Major histocompatibility complex associations of ankylosing spondylitis are complex and involve further epistasis with ERAP1

Ankylosing spondylitis (AS) is a common, highly heritable, inflammatory arthritis for which HLA-B*27 is the major genetic risk factor, although its role in the aetiology of AS remains elusive. To better understand the genetic basis of the MHC susceptibility loci, we genotyped 7,264 MHC SNPs in 22,647 AS cases and controls of European descent. We impute SNPs, classical HLA alleles and amino-acid residues within HLA proteins, and tested these for association to AS status. Here we show that in addition to effects due to HLA-B*27 alleles, several other HLA-B alleles also affect susceptibility. After controlling for the associated haplotypes in HLA-B, we observe independent associations with variants in the HLA-A, HLA-DPB1 and HLA-DRB1 loci. We also demonstrate that the ERAP1 SNP rs30187 association is not restricted only to carriers of HLA-B*27 but also found in HLA-B*40:01 carriers independently of HLA-B*27 genotype.

Genetic insights into common pathways and complex relationships among iImmune-mediated diseases

Shared aetiopathogenic factors among immune-mediated diseases have long been suggested by their co-familiality and co-occurrence, and molecular support has been provided by analysis of human leukocyte antigen (HLA) haplotypes and genome-wide association studies. The interrelationships can now be better appreciated following the genotyping of large immune disease sample sets on a shared SNP array: the 'Immunochip'. Here, we systematically analyse loci shared among major immune-mediated diseases. This reveals that several diseases share multiple susceptibility loci, but there are many nuances. The most associated variant at a given locus frequently differs and, even when shared, the same allele often has opposite associations. Interestingly, risk alleles conferring the largest effect sizes are usually disease-specific. These factors help to explain why early evidence of extensive 'sharing' is not always reflected in epidemiological overlap. © 2013 Macmillan Publishers Limited. All rights reserved.

Using genome-Wwide complex trait analysis to quantify 'missing heritability' in Parkinson's disease

Genome-wide association studies (GWASs) have been successful at identifying single-nucleotide polymorphisms (SNPs) highly associated with common traits; however, a great deal of the heritable variation associated with common traits remains unaccounted for within the genome. Genome-wide complex trait analysis (GCTA) is a statistical method that applies a linear mixed model to estimate phenotypic variance of complex traits explained by genome-wide SNPs, including those not associated with the trait in a GWAS. We applied GCTA to 8 cohorts containing 7096 case and 19 455 control individuals of European ancestry in order to examine the missing heritability present in Parkinson's disease (PD). We meta-analyzed our initial results to produce robust heritability estimates for PD types across cohorts. Our results identify 27% (95% CI 17-38, P = 8.08E - 08) phenotypic variance associated with all types of PD, 15% (95% CI -0.2 to 33, P = 0.09) phenotypic variance associated with early-onset PD and 31% (95% CI 17-44, P = 1.34E - 05) phenotypic variance associated with late-onset PD. This is a substantial increase from the genetic variance identified by top GWAS hits alone (between 3 and 5%) and indicates there are substantially more risk loci to be identified. Our results suggest that although GWASs are a useful tool in identifying the most common variants associated with complex disease, a great deal of common variants of small effect remain to be discovered. © Published by Oxford University Press 2012.

Complex crystallization dynamics in amorphous germanium observed with dynamic transmission electron microscopy

Crystallization of amorphous germanium (a-Ge) by laser or electron beam heating is a remarkably complex process that involves several distinct modes of crystal growth and the development of intricate microstructural patterns on the nanosecond to ten microsecond time scales. Here we use dynamic transmission electron microscopy (DTEM) to study the fast, complex crystallization dynamics with 10 nm spatial and 15 ns temporal resolution. We have obtained time-resolved real-space images of nanosecond laser-induced crystallization in a-Ge with unprecedentedly high spatial resolution. Direct visualization of the crystallization front allows for time-resolved snapshots of the initiation and roughening of the dendrites on submicrosecond time scales. This growth is followed by a rapid transition to a ledgelike growth mechanism that produces a layered microstructure on a time scale of several microseconds. This study provides insights into the mechanisms governing this complex crystallization process and is a dramatic demonstration of the power of DTEM for studying time-dependent material processes far from equilibrium.

