GMM). We also interpret the meaning of each of the mixture components of the MDL-GMM for the key audio class (the "highlight" class) that is correlated with highlight moments. Our results show that the "highlight" class is a mixture of audience cheering and commentator's excited speech. Furthermore, we show that the precision-recall performance for highlights extraction based on this "highlight" class is better than that of our previous approach which uses only audience cheering as the key highlight class.

Mining patterns in persistent surveillance systems with smart query and visual analytics

NASA Astrophysics Data System (ADS)

Habibi, Mohammad S.; Shirkhodaie, Amir

2013-05-01

In Persistent Surveillance Systems (PSS) the ability to detect and characterize events geospatially help take pre-emptive steps to counter adversary's actions. Interactive Visual Analytic (VA) model offers this platform for pattern investigation and reasoning to comprehend and/or predict such occurrences. The need for identifying and offsetting these threats requires collecting information from diverse sources, which brings with it increasingly abstract data. These abstract semantic data have a degree of inherent uncertainty and imprecision, and require a method for their filtration before being processed further. In this paper, we have introduced an approach based on Vector Space Modeling (VSM) technique for classification of spatiotemporal sequential patterns of group activities. The feature vectors consist of an array of attributes extracted from generated sensors semantic annotated messages. To facilitate proper similarity matching and detection of time-varying spatiotemporal patterns, a Temporal-Dynamic Time Warping (DTW) method with Gaussian Mixture Model (GMM) for Expectation Maximization (EM) is introduced. DTW is intended for detection of event patterns from neighborhood-proximity semantic frames derived from established ontology. GMM with EM, on the other hand, is employed as a Bayesian probabilistic model to estimated probability of events associated with a detected spatiotemporal pattern. In this paper, we present a new visual analytic tool for testing and evaluation group activities detected under this control scheme. Experimental results demonstrate the effectiveness of proposed approach for discovery and matching of subsequences within sequentially generated patterns space of our experiments.

Semi-supervised anomaly detection - towards model-independent searches of new physics

NASA Astrophysics Data System (ADS)

Kuusela, Mikael; Vatanen, Tommi; Malmi, Eric; Raiko, Tapani; Aaltonen, Timo; Nagai, Yoshikazu

2012-06-01

Most classification algorithms used in high energy physics fall under the category of supervised machine learning. Such methods require a training set containing both signal and background events and are prone to classification errors should this training data be systematically inaccurate for example due to the assumed MC model. To complement such model-dependent searches, we propose an algorithm based on semi-supervised anomaly detection techniques, which does not require a MC training sample for the signal data. We first model the background using a multivariate Gaussian mixture model. We then search for deviations from this model by fitting to the observations a mixture of the background model and a number of additional Gaussians. This allows us to perform pattern recognition of any anomalous excess over the background. We show by a comparison to neural network classifiers that such an approach is a lot more robust against misspecification of the signal MC than supervised classification. In cases where there is an unexpected signal, a neural network might fail to correctly identify it, while anomaly detection does not suffer from such a limitation. On the other hand, when there are no systematic errors in the training data, both methods perform comparably.

Cross-Diffusion Induced Turing Instability and Amplitude Equation for a Toxic-Phytoplankton-Zooplankton Model with Nonmonotonic Functional Response

NASA Astrophysics Data System (ADS)

Han, Renji; Dai, Binxiang

2017-06-01

The spatiotemporal pattern induced by cross-diffusion of a toxic-phytoplankton-zooplankton model with nonmonotonic functional response is investigated in this paper. The linear stability analysis shows that cross-diffusion is the key mechanism for the formation of spatial patterns. By taking cross-diffusion rate as bifurcation parameter, we derive amplitude equations near the Turing bifurcation point for the excited modes in the framework of a weakly nonlinear theory, and the stability analysis of the amplitude equations interprets the structural transitions and stability of various forms of Turing patterns. Furthermore, we illustrate the theoretical results via numerical simulations. It is shown that the spatiotemporal distribution of the plankton is homogeneous in the absence of cross-diffusion. However, when the cross-diffusivity is greater than the critical value, the spatiotemporal distribution of all the plankton species becomes inhomogeneous in spaces and results in different kinds of patterns: spot, stripe, and the mixture of spot and stripe patterns depending on the cross-diffusivity. Simultaneously, the impact of toxin-producing rate of toxic-phytoplankton (TPP) species and natural death rate of zooplankton species on pattern selection is also explored.

A mixture toxicity approach to predict the toxicity of Ag decorated ZnO nanomaterials.

PubMed

Azevedo, S L; Holz, T; Rodrigues, J; Monteiro, T; Costa, F M; Soares, A M V M; Loureiro, S

2017-02-01

Nanotechnology is a rising field and nanomaterials can now be found in a vast variety of products with different chemical compositions, sizes and shapes. New nanostructures combining different nanomaterials are being developed due to their enhancing characteristics when compared to nanomaterials alone. In the present study, the toxicity of a nanostructure composed by a ZnO nanomaterial with Ag nanomaterials on its surface (designated as ZnO/Ag nanostructure) was assessed using the model-organism Daphnia magna and its toxicity predicted based on the toxicity of the single components (Zn and Ag). For that ZnO and Ag nanomaterials as single components, along with its mixture prepared in the laboratory, were compared in terms of toxicity to ZnO/Ag nanostructures. Toxicity was assessed by immobilization and reproduction tests. A mixture toxicity approach was carried out using as starting point the conceptual model of Concentration Addition. The laboratory mixture of both nanomaterials showed that toxicity was dependent on the doses of ZnO and Ag used (immobilization) or presented a synergistic pattern (reproduction). The ZnO/Ag nanostructure toxicity prediction, based on the percentage of individual components, showed an increase in toxicity when compared to the expected (immobilization) and dependent on the concentration used (reproduction). This study demonstrates that the toxicity of the prepared mixture of ZnO and Ag and of the ZnO/Ag nanostructure cannot be predicted based on the toxicity of their components, highlighting the importance of taking into account the interaction between nanomaterials when assessing hazard and risk. Copyright © 2016 Elsevier B.V. All rights reserved.

Patterns of Crime and Drug Use Trajectories in Relation to Treatment Initiation and 5-Year Outcomes: An Application of Growth Mixture Modeling across Three Data Sets

ERIC Educational Resources Information Center

Prendergast, Michael; Huang, David; Hser, Yih-Ing

2008-01-01

Drug abusers vary considerably in their drug use and criminal behavior over time, and these trajectories are likely to influence drug treatment participation and treatment outcomes. Drawing on longitudinal natural history data from three samples of adult male drug users, we identify four groups with distinctive drug use and crime trajectories…

Discovering Condition-Specific Gene Co-Expression Patterns Using Gaussian Mixture Models: A Cancer Case Study.

PubMed

Ficklin, Stephen P; Dunwoodie, Leland J; Poehlman, William L; Watson, Christopher; Roche, Kimberly E; Feltus, F Alex

2017-08-17

A gene co-expression network (GCN) describes associations between genes and points to genetic coordination of biochemical pathways. However, genetic correlations in a GCN are only detectable if they are present in the sampled conditions. With the increasing quantity of gene expression samples available in public repositories, there is greater potential for discovery of genetic correlations from a variety of biologically interesting conditions. However, even if gene correlations are present, their discovery can be masked by noise. Noise is introduced from natural variation (intrinsic and extrinsic), systematic variation (caused by sample measurement protocols and instruments), and algorithmic and statistical variation created by selection of data processing tools. A variety of published studies, approaches and methods attempt to address each of these contributions of variation to reduce noise. Here we describe an approach using Gaussian Mixture Models (GMMs) to address natural extrinsic (condition-specific) variation during network construction from mixed input conditions. To demonstrate utility, we build and analyze a condition-annotated GCN from a compendium of 2,016 mixed gene expression data sets from five tumor subtypes obtained from The Cancer Genome Atlas. Our results show that GMMs help discover tumor subtype specific gene co-expression patterns (modules) that are significantly enriched for clinical attributes.

Associations between trajectories of perceived racial discrimination and psychological symptoms among African American adolescents

PubMed Central

Smith-Bynum, Mia A.; Lambert, Sharon F.; English, Devin; Ialongo, Nicholas S.

2014-01-01

Many African American adolescents experience racial discrimination, with adverse consequences; however, stability and change in these experiences over time have not been examined. We examined longitudinal patterns of perceived racial discrimination assessed in grades 7 – 10 and how these discrimination trajectories related to patterns of change in depressive and anxious symptoms and aggressive behaviors assessed over the same 4-year period. Growth mixture modeling performed on a community epidemiologically-defined sample of urban African American adolescents (n = 504) revealed three trajectories of discrimination: (1) increasing, (2) decreasing, and (3) stable low. As predicted, African American boys were more frequent targets for racial discrimination as they aged, and were more likely to be in the increasing group. Results of parallel process growth mixture modeling revealed that youth in the increasing racial discrimination group were four times more likely to be in an increasing depression trajectory than youth in the low stable discrimination trajectory. Though youth in the increasing racial discrimination group were nearly twice as likely to be in the high aggression trajectory, results were not statistically significant. These results indicate an association between variation in the growth of perceived racial discrimination and youth behavior and psychological well-being over the adolescent years. PMID:24955844

Associations between trajectories of perceived racial discrimination and psychological symptoms among African American adolescents.

PubMed

Smith-Bynum, Mia A; Lambert, Sharon F; English, Devin; Ialongo, Nicholas S

2014-11-01

Many African American adolescents experience racial discrimination, with adverse consequences; however, stability and change in these experiences over time have not been examined. We examined longitudinal patterns of perceived racial discrimination assessed in Grades 7-10 and how these discrimination trajectories related to patterns of change in depressive and anxious symptoms and aggressive behaviors assessed over the same 4-year period. Growth mixture modeling performed on a community epidemiologically defined sample of urban African American adolescents (n = 504) revealed three trajectories of discrimination: increasing, decreasing, and stable low. As predicted, African American boys were more frequent targets for racial discrimination as they aged, and they were more likely to be in the increasing group. The results of parallel process growth mixture modeling revealed that youth in the increasing racial discrimination group were four times more likely to be in an increasing depression trajectory than were youth in the low stable discrimination trajectory. Though youth in the increasing racial discrimination group were nearly twice as likely to be in the high aggression trajectory, results were not statistically significant. These results indicate an association between variation in the growth of perceived racial discrimination and youth behavior and psychological well-being over the adolescent years.

Characterization of Sheet Fracture Patterns in Polygonal-Jointed Lavas at Kokostick Butte, OR, and Mazama Ridge, WA: Investigation and Interpretation of Their Formation and Significance

NASA Astrophysics Data System (ADS)

Lodge, R. W.; Lescinsky, D. T.

2006-12-01

Polygonal joints in lava flows ("columns") are commonly equant leading to a model of formation associated with cooling in an isotropic stress field. This model, however, does not explain rectangular columns, sheet-like fractures, fractures with crosscutting relationships, and fractures with orientations other than perpendicular to the cooling surface. These fracture patterns are often observed at glaciated volcanoes. The presence of preferential fracture orientations suggests an applied stress component likely due to environmental conditions such as the presence of glaciers or flow dynamics such as down-slope settling or flow margin inflation. During this study we investigated the formation and significance of these non-equant fracture patterns to propose a model for their formation. These `abnormal' fracture patterns have not been discussed in the literature and may be important to better understanding the cooling conditions of such lava flows. To test these possibilities we studied Kokostick Butte dacite flow, OR (near South Sister), and Mazama Ridge andesite flow at Mount Rainier, WA. Both of these flows have well developed sheet-like fractures and display evidence of ice-contact during eruption and emplacement. Sheet fractures are long and continuous fractures that have perpendicular connecting fractures forming rectangular columns. The sheet-like fractures are largely parallel to each other on the exposure surface and the connecting fractures vary locally from primary fractures (associated with cooling toward flow interior) to secondary fractures (associated with cooling by water infiltration). Detailed measurements of fracture orientations and spacing were collected at Kokostick Butte and Mazama Ridge to examine the relationship between the sheet fractures and flow geometry. Preliminary results support this relationship and suggest these patterns likely form due to shear associated with small amounts of flow advance by the rapidly cooling lava. Laboratory studies have been undertaken to complement the field observations and measurements. Starch- water experiments have been proven a useful analogue for lava column formation. Various experimental setups involving different mixture thicknesses and compression of the mixture were utilized to simulate the stresses acting during ponding of lava against glacial ice and to produce different fracture morphologies and patterns. Initial results show that compression of the starch slurry results in non-equant fracture patterns with some sheet-like fracturing present.

Computational Fluid Dynamics modeling of contrast transport in basilar aneurysms following flow-altering surgeries.

PubMed

Vali, Alireza; Abla, Adib A; Lawton, Michael T; Saloner, David; Rayz, Vitaliy L

2017-01-04

In vivo measurement of blood velocity fields and flow descriptors remains challenging due to image artifacts and limited resolution of current imaging methods; however, in vivo imaging data can be used to inform and validate patient-specific computational fluid dynamics (CFD) models. Image-based CFD can be particularly useful for planning surgical interventions in complicated cases such as fusiform aneurysms of the basilar artery, where it is crucial to alter pathological hemodynamics while preserving flow to the distal vasculature. In this study, patient-specific CFD modeling was conducted for two basilar aneurysm patients considered for surgical treatment. In addition to velocity fields, transport of contrast agent was simulated for the preoperative and postoperative conditions using two approaches. The transport of a virtual contrast passively following the flow streamlines was simulated to predict post-surgical flow regions prone to thrombus deposition. In addition, the transport of a mixture of blood with an iodine-based contrast agent was modeled to compare and verify the CFD results with X-ray angiograms. The CFD-predicted patterns of contrast flow were qualitatively compared to in vivo X-ray angiograms acquired before and after the intervention. The results suggest that the mixture modeling approach, accounting for the flow rates and properties of the contrast injection, is in better agreement with the X-ray angiography data. The virtual contrast modeling assessed the residence time based on flow patterns unaffected by the injection procedure, which makes the virtual contrast modeling approach better suited for prediction of thrombus deposition, which is not limited to the peri-procedural state. Copyright © 2016 Elsevier Ltd. All rights reserved.

Nebulisation of corticosteroid suspensions and solutions with a beta(2) agonist.

PubMed

O'Callaghan, Christopher L; White, Judy A; Jackson, Judith M; Barry, Peter W; Kantar, Ahmad

2008-05-01

The aim of this study was to determine the output of salbutamol nebulised in combination with either flunisolide or beclometasone dipropionate (BDP) from two different nebulisers under simulated breathing conditions. The BimboNeb and Nebula nebulisers were used to nebulise 3.0 mL of the two drug mixtures (salbutamol, 5000 microg plus either flunisolide, 600 microg, or BDP, 800 microg). Particle size was determined by inertial impaction. Total outputs of all drugs from both nebulisers were measured using a sinus flow pump under simulated paediatric and adult breathing patterns. The mass median aerodynamic diameter (MMAD) of BDP particles from the mixture was 6.34 mum using the BimboNeb and 5.34 mum using the Nebula. Values for salbutamol in this mixture were 3.93 and 3.32 microm, respectively. The MMAD of flunisolide particles from the BimboNeb and Nebula were 3.74 and 3.65 microm, respectively, while for salbutamol were 3.79 and 3.74 microm, respectively. With the simulated adult breathing pattern, all drug outputs from both mixtures were greater from the BimboNeb than from the Nebula after 5 and 10 min' nebulisation. Drug delivery from the BimboNeb, but not the Nebula, was affected by the simulated breathing pattern. Outputs with the BimboNeb were lower with the paediatric breathing pattern than with the adult pattern. In the majority of cases, nebulising for 10 min produced significantly greater drug output than after 5 min. For the Nebula, outputs were generally similar at 5 and 10 min, irrespective of the breathing pattern. These results highlight the need to assess the amount of aerosolised drug available when drugs are combined, when different nebulisers are used and when they are used with patients of different ages.

Application of DNA Machineries for the Barcode Patterned Detection of Genes or Proteins.

PubMed

Zhou, Zhixin; Luo, Guofeng; Wulf, Verena; Willner, Itamar

2018-06-05

The study introduces an analytical platform for the detection of genes or aptamer-ligand complexes by nucleic acid barcode patterns generated by DNA machineries. The DNA machineries consist of nucleic acid scaffolds that include specific recognition sites for the different genes or aptamer-ligand analytes. The binding of the analytes to the scaffolds initiate, in the presence of the nucleotide mixture, a cyclic polymerization/nicking machinery that yields displaced strands of variable lengths. The electrophoretic separation of the resulting strands provides barcode patterns for the specific detection of the different analytes. Mixtures of DNA machineries that yield, upon sensing of different genes (or aptamer ligands), one-, two-, or three-band barcode patterns are described. The combination of nucleic acid scaffolds acting, in the presence of polymerase/nicking enzyme and nucleotide mixture, as DNA machineries, that generate multiband barcode patterns provide an analytical platform for the detection of an individual gene out of many possible genes. The diversity of genes (or other analytes) that can be analyzed by the DNA machineries and the barcode patterned imaging is given by the Pascal's triangle. As a proof-of-concept, the detection of one of six genes, that is, TP53, Werner syndrome, Tay-Sachs normal gene, BRCA1, Tay-Sachs mutant gene, and cystic fibrosis disorder gene by six two-band barcode patterns is demonstrated. The advantages and limitations of the detection of analytes by polymerase/nicking DNA machineries that yield barcode patterns as imaging readout signals are discussed.

Modeling the interaction and toxicity of Cu-Cd mixture to wheat roots affected by humic acids, in terms of cell membrane surface characteristics.

PubMed

Wang, Yi-Min; Zhou, Dong-Mei; Yuan, Xu-Yin; Zhang, Xiao-Hui; Li, Yi

2018-05-01

Responses of wheat (Triticum aestivum L.) seedling roots to the mixtures of copper (Cu), cadmium (Cd) and humic acids (HA) were investigated using the solution culture experiments, focusing on the interaction patterns between multiple metals and their influences on root proton release. A concentration-addition multiplication (CA) model was introduced into the modeling analysis. In comparison with metal ion activities in bulk-phase solutions, the incorporation of ion activities at the root cell membrane surfaces (CMs) (denoted as {Cu 2+ } 0 and {Cd 2+ } 0 ) into the CA model could significantly improve their correlation with RRE (relative root elongation) from 0.819 to 0.927. Modeling analysis indicated that the co-existence of {Cu 2+ } 0 significantly enhanced the rhizotoxicity of {Cd 2+ } 0 , while no significant effect of {Cd 2+ } 0 on the {Cu 2+ } 0 rhizotoxicity. 10 mg/L HA stimulated the root elongation even under metal stress. Although high concentration of metal ions inhibited the root proton release rate (ΔH + ), both the low concentration of metal ions and HA treatments increased the values of ΔH + . In HA-Cu-Cd mixtures, actions of metal ions on ΔH + values were varied intricately among treatments but well modeled by the CA model. We concluded from the CA models that the electrostatic effect is vitally important for explaining the effect of {Cu 2+ } 0 on the rhizotoxicity of {Cd 2+ } 0 , while it plays no unique role in understanding the influence of {Cd 2+ } 0 on the rhizotoxicity of {Cu 2+ } 0. Thus our study provide a novel way for modeling multiple metals behaviors in the environment and understanding the mechanisms of ion interactions. Copyright © 2018 Elsevier Ltd. All rights reserved.

How good is the turbid medium-based approach for accounting for light partitioning in contrasted grass--legume intercropping systems?

PubMed

Barillot, Romain; Louarn, Gaëtan; Escobar-Gutiérrez, Abraham J; Huynh, Pierre; Combes, Didier

2011-10-01

Most studies dealing with light partitioning in intercropping systems have used statistical models based on the turbid medium approach, thus assuming homogeneous canopies. However, these models could not be directly validated although spatial heterogeneities could arise in such canopies. The aim of the present study was to assess the ability of the turbid medium approach to accurately estimate light partitioning within grass-legume mixed canopies. Three contrasted mixtures of wheat-pea, tall fescue-alfalfa and tall fescue-clover were sown according to various patterns and densities. Three-dimensional plant mock-ups were derived from magnetic digitizations carried out at different stages of development. The benchmarks for light interception efficiency (LIE) estimates were provided by the combination of a light projective model and plant mock-ups, which also provided the inputs of a turbid medium model (SIRASCA), i.e. leaf area index and inclination. SIRASCA was set to gradually account for vertical heterogeneity of the foliage, i.e. the canopy was described as one, two or ten horizontal layers of leaves. Mixtures exhibited various and heterogeneous profiles of foliar distribution, leaf inclination and component species height. Nevertheless, most of the LIE was satisfactorily predicted by SIRASCA. Biased estimations were, however, observed for (1) grass species and (2) tall fescue-alfalfa mixtures grown at high density. Most of the discrepancies were due to vertical heterogeneities and were corrected by increasing the vertical description of canopies although, in practice, this would require time-consuming measurements. The turbid medium analogy could be successfully used in a wide range of canopies. However, a more detailed description of the canopy is required for mixtures exhibiting vertical stratifications and inter-/intra-species foliage overlapping. Architectural models remain a relevant tool for studying light partitioning in intercropping systems that exhibit strong vertical heterogeneities. Moreover, these models offer the possibility to integrate the effects of microclimate variations on plant growth.

Using Bayesian statistics for modeling PTSD through Latent Growth Mixture Modeling: implementation and discussion.

PubMed

Depaoli, Sarah; van de Schoot, Rens; van Loey, Nancy; Sijbrandij, Marit

2015-01-01

After traumatic events, such as disaster, war trauma, and injuries including burns (which is the focus here), the risk to develop posttraumatic stress disorder (PTSD) is approximately 10% (Breslau & Davis, 1992). Latent Growth Mixture Modeling can be used to classify individuals into distinct groups exhibiting different patterns of PTSD (Galatzer-Levy, 2015). Currently, empirical evidence points to four distinct trajectories of PTSD patterns in those who have experienced burn trauma. These trajectories are labeled as: resilient, recovery, chronic, and delayed onset trajectories (e.g., Bonanno, 2004; Bonanno, Brewin, Kaniasty, & Greca, 2010; Maercker, Gäbler, O'Neil, Schützwohl, & Müller, 2013; Pietrzak et al., 2013). The delayed onset trajectory affects only a small group of individuals, that is, about 4-5% (O'Donnell, Elliott, Lau, & Creamer, 2007). In addition to its low frequency, the later onset of this trajectory may contribute to the fact that these individuals can be easily overlooked by professionals. In this special symposium on Estimating PTSD trajectories (Van de Schoot, 2015a), we illustrate how to properly identify this small group of individuals through the Bayesian estimation framework using previous knowledge through priors (see, e.g., Depaoli & Boyajian, 2014; Van de Schoot, Broere, Perryck, Zondervan-Zwijnenburg, & Van Loey, 2015). We used latent growth mixture modeling (LGMM) (Van de Schoot, 2015b) to estimate PTSD trajectories across 4 years that followed a traumatic burn. We demonstrate and compare results from traditional (maximum likelihood) and Bayesian estimation using priors (see, Depaoli, 2012, 2013). Further, we discuss where priors come from and how to define them in the estimation process. We demonstrate that only the Bayesian approach results in the desired theory-driven solution of PTSD trajectories. Since the priors are chosen subjectively, we also present a sensitivity analysis of the Bayesian results to illustrate how to check the impact of the prior knowledge integrated into the model. We conclude with recommendations and guidelines for researchers looking to implement theory-driven LGMM, and we tailor this discussion to the context of PTSD research.

Cold-development tool and technique for the ultimate resolution of ZEP520A to fabricate an EB master mold for nano-imprint lithography for 1Tbit/inch2 BPM development

NASA Astrophysics Data System (ADS)

Kobayashi, Hideo; Iyama, Hiromasa; Kagatsume, Takeshi; Watanabe, Tsuyoshi

2012-11-01

Cold-development is well-known for resolution enhancement on ZEP520A. Dipping a wafer in a developer solvent chilled by a freezer, such a typical method had been employed. But, it is obvious that the dip-development method has several inferiorities such as developer temperature instability, temperature inconsistency between developer and a wafer, water-condensation on drying. We then built a single wafer spin-develop tool, and established a process sequence, to solve those difficulties. And, we tried to see their effect down to -10degC over various developers. In specific, we tried to make hole patterns in hexagonal closest packing in 40nm, 35nm, 30nm, 25nm pitch, and examined holes pattern quality and resolution limit by varying setting temperature from room temperature to -10degC in the cold-development, as well as varying developer chemistry from the standard developer ZED N-50 (n-amyl acetate, 100%) to MiBK and IPA mixture which was a rinsing solvent mixture originally. We also examined the other developer (poor solvent mixture) we designed, N-50 and fluorocarbon (FC) mixture, MiBK and FC mixture, and IPA+FC mixture. This paper describes cold-development tool and technique, and its results down to minus (-) 10degC, for ZEP520A resolution enhancement to obtain 1Xnm bits (holes) in 25nm pitch to fabricate an EB master mold for Nano-Imprinting Lithography for 1Tbit/in2 bit patterned media (BPM) in HDD development and production.

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

A Bayesian Joint Model of Menstrual Cycle Length and Fecundity

PubMed Central

Lum, Kirsten J.; Sundaram, Rajeshwari; Louis, Germaine M. Buck; Louis, Thomas A.

2015-01-01

Summary Menstrual cycle length (MCL) has been shown to play an important role in couple fecundity, which is the biologic capacity for reproduction irrespective of pregnancy intentions. However, a comprehensive assessment of its role requires a fecundity model that accounts for male and female attributes and the couple’s intercourse pattern relative to the ovulation day. To this end, we employ a Bayesian joint model for MCL and pregnancy. MCLs follow a scale multiplied (accelerated) mixture model with Gaussian and Gumbel components; the pregnancy model includes MCL as a covariate and computes the cycle-specific probability of pregnancy in a menstrual cycle conditional on the pattern of intercourse and no previous fertilization. Day-specific fertilization probability is modeled using natural, cubic splines. We analyze data from the Longitudinal Investigation of Fertility and the Environment Study (the LIFE Study), a couple based prospective pregnancy study, and find a statistically significant quadratic relation between fecundity and menstrual cycle length, after adjustment for intercourse pattern and other attributes, including male semen quality, both partner’s age, and active smoking status (determined by baseline cotinine level 100ng/mL). We compare results to those produced by a more basic model and show the advantages of a more comprehensive approach. PMID:26295923

Fibrous dosage forms by wet 3D-micro-patterning: process design, manufacture, and drug release rate.

PubMed

Blaesi, Aron H; Saka, Nannaji

2018-06-19

Recently, we have introduced fibrous dosage forms prepared by 3D-micro-patterning of drug-laden viscous melts. Such dosage forms enable predictable microstructures and increased drug release rates, and they can be manufactured continuously. However, melt processing is not applicable if the melting temperature of the formulation is greater than the degradation temperature of the drug or of the excipient. In this work, therefore, a continuous wet micro-patterning process that operates at ambient temperature is presented. The excipient is plasticized by a solvent and the patterned dosage form is solidified by air drying. Process models show that the micro-patterning time is the ratio of the fiber length in the dosage form and the velocity of the fiber stream. It was 1.3 minutes in the experiments, but can be reduced further. The drying time is limited by the diffusive flux of solvent through the fibers: it was about 3 minutes for the experimental conditions. Furthermore, models are developed to illustrate the effects of fiber radius, inter-fiber spacing, viscosity of the drug-excipient-solvent mixture, and drying conditions on the microstructure of the dosage form. Models and experimental results show that for a viscosity of the wet fibers of the order 10 3 Pa·s, both the patterned microstructure is well preserved and the crossed fibers are well bonded. Finally, the drug release rate by the dosage forms is experimentally determined and theoretically modeled. The results of the experiments validate the models fairly. Copyright © 2018. Published by Elsevier B.V.

Heterogeneity in Trajectories of Child Maltreatment Severity: A Two-Part Growth Mixture Model

PubMed Central

Yampolskaya, Svetlana; Greenbaum, Paul E.; Brown, C. Hendricks; Armstrong, Mary I.

2016-01-01

This study examined the trajectories of maltreatment severity and substantiation over a 24-month period among children (N = 82,396) with repeated maltreatment reports. Findings revealed two different longitudinal patterns. The first pattern, Elevated Severity, showed a higher level of maltreatment during the initial incident and increased maltreatment severity during subsequent incidents but the substantiation rates for this class decreased over time. The second pattern, Lowered Severity, showed a much lower level of severity, but the likelihood of substantiation increased over time. The Elevated Severity class was comprised of children with an elevated risk profile due to both individual and contextual risk factors including older age, female gender, caregivers’ substance use problems, and a higher number of previous maltreatment reports. Implications of the findings are discussed. PMID:26300381

On the modeling of the bottom particles segregation with non-linear diffusion equations: application to the marine sand ripples

NASA Astrophysics Data System (ADS)

Tiguercha, Djlalli; Bennis, Anne-claire; Ezersky, Alexander

2015-04-01

The elliptical motion in surface waves causes an oscillating motion of the sand grains leading to the formation of ripple patterns on the bottom. Investigation how the grains with different properties are distributed inside the ripples is a difficult task because of the segration of particle. The work of Fernandez et al. (2003) was extended from one-dimensional to two-dimensional case. A new numerical model, based on these non-linear diffusion equations, was developed to simulate the grain distribution inside the marine sand ripples. The one and two-dimensional models are validated on several test cases where segregation appears. Starting from an homogeneous mixture of grains, the two-dimensional simulations demonstrate different segregation patterns: a) formation of zones with high concentration of light and heavy particles, b) formation of «cat's eye» patterns, c) appearance of inverse Brazil nut effect. Comparisons of numerical results with the new set of field data and wave flume experiments show that the two-dimensional non-linear diffusion equations allow us to reproduce qualitatively experimental results on particles segregation.

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.

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

Häusler, I., E-mail: ines.haeusler@bam.de; Dörfel, I., E-mail: Ilona.doerfel@bam.de; Peplinski, B., E-mail: Burkhard.peplinski@bam.de

A model system was used to simulate the properties of tribofilms which form during automotive braking. The model system was prepared by ball milling of a blend of 70 vol.% iron oxides, 15 vol.% molybdenum disulfide and 15 vol.% graphite. The resulting mixture was characterized by X-ray powder diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and various transmission electron microscopic (TEM) methods, including energy dispersive X-ray spectroscopy (EDXS), high resolution investigations (HRTEM) with corresponding simulation of the HRTEM images, diffraction methods such as scanning nano-beam electron diffraction (SNBED) and selected area electron diffraction (SAED). It could be shown that the ballmore » milling caused a reduction of the grain size of the initial components to the nanometer range. Sometimes even amorphization or partial break-down of the crystal structure was observed for MoS{sub 2} and graphite. Moreover, chemical reactions lead to a formation of surface coverings of the nanoparticles by amorphous material, molybdenum oxides, and iron sulfates as derived from XPS. - Highlights: • Ball milling of iron oxides, MoS{sub 2}, and graphite to simulate a tribofilm • Increasing coefficient of friction after ball milling of the model blend • Drastically change of the diffraction pattern of the powder mixture • TEM & XPS showed the components of the milled mixture and the process during milling. • MoS{sub 2} and graphite suffered a loss in translation symmetry or became amorphous.« less

Trajectories of change in depression severity during treatment with antidepressants.

PubMed

Uher, R; Muthén, B; Souery, D; Mors, O; Jaracz, J; Placentino, A; Petrovic, A; Zobel, A; Henigsberg, N; Rietschel, M; Aitchison, K J; Farmer, A; McGuffin, P

2010-08-01

Response and remission defined by cut-off values on the last observed depression severity score are commonly used as outcome criteria in clinical trials, but ignore the time course of symptomatic change and may lead to inefficient analyses. We explore alternative categorization of outcome by naturally occurring trajectories of symptom change. Growth mixture models were applied to repeated measurements of depression severity in 807 participants with major depression treated for 12 weeks with escitalopram or nortriptyline in the part-randomized Genome-based Therapeutic Drugs for Depression study. Latent trajectory classes were validated as outcomes in drug efficacy comparison and pharmacogenetic analyses. The final two-piece growth mixture model categorized participants into a majority (75%) following a gradual improvement trajectory and the remainder following a trajectory with rapid initial improvement. The rapid improvement trajectory was over-represented among nortriptyline-treated participants and showed an antidepressant-specific pattern of pharmacogenetic associations. In contrast, conventional response and remission favoured escitalopram and produced chance results in pharmacogenetic analyses. Controlling for drop-out reduced drug differences on response and remission but did not affect latent trajectory results. Latent trajectory mixture models capture heterogeneity in the development of clinical response after the initiation of antidepressants and provide an outcome that is distinct from traditional endpoint measures. It differentiates between antidepressants with different modes of action and is robust against bias due to differential discontinuation.

Estimating wetland vegetation abundance from Landsat-8 operational land imager imagery: a comparison between linear spectral mixture analysis and multinomial logit modeling methods

NASA Astrophysics Data System (ADS)

Zhang, Min; Gong, Zhaoning; Zhao, Wenji; Pu, Ruiliang; Liu, Ke

2016-01-01

Mapping vegetation abundance by using remote sensing data is an efficient means for detecting changes of an eco-environment. With Landsat-8 operational land imager (OLI) imagery acquired on July 31, 2013, both linear spectral mixture analysis (LSMA) and multinomial logit model (MNLM) methods were applied to estimate and assess the vegetation abundance in the Wild Duck Lake Wetland in Beijing, China. To improve mapping vegetation abundance and increase the number of endmembers in spectral mixture analysis, normalized difference vegetation index was extracted from OLI imagery along with the seven reflective bands of OLI data for estimating the vegetation abundance. Five endmembers were selected, which include terrestrial plants, aquatic plants, bare soil, high albedo, and low albedo. The vegetation abundance mapping results from Landsat OLI data were finally evaluated by utilizing a WorldView-2 multispectral imagery. Similar spatial patterns of vegetation abundance produced by both fully constrained LSMA algorithm and MNLM methods were observed: higher vegetation abundance levels were distributed in agricultural and riparian areas while lower levels in urban/built-up areas. The experimental results also indicate that the MNLM model outperformed the LSMA algorithm with smaller root mean square error (0.0152 versus 0.0252) and higher coefficient of determination (0.7856 versus 0.7214) as the MNLM model could handle the nonlinear reflection phenomenon better than the LSMA with mixed pixels.

