The effect of HLA-DR genes on susceptibility to and severity of ankylosing spondylitis

Objective. To analyze the effect of HLA-DR genes on susceptibility to and severity of ankylosing spondylitis (AS). Methods. Three hundred sixty- three white British AS patients were studied; 149 were carefully assessed for a range of clinical manifestations, and disease severity was assessed using a structured questionnaire. Limited HLA class I typing and complete HLA-DR typing were performed using DNA-based methods. HLA data from 13,634 healthy white British bone marrow donors were used for comparison. Results. A significant association between DR1 and AS was found, independent of HLA-B27 (overall odds ratio [OR] 1.4, 95% confidence interval [95% CI] 1.1-1.8, P = 0.02; relative risk [RR] 2.7, 95% CI 1.5-4.8, P = 6 x 10-4 among homozygotes; RR 2.1, 95% CI 1.5-2.8, P = 5 x 10-6 among heterozygotes). A large but weakly significant association between DR8 and AS was noted, particularly among DR8 homozygotes (RR 6.8, 95% CI 1.6-29.2, P = 0.01 among homozygotes; RR 1.6, 95% CI 1.0-2.7, P = 0.07 among heterozygotes). A negative association with DR12 (OR 0.22, 95% CI 0.09-0.5, P = 0.001) was noted. HLA-DR7 was associated with younger age at onset of disease (mean age at onset 18 years for DR7-positive patients and 23 years for DR7-negative patients; Z score 3.21, P = 0.001). No other HLA class I or class H associations with disease severity or with different clinical manifestations of AS were found. Conclusion. The results of this study suggest that HLA-DR genes may have a weak effect on susceptibility to AS independent of HLA-B27, but do not support suggestions that they affect disease severity or different clinical manifestations.

A genome-wide screen for susceptibility loci in ankylosing spondylitis

Objective. To localize the regions containing genes that determine susceptibility to ankylosing spondylitis (AS). Methods. One hundred five white British families with 121 affected sibling pairs with AS were recruited, largely from the Royal National Hospital for Rheumatic Diseases AS database. A genome-wide linkage screen was undertaken using 254 highly polymorphic microsatellite markers from the Medical Research Council (UK) (MRC) set. The major histocompatibility complex (MHC) region was studied more intensively using 5 microsatellites lying within the HLA class III region and HLA-DRB1 typing. The Analyze package was used for 2-point analysis, and GeneHunter for multipoint analysis. Results. When only the MRC set was considered, 11 markers in 7 regions achieved a P value of ≤0.01. The maximum logarithm of odds score obtained was 3.8 (P = 1.4 x 10-5) using marker D6S273, which lies in the HLA class III region. A further marker used in mapping of the MHC class III region achieved a LOD score of 8.1 (P = 1 x 10-9). Nine of 118 affected sibling pairs (7.6%) did not share parental haplotypes identical by descent across the MHC, suggesting that only 31% of the susceptibility to AS is coded by genes linked to the MHC. The maximum non-MHC LOD score obtained was 2.6 (P = 0.0003) for marker D16S422. Conclusion. The results of this study confirm the strong linkage of the MHC with AS, and provide suggestive evidence regarding the presence and location of non-MHC genes influencing susceptibility to the disease.

A linkage study across the T cell receptor A and T cell receptor B loci in families with rheumatoid arthritis

Objective. To examine whether the T cell receptor (TCR) A or TCRB loci exhibit linkage with disease in multiplex rheumatoid arthritis (RA) families. Methods. A linkage study was performed in 184 RA families from the UK Arthritis and Rheumatism Council Repository, each containing at least 1 affected sibpair. The microsatellites D14S50, TCRA, and D14S64 spanning the TCRA locus and D7S509, Vβ6.7, and D7S688 spanning the TCRB locus were used as DNA markers. The subjects were genotyped using a semiautomated polymerase chain reaction-based method. Two-point and multipoint linkage analyses were performed. Results. Nonparametric single-marker likelihood odds (LOD) scores were 0.49 (P = 0.07) for D14S50, 0.65 (P = 0.04) for TCRA, 0.07 (P = 0.29) for D14S64, 0.01 (P = 0.43) for D7S509, 0.0 (P = 0.50) for Vβ6.7, and 0.0 (P = 0.50) for D7S688. By multipoint analysis, there was no evidence of linkage at TCRB (LOD score 0), and the maximum LOD score at the TCRA locus was 0.37 (at D14S50). The presence of a susceptibility locus (LOD score < -2.0) was excluded, with lambda ≤ 1.8 at TCRA and ≤1.4 at TCRB. Conclusion. These linkage studies provide no significant evidence of a major germline-encoded TCRA or TCRB component of susceptibility to RA.

