Segregation of particles and its influence on the morphology of the eutectic silicon phase in Al-7 wt.% Si alloys

A new modification phenomenon is reported for Al-Si alloys, where the Al-Si eutectic is refined by segregated TiB2 particles. The TiB2 particles are pushed to the Al-Si phase boundary during solidification of the eutectic and it is believed that at high concentrations the TiB2 particles restrict solute redistribution causing refinement of the Si. (c) 2005 Acta Materialia Inc. Published by Elsevier Ltd. 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.

Eutectic grain size and strontium concentration in hypoeutectic aluminium-silicon alloys

Recently it has been shown that modification with strontium causes an increase in the size of eutectic grains. The eutectic grain size increases because there are fewer nucleation events, possibly due to the poisoning of phosphorus-based nuclei that are active in the unmodified alloy. The current paper investigates the effect of strontium concentration on the eutectic grain size. In the aluminium-10 wt.% silicon alloy used in this research, for fixed casting conditions, the eutectic grain size increases as the strontium concentration increases up to approximately 150ppm, beyond which the grain size is relatively stable. This critical strontium concentration is likely to differ depending on the composition of the base alloy, including the concentration of minor elements and impurities. It is concluded that processing and in-service properties of strontium modified aluminium-silicon castings are likely to be more stable if a minimum critical strontium concentration is exceeded. If operating below this critical strontium concentration exceptional control over composition and casting conditions is required. (c) 2005 Elsevier B.V. All rights reserved.

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.

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.

Simulation of binary mixture adsorption of methane and CO2 at supercritical conditions in carbons

Knowledge of the adsorption behavior of coal-bed gases, mainly under supercritical high-pressure conditions, is important for optimum design of production processes to recover coal-bed methane and to sequester CO2 in coal-beds. Here, we compare the two most rigorous adsorption methods based on the statistical mechanics approach, which are Density Functional Theory (DFT) and Grand Canonical Monte Carlo (GCMC) simulation, for single and binary mixtures of methane and carbon dioxide in slit-shaped pores ranging from around 0.75 to 7.5 nm in width, for pressure up to 300 bar, and temperature range of 308-348 K, as a preliminary study for the CO2 sequestration problem. For single component adsorption, the isotherms generated by DFT, especially for CO2, do not match well with GCMC calculation, and simulation is subsequently pursued here to investigate the binary mixture adsorption. For binary adsorption, upon increase of pressure, the selectivity of carbon dioxide relative to methane in a binary mixture initially increases to a maximum value, and subsequently drops before attaining a constant value at pressures higher than 300 bar. While the selectivity increases with temperature in the initial pressure-sensitive region, the constant high-pressure value is also temperature independent. Optimum selectivity at any temperature is attained at a pressure of 90-100 bar at low bulk mole fraction of CO2, decreasing to approximately 35 bar at high bulk mole fractions. (c) 2005 American Institute of Chemical Engineers.

Mixture models for detecting differentially expressed genes in microarrays

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.

Thin-layer isothermal drying of fructose, maltodextrin, and their mixture solutions

Solutions of fructose, maltodextrin (DE 5), and their mixtures at the ratios of 20:80, 40:60, 50:50, 60:40, and 80:20 were gelled with 1% agar-agar and dried under convective-conductive drying conditions. The thin slabs were maintained at isothermal drying condition of 30 and 50 degrees C. Yamamoto's simplified method based on regular regime approach was used to calculate the (effective) moisture diffusivity. Both the drying rates and the moisture diffusivity exhibited strong concentration dependence. The concentration dependence was stronger in the case of fructose and fructose rich solutions. Both the moisture diffusivity and drying rates of the mixture solutions were enhanced due to plasticization of fructose on maltodextrin, which is explained through free volume theory.

Interactions between iron, manganese, and the Al-Si eutectic in hypoeutectic Al-Si alloys

Sand-cast plates were used to determine the effect of iron and manganese concentrations on porosity levels in Al-9 pet Si-0.5 pet Mg alloys. Iron increased porosity levels. Manganese additions increased porosity levels in alloys with 0.1 pet Fe, but reduced porosity in alloys with 0.6 and I pet Fe. Thermal analysis and quenching were undertaken to determine the effect of iron and manganese on the solidification of the Al-Si eutectic. At high iron levels, the presence of large beta-Al5FeSi was found to reduce the number of eutectic nucleation events and increase the eutectic grain size. The preferential formation of alpha-Al15Mn3Si2 upon addition of manganese reversed these effects. It is proposed that this interaction is due to beta-Al5FeSi and the Al-Si eutectic having common nuclei. Porosity levels are proposed to be controlled by the eutectic grain size and the size of the iron-bearing intermetallic particles rather than the specific intermetallic phase that forms.

Performance analysis using equivariant kernel density estimator in nonlinear mixture

This paper investigates the performance analysis of separation of mutually independent sources in nonlinear models. The nonlinear mapping constituted by an unsupervised linear mixture is followed by an unknown and invertible nonlinear distortion, are found in many signal processing cases. Generally, blind separation of sources from their nonlinear mixtures is rather difficult. We propose using a kernel density estimator incorporated with equivariant gradient analysis to separate the sources with nonlinear distortion. The kernel density estimator parameters of which are iteratively updated to minimize the output independence expressed as a mutual information criterion. The equivariant gradient algorithm has the form of nonlinear decorrelation to perform the convergence analysis. Experiments are proposed to illustrate these results.