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…

Partitioning into hazard subregions for regional peaks-over-threshold modeling of heavy precipitation

NASA Astrophysics Data System (ADS)

Carreau, J.; Naveau, P.; Neppel, L.

2017-05-01

The French Mediterranean is subject to intense precipitation events occurring mostly in autumn. These can potentially cause flash floods, the main natural danger in the area. The distribution of these events follows specific spatial patterns, i.e., some sites are more likely to be affected than others. The peaks-over-threshold approach consists in modeling extremes, such as heavy precipitation, by the generalized Pareto (GP) distribution. The shape parameter of the GP controls the probability of extreme events and can be related to the hazard level of a given site. When interpolating across a region, the shape parameter should reproduce the observed spatial patterns of the probability of heavy precipitation. However, the shape parameter estimators have high uncertainty which might hide the underlying spatial variability. As a compromise, we choose to let the shape parameter vary in a moderate fashion. More precisely, we assume that the region of interest can be partitioned into subregions with constant hazard level. We formalize the model as a conditional mixture of GP distributions. We develop a two-step inference strategy based on probability weighted moments and put forward a cross-validation procedure to select the number of subregions. A synthetic data study reveals that the inference strategy is consistent and not very sensitive to the selected number of subregions. An application on daily precipitation data from the French Mediterranean shows that the conditional mixture of GPs outperforms two interpolation approaches (with constant or smoothly varying shape parameter).

Effects of grinding and humidification on the transformation of conglomerate to racemic compound in optically active drugs.

PubMed

Piyarom, S; Yonemochi, E; Oguchi, T; Yamamoto, K

1997-04-01

The effects of grinding and humidification on the transformation of conglomerate to racemic compound have been investigated by X-ray powder diffraction (XPD), differential scanning calorimetry (DSC) and infrared (IR) spectroscopy for leucine, norleucine, valine, serine, tartaric acid and malic acid. Racemic physical mixtures were prepared by physical mixing of equimolar quantities of D and I. crystals using a mortar and pestle. Ground mixtures were obtained by grinding the physical mixtures with a vibrational mill. Humidification was performed by storing the physical mixtures and the ground mixtures in a desiccator containing saturated aqueous salt solutions at 40 degrees C. When physical mixtures of malic acid, tartaric acid and serine were ground, the XPD peaks of the racemic compounds were observed. The XPD patterns of humidified physical mixtures of these compounds also showed the formation of the racemic compounds. This indicated that grinding or humidification of malic acid, tartaric acid and serine induced the transformation of conglomerate to racemic compound crystals. When, on the other hand, the physical mixtures of valine, leucine and norleucine were ground, peaks of racemic compounds were not detected in the XPD pattern. After humidification of the ground mixtures of valine, leucine and norleucine, however, the XPD peaks of racemic compounds were observed. DSC and IR studies revealed consistent results. We concluded that grinding or humidification of malic acid, tartaric acid and serine could induce the transformation of a conglomerate to racemic compound. In contrast, humidifying after grinding was needed to bring about the transformation in leucine, norleucine and valine.

Slug Flow Analysis in Vertical Large Diameter Pipes

NASA Astrophysics Data System (ADS)

Roullier, David

The existence of slug flow in vertical co-current two-phase flow is studied experimentally and theoretically. The existence of slug flow in vertical direction implies the presence of Taylor bubbles separated by hydraulically sealed liquid slugs. Previous experimental studies such as Ombere-Ayari and Azzopardi (2007) showed the evidence of the non-existence of Taylor bubbles for extensive experimental conditions. Models developed to predict experimental behavior [Kocamustafaogullari et al. (1984), Jayanti and Hewitt. (1990) and Kjoolas et al. (2017)] suggest that Taylor bubbles may disappear at large diameters and high velocities. A 73-ft tall and 101.6-mm internal diameter test facility was used to conduct the experiments allowing holdup and pressure drop measurements at large L/D. Superficial liquid and gas velocities varied from 0.05-m/s to 0.2 m/s and 0.07 m/s to 7.5 m/s, respectively. Test section pressure varied from 38 psia to 84 psia. Gas compressibility effect was greatly reduced at 84 psia. The experimental program allowed to observe the flow patterns for flowing conditions near critical conditions predicted by previous models (air-water, 1016 mm ID, low mixture velocities). Flow patterns were observed in detail using wire-mesh sensor measurements. Slug-flow was observed for a narrow range of experimental conditions at low velocities. Churn-slug and churn-annular flows were observed for most of the experimental data-points. Cap-bubble flow was observed instead of bubbly flow at low vSg. Wire-mesh measurements showed that the liquid has a tendency to remain near to the walls. The standard deviation of radial holdup profile correlates to the flow pattern observed. For churn-slug flow, the profile is convex with a single maximum near the pipe center while it exhibits a concave shape with two symmetric maxima close to the wall for churn-annular flow. The translational velocity was measured by two consecutive wire-mesh sensor crosscorrelation. The results show linear trends at low mixture velocities and non-linear behaviors at high mixture velocities. The translational velocity trends seem to be related to the flow-pattern observed, namely to the ability of the gas to flow through the liquid structures. A simplified Taylor bubble stability model is proposed. The model allows to estimate under which conditions Taylor bubbles disappear, properly accounting for the diameter effect and velocity effect observed experimentally. In addition, annular flow distribution coefficient relating true holdup to centerline holdup in vertical flow is proposed. The proposed coefficient defines the tendency of the liquid to remain near the walls. This coefficient increases linearly with the void fraction.

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

Fuzzy membership functions for analysis of high-resolution CT images of diffuse pulmonary diseases.

PubMed

Almeida, Eliana; Rangayyan, Rangaraj M; Azevedo-Marques, Paulo M

2015-08-01

We propose the use of fuzzy membership functions to analyze images of diffuse pulmonary diseases (DPDs) based on fractal and texture features. The features were extracted from preprocessed regions of interest (ROIs) selected from high-resolution computed tomography images. The ROIs represent five different patterns of DPDs and normal lung tissue. A Gaussian mixture model (GMM) was constructed for each feature, with six Gaussians modeling the six patterns. Feature selection was performed and the GMMs of the five significant features were used. From the GMMs, fuzzy membership functions were obtained by a probability-possibility transformation and further statistical analysis was performed. An average classification accuracy of 63.5% was obtained for the six classes. For four of the six classes, the classification accuracy was superior to 65%, and the best classification accuracy was 75.5% for one class. The use of fuzzy membership functions to assist in pattern classification is an alternative to deterministic approaches to explore strategies for medical diagnosis.

Patterned surfaces in the drying of films composed of water, polymer, and alcohol

NASA Astrophysics Data System (ADS)

Fichot, Julie; Heyd, Rodolphe; Josserand, Christophe; Chourpa, Igor; Gombart, Emilie; Tranchant, Jean-Francois; Saboungi, Marie-Louise

2012-12-01

A study of the complex drying dynamics of polymeric mixtures with optical microscopy and gravimetric measurement is presented. Droplet formation is observed, followed by a collapse that leads to the residual craters in the dried film. The process is followed in situ under well-defined temperature and hygrometric conditions to determine the origin and nature of these droplets and craters. The drying process is usually completed within 1 h. The observations are explained using a simple diffusion model based on experimental results collected from mass and optical measurements as well as Raman confocal microspectrometry. Although the specific polymeric mixtures used here are of interest to the cosmetic industry, the general conclusions reached can apply to other polymeric aqueous solutions with applications to commercial and artistic painting.

Stabilization of the Thermal Decomposition of Poly(Propylene Carbonate) Through Copper Ion Incorporation and Use in Self-Patterning

NASA Astrophysics Data System (ADS)

Spencer, Todd J.; Chen, Yu-Chun; Saha, Rajarshi; Kohl, Paul A.

2011-06-01

Incorporation of copper ions into poly(propylene carbonate) (PPC) films cast from γ-butyrolactone (GBL), trichloroethylene (TCE) or methylene chloride (MeCl) solutions containing a photo-acid generator is shown to stabilize the PPC from thermal decomposition. Copper ions were introduced into the PPC mixtures by bringing the polymer mixture into contact with copper metal. The metal was oxidized and dissolved into the PPC mixture. The dissolved copper interferes with the decomposition mechanism of PPC, raising its decomposition temperature. Thermogravimetric analysis shows that copper ions make PPC more stable by up to 50°C. Spectroscopic analysis indicates that copper ions may stabilize terminal carboxylic acid groups, inhibiting PPC decomposition. The change in thermal stability based on PPC exposure to patterned copper substrates was used to provide a self-aligned patterning method for PPC on copper traces without the need for an additional photopatterning registration step. Thermal decomposition of PPC is then used to create air isolation regions around the copper traces. The spatial resolution of the self-patterning PPC process is limited by the lateral diffusion of the copper ions within the PPC. The concentration profiles of copper within the PPC, patterning resolution, and temperature effects on the PPC decomposition have been studied.

Quantile regression in the presence of monotone missingness with sensitivity analysis

PubMed Central

Liu, Minzhao; Daniels, Michael J.; Perri, Michael G.

2016-01-01

In this paper, we develop methods for longitudinal quantile regression when there is monotone missingness. In particular, we propose pattern mixture models with a constraint that provides a straightforward interpretation of the marginal quantile regression parameters. Our approach allows sensitivity analysis which is an essential component in inference for incomplete data. To facilitate computation of the likelihood, we propose a novel way to obtain analytic forms for the required integrals. We conduct simulations to examine the robustness of our approach to modeling assumptions and compare its performance to competing approaches. The model is applied to data from a recent clinical trial on weight management. PMID:26041008

Patterning in systems driven by nonlocal external forces.

PubMed

Luneville, L; Mallick, K; Pontikis, V; Simeone, D

2016-11-01

This work focuses on systems displaying domain patterns resulting from competing external and internal dynamics. To this end, we introduce a Lyapunov functional capable of describing the steady states of systems subject to external forces, by adding nonlocal terms to the Landau Ginzburg free energy of the system. Thereby, we extend the existing methodology treating long-range order interactions, to the case of external nonlocal forces. By studying the quadratic term of this Lyapunov functional, we compute the phase diagram in the temperature versus external field and we determine all possible modulated phases (domain patterns) as a function of the external forces and the temperature. Finally, we investigate patterning in chemical reactive mixtures and binary mixtures under irradiation, and we show that the last case opens the path toward micro-structural engineering of materials.

Patterning in systems driven by nonlocal external forces

NASA Astrophysics Data System (ADS)

Luneville, L.; Mallick, K.; Pontikis, V.; Simeone, D.

2016-11-01

This work focuses on systems displaying domain patterns resulting from competing external and internal dynamics. To this end, we introduce a Lyapunov functional capable of describing the steady states of systems subject to external forces, by adding nonlocal terms to the Landau Ginzburg free energy of the system. Thereby, we extend the existing methodology treating long-range order interactions, to the case of external nonlocal forces. By studying the quadratic term of this Lyapunov functional, we compute the phase diagram in the temperature versus external field and we determine all possible modulated phases (domain patterns) as a function of the external forces and the temperature. Finally, we investigate patterning in chemical reactive mixtures and binary mixtures under irradiation, and we show that the last case opens the path toward micro-structural engineering of materials.

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.

Diel periodicity and chronology of upstream migration in yellow-phase American eels (Anguilla rostrata)

USGS Publications Warehouse

Aldinger, Joni L.; Welsh, Stuart A.

2017-01-01

Yellow-phase American eel (Anguilla rostrata) upstream migration is temporally punctuated, yet migration chronology within diel time periods is not well-understood. This study examined diel periodicity, chronology, and total length (TL) of six multi-day, high-count (285–1,868 eels) passage events of upstream migrant yellow-phase American eels at the Millville Dam eel ladder, lower Shenandoah River, West Virginia during 2011–2014. We categorized passage by diel periods (vespertine, nocturnal, matutinal, diurnal) and season (spring, summer, late summer/early fall, fall). We depicted passage counts as time-series histograms and used time-series spectral analysis (Fast Fourier Transformation) to identify cyclical patterns and diel periodicity of upstream migration. We created histograms to examine movement patterns within diel periods for each passage event and fit normal mixture models (2–9 mixtures) to describe multiple peaks of passage counts. Periodicity of movements for each passage event followed a 24-h activity cycle with mostly nocturnal movement. Multimodal models were supported by the data; most modes represented nocturnal movements, but modes at or near the transition between twilight and night were also common. We used mixed-model methodology to examine relationships among TL, diel period, and season. An additive-effects model of diel period + season was the best approximating model. A decreasing trend of mean TL occurred across diel movement periods, with the highest mean TL occurring during fall relative to similar mean values of TL for spring, summer, and late summer/early fall. This study increased our understanding of yellow-phase American eels by demonstrating the non-random nature of their upstream migration.

Chemotaxis migration and morphogenesis of living colonies.

PubMed

Ben Amar, Martine

2013-06-01

Development of forms in living organisms is complex and fascinating. Morphogenetic theories that investigate these shapes range from discrete to continuous models, from the variational elasticity to time-dependent fluid approach. Here a mixture model is chosen to describe the mass transport in a morphogenetic gradient: it gives a mathematical description of a mixture involving several constituents in mechanical interactions. This model, which is highly flexible can incorporate many biological processes but also complex interactions between cells as well as between cells and their environment. We use this model to derive a free-boundary problem easier to handle analytically. We solve it in the simplest geometry: an infinite linear front advancing with a constant velocity. In all the cases investigated here as the 3 D diffusion, the increase of mitotic activity at the border, nonlinear laws for the uptake of morphogens or for the mobility coefficient, a planar front exists above a critical threshold for the mobility coefficient but it becomes unstable just above the threshold at long wavelengths due to the existence of a Goldstone mode. This explains why sparsely bacteria exhibit dendritic patterns experimentally in opposition to other colonies such as biofilms and epithelia which are more compact. In the most unstable situation, where all the laws: diffusion, chemotaxis driving and chemoattractant uptake are linear, we show also that the system can recover a dynamic stability. A second threshold for the mobility exists which has a lower value as the ratio between diffusion coefficients decreases. Within the framework of this model where the biomass is treated mainly as a viscous and diffusive fluid, we show that the multiplicity of independent parameters in real biologic experimental set-up may explain varieties of observed patterns.

Growth of mixed cultures on mixtures of substitutable substrates: the operating diagram for a structured model.

PubMed

Reeves, Gregory T; Narang, Atul; Pilyugin, Sergei S

2004-01-21

The growth of mixed microbial cultures on mixtures of substrates is a problem of fundamental biological interest. In the last two decades, several unstructured models of mixed-substrate growth have been studied. It is well known, however, that the growth patterns in mixed-substrate environments are dictated by the enzymes that catalyse the transport of substrates into the cell. We have shown previously that a model taking due account of transport enzymes captures and explains all the observed patterns of growth of a single species on two substitutable substrates (J. Theor. Biol. 190 (1998) 241). Here, we extend the model to study the steady states of growth of two species on two substitutable substrates. The model is analysed to determine the conditions for existence and stability of the various steady states. Simulations are performed to determine the flow rates and feed concentrations at which both species coexist. We show that if the interaction between the two species is purely competitive, then at any given flow rate, coexistence is possible only if the ratio of the two feed concentrations lies within a certain interval; excessive supply of either one of the two substrates leads to annihilation of one of the species. This result simplifies the construction of the operating diagram for purely competing species. This is because the two-dimensional surface that bounds the flow rates and feed concentrations at which both species coexist has a particularly simple geometry: It is completely determined by only two coordinates, the flow rate and the ratio of the two feed concentrations. We also study commensalistic interactions between the two species by assuming that one of the species excretes a product that can support the growth of the other species. We show that such interactions enhance the coexistence region.

Weed suppression greatly increased by plant diversity in intensively managed grasslands: A continental-scale experiment.

PubMed

Connolly, John; Sebastià, Maria-Teresa; Kirwan, Laura; Finn, John Anthony; Llurba, Rosa; Suter, Matthias; Collins, Rosemary P; Porqueddu, Claudio; Helgadóttir, Áslaug; Baadshaug, Ole H; Bélanger, Gilles; Black, Alistair; Brophy, Caroline; Čop, Jure; Dalmannsdóttir, Sigridur; Delgado, Ignacio; Elgersma, Anjo; Fothergill, Michael; Frankow-Lindberg, Bodil E; Ghesquiere, An; Golinski, Piotr; Grieu, Philippe; Gustavsson, Anne-Maj; Höglind, Mats; Huguenin-Elie, Olivier; Jørgensen, Marit; Kadziuliene, Zydre; Lunnan, Tor; Nykanen-Kurki, Paivi; Ribas, Angela; Taube, Friedhelm; Thumm, Ulrich; De Vliegher, Alex; Lüscher, Andreas

2018-03-01

Grassland diversity can support sustainable intensification of grassland production through increased yields, reduced inputs and limited weed invasion. We report the effects of diversity on weed suppression from 3 years of a 31-site continental-scale field experiment.At each site, 15 grassland communities comprising four monocultures and 11 four-species mixtures based on a wide range of species' proportions were sown at two densities and managed by cutting. Forage species were selected according to two crossed functional traits, "method of nitrogen acquisition" and "pattern of temporal development".Across sites, years and sown densities, annual weed biomass in mixtures and monocultures was 0.5 and 2.0 t  DM ha -1 (7% and 33% of total biomass respectively). Over 95% of mixtures had weed biomass lower than the average of monocultures, and in two-thirds of cases, lower than in the most suppressive monoculture (transgressive suppression). Suppression was significantly transgressive for 58% of site-years. Transgressive suppression by mixtures was maintained across years, independent of site productivity.Based on models, average weed biomass in mixture over the whole experiment was 52% less (95% confidence interval: 30%-75%) than in the most suppressive monoculture. Transgressive suppression of weed biomass was significant at each year across all mixtures and for each mixture.Weed biomass was consistently low across all mixtures and years and was in some cases significantly but not largely different from that in the equiproportional mixture. The average variability (standard deviation) of annual weed biomass within a site was much lower for mixtures (0.42) than for monocultures (1.77). Synthesis and applications . Weed invasion can be diminished through a combination of forage species selected for complementarity and persistence traits in systems designed to reduce reliance on fertiliser nitrogen. In this study, effects of diversity on weed suppression were consistently strong across mixtures varying widely in species' proportions and over time. The level of weed biomass did not vary greatly across mixtures varying widely in proportions of sown species. These diversity benefits in intensively managed grasslands are relevant for the sustainable intensification of agriculture and, importantly, are achievable through practical farm-scale actions.

Phylogenetic relationships of South American lizards of the genus Stenocercus (Squamata: Iguania): A new approach using a general mixture model for gene sequence data.

PubMed

Torres-Carvajal, Omar; Schulte, James A; Cadle, John E

2006-04-01

The South American iguanian lizard genus Stenocercus includes 54 species occurring mostly in the Andes and adjacent lowland areas from northern Venezuela and Colombia to central Argentina at elevations of 0-4000m. Small taxon or character sampling has characterized all phylogenetic analyses of Stenocercus, which has long been recognized as sister taxon to the Tropidurus Group. In this study, we use mtDNA sequence data to perform phylogenetic analyses that include 32 species of Stenocercus and 12 outgroup taxa. Monophyly of this genus is strongly supported by maximum parsimony and Bayesian analyses. Evolutionary relationships within Stenocercus are further analyzed with a Bayesian implementation of a general mixture model, which accommodates variability in the pattern of evolution across sites. These analyses indicate a basal split of Stenocercus into two clades, one of which receives very strong statistical support. In addition, we test previous hypotheses using non-parametric and parametric statistical methods, and provide a phylogenetic classification for Stenocercus.

The effect of particle shape and size distribution on the acoustical properties of mixtures of hemp particles.

PubMed

Glé, Philippe; Gourdon, Emmanuel; Arnaud, Laurent; Horoshenkov, Kirill-V; Khan, Amir

2013-12-01

Hemp concrete is an attractive alternative to traditional materials used in building construction. It has a very low environmental impact, and it is characterized by high thermal insulation. Hemp aggregate particles are parallelepiped in shape and can be organized in a plurality of ways to create a considerable proportion of open pores with a complex connectivity pattern, the acoustical properties of which have never been examined systematically. Therefore this paper is focused on the fundamental understanding of the relations between the particle shape and size distribution, pore size distribution, and the acoustical properties of the resultant porous material mixture. The sound absorption and the transmission loss of various hemp aggregates is characterized using laboratory experiments and three theoretical models. These models are used to relate the particle size distribution to the pore size distribution. It is shown that the shape of particles and particle size control the pore size distribution and tortuosity in shiv. These properties in turn relate directly to the observed acoustical behavior.

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.

Quantifying Synergy: A Systematic Review of Mixture Toxicity Studies within Environmental Toxicology

PubMed Central

Cedergreen, Nina

2014-01-01

Cocktail effects and synergistic interactions of chemicals in mixtures are an area of great concern to both the public and regulatory authorities. The main concern is whether some chemicals can enhance the effect of other chemicals, so that they jointly exert a larger effect than predicted. This phenomenon is called synergy. Here we present a review of the scientific literature on three main groups of environmentally relevant chemical toxicants: pesticides, metal ions and antifouling compounds. The aim of the review is to determine 1) the frequency of synergy, 2) the extent of synergy, 3) whether any particular groups or classes of chemicals tend to induce synergy, and 4) which physiological mechanisms might be responsible for this synergy. Synergy is here defined as mixtures with minimum two-fold difference between observed and predicted effect concentrations using Concentration Addition (CA) as a reference model and including both lethal and sub-lethal endpoints. The results showed that synergy occurred in 7%, 3% and 26% of the 194, 21 and 136 binary pesticide, metal and antifoulants mixtures included in the data compilation on frequency. The difference between observed and predicted effect concentrations was rarely more than 10-fold. For pesticides, synergistic mixtures included cholinesterase inhibitors or azole fungicides in 95% of 69 described cases. Both groups of pesticides are known to interfere with metabolic degradation of other xenobiotics. For the four synergistic metal and 47 synergistic antifoulant mixtures the pattern in terms of chemical groups inducing synergy was less clear. Hypotheses in terms of mechanisms governing these interactions are discussed. It was concluded that true synergistic interactions between chemicals are rare and often occur at high concentrations. Addressing the cumulative rather than synergistic effect of co-occurring chemicals, using standard models as CA, is therefore regarded as the most important step in the risk assessment of chemical cocktails. PMID:24794244

Phase-coexisting patterns, horizontal segregation, and controlled convection in vertically vibrated binary granular mixtures

NASA Astrophysics Data System (ADS)

Ansari, Istafaul Haque; Rivas, Nicolas; Alam, Meheboob

2018-01-01

We report patterns consisting of coexistence of synchronous and asynchronous states [for example, a granular gas co-existing with (i) bouncing bed, (ii) undulatory subharmonic waves, and (iii) Leidenfrost-like states] in experiments on vertically vibrated binary granular mixtures in a Hele-Shaw cell. Most experiments have been carried out with equimolar binary mixtures of glass and steel balls of same diameter by varying the total layer height (F ) for a range of shaking acceleration (Γ ). All patterns as well as the related phase diagram in the (Γ ,F ) plane have been reproduced via molecular dynamics simulations of the same system. The segregation of heavier and lighter particles along the horizontal direction is shown to be the progenitor of such phase-coexisting patterns as confirmed in both experiment and simulation. At strong shaking we uncover a partial convection state in which a pair of convection rolls is found to coexist with a Leidenfrost-like state. The crucial role of the relative number density of two species on controlling the buoyancy-driven granular convection is demonstrated. The onset of horizontal segregation can be explained in terms of an anisotropic diffusion tensor.

Patterns of glaucomatous visual field loss in sita fields automatically identified using independent component analysis.

PubMed

Goldbaum, Michael H; Jang, Gil-Jin; Bowd, Chris; Hao, Jiucang; Zangwill, Linda M; Liebmann, Jeffrey; Girkin, Christopher; Jung, Tzyy-Ping; Weinreb, Robert N; Sample, Pamela A

2009-12-01

To determine if the patterns uncovered with variational Bayesian-independent component analysis-mixture model (VIM) applied to a large set of normal and glaucomatous fields obtained with the Swedish Interactive Thresholding Algorithm (SITA) are distinct, recognizable, and useful for modeling the severity of the field loss. SITA fields were obtained with the Humphrey Visual Field Analyzer (Carl Zeiss Meditec, Inc, Dublin, California) on 1,146 normal eyes and 939 glaucoma eyes from subjects followed by the Diagnostic Innovations in Glaucoma Study and the African Descent and Glaucoma Evaluation Study. VIM modifies independent component analysis (ICA) to develop separate sets of ICA axes in the cluster of normal fields and the 2 clusters of abnormal fields. Of 360 models, the model with the best separation of normal and glaucomatous fields was chosen for creating the maximally independent axes. Grayscale displays of fields generated by VIM on each axis were compared. SITA fields most closely associated with each axis and displayed in grayscale were evaluated for consistency of pattern at all severities. The best VIM model had 3 clusters. Cluster 1 (1,193) was mostly normal (1,089, 95% specificity) and had 2 axes. Cluster 2 (596) contained mildly abnormal fields (513) and 2 axes; cluster 3 (323) held mostly moderately to severely abnormal fields (322) and 5 axes. Sensitivity for clusters 2 and 3 combined was 88.9%. The VIM-generated field patterns differed from each other and resembled glaucomatous defects (eg, nasal step, arcuate, temporal wedge). SITA fields assigned to an axis resembled each other and the VIM-generated patterns for that axis. Pattern severity increased in the positive direction of each axis by expansion or deepening of the axis pattern. VIM worked well on SITA fields, separating them into distinctly different yet recognizable patterns of glaucomatous field defects. The axis and pattern properties make VIM a good candidate as a preliminary process for detecting progression.

Environmental chemical mixtures: Assessing ecological exposure and effects in streams

EPA Science Inventory

This product is a USGS fact sheet that describes a collaborative effort between USGS and US EPA to characterize exposures to chemical mixtures and associated biological effects for a diverse range of US streams representing varying watershed size, land-use patterns, and ecotypes.

Synergy in Protein–Osmolyte Mixtures

PubMed Central

2014-01-01

Virtually all taxa use osmolytes to protect cells against biochemical stress. Osmolytes often occur in mixtures, such as the classical combination of urea with TMAO (trimethylamine N-oxide) in cartilaginous fish or the cocktail of at least six different osmolytes in the kidney. The concentration patterns of osmolyte mixtures found in vivo make it likely that synergy between them plays an important role. Using statistical mechanical n-component Kirkwood–Buff theory, we show from first principles that synergy in protein–osmolyte systems can arise from two separable sources: (1) mutual alteration of protein surface solvation and (2) effects mediated through bulk osmolyte chemical activities. We illustrate both effects in a four-component system with the experimental example of the unfolding of a notch ankyrin domain in urea–TMAO mixtures, which make urea a less effective denaturant and TMAO a more effective stabilizer. Protein surface effects are primarily responsible for this synergy. The specific patterns of surface solvation point to denatured state expansion as the main factor, as opposed to direct competition. PMID:25490052

Optimization of fruit punch using mixture design.

PubMed

Kumar, S Bharath; Ravi, R; Saraswathi, G

2010-01-01

A highly acceptable dehydrated fruit punch was developed with selected fruits, namely lemon, orange, and mango, using a mixture design and optimization technique. The fruit juices were freeze dried, powdered, and used in the reconstitution studies. Fruit punches were prepared according to the experimental design combinations (total 10) based on a mixture design and then subjected to sensory evaluation for acceptability. Response surfaces of sensory attributes were also generated as a function of fruit juices. Analysis of data revealed that the fruit punch prepared using 66% of mango, 33% of orange, and 1% of lemon had highly desirable sensory scores for color (6.00), body (5.92), sweetness (5.68), and pleasantness (5.94). The aroma pattern of individual as well as combinations of fruit juices were also analyzed by electronic nose. The electronic nose could discriminate the aroma patterns of individual as well as fruit juice combinations by mixture design. The results provide information on the sensory quality of best fruit punch formulations liked by the consumer panel based on lemon, orange, and mango.

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

Formation and photopatterning of nanoporous titania thin films

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

Park, Oun-Ho; Cheng, Joy Y.; Kim, Hyun Suk

2007-06-04

Photopatternable nanoporous titania thin films were generated from mixtures of an organic diblock copolymer, poly(styrene-b-ethylene oxide) (PS-b-PEO), and an oligomeric titanate (OT) prepared from a chelated titanium isopropoxide. The PS-b-PEO templates well-defined microdomains in thin films of the mixtures, which upon thermal treatment at 450 deg. C, become nanopores in titania. Average pore size and porosity are controlled by the molecular weight and loading level of the PS-b-PEO, respectively. Patterns of nanoporous titania were created by selectively exposing UV light on the mixture films. The UV irradiation destroys the chelating bond and induces the cross-linking reaction of the OT. Subsequentmore » wet development followed by thermal treatment gives patterned nanoporous films of anatase phase titania.« less

Detecting Mixtures from Structural Model Differences Using Latent Variable Mixture Modeling: A Comparison of Relative Model Fit Statistics

ERIC Educational Resources Information Center

Henson, James M.; Reise, Steven P.; Kim, Kevin H.

2007-01-01

The accuracy of structural model parameter estimates in latent variable mixture modeling was explored with a 3 (sample size) [times] 3 (exogenous latent mean difference) [times] 3 (endogenous latent mean difference) [times] 3 (correlation between factors) [times] 3 (mixture proportions) factorial design. In addition, the efficacy of several…

The role of chemometrics in single and sequential extraction assays: a review. Part II. Cluster analysis, multiple linear regression, mixture resolution, experimental design and other techniques.

PubMed

Giacomino, Agnese; Abollino, Ornella; Malandrino, Mery; Mentasti, Edoardo

2011-03-04

Single and sequential extraction procedures are used for studying element mobility and availability in solid matrices, like soils, sediments, sludge, and airborne particulate matter. In the first part of this review we reported an overview on these procedures and described the applications of chemometric uni- and bivariate techniques and of multivariate pattern recognition techniques based on variable reduction to the experimental results obtained. The second part of the review deals with the use of chemometrics not only for the visualization and interpretation of data, but also for the investigation of the effects of experimental conditions on the response, the optimization of their values and the calculation of element fractionation. We will describe the principles of the multivariate chemometric techniques considered, the aims for which they were applied and the key findings obtained. The following topics will be critically addressed: pattern recognition by cluster analysis (CA), linear discriminant analysis (LDA) and other less common techniques; modelling by multiple linear regression (MLR); investigation of spatial distribution of variables by geostatistics; calculation of fractionation patterns by a mixture resolution method (Chemometric Identification of Substrates and Element Distributions, CISED); optimization and characterization of extraction procedures by experimental design; other multivariate techniques less commonly applied. Copyright © 2010 Elsevier B.V. All rights reserved.

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.

Mechanistic explanation of time-dependent cross-phenomenon based on quorum sensing: A case study of the mixture of sulfonamide and quorum sensing inhibitor to bioluminescence of Aliivibrio fischeri.

PubMed

Sun, Haoyu; Pan, Yongzheng; Gu, Yue; Lin, Zhifen

2018-07-15

Cross-phenomenon in which the concentration-response curve (CRC) for a mixture crosses the CRC for the reference model has been identified in many studies, expressed as a heterogeneous pattern of joint toxic action. However, a mechanistic explanation of the cross-phenomenon has thus far been extremely insufficient. In this study, a time-dependent cross-phenomenon was observed, in which the cross-concentration range between the CRC for the mixture of sulfamethoxypyridazine (SMP) and (Z-)-4-Bromo-5-(bromomethylene)-2(5H)-furanone (C30) to the bioluminescence of Aliivibrio fischeri (A. fischeri) and the CRC for independent action model with 95% confidence bands varied from low-concentration to higher-concentration regions in a timely manner expressed the joint toxic action of the mixture changing with an increase of both concentration and time. Through investigating the time-dependent hormetic effects of SMP and C30 (by measuring the expression of protein mRNA, simulating the bioluminescent reaction and analyzing the toxic action), the underlying mechanism was as follows: SMP and C30 acted on the quorum sensing (QS) system of A. fischeri, which induced low-concentration stimulatory effects and high-concentration inhibitory effects; in the low-concentration region, the stimulatory effects of SMP and C30 made the mixture produce a synergistic stimulation on the bioluminescence; thus, the joint toxic action exhibited antagonism. In the high-concentration region, the inhibitory effects of SMP and C30 in the mixture caused a double block in the loop circuit of the QS system; thus, the joint toxic action exhibited synergism. With the increase of time, these stimulatory and inhibitory effects of SMP and C30 were changed by the variation of the QS system at different growth phases, resulting in the time-dependent cross-phenomenon. This study proposes an induced mechanism for time-dependent cross-phenomenon based on QS, which may provide new insight into the mechanistic investigation of time-dependent cross-phenomenon, benefitting the environmental risk assessment of mixtures. Copyright © 2018 Elsevier B.V. All rights reserved.

On The Value at Risk Using Bayesian Mixture Laplace Autoregressive Approach for Modelling the Islamic Stock Risk Investment

NASA Astrophysics Data System (ADS)

Miftahurrohmah, Brina; Iriawan, Nur; Fithriasari, Kartika

2017-06-01

Stocks are known as the financial instruments traded in the capital market which have a high level of risk. Their risks are indicated by their uncertainty of their return which have to be accepted by investors in the future. The higher the risk to be faced, the higher the return would be gained. Therefore, the measurements need to be made against the risk. Value at Risk (VaR) as the most popular risk measurement method, is frequently ignore when the pattern of return is not uni-modal Normal. The calculation of the risks using VaR method with the Normal Mixture Autoregressive (MNAR) approach has been considered. This paper proposes VaR method couple with the Mixture Laplace Autoregressive (MLAR) that would be implemented for analysing the first three biggest capitalization Islamic stock return in JII, namely PT. Astra International Tbk (ASII), PT. Telekomunikasi Indonesia Tbk (TLMK), and PT. Unilever Indonesia Tbk (UNVR). Parameter estimation is performed by employing Bayesian Markov Chain Monte Carlo (MCMC) approaches.

Investigation of hydrate formation in the system H2-CH4-H2O at a pressure up to 250 MPa.