Whole-genome linkage analysis of rheumatoid arthritis susceptibility loci in 252 affected sibling pairs in the United Kingdom

Objective. To undertake a systematic wholegenome screen to identify regions exhibiting genetic linkage to rheumatoid arthritis (RA). Methods. Two hundred fifty-two RA-affected sibling pairs from 182 UK families were genotyped using 365 highly informative microsatellite markers. Microsatellite genotyping was performed using fluorescent polymerase chain reaction primers and semiautomated DNA sequencing technology. Linkage analysis was undertaken using MAPMAKER/SIBS for single-point and multipoint analysis. Results. Significant linkage (maximum logarithm of odds score 4.7 [P = 0.000003] at marker D6S276, 1 cM from HLA-DRB1) was identified around the major histocompatibility complex (MHC) region on chromosome 6. Suggestive linkage (P < 7.4 × 10-4) was identified on chromosome 6q by single- and multipoint analysis. Ten other sites of nominal linkage (P < 0.05) were identified on chromosomes 3p, 4q, 7p, 2 regions of 10q, 2 regions of 14q, 16p, 21q, and Xq by single-point analysis and on 3 sites (1q, 14q, and 14q) by multipoint analysis. Conclusion. Linkage to the MHC region was confirmed. Eleven non-HLA regions demonstrated evidence of suggestive or nominal linkage, but none reached the genome-wide threshold for significant linkage (P = 2.2 × 10-5). Results of previous genome screens have suggested that 6 of these regions may be involved in RA susceptibility.

The effect of HLA-DR on susceptibility to rheumatoid arthritis is influenced by the associated lymphotoxin α-tumor necrosis factor haplotype

Objective. HLA-DRB1, a major genetic determinant of susceptibility to rheumatoid arthritis (RA), is located within 1,000 kb of the gene encoding tumor necrosis factor (TNF). Because certain HLA-DRB1*04 subtypes increase susceptibility to RA, investigation of the role of the TNF gene is complicated by linkage disequilibrium (LD) between TNF and DRB1 alleles. By adequately controlling for this LD, we aimed to investigate the presence of additional major histocompatibility complex (MHC) susceptibility genes. Methods. We identified 274 HLA-DRB1*04-positive cases of RA and 271 HLA-DRB1*04-positive population controls. Each subject was typed for 6 single-nucleotide polymorphisms within a 4.5-kb region encompassing TNF and lymphotoxin a (LTA). LTA-TNF haplotypes in these unrelated individuals were determined using a combination of family data and the PHASE software program. Results. Significant differences in LTA-TNF haplotype frequencies were observed between different subtypes of HLA-DRB1*04. The LTA-TNF haplotypes observed were very restricted, with only 4 haplotypes constituting 81% of all haplotypes present. Among individuals carrying DRB1*0401, the LTA-TNF 2 haplotype was significantly underrepresented in cases compared with controls (odds ratio 0.5 [95% confidence interval 0.3-0.8], P = 0.007), while in those with DRB1*0404, the opposite effect was observed (P = 0.007). Conclusion. These findings suggest that the MHC contains genetic elements outside the LTA-TNF region that modify the effect of HLA-DRB1 on susceptibility to RA.

