Multifractal characterisation and analysis of complex networks

Complex networks have been studied extensively due to their relevance to many real-world systems such as the world-wide web, the internet, biological and social systems. During the past two decades, studies of such networks in different fields have produced many significant results concerning their structures, topological properties, and dynamics. Three well-known properties of complex networks are scale-free degree distribution, small-world effect and self-similarity. The search for additional meaningful properties and the relationships among these properties is an active area of current research. This thesis investigates a newer aspect of complex networks, namely their multifractality, which is an extension of the concept of selfsimilarity. The first part of the thesis aims to confirm that the study of properties of complex networks can be expanded to a wider field including more complex weighted networks. Those real networks that have been shown to possess the self-similarity property in the existing literature are all unweighted networks. We use the proteinprotein interaction (PPI) networks as a key example to show that their weighted networks inherit the self-similarity from the original unweighted networks. Firstly, we confirm that the random sequential box-covering algorithm is an effective tool to compute the fractal dimension of complex networks. This is demonstrated on the Homo sapiens and E. coli PPI networks as well as their skeletons. Our results verify that the fractal dimension of the skeleton is smaller than that of the original network due to the shortest distance between nodes is larger in the skeleton, hence for a fixed box-size more boxes will be needed to cover the skeleton. Then we adopt the iterative scoring method to generate weighted PPI networks of five species, namely Homo sapiens, E. coli, yeast, C. elegans and Arabidopsis Thaliana. By using the random sequential box-covering algorithm, we calculate the fractal dimensions for both the original unweighted PPI networks and the generated weighted networks. The results show that self-similarity is still present in generated weighted PPI networks. This implication will be useful for our treatment of the networks in the third part of the thesis. The second part of the thesis aims to explore the multifractal behavior of different complex networks. Fractals such as the Cantor set, the Koch curve and the Sierspinski gasket are homogeneous since these fractals consist of a geometrical figure which repeats on an ever-reduced scale. Fractal analysis is a useful method for their study. However, real-world fractals are not homogeneous; there is rarely an identical motif repeated on all scales. Their singularity may vary on different subsets; implying that these objects are multifractal. Multifractal analysis is a useful way to systematically characterize the spatial heterogeneity of both theoretical and experimental fractal patterns. However, the tools for multifractal analysis of objects in Euclidean space are not suitable for complex networks. In this thesis, we propose a new box covering algorithm for multifractal analysis of complex networks. This algorithm is demonstrated in the computation of the generalized fractal dimensions of some theoretical networks, namely scale-free networks, small-world networks, random networks, and a kind of real networks, namely PPI networks of different species. Our main finding is the existence of multifractality in scale-free networks and PPI networks, while the multifractal behaviour is not confirmed for small-world networks and random networks. As another application, we generate gene interactions networks for patients and healthy people using the correlation coefficients between microarrays of different genes. Our results confirm the existence of multifractality in gene interactions networks. This multifractal analysis then provides a potentially useful tool for gene clustering and identification. The third part of the thesis aims to investigate the topological properties of networks constructed from time series. Characterizing complicated dynamics from time series is a fundamental problem of continuing interest in a wide variety of fields. Recent works indicate that complex network theory can be a powerful tool to analyse time series. Many existing methods for transforming time series into complex networks share a common feature: they define the connectivity of a complex network by the mutual proximity of different parts (e.g., individual states, state vectors, or cycles) of a single trajectory. In this thesis, we propose a new method to construct networks of time series: we define nodes by vectors of a certain length in the time series, and weight of edges between any two nodes by the Euclidean distance between the corresponding two vectors. We apply this method to build networks for fractional Brownian motions, whose long-range dependence is characterised by their Hurst exponent. We verify the validity of this method by showing that time series with stronger correlation, hence larger Hurst exponent, tend to have smaller fractal dimension, hence smoother sample paths. We then construct networks via the technique of horizontal visibility graph (HVG), which has been widely used recently. We confirm a known linear relationship between the Hurst exponent of fractional Brownian motion and the fractal dimension of the corresponding HVG network. In the first application, we apply our newly developed box-covering algorithm to calculate the generalized fractal dimensions of the HVG networks of fractional Brownian motions as well as those for binomial cascades and five bacterial genomes. The results confirm the monoscaling of fractional Brownian motion and the multifractality of the rest. As an additional application, we discuss the resilience of networks constructed from time series via two different approaches: visibility graph and horizontal visibility graph. Our finding is that the degree distribution of VG networks of fractional Brownian motions is scale-free (i.e., having a power law) meaning that one needs to destroy a large percentage of nodes before the network collapses into isolated parts; while for HVG networks of fractional Brownian motions, the degree distribution has exponential tails, implying that HVG networks would not survive the same kind of attack.