Energy band engineering of complex metal oxides

This doctoral studies focused on the development of new materials for efficient use of solar energy for environmental applications. The research investigated the engineering of the band gap of semiconductor materials to design and optimise visible-light-sensitive photocatalysts. Experimental studies have been combined with computational simulation in order to develop predictive tools for a systematic understanding and design on the crystal and energy band structures of multi-component metal oxides.

Growth and meaning from negotiating the complex journey of being an Emergency Medical Dispatcher

Emergency Medical Dispatchers (EMDs) are charged with taking the calls of those who ring the national emergency number for urgent medical assistance, for dispatching paramedical crews, and for providing as much assistance as can be offered remotely until paramedics arrive. In a job role which is filled with vicarious trauma, emergency situations, pressure, abuse, grief and loss, EMDs are often challenged in maintaining their mental health. The seemingly senseless death of a teenager who commits suicide, the devastating loss of a baby to Sudden Infant Death Syndrome, lives lost through natural disasters, and multiple vehicle fatalities are only a few of the types of experiences EMDs are faced with in the course of their work. However, amongst the horror are positive stories such as coaching a caller to negotiate the birth of a baby and saving a life in jeopardy from heart failure. EMD’s need to cope with the daily challenges of the role; make sense of their work and create meaning in order to have a fulfilled and sustainable career. Although some people in this work struggle greatly to withstand the impacts of vicarious trauma, there are also stories of personal growth. In this Chapter we use a case study to explore how meaning is made for those who are an auditory witness to a continual flux of trauma for others and how the traumatic experiences EMDs bear witness to can also be a catalyst for posttraumatic growth.

Complex symbolic sequence clustering and multiple classifiers for predictive process monitoring

This paper addresses the following predictive business process monitoring problem: Given the execution trace of an ongoing case,and given a set of traces of historical (completed) cases, predict the most likely outcome of the ongoing case. In this context, a trace refers to a sequence of events with corresponding payloads, where a payload consists of a set of attribute-value pairs. Meanwhile, an outcome refers to a label associated to completed cases, like, for example, a label indicating that a given case completed “on time” (with respect to a given desired duration) or “late”, or a label indicating that a given case led to a customer complaint or not. The paper tackles this problem via a two-phased approach. In the first phase, prefixes of historical cases are encoded using complex symbolic sequences and clustered. In the second phase, a classifier is built for each of the clusters. To predict the outcome of an ongoing case at runtime given its (uncompleted) trace, we select the closest cluster(s) to the trace in question and apply the respective classifier(s), taking into account the Euclidean distance of the trace from the center of the clusters. We consider two families of clustering algorithms – hierarchical clustering and k-medoids – and use random forests for classification. The approach was evaluated on four real-life datasets.

Multifaceted Modelling of Complex Business Enterprises

We formalise and present a new generic multifaceted complex system approach for modelling complex business enterprises. Our method has a strong focus on integrating the various data types available in an enterprise which represent the diverse perspectives of various stakeholders. We explain the challenges faced and define a novel approach to converting diverse data types into usable Bayesian probability forms. The data types that can be integrated include historic data, survey data, and management planning data, expert knowledge and incomplete data. The structural complexities of the complex system modelling process, based on various decision contexts, are also explained along with a solution. This new application of complex system models as a management tool for decision making is demonstrated using a railway transport case study. The case study demonstrates how the new approach can be utilised to develop a customised decision support model for a specific enterprise. Various decision scenarios are also provided to illustrate the versatility of the decision model at different phases of enterprise operations such as planning and control.