Integration of Geospatial and Built Environment - National Data Policy

Digital technology offers enormous benefits (economic, quality of design and efficiency in use) if adopted to implement integrated ways of representing the physical world in a digital form. When applied across the full extent of the built and natural world, it is referred to as the Digital Built Environment (DBE) and encompasses a wide range of approaches and technology initiatives, all aimed at the same end goal: the development of a virtual world that sufficiently mirrors the real world to form the basis for the smart cities of the present and future, enable efficient infrastructure design and programmed maintenance, and create a new foundation for economic growth and social well-being through evidence-based analysis. The creation of a National Data Policy for the DBE will facilitate the creation of additional high technology industries in Australia; provide Governments, industries and citizens with greater knowledge of the environments they occupy and plan; and offer citizen-driven innovations for the future. Australia has slipped behind other nations in the adoption and execution of Building Information Modelling (BIM) and the principal concern is that the gap is widening. Data driven innovation added $67 billion to the Australian economy in 20131. Strong open data policy equates to $16 billion in new value2. Australian Government initiatives such as the Digital Earth inspired “National Map” offer a platform and pathway to embrace the concept of a “BIM Globe”, while also leveraging unprecedented growth in open source / open data collaboration. Australia must address the challenges by learning from international experiences—most notably the UK and NZ—and mandate the use of BIM across Government, extending the Framework for Spatial Data Foundation to include the Built Environment as a theme and engaging collaboration through a “BIM globe” metaphor. This proposed DBE strategy will modernise the Australian urban planning and the construction industry. It will change the way we develop our cities by fundamentally altering the dynamics and behaviours of the supply chains and unlocking new and more efficient ways of collaborating at all stages of the project life-cycle. There are currently two major modelling approaches that contribute to the challenge of delivering the DBE. Though these collectively encompass many (often competing) approaches or proprietary software systems, all can be categorised as either: a spatial modelling approach, where the focus is generally on representing the elements that make up the world within their geographic context; and a construction modelling approach, where the focus is on models that support the life cycle management of the built environment. These two approaches have tended to evolve independently, addressing two broad industry sectors: the one concerned with understanding and managing global and regional aspects of the world that we inhabit, including disciplines concerned with climate, earth sciences, land ownership, urban and regional planning and infrastructure management; the other is concerned with planning, design, construction and operation of built facilities and includes architectural and engineering design, product manufacturing, construction, facility management and related disciplines (a process/technology commonly known as Building Information Modelling, BIM). The spatial industries have a strong voice in the development of public policy in Australia, while the construction sector, which in 2014 accounted for around 8.5% of Australia’s GDP3, has no single voice and because of its diversity, is struggling to adapt to and take advantage of the opportunity presented by these digital technologies. The experience in the UK over the past few years has demonstrated that government leadership is very effective in stimulating industry adoption of digital technologies by, on the one hand, mandating the use of BIM on public procurement projects while at the same time, providing comparatively modest funding to address the common issues that confront the industry in adopting that way of working across the supply chain. The reported result has been savings of £840m in construction costs in 2013/14 according to UK Cabinet Office figures4. There is worldwide recognition of the value of bringing these two modelling technologies together. Australia has the expertise to exercise leadership in this work, but it requires a commitment by government to recognise the importance of BIM as a companion methodology to the spatial technologies so that these two disciplinary domains can cooperate in the development of data policies and information exchange standards to smooth out common workflows. buildingSMART Australasia, SIBA and their academic partners have initiated this dialogue in Australia and wish to work collaboratively, with government support and leadership, to explore the opportunities open to us as we develop an Australasian Digital Built Environment. As part of that programme, we must develop and implement a strategy to accelerate the adoption of BIM processes across the Australian construction sector while at the same time, developing an integrated approach in concert with the spatial sector that will position Australia at the forefront of international best practice in this area. Australia and New Zealand cannot afford to be on the back foot as we face the challenges of rapid urbanisation and change in the global environment. Although we can identify some exemplary initiatives in this area, particularly in New Zealand in response to the need for more resilient urban development in the face of earthquake threats, there is still much that needs to be done. We are well situated in the Asian region to take a lead in this challenge, but we are at imminent risk of losing the initiative if we do not take action now. Strategic collaboration between Governments, Industry and Academia will create new jobs and wealth, with the potential, for example, to save around 20% on the delivery costs of new built assets, based on recent UK estimates.

A principled experimental design approach to Big Data analysis

Big Datasets are endemic, but they are often notoriously difficult to analyse because of their size, heterogeneity, history and quality. The purpose of this paper is to open a discourse on the use of modern experimental design methods to analyse Big Data in order to answer particular questions of interest. By appealing to a range of examples, it is suggested that this perspective on Big Data modelling and analysis has wide generality and advantageous inferential and computational properties. In particular, the principled experimental design approach is shown to provide a flexible framework for analysis that, for certain classes of objectives and utility functions, delivers near equivalent answers compared with analyses of the full dataset under a controlled error rate. It can also provide a formalised method for iterative parameter estimation, model checking, identification of data gaps and evaluation of data quality. Finally, it has the potential to add value to other Big Data sampling algorithms, in particular divide-and-conquer strategies, by determining efficient sub-samples.