Bayesian mixtures for modelling complex medical data : a case study in Parkinson’s disease

Mixture models are a flexible tool for unsupervised clustering that have found popularity in a vast array of research areas. In studies of medicine, the use of mixtures holds the potential to greatly enhance our understanding of patient responses through the identification of clinically meaningful clusters that, given the complexity of many data sources, may otherwise by intangible. Furthermore, when developed in the Bayesian framework, mixture models provide a natural means for capturing and propagating uncertainty in different aspects of a clustering solution, arguably resulting in richer analyses of the population under study. This thesis aims to investigate the use of Bayesian mixture models in analysing varied and detailed sources of patient information collected in the study of complex disease. The first aim of this thesis is to showcase the flexibility of mixture models in modelling markedly different types of data. In particular, we examine three common variants on the mixture model, namely, finite mixtures, Dirichlet Process mixtures and hidden Markov models. Beyond the development and application of these models to different sources of data, this thesis also focuses on modelling different aspects relating to uncertainty in clustering. Examples of clustering uncertainty considered are uncertainty in a patient’s true cluster membership and accounting for uncertainty in the true number of clusters present. Finally, this thesis aims to address and propose solutions to the task of comparing clustering solutions, whether this be comparing patients or observations assigned to different subgroups or comparing clustering solutions over multiple datasets. To address these aims, we consider a case study in Parkinson’s disease (PD), a complex and commonly diagnosed neurodegenerative disorder. In particular, two commonly collected sources of patient information are considered. The first source of data are on symptoms associated with PD, recorded using the Unified Parkinson’s Disease Rating Scale (UPDRS) and constitutes the first half of this thesis. The second half of this thesis is dedicated to the analysis of microelectrode recordings collected during Deep Brain Stimulation (DBS), a popular palliative treatment for advanced PD. Analysis of this second source of data centers on the problems of unsupervised detection and sorting of action potentials or "spikes" in recordings of multiple cell activity, providing valuable information on real time neural activity in the brain.

Chaos or complex systems? Identifying factors influencing the success of international and NESB graduate research students in Engineering and Information Technology Fields

The paper explores the results an on-going research project to identify factors influencing the success of international and non-English speaking background (NESB) gradúate students in the fields of Engineering and IT at three Australian universities: the Queensland University of Technology (QUT), the University of Western Australia (UWA), and Curtin University (CU). While the larger study explores the influence of factors from both sides of the supervision equation (e.g., students and supervisors), this paper focusses primarily on the results of an online survey involving 227 international and/or NESB graduate students in the areas of Engineering and IT at the three universities. The study reveals cross-cultural differences in perceptions of student and supervisor roles, as well as differences in the understanding of the requirements of graduate study within the Australian Higher Education context. We argue that in order to assist international and NESB research students to overcome such culturally embedded challenges, it is important to develop a model which recognizes the complex interactions of factors from both sides of the supervision relationship, in order to understand this cohort‟s unique pedagogical needs and develop intercultural sensitivity within postgraduate research supervision.