PubMed

Skiba, Sergei S; Larionov, Eduard G; Manakov, Andrey Y; Kolesov, Boris A; Kosyakov, Viktor I

2007-09-27

Phase equilibria in the system H2-CH4-H2O are investigated by means of differential thermal analysis within hydrogen concentration range 0-70 mol % and at a pressure up to 250 MPa. All the experiments were carried out under the conditions of gas excess. With an increase in hydrogen concentration in the initial gas mixture, decomposition temperature of the formed hydrates decreased. X-ray diffraction patterns and Raman spectra of the quenched hydrate samples obtained at a pressure of 20 MPA from a gas mixture containing 40 mol % hydrogen were recorded. It turned out that the hydrate has cubic structure I under these conditions. The Raman spectra showed that hydrogen molecules are not detected in the hydrate within the sensitivity of the method, that is, almost pure methane hydrate is formed. The general view of the phase diagram of the investigated system is proposed. A thermodynamic model was proposed to explain a decrease in hydrate decomposition temperature in the system with an increase in the concentration of hydrogen in the initial mixture.

The pitcher plant flesh fly exhibits a mixture of patchy and metapopulation attributes.

PubMed

Rasic, Gordana; Keyghobadi, Nusha

2012-01-01

We investigated the pattern of spatial genetic structure and the extent of gene flow in the pitcher plant flesh fly Fletcherimyia fletcheri, the largest member of the inquiline community of the purple pitcher plant Sarracenia purpurea. Using microsatellite loci, we tested the theoretical predictions of different hypothesized population models (patchy population, metapopulation, or isolated populations) among 11 bogs in Algonquin Provincial Park (Canada). Our results revealed that the pitcher plant flesh fly exhibits a mixture of patchy and metapopulation characteristics. There is significant differentiation among bogs and limited gene flow at larger spatial scales, but local populations do not experience frequent local extinctions/recolonizations. Our findings suggest a strong dispersal ability and stable population sizes in F. fletcheri, providing novel insights into the ecology of this member of a unique ecological microcosm.

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.

An integrative approach to assess X-chromosome inactivation using allele-specific expression with applications to epithelial ovarian cancer.

PubMed

Larson, Nicholas B; Fogarty, Zachary C; Larson, Melissa C; Kalli, Kimberly R; Lawrenson, Kate; Gayther, Simon; Fridley, Brooke L; Goode, Ellen L; Winham, Stacey J

2017-12-01

X-chromosome inactivation (XCI) epigenetically silences transcription of an X chromosome in females; patterns of XCI are thought to be aberrant in women's cancers, but are understudied due to statistical challenges. We develop a two-stage statistical framework to assess skewed XCI and evaluate gene-level patterns of XCI for an individual sample by integration of RNA sequence, copy number alteration, and genotype data. Our method relies on allele-specific expression (ASE) to directly measure XCI and does not rely on male samples or paired normal tissue for comparison. We model ASE using a two-component mixture of beta distributions, allowing estimation for a given sample of the degree of skewness (based on a composite likelihood ratio test) and the posterior probability that a given gene escapes XCI (using a Bayesian beta-binomial mixture model). To illustrate the utility of our approach, we applied these methods to data from tumors of ovarian cancer patients. Among 99 patients, 45 tumors were informative for analysis and showed evidence of XCI skewed toward a particular parental chromosome. For 397 X-linked genes, we observed tumor XCI patterns largely consistent with previously identified consensus states based on multiple normal tissue types. However, 37 genes differed in XCI state between ovarian tumors and the consensus state; 17 genes aberrantly escaped XCI in ovarian tumors (including many oncogenes), whereas 20 genes were unexpectedly inactivated in ovarian tumors (including many tumor suppressor genes). These results provide evidence of the importance of XCI in ovarian cancer and demonstrate the utility of our two-stage analysis. © 2017 WILEY PERIODICALS, INC.

Automating spectral unmixing of AVIRIS data using convex geometry concepts

NASA Technical Reports Server (NTRS)

Boardman, Joseph W.

1993-01-01

Spectral mixture analysis, or unmixing, has proven to be a useful tool in the semi-quantitative interpretation of AVIRIS data. Using a linear mixing model and a set of hypothesized endmember spectra, unmixing seeks to estimate the fractional abundance patterns of the various materials occurring within the imaged area. However, the validity and accuracy of the unmixing rest heavily on the 'user-supplied' set of endmember spectra. Current methods for emdmember determination are the weak link in the unmixing chain.

Spectral pattern recognition of controlled substances in street samples using artificial neural network system

NASA Astrophysics Data System (ADS)

Poryvkina, Larisa; Aleksejev, Valeri; Babichenko, Sergey M.; Ivkina, Tatjana

2011-04-01

The NarTest fluorescent technique is aimed at the detection of analyte of interest in street samples by recognition of its specific spectral patterns in 3-dimentional Spectral Fluorescent Signatures (SFS) measured with NTX2000 analyzer without chromatographic or other separation of controlled substances from a mixture with cutting agents. The illicit drugs have their own characteristic SFS features which can be used for detection and identification of narcotics, however typical street sample consists of a mixture with cutting agents: adulterants and diluents. Many of them interfere the spectral shape of SFS. The expert system based on Artificial Neural Networks (ANNs) has been developed and applied for such pattern recognition in SFS of street samples of illicit drugs.

Shell Tectonics: A Mechanical Model for Strike-slip Displacement on Europa

NASA Technical Reports Server (NTRS)

Rhoden, Alyssa Rose; Wurman, Gilead; Huff, Eric M.; Manga, Michael; Hurford, Terry A.

2012-01-01

We introduce a new mechanical model for producing tidally-driven strike-slip displacement along preexisting faults on Europa, which we call shell tectonics. This model differs from previous models of strike-slip on icy satellites by incorporating a Coulomb failure criterion, approximating a viscoelastic rheology, determining the slip direction based on the gradient of the tidal shear stress rather than its sign, and quantitatively determining the net offset over many orbits. This model allows us to predict the direction of net displacement along faults and determine relative accumulation rate of displacement. To test the shell tectonics model, we generate global predictions of slip direction and compare them with the observed global pattern of strike-slip displacement on Europa in which left-lateral faults dominate far north of the equator, right-lateral faults dominate in the far south, and near-equatorial regions display a mixture of both types of faults. The shell tectonics model reproduces this global pattern. Incorporating a small obliquity into calculations of tidal stresses, which are used as inputs to the shell tectonics model, can also explain regional differences in strike-slip fault populations. We also discuss implications for fault azimuths, fault depth, and Europa's tectonic history.

Comparative Analysis of InSAR Digital Surface Models for Test Area Bucharest

NASA Astrophysics Data System (ADS)

Dana, Iulia; Poncos, Valentin; Teleaga, Delia

2010-03-01

This paper presents the results of the interferometric processing of ERS Tandem, ENVISAT and TerraSAR- X for digital surface model (DSM) generation. The selected test site is Bucharest (Romania), a built-up area characterized by the usual urban complex pattern: mixture of buildings with different height levels, paved roads, vegetation, and water bodies. First, the DSMs were generated following the standard interferometric processing chain. Then, the accuracy of the DSMs was analyzed against the SPOT HRS model (30 m resolution at the equator). A DSM derived by optical stereoscopic processing of SPOT 5 HRG data and also the SRTM (3 arc seconds resolution at the equator) DSM have been included in the comparative analysis.

The comparative clinical study of efficacy of Gamisoyo-San (Jiaweixiaoyaosan) on generalized anxiety disorder according to differently manufactured preparations: multicenter, randomized, double blind, placebo controlled trial.

PubMed

Park, Dae-Myung; Kim, Seok-Hwan; Park, Yang-Chun; Kang, Wee-Chang; Lee, Sang-Ryong; Jung, In-Chul

2014-12-02

Gamisoyo-San (GSS) is a well-known Traditional Korean Medicine shown to be effective on mood disorders. The purpose of this research is to examine the effect of Gamisoyo-San on generalized anxiety disorder by its differently manufactured preparations. Multicenter, randomized, double-blinded, placebo-controlled study was set for 147 patients with generalized anxiety disorder recruited from November 1st 2009 to December 16th 2010. They were given Gamisoyo-San individual extract mixture (extraction done for each crude materia medica separately) or Gamisoyo-San multi-compound extract (extraction done for whole materia medica at once) or controlled medication. Hamilton Rating Scale for Anxiety (HAM-A), Korean State-Trait Anxiety Inventory (K-STAI), Penn State Worry Questionnaire (PSWQ), Korean Beck Depression Inventroy (K-BDI), Symptom Checklist-90-Revised (SCL-90-R), and Korean WHO Quality of Life Scale Abbreviated Version (WHOQOL-BREF) were evaluated. We also applied Pattern Identification tool for 'JingJi and ZhengChong (, Traditional Korean Medicine term which correlates with generalized anxiety disorder)' to patients to evaluate different responses among 9 patterns. HAM-A scores of Gamisoyo-San multi-compound extract group showed greater decrease compared to Gamisoyo-San individual extract mixture group and placebo group, but the difference was insignificant. WHOQOL-BREF scores of Gamisoyo-San multi-compound extract group showed significant increase compared to Gamisoyo-San individual extract mixture group and placebo group. In Heart blood deficiency pattern, the Gamisoyo-San multi-compound extract group showed significant decrease in K-BDI compared to the Gamisoyo-San individual extract mixture group. Gamisoyo-San did not improve anxiety level of GAD patients. However, it can be useful to improve quality of life, and reduce depressive, obsessive-compulsive, somatic symptoms of generalized anxiety disorder. Gamisoyo-San multi-compound seemed more effective than Gamisoyo-San individual extract mixture, especially in Heart blood deficiency pattern. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Early Menarche and Gestational Diabetes Mellitus at First Live Birth.

PubMed

Shen, Yun; Hu, Hui; D Taylor, Brandie; Kan, Haidong; Xu, Xiaohui

2017-03-01

To examine the association between early menarche and gestational diabetes mellitus (GDM). Data from the National Health and Nutrition Examination Survey 2007-2012 were used to investigate the association between age at menarche and the risk of GDM at first birth among 5914 women. A growth mixture model was used to detect distinctive menarche onset patterns based on self-reported age at menarche. Logistic regression models were then used to examine the associations between menarche initiation patterns and GDM after adjusting for sociodemographic factors, family history of diabetes mellitus, lifetime greatest Body Mass Index, smoking status, and physical activity level. Among the 5914 first-time mothers, 3.4 % had self-reported GDM. We detected three groups with heterogeneous menarche onset patterns, the Early, Normal, and Late Menarche Groups. The regression model shows that compared to the Normal Menarche Group, the Early Menarche Group had 1.75 (95 % CI 1.10, 2.79) times the odds of having GDM. No statistically significant difference was observed between the Normal and the Late Menarche Group. This study suggests that early menarche may be a risk factor of GDM. Future studies are warranted to examine and confirm this finding.

Numerical simulation of magma chamber dynamics.

NASA Astrophysics Data System (ADS)

Longo, Antonella; Papale, Paolo; Montagna, Chiara Paola; Vassalli, Melissa; Giudice, Salvatore; Cassioli, Andrea

2010-05-01

Magma chambers are characterized by periodic arrivals of deep magma batches that give origin to complex patterns of magma convection and mixing, and modify the distribution of physical quantities inside the chamber. We simulate the transient, 2D, multi-component homogeneous dynamics in geometrically complex dyke+chamber systems, by means of GALES, a finite element parallel C++ code solving mass, momentum and energy equations for multi-component homogeneous gas-liquid (± crystals) mixtures in compressible-to-incompressible flow conditions. Code validation analysis includes several cases from the classical engineering literature, corresponding to a variety of subsonic to supersonic gas-liquid flow regimes (see http://www.pi.ingv.it/~longo/gales/gales.html). The model allows specification of the composition of the different magmas in the domain, in terms of ten major oxides plus the two volatile species H2O and CO2. Gas-liquid thermodynamics are modeled by using the compositional dependent, non-ideal model in Papale et al. (Chem.. Geol., 2006). Magma properties are defined in terms of local pressure, temperature, and composition including volatiles. Several applications are performed within domains characterized by the presence of one or more magma chambers and one or more dykes, with different geometries and characteristic size from hundreds of m to several km. In most simulations an initial compositional interface is placed at the top of a feeding dyke, or at larger depth, with the deeper magma having a lower density as a consequence of larger volatile content. The numerical results show complex patterns of magma refilling in the chamber, with alternating phases of magma ingression and magma sinking from the chamber into the feeding dyke. Intense mixing takes place in feeding dykes, so that the new magma entering the chamber is always a mixture of the deep and the initially resident magma. Buoyant plume rise occurs through the formation of complex convective patterns, giving origin to a density-stratified magma chamber.

Dispersal and population state of an endangered island lizard following a conservation translocation.

PubMed

Angeli, Nicole F; Lundgren, Ian F; Pollock, Clayton G; Hillis-Starr, Zandy M; Fitzgerald, Lee A

2018-03-01

Population size is widely used as a unit of ecological analysis, yet to estimate population size requires accounting for observed and latent heterogeneity influencing dispersion of individuals across landscapes. In newly established populations, such as when animals are translocated for conservation, dispersal and availability of resources influence patterns of abundance. We developed a process to estimate population size using N-mixture models and spatial models for newly established and dispersing populations. We used our approach to estimate the population size of critically endangered St. Croix ground lizards (Ameiva polops) five years after translocation of 57 individuals to Buck Island, an offshore island of St. Croix, United States Virgin Islands. Estimates of population size incorporated abiotic variables, dispersal limits, and operative environmental temperature available to the lizards to account for low species detection. Operative environmental temperature and distance from the translocation site were always important in fitting the N-mixture model indicating effects of dispersal and species biology on estimates of population size. We found that the population is increasing its range across the island by 5-10% every six months. We spatially interpolated site-specific abundance from the N-mixture model to the entire island, and we estimated 1,473 (95% CI, 940-1,802) St. Croix ground lizards on Buck Island in 2013 corresponding to survey results. This represents a 26-fold increase since the translocation. We predicted the future dispersal of the lizards to all habitats on Buck Island, with the potential for the population to increase by another five times in the future. Incorporating biologically relevant covariates as explicit parameters in population models can improve predictions of population size and the future spread of species introduced to new localities. © 2018 by the Ecological Society of America.

Quantitative energy-dispersive x-ray diffraction for identification of counterfeit medicines: a preliminary study

NASA Astrophysics Data System (ADS)

Crews, Chiaki C. E.; O'Flynn, Daniel; Sidebottom, Aiden; Speller, Robert D.

2015-06-01

The prevalence of counterfeit and substandard medicines has been growing rapidly over the past decade, and fast, nondestructive techniques for their detection are urgently needed to counter this trend. In this study, energy-dispersive X-ray diffraction (EDXRD) combined with chemometrics was assessed for its effectiveness in quantitative analysis of compressed powder mixtures. Although EDXRD produces lower-resolution diffraction patterns than angular-dispersive X-ray diffraction (ADXRD), it is of interest for this application as it carries the advantage of allowing the analysis of tablets within their packaging, due to the higher energy X-rays used. A series of caffeine, paracetamol and microcrystalline cellulose mixtures were prepared with compositions between 0 - 100 weight% in 20 weight% steps (22 samples in total, including a centroid mixture), and were pressed into tablets. EDXRD spectra were collected in triplicate, and a principal component analysis (PCA) separated these into their correct positions in the ternary mixture design. A partial least-squares (PLS) regression model calibrated using this training set was validated using both segmented cross-validation, and with a test set of six samples (mixtures in 8:1:1 and 5⅓:2⅓:2⅓ ratios) - the latter giving a root-mean square error of prediction (RMSEP) of 1.30, 2.25 and 2.03 weight% for caffeine, paracetamol and cellulose respectively. These initial results are promising, with RMSEP values on a par with those reported in the ADXRD literature.

Measurement and Structural Model Class Separation in Mixture CFA: ML/EM versus MCMC

ERIC Educational Resources Information Center

Depaoli, Sarah

2012-01-01

Parameter recovery was assessed within mixture confirmatory factor analysis across multiple estimator conditions under different simulated levels of mixture class separation. Mixture class separation was defined in the measurement model (through factor loadings) and the structural model (through factor variances). Maximum likelihood (ML) via the…

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.

A discrete mathematical model applied to genetic regulation and metabolic networks.

PubMed

Asenjo, A J; Ramirez, P; Rapaport, I; Aracena, J; Goles, E; Andrews, B A

2007-03-01

This paper describes the use of a discrete mathematical model to represent the basic mechanisms of regulation of the bacteria E. coli in batch fermentation. The specific phenomena studied were the changes in metabolism and genetic regulation when the bacteria use three different carbon substrates (glucose, glycerol, and acetate). The model correctly predicts the behavior of E. coli vis-à-vis substrate mixtures. In a mixture of glucose, glycerol, and acetate, it prefers glucose, then glycerol, and finally acetate. The model included 67 nodes; 28 were genes, 20 enzymes, and 19 regulators/biochemical compounds. The model represents both the genetic regulation and metabolic networks in an inrtegrated form, which is how they function biologically. This is one of the first attempts to include both of these networks in one model. Previously, discrete mathematical models were used only to describe genetic regulation networks. The study of the network dynamics generated 8 (2(3)) fixed points, one for each nutrient configuration (substrate mixture) in the medium. The fixed points of the discrete model reflect the phenotypes described. Gene expression and the patterns of the metabolic fluxes generated are described accurately. The activation of the gene regulation network depends basically on the presence of glucose and glycerol. The model predicts the behavior when mixed carbon sources are utilized as well as when there is no carbon source present. Fictitious jokers (Joker1, Joker2, and Repressor SdhC) had to be created to control 12 genes whose regulation mechanism is unknown, since glycerol and glucose do not act directly on the genes. The approach presented in this paper is particularly useful to investigate potential unknown gene regulation mechanisms; such a novel approach can also be used to describe other gene regulation situations such as the comparison between non-recombinant and recombinant yeast strain, producing recombinant proteins, presently under investigation in our group.

Facile hyphenation of gas chromatography and a microcantilever array sensor for enhanced selectivity.

PubMed

Chapman, Peter J; Vogt, Frank; Dutta, Pampa; Datskos, Panos G; Devault, Gerald L; Sepaniak, Michael J

2007-01-01

The very simple coupling of a standard, packed-column gas chromatograph with a microcantilever array (MCA) is demonstrated for enhanced selectivity and potential analyte identification in the analysis of volatile organic compounds (VOCs). The cantilevers in MCAs are differentially coated on one side with responsive phases (RPs) and produce bending responses of the cantilevers due to analyte-induced surface stresses. Generally, individual components are difficult to elucidate when introduced to MCA systems as mixtures, although pattern recognition techniques are helpful in identifying single components, binary mixtures, or composite responses of distinct mixtures (e.g., fragrances). In the present work, simple test VOC mixtures composed of acetone, ethanol, and trichloroethylene (TCE) in pentane and methanol and acetonitrile in pentane are first separated using a standard gas chromatograph and then introduced into a MCA flow cell. Significant amounts of response diversity to the analytes in the mixtures are demonstrated across the RP-coated cantilevers of the array. Principal component analysis is used to demonstrate that only three components of a four-component VOC mixture could be identified without mixture separation. Calibration studies are performed, demonstrating a good linear response over 2 orders of magnitude for each component in the primary study mixture. Studies of operational parameters including column temperature, column flow rate, and array cell temperature are conducted. Reproducibility studies of VOC peak areas and peak heights are also carried out showing RSDs of less than 4 and 3%, respectively, for intra-assay studies. Of practical significance is the facile manner by which the hyphenation of a mature separation technique and the burgeoning sensing approach is accomplished, and the potential to use pattern recognition techniques with MCAs as a new type of detector for chromatography with analyte-identifying capabilities.

Patterns of Weight Change in Black Americans: Pooled Analysis from Three Behavioral Weight Loss Trials

PubMed Central

Morales, Knashawn H.; Kumanyika, Shiriki K.; Fassbender, Jennifer E.; Good, Jerene; Localio, A. Russell; Wadden, Thomas A.

2014-01-01

Objective Differentiating trajectories of weight change and identifying associated baseline predictors can provide insights for improving behavioral obesity treatment outcomes. Design and Methods Secondary, observational analyses using growth mixture models were conducted in pooled data for 604 black American, primarily female adults in three completed clinical trials. Covariates of identified patterns were evaluated. Results The best fitting model identified three patterns over 2 years: 1) mean weight loss of approximately 2 kg (n=519); 2) mean weight loss of approximately 3 kg at 1 year, followed by ~ 4 kg regain (n=61); and 3) mean weight loss of approximately 20 kg at 1 year followed by ~ 4 kg regain (n=24, with 23 from one study). In final multivariate analyses, higher BMI predicted having pattern 2 (OR[95% CI]) 1.10[1.03, 1.17]) or 3 (OR[95% CI] 1.42[1.25, 1.63]), and higher dietary fat score was predictive of a lower odds of having patterns 2 (OR[95% CI] 0.37[0.15, 0.94]) or 3 (OR[95% CI] 0.23[0.07, 0.79]). Conclusions Findings were consistent with moderate, clinically non-significant weight loss as the predominant pattern across all studies. Results underscore the need to develop novel and more carefully targeted and tailored approaches to facilitating weight loss in black American adults. PMID:25251464

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.

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.

Discrete element modelling of bedload transport

NASA Astrophysics Data System (ADS)

Loyer, A.; Frey, P.

2011-12-01

Discrete element modelling (DEM) has been widely used in solid mechanics and in granular physics. In this type of modelling, each individual particle is taken into account and intergranular interactions are modelled with simple laws (e.g. Coulomb friction). Gravity and contact forces permit to solve the dynamical behaviour of the system. DEM is interesting to model configurations and access to parameters not directly available in laboratory experimentation, hence the term "numerical experimentations" sometimes used to describe DEM. DEM was used to model bedload transport experiments performed at the particle scale with spherical glass beads in a steep and narrow flume. Bedload is the larger material that is transported on the bed on stream channels. It has a great geomorphic impact. Physical processes ruling bedload transport and more generally coarse-particle/fluid systems are poorly known, arguably because granular interactions have been somewhat neglected. An existing DEM code (PFC3D) already computing granular interactions was used. We implemented basic hydrodynamic forces to model the fluid interactions (buoyancy, drag, lift). The idea was to use the minimum number of ingredients to match the experimental results. Experiments were performed with one-size and two-size mixtures of coarse spherical glass beads entrained by a shallow turbulent and supercritical water flow down a steep channel with a mobile bed. The particle diameters were 4 and 6mm, the channel width 6.5mm (about the same width as the coarser particles) and the channel inclination was typically 10%. The water flow rate and the particle rate were kept constant at the upstream entrance and adjusted to obtain bedload transport equilibrium. Flows were filmed from the side by a high-speed camera. Using image processing algorithms made it possible to determine the position, velocity and trajectory of both smaller and coarser particles. Modelled and experimental particle velocity and concentration depth profiles were compared in the case of the one-size mixture. The turbulent fluid velocity profile was prescribed and attached to the variable upper bedline. Provided the upper bedline was calculated with a refined space and time resolution, a fair agreement between DEM and experiments was reached. Experiments with two-size mixtures were designed to study vertical grain size sorting or segregation patterns. Sorting is arguably the reason why the predictive capacity of bedload formulations remains so poor. Modelling of the two-size mixture was also performed and gave promising qualitative results.

Modeling carbachol-induced hippocampal network synchronization using hidden Markov models

NASA Astrophysics Data System (ADS)

Dragomir, Andrei; Akay, Yasemin M.; Akay, Metin

2010-10-01

In this work we studied the neural state transitions undergone by the hippocampal neural network using a hidden Markov model (HMM) framework. We first employed a measure based on the Lempel-Ziv (LZ) estimator to characterize the changes in the hippocampal oscillation patterns in terms of their complexity. These oscillations correspond to different modes of hippocampal network synchronization induced by the cholinergic agonist carbachol in the CA1 region of mice hippocampus. HMMs are then used to model the dynamics of the LZ-derived complexity signals as first-order Markov chains. Consequently, the signals corresponding to our oscillation recordings can be segmented into a sequence of statistically discriminated hidden states. The segmentation is used for detecting transitions in neural synchronization modes in data recorded from wild-type and triple transgenic mice models (3xTG) of Alzheimer's disease (AD). Our data suggest that transition from low-frequency (delta range) continuous oscillation mode into high-frequency (theta range) oscillation, exhibiting repeated burst-type patterns, occurs always through a mode resembling a mixture of the two patterns, continuous with burst. The relatively random patterns of oscillation during this mode may reflect the fact that the neuronal network undergoes re-organization. Further insight into the time durations of these modes (retrieved via the HMM segmentation of the LZ-derived signals) reveals that the mixed mode lasts significantly longer (p < 10-4) in 3xTG AD mice. These findings, coupled with the documented cholinergic neurotransmission deficits in the 3xTG mice model, may be highly relevant for the case of AD.

Mixed Convective Condensation in Enclosures with Noncondensable Gases

NASA Astrophysics Data System (ADS)

Fox, Richard John

1994-01-01

A transient, two-dimensional, numerical model was developed in order to study the laminar flow, heat, and mass transfer in a vertical reflux condenser loaded with vapor and noncondensable gas. The simplified model treats the two-component (gas/vapor), two-phase (vapor/liquid) mixture as a continuum by making use of conservation equations for mass continuity, momentum, species, and energy. The liquid mist phase is formed in such a way as to obey one of three conditions: thermodynamic equilibrium, complete nonequilibrium (no mist formation), or partial equilibrium (partial supersaturation). In developing the model, special attention was paid to the formulation of the boundary conditions, global continuity, and numerical efficiency. Two different mixture combinations were used in order to create stable and unstable systems. Steam-helium mixtures (Mv, = 18, Mg = 4) were found to exhibit stable flows with the lighter helium trapped in the upper portion of the condenser, shutting off condensation in that region. Steam-air mixtures (M_ {v}, = 18, Mg = 28) were found to exhibit varying degrees of instability, depending on the noncondensable gas and heat load, owing to the accumulation of the heavy gas near the condensing surface. Under low gas loading cases (Pg = 0.031 kg/m^3) the natural convective fluctuations were found to be weak and the flow was more easily dominated by the forced convective inlet flow and wall suction. At such low gas loadings, stable, asymmetric flow patterns persisted up to high powers. Large gas loadings (Pg = 0.196 kg/m^3) showed much stronger natural convective effects. Regions of counterflowing vapor and gas were found to promote stronger mixing as the power was increased. Regions of noncondensing gas were found to blanket the condenser walls as the suction velocity increased, resulting in a strong resistance to heat and mass transfer and consequent increase in system pressure. Moderate gas loadings (Pg = 0.065 kg/m ^3) were found to exhibit intermediate behavior between the low and high gas loading cases. For the moderate gas loading cases, a bifurcation was found to occur when Re was increased beyond a critical value, forcing the system into one of two stable, distinct flow patterns. Each branch of the bifurcation was found to correspond to the flows that occur in either the low or high gas loading cases, and radically different heat transfer performance was encountered for the same system parameters. The model was also used to simulate experiments conducted in a vertical reflux thermosyphon using steam -air mixtures. The qualitative aspects of the flow were in reasonable agreement between the model and experiment and trends in the local heat transfer were similar. By converting latent heat energy into sensible heat energy, mist formation was found to increase the system temperature and, as a consequence, the overall heat transfer coefficient was lowered. However, the total heat transfer rate was not sensitive to mist formation since the reduction in the latent heat transfer was accompanied by a corresponding increase in the sensible heat transfer, altering the mode but not the magnitude of the total heat transfer.

Identification of degenerate neuronal systems based on intersubject variability.

PubMed

Noppeney, Uta; Penny, Will D; Price, Cathy J; Flandin, Guillaume; Friston, Karl J

2006-04-15

Group studies implicitly assume that all subjects activate one common system to sustain a particular cognitive task. Intersubject variability is generally treated as well-behaved and uninteresting noise. However, intersubject variability might result from subjects engaging different degenerate neuronal systems that are each sufficient for task performance. This would produce a multimodal distribution of intersubject variability. We have explored this idea with the help of Gaussian Mixture Modeling and Bayesian model comparison procedures. We illustrate our approach using a crossmodal priming paradigm, in which subjects perform a semantic decision on environmental sounds or their spoken names that were preceded by a semantically congruent or incongruent picture or written name. All subjects consistently activated the superior temporal gyri bilaterally, the left fusiform gyrus and the inferior frontal sulcus. Comparing a One and Two Gaussian Mixture Model of the unexplained residuals provided very strong evidence for two groups with distinct activation patterns: 6 subjects exhibited additional activations in the superior temporal sulci bilaterally, the right superior frontal and central sulcus. 11 subjects showed increased activation in the striate and the right inferior parietal cortex. These results suggest that semantic decisions on auditory-visual compound stimuli might be accomplished by two overlapping degenerate neuronal systems.

Laser Printing of Superhydrophobic Patterns from Mixtures of Hydrophobic Silica Nanoparticles and Toner Powder.

PubMed

Ngo, Chi-Vinh; Chun, Doo-Man

2016-11-08

In this work, a new and facile dry printing method was developed for the direct fabrication of superhydrophobic patterns based on silica nanoparticles. Mixtures of hydrophobic fumed silica nanoparticles and toner powder were printed on paper and polymer sheets using a commercial laser printer to produce the superhydrophobic patterns. The mixing ratio of the toner powder (for the laser printer) to hydrophobic silica was also investigated to optimize both the printing quality and the superhydrophobicity of the printed areas. The proper mixing ratio was then used to print various superhydrophobic patterns, including triangular, square, circular, and complex arrangements, to demonstrate that superhydrophobic surfaces with different patterns can be fabricated in a few seconds without any post-processing. The superhydrophobicity of each sample was evaluated by contact angle measurements, and all printed areas showed contact angles greater than 150°. The research described here opens the possibility of rapid production of superhydrophobic surfaces with various patterns. Ultimately, the obtained findings may have a significant impact on applications related to self-cleaning, control of water geometry and position, fluid mixing and fluid transport.

Laser Printing of Superhydrophobic Patterns from Mixtures of Hydrophobic Silica Nanoparticles and Toner Powder

PubMed Central

Ngo, Chi-Vinh; Chun, Doo-Man

2016-01-01

In this work, a new and facile dry printing method was developed for the direct fabrication of superhydrophobic patterns based on silica nanoparticles. Mixtures of hydrophobic fumed silica nanoparticles and toner powder were printed on paper and polymer sheets using a commercial laser printer to produce the superhydrophobic patterns. The mixing ratio of the toner powder (for the laser printer) to hydrophobic silica was also investigated to optimize both the printing quality and the superhydrophobicity of the printed areas. The proper mixing ratio was then used to print various superhydrophobic patterns, including triangular, square, circular, and complex arrangements, to demonstrate that superhydrophobic surfaces with different patterns can be fabricated in a few seconds without any post-processing. The superhydrophobicity of each sample was evaluated by contact angle measurements, and all printed areas showed contact angles greater than 150°. The research described here opens the possibility of rapid production of superhydrophobic surfaces with various patterns. Ultimately, the obtained findings may have a significant impact on applications related to self-cleaning, control of water geometry and position, fluid mixing and fluid transport. PMID:27824132

Estimating the incidence of rotavirus infection in children from India and Malawi from serial anti-rotavirus IgA titres.

PubMed

Bennett, Aisleen; Nagelkerke, Nico; Heinsbroek, Ellen; Premkumar, Prasanna S; Wnęk, Małgorzata; Kang, Gagandeep; French, Neil; Cunliffe, Nigel A; Bar-Zeev, Naor; Lopman, Ben; Iturriza-Gomara, Miren

2017-01-01

Accurate estimates of rotavirus incidence in infants are crucial given disparities in rotavirus vaccine effectiveness from low-income settings. Sero-surveys are a pragmatic means of estimating incidence however serological data is prone to misclassification. This study used mixture models to estimate incidence of rotavirus infection from anti-rotavirus immunoglobulin A (IgA) titres in infants from Vellore, India, and Karonga, Malawi. IgA titres were measured using serum samples collected at 6 month intervals for 36 months from 373 infants from Vellore and 12 months from 66 infants from Karonga. Mixture models (two component Gaussian mixture distributions) were fit to the difference in titres between time points to estimate risk of sero-positivity and derive incidence estimates. A peak incidence of 1.05(95% confidence interval [CI]: 0.64, 1.64) infections per child-year was observed in the first 6 months of life in Vellore. This declined incrementally with each subsequent time interval. Contrastingly in Karonga incidence was greatest in the second 6 months of life (1.41 infections per child year [95% CI: 0.79, 2.29]). This study demonstrates that infants from Vellore experience peak rotavirus incidence earlier than those from Karonga. Identifying such differences in transmission patterns is important in informing vaccine strategy, particularly where vaccine effectiveness is modest.

Estimating the incidence of rotavirus infection in children from India and Malawi from serial anti-rotavirus IgA titres

PubMed Central

Nagelkerke, Nico; Heinsbroek, Ellen; Premkumar, Prasanna S.; Wnęk, Małgorzata; Kang, Gagandeep; French, Neil; Cunliffe, Nigel A.; Bar-Zeev, Naor

2017-01-01

Accurate estimates of rotavirus incidence in infants are crucial given disparities in rotavirus vaccine effectiveness from low-income settings. Sero-surveys are a pragmatic means of estimating incidence however serological data is prone to misclassification. This study used mixture models to estimate incidence of rotavirus infection from anti-rotavirus immunoglobulin A (IgA) titres in infants from Vellore, India, and Karonga, Malawi. IgA titres were measured using serum samples collected at 6 month intervals for 36 months from 373 infants from Vellore and 12 months from 66 infants from Karonga. Mixture models (two component Gaussian mixture distributions) were fit to the difference in titres between time points to estimate risk of sero-positivity and derive incidence estimates. A peak incidence of 1.05(95% confidence interval [CI]: 0.64, 1.64) infections per child-year was observed in the first 6 months of life in Vellore. This declined incrementally with each subsequent time interval. Contrastingly in Karonga incidence was greatest in the second 6 months of life (1.41 infections per child year [95% CI: 0.79, 2.29]). This study demonstrates that infants from Vellore experience peak rotavirus incidence earlier than those from Karonga. Identifying such differences in transmission patterns is important in informing vaccine strategy, particularly where vaccine effectiveness is modest. PMID:29287122

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.

Comparison of Two-Phase Pipe Flow in OpenFOAM with a Mechanistic Model

NASA Astrophysics Data System (ADS)

Shuard, Adrian M.; Mahmud, Hisham B.; King, Andrew J.

