Matching conditional moments in PDF modelling of nonpremixed combustion

The rate of generation of fluctuations with respect to the scalar values conditioned on the mixture fraction, which significantly affects turbulent nonpremixed combustion processes, is examined. Simulation of the rate in a major mixing model is investigated and the derived equations can assist in selecting the model parameters so that the level of conditional fluctuations is better reproduced by the models. A more general formulation of the multiple mapping conditioning (MMC) model that distinguishes the reference and conditioning variables is suggested. This formulation can be viewed as a methodology of enforcing certain desired conditional properties onto conventional mixing models. Examples of constructing consistent MMC models with dissipation and velocity conditioning as well as of combining MMC with large eddy simulations (LES) are also provided. (c) 2005 The Combustion Institute. Published by Elsevier Inc. All rights reserved.

Matching poultry production with available feed resources: issues and constraints

Corn and soyabeans may not be available in many countries particularly those which do not have sufficient foreign currency or the capacity to grow them. This paper outlines strategies that may be important under these circumstances. Alternative feedstuffs and various feeding systems may be used to support poultry production. Alternative ingredients such as rice bran, pearl millet, cottonseed meal and grain legumes are discussed. Evidence is presented showing that amino acid requirements of layers and broilers may be too generous particularly in countries where climate, management and disease can impose production constraints. The ability of finishing broilers to perform well on very low-energy diets allows the inclusion of alternative feeds and by-products into formulations. Very low protein diets based on cereals and free amino acids can be used for layers without loss of performance. Self-selection of feedstuffs may be an important strategy in reducing feed costs of broilers and layers. The concept of matching production with available feed resources may compromise broiler growth and egg production, but in many countries this may be the most economical choice. Countries in the humid tropics usually have reduced poultry performance. The effects of high temperature and humidity are difficult to overcome. The vexed questions of the escalation in the price of fossil fuel and the outbreak of avian influenza, both seemingly without a solution, are clouds hanging over an otherwise buoyant industry.

Application of edge-to-edge matching model to understand the in-plane texture of TiSi2 (C49) thin films on (001)(Si) surface

The edge-to-edge matching model, which was originally developed for predicting crystallographic features in diffusional phase transformations in solids, has been used to understand the formation of in-plane textures in TiSi2 (C49) thin films on Si single crystal (001)si surface. The model predicts all the four previously reported orientation relationships between C49 and Si substrate based on the actual atom matching across the interface and the basic crystallographic data only. The model has strong potential to be used to develop new thin film materials. (c) 2006 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Extreme learning machine for predicting HLA-peptide binding

Machine learning techniques have been recognized as powerful tools for learning from data. One of the most popular learning techniques, the Back-Propagation (BP) Artificial Neural Networks, can be used as a computer model to predict peptides binding to the Human Leukocyte Antigens (HLA). The major advantage of computational screening is that it reduces the number of wet-lab experiments that need to be performed, significantly reducing the cost and time. A recently developed method, Extreme Learning Machine (ELM), which has superior properties over BP has been investigated to accomplish such tasks. In our work, we found that the ELM is as good as, if not better than, the BP in term of time complexity, accuracy deviations across experiments, and most importantly - prevention from over-fitting for prediction of peptide binding to HLA.

Matching of catalogues by probabilistic pattern classification

We consider the statistical problem of catalogue matching from a machine learning perspective with the goal of producing probabilistic outputs, and using all available information. A framework is provided that unifies two existing approaches to producing probabilistic outputs in the literature, one based on combining distribution estimates and the other based on combining probabilistic classifiers. We apply both of these to the problem of matching the HI Parkes All Sky Survey radio catalogue with large positional uncertainties to the much denser SuperCOSMOS catalogue with much smaller positional uncertainties. We demonstrate the utility of probabilistic outputs by a controllable completeness and efficiency trade-off and by identifying objects that have high probability of being rare. Finally, possible biasing effects in the output of these classifiers are also highlighted and discussed.

Prediction of HLA-DQ3.2 beta ligands: evidence of multiple registers in class II binding peptides

Motivation: While processing of MHC class II antigens for presentation to helper T-cells is essential for normal immune response, it is also implicated in the pathogenesis of autoimmune disorders and hypersensitivity reactions. Sequence-based computational techniques for predicting HLA-DQ binding peptides have encountered limited success, with few prediction techniques developed using three-dimensional models. Methods: We describe a structure-based prediction model for modeling peptide-DQ3.2 beta complexes. We have developed a rapid and accurate protocol for docking candidate peptides into the DQ3.2 beta receptor and a scoring function to discriminate binders from the background. The scoring function was rigorously trained, tested and validated using experimentally verified DQ3.2 beta binding and non-binding peptides obtained from biochemical and functional studies. Results: Our model predicts DQ3.2 beta binding peptides with high accuracy [area under the receiver operating characteristic (ROC) curve A(ROC) > 0.90], compared with experimental data. We investigated the binding patterns of DQ3.2 beta peptides and illustrate that several registers exist within a candidate binding peptide. Further analysis reveals that peptides with multiple registers occur predominantly for high-affinity binders.