A Bayesian analysis of an agricultural field trial with three spatial dimensions

Modern technology now has the ability to generate large datasets over space and time. Such data typically exhibit high autocorrelations over all dimensions. The field trial data motivating the methods of this paper were collected to examine the behaviour of traditional cropping and to determine a cropping system which could maximise water use for grain production while minimising leakage below the crop root zone. They consist of moisture measurements made at 15 depths across 3 rows and 18 columns, in the lattice framework of an agricultural field. Bayesian conditional autoregressive (CAR) models are used to account for local site correlations. Conditional autoregressive models have not been widely used in analyses of agricultural data. This paper serves to illustrate the usefulness of these models in this field, along with the ease of implementation in WinBUGS, a freely available software package. The innovation is the fitting of separate conditional autoregressive models for each depth layer, the ‘layered CAR model’, while simultaneously estimating depth profile functions for each site treatment. Modelling interest also lay in how best to model the treatment effect depth profiles, and in the choice of neighbourhood structure for the spatial autocorrelation model. The favoured model fitted the treatment effects as splines over depth, and treated depth, the basis for the regression model, as measured with error, while fitting CAR neighbourhood models by depth layer. It is hierarchical, with separate onditional autoregressive spatial variance components at each depth, and the fixed terms which involve an errors-in-measurement model treat depth errors as interval-censored measurement error. The Bayesian framework permits transparent specification and easy comparison of the various complex models compared.

Cyclin-dependent kinase-mediated phosphorylation of RBP1 and pRb promotes their dissociation to mediate release of the SAP30·mSin3·HDAC transcriptional repressor complex

Eukaryotic cell cycle progression is mediated by phosphorylation of protein substrates by cyclin-dependent kinases (CDKs). A critical substrate of CDKs is the product of the retinoblastoma tumor suppressor gene, pRb, which inhibits G1-S phase cell cycle progression by binding and repressing E2F transcription factors. CDK-mediated phosphorylation of pRb alleviates this inhibitory effect to promote G1-S phase cell cycle progression. pRb represses transcription by binding to the E2F transactivation domain and recruiting the mSin3·histone deacetylase (HDAC) transcriptional repressor complex via the retinoblastoma-binding protein 1 (RBP1). RBP1 binds to the pocket region of pRb via an LXCXE motif and to the SAP30 subunit of the mSin3·HDAC complex and, thus, acts as a bridging protein in this multisubunit complex. In the present study we identified RBP1 as a novel CDK substrate. RBP1 is phosphorylated by CDK2 on serines 864 and 1007, which are N- and C-terminal to the LXCXE motif, respectively. CDK2-mediated phosphorylation of RBP1 or pRb destabilizes their interaction in vitro, with concurrent phosphorylation of both proteins leading to their dissociation. Consistent with these findings, RBP1 phosphorylation is increased during progression from G 1 into S-phase, with a concurrent decrease in its association with pRb in MCF-7 breast cancer cells. These studies provide new mechanistic insights into CDK-mediated regulation of the pRb tumor suppressor during cell cycle progression, demonstrating that CDK-mediated phosphorylation of both RBP1 and pRb induces their dissociation to mediate release of the mSin3·HDAC transcriptional repressor complex from pRb to alleviate transcriptional repression of E2F.

The reliability, accuracy and minimal detectable difference of a multi-segment kinematic model of the foot-shoe complex