Dynamic Bayesian network inferencing for non-homogeneous complex systems

Dynamic Bayesian Networks (DBNs) provide a versatile platform for predicting and analysing the behaviour of complex systems. As such, they are well suited to the prediction of complex ecosystem population trajectories under anthropogenic disturbances such as the dredging of marine seagrass ecosystems. However, DBNs assume a homogeneous Markov chain whereas a key characteristics of complex ecosystems is the presence of feedback loops, path dependencies and regime changes whereby the behaviour of the system can vary based on past states. This paper develops a method based on the small world structure of complex systems networks to modularise a non-homogeneous DBN and enable the computation of posterior marginal probabilities given evidence in forwards inference. It also provides an approach for an approximate solution for backwards inference as convergence is not guaranteed for a path dependent system. When applied to the seagrass dredging problem, the incorporation of path dependency can implement conditional absorption and allows release from the zero state in line with environmental and ecological observations. As dredging has a marked global impact on seagrass and other marine ecosystems of high environmental and economic value, using such a complex systems model to develop practical ways to meet the needs of conservation and industry through enhancing resistance and/or recovery is of paramount importance.

How does complex adaptive system theory inform innovation in complex project based organisations?

Innovation enables organisations to endure by responding to emergence and to improve efficiency. Innovation in a complex organisation can be difficult due to complexities contributing to slow decision-making. Complex projects fail due to an inability to respond to emergence which consumes finances and impacts on resources and organisational success. Therefore, for complex organisations to improve on performance and resilience, it would be advantageous to understand how to improve the management of innovation and thus, the ability to respond to emergence. The benefits to managers are an increase in the number of successful projects and improved productivity. This study will explore innovation management in a complex project based organisation. The contribution to the academic literature will be an in-depth, qualitative exploration of innovation in a complex project based organisation using a comparative case study approach.

Complex permittivity measurements of RF plasma polymerized polyterpenol organic thin films employing split post dielectric resonator

In the fields of organic electronics and biotechnology, applications for organic polymer thin films fabricated using low-temperature non-equilibrium plasma techniques are gaining significant attention because of the physical and chemical stability of thin films and the low cost of production. Polymer thin films were fabricated from non-synthetic terpinen-4-ol using radiofrequency polymerization (13.56 MHz) on low loss dielectric substrates and their permittivity properties were ascertained to determine potential applications for these organic films. Real and imaginary parts of permittivity as a function of frequency were measured using the variable angle spectroscopic ellipsometer. The real part of permittivity (k) was found to be between 2.34 and 2.65 in the wavelength region of 400–1100 nm, indicating a potential low-k material. These permittivity values were confirmed at microwave frequencies. Dielectric properties of polyterpenol films were measured by means of split post dielectric resonators (SPDRs) operating at frequencies of 10 GHz and 20 GHz. Permittivity increased for samples deposited at higher RF energy – from 2.65 (25 W) to 2.83 (75 W) measured by a 20-GHz SPDR and from 2.32 (25 W) to 2.53 (100 W) obtained using a 10-GHz SPDR. The error in permittivity measurement was predominantly attributed to the uncertainty in film thickness measurement.

The importance of modelling heterogeneity in complex disease: application to NIMH Schizophrenia Genetics Initiative data

As for other complex diseases, linkage analyses of schizophrenia (SZ) have produced evidence for numerous chromosomal regions, with inconsistent results reported across studies. The presence of locus heterogeneity appears likely and may reduce the power of linkage analyses if homogeneity is assumed. In addition, when multiple heterogeneous datasets are pooled, inter-sample variation in the proportion of linked families (alpha) may diminish the power of the pooled sample to detect susceptibility loci, in spite of the larger sample size obtained. We compare the significance of linkage findings obtained using allele-sharing LOD scores (LOD(exp))-which assume homogeneity-and heterogeneity LOD scores (HLOD) in European American and African American NIMH SZ families. We also pool these two samples and evaluate the relative power of the LOD(exp) and two different heterogeneity statistics. One of these (HLOD-P) estimates the heterogeneity parameter alpha only in aggregate data, while the second (HLOD-S) determines alpha separately for each sample. In separate and combined data, we show consistently improved performance of HLOD scores over LOD(exp). Notably, genome-wide significant evidence for linkage is obtained at chromosome 10p in the European American sample using a recessive HLOD score. When the two samples are combined, linkage at the 10p locus also achieves genome-wide significance under HLOD-S, but not HLOD-P. Using HLOD-S, improved evidence for linkage was also obtained for a previously reported region on chromosome 15q. In linkage analyses of complex disease, power may be maximised by routinely modelling locus heterogeneity within individual datasets, even when multiple datasets are combined to form larger samples.