2016-03-01

Two-phase pipe flow is a common occurrence in many industrial applications such as power generation and oil and gas transportation. Accurate prediction of liquid holdup and pressure drop is of vast importance to ensure effective design and operation of fluid transport systems. In this paper, a Computational Fluid Dynamics (CFD) study of a two-phase flow of air and water is performed using OpenFOAM. The two-phase solver, interFoam is used to identify flow patterns and generate values of liquid holdup and pressure drop, which are compared to results obtained from a two-phase mechanistic model developed by Petalas and Aziz (2002). A total of 60 simulations have been performed at three separate pipe inclinations of 0°, +10° and -10° respectively. A three dimensional, 0.052m diameter pipe of 4m length is used with the Shear Stress Transport (SST) k - ɷ turbulence model to solve the turbulent mixtures of air and water. Results show that the flow pattern behaviour and numerical values of liquid holdup and pressure drop compare reasonably well to the mechanistic model.

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.

Modeling and analysis of personal exposures to VOC mixtures using copulas

PubMed Central

Su, Feng-Chiao; Mukherjee, Bhramar; Batterman, Stuart

2014-01-01

Environmental exposures typically involve mixtures of pollutants, which must be understood to evaluate cumulative risks, that is, the likelihood of adverse health effects arising from two or more chemicals. This study uses several powerful techniques to characterize dependency structures of mixture components in personal exposure measurements of volatile organic compounds (VOCs) with aims of advancing the understanding of environmental mixtures, improving the ability to model mixture components in a statistically valid manner, and demonstrating broadly applicable techniques. We first describe characteristics of mixtures and introduce several terms, including the mixture fraction which represents a mixture component's share of the total concentration of the mixture. Next, using VOC exposure data collected in the Relationship of Indoor Outdoor and Personal Air (RIOPA) study, mixtures are identified using positive matrix factorization (PMF) and by toxicological mode of action. Dependency structures of mixture components are examined using mixture fractions and modeled using copulas, which address dependencies of multiple variables across the entire distribution. Five candidate copulas (Gaussian, t, Gumbel, Clayton, and Frank) are evaluated, and the performance of fitted models was evaluated using simulation and mixture fractions. Cumulative cancer risks are calculated for mixtures, and results from copulas and multivariate lognormal models are compared to risks calculated using the observed data. Results obtained using the RIOPA dataset showed four VOC mixtures, representing gasoline vapor, vehicle exhaust, chlorinated solvents and disinfection by-products, and cleaning products and odorants. Often, a single compound dominated the mixture, however, mixture fractions were generally heterogeneous in that the VOC composition of the mixture changed with concentration. Three mixtures were identified by mode of action, representing VOCs associated with hematopoietic, liver and renal tumors. Estimated lifetime cumulative cancer risks exceeded 10−3 for about 10% of RIOPA participants. Factors affecting the likelihood of high concentration mixtures included city, participant ethnicity, and house air exchange rates. The dependency structures of the VOC mixtures fitted Gumbel (two mixtures) and t (four mixtures) copulas, types that emphasize tail dependencies. Significantly, the copulas reproduced both risk predictions and exposure fractions with a high degree of accuracy, and performed better than multivariate lognormal distributions. Copulas may be the method of choice for VOC mixtures, particularly for the highest exposures or extreme events, cases that poorly fit lognormal distributions and that represent the greatest risks. PMID:24333991

Daily Mood Patterns and Bulimic Behaviors in the Natural Environment

PubMed Central

Crosby, Ross D.; Wonderlich, Stephen A.; Engel, Scott G.; Simonich, Heather; Smyth, Joshua; Mitchell, James E.

2009-01-01

Objective Negative affect has been purported to play an important role in the etiology and maintenance of bulimic behaviors. The objective of this study was to identify daily mood patterns in the natural environment exhibited by individuals with bulimia nervosa and to examine the relationship between these patterns and bulimic behaviors. Method One hundred thirty-three women aged 18–55 meeting DSM-IV criteria for bulimia nervosa were recruited through clinical referrals and community advertisements. Ecological momentary assessment was used to collect multiple ratings of negative affect, binge eating and purging each day for a two-week period using palmtop computers. Latent growth mixture modeling was used to identify daily mood patterns. Results Nine distinct daily mood patterns were identified. The highest rates of binge eating and purging episodes occurred on days characterized by stable high negative affect or increasing negative affect over the course of the day. Conclusions These findings support the conclusion that negative mood states are intimately tied to bulimic behaviors and may in fact precipitate such behavior. PMID:19152874

Mixing, diffusion, and percolation in binary supported membranes containing mixtures of lipids and amphiphilic block copolymers.

PubMed

Gettel, Douglas L; Sanborn, Jeremy; Patel, Mira A; de Hoog, Hans-Peter; Liedberg, Bo; Nallani, Madhavan; Parikh, Atul N

2014-07-23

Substrate-mediated fusion of small polymersomes, derived from mixtures of lipids and amphiphilic block copolymers, produces hybrid, supported planar bilayers at hydrophilic surfaces, monolayers at hydrophobic surfaces, and binary monolayer/bilayer patterns at amphiphilic surfaces, directly responding to local measures of (and variations in) surface free energy. Despite the large thickness mismatch in their hydrophobic cores, the hybrid membranes do not exhibit microscopic phase separation, reflecting irreversible adsorption and limited lateral reorganization of the polymer component. With increasing fluid-phase lipid fraction, these hybrid, supported membranes undergo a fluidity transition, producing a fully percolating fluid lipid phase beyond a critical area fraction, which matches the percolation threshold for the immobile point obstacles. This then suggests that polymer-lipid hybrid membranes might be useful models for studying obstructed diffusion, such as occurs in lipid membranes containing proteins.

A Computational Algorithm for Functional Clustering of Proteome Dynamics During Development

PubMed Central

Wang, Yaqun; Wang, Ningtao; Hao, Han; Guo, Yunqian; Zhen, Yan; Shi, Jisen; Wu, Rongling

2014-01-01

Phenotypic traits, such as seed development, are a consequence of complex biochemical interactions among genes, proteins and metabolites, but the underlying mechanisms that operate in a coordinated and sequential manner remain elusive. Here, we address this issue by developing a computational algorithm to monitor proteome changes during the course of trait development. The algorithm is built within the mixture-model framework in which each mixture component is modeled by a specific group of proteins that display a similar temporal pattern of expression in trait development. A nonparametric approach based on Legendre orthogonal polynomials was used to fit dynamic changes of protein expression, increasing the power and flexibility of protein clustering. By analyzing a dataset of proteomic dynamics during early embryogenesis of the Chinese fir, the algorithm has successfully identified several distinct types of proteins that coordinate with each other to determine seed development in this forest tree commercially and environmentally important to China. The algorithm will find its immediate applications for the characterization of mechanistic underpinnings for any other biological processes in which protein abundance plays a key role. PMID:24955031

CFD Modelling of Particle Mixtures in a 2D CFB

NASA Astrophysics Data System (ADS)

Seppälä, M.; Kallio, S.

The capability of Fluent 6.2.16 to simulate particle mixtures in a laboratory scale 2D circulating fluidized bed (CFB) unit has been tested. In the simulations, the solids were described as one or two particle phases. The loading ratio of small to large particles, particle diameters and the gas inflow velocity were varied. The 40 cm wide and 3 m high 2D CFB was modeled using a grid with 31080 cells. The outflow of particles at the top of the CFB was monitored and emanated particles were fed back to the riser through a return duct. The paper presents the segregation patterns of the particle phases obtained from the simulations. When the fraction of large particles was 50% or larger, large particles segregated, as expected, to the wall regions and to the bottom part of the riser. However, when the fraction of large particles was 10%, an excess of large particles was found in the upper half of the riser. The explanation for this unexpected phenomenon was found in the distribution of the large particles between the slow clusters and the faster moving lean suspension.

Using Gaussian mixture models to detect and classify dolphin whistles and pulses.

PubMed

Peso Parada, Pablo; Cardenal-López, Antonio

2014-06-01

In recent years, a number of automatic detection systems for free-ranging cetaceans have been proposed that aim to detect not just surfaced, but also submerged, individuals. These systems are typically based on pattern-recognition techniques applied to underwater acoustic recordings. Using a Gaussian mixture model, a classification system was developed that detects sounds in recordings and classifies them as one of four types: background noise, whistles, pulses, and combined whistles and pulses. The classifier was tested using a database of underwater recordings made off the Spanish coast during 2011. Using cepstral-coefficient-based parameterization, a sound detection rate of 87.5% was achieved for a 23.6% classification error rate. To improve these results, two parameters computed using the multiple signal classification algorithm and an unpredictability measure were included in the classifier. These parameters, which helped to classify the segments containing whistles, increased the detection rate to 90.3% and reduced the classification error rate to 18.1%. Finally, the potential of the multiple signal classification algorithm and unpredictability measure for estimating whistle contours and classifying cetacean species was also explored, with promising results.

Mobility of maerl-siliciclastic mixtures: Impact of waves, currents and storm events

NASA Astrophysics Data System (ADS)

Joshi, Siddhi; Duffy, Garret Patrick; Brown, Colin

2017-04-01

Maerl beds are free-living, non-geniculate coralline algae habitats which form biogenic reefs with high micro-scale complexity supporting a diversity and abundance of rare epifauna and epiflora. These habitats are highly mobile in shallow marine environments where substantial maerl beds co-exist with siliciclastic sediment, exemplified by our study site of Galway Bay. Coupled hydrodynamic-wave-sediment transport models have been used to explore the transport patterns of maerl-siliciclastic sediment during calm summer conditions and severe winter storms. The sediment distribution is strongly influenced by storm waves even in water depths greater than 100 m. Maerl is present at the periphery of wave-induced residual current gyres during storm conditions. A combined wave-current Sediment Mobility Index during storm conditions shows correlation with multibeam backscatter and surficial sediment distribution. A combined wave-current Mobilization Frequency Index during storm conditions acts as a physical surrogate for the presence of maerl-siliciclastic mixtures in Galway Bay. Both indices can provide useful integrated oceanographic and sediment information to complement coupled numerical hydrodynamic, sediment transport and erosion-deposition models.

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…

Recognizing patterns of visual field loss using unsupervised machine learning

NASA Astrophysics Data System (ADS)

Yousefi, Siamak; Goldbaum, Michael H.; Zangwill, Linda M.; Medeiros, Felipe A.; Bowd, Christopher

2014-03-01

Glaucoma is a potentially blinding optic neuropathy that results in a decrease in visual sensitivity. Visual field abnormalities (decreased visual sensitivity on psychophysical tests) are the primary means of glaucoma diagnosis. One form of visual field testing is Frequency Doubling Technology (FDT) that tests sensitivity at 52 points within the visual field. Like other psychophysical tests used in clinical practice, FDT results yield specific patterns of defect indicative of the disease. We used Gaussian Mixture Model with Expectation Maximization (GEM), (EM is used to estimate the model parameters) to automatically separate FDT data into clusters of normal and abnormal eyes. Principal component analysis (PCA) was used to decompose each cluster into different axes (patterns). FDT measurements were obtained from 1,190 eyes with normal FDT results and 786 eyes with abnormal (i.e., glaucomatous) FDT results, recruited from a university-based, longitudinal, multi-center, clinical study on glaucoma. The GEM input was the 52-point FDT threshold sensitivities for all eyes. The optimal GEM model separated the FDT fields into 3 clusters. Cluster 1 contained 94% normal fields (94% specificity) and clusters 2 and 3 combined, contained 77% abnormal fields (77% sensitivity). For clusters 1, 2 and 3 the optimal number of PCA-identified axes were 2, 2 and 5, respectively. GEM with PCA successfully separated FDT fields from healthy and glaucoma eyes and identified familiar glaucomatous patterns of loss.

Multimodality Prediction of Chaotic Time Series with Sparse Hard-Cut EM Learning of the Gaussian Process Mixture Model

NASA Astrophysics Data System (ADS)

Zhou, Ya-Tong; Fan, Yu; Chen, Zi-Yi; Sun, Jian-Cheng

2017-05-01

The contribution of this work is twofold: (1) a multimodality prediction method of chaotic time series with the Gaussian process mixture (GPM) model is proposed, which employs a divide and conquer strategy. It automatically divides the chaotic time series into multiple modalities with different extrinsic patterns and intrinsic characteristics, and thus can more precisely fit the chaotic time series. (2) An effective sparse hard-cut expectation maximization (SHC-EM) learning algorithm for the GPM model is proposed to improve the prediction performance. SHC-EM replaces a large learning sample set with fewer pseudo inputs, accelerating model learning based on these pseudo inputs. Experiments on Lorenz and Chua time series demonstrate that the proposed method yields not only accurate multimodality prediction, but also the prediction confidence interval. SHC-EM outperforms the traditional variational learning in terms of both prediction accuracy and speed. In addition, SHC-EM is more robust and insusceptible to noise than variational learning. Supported by the National Natural Science Foundation of China under Grant No 60972106, the China Postdoctoral Science Foundation under Grant No 2014M561053, the Humanity and Social Science Foundation of Ministry of Education of China under Grant No 15YJA630108, and the Hebei Province Natural Science Foundation under Grant No E2016202341.

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.

Identification of homogeneous regions for rainfall regional frequency analysis considering typhoon event in South Korea

NASA Astrophysics Data System (ADS)

Heo, J. H.; Ahn, H.; Kjeldsen, T. R.

2017-12-01

South Korea is prone to large, and often disastrous, rainfall events caused by a mixture of monsoon and typhoon rainfall phenomena. However, traditionally, regional frequency analysis models did not consider this mixture of phenomena when fitting probability distributions, potentially underestimating the risk posed by the more extreme typhoon events. Using long-term observed records of extreme rainfall from 56 sites combined with detailed information on the timing and spatial impact of past typhoons from the Korea Meteorological Administration (KMA), this study developed and tested a new mixture model for frequency analysis of two different phenomena; events occurring regularly every year (monsoon) and events only occurring in some years (typhoon). The available annual maximum 24 hour rainfall data were divided into two sub-samples corresponding to years where the annual maximum is from either (1) a typhoon event, or (2) a non-typhoon event. Then, three-parameter GEV distribution was fitted to each sub-sample along with a weighting parameter characterizing the proportion of historical events associated with typhoon events. Spatial patterns of model parameters were analyzed and showed that typhoon events are less commonly associated with annual maximum rainfall in the North-West part of the country (Seoul area), and more prevalent in the southern and eastern parts of the country, leading to the formation of two distinct typhoon regions: (1) North-West; and (2) Southern and Eastern. Using a leave-one-out procedure, a new regional frequency model was tested and compared to a more traditional index flood method. The results showed that the impact of typhoon on design events might previously have been underestimated in the Seoul area. This suggests that the use of the mixture model should be preferred where the typhoon phenomena is less frequent, and thus can have a significant effect on the rainfall-frequency curve. This research was supported by a grant(2017-MPSS31-001) from Supporting Technology Development Program for Disaster Management funded by Ministry of Public Safety and Security(MPSS) of the Korean government.

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.

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.

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.

Mapping behavioral landscapes for animal movement: a finite mixture modeling approach

USGS Publications Warehouse

Tracey, Jeff A.; Zhu, Jun; Boydston, Erin E.; Lyren, Lisa M.; Fisher, Robert N.; Crooks, Kevin R.

2013-01-01

Because of its role in many ecological processes, movement of animals in response to landscape features is an important subject in ecology and conservation biology. In this paper, we develop models of animal movement in relation to objects or fields in a landscape. We take a finite mixture modeling approach in which the component densities are conceptually related to different choices for movement in response to a landscape feature, and the mixing proportions are related to the probability of selecting each response as a function of one or more covariates. We combine particle swarm optimization and an Expectation-Maximization (EM) algorithm to obtain maximum likelihood estimates of the model parameters. We use this approach to analyze data for movement of three bobcats in relation to urban areas in southern California, USA. A behavioral interpretation of the models revealed similarities and differences in bobcat movement response to urbanization. All three bobcats avoided urbanization by moving either parallel to urban boundaries or toward less urban areas as the proportion of urban land cover in the surrounding area increased. However, one bobcat, a male with a dispersal-like large-scale movement pattern, avoided urbanization at lower densities and responded strictly by moving parallel to the urban edge. The other two bobcats, which were both residents and occupied similar geographic areas, avoided urban areas using a combination of movements parallel to the urban edge and movement toward areas of less urbanization. However, the resident female appeared to exhibit greater repulsion at lower levels of urbanization than the resident male, consistent with empirical observations of bobcats in southern California. Using the parameterized finite mixture models, we mapped behavioral states to geographic space, creating a representation of a behavioral landscape. This approach can provide guidance for conservation planning based on analysis of animal movement data using statistical models, thereby linking connectivity evaluations to empirical data.

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…

Method translation and full metadata transfer from thermal to differential flow modulated comprehensive two dimensional gas chromatography: Profiling of suspected fragrance allergens.

PubMed

Cordero, Chiara; Rubiolo, Patrizia; Reichenbach, Stephen E; Carretta, Andrea; Cobelli, Luigi; Giardina, Matthew; Bicchi, Carlo

2017-01-13

The possibility to transfer methods from thermal to differential-flow modulated comprehensive two-dimensional gas chromatographic (GC×GC) platforms is of high interest to improve GC×GC flexibility and increase the compatibility of results from different platforms. The principles of method translation are here applied to an original method, developed for a loop-type thermal modulated GC×GC-MS/FID system, suitable for quali-quantitative screening of suspected fragrance allergens. The analysis conditions were translated to a reverse-injection differential flow modulated platform (GC×2GC-MS/FID) with a dual-parallel secondary column and dual detection. The experimental results, for a model mixture of suspected volatile allergens and for raw fragrance mixtures of different composition, confirmed the feasibility of translating methods by preserving 1 D elution order, as well as the relative alignment of resulting 2D peak patterns. A correct translation produced several benefits including an effective transfer of metadata (compound names, MS fragmentation pattern, response factors) by automatic template transformation and matching from the original/reference method to its translated counterpart. The correct translation provided: (a) 2D pattern repeatability, (b) MS fragmentation pattern reliability for identity confirmation, and (c) comparable response factors and quantitation accuracy within a concentration range of three orders of magnitude. The adoption of a narrow bore (i.e. 0.1mm d c ) first-dimension column to operate under close-to-optimal conditions with the differential-flow modulation GC×GC platform was also advantageous in halving the total analysis under the translated conditions. Copyright © 2016 Elsevier B.V. All rights reserved.

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.

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,…

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.

Differential Activity-Driven Instabilities in Biphasic Active Matter

NASA Astrophysics Data System (ADS)

Weber, Christoph A.; Rycroft, Chris H.; Mahadevan, L.

2018-06-01

Active stresses can cause instabilities in contractile gels and living tissues. Here we provide a generic hydrodynamic theory that treats these systems as a mixture of two phases of varying activity and different mechanical properties. We find that differential activity between the phases causes a uniform mixture to undergo a demixing instability. We follow the nonlinear evolution of the instability and characterize a phase diagram of the resulting patterns. Our study complements other instability mechanisms in mixtures driven by differential adhesion, differential diffusion, differential growth, and differential motion.

Understanding the spatiotemporal pattern of grazing cattle movement

NASA Astrophysics Data System (ADS)

Zhao, Kun; Jurdak, Raja

2016-08-01

Understanding the drivers of animal movement is significant for ecology and biology. Yet researchers have so far been unable to fully understand these drivers, largely due to low data resolution. In this study, we analyse a high-frequency movement dataset for a group of grazing cattle and investigate their spatiotemporal patterns using a simple two-state ‘stop-and-move’ mobility model. We find that the dispersal kernel in the moving state is best described by a mixture exponential distribution, indicating the hierarchical nature of the movement. On the other hand, the waiting time appears to be scale-invariant below a certain cut-off and is best described by a truncated power-law distribution, suggesting that the non-moving state is governed by time-varying dynamics. We explore possible explanations for the observed phenomena, covering factors that can play a role in the generation of mobility patterns, such as the context of grazing environment, the intrinsic decision-making mechanism or the energy status of different activities. In particular, we propose a new hypothesis that the underlying movement pattern can be attributed to the most probable observable energy status under the maximum entropy configuration. These results are not only valuable for modelling cattle movement but also provide new insights for understanding the underlying biological basis of grazing behaviour.

Deep Independence Network Analysis of Structural Brain Imaging: Application to Schizophrenia

PubMed Central

Castro, Eduardo; Hjelm, R. Devon; Plis, Sergey M.; Dinh, Laurent; Turner, Jessica A.; Calhoun, Vince D.

2016-01-01

Linear independent component analysis (ICA) is a standard signal processing technique that has been extensively used on neuroimaging data to detect brain networks with coherent brain activity (functional MRI) or covarying structural patterns (structural MRI). However, its formulation assumes that the measured brain signals are generated by a linear mixture of the underlying brain networks and this assumption limits its ability to detect the inherent nonlinear nature of brain interactions. In this paper, we introduce nonlinear independent component estimation (NICE) to structural MRI data to detect abnormal patterns of gray matter concentration in schizophrenia patients. For this biomedical application, we further addressed the issue of model regularization of nonlinear ICA by performing dimensionality reduction prior to NICE, together with an appropriate control of the complexity of the model and the usage of a proper approximation of the probability distribution functions of the estimated components. We show that our results are consistent with previous findings in the literature, but we also demonstrate that the incorporation of nonlinear associations in the data enables the detection of spatial patterns that are not identified by linear ICA. Specifically, we show networks including basal ganglia, cerebellum and thalamus that show significant differences in patients versus controls, some of which show distinct nonlinear patterns. PMID:26891483

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.

The Impact of Parents, Child Care Providers, Teachers, and Peers on Early Externalizing Trajectories

PubMed Central

Silver, Rebecca B.; Measelle, Jeffrey R.; Armstrong, Jeffrey M.; Essex, Marilyn J.

2010-01-01

This study utilized growth mixture modeling to examine the impact of parents, child care providers, teachers, and peers on the prediction of distinct developmental patterns of classroom externalizing behavior in elementary school. Among 241 children, three groups were identified. 84.6% of children exhibited consistently low externalizing behavior. The externalizing behavior of the Chronic High group (5.8%) remained elevated throughout elementary school; it increased over time in the Low Increasing group (9.5%). Negative relationships with teachers and peers in the kindergarten classroom increased the odds of having chronically high externalizing behavior. Teacher–child conflict increased the likelihood of a developmental pattern of escalating externalizing behavior. Boys were overrepresented in the behaviorally risky groups, and no sex differences in trajectory types were found. PMID:21094398

Similarity measure and domain adaptation in multiple mixture model clustering: An application to image processing.

PubMed

Leong, Siow Hoo; Ong, Seng Huat

2017-01-01

This paper considers three crucial issues in processing scaled down image, the representation of partial image, similarity measure and domain adaptation. Two Gaussian mixture model based algorithms are proposed to effectively preserve image details and avoids image degradation. Multiple partial images are clustered separately through Gaussian mixture model clustering with a scan and select procedure to enhance the inclusion of small image details. The local image features, represented by maximum likelihood estimates of the mixture components, are classified by using the modified Bayes factor (MBF) as a similarity measure. The detection of novel local features from MBF will suggest domain adaptation, which is changing the number of components of the Gaussian mixture model. The performance of the proposed algorithms are evaluated with simulated data and real images and it is shown to perform much better than existing Gaussian mixture model based algorithms in reproducing images with higher structural similarity index.

Similarity measure and domain adaptation in multiple mixture model clustering: An application to image processing

PubMed Central

Leong, Siow Hoo

2017-01-01

This paper considers three crucial issues in processing scaled down image, the representation of partial image, similarity measure and domain adaptation. Two Gaussian mixture model based algorithms are proposed to effectively preserve image details and avoids image degradation. Multiple partial images are clustered separately through Gaussian mixture model clustering with a scan and select procedure to enhance the inclusion of small image details. The local image features, represented by maximum likelihood estimates of the mixture components, are classified by using the modified Bayes factor (MBF) as a similarity measure. The detection of novel local features from MBF will suggest domain adaptation, which is changing the number of components of the Gaussian mixture model. The performance of the proposed algorithms are evaluated with simulated data and real images and it is shown to perform much better than existing Gaussian mixture model based algorithms in reproducing images with higher structural similarity index. PMID:28686634

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

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.

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…

A Bayesian model for time-to-event data with informative censoring

PubMed Central

Kaciroti, Niko A.; Raghunathan, Trivellore E.; Taylor, Jeremy M. G.; Julius, Stevo

2012-01-01

Randomized trials with dropouts or censored data and discrete time-to-event type outcomes are frequently analyzed using the Kaplan–Meier or product limit (PL) estimation method. However, the PL method assumes that the censoring mechanism is noninformative and when this assumption is violated, the inferences may not be valid. We propose an expanded PL method using a Bayesian framework to incorporate informative censoring mechanism and perform sensitivity analysis on estimates of the cumulative incidence curves. The expanded method uses a model, which can be viewed as a pattern mixture model, where odds for having an event during the follow-up interval (tk−1,tk], conditional on being at risk at tk−1, differ across the patterns of missing data. The sensitivity parameters relate the odds of an event, between subjects from a missing-data pattern with the observed subjects for each interval. The large number of the sensitivity parameters is reduced by considering them as random and assumed to follow a log-normal distribution with prespecified mean and variance. Then we vary the mean and variance to explore sensitivity of inferences. The missing at random (MAR) mechanism is a special case of the expanded model, thus allowing exploration of the sensitivity to inferences as departures from the inferences under the MAR assumption. The proposed approach is applied to data from the TRial Of Preventing HYpertension. PMID:22223746

Investigation on Constrained Matrix Factorization for Hyperspectral Image Analysis

DTIC Science & Technology

2005-07-25

analysis. Keywords: matrix factorization; nonnegative matrix factorization; linear mixture model ; unsupervised linear unmixing; hyperspectral imagery...spatial resolution permits different materials present in the area covered by a single pixel. The linear mixture model says that a pixel reflectance in...in r. In the linear mixture model , r is considered as the linear mixture of m1, m2, …, mP as nMαr += (1) where n is included to account for

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.

Generalized concentration addition: a method for examining mixtures containing partial agonists.

PubMed

Howard, Gregory J; Webster, Thomas F

2009-08-07

Environmentally relevant toxic exposures often consist of simultaneous exposure to multiple agents. Methods to predict the expected outcome of such combinations are critical both to risk assessment and to an accurate judgment of whether combinations are synergistic or antagonistic. Concentration addition (CA) has commonly been used to assess the presence of synergy or antagonism in combinations of similarly acting chemicals, and to predict effects of combinations of such agents. CA has the advantage of clear graphical interpretation: Curves of constant joint effect (isoboles) must be negatively sloped straight lines if the mixture is concentration additive. However, CA cannot be directly used to assess combinations that include partial agonists, although such agents are of considerable interest. Here, we propose a natural extension of CA to a functional form that may be applied to mixtures including full agonists and partial agonists. This extended definition, for which we suggest the term "generalized concentration addition," encompasses linear isoboles with slopes of any sign. We apply this approach to the simple example of agents with dose-response relationships described by Hill functions with slope parameter n=1. The resulting isoboles are in all cases linear, with negative, zero and positive slopes. Using simple mechanistic models of ligand-receptor systems, we show that the same isobole pattern and joint effects are generated by modeled combinations of full and partial agonists. Special cases include combinations of two full agonists and a full agonist plus a competitive antagonist.

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.

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

Novel associations between contaminant body burdens and biomarkers of reproductive condition in male Common Carp along multiple gradients of contaminant exposure in Lake Mead National Recreation Area, USA

USGS Publications Warehouse

Patino, Reynaldo; VanLandeghem, Matthew M.; Goodbred, Steven L.; Orsak, Erik; Jenkins, Jill A.; Echols, Kathy R.; Rosen, Michael R.; Torres, Leticia

2015-01-01

Adult male Common Carp were sampled in 2007/08 over a full reproductive cycle at Lake Mead National Recreation Area. Sites sampled included a stream dominated by treated wastewater effluent, a lake basin receiving the streamflow, an upstream lake basin (reference), and a site below Hoover Dam. Individual body burdens for 252 contaminants were measured, and biological variables assessed included physiological [plasma vitellogenin (VTG), estradiol-17β (E2), 11-ketotestosterone (11KT)] and organ [gonadosomatic index (GSI)] endpoints. Patterns in contaminant composition and biological condition were determined by Principal Component Analysis, and their associations modeled by Principal Component Regression. Three spatially distinct but temporally stable gradients of contaminant distribution were recognized: a contaminant mixture typical of wastewaters (PBDEs, methyl triclosan, galaxolide), PCBs, and DDTs. Two spatiotemporally variable patterns of biological condition were recognized: a primary pattern consisting of reproductive condition variables (11KT, E2, GSI), and a secondary pattern including general condition traits (condition factor, hematocrit, fork length). VTG was low in all fish, indicating low estrogenic activity of water at all sites. Wastewater contaminants associated negatively with GSI, 11KT and E2; PCBs associated negatively with GSI and 11KT; and DDTs associated positively with GSI and 11KT. Regression of GSI on sex steroids revealed a novel, nonlinear association between these variables. Inclusion of sex steroids in the GSI regression on contaminants rendered wastewater contaminants nonsignificant in the model and reduced the influence of PCBs and DDTs. Thus, the influence of contaminants on GSI may have been partially driven by organismal modes-of-action that include changes in sex steroid production. The positive association of DDTs with 11KT and GSI suggests that lifetime, sub-lethal exposures to DDTs have effects on male carp opposite of those reported by studies where exposure concentrations were relatively high. Lastly, this study highlighted advantages of multivariate/multiple regression approaches for exploring associations between complex contaminant mixtures and gradients and reproductive condition in wild fishes.

Novel associations between contaminant body burdens and biomarkers of reproductive condition in male Common Carp along multiple gradients of contaminant exposure in Lake Mead National Recreation Area, USA.

PubMed

Patiño, Reynaldo; VanLandeghem, Matthew M; Goodbred, Steven L; Orsak, Erik; Jenkins, Jill A; Echols, Kathy; Rosen, Michael R; Torres, Leticia

2015-08-01

Adult male Common Carp were sampled in 2007/08 over a full reproductive cycle at Lake Mead National Recreation Area. Sites sampled included a stream dominated by treated wastewater effluent, a lake basin receiving the streamflow, an upstream lake basin (reference), and a site below Hoover Dam. Individual body burdens for 252 contaminants were measured, and biological variables assessed included physiological [plasma vitellogenin (VTG), estradiol-17β (E2), 11-ketotestosterone (11KT)] and organ [gonadosomatic index (GSI)] endpoints. Patterns in contaminant composition and biological condition were determined by Principal Component Analysis, and their associations modeled by Principal Component Regression. Three spatially distinct but temporally stable gradients of contaminant distribution were recognized: a contaminant mixture typical of wastewaters (PBDEs, methyl triclosan, galaxolide), PCBs, and DDTs. Two spatiotemporally variable patterns of biological condition were recognized: a primary pattern consisting of reproductive condition variables (11KT, E2, GSI), and a secondary pattern including general condition traits (condition factor, hematocrit, fork length). VTG was low in all fish, indicating low estrogenic activity of water at all sites. Wastewater contaminants associated negatively with GSI, 11KT and E2; PCBs associated negatively with GSI and 11KT; and DDTs associated positively with GSI and 11KT. Regression of GSI on sex steroids revealed a novel, nonlinear association between these variables. Inclusion of sex steroids in the GSI regression on contaminants rendered wastewater contaminants nonsignificant in the model and reduced the influence of PCBs and DDTs. Thus, the influence of contaminants on GSI may have been partially driven by organismal modes-of-action that include changes in sex steroid production. The positive association of DDTs with 11KT and GSI suggests that lifetime, sub-lethal exposures to DDTs have effects on male carp opposite of those reported by studies where exposure concentrations were relatively high. Lastly, this study highlighted advantages of multivariate/multiple regression approaches for exploring associations between complex contaminant mixtures and gradients and reproductive condition in wild fishes. Published by Elsevier 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.

USAXS analysis of concentration-dependent self-assembling of polymer-brush-modified nanoparticles in ionic liquid: [I] concentrated-brush regime

NASA Astrophysics Data System (ADS)

Nakanishi, Yohei; Ishige, Ryohei; Ogawa, Hiroki; Sakakibara, Keita; Ohno, Kohji; Morinaga, Takashi; Sato, Takaya; Kanaya, Toshiji; Tsujii, Yoshinobu

2018-03-01

Using ultra-small angle X-ray scattering (USAXS), we analyzed the higher-order structures of nanoparticles with a concentrated brush of an ionic liquid (IL)-type polymer (concentrated-polymer-brush-modified silica particle; PSiP) in an IL and the structure of the swollen shell layer of PSiP. Homogeneous mixtures of PSiP and IL were successfully prepared by the solvent-casting method involving the slow evaporation of a volatile solvent, which enabled a systematic study over an exceptionally wide range of compositions. Different diffraction patterns as a function of PSiP concentration were observed in the USAXS images of the mixtures. At suitably low PSiP concentrations, the USAXS intensity profile was analyzed using the Percus-Yevick model by matching the contrast between the shell layer and IL, and the swollen structure of the shell and "effective diameter" of the PSiP were evaluated. This result confirms that under sufficiently low pressures below and near the liquid/crystal-threshold concentration, the studied PSiP can be well described using the "hard sphere" model in colloidal science. Above the threshold concentration, the PSiP forms higher-order structures. The analysis of diffraction patterns revealed structural changes from disorder to random hexagonal-closed-packing and then face-centered-cubic as the PSiP concentration increased. These results are discussed in terms of thermodynamically stable "hard" and/or "semi-soft" colloidal crystals, wherein the swollen layer of the concentrated polymer brush and its structure play an important role.

Classification of Anticipatory Signals for Grasp and Release from Surface Electromyography.

