Location and scale mixtures of Gaussians with flexible tail behaviour: Properties, inference and application to multivariate clustering

The family of location and scale mixtures of Gaussians has the ability to generate a number of flexible distributional forms. The family nests as particular cases several important asymmetric distributions like the Generalized Hyperbolic distribution. The Generalized Hyperbolic distribution in turn nests many other well known distributions such as the Normal Inverse Gaussian. In a multivariate setting, an extension of the standard location and scale mixture concept is proposed into a so called multiple scaled framework which has the advantage of allowing different tail and skewness behaviours in each dimension with arbitrary correlation between dimensions. Estimation of the parameters is provided via an EM algorithm and extended to cover the case of mixtures of such multiple scaled distributions for application to clustering. Assessments on simulated and real data confirm the gain in degrees of freedom and flexibility in modelling data of varying tail behaviour and directional shape.

A new family of multivariate heavy-tailed distributions with variable marginal amounts of tailweight: Application to robust clustering

We propose a family of multivariate heavy-tailed distributions that allow variable marginal amounts of tailweight. The originality comes from introducing multidimensional instead of univariate scale variables for the mixture of scaled Gaussian family of distributions. In contrast to most existing approaches, the derived distributions can account for a variety of shapes and have a simple tractable form with a closed-form probability density function whatever the dimension. We examine a number of properties of these distributions and illustrate them in the particular case of Pearson type VII and t tails. For these latter cases, we provide maximum likelihood estimation of the parameters and illustrate their modelling flexibility on simulated and real data clustering examples.

The plasticity of NBS resistance genes in sorghum is driven by multiple evolutionary processes

Background Increased disease resistance is a key target of cereal breeding programs, with disease outbreaks continuing to threaten global food production, particularly in Africa. Of the disease resistance gene families, the nucleotide-binding site plus leucine-rich repeat (NBS-LRR) family is the most prevalent and ancient and is also one of the largest gene families known in plants. The sequence diversity in NBS-encoding genes was explored in sorghum, a critical food staple in Africa, with comparisons to rice and maize and with comparisons to fungal pathogen resistance QTL. Results In sorghum, NBS-encoding genes had significantly higher diversity in comparison to non NBS-encoding genes and were significantly enriched in regions of the genome under purifying and balancing selection, both through domestication and improvement. Ancestral genes, pre-dating species divergence, were more abundant in regions with signatures of selection than in regions not under selection. Sorghum NBS-encoding genes were also significantly enriched in the regions of the genome containing fungal pathogen disease resistance QTL; with the diversity of the NBS-encoding genes influenced by the type of co-locating biotic stress resistance QTL. Conclusions NBS-encoding genes are under strong selection pressure in sorghum, through the contrasting evolutionary processes of purifying and balancing selection. Such contrasting evolutionary processes have impacted ancestral genes more than species-specific genes. Fungal disease resistance hot-spots in the genome, with resistance against multiple pathogens, provides further insight into the mechanisms that cereals use in the “arms race” with rapidly evolving pathogens in addition to providing plant breeders with selection targets for fast-tracking the development of high performing varieties with more durable pathogen resistance.

Feature selection and the concept of immediate neighborhood in the context of clustering techniques

The concept of feature selection in a nonparametric unsupervised learning environment is practically undeveloped because no true measure for the effectiveness of a feature exists in such an environment. The lack of a feature selection phase preceding the clustering process seriously affects the reliability of such learning. New concepts such as significant features, level of significance of features, and immediate neighborhood are introduced which result in meeting implicitly the need for feature slection in the context of clustering techniques.

Robust automatic face clustering in news video

Clustering identities in a video is a useful task to aid in video search, annotation and retrieval, and cast identification. However, reliably clustering faces across multiple videos is challenging task due to variations in the appearance of the faces, as videos are captured in an uncontrolled environment. A person's appearance may vary due to session variations including: lighting and background changes, occlusions, changes in expression and make up. In this paper we propose the novel Local Total Variability Modelling (Local TVM) approach to cluster faces across a news video corpus; and incorporate this into a novel two stage video clustering system. We first cluster faces within a single video using colour, spatial and temporal cues; after which we use face track modelling and hierarchical agglomerative clustering to cluster faces across the entire corpus. We compare different face recognition approaches within this framework. Experiments on a news video database show that the Local TVM technique is able effectively model the session variation observed in the data, resulting in improved clustering performance, with much greater computational efficiency than other methods.

