Mixture modelling for cluster analysis

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.

Comparison of texture of yogurt made from conventionally treated milk and UHT milk fortified with low-heat skim milk powder

The textures of yogurt made from ultra-high temperature (UHT) treated and conventionally treated milks at high total solids were investigated. The yogurt premixes, fortified with low-heat skim milk powder to 16%, 18%, and 20% total solids, were UHT processed at 143 degreesC for 6 s and heated at 85 degreesC for 30 min using the conventional method. The onset of gelation was delayed in the UHT-processed milk compared with conventionally heated milk. During fermentation, the viscosity of yogurt made, from UHT-treated milk at 20% total solids was close to that of yogurt made from conventionally treated milk with 16% total solids. However, after storage for greater than or equal to1 d, the yogurt made from UHT-treated milk had lower viscosity and gel strength than the yogurt made from conventionally treated milk. The solids level had no influence on yogurt culture growth.

Adsorption of N-2, Ar, acetone, chloroform and acetone-chloroform mixture on carbon black in the framework of molecular layer structure theory (MLST)

Adsorption of pure nitrogen, argon, acetone, chloroform and acetone-chloroform mixture on graphitized thermal carbon black is considered at sub-critical conditions by means of molecular layer structure theory (MLST). In the present version of the MLST an adsorbed fluid is considered as a sequence of 2D molecular layers, whose Helmholtz free energies are obtained directly from the analysis of experimental adsorption isotherm of pure components. The interaction of the nearest layers is accounted for in the framework of mean field approximation. This approach allows quantitative correlating of experimental nitrogen and argon adsorption isotherm both in the monolayer region and in the range of multi-layer coverage up to 10 molecular layers. In the case of acetone and chloroform the approach also leads to excellent quantitative correlation of adsorption isotherms, while molecular approaches such as the non-local density functional theory (NLDFT) fail to describe those isotherms. We extend our new method to calculate the Helmholtz free energy of an adsorbed mixture using a simple mixing rule, and this allows us to predict mixture adsorption isotherms from pure component adsorption isotherms. The approach, which accounts for the difference in composition in different molecular layers, is tested against the experimental data of acetone-chloroform mixture (non-ideal mixture) adsorption on graphitized thermal carbon black at 50 degrees C. (C) 2005 Elsevier Ltd. All rights reserved.

Application of mixture models to detect differentially expressed genes

An important and common problem in microarray experiments is the detection of genes that are differentially expressed in a given number of classes. As this problem concerns the selection of significant genes from a large pool of candidate genes, it needs to be carried out within the framework of multiple hypothesis testing. In this paper, we focus on the use of mixture models to handle the multiplicity issue. With this approach, a measure of the local FDR (false discovery rate) is provided for each gene. An attractive feature of the mixture model approach is that it provides a framework for the estimation of the prior probability that a gene is not differentially expressed, and this probability can subsequently be used in forming a decision rule. The rule can also be formed to take the false negative rate into account. We apply this approach to a well-known publicly available data set on breast cancer, and discuss our findings with reference to other approaches.

Measurement of glass-rubber transition temperature of skim milk powder by static mechanical test

Stickiness behavior of skim milk powder was investigated based on the mechanical property of the material during the glass-rubber transition. A thermally controlled device was developed for the static mechanical test. This device was attached to a texture analyzer, and skim milk powder, which was used as a model sample, was tested for its glass-rubber transition temperature (Tg-r) using static compression technique (creep test). Changes in compression probe distance as a function of temperature were recorded. Tg-r was determined, in the region where changes in the probe distance were observed, by using linear regression technique. The effect of sample quantity, compression force, and heating rate on the determination of Tg-r was investigated. All these parameters significantly influenced the Tg-r determination (p < 0.05). The Tg-r of skim milk powder measured by this novel technique was found closely correlated to its glass transition temperature (T-g) measured by DSC.

Using mixture models to detect differentially expressed genes

An important and common problem in microarray experiments is the detection of genes that are differentially expressed in a given number of classes. As this problem concerns the selection of significant genes from a large pool of candidate genes, it needs to be carried out within the framework of multiple hypothesis testing. In this paper, we focus on the use of mixture models to handle the multiplicity issue. With this approach, a measure of the local false discovery rate is provided for each gene, and it can be implemented so that the implied global false discovery rate is bounded as with the Benjamini-Hochberg methodology based on tail areas. The latter procedure is too conservative, unless it is modified according to the prior probability that a gene is not differentially expressed. An attractive feature of the mixture model approach is that it provides a framework for the estimation of this probability and its subsequent use in forming a decision rule. The rule can also be formed to take the false negative rate into account.

Quil A-lipid powder formulations releasing ISCOMs and related colloidal stuctures upon hydration