PubMed

Siu, Ho Chit; Shah, Julie A; Stirling, Leia A

2016-10-25

Surface electromyography (sEMG) is a technique for recording natural muscle activation signals, which can serve as control inputs for exoskeletons and prosthetic devices. Previous experiments have incorporated these signals using both classical and pattern-recognition control methods in order to actuate such devices. We used the results of an experiment incorporating grasp and release actions with object contact to develop an intent-recognition system based on Gaussian mixture models (GMM) and continuous-emission hidden Markov models (HMM) of sEMG data. We tested this system with data collected from 16 individuals using a forearm band with distributed sEMG sensors. The data contain trials with shifted band alignments to assess robustness to sensor placement. This study evaluated and found that pattern-recognition-based methods could classify transient anticipatory sEMG signals in the presence of shifted sensor placement and object contact. With the best-performing classifier, the effect of label lengths in the training data was also examined. A mean classification accuracy of 75.96% was achieved through a unigram HMM method with five mixture components. Classification accuracy on different sub-movements was found to be limited by the length of the shortest sub-movement, which means that shorter sub-movements within dynamic sequences require larger training sets to be classified correctly. This classification of user intent is a potential control mechanism for a dynamic grasping task involving user contact with external objects and noise. Further work is required to test its performance as part of an exoskeleton controller, which involves contact with actuated external surfaces.

Classification of Anticipatory Signals for Grasp and Release from Surface Electromyography

PubMed Central

Siu, Ho Chit; Shah, Julie A.; Stirling, Leia A.

2016-01-01

Surface electromyography (sEMG) is a technique for recording natural muscle activation signals, which can serve as control inputs for exoskeletons and prosthetic devices. Previous experiments have incorporated these signals using both classical and pattern-recognition control methods in order to actuate such devices. We used the results of an experiment incorporating grasp and release actions with object contact to develop an intent-recognition system based on Gaussian mixture models (GMM) and continuous-emission hidden Markov models (HMM) of sEMG data. We tested this system with data collected from 16 individuals using a forearm band with distributed sEMG sensors. The data contain trials with shifted band alignments to assess robustness to sensor placement. This study evaluated and found that pattern-recognition-based methods could classify transient anticipatory sEMG signals in the presence of shifted sensor placement and object contact. With the best-performing classifier, the effect of label lengths in the training data was also examined. A mean classification accuracy of 75.96% was achieved through a unigram HMM method with five mixture components. Classification accuracy on different sub-movements was found to be limited by the length of the shortest sub-movement, which means that shorter sub-movements within dynamic sequences require larger training sets to be classified correctly. This classification of user intent is a potential control mechanism for a dynamic grasping task involving user contact with external objects and noise. Further work is required to test its performance as part of an exoskeleton controller, which involves contact with actuated external surfaces. PMID:27792155

Modeling and clustering water demand patterns from real-world smart meter data

NASA Astrophysics Data System (ADS)

Cheifetz, Nicolas; Noumir, Zineb; Samé, Allou; Sandraz, Anne-Claire; Féliers, Cédric; Heim, Véronique

2017-08-01

Nowadays, drinking water utilities need an acute comprehension of the water demand on their distribution network, in order to efficiently operate the optimization of resources, manage billing and propose new customer services. With the emergence of smart grids, based on automated meter reading (AMR), a better understanding of the consumption modes is now accessible for smart cities with more granularities. In this context, this paper evaluates a novel methodology for identifying relevant usage profiles from the water consumption data produced by smart meters. The methodology is fully data-driven using the consumption time series which are seen as functions or curves observed with an hourly time step. First, a Fourier-based additive time series decomposition model is introduced to extract seasonal patterns from time series. These patterns are intended to represent the customer habits in terms of water consumption. Two functional clustering approaches are then used to classify the extracted seasonal patterns: the functional version of K-means, and the Fourier REgression Mixture (FReMix) model. The K-means approach produces a hard segmentation and K representative prototypes. On the other hand, the FReMix is a generative model and also produces K profiles as well as a soft segmentation based on the posterior probabilities. The proposed approach is applied to a smart grid deployed on the largest water distribution network (WDN) in France. The two clustering strategies are evaluated and compared. Finally, a realistic interpretation of the consumption habits is given for each cluster. The extensive experiments and the qualitative interpretation of the resulting clusters allow one to highlight the effectiveness of the proposed methodology.

Environmentally relevant chemical mixtures of concern in waters of United States tributaries to the Great Lakes

USGS Publications Warehouse

Elliott, Sarah M.; Brigham, Mark E.; Kiesling, Richard L.; Schoenfuss, Heiko L.; Jorgenson, Zachary G.

2018-01-01

The North American Great Lakes are a vital natural resource that provide fish and wildlife habitat, as well as drinking water and waste assimilation services for millions of people. Tributaries to the Great Lakes receive chemical inputs from various point and nonpoint sources, and thus are expected to have complex mixtures of chemicals. However, our understanding of the co‐occurrence of specific chemicals in complex mixtures is limited. To better understand the occurrence of specific chemical mixtures in the US Great Lakes Basin, surface water from 24 US tributaries to the Laurentian Great Lakes was collected and analyzed for diverse suites of organic chemicals, primarily focused on chemicals of concern (e.g., pharmaceuticals, personal care products, fragrances). A total of 181 samples and 21 chemical classes were assessed for mixture compositions. Basin wide, 1664 mixtures occurred in at least 25% of sites. The most complex mixtures identified comprised 9 chemical classes and occurred in 58% of sampled tributaries. Pharmaceuticals typically occurred in complex mixtures, reflecting pharmaceutical‐use patterns and wastewater facility outfall influences. Fewer mixtures were identified at lake or lake‐influenced sites than at riverine sites. As mixture complexity increased, the probability of a specific mixture occurring more often than by chance greatly increased, highlighting the importance of understanding source contributions to the environment. This empirically based analysis of mixture composition and occurrence may be used to focus future sampling efforts or mixture toxicity assessments. 

Using natural stand development patterns in artificial mixtures: a case study with cherrybark oak and sweetgum in east-central Mississippi, USA

Treesearch

Brian Roy Lockhart; Andrew W. Ezell; John D. Hodges; Wayne K. Clatterbuck

2006-01-01

Results from a long-term planted mixture of cherrybark oak (Quercus pagoda Raf.) and sweetgum (Liquidambar styraciflua L.) showed sweetgum taller in height and larger in diameter than cherrybark oak early in plantation development. By age 17 years, cherrybark oak was similar in height and diameter with sweetgum and by age 21...

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…

Growth Modeling with Non-Ignorable Dropout: Alternative Analyses of the STAR*D Antidepressant Trial

PubMed Central

Muthén, Bengt; Asparouhov, Tihomir; Hunter, Aimee; Leuchter, Andrew

2011-01-01

This paper uses a general latent variable framework to study a series of models for non-ignorable missingness due to dropout. Non-ignorable missing data modeling acknowledges that missingness may depend on not only covariates and observed outcomes at previous time points as with the standard missing at random (MAR) assumption, but also on latent variables such as values that would have been observed (missing outcomes), developmental trends (growth factors), and qualitatively different types of development (latent trajectory classes). These alternative predictors of missing data can be explored in a general latent variable framework using the Mplus program. A flexible new model uses an extended pattern-mixture approach where missingness is a function of latent dropout classes in combination with growth mixture modeling using latent trajectory classes. A new selection model allows not only an influence of the outcomes on missingness, but allows this influence to vary across latent trajectory classes. Recommendations are given for choosing models. The missing data models are applied to longitudinal data from STAR*D, the largest antidepressant clinical trial in the U.S. to date. Despite the importance of this trial, STAR*D growth model analyses using non-ignorable missing data techniques have not been explored until now. The STAR*D data are shown to feature distinct trajectory classes, including a low class corresponding to substantial improvement in depression, a minority class with a U-shaped curve corresponding to transient improvement, and a high class corresponding to no improvement. The analyses provide a new way to assess drug efficiency in the presence of dropout. PMID:21381817

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

Diversity of developmental patterns in achelate lobsters-today and in the Mesozoic.

PubMed

Haug, Joachim T; Audo, Denis; Charbonnier, Sylvain; Haug, Carolin

2013-11-01

Modern achelate lobsters, slipper and spiny lobsters, have a specific post-embryonic developmental pattern with the following phases: phyllosoma, nisto (slipper lobsters) or puerulus (spiny lobsters), juvenile and adult. The phyllosoma is a peculiar larva, which transforms through a metamorphic moult into another larval form, the nisto or puerulus which largely resembles the juvenile. Unlike the nisto and puerulus, the phyllosoma is characterised by numerous morphological differences to the adult, e.g. a thin head shield, elongate appendages, exopods on these appendages and a special claw. Our reinvestigation of the 85 million years old fossil "Eryoneicus sahelalmae" demonstrates that it represents an unusual type of achelatan lobster larva, characterised by a mixture of phyllosoma and post-phyllosoma characters. We ascribe it to its own genus: Polzicaris nov. gen. We study its significance by comparisons with other cases of Mesozoic fossil larvae also characterised by a mixture of characters. Accordingly, all these larvae are interpreted as ontogenetic intermediates between phyllosoma and post-phyllosoma morphology. Remarkably, most of the larvae show a unique mixture of retained larval and already developed post-larval features. Considering the different-and incompatible-mixture of characters of each of these larvae and their wide geographical and temporal distribution, we interpret all these larvae as belonging to distinct species. The particular character combinations in the different larvae make it currently difficult to reconstruct an evolutionary scenario with a stepwise character acquisition. Yet, it can be concluded that a larger diversity of larval forms and developmental patterns occurred in Mesozoic than in modern faunas.

Two Salix Genotypes Differ in Productivity and Nitrogen Economy When Grown in Monoculture and Mixture

PubMed Central

Hoeber, Stefanie; Fransson, Petra; Prieto-Ruiz, Inés; Manzoni, Stefano; Weih, Martin

2017-01-01

Individual plant species or genotypes often differ in their demand for nutrients; to compete in a community they must be able to acquire more nutrients (i.e., uptake efficiency) and/or use them more efficiently for biomass production than their competitors. These two mechanisms are often complementary, as there are inherent trade-offs between them. In a mixed-stand, species with contrasting nutrient use patterns interact and may use their resources to increase productivity in different ways. Under contrasting nutrient availabilities, the competitive advantages conferred by either strategy may also shift, so that the interaction between resource use strategy and resource availability ultimately determines the performance of individual genotypes in mixtures. The aim was to investigate growth and nitrogen (N) use efficiency of two willow (Salix) genotypes grown in monoculture and mixture in a fertilizer contrast. We explored the hypotheses that (1) the biomass production of at least one of the involved genotypes should be greater when grown in mixture as compared to the corresponding monoculture when nutrients are the most growth-limiting factor; and (2) the N economy of individual genotypes differs when grown in mixture compared to the corresponding monoculture. The genotypes ‘Tora’ (Salix schwerinii ×S. viminalis) and ‘Loden’ (S. dasyclados), with contrasting phenology and functional traits, were grown from cuttings in a growth container experiment under two nutrient fertilization treatments (high and low) in mono- and mixed-culture for 17 weeks. Under low nutrient level, ‘Tora’ showed a higher biomass production (aboveground biomass, leaf area productivity) and N uptake efficiency in mixture than in monoculture, whereas ‘Loden’ showed the opposite pattern. In addition, ‘Loden’ showed higher leaf N productivity but lower N uptake efficiency than ‘Tora.’ The results demonstrated that the specific functional trait combinations of individual genotypes affect their response to mixture as compared to monoculture. Plants grown in mixture as opposed to monoculture may thus increase biomass and vary in their response of N use efficiency traits. However, young plants were investigated here, and as we cannot predict mixture response in mature stands, our results need to be validated at field scale. PMID:28270828

Two Salix Genotypes Differ in Productivity and Nitrogen Economy When Grown in Monoculture and Mixture.

PubMed

Hoeber, Stefanie; Fransson, Petra; Prieto-Ruiz, Inés; Manzoni, Stefano; Weih, Martin

2017-01-01

Individual plant species or genotypes often differ in their demand for nutrients; to compete in a community they must be able to acquire more nutrients (i.e., uptake efficiency) and/or use them more efficiently for biomass production than their competitors. These two mechanisms are often complementary, as there are inherent trade-offs between them. In a mixed-stand, species with contrasting nutrient use patterns interact and may use their resources to increase productivity in different ways. Under contrasting nutrient availabilities, the competitive advantages conferred by either strategy may also shift, so that the interaction between resource use strategy and resource availability ultimately determines the performance of individual genotypes in mixtures. The aim was to investigate growth and nitrogen (N) use efficiency of two willow ( Salix ) genotypes grown in monoculture and mixture in a fertilizer contrast. We explored the hypotheses that (1) the biomass production of at least one of the involved genotypes should be greater when grown in mixture as compared to the corresponding monoculture when nutrients are the most growth-limiting factor; and (2) the N economy of individual genotypes differs when grown in mixture compared to the corresponding monoculture. The genotypes 'Tora' ( Salix schwerinii × S. viminalis ) and 'Loden' ( S. dasyclados ), with contrasting phenology and functional traits, were grown from cuttings in a growth container experiment under two nutrient fertilization treatments (high and low) in mono- and mixed-culture for 17 weeks. Under low nutrient level, 'Tora' showed a higher biomass production (aboveground biomass, leaf area productivity) and N uptake efficiency in mixture than in monoculture, whereas 'Loden' showed the opposite pattern. In addition, 'Loden' showed higher leaf N productivity but lower N uptake efficiency than 'Tora.' The results demonstrated that the specific functional trait combinations of individual genotypes affect their response to mixture as compared to monoculture. Plants grown in mixture as opposed to monoculture may thus increase biomass and vary in their response of N use efficiency traits. However, young plants were investigated here, and as we cannot predict mixture response in mature stands, our results need to be validated at field scale.

Stripe-patterned thermo-responsive cell culture dish for cell separation without cell labeling.

PubMed

Kumashiro, Yoshikazu; Ishihara, Jun; Umemoto, Terumasa; Itoga, Kazuyoshi; Kobayashi, Jun; Shimizu, Tatsuya; Yamato, Masayuki; Okano, Teruo

2015-02-11

A stripe-patterned thermo-responsive surface is prepared to enable cell separation without labeling. The thermo-responsive surface containing a 3 μm striped pattern exhibits various cell adhesion and detachment properties. A mixture of three cell types is separated on the patterned surface based on their distinct cell-adhesion properties, and the composition of the cells is analyzed by flow cytometry. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Inactivity periods and postural change speed can explain atypical postural change patterns of Caenorhabditis elegans mutants.

PubMed

Fukunaga, Tsukasa; Iwasaki, Wataru

2017-01-19

With rapid advances in genome sequencing and editing technologies, systematic and quantitative analysis of animal behavior is expected to be another key to facilitating data-driven behavioral genetics. The nematode Caenorhabditis elegans is a model organism in this field. Several video-tracking systems are available for automatically recording behavioral data for the nematode, but computational methods for analyzing these data are still under development. In this study, we applied the Gaussian mixture model-based binning method to time-series postural data for 322 C. elegans strains. We revealed that the occurrence patterns of the postural states and the transition patterns among these states have a relationship as expected, and such a relationship must be taken into account to identify strains with atypical behaviors that are different from those of wild type. Based on this observation, we identified several strains that exhibit atypical transition patterns that cannot be fully explained by their occurrence patterns of postural states. Surprisingly, we found that two simple factors-overall acceleration of postural movement and elimination of inactivity periods-explained the behavioral characteristics of strains with very atypical transition patterns; therefore, computational analysis of animal behavior must be accompanied by evaluation of the effects of these simple factors. Finally, we found that the npr-1 and npr-3 mutants have similar behavioral patterns that were not predictable by sequence homology, proving that our data-driven approach can reveal the functions of genes that have not yet been characterized. We propose that elimination of inactivity periods and overall acceleration of postural change speed can explain behavioral phenotypes of strains with very atypical postural transition patterns. Our methods and results constitute guidelines for effectively finding strains that show "truly" interesting behaviors and systematically uncovering novel gene functions by bioimage-informatic approaches.

UP-HILIC-MS/MS to Determine the Action Pattern of Penicillium sp. Dextranase.

PubMed

Yi, Lin; Sun, Xue; Du, Kenze; Ouyang, Yilan; Wu, Chengling; Xu, Naiyu; Linhardt, Robert J; Zhang, Zhenqing

2015-07-01

Investigation of the action pattern of enzymes acting on carbohydrates is challenging, as both the substrate and the digestion products are complex mixtures. Dextran and its enzyme-derived oligosaccharides are widely used for many industrial applications. In this work, a new method relying on ultra-performance hydrophilic interaction liquid chromatography quadrupole time-of-flight tandem mass spectrometry (UP-HILIC-Q/TOF-MS/MS) was developed to analyze a complex mixture of dextran oligosaccharide products to determine the action pattern of dextranase. No derivatization of oligosaccharides was required and the impact of the α- and β-configurations of the native oligosaccharides on the chromatographic separation was eliminated. The 1→6, 1→3, 1→4 backbone linkages and the branch linkages of these oligosaccharides were all distinguished from diagnostic ions in their MS/MS spectra, including fragments corresponding to (0,2)A, (0,3)A, (0,4)A, B-H2O, (2,5)A, and (3,5)A. The sequences of the oligosaccharide products were similarly established. Thus, the complex oligosaccharide mixtures in dextran digestion products were profiled and identified using this method. The more enzyme-resistant structures in dextran were established using much less sample, labor, time, and uncertainty than in previous studies. This method provides an efficient, sensitive, and straightforward way to monitor the entire process of digestion, establish the action pattern of the dextranase from Penicillium sp., and to support the proper industrial application of dextranase.

UP-HILIC-MS/MS to Determine the Action Pattern of Penicillium sp. Dextranase

NASA Astrophysics Data System (ADS)

Yi, Lin; Sun, Xue; Du, Kenze; Ouyang, Yilan; Wu, Chengling; Xu, Naiyu; Linhardt, Robert J.; Zhang, Zhenqing

2015-07-01

Investigation of the action pattern of enzymes acting on carbohydrates is challenging, as both the substrate and the digestion products are complex mixtures. Dextran and its enzyme-derived oligosaccharides are widely used for many industrial applications. In this work, a new method relying on ultra-performance hydrophilic interaction liquid chromatography quadrupole time-of-flight tandem mass spectrometry (UP-HILIC- Q/TOF-MS/MS) was developed to analyze a complex mixture of dextran oligosaccharide products to determine the action pattern of dextranase. No derivatization of oligosaccharides was required and the impact of the α- and β-configurations of the native oligosaccharides on the chromatographic separation was eliminated. The 1→6, 1→3, 1→4 backbone linkages and the branch linkages of these oligosaccharides were all distinguished from diagnostic ions in their MS/MS spectra, including fragments corresponding to 0,2A, 0,3A, 0,4A, B-H2O, 2,5A, and 3,5A. The sequences of the oligosaccharide products were similarly established. Thus, the complex oligosaccharide mixtures in dextran digestion products were profiled and identified using this method. The more enzyme-resistant structures in dextran were established using much less sample, labor, time, and uncertainty than in previous studies. This method provides an efficient, sensitive, and straightforward way to monitor the entire process of digestion, establish the action pattern of the dextranase from Penicillium sp., and to support the proper industrial application of dextranase.

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.

Structure and dynamics of propylammonium nitrate-acetonitrile mixtures: An intricate multi-scale system probed with experimental and theoretical techniques.

PubMed

Campetella, Marco; Mariani, Alessandro; Sadun, Claudia; Wu, Boning; Castner, Edward W; Gontrani, Lorenzo

2018-04-07

In this article, we report the study of structural and dynamical properties for a series of acetonitrile/propylammonium nitrate mixtures as a function of their composition. These systems display an unusual increase in intensity in their X-ray diffraction patterns in the low-q regime, and their 1 H-NMR diffusion-ordered NMR spectroscopy (DOSY) spectra display unusual diffusivities. However, the magnitude of both phenomena for mixtures of propylammonium nitrate is smaller than those observed for ethylammonium nitrate mixtures with the same cosolvent, suggesting that the cation alkyl tail plays an important role in these observations. The experimental X-ray scattering data are compared with the results of molecular dynamics simulations, including both ab initio studies used to interpret short-range interactions and classical simulations to describe longer range interactions. The higher level calculations highlight the presence of a strong hydrogen bond network within the ionic liquid, only slightly perturbed even at high acetonitrile concentration. These strong interactions lead to the symmetry breaking of the NO 3 - vibrations, with a splitting of about 88 cm -1 in the ν 3 antisymmetric stretch. The classical force field simulations use a greater number of ion pairs, but are not capable of fully describing the longest range interactions, although they do successfully account for the observed concentration trend, and the analysis of the models confirms the nano-inhomogeneity of these kinds of samples.

Structure and dynamics of propylammonium nitrate-acetonitrile mixtures: An intricate multi-scale system probed with experimental and theoretical techniques

NASA Astrophysics Data System (ADS)

Campetella, Marco; Mariani, Alessandro; Sadun, Claudia; Wu, Boning; Castner, Edward W.; Gontrani, Lorenzo

2018-04-01

In this article, we report the study of structural and dynamical properties for a series of acetonitrile/propylammonium nitrate mixtures as a function of their composition. These systems display an unusual increase in intensity in their X-ray diffraction patterns in the low-q regime, and their 1H-NMR diffusion-ordered NMR spectroscopy (DOSY) spectra display unusual diffusivities. However, the magnitude of both phenomena for mixtures of propylammonium nitrate is smaller than those observed for ethylammonium nitrate mixtures with the same cosolvent, suggesting that the cation alkyl tail plays an important role in these observations. The experimental X-ray scattering data are compared with the results of molecular dynamics simulations, including both ab initio studies used to interpret short-range interactions and classical simulations to describe longer range interactions. The higher level calculations highlight the presence of a strong hydrogen bond network within the ionic liquid, only slightly perturbed even at high acetonitrile concentration. These strong interactions lead to the symmetry breaking of the NO3 - vibrations, with a splitting of about 88 cm-1 in the ν3 antisymmetric stretch. The classical force field simulations use a greater number of ion pairs, but are not capable of fully describing the longest range interactions, although they do successfully account for the observed concentration trend, and the analysis of the models confirms the nano-inhomogeneity of these kinds of samples.

The 'triple contrast' method in experimental wound ballistics and backspatter analysis.

PubMed

Schyma, Christian; Lux, Constantin; Madea, Burkhard; Courts, Cornelius

2015-09-01

In practical forensic casework, backspatter recovered from shooters' hands can be an indicator of self-inflicted gunshot wounds to the head. In such cases, backspatter retrieved from inside the barrel indicates that the weapon found at the death scene was involved in causing the injury to the head. However, systematic research on the aspects conditioning presence, amount and specific patterns of backspatter is lacking so far. Herein, a new concept of backspatter investigation is presented, comprising staining technique, weapon and target medium: the 'triple contrast method' was developed, tested and is introduced for experimental backspatter analysis. First, mixtures of various proportions of acrylic paint for optical detection, barium sulphate for radiocontrast imaging in computed tomography and fresh human blood for PCR-based DNA profiling were generated (triple mixture) and tested for DNA quantification and short tandem repeat (STR) typing success. All tested mixtures yielded sufficient DNA that produced full STR profiles suitable for forensic identification. Then, for backspatter analysis, sealed foil bags containing the triple mixture were attached to plastic bottles filled with 10% ballistic gelatine and covered by a 2-3-mm layer of silicone. To simulate backspatter, close contact shots were fired at these models. Endoscopy of the barrel inside revealed coloured backspatter containing typable DNA and radiographic imaging showed a contrasted bullet path in the gelatine. Cross sections of the gelatine core exhibited cracks and fissures stained by the acrylic paint facilitating wound ballistic analysis.

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…

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...

Quality improvement of melt extruded laminar systems using mixture design.

PubMed

Hasa, D; Perissutti, B; Campisi, B; Grassi, M; Grabnar, I; Golob, S; Mian, M; Voinovich, D

2015-07-30

This study investigates the application of melt extrusion for the development of an oral retard formulation with a precise drug release over time. Since adjusting the formulation appears to be of the utmost importance in achieving the desired drug release patterns, different formulations of laminar extrudates were prepared according to the principles of Experimental Design, using a design for mixtures to assess the influence of formulation composition on the in vitro drug release from the extrudates after 1h and after 8h. The effect of each component on the two response variables was also studied. Ternary mixtures of theophylline (model drug), monohydrate lactose and microcrystalline wax (as thermoplastic binder) were extruded in a lab scale vertical ram extruder in absence of solvents at a temperature below the melting point of the binder (so that the crystalline state of the drug could be maintained), through a rectangular die to obtain suitable laminar systems. Thanks to the desirability approach and a reliability study for ensuring the quality of the formulation, a very restricted optimal zone was defined within the experimental domain. Among the mixture components, the variation of microcrystalline wax content played the most significant role in overall influence on the in vitro drug release. The formulation theophylline:lactose:wax, 57:14:29 (by weight), selected based on the desirability zone, was subsequently used for in vivo studies. The plasma profile, obtained after oral administration of the laminar extruded system in hard gelatine capsules, revealed the typical trend of an oral retard formulation. The application of the mixture experimental design associated to a desirability function permitted to optimize the extruded system and to determine the composition space that ensures final product quality. Copyright © 2015 Elsevier B.V. All rights reserved.

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.

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.

Growth Mixture Modeling of Academic Achievement in Children of Varying Birth Weight Risk

PubMed Central

Espy, Kimberly Andrews; Fang, Hua; Charak, David; Minich, Nori; Taylor, H. Gerry

2009-01-01

The extremes of birth weight and preterm birth are known to result in a host of adverse outcomes, yet studies to date largely have used cross-sectional designs and variable-centered methods to understand long-term sequelae. Growth mixture modeling (GMM) that utilizes an integrated person- and variable-centered approach was applied to identify latent classes of achievement from a cohort of school-age children born at varying birth weights. GMM analyses revealed two latent achievement classes for calculation, problem-solving, and decoding abilities. The classes differed substantively and persistently in proficiency and in growth trajectories. Birth weight was a robust predictor of class membership for the two mathematics achievement outcomes and a marginal predictor of class membership for decoding. Neither visuospatial-motor skills nor environmental risk at study entry added to class prediction for any of the achievement skills. Among children born preterm, neonatal medical variables predicted class membership uniquely beyond birth weight. More generally, GMM is useful in revealing coherence in the developmental patterns of academic achievement in children of varying weight at birth, and is well suited to investigations of sources of heterogeneity. PMID:19586210

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,...

Capturing the Temporal Sequence of Interaction in Young Siblings

PubMed Central

Steele, Fiona; Jenkins, Jennifer

2015-01-01

We explored whether young children exhibit subtypes of behavioral sequences during sibling interaction. Ten-minute, free-play observations of over 300 sibling dyads were coded for positivity, negativity and disengagement. The data were analyzed using growth mixture modeling (GMM). Younger (18-month-old) children’s temporal behavioral sequences showed a harmonious (53%) and a casual (47%) class. Older (approximately four-year-old) children’s behavior was more differentiated revealing a harmonious (25%), a deteriorating (31%), a recovery (22%) and a casual (22%) class. A more positive maternal affective climate was associated with more positive patterns. Siblings’ sequential behavioral patterns tended to be complementary rather than reciprocal in nature. The study illustrates a novel use of GMM and makes a theoretical contribution by showing that young children exhibit distinct types of temporal behavioral sequences that are related to parenting processes. PMID:25996957

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

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.

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.

Controlled irrigation of a structured packing as a method for increasing the efficiency of liquid mixture separation in the distillation column

NASA Astrophysics Data System (ADS)

Pavlenko, A. N.; Zhukov, V. E.; Pecherkin, N. I.; Nazarov, A. D.; Li, X.; Li, H.; Gao, X.; Sui, H.

2017-09-01

The use of modern structured packing in the distillation columns allows much more even distribution of the liquid film over the packing surface, but it does not completely solve the problem of uniform distribution of flow parameters over the entire height of the packing. Negative stratification of vapor along the packing height caused by different densities of vapor mixture components and higher temperature in the lower part of the column leads to formation of large-scale maldistributions of temperature and mixture composition over the column cross-section even under the conditions of uniform irrigation of packing with liquid. In these experiments, the idea of compensatory action of liquid distributor on the large-scale maldistribution of mixture composition over the column cross-section was implemented. The experiments were carried out in the distillation column with the diameter of 0.9 m on 10 layers of the Mellapak 350Y packing with the total height of 2.1 m. The mixture of R-21 and R-114 was used as the working mixture. To irrigate the packing, the liquid distributorr with 126 independently controlled solenoid valves overlapping the holes with the diameter of 5 mm, specially designed by the authors, was used. Response of the column to the action of liquid distributor was observed in real time according to the indications of 3 groups of thermometers mounted in 3 different cross-sections of the column. The experiments showed that the minimal correction of the drip point pattern in the controlled liquid distributor can significantly affect the pattern of flow parameter distribution over the cross-section and height of the mass transfer surface and increase separation efficiency of the column within 20%.

Effect of lipid types on physicochemical characteristics, stability and antioxidant activity of gamma-oryzanol-loaded lipid nanoparticles.

PubMed

Ruktanonchai, Uracha; Sakulkhu, Usawadee; Bejrapha, Piyawan; Opanasopit, Praneet; Bunyapraphatsara, Nuntavan; Junyaprasert, Varaporn; Puttipipatkhachorn, Satit

2009-11-01

In the present study gamma-oryzanol, an antioxidant, was incorporated into three different types of solid lipid: wax, triglycerides, a mixture of glycerides as solid lipid nanoparticles (SLN) and liquid lipid (Miglyol 812) as nanoemulsion (NE). Instability was found only from NE due to its significant increase in particle size and decreased entrapment efficiency (%EE) at a storage temperature of 45 degrees C. Solid lipid type in SLN plays an important role only on %EE, but not chemical stability. A decrease in crystallinity of SLN was observed with the incorporation of gamma-oryzanol and low recrystallization index were found with two glycerides-based SLN. The in vitro release studies demonstrated that a biphasic release pattern fitted well with the Higuchi model of SLN formulations. In comparison, nearly constant release was observed in NE comprised of similar composition. Wax-based SLN demonstrated the lowest cytotoxicity. NE, wax-based SLN and a mixture of glycerides-based SLN were considered to enhance the antioxidant activity of gamma-oryzanol.

Monolayers of derivatized poly(l-lysine)-grafted poly(ethylene glycol) on metal oxides as a class of biomolecular interfaces

PubMed Central

Ruiz-Taylor, L. A.; Martin, T. L.; Zaugg, F. G.; Witte, K.; Indermuhle, P.; Nock, S.; Wagner, P.

2001-01-01

We report on the design and characterization of a class of biomolecular interfaces based on derivatized poly(l-lysine)-grafted poly(ethylene glycol) copolymers adsorbed on negatively charged surfaces. As a model system, we synthesized biotin-derivatized poly(l-lysine)-grafted poly(ethylene glycol) copolymers, PLL-g-[(PEGm)(1−x) (PEG-biotin)x], where x varies from 0 to 1. Monolayers were produced on titanium dioxide substrates and characterized by x-ray photoelectron spectroscopy. The specific biorecognition properties of these biotinylated surfaces were investigated with the use of radiolabeled streptavidin alone and within complex protein mixtures. The PLL-g-PEG-biotin monolayers specifically capture streptavidin, even from a complex protein mixture, while still preventing nonspecific adsorption of other proteins. This streptavidin layer can subsequently capture biotinylated proteins. Finally, with the use of microfluidic networks and protein arraying, we demonstrate the potential of this class of biomolecular interfaces for applications based on protein patterning. PMID:11158560

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.

Alfven wave dispersion behavior in single- and multicomponent plasmas

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

Rahbarnia, K.; Grulke, O.; Klinger, T.

Dispersion relations of driven Alfven waves (AWs) are measured in single- and multicomponent plasmas consisting of mixtures of argon, helium, and oxygen in a magnetized linear cylindrical plasma device VINETA [C. Franck, O. Grulke, and T. Klinger, Phys. Plasmas 9, 3254 (2002)]. The decomposition of the measured three-dimensional magnetic field fluctuations and the corresponding parallel current pattern reveals that the wave field is a superposition of L- and R-wave components. The dispersion relation measurements agree well with calculations based on a multifluid Hall-magnetohydrodynamic model if the plasma resistivity is correctly taken into account.

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.

Novel selective TOCSY method enables NMR spectral elucidation of metabolomic mixtures

NASA Astrophysics Data System (ADS)

MacKinnon, Neil; While, Peter T.; Korvink, Jan G.

2016-11-01

Complex mixture analysis is routinely encountered in NMR-based investigations. With the aim of component identification, spectral complexity may be addressed chromatographically or spectroscopically, the latter being favored to reduce sample handling requirements. An attractive experiment is selective total correlation spectroscopy (sel-TOCSY), which is capable of providing tremendous spectral simplification and thereby enhancing assignment capability. Unfortunately, isolating a well resolved resonance is increasingly difficult as the complexity of the mixture increases and the assumption of single spin system excitation is no longer robust. We present TOCSY optimized mixture elucidation (TOOMIXED), a technique capable of performing spectral assignment particularly in the case where the assumption of single spin system excitation is relaxed. Key to the technique is the collection of a series of 1D sel-TOCSY experiments as a function of the isotropic mixing time (τm), resulting in a series of resonance intensities indicative of the underlying molecular structure. By comparing these τm -dependent intensity patterns with a library of pre-determined component spectra, one is able to regain assignment capability. After consideration of the technique's robustness, we tested TOOMIXED firstly on a model mixture. As a benchmark we were able to assign a molecule with high confidence in the case of selectively exciting an isolated resonance. Assignment confidence was not compromised when performing TOOMIXED on a resonance known to contain multiple overlapping signals, and in the worst case the method suggested a follow-up sel-TOCSY experiment to confirm an ambiguous assignment. TOOMIXED was then demonstrated on two realistic samples (whisky and urine), where under our conditions an approximate limit of detection of 0.6 mM was determined. Taking into account literature reports for the sel-TOCSY limit of detection, the technique should reach on the order of 10 μ M sensitivity. We anticipate this technique will be highly attractive to various analytical fields facing mixture analysis, including metabolomics, foodstuff analysis, pharmaceutical analysis, and forensics.