Contemplating an evolutionary approach to entrepreneurship

This paper explores that application of evolutionary approaches to the study of entrepreneurship. It is argued an evolutionary theory of entrepreneurship must give as much concern to the foundations of evolutionary thought as it does the nature entrepreneurship. The central point being that we must move beyond a debate or preference of the natural selection and adaptationist viewpoints. Only then can the interrelationships between individuals, firms, populations and the environments within which they interact be better appreciated.

e-Extinction: an illusion of knowledge, the presence of ignorance, or evolutionary fate?

At present, the rate of small firm adoption of the Internet's ubiquitous World Wide Web (the web) far exceeds the actual exploitation its commercial potential. An inability to strategically acquire, comprehend and use external knowledge is proposed as a major barrier to optimal exploitation of the Internet. This paper discusses the limitations of applying market orientation theory to explain and guide small firm exploitation of the web. Absorptive capacity is introduced as an alternative theory that when viewed from an evolutionary perspective provides potentially more insightful discussion. An inability to detect emerging business model dominant designs is suggested to be a mixture of the nature of the technology that supports the Internet and underdeveloped small firm knowledge processing capabilities. We conclude with consideration of the practical and theoretical implications that arise from the paper.

Bayesian statistical analysis of bacterial diversity

Bacteria play an important role in many ecological systems. The molecular characterization of bacteria using either cultivation-dependent or cultivation-independent methods reveals the large scale of bacterial diversity in natural communities, and the vastness of subpopulations within a species or genus. Understanding how bacterial diversity varies across different environments and also within populations should provide insights into many important questions of bacterial evolution and population dynamics. This thesis presents novel statistical methods for analyzing bacterial diversity using widely employed molecular fingerprinting techniques. The first objective of this thesis was to develop Bayesian clustering models to identify bacterial population structures. Bacterial isolates were identified using multilous sequence typing (MLST), and Bayesian clustering models were used to explore the evolutionary relationships among isolates. Our method involves the inference of genetic population structures via an unsupervised clustering framework where the dependence between loci is represented using graphical models. The population dynamics that generate such a population stratification were investigated using a stochastic model, in which homologous recombination between subpopulations can be quantified within a gene flow network. The second part of the thesis focuses on cluster analysis of community compositional data produced by two different cultivation-independent analyses: terminal restriction fragment length polymorphism (T-RFLP) analysis, and fatty acid methyl ester (FAME) analysis. The cluster analysis aims to group bacterial communities that are similar in composition, which is an important step for understanding the overall influences of environmental and ecological perturbations on bacterial diversity. A common feature of T-RFLP and FAME data is zero-inflation, which indicates that the observation of a zero value is much more frequent than would be expected, for example, from a Poisson distribution in the discrete case, or a Gaussian distribution in the continuous case. We provided two strategies for modeling zero-inflation in the clustering framework, which were validated by both synthetic and empirical complex data sets. We show in the thesis that our model that takes into account dependencies between loci in MLST data can produce better clustering results than those methods which assume independent loci. Furthermore, computer algorithms that are efficient in analyzing large scale data were adopted for meeting the increasing computational need. Our method that detects homologous recombination in subpopulations may provide a theoretical criterion for defining bacterial species. The clustering of bacterial community data include T-RFLP and FAME provides an initial effort for discovering the evolutionary dynamics that structure and maintain bacterial diversity in the natural environment.