Collaborative data exploration interfaces - From participatory sensing to participatory sensemaking

As technological capabilities for capturing, aggregating, and processing large quantities of data continue to improve, the question becomes how to effectively utilise these resources. Whenever automatic methods fail, it is necessary to rely on human background knowledge, intuition, and deliberation. This creates demand for data exploration interfaces that support the analytical process, allowing users to absorb and derive knowledge from data. Such interfaces have historically been designed for experts. However, existing research has shown promise in involving a broader range of users that act as citizen scientists, placing high demands in terms of usability. Visualisation is one of the most effective analytical tools for humans to process abstract information. Our research focuses on the development of interfaces to support collaborative, community-led inquiry into data, which we refer to as Participatory Data Analytics. The development of data exploration interfaces to support independent investigations by local communities around topics of their interest presents a unique set of challenges, which we discuss in this paper. We present our preliminary work towards suitable high-level abstractions and interaction concepts to allow users to construct and tailor visualisations to their own needs.

Medical Data Access Accountability in EHR Systems, A Practical Perspective

The world has experienced a large increase in the amount of available data. Therefore, it requires better and more specialized tools for data storage and retrieval and information privacy. Recently Electronic Health Record (EHR) Systems have emerged to fulfill this need in health systems. They play an important role in medicine by granting access to information that can be used in medical diagnosis. Traditional systems have a focus on the storage and retrieval of this information, usually leaving issues related to privacy in the background. Doctors and patients may have different objectives when using an EHR system: patients try to restrict sensible information in their medical records to avoid misuse information while doctors want to see as much information as possible to ensure a correct diagnosis. One solution to this dilemma is the Accountable e-Health model, an access protocol model based in the Information Accountability Protocol. In this model patients are warned when doctors access their restricted data. They also enable a non-restrictive access for authenticated doctors. In this work we use FluxMED, an EHR system, and augment it with aspects of the Information Accountability Protocol to address these issues. The Implementation of the Information Accountability Framework (IAF) in FluxMED provides ways for both patients and physicians to have their privacy and access needs achieved. Issues related to storage and data security are secured by FluxMED, which contains mechanisms to ensure security and data integrity. The effort required to develop a platform for the management of medical information is mitigated by the FluxMED's workflow-based architecture: the system is flexible enough to allow the type and amount of information being altered without the need to change in your source code.

Predicting the speed of a Wave Glider autonomous surface vehicle from wave model data

A key component of robotic path planning is ensuring that one can reliably navigate a vehicle to a desired location. In addition, when the features of interest are dynamic and move with oceanic currents, vehicle speed plays an important role in the planning exercise to ensure that vehicles are in the right place at the right time. Aquatic robot design is moving towards utilizing the environment for propulsion rather than traditional motors and propellers. These new vehicles are able to realize significantly increased endurance, however the mission planning problem, in turn, becomes more difficult as the vehicle velocity is not directly controllable. In this paper, we examine Gaussian process models applied to existing wave model data to predict the behavior, i.e., velocity, of a Wave Glider Autonomous Surface Vehicle. Using training data from an on-board sensor and forecasting with the WAVEWATCH III model, our probabilistic regression models created an effective method for forecasting WG velocity.

FlexAnalytics: A flexible data analytics framework for big data applications with I/O performance improvement

Increasingly larger scale applications are generating an unprecedented amount of data. However, the increasing gap between computation and I/O capacity on High End Computing machines makes a severe bottleneck for data analysis. Instead of moving data from its source to the output storage, in-situ analytics processes output data while simulations are running. However, in-situ data analysis incurs much more computing resource contentions with simulations. Such contentions severely damage the performance of simulation on HPE. Since different data processing strategies have different impact on performance and cost, there is a consequent need for flexibility in the location of data analytics. In this paper, we explore and analyze several potential data-analytics placement strategies along the I/O path. To find out the best strategy to reduce data movement in given situation, we propose a flexible data analytics (FlexAnalytics) framework in this paper. Based on this framework, a FlexAnalytics prototype system is developed for analytics placement. FlexAnalytics system enhances the scalability and flexibility of current I/O stack on HEC platforms and is useful for data pre-processing, runtime data analysis and visualization, as well as for large-scale data transfer. Two use cases – scientific data compression and remote visualization – have been applied in the study to verify the performance of FlexAnalytics. Experimental results demonstrate that FlexAnalytics framework increases data transition bandwidth and improves the application end-to-end transfer performance.

