Deriving a preference-based utility measure for cancer patients from the European Organisation for the Research and Treatment of Cancer's Quality of Life Questionnaire C30 : a confirmatory versus exploratory approach

Background Multi attribute utility instruments (MAUIs) are preference-based measures that comprise a health state classification system (HSCS) and a scoring algorithm that assigns a utility value to each health state in the HSCS. When developing a MAUI from a health-related quality of life (HRQOL) questionnaire, first a HSCS must be derived. This typically involves selecting a subset of domains and items because HRQOL questionnaires typically have too many items to be amendable to the valuation task required to develop the scoring algorithm for a MAUI. Currently, exploratory factor analysis (EFA) followed by Rasch analysis is recommended for deriving a MAUI from a HRQOL measure. Aim To determine whether confirmatory factor analysis (CFA) is more appropriate and efficient than EFA to derive a HSCS from the European Organisation for the Research and Treatment of Cancer’s core HRQOL questionnaire, Quality of Life Questionnaire (QLQ-C30), given its well-established domain structure. Methods QLQ-C30 (Version 3) data were collected from 356 patients receiving palliative radiotherapy for recurrent/metastatic cancer (various primary sites). The dimensional structure of the QLQ-C30 was tested with EFA and CFA, the latter informed by the established QLQ-C30 structure and views of both patients and clinicians on which are the most relevant items. Dimensions determined by EFA or CFA were then subjected to Rasch analysis. Results CFA results generally supported the proposed QLQ-C30 structure (comparative fit index =0.99, Tucker–Lewis index =0.99, root mean square error of approximation =0.04). EFA revealed fewer factors and some items cross-loaded on multiple factors. Further assessment of dimensionality with Rasch analysis allowed better alignment of the EFA dimensions with those detected by CFA. Conclusion CFA was more appropriate and efficient than EFA in producing clinically interpretable results for the HSCS for a proposed new cancer-specific MAUI. Our findings suggest that CFA should be recommended generally when deriving a preference-based measure from a HRQOL measure that has an established domain structure.

Modelling the intention to adopt cloud computing services : a transaction cost theory perspective

This paper uses transaction cost theory to study cloud computing adoption. A model is developed and tested with data from an Australian survey. According to the results, perceived vendor opportunism and perceived legislative uncertainty around cloud computing were significantly associated with perceived cloud computing security risk. There was also a significant negative relationship between perceived cloud computing security risk and the intention to adopt cloud services. This study also reports on adoption rates of cloud computing in terms of applications, as well as the types of services used.

Building energy simulation and parallel computing : opportunities and challenges

Increased focus on energy cost savings and carbon footprint reduction efforts improved the visibility of building energy simulation, which became a mandatory requirement of several building rating systems. Despite developments in building energy simulation algorithms and user interfaces, there are some major challenges associated with building energy simulation; an important one is the computational demands and processing time. In this paper, we analyze the opportunities and challenges associated with this topic while executing a set of 275 parametric energy models simultaneously in EnergyPlus using a High Performance Computing (HPC) cluster. Successful parallel computing implementation of building energy simulations will not only improve the time necessary to get the results and enable scenario development for different design considerations, but also might enable Dynamic-Building Information Modeling (BIM) integration and near real-time decision-making. This paper concludes with the discussions on future directions and opportunities associated with building energy modeling simulations.

The clinical utility of the Cornell Scale for Depression in Dementia as a routine assessment in nursing homes

Objective To examine the clinical utility of the Cornell Scale for Depression in Dementia (CSDD) in nursing homes. Setting 14 nursing homes in Sydney and Brisbane, Australia. Participants 92 residents with a mean age of 85 years. Measurements Consenting residents were assessed by care staff for depression using the CSDD as part of their routine assessment. Specialist clinicians conducted assessment of depression using the Semi-structured Clinical Diagnostic Interview for DSM-IV-TR Axis I Disorders for residents without dementia or the Provisional Diagnostic Criteria for Depression in Alzheimer Disease for residents with dementia to establish expert clinical diagnoses of depression. The diagnostic performance of the staff completed CSDD was analyzed against expert diagnosis using receiver operating characteristic (ROC) curves. Results The CSDD showed low diagnostic accuracy, with areas under the ROC curve being 0.69, 0.68 and 0.70 for the total sample, residents with dementia and residents without dementia, respectively. At the standard CSDD cutoff score, the sensitivity and specificity were 71% and 59% for the total sample, 69% and 57% for residents with dementia, and 75% and 61% for residents without dementia. The Youden index (for optimizing cut-points) suggested different depression cutoff scores for residents with and without dementia. Conclusion When administered by nursing home staff the clinical utility of the CSDD is highly questionable in identifying depression. The complexity of the scale, the time required for collecting relevant information, and staff skills and knowledge of assessing depression in older people must be considered when using the CSDD in nursing homes.

In silico biology : making the most of parallel computing

Biological systems are typically complex and adaptive, involving large numbers of entities, or organisms, and many-layered interactions between these. System behaviour evolves over time, and typically benefits from previous experience by retaining memory of previous events. Given the dynamic nature of these phenomena, it is non-trivial to provide a comprehensive description of complex adaptive systems and, in particular, to define the importance and contribution of low-level unsupervised interactions to the overall evolution process. In this chapter, the authors focus on the application of the agent-based paradigm in the context of the immune response to HIV. Explicit implementation of lymph nodes and the associated lymph network, including lymphatic chain structure, is a key objective, and requires parallelisation of the model. Steps taken towards an optimal communication strategy are detailed.