Developing an evolutionary/ecological approach in enterprise education

This paper presents an ecological/evolutionary approach to enterprise education. Ecological approaches are used at the University of Tasmania to heighten the awareness of students to a raft of difficult to observe environmental factors associated with developing enterprising ideas. At Sheffield University, the discovery and exploitation of entrepreneurial opportunities is viewed as a co-evolving system of emerging business ideas, and routines/heuristics respectively. It is argued that using both approaches enables students to develop a greater awareness of their situated environment, and ultimately the degree of fit between their learning process and a changing external world. The authors argue that in order to improve the chances of longer-term survival what is needed is a new level of organisation where the individual is capable of developing a representation of the external world that he or she can use to sense the appropriateness of local decisions. This reinterpretation of events allows individuals to step back and examine the broader consequences of their actions through the interpretation and anticipation of feedback from the environment. These approaches thus seek to develop practice-based heuristics which individuals can use to make sense of their lived experiences, as they learn to evolve in an increasingly complex world.

A co-evolutionary relationship exists between Endoraecium (Pucciniales) and its Acacia hosts in Australia

Endoraecium is a genus of rust fungi that infects several species of Acacia in Australia, South-East Asia and Hawaii. This study investigated the systematics of Endoraecium from 55 specimens in Australia based on a combined morphological and molecular approach. Phylogenetic analyses were conducted on partitioned datasets of loci from ribosomal and mitochondrial DNA. The recovered molecular phylogeny supported a recently published taxonomy based on morphology and host range that divided Endoraecium digitatum into five species. Spore morphology is synapomorphic and there is evidence Endoraecium co-evolved with its Acacia hosts. The broad host ranges of E. digitatum, E. parvum, E. phyllodiorum and E. violae-faustiae are revised in light of this study, and nine new species of Endoraecium are described from Australia based on host taxonomy, morphology and phylogenetic concordance.

Clustering Users of Online Content Service

Online content services can greatly benefit from personalisation features that enable delivery of content that is suited to each user's specific interests. This thesis presents a system that applies text analysis and user modeling techniques in an online news service for the purpose of personalisation and user interest analysis. The system creates a detailed thematic profile for each content item and observes user's actions towards content items to learn user's preferences. A handcrafted taxonomy of concepts, or ontology, is used in profile formation to extract relevant concepts from the text. User preference learning is automatic and there is no need for explicit preference settings or ratings from the user. Learned user profiles are segmented into interest groups using clustering techniques with the objective of providing a source of information for the service provider. Some theoretical background for chosen techniques is presented while the main focus is in finding practical solutions to some of the current information needs, which are not optimally served with traditional techniques.

Feature selection and the concept of immediate neighborhood in the context of clustering techniques

The concept of feature selection in a nonparametric unsupervised learning environment is practically undeveloped because no true measure for the effectiveness of a feature exists in such an environment. The lack of a feature selection phase preceding the clustering process seriously affects the reliability of such learning. New concepts such as significant features, level of significance of features, and immediate neighborhood are introduced which result in meeting implicitly the need for feature slection in the context of clustering techniques.

An innovative clustering technique for unsupervized learning in the context of remotely sensed earth resources data analysis

A new clustering technique, based on the concept of immediato neighbourhood, with a novel capability to self-learn the number of clusters expected in the unsupervized environment, has been developed. The method compares favourably with other clustering schemes based on distance measures, both in terms of conceptual innovations and computational economy. Test implementation of the scheme using C-l flight line training sample data in a simulated unsupervized mode has brought out the efficacy of the technique. The technique can easily be implemented as a front end to established pattern classification systems with supervized learning capabilities to derive unified learning systems capable of operating in both supervized and unsupervized environments. This makes the technique an attractive proposition in the context of remotely sensed earth resources data analysis wherein it is essential to have such a unified learning system capability.