Topical or oral treatment of peach flower extract attenuates UV-induced epidermal thickening, matrix metalloproteinase-13 expression and pro-inflammatory cytokine production in hairless mice skin

PubMed Central

Yang, Jiwon; Shin, Chang-Yup; Chung, Jin Ho

2018-01-01

BACKGROUND/OBJECTIVES Ultraviolet radiation (UV) is a major cause of skin photoaging. Previous studies reported that ethanol extract (PET) of Prunus persica (L.) Batsch flowers (PPF, peach flowers) and its subfractions, particularly the ethylacetate (PEA) and n-butanol extracts (PBT), have potent antioxidant activity and attenuate the UV-induced matrix metalloproteinase (MMP) expression in human skin cells. In this study, we investigated the protective activity of PPF extract against UV-induced photoaging in a mouse model. MATERIALS/METHODS Hairless mice were treated with PET or a mixture of PEA and PBT either topically or orally along with UV irradiation. Histological changes and biochemical alterations of mouse skin were examined. Major phenolic compounds in PPF extract were analyzed using an ACQUITY UPLC system. RESULTS The overall effects of topical and oral treatments with PPF extract on the UV-induced skin responses exhibited similar patterns. In both experiments, the mixture of PEA and PBT significantly inhibited the UV-induced skin and epidermal thickening, while PET inhibited only the UV-induced epidermal thickening. Treatment of PET or the mixture of PEA and PBT significantly inhibited the UV-induced MMP-13 expression, but not typeⅠ collagen expression. Topical treatment of the mixture of PEA and PBT with UV irradiation significantly elevated catalase, superoxide dismutase (SOD) and glutathione-peroxidase (GPx) activities in the skin compared to those in the UV irradiated control group, while oral treatment of the mixture of PEA and PBT or PET elevated only catalase and SOD activities, but not GPx. Thirteen phytochemical compounds including 4-O-caffeoylquinic acid, cimicifugic acid E and B, quercetin-3-O-rhamnoside and kaempferol glycoside derivatives were identified in the PPF extract. CONCLUSIONS These results demonstrate that treatment with PET or the mixture of PEA and PBT, both topically or orally, attenuates UV-induced photoaging via the cooperative interactions of phenolic components having anti-oxidative and collagen-protective activities. PMID:29399294

Intrinsic capacities of soil microflorae for gasoline degradation.

PubMed

Solano-Serena, F; Marchal, R; Blanchet, D; Vandecasteele, J P

1998-01-01

A methodology to determine the intrinsic capacities of a microflora to degrade gasoline was developed, in particular for assessing the potential of autochtonous populations of polluted and non polluted soils for natural attenuation and engineered bioremediation. A model mixture (GM23) constituted of the 23 most representative hydrocarbons of a commercial gasoline was used. The capacities of the microflorae (kinetics and extent of biodegradation) were assessed by chromatographic analysis of hydrocarbon consumption and of CO2 production. The degradation of the components of GM23 was assayed in separate incubations of each component and in the complete mixture. For the microflora of an unpolluted spruce forest soil, all hydrocarbons of GM23 except cyclohexane, 2,2,4- and 2,3,4-trimethylpentane isomers were degraded to below detection limit in 28 days. This microflora was reinforced with two mixed microbial communities selected from gasoline-polluted sites and shown to degrade cyclohexane and 2,2,4-trimethylpentane. With the reinforced microflora, complete degradation of GM23 was observed. The degradation patterns of individual components of GM23 were similar when the compounds were present individually or in the GM23 mixture, as long as the concentrations of 2-ethyltoluene and trimethylbenzene isomers were kept sufficiently low (< or = 35 mg.l-1) to remain below their inhibitory level.

CFD study of the flow pattern in an ultrasonic horn reactor: Introducing a realistic vibrating boundary condition.

PubMed

Rahimi, Masoud; Movahedirad, Salman; Shahhosseini, Shahrokh

2017-03-01

Recently, great attention has been paid to predict the acoustic streaming field distribution inside the sonoreactors, induced by high-power ultrasonic wave generator. The focus of this paper is to model an ultrasonic vibrating horn and study the induced flow pattern with a newly developed moving boundary condition. The numerical simulation utilizes the modified cavitation model along with the "mixture" model for turbulent flow (RNG, k-ε), and a moving boundary condition with an oscillating parabolic-logarithmic profile, applied to the horn tip. This moving-boundary provides the situation in which the center of the horn tip vibrates stronger than that of the peripheral regions. The velocity field obtained by computational fluid dynamic was in a reasonably good agreement with the PIV results. The moving boundary model is more accurate since it better approximates the movement of the horn tip in the ultrasonic assisted process. From an optimizing point of view, the model with the new moving boundary is more suitable than the conventional models for design purposes because the displacement magnitude of the horn tip is the only fitting parameter. After developing and validating the numerical model, the model was utilized to predict various quantities such as cavitation zone, pressure field and stream function that are not experimentally feasible to measure. Copyright © 2016 Elsevier B.V. All rights reserved.

Artificial neural network model for photosynthetic pigments identification using multi wavelength chromatographic data

NASA Astrophysics Data System (ADS)

Prilianti, K. R.; Hariyanto, S.; Natali, F. D. D.; Indriatmoko, Adhiwibawa, M. A. S.; Limantara, L.; Brotosudarmo, T. H. P.

2016-04-01

The development of rapid and automatic pigment characterization method become an important issue due to the fact that there are only less than 1% of plant pigments in the earth have been explored. In this research, a mathematical model based on artificial intelligence approach was developed to simplify and accelerate pigment characterization process from HPLC (high-performance liquid chromatography) procedure. HPLC is a widely used technique to separate and identify pigments in a mixture. Input of the model is chromatographic data from HPLC device and output of the model is a list of pigments which is the spectrum pattern is discovered in it. This model provides two dimensional (retention time and wavelength) fingerprints for pigment characterization which is proven to be more accurate than one dimensional fingerprint (fixed wavelength). Moreover, by mimicking interconnection of the neuron in the nervous systems of the human brain, the model have learning ability that could be replacing expert judgement on evaluating spectrum pattern. In the preprocessing step, principal component analysis (PCA) was used to reduce the huge dimension of the chromatographic data. The aim of this step is to simplify the model and accelerate the identification process. Six photosynthetic pigments i.e. zeaxantin, pheophytin a, α-carotene, β-carotene, lycopene and lutein could be well identified by the model with accuracy up to 85.33% and processing time less than 1 second.

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

Coaxial twin-fluid atomization with pattern air gas streams

NASA Astrophysics Data System (ADS)

Hei Ng, Chin; Aliseda, Alberto

2010-11-01

Coaxial twin-fluid atomization has numerous industrial applications, most notably fuel injection and spray coating. In the coating process of pharmaceutical tablets, the coaxial atomizing air stream is accompanied by two diametrically opposed side jets that impinge on the liquid/gas coaxial jets at an angle to produce an elliptical shape of the spray's cross section. Our study focuses on the influence of these side jets on the break up process and on the droplet velocity and diameter distribution along the cross section. The ultimate goal is to predict the size distribution and volume flux per unit area in the spray. With this predictive model, an optimal atomizing air/pattern air ratio can be found to achieve the desired coating result. This model is also crucial in scaling up the laboratory setup to production level. We have performed experiments with different atomized liquids, such as water and glycerine-water mixtures, that allow us to establish the effect of liquid viscosity, through the Ohnesorge number, in the spray characteristics. The gas Reynolds number of our experiments ranges from 9000 to 18000 and the Weber number ranges from 400 to 1600. We will present the effect of pattern air in terms of the resulting droplets size, droplet number density and velocity at various distances downstream of the nozzle where the effect of pattern air is significant.

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.

College drinking behaviors: mediational links between parenting styles, impulse control, and alcohol-related outcomes.

PubMed

Patock-Peckham, Julie A; Morgan-Lopez, Antonio A

2006-06-01

Mediational links between parenting styles (authoritative, authoritarian, permissive), impulsiveness (general control), drinking control (specific control), and alcohol use and abuse were tested. A pattern-mixture approach (for modeling non-ignorable missing data) with multiple-group structural equation models with 421 (206 female, 215 male) college students was used. Gender was examined as a potential moderator of parenting styles on control processes related to drinking. Specifically, the parent-child gender match was found to have implications for increased levels of impulsiveness (a significant mediator of parenting effects on drinking control). These findings suggest that a parent with a permissive parenting style who is the same gender as the respondent can directly influence control processes and indirectly influence alcohol use and abuse.

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

Associations Between Sleep Duration Patterns and Behavioral/Cognitive Functioning at School Entry

PubMed Central

Touchette, Évelyne; Petit, Dominique; Séguin, Jean R.; Boivin, Michel; Tremblay, Richard E.; Montplaisir, Jacques Y.

2007-01-01

Objective: The aim of the study was to investigate the associations between longitudinal sleep duration patterns and behavioral/cognitive functioning at school entry. Design, Setting, and Participants: Hyperactivity-impulsivity (HI), inattention, and daytime sleepiness scores were measured by questionnaire at 6 years of age in a sample of births from 1997 to 1998 in a Canadian province (N=1492). The Peabody Picture Vocabulary Test - Revised (PPVT-R) was administered at 5 years of age and the Block Design subtest (WISC-III) was administered at 6 years of age. Sleep duration was reported yearly by the children's mothers from age 2.5 to 6 years. A group-based semiparametric mixture model was used to estimate developmental patterns of sleep duration. The relationships between sleep duration patterns and both behavioral items and neurodevelopmental tasks were tested using weighted multivariate logistic regression models to control for potentially confounding psychosocial factors. Results: Four sleep duration patterns were identified: short persistent (6.0%), short increasing (4.8%),10-hour persistent (50.3%), and 11-hour persistent (38.9%). The association of short sleep duration patterns with high HI scores (P=0.001), low PPVT-R performance (P=0.002), and low Block Design subtest performance (P=0.004) remained significant after adjusting for potentially confounding variables. Conclusions: Shortened sleep duration, especially before the age of 41 months, is associated with externalizing problems such as HI and lower cognitive performance on neurodevelopmental tests. Results highlight the importance of giving a child the opportunity to sleep at least 10 hours per night throughout early childhood. Citation: Touchette E; Petit D; Séguin JR; Boivin M; Tremblay RE; Montplaisir JY. Associations between sleep duration patterns and behavioral/cognitive functioning at school entry. SLEEP 2007;30(9):1213-1219. PMID:17910393

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.

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.

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.

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.

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.

Estimating statistical power for open-enrollment group treatment trials.

PubMed

Morgan-Lopez, Antonio A; Saavedra, Lissette M; Hien, Denise A; Fals-Stewart, William

2011-01-01

Modeling turnover in group membership has been identified as a key barrier contributing to a disconnect between the manner in which behavioral treatment is conducted (open-enrollment groups) and the designs of substance abuse treatment trials (closed-enrollment groups, individual therapy). Latent class pattern mixture models (LCPMMs) are emerging tools for modeling data from open-enrollment groups with membership turnover in recently proposed treatment trials. The current article illustrates an approach to conducting power analyses for open-enrollment designs based on the Monte Carlo simulation of LCPMM models using parameters derived from published data from a randomized controlled trial comparing Seeking Safety to a Community Care condition for women presenting with comorbid posttraumatic stress disorder and substance use disorders. The example addresses discrepancies between the analysis framework assumed in power analyses of many recently proposed open-enrollment trials and the proposed use of LCPMM for data analysis. Copyright © 2011 Elsevier Inc. All rights reserved.

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.

An odorant congruent with a colour cue is selectively perceived in an odour mixture.

PubMed

Arao, Mari; Suzuki, Maya; Katayama, Jun'ich; Akihiro, Yagi

2012-01-01

Odour identification can be influenced by colour cues. This study examined the mechanism underlying this colour context effect. We hypothesised that a specific odour component congruent with a colour would be selectively perceived in preference to another odour component in a binary odour mixture. We used a ratio estimation method under two colour conditions, a binary odour mixture (experiment 1) and single chemicals presented individually (experiment 2). Each colour was congruent with one of the odour components. Participants judged the perceived mixture ratio in each odour container on which a colour patch was pasted. An influence of colour was not observed when the odour stimulus did not contain the odour component congruent with the colour (experiment 2); however, the odour component congruent with the colour was perceived as more dominant when the odour stimulus did contain the colour-congruent odorant (experiment 1). This pattern indicates that a colour-congruent odour component is selectively perceived in an odour mixture. This finding suggests that colours can enhance the perceptual representation of the colour-associated component in an odour mixture.

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.

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.

Determination of solvents permeating through chemical protective clothing with a microsensor array.

PubMed

Park, J; Zellers, E T

2000-08-01

The performance of a novel prototype instrument in determining solvents and solvent mixtures permeating through samples of chemical protective clothing (CPC) materials was evaluated. The instrument contains a mini-preconcentrator and an array of three polymer-coated surface-acoustic-wave (SAW) microsensors whose collective response patterns are used to discriminate among multiple permeants. Permeation tests were performed with a 2.54 cm diameter test cell in an open-loop configuration on samples of common glove materials challenged with four individual solvents, three binary mixtures, and two ternary mixtures. Breakthrough times, defined as the times required for the permeation rate to reach a value of 1 microg cm(-2) min(-1), determined by the instrument were within 3 min of those determined in parallel by manual sampling and gas chromatographic analysis. Permeating solvents were recognized (identified) from their response patterns in 59 out of 64 measurements (92%) and their vapor concentrations were quantified to an accuracy of +/- 31% (typically +/- 10%). These results demonstrate the potential for such instrumentation to provide semi-automated field or bench-top screening of CPC permeation resistance.

Nano- and Micro-Scale Oxidative Patterning of Titanium Implant Surfaces for Improved Surface Wettability.

PubMed

Kim, In-hye; Son, Jun Sik; Choi, Seok Hwa; Kim, Kyo-han; Kwon, Tae-yub

2016-02-01

A simple and scalable surface modification treatment is demonstrated, in which nano- and microscale features are introduced into the surface of titanium (Ti) substrates by means of a novel and eco-friendly oxidative aqueous solution composed of hydrogen peroxide (H202) and sodium bicarbonate (NaHCO3). By immersing mirror-polished Ti discs in an aqueous mixture of 30 wt% H2O2/5 wt% NaHCO3 at 23 +/- 3 degrees C for 4 h, it was confirmed that this mixture is capable of generating microscale topographies on Ti surfaces. It also simultaneously formed nanochannels that were regularly arranged in a comb-like pattern on the Ti surface, thus forming a hierarchical surface structure. Further, these nano/micro-textured Ti surfaces showed great surface roughness and excellent wettability when compared with control Ti surfaces. This study demonstrates that a H2O2/NaHCO3 mixture can be effectively utilized to create reproducible nano/microscale topographies on Ti implant surfaces, thus providing an economical new oxidative solution that may be used effectively and safely as a Ti surface modification treatment.

Simulation of Complex Cracking in Plain Weave C/SiC Composite under Biaxial Loading

NASA Technical Reports Server (NTRS)

Cheng, Ron-Bin; Hsu, Su-Yuen

2012-01-01

Finite element analysis is performed on a mesh, based on computed geometry of a plain weave C/SiC composite with assumed internal stacking, to reveal the pattern of internal damage due to biaxial normal cyclic loading. The simulation encompasses intertow matrix cracking, matrix cracking inside the tows, and separation at the tow-intertow matrix and tow-tow interfaces. All these dissipative behaviors are represented by traction-separation cohesive laws. Not aimed at quantitatively predicting the overall stress-strain relation, the simulation, however, does not take the actual process of fiber debonding into account. The fiber tows are represented by a simple rule-of-mixture model where the reinforcing phase is a hypothetical one-dimensional material. Numerical results indicate that for the plain weave C/SiC composite, 1) matrix-crack initiation sites are primarily determined by large intertow matrix voids and interlayer tow-tow contacts, 2) the pattern of internal damage strongly depends on the loading path and initial stress, 3) compressive loading inflicts virtually no damage evolution. KEY WORDS: ceramic matrix composite, plain weave, cohesive model, brittle failure, smeared crack model, progressive damage, meso-mechanical analysis, finite element.

Dynamic Response of Functionally Graded Carbon Nanotube Reinforced Sandwich Plate

NASA Astrophysics Data System (ADS)

Mehar, Kulmani; Panda, Subrata Kumar

2018-03-01

In this article, the dynamic response of the carbon nanotube-reinforced functionally graded sandwich composite plate has been studied numerically with the help of finite element method. The face sheets of the sandwich composite plate are made of carbon nanotube- reinforced composite for two different grading patterns whereas the core phase is taken as isotropic material. The final properties of the structure are calculated using the rule of mixture. The geometrical model of the sandwich plate is developed and discretized suitably with the help of available shell element in ANSYS library. Subsequently, the corresponding numerical dynamic responses computed via batch input technique (parametric design language code in ANSYS) of ANSYS including Newmark’s integration scheme. The stability of the sandwich structural numerical model is established through the proper convergence study. Further, the reliability of the sandwich model is checked by comparison study between present and available results from references. As a final point, some numerical problems have been solved to examine the effect of different design constraints (carbon nanotube distribution pattern, core to face thickness ratio, volume fractions of the nanotube, length to thickness ratio, aspect ratio and constraints at edges) on the time-responses of sandwich plate.

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.

Incorporating concentration dependence in stable isotope mixing models.

PubMed

Phillips, Donald L; Koch, Paul L

2002-01-01

Stable isotopes are often used as natural labels to quantify the contributions of multiple sources to a mixture. For example, C and N isotopic signatures can be used to determine the fraction of three food sources in a consumer's diet. The standard dual isotope, three source linear mixing model assumes that the proportional contribution of a source to a mixture is the same for both elements (e.g., C, N). This may be a reasonable assumption if the concentrations are similar among all sources. However, one source is often particularly rich or poor in one element (e.g., N), which logically leads to a proportionate increase or decrease in the contribution of that source to the mixture for that element relative to the other element (e.g., C). We have developed a concentration-weighted linear mixing model, which assumes that for each element, a source's contribution is proportional to the contributed mass times the elemental concentration in that source. The model is outlined for two elements and three sources, but can be generalized to n elements and n+1 sources. Sensitivity analyses for C and N in three sources indicated that varying the N concentration of just one source had large and differing effects on the estimated source contributions of mass, C, and N. The same was true for a case study of bears feeding on salmon, moose, and N-poor plants. In this example, the estimated biomass contribution of salmon from the concentration-weighted model was markedly less than the standard model estimate. Application of the model to a captive feeding study of captive mink fed on salmon, lean beef, and C-rich, N-poor beef fat reproduced very closely the known dietary proportions, whereas the standard model failed to yield a set of positive source proportions. Use of this concentration-weighted model is recommended whenever the elemental concentrations vary substantially among the sources, which may occur in a variety of ecological and geochemical applications of stable isotope analysis. Possible examples besides dietary and food web studies include stable isotope analysis of water sources in soils, plants, or water bodies; geological sources for soils or marine systems; decomposition and soil organic matter dynamics, and tracing animal migration patterns. A spreadsheet for performing the calculations for this model is available at http://www.epa.gov/wed/pages/models.htm.

Element Abundances in Meteorites and the Earth: Implication for the Accretion of Planetary Bodies

NASA Astrophysics Data System (ADS)

Mezger, K.; Vollstaedt, H.; Maltese, A.

2017-12-01

Essentially all known inner solar system materials show near chondritic relative abundances of refractory elements and depletion in volatile elements. To a first approximation volatile element depletion correlates with the respective condensation temperature (TC) of the elements. Possible mechanisms for this depletion are incomplete condensation and partial loss by evaporation caused by heating prior to or during the planetesimal accretion. The stable isotope compositions of almost all moderately volatile elements in different meteorite classes show only minor, or no evidence for a Rayleigh-type fractionation that could be attributed to partial condensation or evaporation. The different classes of meteorites also show that the degree of depletion in their parent bodies (i.e. mostly planetesimals) is quite variable, but nevertheless systematic. For primitive and least disturbed carbonaceous chondrites the element depletion pattern is a smooth function of TC. The accessible silicate Earth also shows this general depletion pattern, but in detail it is highly complex and requires differentiation processes that are not solely controlled by TC. If only highly lithophile elements are considered the depletion pattern of the silicate Earth reveals a step function that shows that moderately volatile lithophile elements have abundances that are ca. 0.1 times the chondritic value, irrespective of their TC. This element pattern observed for bulk silicate Earth can be modelled as a mixture of two distinct components: ca. 90% of a strongly reduced planetary body that is depleted in highly volatile elements and ca. 10% of a more volatile element rich and oxidized component. This mixture can account for the apparent Pb- paradox observed in melts derived from the silicate Earth and provides a time constraint for the mixing event, which is ca. 70 My after the beginning of the solar system. This event corresponds to the giant impact that also formed the Moon.

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.

Application of four anti-human interferon-alpha monoclonal antibodies for immunoassay and comparative analysis of natural interferon-alpha mixtures

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

Andersson, G.; Lundgren, E.; Ekre, H.P.

Four different mouse monoclonal antibodies to human interferon-alpha (IFN-alpha) were evaluated for application in quantitative and comparative analysis of natural IFN-alpha mixtures. Binding to IFN-alpha subtypes in solution revealed individual reactivity patterns. These patterns changed if the IFN-alpha molecules were immobilized either passively to a surface or bound by another antibody. Also, substitution of a single amino acid in IFN-alpha 2 affected the binding, apparently by altering the conformation. Isoelectric focusing of three natural IFN-alpha preparations from different sources, followed by immunoblotting, resulted in individual patterns with each of the four mAbs and also demonstrated variation in the composition ofmore » the IFN-alpha preparations. None of the mAbs was subtype specific, but by combining the different mAbs, and also applying polyclonal anti-human IFN-alpha antibodies, it was possible to design sensitive sandwich ELISAs with broad or more limited IFN-alpha subtype specificity.« less

Taste Mixture Interactions: Suppression, Additivity, and the Predominance of Sweetness

PubMed Central

Green, Barry G.; Lim, Juyun; Osterhoff, Floor; Blacher, Karen; Nachtigal, Danielle

2010-01-01

Most of what is known about taste interactions has come from studies of binary mixtures. The primary goal of this study was to determine whether asymmetries in suppression between stimuli in binary mixtures predict the perception of tastes in more complex mixtures (e.g., ternary, quaternary mixtures). Also of interest was the longstanding question of whether overall taste intensity derives from the sum of the tastes perceived within a mixture (perceptual additivity) or from the sum of the perceived intensities of the individual stimuli (stimulus additivity). Using the general Labeled Magnitude Scale together with a sip-and-spit procedure, we asked subjects to rate overall taste intensity and the sweetness, sourness, saltiness and bitterness of approximately equi- intense sucrose, NaCl, citric acid and QSO4 stimuli presented alone and in all possible binary, ternary and quaternary mixtures. The results showed a consistent pattern of mixture suppression in which sucrose sweetness tended to be both the least suppressed quality and the strongest suppressor of other tastes. The overall intensity of mixtures was found to be predicted best by perceptual additivity. A second experiment that was designed to rule out potentially confounding effects of the order of taste ratings and the temperature of taste solutions replicated the main findings of the first experiment. Overall, the results imply that mixture suppression favors perception of sweet carbohydrates in foods at the expense of other potentially harmful ingredients, such as high levels of sodium (saltiness) and potential poisons or spoilage (bitterness, sourness). PMID:20800076

Investigation of Embedded Si/C System Exposed to a Hybrid Reaction of Centrifugal-Assisted Thermite Method

PubMed Central

Mahmoodian, Reza; Yahya, Rosiyah; Dabbagh, Ali; Hamdi, Mohd; Hassan, Mohsen A.

2015-01-01

A novel method is proposed to study the behavior and phase formation of a Si+C compacted pellet under centrifugal acceleration in a hybrid reaction. Si+C as elemental mixture in the form of a pellet is embedded in a centrifugal tube. The pellet assembly and tube are exposed to the sudden thermal energy of a thermite reaction resulted in a hybrid reaction. The hybrid reaction of thermite and Si+C produced unique phases. X-ray diffraction pattern (XRD) as well as microstructural and elemental analyses are then investigated. XRD pattern showed formation of materials with possible electronic and magnetic properties. The cooling rate and the molten particle viscosity mathematical model of the process are meant to assist in understanding the physical and chemical phenomena took place during and after reaction. The results analysis revealed that up to 85% of materials converted into secondary products as ceramics-matrix composite. PMID:26641651

Investigation of Embedded Si/C System Exposed to a Hybrid Reaction of Centrifugal-Assisted Thermite Method.

PubMed

Mahmoodian, Reza; Yahya, Rosiyah; Dabbagh, Ali; Hamdi, Mohd; Hassan, Mohsen A

2015-01-01

A novel method is proposed to study the behavior and phase formation of a Si+C compacted pellet under centrifugal acceleration in a hybrid reaction. Si+C as elemental mixture in the form of a pellet is embedded in a centrifugal tube. The pellet assembly and tube are exposed to the sudden thermal energy of a thermite reaction resulted in a hybrid reaction. The hybrid reaction of thermite and Si+C produced unique phases. X-ray diffraction pattern (XRD) as well as microstructural and elemental analyses are then investigated. XRD pattern showed formation of materials with possible electronic and magnetic properties. The cooling rate and the molten particle viscosity mathematical model of the process are meant to assist in understanding the physical and chemical phenomena took place during and after reaction. The results analysis revealed that up to 85% of materials converted into secondary products as ceramics-matrix composite.

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

iASeq: integrative analysis of allele-specificity of protein-DNA interactions in multiple ChIP-seq datasets

PubMed Central

2012-01-01

Background ChIP-seq provides new opportunities to study allele-specific protein-DNA binding (ASB). However, detecting allelic imbalance from a single ChIP-seq dataset often has low statistical power since only sequence reads mapped to heterozygote SNPs are informative for discriminating two alleles. Results We develop a new method iASeq to address this issue by jointly analyzing multiple ChIP-seq datasets. iASeq uses a Bayesian hierarchical mixture model to learn correlation patterns of allele-specificity among multiple proteins. Using the discovered correlation patterns, the model allows one to borrow information across datasets to improve detection of allelic imbalance. Application of iASeq to 77 ChIP-seq samples from 40 ENCODE datasets and 1 genomic DNA sample in GM12878 cells reveals that allele-specificity of multiple proteins are highly correlated, and demonstrates the ability of iASeq to improve allelic inference compared to analyzing each individual dataset separately. Conclusions iASeq illustrates the value of integrating multiple datasets in the allele-specificity inference and offers a new tool to better analyze ASB. PMID:23194258

A Recurrent Probabilistic Neural Network with Dimensionality Reduction Based on Time-series Discriminant Component Analysis.

PubMed

Hayashi, Hideaki; Shibanoki, Taro; Shima, Keisuke; Kurita, Yuichi; Tsuji, Toshio

2015-12-01

This paper proposes a probabilistic neural network (NN) developed on the basis of time-series discriminant component analysis (TSDCA) that can be used to classify high-dimensional time-series patterns. TSDCA involves the compression of high-dimensional time series into a lower dimensional space using a set of orthogonal transformations and the calculation of posterior probabilities based on a continuous-density hidden Markov model with a Gaussian mixture model expressed in the reduced-dimensional space. The analysis can be incorporated into an NN, which is named a time-series discriminant component network (TSDCN), so that parameters of dimensionality reduction and classification can be obtained simultaneously as network coefficients according to a backpropagation through time-based learning algorithm with the Lagrange multiplier method. The TSDCN is considered to enable high-accuracy classification of high-dimensional time-series patterns and to reduce the computation time taken for network training. The validity of the TSDCN is demonstrated for high-dimensional artificial data and electroencephalogram signals in the experiments conducted during the study.

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

Modulation of Spatiotemporal Particle Patterning in Evaporating Droplets: Applications to Diagnostics and Materials Science.

PubMed

Guha, Rajarshi; Mohajerani, Farzad; Mukhopadhyay, Ahana; Collins, Matthew D; Sen, Ayusman; Velegol, Darrell

2017-12-13

Spatiotemporal particle patterning in evaporating droplets lacks a common design framework. Here, we demonstrate autonomous control of particle distribution in evaporating droplets through the imposition of a salt-induced self-generated electric field as a generalized patterning strategy. Through modeling, a new dimensionless number, termed "capillary-phoresis" (CP) number, arises, which determines the relative contributions of electrokinetic and convective transport to pattern formation, enabling one to accurately predict the mode of particle assembly by controlling the spontaneous electric field and surface potentials. Modulation of the CP number allows the particles to be focused in a specific region in space or distributed evenly. Moreover, starting with a mixture of two different particle types, their relative placement in the ensuing pattern can be controlled, allowing coassemblies of multiple, distinct particle populations. By this approach, hypermethylated DNA, prevalent in cancerous cells, can be qualitatively distinguished from normal DNA of comparable molecular weights. In other examples, we show uniform dispersion of several particle types (polymeric colloids, multiwalled carbon nanotubes, and molecular dyes) on different substrates (metallic Cu, metal oxide, and flexible polymer), as dictated by the CP number. Depending on the particle, the highly uniform distribution leads to surfaces with a lower sheet resistance, as well as superior dye-printed displays.

Changes in the pattern of distribution of von Willebrand factor in rat aortic endothelial cells following thrombin generation in vivo.

PubMed

Senis, Y A; Richardson, M; Tinlin, S; Maurice, D H; Giles, A R

1996-04-01

The pattern of distribution of von Willebrand factor (VWF) in relatively large sheets of rat aortic endothelial cells (EC) obtained by the Häutchen technique were analysed by immunocytochemistry and light microscopy. EC were examined pre and post administration of a procoagulant mixture of factor Xa (F.Xa) and phosphotidylcholine/phosphotidylserine (PCPS) vesicles which was demonstrated to result in the selective loss of high molecular weight multimers (HMWM) of plasma VWF in the rat. In placebo animals the pattern was heterogenous both in overall distribution and in individual cells which showed both a diffuse and granular pattern. Groups of intensely stained EC were oriented parallel to the longitudinal axis of the aorta and staining was particularly prominent around the orifices of the intercostal arteries, implicating shear-stress as a possible factor in VWF expression by EC. Changes in the pattern of distribution of staining were observed at various time points post-infusion of F.Xa/PCPS, suggesting the immediate release of VWF from EC stores followed by the recruitment of EC to synthesize and store VWF. These changes are consistent with the decrease in EC Weibel-Palade Body (WPB) content observed by EM in previously reported studies using this model.

Three-dimensional convection of binary mixtures in porous media.

PubMed

Umla, R; Augustin, M; Huke, B; Lücke, M

2011-11-01

We investigate convection patterns of binary mixtures with a positive separation ratio in porous media. As setup, we choose the Rayleigh-Bénard system of a fluid layer heated from below. Results are obtained by a multimode Galerkin method. Using this method, we compute square and crossroll patterns, and we analyze their structural, bifurcation, and stability properties. Evidence is provided that, for a strong enough Soret effect, both structures exist as stable forms of convection. Some of their properties are found to be similar to square and crossroll convection in the system without porous medium. However, there are also qualitative differences. For example, squares can be destabilized by oscillatory perturbations with square symmetry in porous media, and their velocity isolines are deformed in the so-called Soret regime.

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

Numerical investigation of diesel exhaust particle transport and deposition in the CT-scan based lung airway

NASA Astrophysics Data System (ADS)

Islam, Mohammad S.; Saha, Suvash C.; Sauret, Emilie; Gu, Y. T.; Molla, Md Mamun

2017-06-01

Diesel exhaust particulates matter (DEPM) is a compound mixture of gasses and fine particles that contain more than 40 toxic air pollutants including benzene, formaldehyde, and nitrogen oxides. Exposure of DEPM to human lung airway during respiratory inhalation causes severe health hazards like diverse pulmonary diseases. This paper studies the DEPM transport and deposition in upper three generations of the realistic lung airways. A 3-D digital airway bifurcation model is constructed from the computerized tomography (CT) scan data of a healthy adult man. The Euler-Lagrange approach is used to solve the continuum and disperse phases of the calculation. Local averaged Navier-Stokes equations are solved to calculate the transport of the continuum phase. Lagrangian based Discrete Phase Model (DPM) is used to investigate the particle transport and deposition in the current anatomical model. The effects of size specific monodispersed particles on deposition are extensively investigated during different breathing pattern. The numerical results illustrate that particle diameter and breathing pattern have a substantial impact on particles transport and deposition in the tracheobronchial airways. The present realistic bifurcation model also depicts a new deposition hot spot which could advance the understanding of the therapeutic drug delivery system to the specific position of the respiratory airways.

Depletion forces drive polymer-like self-assembly in vibrofluidized granular materials†

PubMed Central

Nossal, Ralph

2011-01-01

Ranging from nano- to granular-scales, control of particle assembly can be achieved by limiting the available free space, for example by increasing the concentration of particles (“crowding”) or through their restriction to 2D environments. It is unclear, however, if self-assembly principles governing thermally-equilibrated molecules can also apply to mechanically-excited macroscopic particles in non-equilibrium steady-state. Here we show that low densities of vibrofluidized steel rods, when crowded by high densities of spheres and confined to quasi-2D planes, can self-assemble into linear polymer-like structures. Our 2D Monte Carlo simulations show similar finite sized aggregates in thermally equilibrated binary mixtures. Using theory and simulations, we demonstrate how depletion interactions create oriented “binding” forces between rigid rods to form these “living polymers.” Unlike rod-sphere mixtures in 3D that can demonstrate well-defined equilibrium phases, our mixtures confined to 2D lack these transitions because lower dimensionality favors the formation of linear aggregates, thus suppressing a true phase transition. The qualitative and quantitative agreement between equilibrium and granular patterning for these mixtures suggests that entropy maximization is the determining driving force for bundling. Furthermore, this study uncovers a previously unknown patterning behavior at both the granular and nanoscales, and may provide insights into the role of crowding at interfaces in molecular assembly. PMID:22039392

Quantifying Short-Chain Chlorinated Paraffin Congener Groups.

PubMed

Yuan, Bo; Bogdal, Christian; Berger, Urs; MacLeod, Matthew; Gebbink, Wouter A; Alsberg, Tomas; de Wit, Cynthia A

2017-09-19

Accurate quantification of short-chain chlorinated paraffins (SCCPs) poses an exceptional challenge to analytical chemists. SCCPs are complex mixtures of chlorinated alkanes with variable chain length and chlorination level; congeners with a fixed chain length (n) and number of chlorines (m) are referred to as a "congener group" C n Cl m . Recently, we resolved individual C n Cl m by mathematically deconvolving soft ionization high-resolution mass spectra of SCCP mixtures. Here we extend the method to quantifying C n Cl m by introducing C n Cl m specific response factors (RFs) that are calculated from 17 SCCP chain-length standards with a single carbon chain length and variable chlorination level. The signal pattern of each standard is measured on APCI-QTOF-MS. RFs of each C n Cl m are obtained by pairwise optimization of the normal distribution's fit to the signal patterns of the 17 chain-length standards. The method was verified by quantifying SCCP technical mixtures and spiked environmental samples with accuracies of 82-123% and 76-109%, respectively. The absolute differences between calculated and manufacturer-reported chlorination degrees were -0.9 to 1.0%Cl for SCCP mixtures of 49-71%Cl. The quantification method has been replicated with ECNI magnetic sector MS and ECNI-Q-Orbitrap-MS. C n Cl m concentrations determined with the three instruments were highly correlated (R 2 > 0.90) with each other.