Kinematic models are commonly used to quantify foot and ankle kinematics, yet no marker sets or models have been proven reliable or accurate when wearing shoes. Further, the minimal detectable difference of a developed model is often not reported. We present a kinematic model that is reliable, accurate and sensitive to describe the kinematics of the foot–shoe complex and lower leg during walking gait. In order to achieve this, a new marker set was established, consisting of 25 markers applied on the shoe and skin surface, which informed a four segment kinematic model of the foot–shoe complex and lower leg. Three independent experiments were conducted to determine the reliability, accuracy and minimal detectable difference of the marker set and model. Inter-rater reliability of marker placement on the shoe was proven to be good to excellent (ICC = 0.75–0.98) indicating that markers could be applied reliably between raters. Intra-rater reliability was better for the experienced rater (ICC = 0.68–0.99) than the inexperienced rater (ICC = 0.38–0.97). The accuracy of marker placement along each axis was <6.7 mm for all markers studied. Minimal detectable difference (MDD90) thresholds were defined for each joint; tibiocalcaneal joint – MDD90 = 2.17–9.36°, tarsometatarsal joint – MDD90 = 1.03–9.29° and the metatarsophalangeal joint – MDD90 = 1.75–9.12°. These thresholds proposed are specific for the description of shod motion, and can be used in future research designed at comparing between different footwear.

Complex events in business processes

Flow-oriented process modeling languages have a long tradition in the area of Business Process Management and are widely used for capturing activities with their behavioral and data dependencies. Individual events were introduced for triggering process instantiation and activities. However, real-world business cases drive the need for also covering complex event patterns as they are known in the field of Complex Event Processing. Therefore, this paper puts forward a catalog of requirements for handling complex events in process models, which can be used as reference framework for assessing process definition languages and systems. An assessment of BPEL and BPMN is provided.

Infrastructure transitions toward sustainability : a complex adaptive systems perspective

To ensure infrastructure assets are procured and maintained by government on behalf of citizens, appropriate policy and institutional architecture is needed, particularly if a fundamental shift to more sustainable infrastructure is the goal. The shift in recent years from competitive and resource-intensive procurement to more collaborative and sustainable approaches to infrastructure governance is considered a major transition in infrastructure procurement systems. In order to better understand this transition in infrastructure procurement arrangements, the concept of emergence from Complex Adaptive Systems (CAS) theory is offered as a key construct. Emergence holds that micro interactions can result in emergent macro order. Applying the concept of emergence to infrastructure procurement, this research examines how interaction of agents in individual projects can result in different industry structural characteristics. The paper concludes that CAS theory, and particularly the concept of ‘emergence’, provides a useful construct to understand infrastructure procurement dynamics and progress towards sustainability.

A review of models and model usage scenarios for an airport complex system

Airports represent the epitome of complex systems with multiple stakeholders, multiple jurisdictions and complex interactions between many actors. The large number of existing models that capture different aspects of the airport are a testament to this. However, these existing models do not consider in a systematic sense modelling requirements nor how stakeholders such as airport operators or airlines would make use of these models. This can detrimentally impact on the verification and validation of models and makes the development of extensible and reusable modelling tools difficult. This paper develops from the Concept of Operations (CONOPS) framework a methodology to help structure the review and development of modelling capabilities and usage scenarios. The method is applied to the review of existing airport terminal passenger models. It is found that existing models can be broadly categorised according to four usage scenarios: capacity planning, operational planning and design, security policy and planning, and airport performance review. The models, the performance metrics that they evaluate and their usage scenarios are discussed. It is found that capacity and operational planning models predominantly focus on performance metrics such as waiting time, service time and congestion whereas performance review models attempt to link those to passenger satisfaction outcomes. Security policy models on the other hand focus on probabilistic risk assessment. However, there is an emerging focus on the need to be able to capture trade-offs between multiple criteria such as security and processing time. Based on the CONOPS framework and literature findings, guidance is provided for the development of future airport terminal models.