Social work and complex care systems: The case of people hospitalised with a disability

Health and hospital system reforms prioritise efficiency. However, initiatives can impact on people with new or existing disabilities who require time to maximise functional independence. With greater demands for shorter hospital stays social workers face increasing pressure to facilitate discharge. This paper reports findings from research identifying factors contributing to extended stays for adults with disabilities. We sought to better understand patient characteristics and discharge planning challenges by analysing a clinical data set of 80 patients and qualitative interviews with five experienced hospital social workers. Three key factors are identified: issues around rehabilitation services; assessment and planning for community care; and availability of and access to discharge options. Strategies to reduce length of stay are reported. We argue that building collaborative partnerships and working across multiple, complex systems and disciplines are vital to ensure these patients access appropriate community-based resources within the current health reform environment.

Superhydrophobic fabrics for oil-water separation based on the metal organic charge transfer complex CuTCNAQ

This work reports on the fabrication of a superhydrophobic nylon textile based on the organic charge transfer complex CuTCNAQ (TCNAQ = 11,11,12,12-tetracyanoanthraquinodimethane). The nylon fabric that is metallized with copper undergoes a spontaneous chemical reaction with TCNAQ dissolved in acetonitrile to form nanorods of CuTCNAQ that are intertwined over the entire surface of the fabric. This creates the necessary micro and nanoscale roughness that is required for the Cassie-Baxter state thereby achieving a superhydrophobic/superoleophilic surface without the need for a fluorinated surface. The material is characterised with SEM, FT-IR and XPS spectroscopy and investigated for its ability to separate oil and water in two modes, namely under gravity and as an absorbent. It is found that the fabric can separate dichloromethane, olive oil and crude oil from water and in fact reduce the water content of the oil during the separation process. The fabric is reusable and tolerant to conditions such as seawater, hydrochloric acid and extensive time periods on the shelf. Given that CuTCNAQ is a copper based semiconductor may also open up the possibility of other applications in areas such as photocatalysis and antibacterial applications.

Assessing the integrated sustainability of a public rental housing project from the perspective of complex eco-system

Public rental housing (PRH) projects are the mainstream of China's new affordable housing policies, and their integrated sustainability has a far-reaching effect on medium-low income families' well-being and social stability. However, there are few quantitative researches on the integrated sustainability of PRH projects. Our study tries to fill this gap through proposing an assessment model of the integrated sustainability for PRH projects. First, this paper defines what the sustainability of a PRH project is. Second, after constructing the sustainable system of a PRH project from the perspective of complex eco-system, the paper explores the internal operation mechanism and the coupling mechanism among the ecological, economic and social subsystems. Third, it identifies fourteen indices to represent the sustainability system of a PRH project, including six indices of ecological subsystem, five of economic subsystem and three of social subsystem. Fourth, it qualifies the weights of three subsystems and their internal representative indices. In addition, an assessment model is established through expert surveys and analytic network process (ANP). Finally, the paper carries out an empirical research on a PRH project in Nanjing city of China, followed by suggestions to enhance the integrated sustainability. The sustainability system and its evaluation model proposed in this paper are concise and easy to understand and can provide a theoretical foundation and a scientific basis for the evaluation and optimization of PRH projects.