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.

Model Selection Methods for Mixture Dichotomous IRT Models

ERIC Educational Resources Information Center

Li, Feiming; Cohen, Allan S.; Kim, Seock-Ho; Cho, Sun-Joo

2009-01-01

This study examines model selection indices for use with dichotomous mixture item response theory (IRT) models. Five indices are considered: Akaike's information coefficient (AIC), Bayesian information coefficient (BIC), deviance information coefficient (DIC), pseudo-Bayes factor (PsBF), and posterior predictive model checks (PPMC). The five…

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.

Isoform-level gene expression patterns in single-cell RNA-sequencing data.

PubMed

Vu, Trung Nghia; Wills, Quin F; Kalari, Krishna R; Niu, Nifang; Wang, Liewei; Pawitan, Yudi; Rantalainen, Mattias

2018-02-27

RNA sequencing of single cells enables characterization of transcriptional heterogeneity in seemingly homogeneous cell populations. Single-cell sequencing has been applied in a wide range of researches fields. However, few studies have focus on characterization of isoform-level expression patterns at the single-cell level. In this study we propose and apply a novel method, ISOform-Patterns (ISOP), based on mixture modeling, to characterize the expression patterns of isoform pairs from the same gene in single-cell isoform-level expression data. We define six principal patterns of isoform expression relationships and describe a method for differential-pattern analysis. We demonstrate ISOP through analysis of single-cell RNA-sequencing data from a breast cancer cell line, with replication in three independent datasets. We assigned the pattern types to each of 16,562 isoform-pairs from 4,929 genes. Among those, 26% of the discovered patterns were significant (p<0.05), while remaining patterns are possibly effects of transcriptional bursting, drop-out and stochastic biological heterogeneity. Furthermore, 32% of genes discovered through differential-pattern analysis were not detected by differential-expression analysis. The effect of drop-out events, mean expression level, and properties of the expression distribution on the performances of ISOP were also investigated through simulated datasets. To conclude, ISOP provides a novel approach for characterization of isoformlevel preference, commitment and heterogeneity in single-cell RNA-sequencing data. The ISOP method has been implemented as a R package and is available at https://github.com/nghiavtr/ISOP under a GPL-3 license. mattias.rantalainen@ki.se. Supplementary data are available at Bioinformatics online.

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.

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

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.

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.

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.

Isotopic characteristics of simulated meteoritic organic matter. I - Kerogen-like material

NASA Technical Reports Server (NTRS)

Kerridge, John F.; Mariner, Ruth; Flores, Jose; Chang, Sherwood

1989-01-01

Carbonaceous residues from a variety of laboratory syntheses yield release patterns for C and H isotopes during stepwise combustion that fail to mimic the striking patterns characteristic of meteoritic kerogen-like residues that otherwise superficially resemble them. It seems likely that the meteoritic material comprises a complex mixture of substances having different origins and/or synthesis conditions.

More than Child's Play: Variable- And Pattern-Centered Approaches for Examining Effects of Sports Participation on Youth Development

ERIC Educational Resources Information Center

Zarrett, Nicole; Fay, Kristen; Li, Yibing; Carrano, Jennifer; Phelps, Erin; Lerner, Richard M.

2009-01-01

The authors used data from Grades 5 through 7 of the longitudinal 4-H Study of Positive Youth Development to assess relations among sports participation, other out-of-school-time (OST) activities, and indicators of youth development. They used a mixture of variable- and pattern-centered analyses aimed at disentangling different features of…

ZEP520A cold-development technique and tool for ultimate resolution to fabricate 1Xnm bit pattern EB master mold for nano-imprinting lithography for HDD/BPM development

NASA Astrophysics Data System (ADS)

Kobayashi, Hideo; Iyama, Hiromasa

2012-06-01

Poor solvent developers are effective for resolution enhancement on a polymer-type EB resist such as ZEP520A. Another way is to utilize "cold-development" technique which was accomplished by a dip-development technique usually. We then designed and successfully built a single-wafer spin-development tool for the cold-development down to -10degC in order to dissolve difficulties of the dip-development. The cold-development certainly helped improve ZEP520A resolution and hole CD size uniformity, and achieved 35nm pitch BPM patterns with the standard developer ZED-N50, but not 25nm pitch yet. By employing a poor solvent mixture of iso-Propyl Alcohol (IPA) and Fluoro-Carbon (FC), 25nm pitch BPM patterns were accomplished. However, the cold-development showed almost no improvement on the IPA/FC mixture developer solvent. This paper describes cold-development technique and a tool, as well as its results, for ZEP520A resolution enhancement to fabricate 1Xnm bits (holes) for EB master-mold for Nano-Imprinting Lithography for 1Tbit/inch2 and 25nm pitch Bit Patterned Media development.

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.

Modelling of sequential groundwater treatment with zero valent iron and granular activated carbon.

PubMed

Bayer, Peter; Finkel, Michael

2005-06-01

Multiple contaminant mixtures in groundwater may not efficiently be treated by a single technology if contaminants possess rather different properties with respect to sorptivity, solubility, and degradation potential. An obvious choice is to use sequenced units of the generally accepted treatment materials zero valent iron (ZVI) and granular activated carbon (GAC). However, as the results of this modelling study suggest, the required dimensions of both reactor units may strongly differ from those expected on the grounds of a contaminant-specific design. This is revealed by performing an analysis for a broad spectrum of design alternatives through numerical experiments for selected patterns of contaminant mixtures consisting of monochlorobenzene, tetrachloroethylene, trichloroethylene (TCE), cis-1,2-dichloroethylene (cis-DCE), and vinyl chloride (VC). It is shown that efficient treatment can be achieved only if competitive sorption effects in the GAC unit as well as the formation of intermediate products in the ZVI unit are carefully taken into account. Cost-optimal designs turned out to vary extremely depending on the prevailing conditions concerning contaminant concentrations, branching ratios, and unit costs of both reactor materials. Where VC is the critical contaminant, due to high initial concentration or extensive production as an intermediate, two options are cost-effective: an oversized ZVI unit with an oversized GAC unit or a pure GAC reactor.

Tailoring plasmonic properties of gold nanohole arrays for surface-enhanced Raman scattering

PubMed Central

Zheng, Peng; Cushing, Scott K.; Suri, Savan; Wu, Nianqiang

2015-01-01

The wide plasmonic tuning range of nanotriangle and nanohole array patterns fabricated by nanosphere lithography makes them promising in surface-enhanced Raman scattering (SERS) sensors. Unfortunately, it is challenging to optimize these patterns for SERS sensing because their optical response is a complex mixture of localized and propagating surface plasmons. In this paper, transmission and reflection measurements are combined with finite difference time domain simulations to identify and separate each plasmonic mode, discerning which resonance leads to the electromagnetic field enhancement. The SERS enhancement is found to be dominated by the absorption, which is shifted from the transmission and reflection dips usually used as tuning points, and by the ‘gap’ defects formed within the pattern. These effects have different spectral and geometric dependences, forming two optimization curves which can be used to predict the best performance for a given excitation wavelength. The developed model is verified with experimental SERS measurements for several nanohole sizes and periodicities, and then used to give optimal fabrication parameters for a range of measurement conditions. The results will promote the application of two-dimensional plasmonic nanoarrays in SERS sensors. PMID:25586930

Conceptual recurrence plots: revealing patterns in human discourse.

PubMed

Angus, Daniel; Smith, Andrew; Wiles, Janet

2012-06-01

Human discourse contains a rich mixture of conceptual information. Visualization of the global and local patterns within this data stream is a complex and challenging problem. Recurrence plots are an information visualization technique that can reveal trends and features in complex time series data. The recurrence plot technique works by measuring the similarity of points in a time series to all other points in the same time series and plotting the results in two dimensions. Previous studies have applied recurrence plotting techniques to textual data; however, these approaches plot recurrence using term-based similarity rather than conceptual similarity of the text. We introduce conceptual recurrence plots, which use a model of language to measure similarity between pairs of text utterances, and the similarity of all utterances is measured and displayed. In this paper, we explore how the descriptive power of the recurrence plotting technique can be used to discover patterns of interaction across a series of conversation transcripts. The results suggest that the conceptual recurrence plotting technique is a useful tool for exploring the structure of human discourse.

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…

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.

Lifetime income patterns and alcohol consumption: Investigating the association between long- and short-term income trajectories and drinking

PubMed Central

Cerdá, Magdalena; Johnson-Lawrence, Vicki; Galea, Sandro

2011-01-01

Lifetime patterns of income may be an important driver of alcohol use. In this study, we evaluated the relationship between long-term and short-term measures of income and the relative odds of abstaining, drinking lightly-moderately and drinking heavily. We used data from the US Panel Study on Income Dynamics (PSID), a national population-based cohort that has been followed annually or biannually since 1968. We examined 3111 adult respondents aged 30-44 in 1997. Latent class growth mixture models with a censored normal distribution were used to estimate income trajectories followed by the respondent families from 1968-1997, while repeated measures multinomial generalized logit models estimated the odds of abstinence (no drinks per day) or heavy drinking (at least 3 drinks a day), relative to light/moderate drinking (<1-2 drinks a day), in 1999-2003. Lower income was associated with higher odds of abstinence and of heavy drinking, relative to light/moderate drinking. For example, belonging to a household with stable low income ($11-20,000) over 30 years was associated with 1.57 odds of abstinence, and 2.14 odds of heavy drinking in adulthood. The association between lifetime income patterns and alcohol use decreased in magnitude and became non-significant once we controlled for past-year income, education and occupation. Lifetime income patterns may have an indirect association with alcohol use, mediated through current socioeconomic conditions. PMID:21890256

Probabilistic n/γ discrimination with robustness against outliers for use in neutron profile monitors

NASA Astrophysics Data System (ADS)

Uchida, Y.; Takada, E.; Fujisaki, A.; Kikuchi, T.; Ogawa, K.; Isobe, M.

2017-08-01

A method to stochastically discriminate neutron and γ-ray signals measured with a stilbene organic scintillator is proposed. Each pulse signal was stochastically categorized into two groups: neutron and γ-ray. In previous work, the Expectation Maximization (EM) algorithm was used with the assumption that the measured data followed a Gaussian mixture distribution. It was shown that probabilistic discrimination between these groups is possible. Moreover, by setting the initial parameters for the Gaussian mixture distribution with a k-means algorithm, the possibility of automatic discrimination was demonstrated. In this study, the Student's t-mixture distribution was used as a probabilistic distribution with the EM algorithm to improve the robustness against the effect of outliers caused by pileup of the signals. To validate the proposed method, the figures of merit (FOMs) were compared for the EM algorithm assuming a t-mixture distribution and a Gaussian mixture distribution. The t-mixture distribution resulted in an improvement of the FOMs compared with the Gaussian mixture distribution. The proposed data processing technique is a promising tool not only for neutron and γ-ray discrimination in fusion experiments but also in other fields, for example, homeland security, cancer therapy with high energy particles, nuclear reactor decommissioning, pattern recognition, and so on.

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.

Calculation of detonation initiation in a hydrogen/oxygen/argon mixture in by a small-diameter spherical projectile

NASA Astrophysics Data System (ADS)

Bedarev, I. A.; Temerbekov, V. M.; Fedorov, A. V.

2018-03-01

The initiation of detonation in a reactive mixture by a small-diameter spherical projectile launched at supersonic velocity was studied for a reduced kinetic scheme of chemical reactions. A mathematical technique based on the ANSYS Fluent package was developed for this purpose. Numerical and experimental data on the flow regimes and detonation cell sizes are compared. There is agreement between the calculated and experimental flow patterns and detonation cell sizes for each regime.

Evidence of temperature-dependent effects on the estrogenic response of fish: implications with regard to climate change.

PubMed

Brian, Jayne V; Harris, Catherine A; Runnalls, Tamsin J; Fantinati, Andrea; Pojana, Giulio; Marcomini, Antonio; Booy, Petra; Lamoree, Marja; Kortenkamp, Andreas; Sumpter, John P

2008-07-01

Chemical risk assessment is fraught with difficulty due to the problem of accounting for the effects of mixtures. In addition to the uncertainty arising from chemical-to-chemical interactions, it is possible that environmental variables, such as temperature, influence the biological response to chemical challenge, acting as confounding factors in the analysis of mixture effects. Here, we investigate the effects of temperature on the response of fish to a defined mixture of estrogenic chemicals. It was anticipated that the response to the mixture may be exacerbated at higher temperatures, due to an increase in the rate of physiological processing. This is a pertinent issue in view of global climate change. Fathead minnows (Pimephales promelas) were exposed to the mixture in parallel exposure studies, which were carried out at different temperatures (20 and 30 degrees C). The estrogenic response was characterised using an established assay, involving the analysis of the egg yolk protein, vitellogenin (VTG). Patterns of VTG gene expression were also analysed using real-time QPCR. The results revealed that there was no effect of temperature on the magnitude of the VTG response after 2 weeks of chemical exposure. However, the analysis of mixture effects at two additional time points (24 h and 7 days) revealed that the response was induced more rapidly at the higher temperature. This trend was apparent from the analysis of effects both at the molecular and biochemical level. Whilst this indicates that climatic effects on water temperature are not a significant issue with regard to the long-term risk assessment of estrogenic chemicals, the relevance of short-term effects is, as yet, unclear. Furthermore, analysis of the patterns of VTG gene expression versus protein induction gives an insight into the physiological mechanisms responsible for temperature-dependent effects on the reproductive phenology of species such as roach. Hence, the data contribute to our understanding of the implications of global climate change for wild fish populations.

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.

Dynamic foraging of a top predator in a seasonal polar marine environment.

PubMed

Weinstein, Ben G; Friedlaender, Ari S

2017-11-01

The seasonal movement of animals at broad spatial scales provides insight into life-history, ecology and conservation. By combining high-resolution satellite-tagged data with hierarchical Bayesian movement models, we can associate spatial patterns of movement with marine animal behavior. We used a multi-state mixture model to describe humpback whale traveling and area-restricted search states as they forage along the West Antarctic Peninsula. We estimated the change in the geography, composition and characteristics of these behavioral states through time. We show that whales later in the austral fall spent more time in movements associated with foraging, traveled at lower speeds between foraging areas, and shifted their distribution northward and inshore. Seasonal changes in movement are likely due to a combination of sea ice advance and regional shifts in the primary prey source. Our study is a step towards dynamic movement models in the marine environment at broad scales.

Revealing common disease mechanisms shared by tumors of different tissues of origin through semantic representation of genomic alterations and topic modeling.

PubMed

Chen, Vicky; Paisley, John; Lu, Xinghua

2017-03-14

Cancer is a complex disease driven by somatic genomic alterations (SGAs) that perturb signaling pathways and consequently cellular function. Identifying patterns of pathway perturbations would provide insights into common disease mechanisms shared among tumors, which is important for guiding treatment and predicting outcome. However, identifying perturbed pathways is challenging, because different tumors can have the same perturbed pathways that are perturbed by different SGAs. Here, we designed novel semantic representations that capture the functional similarity of distinct SGAs perturbing a common pathway in different tumors. Combining this representation with topic modeling would allow us to identify patterns in altered signaling pathways. We represented each gene with a vector of words describing its function, and we represented the SGAs of a tumor as a text document by pooling the words representing individual SGAs. We applied the nested hierarchical Dirichlet process (nHDP) model to a collection of tumors of 5 cancer types from TCGA. We identified topics (consisting of co-occurring words) representing the common functional themes of different SGAs. Tumors were clustered based on their topic associations, such that each cluster consists of tumors sharing common functional themes. The resulting clusters contained mixtures of cancer types, which indicates that different cancer types can share disease mechanisms. Survival analysis based on the clusters revealed significant differences in survival among the tumors of the same cancer type that were assigned to different clusters. The results indicate that applying topic modeling to semantic representations of tumors identifies patterns in the combinations of altered functional pathways in cancer.

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.

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.

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.

Micro acoustic resonant chambers for heating/agitating/mixing (MARCHAM)

NASA Astrophysics Data System (ADS)

Sherrit, Stewart; Noell, Aaron C.; Fisher, Anita M.; Takano, Nobuyuki; Grunthaner, Frank

2016-04-01

A variety of applications require the mixing and/or heating of a slurry made from a powder/fluid mixture. One of these applications, Sub Critical Water Extraction (SCWE), is a process where water and an environmental powder sample (sieved soil, drill cuttings, etc.) are heated in a sealed chamber to temperatures greater than 200 degrees Celsius by allowing the pressure to increase, but without reaching the critical point of water. At these temperatures, the ability of water to extract organics from solid particulate increases drastically. This paper describes the modeling and experimentation on the use of an acoustic resonant chamber which is part of an amino acid detection instrument called Astrobionibbler [Noell et al. 2014, 2015]. In this instrument we use acoustics to excite a fluid- solid fines mixture in different frequency/amplitude regimes to accomplish a variety of sample processing tasks. Driving the acoustic resonant chamber at lower frequencies can create circulation patterns in the fluid and mixes the liquid and fines, while driving the chamber at higher frequencies one can agitate the fluid and powder and create a suspension. If one then drives the chamber at high amplitude at resonance heating of the slurry occurs. In the mixing and agitating cell the particle levitation force depends on the relative densities and compressibility's of the particulate and fluid and on the kinetic and potential energy densities associated with the velocity and pressure fields [Glynne-Jones, Boltryk and Hill 2012] in the cell. When heating, the piezoelectric transducer and chamber is driven at high power in resonance where the solid/fines region is modelled as an acoustic transmission line with a large loss component. In this regime, heat is pumped into the solution/fines mixture and rapidly heats the sample. We have modeled the piezoelectric transducer/chamber/ sample using Mason's equivalent circuit. In order to assess the validity of the model we have built and tested a variety of chambers. This paper describes the experimental results which are in general agreement with theory within the limitations of the modeling.

Differential gene expression pattern in human mammary epithelial cells induced by realistic organochlorine mixtures described in healthy women and in women diagnosed with breast cancer.

PubMed

Rivero, Javier; Henríquez-Hernández, Luis Alberto; Luzardo, Octavio P; Pestano, José; Zumbado, Manuel; Boada, Luis D; Valerón, Pilar F

2016-03-30

Organochlorine pesticides (OCs) have been associated with breast cancer development and progression, but the mechanisms underlying this phenomenon are not well known. In this work, we evaluated the effects exerted on normal human mammary epithelial cells (HMEC) by the OC mixtures most frequently detected in healthy women (H-mixture) and in women diagnosed with breast cancer (BC-mixture), as identified in a previous case-control study developed in Spain. Cytotoxicity and gene expression profile of human kinases (n=68) and non-kinases (n=26) were tested at concentrations similar to those described in the serum of those cases and controls. Although both mixtures caused a down-regulation of genes involved in the ATP binding process, our results clearly indicate that both mixtures may exert a very different effect on the gene expression profile of HMEC. Thus, while BC-mixture up-regulated the expression of oncogenes associated to breast cancer (GFRA1 and BHLHB8), the H-mixture down-regulated the expression of tumor suppressor genes (EPHA4 and EPHB2). Our results indicate that the composition of the OC mixture could play a role in the initiation processes of breast cancer. In addition, the present results suggest that subtle changes in the composition and levels of pollutants involved in environmentally relevant mixtures might induce very different biological effects, which explain, at least partially, why some mixtures seem to be more carcinogenic than others. Nonetheless, our findings confirm that environmentally relevant pollutants may modulate the expression of genes closely related to carcinogenic processes in the breast, reinforcing the role exerted by environment in the regulation of genes involved in breast carcinogenesis. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

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

Mixture class recovery in GMM under varying degrees of class separation: frequentist versus Bayesian estimation.

PubMed

Depaoli, Sarah

2013-06-01

Growth mixture modeling (GMM) represents a technique that is designed to capture change over time for unobserved subgroups (or latent classes) that exhibit qualitatively different patterns of growth. The aim of the current article was to explore the impact of latent class separation (i.e., how similar growth trajectories are across latent classes) on GMM performance. Several estimation conditions were compared: maximum likelihood via the expectation maximization (EM) algorithm and the Bayesian framework implementing diffuse priors, "accurate" informative priors, weakly informative priors, data-driven informative priors, priors reflecting partial-knowledge of parameters, and "inaccurate" (but informative) priors. The main goal was to provide insight about the optimal estimation condition under different degrees of latent class separation for GMM. Results indicated that optimal parameter recovery was obtained though the Bayesian approach using "accurate" informative priors, and partial-knowledge priors showed promise for the recovery of the growth trajectory parameters. Maximum likelihood and the remaining Bayesian estimation conditions yielded poor parameter recovery for the latent class proportions and the growth trajectories. (PsycINFO Database Record (c) 2013 APA, all rights reserved).

Nonlinear hydrodynamic stability and transition; Proceedings of the IUTAM Symposium, Nice, France, Sept. 3-7, 1990

NASA Astrophysics Data System (ADS)

Theoretical and experimental research on nonlinear hydrodynamic stability and transition is presented. Bifurcations, amplitude equations, pattern in experiments, and shear flows are considered. Particular attention is given to bifurcations of plane viscous fluid flow and transition to turbulence, chaotic traveling wave covection, chaotic behavior of parametrically excited surface waves in square geometry, amplitude analysis of the Swift-Hohenberg equation, traveling wave convection in finite containers, focus instability in axisymmetric Rayleigh-Benard convection, scaling and pattern formation in flowing sand, dynamical behavior of instabilities in spherical gap flows, and nonlinear short-wavelength Taylor vortices. Also discussed are stability of a flow past a two-dimensional grid, inertia wave breakdown in a precessing fluid, flow-induced instabilities in directional solidification, structure and dynamical properties of convection in binary fluid mixtures, and instability competition for convecting superfluid mixtures.

High-throughput identification of genotype-specific cancer vulnerabilities in mixtures of barcoded tumor cell lines

PubMed Central

Yu, Channing; Mannan, Aristotle M.; Yvone, Griselda Metta; Ross, Kenneth N.; Zhang, Yan-Ling; Marton, Melissa A.; Taylor, Bradley R.; Crenshaw, Andrew; Gould, Joshua Z.; Tamayo, Pablo; Weir, Barbara A.; Tsherniak, Aviad; Wong, Bang; Garraway, Levi A.; Shamji, Alykhan F.; Palmer, Michelle A.; Foley, Michael A.; Winckler, Wendy; Schreiber, Stuart L.; Kung, Andrew L.; Golub, Todd R.

2016-01-01

Hundreds of genetically characterized cell lines are available for the discovery of genotype-specific cancer vulnerabilities. However, screening large numbers of compounds against large numbers of cell lines is currently impractical, and such experiments are often difficult to control1-4. Here, we report a method called PRISM that allows pooled screening of mixtures of cancer cell lines by labeling each cell line with 24-nucleotide barcodes. PRISM displayed the expected patterns of cell killing seen in conventional (unpooled) assays. In a screen of 102 cell lines across 8,400 compounds, PRISM led to the identification of BRD-7880 as a potent and highly specific inhibitor of aurora kinases B and C. Cell line pools also efficiently formed tumors as xenografts, and PRISM recapitulated the expected pattern of erlotinib sensitivity in vivo. PMID:26928769

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.

[Physicochemical properties of suplatast tosilate racemate and enantiomers].

PubMed

Ushio, T; Endo, K; Yamamoto, K

1996-11-01

The physicochemical properties of the enantiomer and racemates of suplatast tosilate (ST) were investigated by means of infrared spectroscopy, solid-state 13C CP/MAS NMR spectroscopy, thermal analysis, and X-ray diffraction analysis, and by measuring the solubility and hygroscopy. The infrared and NMR spectra and X-ray diffraction pattern of the enantiomer were distinctly different from those of the racemate. The melting point of the enantiomer was lower than that of the racemate by 5 degrees C, while the solubility of the enantiomer was 1.3 times higher than that of the racemate. The hygroscopic rate of the enantiomer was greater than that of the racemate. These results suggested that ST was classified into a racemic compound crystal. Furthermore, by comparing the relative peak intensity ratios on X-ray diffraction patterns of the crystals with various optical purities prepared by recrystallization, it was found that a mixture of racemic compound crystals and either of racemic mixture crystals or racemic solid solutions was obtained by recrystallization of ST in the content of 0 to 64%ee, while the recrystallization of ST in the content of more than 64%ee led to the formation of racemic mixture crystals or racemic solid solutions.

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…

Effects of food conditions on the development of the population of Temora stylifera: A modeling approach

NASA Astrophysics Data System (ADS)

Mazzocchi, M. G.; Buffoni, G.; Carotenuto, Y.; Pasquali, S.; Ribera d'Alcalà, M.

2006-08-01

We integrated field and laboratory data with modeling to determine the extent to which the temporal patterns in population abundance of a copepod species as observed at sea may be explained by differences in production and mortality rates due to diet. A Lagrangian individual-based model utilizing birth and mortality rates whose values and variance were derived from the effects of dietary composition was implemented to simulate the growth of the multi-staged population of Temora stylifera. The four diets considered were represented by unialgal cultures of the dinoflagellate Prorocentrum minimum or the diatom Thalassiosira rotula, a mixture of the two species, and natural particle assemblages < 50 μm. The aim of this work was to set up an exemplary study on a debated issue, i.e., whether the insidious effect of a diatom diet demonstrated in laboratory experiments plays a role in the time course of copepod populations in situ. Our numerical simulations showed that differences in life history parameters, as mainly dependent on diet, caused remarkably different population growth rates. However, our model reproduced the pattern of an average seasonal cycle of T. stylifera in Mediterranean coastal waters only when it utilized time-dependent field data, which evidently integrate all conditions the animals experience at sea. Proper tuning of the mortality term of developmental stages was crucial to reproduce the pattern of the time course of T. stylifera abundance in situ, which confirms that this term plays a major role in shaping the copepod population dynamics. The model also showed that, while dietary composition affects the population growth, it is far from being the only determinant of the cycle of abundance of T. stylifera at sea.

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.

Modeling and simulation of large scale stirred tank

NASA Astrophysics Data System (ADS)

Neuville, John R.

The purpose of this dissertation is to provide a written record of the evaluation performed on the DWPF mixing process by the construction of numerical models that resemble the geometry of this process. There were seven numerical models constructed to evaluate the DWPF mixing process and four pilot plants. The models were developed with Fluent software and the results from these models were used to evaluate the structure of the flow field and the power demand of the agitator. The results from the numerical models were compared with empirical data collected from these pilot plants that had been operated at an earlier date. Mixing is commonly used in a variety ways throughout industry to blend miscible liquids, disperse gas through liquid, form emulsions, promote heat transfer and, suspend solid particles. The DOE Sites at Hanford in Richland Washington, West Valley in New York, and Savannah River Site in Aiken South Carolina have developed a process that immobilizes highly radioactive liquid waste. The radioactive liquid waste at DWPF is an opaque sludge that is mixed in a stirred tank with glass frit particles and water to form slurry of specified proportions. The DWPF mixing process is composed of a flat bottom cylindrical mixing vessel with a centrally located helical coil, and agitator. The helical coil is used to heat and cool the contents of the tank and can improve flow circulation. The agitator shaft has two impellers; a radial blade and a hydrofoil blade. The hydrofoil is used to circulate the mixture between the top region and bottom region of the tank. The radial blade sweeps the bottom of the tank and pushes the fluid in the outward radial direction. The full scale vessel contains about 9500 gallons of slurry with flow behavior characterized as a Bingham Plastic. Particles in the mixture have an abrasive characteristic that cause excessive erosion to internal vessel components at higher impeller speeds. The desire for this mixing process is to ensure the agitation of the vessel is adequate to produce a homogenous mixture but not so high that it produces excessive erosion to internal components. The main findings reported by this study were: (1) Careful consideration of the fluid yield stress characteristic is required to make predictions of fluid flow behavior. Laminar Models can predict flow patterns and stagnant regions in the tank until full movement of the flow field occurs. Power Curves and flow patterns were developed for the full scale mixing model to show the differences in expected performance of the mixing process for a broad range of fluids that exhibit Herschel--Bulkley and Bingham Plastic flow behavior. (2) The impeller power demand is independent of the flow model selection for turbulent flow fields in the region of the impeller. The laminar models slightly over predicted the agitator impeller power demand produced by turbulent models. (3) The CFD results show that the power number produced by the mixing system is independent of size. The 40 gallon model produced the same power number results as the 9300 gallon model for the same process conditions. (4) CFD Results show that the Scale-Up of fluid motion in a 40 gallon tank should compare with fluid motion at full scale, 9300 gallons by maintaining constant impeller Tip Speed.

Time-dependent patterns in quasivertical cylindrical binary convection.

PubMed

Alonso, Arantxa; Mercader, Isabel; Batiste, Oriol

2018-02-01

This paper reports on numerical investigations of the effect of a slight inclination α on pattern formation in a shallow vertical cylindrical cell heated from below for binary mixtures with a positive value of the Soret coefficient. By using direct numerical simulation of the three-dimensional Boussinesq equations with Soret effect in cylindrical geometry, we show that a slight inclination of the cell in the range α≈0.036rad=2^{∘} strongly influences pattern selection. The large-scale shear flow (LSSF) induced by the small tilt of gravity overcomes the squarelike arrangements observed in noninclined cylinders in the Soret regime, stratifies the fluid along the direction of inclination, and produces an enhanced separation of the two components of the mixture. The competition between shear effects and horizontal and vertical buoyancy alters significantly the dynamics observed in noninclined convection. Additional unexpected time-dependent patterns coexist with the basic LSSF. We focus on an unsual periodic state recently discovered in an experiment, the so-called superhighway convection state (SHC), in which ascending and descending regions of fluid move in opposite directions. We provide numerical confirmation that Boussinesq Navier-Stokes equations with standard boundary conditions contain the essential ingredients that allow for the existence of such a state. Also, we obtain a persistent heteroclinic structure where regular oscillations between a SHC pattern and a state of nearly stationary longitudinal rolls take place. We characterize numerically these time-dependent patterns and investigate the dynamics around the threshold of convection.

Time-dependent patterns in quasivertical cylindrical binary convection

NASA Astrophysics Data System (ADS)

Alonso, Arantxa; Mercader, Isabel; Batiste, Oriol

2018-02-01

This paper reports on numerical investigations of the effect of a slight inclination α on pattern formation in a shallow vertical cylindrical cell heated from below for binary mixtures with a positive value of the Soret coefficient. By using direct numerical simulation of the three-dimensional Boussinesq equations with Soret effect in cylindrical geometry, we show that a slight inclination of the cell in the range α ≈0.036 rad =2∘ strongly influences pattern selection. The large-scale shear flow (LSSF) induced by the small tilt of gravity overcomes the squarelike arrangements observed in noninclined cylinders in the Soret regime, stratifies the fluid along the direction of inclination, and produces an enhanced separation of the two components of the mixture. The competition between shear effects and horizontal and vertical buoyancy alters significantly the dynamics observed in noninclined convection. Additional unexpected time-dependent patterns coexist with the basic LSSF. We focus on an unsual periodic state recently discovered in an experiment, the so-called superhighway convection state (SHC), in which ascending and descending regions of fluid move in opposite directions. We provide numerical confirmation that Boussinesq Navier-Stokes equations with standard boundary conditions contain the essential ingredients that allow for the existence of such a state. Also, we obtain a persistent heteroclinic structure where regular oscillations between a SHC pattern and a state of nearly stationary longitudinal rolls take place. We characterize numerically these time-dependent patterns and investigate the dynamics around the threshold of convection.

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.

Arctic lead detection using a waveform mixture algorithm from CryoSat-2 data

NASA Astrophysics Data System (ADS)

Lee, Sanggyun; Kim, Hyun-cheol; Im, Jungho

2018-05-01

We propose a waveform mixture algorithm to detect leads from CryoSat-2 data, which is novel and different from the existing threshold-based lead detection methods. The waveform mixture algorithm adopts the concept of spectral mixture analysis, which is widely used in the field of hyperspectral image analysis. This lead detection method was evaluated with high-resolution (250 m) MODIS images and showed comparable and promising performance in detecting leads when compared to the previous methods. The robustness of the proposed approach also lies in the fact that it does not require the rescaling of parameters (i.e., stack standard deviation, stack skewness, stack kurtosis, pulse peakiness, and backscatter σ0), as it directly uses L1B waveform data, unlike the existing threshold-based methods. Monthly lead fraction maps were produced by the waveform mixture algorithm, which shows interannual variability of recent sea ice cover during 2011-2016, excluding the summer season (i.e., June to September). We also compared the lead fraction maps to other lead fraction maps generated from previously published data sets, resulting in similar spatiotemporal patterns.

Modeling the impact of prostate edema on LDR brachytherapy: a Monte Carlo dosimetry study based on a 3D biphasic finite element biomechanical model.

PubMed

Mountris, K A; Bert, J; Noailly, J; Aguilera, A Rodriguez; Valeri, A; Pradier, O; Schick, U; Promayon, E; Ballester, M A Gonzalez; Troccaz, J; Visvikis, D

2017-03-21

Prostate volume changes due to edema occurrence during transperineal permanent brachytherapy should be taken under consideration to ensure optimal dose delivery. Available edema models, based on prostate volume observations, face several limitations. Therefore, patient-specific models need to be developed to accurately account for the impact of edema. In this study we present a biomechanical model developed to reproduce edema resolution patterns documented in the literature. Using the biphasic mixture theory and finite element analysis, the proposed model takes into consideration the mechanical properties of the pubic area tissues in the evolution of prostate edema. The model's computed deformations are incorporated in a Monte Carlo simulation to investigate their effect on post-operative dosimetry. The comparison of Day1 and Day30 dosimetry results demonstrates the capability of the proposed model for patient-specific dosimetry improvements, considering the edema dynamics. The proposed model shows excellent ability to reproduce previously described edema resolution patterns and was validated based on previous findings. According to our results, for a prostate volume increase of 10-20% the Day30 urethra D10 dose metric is higher by 4.2%-10.5% compared to the Day1 value. The introduction of the edema dynamics in Day30 dosimetry shows a significant global dose overestimation identified on the conventional static Day30 dosimetry. In conclusion, the proposed edema biomechanical model can improve the treatment planning of transperineal permanent brachytherapy accounting for post-implant dose alterations during the planning procedure.