Creativity in policing: building the necessary skills to solve complex and protracted investigations

Despite an increased focus on proactive policing in recent years, criminal investigation is still perhaps the most important task of any law enforcement agency. As a result, the skills required to carry out a successful investigation or to be an ‘effective detective’ have been subjected to much attention and debate (Smith and Flanagan, 2000; Dean, 2000; Fahsing and Gottschalk, 2008:652). Stelfox (2008:303) states that “The service’s capacity to carry out investigations comprises almost entirely the expertise of investigators.” In this respect, Dean (2000) highlighted the need to profile criminal investigators in order to promote further understanding of the cognitive approaches they take to the process of criminal investigation. As a result of his research, Dean (2000) produced a theoretical framework of criminal investigation, which included four disparate cognitive or ‘thinking styles’. These styles were the ‘Method’, ‘Challenge’, ‘Skill’ and ‘Risk’. While the Method and Challenge styles deal with adherence to Standard Operating Procedures (SOPs) and the internal ‘drive’ that keeps an investigator going, the Skill and Risk styles both tap on the concept of creativity in policing. It is these two latter styles that provide the focus for this paper. This paper presents a brief discussion on Dean’s (2000) Skill and Risk styles before giving an overview of the broader literature on creativity in policing. The potential benefits of a creative approach as well as some hurdles which need to be overcome when proposing the integration of creativity within the policing sector are then discussed. Finally, the paper concludes by proposing further research into Dean’s (2000) skill and risk styles and also by stressing the need for significant changes to the structure and approach of the traditional policing organisation before creativity in policing is given the status it deserves.

Syntaxin 6 and Vti1b form a novel SNARE complex, which is upregulated in activated macrophages to facilitate exocytosis of TNF.

A key function of activated macrophages is to secrete proinflammatory cytokines such as TNF; however, the intracellular pathway and machinery responsible for cytokine trafficking and secretion is largely undefined. Here we show that individual SNARE proteins involved in vesicle docking and fusion are regulated at both gene and protein expression upon stimulation with the bacterial cell wall component lipopolysaccharide. Focusing on two intracellular SNARE proteins, Vti1b and syntaxin 6 (Stx6), we show that they are up-regulated in conjunction with increasing cytokine secretion in activated macrophages and that their levels are selectively titrated to accommodate the volume and timing of post-Golgi cytokine trafficking. In macrophages, Vti1b and syntaxin 6 are localized on intracellular membranes and are present on isolated Golgi membranes and on Golgi-derived TNF� vesicles budded in vitro. By immunoprecipitation, we find that Vti1b and syntaxin 6 interact to form a novel intracellular Q-SNARE complex. Functional studies using overexpression of full-length and truncated proteins show that both Vti1b and syntaxin 6 function and have rate-limiting roles in TNF� trafficking and secretion. This study shows how macrophages have uniquely adapted a novel Golgi-associated SNARE complex to accommodate their requirement for increased cytokine secretion.

A new geological model of Cenozoic New Zealand suggests a complex phylogeographic history for New Zealand terrestrial taxa

The new model of North Island Cenozoic palaeogeography developed by Kamp et al. has a range of important implications for the evolution of New Zealand terrestrial taxa over the past 30 Ma. Key aspects include the prolonged isolation of the biota on the North Island landmass from the larger and more diverse greater South Island, and the founding of North Island taxa from the potentially unusual ecosystem of a small island around Northland. The prolonged period of isolation is expected to have generated deep phylogenetic splits within taxa present on both islands, and an important current aim should be to identify such signals in surviving endemics to start building a picture of the historical phylogeography, and inferred ecology of both islands through the Cenozoic. Given the potential differences in founding terrestrial species and climatic conditions, it seems likely that the ecology may have been very diferent between the North and South Islands. New genetic data from the 10 or so species of extinct moa suggest that the radiation of moa was much more recent than previously suggested, and reveals a complex pattern that is inferred to result from the interplay of the Cenozoic biogeography, marine barriers, and glacial cycles.