Modeling ecological and evolutionary processes in spatially structured populations

Many species inhabit fragmented landscapes, resulting either from anthropogenic or from natural processes. The ecological and evolutionary dynamics of spatially structured populations are affected by a complex interplay between endogenous and exogenous factors. The metapopulation approach, simplifying the landscape to a discrete set of patches of breeding habitat surrounded by unsuitable matrix, has become a widely applied paradigm for the study of species inhabiting highly fragmented landscapes. In this thesis, I focus on the construction of biologically realistic models and their parameterization with empirical data, with the general objective of understanding how the interactions between individuals and their spatially structured environment affect ecological and evolutionary processes in fragmented landscapes. I study two hierarchically structured model systems, which are the Glanville fritillary butterfly in the Åland Islands, and a system of two interacting aphid species in the Tvärminne archipelago, both being located in South-Western Finland. The interesting and challenging feature of both study systems is that the population dynamics occur over multiple spatial scales that are linked by various processes. My main emphasis is in the development of mathematical and statistical methodologies. For the Glanville fritillary case study, I first build a Bayesian framework for the estimation of death rates and capture probabilities from mark-recapture data, with the novelty of accounting for variation among individuals in capture probabilities and survival. I then characterize the dispersal phase of the butterflies by deriving a mathematical approximation of a diffusion-based movement model applied to a network of patches. I use the movement model as a building block to construct an individual-based evolutionary model for the Glanville fritillary butterfly metapopulation. I parameterize the evolutionary model using a pattern-oriented approach, and use it to study how the landscape structure affects the evolution of dispersal. For the aphid case study, I develop a Bayesian model of hierarchical multi-scale metapopulation dynamics, where the observed extinction and colonization rates are decomposed into intrinsic rates operating specifically at each spatial scale. In summary, I show how analytical approaches, hierarchical Bayesian methods and individual-based simulations can be used individually or in combination to tackle complex problems from many different viewpoints. In particular, hierarchical Bayesian methods provide a useful tool for decomposing ecological complexity into more tractable components.

ORP1L, a new Rab7 effector and regulator of late endosome functions

Oxysterol binding protein (OSBP) homologues have been found in eukaryotic organisms ranging from yeast to humans. These evolutionary conserved proteins have in common the presence of an OSBP-related domain (ORD) which contains the fully conserved EQVSHHPP sequence motif. The ORD forms a barrel structure that binds sterols in its interior. Other domains and sequence elements found in OSBP-homologues include pleckstrin homology domains, ankyrin repeats and two phenylalanines in an acidic tract (FFAT) motifs, which target the proteins to distinct subcellular compartments. OSBP homologues have been implicated in a wide range of intracellular processes, including vesicle trafficking, lipid metabolism and cell signaling, but little is known about the functional mechanisms of these proteins. The human family of OSBP homologues consists of twelve OSBP-related proteins (ORP). This thesis work is focused on one of the family members, ORP1, of which two variants were found to be expressed tissue-specifically in humans. The shorter variant, ORP1S contains an ORD only. The N-terminally extended variant, ORP1L, comprises a pleckstrin homology domain and three ankyrin repeats in addition to the ORD. The two ORP1 variants differ in intracellular localization. ORP1S is cytosolic, while the ankyrin repeat region of ORP1L targets the protein to late endosomes/lysosomes. This part of ORP1L also has profound effects on late endosomal morphology, inducing perinuclear clustering of late endosomes. A central aim of this study was to identify molecular interactions of ORP1L on late endosomes. The morphological changes of late endosomes induced by overexpressed ORP1L implies involvement of small Rab GTPases, regulators of organelle motility, tethering, docking and/or fusion, in generation of the phenotype. A direct interaction was demonstrated between ORP1L and active Rab7. ORP1L prolongs the active state of Rab7 by stabilizing its GTP-bound form. The clustering of late endosomes/lysosomes was also shown to be linked to the minus end-directed microtubule-based dynein-dynactin motor complex through the ankyrin repeat region of ORP1L. ORP1L, Rab7 and the Rab7-interacting lysosomal protein (RILP) were found to be part of the same effector complex recruiting the dynein-dynactin complex to late endosomes, thereby promoting minus end-directed movement. The proteins were found to be physically close to each other on late endosomes and RILP was found to stabilize the ORP1L-Rab7 interaction. It is possible that ORP1L and RILP bind to each other through their C-terminal and N-terminal regions, respectively, when they are bridged by Rab7. With the results of this study we have been able to place a member of the uncharacterized OSBP-family, ORP1L, in the endocytic pathway, where it regulates motility and possibly fusion of late endosomes through interaction with the small GTPase Rab7.