Model refinement through high-performance computing: an agent-based HIV example

Background Recent advances in Immunology highlighted the importance of local properties on the overall progression of HIV infection. In particular, the gastrointestinal tract is seen as a key area during early infection, and the massive cell depletion associated with it may influence subsequent disease progression. This motivated the development of a large-scale agent-based model. Results Lymph nodes are explicitly implemented, and considerations on parallel computing permit large simulations and the inclusion of local features. The results obtained show that GI tract inclusion in the model leads to an accelerated disease progression, during both the early stages and the long-term evolution, compared to a theoretical, uniform model. Conclusions These results confirm the potential of treatment policies currently under investigation, which focus on this region. They also highlight the potential of this modelling framework, incorporating both agent-based and network-based components, in the context of complex systems where scaling-up alone does not result in models providing additional insights.

Complexity and high-end computing in biology and medicine

Biomedical systems involve a large number of entities and intricate interactions between these. Their direct analysis is, therefore, difficult, and it is often necessary to rely on computational models. These models require significant resources and parallel computing solutions. These approaches are particularly suited, given parallel aspects in the nature of biomedical systems. Model hybridisation also permits the integration and simultaneous study of multiple aspects and scales of these systems, thus providing an efficient platform for multidisciplinary research.

From bio-inspired computing to e-Biology

Several algorithms and techniques widely used in Computer Science have been adapted from, or inspired by, known biological phenomena. This is a consequence of the multidisciplinary background of most early computer scientists. The field has now matured, and permits development of tools and collaborative frameworks which play a vital role in advancing current biomedical research. In this paper, we briefly present examples of the former, and elaborate upon two of the latter, applied to immunological modelling and as a new paradigm in gene expression.

High-performance computing for data analytics

One of the main challenges in data analytics is that discovering structures and patterns in complex datasets is a computer-intensive task. Recent advances in high-performance computing provide part of the solution. Multicore systems are now more affordable and more accessible. In this paper, we investigate how this can be used to develop more advanced methods for data analytics. We focus on two specific areas: model-driven analysis and data mining using optimisation techniques.

Performance evaluation of cloud-based parallel computing

As computational models in fields such as medicine and engineering get more refined, resource requirements are increased. In a first instance, these needs have been satisfied using parallel computing and HPC clusters. However, such systems are often costly and lack flexibility. HPC users are therefore tempted to move to elastic HPC using cloud services. One difficulty in making this transition is that HPC and cloud systems are different, and performance may vary. The purpose of this study is to evaluate cloud services as a means to minimise both cost and computation time for large-scale simulations, and to identify which system properties have the most significant impact on performance. Our simulation results show that, while the performance of Virtual CPU (VCPU) is satisfactory, network throughput may lead to difficulties.

Transdisciplinary approaches to urban computing

The research field of urban computing – defined as “the integration of computing, sensing, and actuation technologies into everyday urban settings and lifestyles” – considers the design and use of ubiquitous computing technology in public and shared urban environments. Its impact on cities, buildings, and spaces evokes innumerable kinds of change. Embedded into our everyday lived environments, urban computing technologies have the potential to alter the meaning of physical space, and affect the activities performed in those spaces. This paper starts a multi-themed discussion of various aspects that make up the, at times, messy and certainly transdisciplinary field of urban computing and urban informatics.

Utility of assessing nerve morphology in central cornea versus whorl area for diagnosing diabetic peripheral neuropathy

Purpose To compare small nerve fiber damage in the central cornea and whorl area in participants with diabetic peripheral neuropathy (DPN) and to examine the accuracy of evaluating these 2 anatomical sites for the diagnosis of DPN. Methods A cohort of 187 participants (107 with type 1 diabetes and 80 controls) was enrolled. The neuropathy disability score (NDS) was used for the identification of DPN. The corneal nerve fiber length at the central cornea (CNFLcenter) and whorl (CNFLwhorl) was quantified using corneal confocal microscopy and a fully automated morphometric technique and compared according to the DPN status. Receiver operating characteristic analyses were used to compare the accuracy of the 2 corneal locations for the diagnosis of DPN. Results CNFLcenter and CNFLwhorl were able to differentiate all 3 groups (diabetic participants with and without DPN and controls) (P < 0.001). There was a weak but significant linear relationship for CNFLcenter and CNFLwhorl versus NDS (P < 0.001); however, the corneal location x NDS interaction was not statistically significant (P = 0.17). The area under the receiver operating characteristic curve was similar for CNFLcenter and CNFLwhorl (0.76 and 0.77, respectively, P = 0.98). The sensitivity and specificity of the cutoff points were 0.9 and 0.5 for CNFLcenter and 0.8 and 0.6 for CNFLwhorl. Conclusions Small nerve fiber pathology is comparable at the central and whorl anatomical sites of the cornea. Quantification of CNFL from the corneal center is as accurate as CNFL quantification of the whorl area for the diagnosis of DPN.

How the energy sector could get it wrong with cloud computing

Cloud computing has significantly impacted a broad range of industries, but these technologies and services have been absorbed throughout the marketplace unevenly. Some industries have moved aggressively towards cloud computing, while others have moved much more slowly. For the most part, the energy sector has approached cloud computing in a measured and cautious way, with progress often in the form of private cloud solutions rather than public ones, or hybridized information technology systems that combine cloud and existing non-cloud architectures. By moving towards cloud computing in a very slow and tentative way, however, the energy industry may prevent itself from reaping the full benefit that a more complete migration to the public cloud has brought about in several other industries. This short communication is accordingly intended to offer a high-level overview of cloud computing, and to put forward the argument that the energy sector should make a more complete migration to the public cloud in order to unlock the major system-wide efficiencies that cloud computing can provide. Also, assets within the energy sector should be designed with as much modularity and flexibility as possible so that they are not locked out of cloud-friendly options in the future.