Concept combination and the origins of complex cognition

At the core of our uniquely human cognitive abilities is the capacity to see things from different perspectives, or to place them in a new context. We propose that this was made possible by two cognitive transitions. First, the large brain of Homo erectus facilitated the onset of recursive recall: the ability to string thoughts together into a stream of potentially abstract or imaginative thought. This hypothesis is sup-ported by a set of computational models where an artificial society of agents evolved to generate more diverse and valuable cultural outputs under conditions of recursive recall. We propose that the capacity to see things in context arose much later, following the appearance of anatomically modern humans. This second transition was brought on by the onset of contextual focus: the capacity to shift between a minimally contextual analytic mode of thought, and a highly contextual associative mode of thought, conducive to combining concepts in new ways and ‘breaking out of a rut’. When contextual focus is implemented in an art-generating computer program, the resulting artworks are seen as more creative and appealing. We summarize how both transitions can be modeled using a theory of concepts which high-lights the manner in which different contexts can lead to modern humans attributing very different meanings to the interpretation of one concept.

Developing tools to support complex infrastructure decision-making

Purpose – The rapidly changing role of capital city airports has placed demands on surrounding infrastructure. The need for infrastructure management and coordination is increasing as airports and cities grow and share common infrastructure frameworks. The purpose of this paper is to document the changing context in Australia, where the privatisation of airports has stimulated considerable land development with resulting pressures on surrounding infrastructure provision. It aims to describe a tool that is being developed to support decision-making between various stakeholders in the airport region. The use of planning support systems improves both communication and data transfer between stakeholders and provides a foundation for complex decisions on infrastructure. Design/methodology/approach – The research uses a case study approach and focuses on Brisbane International Airport and Brisbane City Council. The research is primarily descriptive and provides an empirical assessment of the challenges of developing and implementing planning support systems as a tool for governance and decision-making. Findings – The research assesses the challenges in implementing a common data platform for stakeholders. Agency data platforms and models, traditional roles in infrastructure planning, and integrating similar data platforms all provide barriers to sharing a common language. The use of a decision support system has to be shared by all stakeholders with a common platform that can be versatile enough to support scenarios and changing conditions. The use of iPadss for scenario modelling provides stakeholders the opportunity to interact, compare scenarios and views, and react with the modellers to explore other options. Originality/value – The research confirms that planning support systems have to be accessible and interactive by their users. The Airport City concept is a new and evolving focus for airport development and will place continuing pressure on infrastructure servicing. A coordinated and efficient approach to infrastructure decision-making is critical, and an interactive planning support system that can model infrastructure scenarios provides a sound tool for governance.

A biphasic scaffold design combined with cell sheet technology for simultaneous regeneration of alveolar bone/periodontal ligament complex

This study describes the design of a biphasic scaffold composed of a Fused Deposition Modeling scaffold (bone compartment) and an electrospun membrane (periodontal compartment) for periodontal regeneration. In order to achieve simultaneous alveolar bone and periodontal ligament regeneration a cell-based strategy was carried out by combining osteoblast culture in the bone compartment and placement of multiple periodontal ligament (PDL) cell sheets on the electrospun membrane. In vitro data showed that the osteoblasts formed mineralized matrix in the bone compartment after 21 days in culture and that the PDL cell sheet harvesting did not induce significant cell death. The cell-seeded biphasic scaffolds were placed onto a dentin block and implanted for 8 weeks in an athymic rat subcutaneous model. The scaffolds were analyzed by μCT, immunohistochemistry and histology. In the bone compartment, a more intense ALP staining was obtained following seeding with osteoblasts, confirming the μCT results which showed higher mineralization density for these scaffolds. A thin mineralized cementum-like tissue was deposited on the dentin surface for the scaffolds incorporating the multiple PDL cell sheets, as observed by H&E and Azan staining. These scaffolds also demonstrated better attachment onto the dentin surface compared to no attachment when no cell sheets were used. In addition, immunohistochemistry revealed the presence of CEMP1 protein at the interface with the dentine. These results demonstrated that the combination of multiple PDL cell sheets and a biphasic scaffold allows the simultaneous delivery of the cells necessary for in vivo regeneration of alveolar bone, periodontal ligament and cementum. © 2012 Elsevier Ltd.