The aim of the present study was to prepare solid Quil A-cholesterol-phospholid formulations (as powder mixtures or compressed to pellets) by physical mixing or by freeze-drying of aqueous dispersions of these components in ratios that allow spontaneous formation of ISCOMs and other colloidal stuctures upon hydration. The effect of addition of excess cholesterol to the lipid mixtures on the release of a model antigen (PE-FITC-OVA) from the pellets was also investigated. Physical properties were evaluated by X-ray powder diffractometry (XPRD), differential scanning calorimetry (DSC), scanning electron microscopy (SEM), and polarized light microscopy (PLM). Characterization of aqueous colloidal dispersions was performed by negative staining transmission electron microscopy (TEM). Physically mixed powders (with or without PE-FITC-OVA) and pellets prepared from the same powders did not spontaneously form ISCOM matrices and related colloidal structures such as worm-like micelles, ring-like micelles, lipidic/layered structures and lamellae (hexagonal array of ring-like micelles) upon hydration as expected from the pseudo-temary diagram for aqueous mixtures of Quil A, cholesterol and phospholipid. In contrast, spontaneous formation of the expected colloids was demonstrated for the freeze-dried lipid mixtures. Pellets prepared by compression of freeze-dried powders released PE-FITC-OVA slower than those prepared from physically mixed powders. TEM investigations revealed that the antigen was released in the form of colloidal particles (ISCOMs) from pellets prepared by compression of freeze-dried powders. The addition of excess cholesterol slowed down the release of antigen. The findings obtained in this study are important for the formulation of solid Quil A-containing lipid articles as controlled particulate adjuvant containing antigen delivery systems. (c) 2004 Elsevier B.V. All rights reserved.

A simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays

Motivation: An important problem in microarray experiments is the detection of genes that are differentially expressed in a given number of classes. We provide a straightforward and easily implemented method for estimating the posterior probability that an individual gene is null. The problem can be expressed in a two-component mixture framework, using an empirical Bayes approach. Current methods of implementing this approach either have some limitations due to the minimal assumptions made or with more specific assumptions are computationally intensive. Results: By converting to a z-score the value of the test statistic used to test the significance of each gene, we propose a simple two-component normal mixture that models adequately the distribution of this score. The usefulness of our approach is demonstrated on three real datasets.

A Mixture model with random-effects components for clustering correlated gene-expression profiles

Motivation: The clustering of gene profiles across some experimental conditions of interest contributes significantly to the elucidation of unknown gene function, the validation of gene discoveries and the interpretation of biological processes. However, this clustering problem is not straightforward as the profiles of the genes are not all independently distributed and the expression levels may have been obtained from an experimental design involving replicated arrays. Ignoring the dependence between the gene profiles and the structure of the replicated data can result in important sources of variability in the experiments being overlooked in the analysis, with the consequent possibility of misleading inferences being made. We propose a random-effects model that provides a unified approach to the clustering of genes with correlated expression levels measured in a wide variety of experimental situations. Our model is an extension of the normal mixture model to account for the correlations between the gene profiles and to enable covariate information to be incorporated into the clustering process. Hence the model is applicable to longitudinal studies with or without replication, for example, time-course experiments by using time as a covariate, and to cross-sectional experiments by using categorical covariates to represent the different experimental classes. Results: We show that our random-effects model can be fitted by maximum likelihood via the EM algorithm for which the E(expectation) and M(maximization) steps can be implemented in closed form. Hence our model can be fitted deterministically without the need for time-consuming Monte Carlo approximations. The effectiveness of our model-based procedure for the clustering of correlated gene profiles is demonstrated on three real datasets, representing typical microarray experimental designs, covering time-course, repeated-measurement and cross-sectional data. In these examples, relevant clusters of the genes are obtained, which are supported by existing gene-function annotation. A synthetic dataset is considered too.

Modelling predicts positive and negative interactions between three Australian tropical tree species in monoculture and binary mixture

Computer modelling promises to. be an important tool for analysing and predicting interactions between trees within mixed species forest plantations. This study explored the use of an individual-based mechanistic model as a predictive tool for designing mixed species plantations of Australian tropical trees. The 'spatially explicit individually based-forest simulator' (SeXI-FS) modelling system was used to describe the spatial interaction of individual tree crowns within a binary mixed-species experiment. The three-dimensional model was developed and verified with field data from three forest tree species grown in tropical Australia. The model predicted the interactions within monocultures and binary mixtures of Flindersia brayleyana, Eucalyptus pellita and Elaeocarpus grandis, accounting for an average of 42% of the growth variation exhibited by species in different treatments. The model requires only structural dimensions and shade tolerance as species parameters. By modelling interactions in existing tree mixtures, the model predicted both increases and reductions in the growth of mixtures (up to +/- 50% of stem volume at 7 years) compared to monocultures. This modelling approach may be useful for designing mixed tree plantations. (c) 2006 Published by Elsevier B.V.

On the role of tin in the nitridation of aluminium powder

The role of tin in the mechanism by which aluminium nitride grows on aluminium powder is explored. In the absence of tin, the aluminium powder nitrides rapidly, with growth occurring both into and out from the surface of the particles. In contrast, nitridation occurs more slowly in the presence of tin, which is incorporated in the growing nitride. When the tin is depleted, rapid nitridation occurs. The initial tin concentration determines the point at which the growth rate changes. (c) 2006 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Phase behavior of a phospholipid/fatty acid/water mixture studied in atomic detail

Molecular dynamics simulations have been used to study the phase behavior of a dipalmitoylphosphatidylcholine (DPPC)/palmitic acid (PA)/water 1:2:20 mixture in atomic detail. Starting from a random solution of DPPC and PA in water, the system adopts either a gel phase at temperatures below similar to 330 K or an inverted hexagonal phase above similar to 330 K in good agreement with experiment. It has also been possible to observe the direct transformation from a gel to an inverted hexagonal phase at elevated temperature (similar to 390 K). During this transformation, a metastable fluid lamellar intermediate is observed. Interlamellar connections or stalks form spontaneously on a nanosecond time scale and subsequently elongate, leading to the formation of an inverted hexagonal phase. This work opens the possibility of studying in detail how the formation of nonlamellar phases is affected by lipid composition and (fusion) peptides and, thus, is an important step toward understanding related biological processes, such as membrane fusion.