Combining HPLC-DAD and ICP-MS data for improved analysis of complex samples: Classification of the root samples from Cortex moutan

A combined data matrix consisting of high performance liquid chromatography–diode array detector (HPLC–DAD) and inductively coupled plasma-mass spectrometry (ICP-MS) measurements of samples from the plant roots of the Cortex moutan (CM), produced much better classification and prediction results in comparison with those obtained from either of the individual data sets. The HPLC peaks (organic components) of the CM samples, and the ICP-MS measurements (trace metal elements) were investigated with the use of principal component analysis (PCA) and the linear discriminant analysis (LDA) methods of data analysis; essentially, qualitative results suggested that discrimination of the CM samples from three different provinces was possible with the combined matrix producing best results. Another three methods, K-nearest neighbor (KNN), back-propagation artificial neural network (BP-ANN) and least squares support vector machines (LS-SVM) were applied for the classification and prediction of the samples. Again, the combined data matrix analyzed by the KNN method produced best results (100% correct; prediction set data). Additionally, multiple linear regression (MLR) was utilized to explore any relationship between the organic constituents and the metal elements of the CM samples; the extracted linear regression equations showed that the essential metals as well as some metallic pollutants were related to the organic compounds on the basis of their concentrations

Data driven modeling for power transformer lifespan evaluation

Large sized power transformers are important parts of the power supply chain. These very critical networks of engineering assets are an essential base of a nation’s energy resource infrastructure. This research identifies the key factors influencing transformer normal operating conditions and predicts the asset management lifespan. Engineering asset research has developed few lifespan forecasting methods combining real-time monitoring solutions for transformer maintenance and replacement. Utilizing the rich data source from a remote terminal unit (RTU) system for sensor-data driven analysis, this research develops an innovative real-time lifespan forecasting approach applying logistic regression based on the Weibull distribution. The methodology and the implementation prototype are verified using a data series from 161 kV transformers to evaluate the efficiency and accuracy for energy sector applications. The asset stakeholders and suppliers significantly benefit from the real-time power transformer lifespan evaluation for maintenance and replacement decision support.

Occupational injury risk among Australian paramedics: An analysis of national data

Objective To identify the occupational risks for Australian paramedics, by describing the rate of injuries and fatalities and comparing those rates with other reports. Design and participants Retrospective descriptive study using data provided by Safe Work Australia for the period 2000–2010. The subjects were paramedics who had been injured in the course of their duties and for whom a claim had been made for workers compensation payments. Main outcome measures Rates of injury calculated from the data provided. Results The risk of serious injury among Australian paramedics was found to be more than seven times higher than the Australian national average. The fatality rate for paramedics was about six times higher than the national average. On average, every 2 years during the study period, one paramedic died and 30 were seriously injured in vehicle crashes. Ten Australian paramedics were seriously injured each year as a result of an assault. The injury rate for paramedics was more than two times higher than the rate for police officers. Conclusions The high rate of occupational injuries and fatalities among paramedics is a serious public health issue. The risk of injury in Australia is similar to that in the United States. While it may be anticipated that injury rates would be higher as a result of the nature of the work and environment of paramedics, further research is necessary to identify and validate the strategies required to minimise the rates of occupational injury for paramedics.

Optimal management of the critically Ill: Anaesthesia, monitoring, data capture, and point-of-care technological practices in ovine models of critical care

Animal models of critical illness are vital in biomedical research. They provide possibilities for the investigation of pathophysiological processes that may not otherwise be possible in humans. In order to be clinically applicable, the model should simulate the critical care situation realistically, including anaesthesia, monitoring, sampling, utilising appropriate personnel skill mix, and therapeutic interventions. There are limited data documenting the constitution of ideal technologically advanced large animal critical care practices and all the processes of the animal model. In this paper, we describe the procedure of animal preparation, anaesthesia induction and maintenance, physiologic monitoring, data capture, point-of-care technology, and animal aftercare that has been successfully used to study several novel ovine models of critical illness. The relevant investigations are on respiratory failure due to smoke inhalation, transfusion related acute lung injury, endotoxin-induced proteogenomic alterations, haemorrhagic shock, septic shock, brain death, cerebral microcirculation, and artificial heart studies. We have demonstrated the functionality of monitoring practices during anaesthesia required to provide a platform for undertaking systematic investigations in complex ovine models of critical illness.