Understanding the crystallography of the eutectoid microstructure in a Zn-Al alloy using the edge-to-edge matching model

The orientation relationship (OR) between the beta(Zn) phase and the alpha(Al) phase and the corresponding habit planes in a Zn-Al eutectoid alloy were accurately determined using convergent beam Kikuchi line diffraction patterns. In addition to the previously reported OR. [11 (2) over bar0](beta)parallel to[110](alpha), (0002)(beta)parallel to ((1) over bar 11)alpha, two new ORs were observed. They are: [11 (2) over bar0](beta)parallel to [110], ((1) over bar 101)(beta) 0.82 degrees from (002)(alpha) and [(1) over bar 100](beta)parallel to[112](alpha), (0002)(beta) 4.5 degrees from (111)(alpha). These ORs can be explained and understood using the recently developed edge-to-edge matching model. (c) 2006 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Imitating the neighbours: vocal dialect matching in a mimic-model system

Vocal mimicry provides a unique system for investigating song learning and cultural evolution in birds. Male lyrebirds produce complex vocal displays that include extensive and accurate mimicry of many other bird species. We recorded and analysed the songs of the Albert's lyrebird (Menura alberti) and its most commonly imitated model species, the satin bowerbird (Ptilonorhynchus violaceus), at six sites in southeast Queensland, Australia. We show that each population of lyrebirds faithfully reproduces the song of the local population of bowerbirds. Within a population, lyrebirds show less variation in song structure than the available variation in the songs of the models. These results provide the first quantitative evidence for dialect matching in the songs of two species that have no direct ecological relationship.

HLA and Genomewide Allele Sharing in Dizygotic Twins

Gametic selection during fertilization or the effects of specific genotypes on the viability of embryos may cause a skewed transmission of chromosomes to surviving offspring. A recent analysis of transmission distortion in humans reported significant excess sharing among full siblings. Dizygotic (DZ) twin pairs are a special case of the simultaneous survival of two genotypes, and there have been reports of DZ pairs with excess allele sharing around the HLA locus, a candidate locus for embryo survival. We performed an allele-sharing study of 1,592 DZ twin pairs from two independent Australian cohorts, of which 1,561 pairs were informative for linkage on chromosome 6. We also analyzed allele sharing in 336 DZ twin pairs from The Netherlands. We found no evidence of excess allele sharing, either at the HLA locus or in the rest of the genome. In contrast, we found evidence of a small but significant (P = .003 for the Australian sample) genomewide deficit in the proportion of two alleles shared identical by descent among DZ twin pairs. We reconciled conflicting evidence in the literature for excess genomewide allele sharing by performing a simulation study that shows how undetected genotyping errors can lead to an apparent deficit or excess of allele sharing among sibling pairs, dependent on whether parental genotypes are known. Our results imply that gene-mapping studies based on affected sibling pairs that include DZ pairs will not suffer from false-positive results due to loci involved in embryo survival.

Studies of HLA associations in male and female patients with Guillain-Barre syndrome (GBS) and chronic inflammatory demyelinating polyradiculoneuropathy (CIDP)

HLA associations are found to differ with the gender of the patient in some autoimmune diseases. Here we have investigated whether there are gender-related HLA associations in Guillain-Barre syndrome (GBS) and chronic inflammatory demyelinating polyradiculoneuropathy (CIDP), both of which occur more frequently in male patients than in females. In GBS, no particular HLA associations were noted, except for a slight negative association in both males and females for carriage of HLA-DR5. In CIDP, the gene frequency and the frequency of individuals positive for HLA-DR2 were greater in female patients than female controls, although this was statistically significant only for the gene frequency. Furthermore more female CIDP patients were homozygous for DR2, than male CIDP patients, or male or female controls and patients with GBS. This suggests that sex-related factors may interact with the risk associated with carriage of HLA-DR2 for development of CIDP. (c) 2006 Published by Elsevier B.V.

Edge-to-edge matching - The fundamentals

The basis of the present authors' edge-to-edge matching model for understanding the crystallography of partially coherent precipitates is the minimization of the energy of the interface between the two phases. For relatively simple crystal structures, this energy minimization occurs when close-packed, or relatively close-packed, rows of atoms match across the interface. Hence, the fundamental principle behind edge-to-edge matching is that the directions in each phase that correspond to the edges of the planes that meet in the interface should be close-packed, or relatively close-packed, rows of atoms. A few of the recently reported examples of what is termed edge-to-edge matching appear to ignore this fundamental principle. By comparing theoretical predictions with available experimental data, this article will explore the validity of this critical atom-row coincidence condition, in situations where the two phases have simple crystal Structures and in those where the precipitate has a more complex structure.

Machine learning for matching astronomy catalogues

An emerging issue in the field of astronomy is the integration, management and utilization of databases from around the world to facilitate scientific discovery. In this paper, we investigate application of the machine learning techniques of support vector machines and neural networks to the problem of amalgamating catalogues of galaxies as objects from two disparate data sources: radio and optical. Formulating this as a classification problem presents several challenges, including dealing with a highly unbalanced data set. Unlike the conventional approach to the problem (which is based on a likelihood ratio) machine learning does not require density estimation and is shown here to provide a significant improvement in performance. We also report some experiments that explore the importance of the radio and optical data features for the matching problem.

Fast stereo matching by iterated dynamic programming and quadtree subregioning

The application of energy minimisation methods for stereo matching has been demonstrated to produce high quality disparity maps. However the majority of these methods are known to be computationally expensive, requiring minutes or even hours of computation. We propose a fast minimisation scheme that produces strongly competitive results for significantly reduced computation, requiring only a few seconds of computation. In this paper, we present our iterated dynamic programming algorithm along with a quadtree subregioning process for fast stereo matching.