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.

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.

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

Birthdays and the Binary System: A Magical Mixture.

ERIC Educational Resources Information Center

Karp, Karen S.; Ronau, Robert N.

1997-01-01

Presents an activity involving the use of students' birth dates. Activity includes a classic binary representation of numerical values. In the Green Machine, Sorting Cards, and Window Cards, students observe, describe, and analyze patterns. (PVD)

Rapid method for determination of antimicrobial susceptibilities pattern of urinary bacteria

NASA Technical Reports Server (NTRS)

Picciolo, G. L.; Chapelle, E. W.; Barza, M. J.; Weinstein, L.; Tuttle, S. A.; Vellend, H.

1975-01-01

Method determines bacterial sensitivity to antimicrobial agents by measuring level of adenosine triphosphate remaining in the bacteria. Light emitted during reaction of sample with a mixture of luciferase and luciferin is measured.

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…

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.

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.

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.

Modeling the formation of ordered nano-assemblies comprised by dendrimers and linear polyelectrolytes: The role of Coulombic interactions

NASA Astrophysics Data System (ADS)

Eleftheriou, E.; Karatasos, K.

2012-10-01

Models of mixtures of peripherally charged dendrimers with oppositely charged linear polyelectrolytes in the presence of explicit solvent are studied by means of molecular dynamics simulations. Under the influence of varying strength of electrostatic interactions, these systems appear to form dynamically arrested film-like interconnected structures in the polymer-rich phase. Acting like a pseudo-thermodynamic inverse temperature, the increase of the strength of the Coulombic interactions drive the polymeric constituents of the mixture to a gradual dynamic freezing-in. The timescale of the average density fluctuations of the formed complexes initially increases in the weak electrostatic regime reaching a finite limit as the strength of electrostatic interactions grow. Although the models are overall electrically neutral, during this process the dendrimer/linear complexes develop a polar character with an excess charge mainly close to the periphery of the dendrimers. The morphological characteristics of the resulted pattern are found to depend on the size of the polymer chains on account of the distinct conformational features assumed by the complexed linear polyelectrolytes of different length. In addition, the length of the polymer chain appears to affect the dynamics of the counterions, thus affecting the ionic transport properties of the system. It appears, therefore, that the strength of electrostatic interactions together with the length of the linear polyelectrolytes are parameters to which these systems are particularly responsive, offering thus the possibility for a better control of the resulted structure and the electric properties of these soft-colloidal systems.

Inference of population splits and mixtures from genome-wide allele frequency data.

PubMed

Pickrell, Joseph K; Pritchard, Jonathan K

2012-01-01

Many aspects of the historical relationships between populations in a species are reflected in genetic data. Inferring these relationships from genetic data, however, remains a challenging task. In this paper, we present a statistical model for inferring the patterns of population splits and mixtures in multiple populations. In our model, the sampled populations in a species are related to their common ancestor through a graph of ancestral populations. Using genome-wide allele frequency data and a Gaussian approximation to genetic drift, we infer the structure of this graph. We applied this method to a set of 55 human populations and a set of 82 dog breeds and wild canids. In both species, we show that a simple bifurcating tree does not fully describe the data; in contrast, we infer many migration events. While some of the migration events that we find have been detected previously, many have not. For example, in the human data, we infer that Cambodians trace approximately 16% of their ancestry to a population ancestral to other extant East Asian populations. In the dog data, we infer that both the boxer and basenji trace a considerable fraction of their ancestry (9% and 25%, respectively) to wolves subsequent to domestication and that East Asian toy breeds (the Shih Tzu and the Pekingese) result from admixture between modern toy breeds and "ancient" Asian breeds. Software implementing the model described here, called TreeMix, is available at http://treemix.googlecode.com.

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.

Photodynamic mechanisms induced by a combination of hypericin and a chlorin based-photosensitizer in head and neck squamous cell carcinoma cells.

PubMed

Gyenge, Emina Besic; Lüscher, Daniel; Forny, Patrick; Antoniol, Martina; Geisberger, Georg; Walt, Heinrich; Patzke, Greta; Maake, Caroline

2013-01-01

The aim of this study was to elucidate photodynamic therapy (PDT) effects mediated by hypericin and a liposomal meso-tetrahydroxyphenyl chlorin (mTHPC) derivative, with focus on their 1:1 mixture, on head and neck squamous cell carcinoma cell lines. Absorption, excitation and photobleaching were monitored using fluorescence spectrometry, showing the same spectral patterns for the mixture as measured for single photosensitizers. In the mixture mTHPC showed a prolonged photo-stability. Singlet oxygen yield for light-activated mTHPC was Φ(Δ) = 0.66, for hypericin Φ(Δ) = 0.25 and for the mixture Φ(Δ) = ~0.4. A linear increase of singlet oxygen yield for mTHPC and the mixture was found, whereas hypericin achieved saturation after 35 min. Reactive oxygen species fluorescence was only visible after hypericin and mixture-induced PDT. Cell viability was also more affected with these two treatment options under the selected conditions. Examination of death pathways showed that hypericin-mediated cell death was apoptotic, with mTHPC necrotic and the 1:1 mixture showed features of both. Changes in gene expression after PDT indicated strong up-regulation of selected heat-shock proteins. The application of photosensitizer mixtures with the features of reduced dark toxicity and combined apoptotic and necrotic cell death may be beneficial in clinical PDT. This will be the focus of our future investigations. © 2012 Wiley Periodicals, Inc. Photochemistry and Photobiology © 2012 The American Society of Photobiology.

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.

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.

Bars and spirals in tidal interactions with an ensemble of galaxy mass models

NASA Astrophysics Data System (ADS)

Pettitt, Alex R.; Wadsley, J. W.

2018-03-01

We present simulations of the gaseous and stellar material in several different galaxy mass models under the influence of different tidal fly-bys to assess the changes in their bar and spiral morphology. Five different mass models are chosen to represent the variety of rotation curves seen in nature. We find a multitude of different spiral and bar structures can be created, with their properties dependent on the strength of the interaction. We calculate pattern speeds, spiral wind-up rates, bar lengths, and angular momentum exchange to quantify the changes in disc morphology in each scenario. The wind-up rates of the tidal spirals follow the 2:1 resonance very closely for the flat and dark matter-dominated rotation curves, whereas the more baryon-dominated curves tend to wind-up faster, influenced by their inner bars. Clear spurs are seen in most of the tidal spirals, most noticeable in the flat rotation curve models. Bars formed both in isolation and interactions agree well with those seen in real galaxies, with a mixture of `fast' and `slow' rotators. We find no strong correlation between bar length or pattern speed and the interaction strength. Bar formation is, however, accelerated/induced in four out of five of our models. We close by briefly comparing the morphology of our models to real galaxies, easily finding analogues for nearly all simulations presenter here, showing passages of small companions can easily reproduce an ensemble of observed morphologies.

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

Modeling the impact of prostate edema on LDR brachytherapy: a Monte Carlo dosimetry study based on a 3D biphasic finite element biomechanical model

NASA Astrophysics Data System (ADS)

Mountris, K. A.; Bert, J.; Noailly, J.; Rodriguez Aguilera, A.; Valeri, A.; Pradier, O.; Schick, U.; Promayon, E.; Gonzalez Ballester, M. A.; Troccaz, J.; Visvikis, D.

2017-03-01

Prostate volume changes due to edema occurrence during transperineal permanent brachytherapy should be taken under consideration to ensure optimal dose delivery. Available edema models, based on prostate volume observations, face several limitations. Therefore, patient-specific models need to be developed to accurately account for the impact of edema. In this study we present a biomechanical model developed to reproduce edema resolution patterns documented in the literature. Using the biphasic mixture theory and finite element analysis, the proposed model takes into consideration the mechanical properties of the pubic area tissues in the evolution of prostate edema. The model’s computed deformations are incorporated in a Monte Carlo simulation to investigate their effect on post-operative dosimetry. The comparison of Day1 and Day30 dosimetry results demonstrates the capability of the proposed model for patient-specific dosimetry improvements, considering the edema dynamics. The proposed model shows excellent ability to reproduce previously described edema resolution patterns and was validated based on previous findings. According to our results, for a prostate volume increase of 10-20% the Day30 urethra D10 dose metric is higher by 4.2%-10.5% compared to the Day1 value. The introduction of the edema dynamics in Day30 dosimetry shows a significant global dose overestimation identified on the conventional static Day30 dosimetry. In conclusion, the proposed edema biomechanical model can improve the treatment planning of transperineal permanent brachytherapy accounting for post-implant dose alterations during the planning procedure.

Visualization of nasal airflow patterns in a patient affected with atrophic rhinitis using particle image velocimetry

NASA Astrophysics Data System (ADS)

Garcia, G. J. M.; Mitchell, G.; Bailie, N.; Thornhill, D.; Watterson, J.; Kimbell, J. S.

2007-10-01

The relationship between airflow patterns in the nasal cavity and nasal function is poorly understood. This paper reports an experimental study of the interplay between symptoms and airflow patterns in a patient affected with atrophic rhinitis. This pathology is characterized by mucosal dryness, fetor, progressive atrophy of anatomical structures, a spacious nasal cavity, and a paradoxical sensation of nasal congestion. A physical replica of the patient's nasal geometry was made and particle image velocimetry (PIV) was used to visualize and measure the flow field. The nasal replica was based on computed tomography (CT) scans of the patient and was built in three steps: three-dimensional reconstruction of the CT scans; rapid prototyping of a cast; and sacrificial use of the cast to form a model of the nasal passage in clear silicone. Flow patterns were measured by running a water-glycerol mixture through the replica and evaluating the displacement of particles dispersed in the liquid using PIV. The water-glycerol flow rate used corresponded to an air flow rate representative of a human breathing at rest. The trajectory of the flow observed in the left passage of the nose (more affected by atrophic rhinitis) differed markedly from what is considered normal, and was consistent with patterns of epithelial damage observed in cases of the condition. The data are also useful for validation of computational fluid dynamics predictions.

Formation and Evolution of Target Patterns in Cahn-Hilliard Flows: An Extension of the Flux Expulsion Studies in MHD

NASA Astrophysics Data System (ADS)

Fan, Xiang; P H Diamond Collaboration; Luis Chacon Collaboration

2017-10-01

Spinodal decomposition is a second order phase transition for a binary liquid mixture to evolve from a miscible phase (e.g., water + alcohol) to two co-existing phases (e.g., water + oil). The Cahn-Hilliard model for spinodal decomposition is analogous to 2D MHD. We study the evolution of the concentration field in a single eddy in the 2D Cahn-Hilliard system to better understand scalar mixing processes in that system. This study extends investigations of the classic studies of flux expulsion in 2D MHD and homogenization of potential vorticity in 2D fluids. Simulation results show that there are three stages in the evolution: (A) formation of a ``jelly roll'' pattern, for which the concentration field is constant along spirals; (B) a change in isoconcentration contour topology; and (C) formation of a target pattern, for which the isoconcentration contours follow concentric annuli. In the final target pattern stage, the isoconcentration bands align with stream lines. The results indicate that the target pattern is a metastable state. Band merger process continues on a time scale exponentially long relative to the eddy turnover time. The band merger process resembles step merger in drift-ZF staircases. This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Fusion Energy Sciences, under Award Number DE-FG02-04ER54738.

Glaucomatous patterns in Frequency Doubling Technology (FDT) perimetry data identified by unsupervised machine learning classifiers.

PubMed

Bowd, Christopher; Weinreb, Robert N; Balasubramanian, Madhusudhanan; Lee, Intae; Jang, Giljin; Yousefi, Siamak; Zangwill, Linda M; Medeiros, Felipe A; Girkin, Christopher A; Liebmann, Jeffrey M; Goldbaum, Michael H

2014-01-01

The variational Bayesian independent component analysis-mixture model (VIM), an unsupervised machine-learning classifier, was used to automatically separate Matrix Frequency Doubling Technology (FDT) perimetry data into clusters of healthy and glaucomatous eyes, and to identify axes representing statistically independent patterns of defect in the glaucoma clusters. FDT measurements were obtained from 1,190 eyes with normal FDT results and 786 eyes with abnormal FDT results from the UCSD-based Diagnostic Innovations in Glaucoma Study (DIGS) and African Descent and Glaucoma Evaluation Study (ADAGES). For all eyes, VIM input was 52 threshold test points from the 24-2 test pattern, plus age. FDT mean deviation was -1.00 dB (S.D. = 2.80 dB) and -5.57 dB (S.D. = 5.09 dB) in FDT-normal eyes and FDT-abnormal eyes, respectively (p<0.001). VIM identified meaningful clusters of FDT data and positioned a set of statistically independent axes through the mean of each cluster. The optimal VIM model separated the FDT fields into 3 clusters. Cluster N contained primarily normal fields (1109/1190, specificity 93.1%) and clusters G1 and G2 combined, contained primarily abnormal fields (651/786, sensitivity 82.8%). For clusters G1 and G2 the optimal number of axes were 2 and 5, respectively. Patterns automatically generated along axes within the glaucoma clusters were similar to those known to be indicative of glaucoma. Fields located farther from the normal mean on each glaucoma axis showed increasing field defect severity. VIM successfully separated FDT fields from healthy and glaucoma eyes without a priori information about class membership, and identified familiar glaucomatous patterns of loss.

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...

Traceable Mueller polarimetry and scatterometry for shape reconstruction of grating structures

NASA Astrophysics Data System (ADS)

Hansen, Poul-Erik; Madsen, Morten H.; Lehtolahti, Joonas; Nielsen, Lars

2017-11-01

Dimensional measurements of multi-patterned transmission gratings with a mixture of long and small periods are great challenges for optical metrology today. It is a further challenge when the aspect ratio of the structures is high, that is, when the height of structures is larger than the pitch. Here we consider a double patterned transmission grating with pitches of 500 nm and 20 000 nm. For measuring the geometrical properties of double patterned transmission grating we use a combined spectroscopic Mueller polarimetry and scatterometry setup. For modelling the experimentally obtained data we rigorously compute the scattering signal by solving Maxwell's equations using the RCWA method on a supercell structure. We also present a new method for analyzing the Mueller polarimetry parameters that performs the analysis in the measured variables. This new inversion method for finding the best fit between measured and calculated values are tested on silicon gratings with periods from 300 to 600 nm. The method is shown to give results within the expanded uncertainty of reference AFM measurements. The application of the new inversion method and the supercell structure to the double patterned transmission grating gives best estimates of dimensional quantities that are in fair agreement with those derived from local AFM measurements

Using multilevel spatial models to understand salamander site occupancy patterns after wildfire

USGS Publications Warehouse

Chelgren, Nathan; Adams, Michael J.; Bailey, Larissa L.; Bury, R. Bruce

2011-01-01

Studies of the distribution of elusive forest wildlife have suffered from the confounding of true presence with the uncertainty of detection. Occupancy modeling, which incorporates probabilities of species detection conditional on presence, is an emerging approach for reducing observation bias. However, the current likelihood modeling framework is restrictive for handling unexplained sources of variation in the response that may occur when there are dependence structures such as smaller sampling units that are nested within larger sampling units. We used multilevel Bayesian occupancy modeling to handle dependence structures and to partition sources of variation in occupancy of sites by terrestrial salamanders (family Plethodontidae) within and surrounding an earlier wildfire in western Oregon, USA. Comparison of model fit favored a spatial N-mixture model that accounted for variation in salamander abundance over models that were based on binary detection/non-detection data. Though catch per unit effort was higher in burned areas than unburned, there was strong support that this pattern was due to a higher probability of capture for individuals in burned plots. Within the burn, the odds of capturing an individual given it was present were 2.06 times the odds outside the burn, reflecting reduced complexity of ground cover in the burn. There was weak support that true occupancy was lower within the burned area. While the odds of occupancy in the burn were 0.49 times the odds outside the burn among the five species, the magnitude of variation attributed to the burn was small in comparison to variation attributed to other landscape variables and to unexplained, spatially autocorrelated random variation. While ordinary occupancy models may separate the biological pattern of interest from variation in detection probability when all sources of variation are known, the addition of random effects structures for unexplained sources of variation in occupancy and detection probability may often more appropriately represent levels of uncertainty. ?? 2011 by the Ecological Society of America.

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.

A general mixture theory. I. Mixtures of spherical molecules

NASA Astrophysics Data System (ADS)

Hamad, Esam Z.

1996-08-01

We present a new general theory for obtaining mixture properties from the pure species equations of state. The theory addresses the composition and the unlike interactions dependence of mixture equation of state. The density expansion of the mixture equation gives the exact composition dependence of all virial coefficients. The theory introduces multiple-index parameters that can be calculated from binary unlike interaction parameters. In this first part of the work, details are presented for the first and second levels of approximations for spherical molecules. The second order model is simple and very accurate. It predicts the compressibility factor of additive hard spheres within simulation uncertainty (equimolar with size ratio of three). For nonadditive hard spheres, comparison with compressibility factor simulation data over a wide range of density, composition, and nonadditivity parameter, gave an average error of 2%. For mixtures of Lennard-Jones molecules, the model predictions are better than the Weeks-Chandler-Anderson perturbation theory.

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).

Modeling Multi-Agent Self-Organization through the Lens of Higher Order Attractor Dynamics.

PubMed

Butner, Jonathan E; Wiltshire, Travis J; Munion, A K

2017-01-01

Social interaction occurs across many time scales and varying numbers of agents; from one-on-one to large-scale coordination in organizations, crowds, cities, and colonies. These contexts, are characterized by emergent self-organization that implies higher order coordinated patterns occurring over time that are not due to the actions of any particular agents, but rather due to the collective ordering that occurs from the interactions of the agents. Extant research to understand these social coordination dynamics (SCD) has primarily examined dyadic contexts performing rhythmic tasks. To advance this area of study, we elaborate on attractor dynamics, our ability to depict them visually, and quantitatively model them. Primarily, we combine difference/differential equation modeling with mixture modeling as a way to infer the underlying topological features of the data, which can be described in terms of attractor dynamic patterns. The advantage of this approach is that we are able to quantify the self-organized dynamics that agents exhibit, link these dynamics back to activity from individual agents, and relate it to other variables central to understanding the coordinative functionality of a system's behavior. We present four examples that differ in the number of variables used to depict the attractor dynamics (1, 2, and 6) and range from simulated to non-simulated data sources. We demonstrate that this is a flexible method that advances scientific study of SCD in a variety of multi-agent systems.

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.

Quantification of the fraction poorly deformable red blood cells using ektacytometry.

PubMed

Streekstra, G J; Dobbe, J G G; Hoekstra, A G

2010-06-21

We describe a method to obtain the fraction of poorly deformable red blood cells in a blood sample from the intensity pattern in an ektacytometer. In an ektacytometer red blood cells are transformed into ellipsoids by a shear flow between two transparent cylinders. The intensity pattern, due to a laser beam that is sent through the suspension, is projected on a screen. When measuring a healthy red blood cell population iso-intensity curves are ellipses with an axial ratio equal to that of the average red blood cell. In contrast poorly deformable cells result in circular iso-intensity curves. In this study we show that for mixtures of deformable and poorly deformable red blood cells, iso-intensity curves in the composite intensity pattern are neither elliptical nor circular but obtain cross-like shapes. We propose a method to obtain the fraction of poorly deformable red blood cells from those intensity patterns. Experiments with mixtures of poorly deformable and deformable red blood cells validate the method and demonstrate its accuracy. In a clinical setting our approach is potentially of great value for the detection of the fraction of sickle cells in blood samples of patients with sickle cell disease or to find a measure for the parasitemia in patients infected with malaria.

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.

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 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.

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

Trend estimation in populations with imperfect detection

USGS Publications Warehouse

Kery, Marc; Dorazio, Robert M.; Soldaat, Leo; Van Strien, Arco; Zuiderwijk, Annie; Royle, J. Andrew

2009-01-01

1. Trends of animal populations are of great interest in ecology but cannot be directly observed owing to imperfect detection. Binomial mixture models use replicated counts to estimate abundance, corrected for detection, in demographically closed populations. Here, we extend these models to open populations and illustrate them using sand lizard Lacerta agilis counts from the national Dutch reptile monitoring scheme. 2. Our model requires replicated counts from multiple sites in each of several periods, within which population closure is assumed. Counts are described by a hierarchical generalized linear model, where the state model deals with spatio-temporal patterns in true abundance and the observation model with imperfect counts, given that true state. We used WinBUGS to fit the model to lizard counts from 208 transects with 1–10 (mean 3) replicate surveys during each spring 1994–2005. 3. Our state model for abundance contained two independent log-linear Poisson regressions on year for coastal and inland sites, and random site effects to account for unexplained heterogeneity. The observation model for detection of an individual lizard contained effects of region, survey date, temperature, observer experience and random survey effects. 4. Lizard populations increased in both regions but more steeply on the coast. Detectability increased over the first few years of the study, was greater on the coast and for the most experienced observers, and highest around 1 June. Interestingly, the population increase inland was not detectable when the observed counts were analysed without account of detectability. The proportional increase between 1994 and 2005 in total lizard abundance across all sites was estimated at 86% (95% CRI 35–151). 5. Synthesis and applications. Open-population binomial mixture models are attractive for studying true population dynamics while explicitly accounting for the observation process, i.e. imperfect detection. We emphasize the important conceptual benefit provided by temporal replicate observations in terms of the interpretability of animal counts.

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

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.

Longitudinal burnout-collaboration patterns in Japanese medical care workers at special needs schools: a latent class growth analysis

PubMed Central

Kanayama, Mieko; Suzuki, Machiko; Yuma, Yoshikazu

2016-01-01

The present study aimed to identify and characterize potential burnout types and the relationship between burnout and collaboration over time. Latent class growth analysis and the growth mixture model were used to identify and characterize heterogeneous patterns of longitudinal stability and change in burnout, and the relationship between burnout and collaboration. We collected longitudinal data at three time points based on Japanese academic terms. The 396 study participants included academic teachers, yogo teachers, and registered nurses in Japanese special needs schools. The best model included four types of both burnout and collaboration in latent class growth analysis with intercept, slope, and quadratic terms. The four types of burnout were as follows: low stable, moderate unstable, high unstable, and high decreasing. They were identified as involving inverse collaboration function. The results indicated that there could be dynamic burnout types, namely moderate unstable, high unstable, and high decreasing, when focusing on growth trajectories in latent class analyses. The finding that collaboration was dynamic for dynamic burnout types and stable for stable burnout types is of great interest. This was probably related to the inverse relationship between the two constructs. PMID:27366107

Heterogeneity in perinatal depression: how far have we come? A systematic review.

PubMed

Santos, Hudson; Tan, Xianming; Salomon, Rebecca

2017-02-01

Despite perinatal depression (PND) being a common mental disorder affecting pregnant women and new mothers, limited attention has been paid to the heterogeneous nature of this disorder. We examined heterogeneity in PND symptom profiles and symptom trajectories. Literature searches revealed 247 studies, 23 of which were included in the final review. The most common statistical approaches used to explore symptom and trajectory heterogeneity were latent class model and growth mixture model. All but one study examined PND symptom trajectories and provided collective evidence of at least three heterogeneous patterns: low, medium, or chronic-high symptom levels. Social and psychological risk factors were the most common group of predictors related to a higher burden (high sum of score) of depressive symptoms. These studies were consistent in reporting poorer health outcomes for children of mothers assigned to high burden symptom trajectories. Only one study explored heterogeneity in symptom profile and was the only one to describe the specific constellations of depressive symptoms related to the PND heterogeneous patterns identified. Therefore, there is limited evidence on the specific symptoms and symptom configurations that make up PND heterogeneity. We suggest directions for future research to further clarify the PND heterogeneity and its related mechanisms.

Coastal Seabed Mapping with Hyperspectral and Lidar data

NASA Astrophysics Data System (ADS)

Taramelli, A.; Valentini, E.; Filipponi, F.; Cappucci, S.

2017-12-01

A synoptic view of the coastal seascape and its dynamics needs a quantitative ability to dissect different components over the complexity of the seafloor where a mixture of geo - biological facies determines geomorphological features and their coverage. The present study uses an analytical approach that takes advantage of a multidimensional model to integrate different data sources from airborne Hyperspectral and LiDAR remote sensing and in situ measurements to detect antropogenic features and ecological `tipping points' in coastal seafloors. The proposed approach has the ability to generate coastal seabed maps using: 1) a multidimensional dataset to account for radiometric and morphological properties of waters and the seafloor; 2) a field spectral library to assimilate the high environmental variability into the multidimensional model; 3) a final classification scheme to represent the spatial gradients in the seafloor. The spatial pattern of the response to anthropogenic forcing may be indistinguishable from patterns of natural variability. It is argued that this novel approach to define tipping points following anthropogenic impacts could be most valuable in the management of natural resources and the economic development of coastal areas worldwide. Examples are reported from different sites of the Mediterranean Sea, both from Marine Protected and un-Protected Areas.

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…

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…

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

A spiking neural network model of self-organized pattern recognition in the early mammalian olfactory system.

PubMed

Kaplan, Bernhard A; Lansner, Anders

2014-01-01

Olfactory sensory information passes through several processing stages before an odor percept emerges. The question how the olfactory system learns to create odor representations linking those different levels and how it learns to connect and discriminate between them is largely unresolved. We present a large-scale network model with single and multi-compartmental Hodgkin-Huxley type model neurons representing olfactory receptor neurons (ORNs) in the epithelium, periglomerular cells, mitral/tufted cells and granule cells in the olfactory bulb (OB), and three types of cortical cells in the piriform cortex (PC). Odor patterns are calculated based on affinities between ORNs and odor stimuli derived from physico-chemical descriptors of behaviorally relevant real-world odorants. The properties of ORNs were tuned to show saturated response curves with increasing concentration as seen in experiments. On the level of the OB we explored the possibility of using a fuzzy concentration interval code, which was implemented through dendro-dendritic inhibition leading to winner-take-all like dynamics between mitral/tufted cells belonging to the same glomerulus. The connectivity from mitral/tufted cells to PC neurons was self-organized from a mutual information measure and by using a competitive Hebbian-Bayesian learning algorithm based on the response patterns of mitral/tufted cells to different odors yielding a distributed feed-forward projection to the PC. The PC was implemented as a modular attractor network with a recurrent connectivity that was likewise organized through Hebbian-Bayesian learning. We demonstrate the functionality of the model in a one-sniff-learning and recognition task on a set of 50 odorants. Furthermore, we study its robustness against noise on the receptor level and its ability to perform concentration invariant odor recognition. Moreover, we investigate the pattern completion capabilities of the system and rivalry dynamics for odor mixtures.

A spiking neural network model of self-organized pattern recognition in the early mammalian olfactory system

PubMed Central

Kaplan, Bernhard A.; Lansner, Anders

2014-01-01

Olfactory sensory information passes through several processing stages before an odor percept emerges. The question how the olfactory system learns to create odor representations linking those different levels and how it learns to connect and discriminate between them is largely unresolved. We present a large-scale network model with single and multi-compartmental Hodgkin–Huxley type model neurons representing olfactory receptor neurons (ORNs) in the epithelium, periglomerular cells, mitral/tufted cells and granule cells in the olfactory bulb (OB), and three types of cortical cells in the piriform cortex (PC). Odor patterns are calculated based on affinities between ORNs and odor stimuli derived from physico-chemical descriptors of behaviorally relevant real-world odorants. The properties of ORNs were tuned to show saturated response curves with increasing concentration as seen in experiments. On the level of the OB we explored the possibility of using a fuzzy concentration interval code, which was implemented through dendro-dendritic inhibition leading to winner-take-all like dynamics between mitral/tufted cells belonging to the same glomerulus. The connectivity from mitral/tufted cells to PC neurons was self-organized from a mutual information measure and by using a competitive Hebbian–Bayesian learning algorithm based on the response patterns of mitral/tufted cells to different odors yielding a distributed feed-forward projection to the PC. The PC was implemented as a modular attractor network with a recurrent connectivity that was likewise organized through Hebbian–Bayesian learning. We demonstrate the functionality of the model in a one-sniff-learning and recognition task on a set of 50 odorants. Furthermore, we study its robustness against noise on the receptor level and its ability to perform concentration invariant odor recognition. Moreover, we investigate the pattern completion capabilities of the system and rivalry dynamics for odor mixtures. PMID:24570657

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.

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.

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.

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.

Flow behaviour and structure of heterogeneous particles-water mixture in horizontal and inclined pipes

NASA Astrophysics Data System (ADS)

Vlasák, Pavel; Chára, Zdeněk; Konfršt, Jiří

2018-06-01

The effect of slurry velocity and mean concentration of heterogeneous particle-water mixture on flow behaviour and structure in the turbulent regime was studied in horizontal and inclined pipe sections of inner diameter D = 100 mm. The stratified flow pattern of heterogeneous particle-water mixture in the inclined pipe sections was revealed. The particles moved mostly near to the pipe invert. Concentration distribution in ascending and descending vertical pipe sections confirmed the effect of fall velocity on particle-carrier liquid slip velocity and increase of in situ concentration in the ascending pipe section. Slip velocity in two-phase flow, which is defined as the velocity difference between the solid and liquid phase, is one of mechanism of particle movement in two-phase flow. Due to the slip velocity, there is difference between transport and in situ concentrations, and the slip velocity can be determined from comparison of the in situ and transport concentration. For heterogeneous particle-water mixture flow the slip velocity depends on the flow structure.

Non-Genomic Effects of Xenoestrogen Mixtures

PubMed Central

Viñas, René; Jeng, Yow-Jiun; Watson, Cheryl S.

2012-01-01

Xenoestrogens (XEs) are chemicals derived from a variety of natural and anthropogenic sources that can interfere with endogenous estrogens by either mimicking or blocking their responses via non-genomic and/or genomic signaling mechanisms. Disruption of estrogens’ actions through the less-studied non-genomic pathway can alter such functional end points as cell proliferation, peptide hormone release, catecholamine transport, and apoptosis, among others. Studies of potentially adverse effects due to mixtures and to low doses of endocrine-disrupting chemicals have recently become more feasible, though few so far have included actions via the non-genomic pathway. Physiologic estrogens and XEs evoke non-monotonic dose responses, with different compounds having different patterns of actions dependent on concentration and time, making mixture assessments all the more challenging. In order to understand the spectrum of toxicities and their mechanisms, future work should focus on carefully studying individual and mixture components across a range of concentrations and cellular pathways in a variety of tissue types. PMID:23066391

Avalanches and diffusion in bubble rafts

NASA Astrophysics Data System (ADS)

Maloney, C. E.

2015-07-01

Energy dissipation distributions and particle displacement statistics are studied in the mean-field version of Durian's bubble model. A two-dimensional (2D) bi-disperse mixture is simulated at various strain rates, \\dotγ , and packing ratios, ϕ, above the rigidity onset at φ=φc . Well above φc , and at sufficiently low \\dotγ , the system responds in a highly bursty way, reminiscent of other dynamically critical systems with a power-law distribution of energy dissipation. As one increases \\dotγ at fixed ϕ or tunes φ→ φc at fixed \\dotγ , the bursty behavior vanishes. Displacement distributions are non-Fickian at short times but cross to a Fickian regime at a universal strain, Δγ* , independent of \\dotγ and ϕ. Despite the profound differences in short-time dynamics, at intermediate Δγ the systems exhibit qualitatively similar spatial patterns of deformation with lines of slip extending across large fractions of the simulation cell. These deformation patterns explain the observed diffusion constants and the universal crossover time to Fickian behavior.

A comparative study of mixture cure models with covariate

NASA Astrophysics Data System (ADS)

Leng, Oh Yit; Khalid, Zarina Mohd

2017-05-01

In survival analysis, the survival time is assumed to follow a non-negative distribution, such as the exponential, Weibull, and log-normal distributions. In some cases, the survival time is influenced by some observed factors. The absence of these observed factors may cause an inaccurate estimation in the survival function. Therefore, a survival model which incorporates the influences of observed factors is more appropriate to be used in such cases. These observed factors are included in the survival model as covariates. Besides that, there are cases where a group of individuals who are cured, that is, not experiencing the event of interest. Ignoring the cure fraction may lead to overestimate in estimating the survival function. Thus, a mixture cure model is more suitable to be employed in modelling survival data with the presence of a cure fraction. In this study, three mixture cure survival models are used to analyse survival data with a covariate and a cure fraction. The first model includes covariate in the parameterization of the susceptible individuals survival function, the second model allows the cure fraction to depend on covariate, and the third model incorporates covariate in both cure fraction and survival function of susceptible individuals. This study aims to compare the performance of these models via a simulation approach. Therefore, in this study, survival data with varying sample sizes and cure fractions are simulated and the survival time is assumed to follow the Weibull distribution. The simulated data are then modelled using the three mixture cure survival models. The results show that the three mixture cure models are more appropriate to be used in modelling survival data with the presence of cure fraction and an observed factor.

Effects of alkyl chain length and substituent pattern of fullerene bis-adducts on film structures and photovoltaic properties of bulk heterojunction solar cells.

PubMed

Tao, Ran; Umeyama, Tomokazu; Kurotobi, Kei; Imahori, Hiroshi

2014-10-08

A series of alkoxycarbonyl-substituted dihydronaphthyl-based [60]fullerene bis-adduct derivatives (denoted as C2BA, C4BA, and C6BA with the alkyl chain of ethyl, n-butyl, and n-hexyl, respectively) have been synthesized to investigate the effects of alkyl chain length and substituent pattern of fullerene bis-adducts on the film structures and photovoltaic properties of bulk heterojunction polymer solar cells. The shorter alkyl chain length caused lower solubility of the fullerene bis-adducts (C6BA > C4BA > C2BA), thereby resulting in the increased separation difficulty of respective bis-adduct isomers. The device performance based on poly(3-hexylthiophene) (P3HT) and the fullerene bis-adduct regioisomer mixtures was enhanced by shortening the alkyl chain length. When using the regioisomerically separated fullerene bis-adducts, the devices based on trans-2 and a mixture of trans-4 and e of C4BA exhibited the highest power conversion efficiencies of ca. 2.4%, which are considerably higher than those of the C6BA counterparts (ca. 1.4%) and the C4BA regioisomer mixture (1.10%). The film morphologies as well as electron mobilities of the P3HT:bis-adduct blend films were found to affect the photovoltaic properties considerably. These results reveal that the alkyl chain length and substituent pattern of fullerene bis-adducts significantly influence the photovoltaic properties as well as the film structures of bulk heterojunction solar cells.