Hui and Walter's latent-class model extended to estimate diagnostic test properties from surveillance data: a latent model for latent data

Diagnostic test sensitivity and specificity are probabilistic estimates with far reaching implications for disease control, management and genetic studies. In the absence of 'gold standard' tests, traditional Bayesian latent class models may be used to assess diagnostic test accuracies through the comparison of two or more tests performed on the same groups of individuals. The aim of this study was to extend such models to estimate diagnostic test parameters and true cohort-specific prevalence, using disease surveillance data. The traditional Hui-Walter latent class methodology was extended to allow for features seen in such data, including (i) unrecorded data (i.e. data for a second test available only on a subset of the sampled population) and (ii) cohort-specific sensitivities and specificities. The model was applied with and without the modelling of conditional dependence between tests. The utility of the extended model was demonstrated through application to bovine tuberculosis surveillance data from Northern and the Republic of Ireland. Simulation coupled with re-sampling techniques, demonstrated that the extended model has good predictive power to estimate the diagnostic parameters and true herd-level prevalence from surveillance data. Our methodology can aid in the interpretation of disease surveillance data, and the results can potentially refine disease control strategies.

The utility of animal models in developing immunosuppressive agents

The immune system comprises an integrated network of cellular interactions. Some responses are predictable, while others are more stochastic. While in vitro the outcome of stimulating a single type of cell may be stereotyped and reproducible, in vivo this is often not the case. This phenomenon often merits the use of animal models in predicting the impact of immunosuppressant drugs. A heavy burden of responsibility lies on the shoulders of the investigator when using animal models to study immunosuppressive agents. The principles of the three R׳s: refine (less suffering,), reduce (lower animal numbers) and replace (alternative in vitro assays) must be applied, as described elsewhere in this issue. Well designed animal model experiments have allowed us to develop all the immunosuppressive agents currently available for treating autoimmune disease and transplant recipients. In this review, we examine the common animal models used in developing immunosuppressive agents, focusing on drugs used in transplant surgery. Autoimmune diseases, such as multiple sclerosis, are covered elsewhere in this issue. We look at the utility and limitations of small and large animal models in measuring potency and toxicity of immunosuppressive therapies.

A Conditional Approach for Modelling Patient Re-admissions in Hospital using a mixture of Coxian phase-type distributions incorporating Bayes Theorem

The number of elderly patients requiring hospitalisation in Europe is rising. With a greater proportion of elderly people in the population comes a greater demand for health services and, in particular, hospital care. Thus, with a growing number of elderly patients requiring hospitalisation competing with non-elderly patients for a fixed (and in some cases, decreasing) number of hospital beds, this results in much longer waiting times for patients, often with a less satisfactory hospital experience. However, if a better understanding of the recurring nature of elderly patient movements between the community and hospital can be developed, then it may be possible for alternative provisions of care in the community to be put in place and thus prevent readmission to hospital. The research in this paper aims to model the multiple patient transitions between hospital and community by utilising a mixture of conditional Coxian phase-type distributions that incorporates Bayes' theorem. For the purpose of demonstration, the results of a simulation study are presented and the model is applied to hospital readmission data from the Lombardy region of Italy.

Discrete Conditional Phase-type Model Utilising a Multiclass Support Vector Machine for the Prediction of Retinopathy of Prematurity

Retinopathy of prematurity (ROP) is a rare disease in which retinal blood vessels of premature infants fail to develop normally, and is one of the major causes of childhood blindness throughout the world. The Discrete Conditional Phase-type (DC-Ph) model consists of two components, the conditional component measuring the inter-relationships between covariates and the survival component which models the survival distribution using a Coxian phase-type distribution. This paper expands the DC-Ph models by introducing a support vector machine (SVM), in the role of the conditional component. The SVM is capable of classifying multiple outcomes and is used to identify the infant's risk of developing ROP. Class imbalance makes predicting rare events difficult. A new class decomposition technique, which deals with the problem of multiclass imbalance, is introduced. Based on the SVM classification, the length of stay in the neonatal ward is modelled using a 5, 8 or 9 phase Coxian distribution.

Investigating Cyber-Physical Attacks against IEC 61850 Photovoltaic Inverter Installations

Cyber-attacks against Smart Grids have been found in the real world. Malware such as Havex and BlackEnergy have been found targeting industrial control systems (ICS) and researchers have shown that cyber-attacks can exploit vulnerabilities in widely used Smart Grid communication standards. This paper addresses a deep investigation of attacks against the manufacturing message specification of IEC 61850, which is expected to become one of the most widely used communication services in Smart Grids. We investigate how an attacker can build a custom tool to execute man-in-the-middle attacks, manipulate data, and affect the physical system. Attack capabilities are demonstrated based on NESCOR scenarios to make it possible to thoroughly test these scenarios in a real system. The goal is to help understand the potential for such attacks, and to aid the development and testing of cyber security solutions. An attack use-case is presented that focuses on the standard for power utility automation, IEC 61850 in the context of inverter-based distributed energy resource devices; especially photovoltaic (PV) generators.

Homogenization and analysis of an expanded long-term monthly rainfall network for the Island of Ireland (1850–2010)

Long-term precipitation series are critical for understanding emerging changes to the hydrological cycle. To this end we construct a homogenized Island of Ireland Precipitation (IIP) network comprising 25 stations and a composite series covering the period 1850–2010, providing the second-longest regional precipitation archive in the British-Irish Isles. We expand the existing catalogue of long-term precipitation records for the island by recovering archived data for an additional eight stations. Following bridging and updating of stations HOMogenisation softwarE in R (HOMER) homogenization software is used to detect breaks using pairwise and joint detection. A total of 25 breakpoints are detected across 14 stations, and the majority (20) are corroborated by metadata. Assessment of variability and change in homogenized and extended precipitation records reveal positive (winter) and negative (summer) trends. Trends in records covering the typical period of digitization (1941 onwards) are not always representative of longer records. Furthermore, trends in post-homogenization series change magnitude and even direction at some stations. While cautionary flags are raised for some series, confidence in the derived network is high given attention paid to metadata, coherence of behaviour across the network and consistency of findings with other long-term climatic series such as England and Wales precipitation. As far as we are aware, this work represents the first application of HOMER to a long-term precipitation network and bodes well for use in other regions. It is expected that the homogenized IIP network will find wider utility in benchmarking and supporting climate services across the Island of Ireland, a sentinel location in the North Atlantic.

Picturing Probation: Exploring the utility of visual methods in comparative research

A previous review of research on the practice of offender supervision identified the predominant use of interview-based methodologies and limited use of other research approaches (Robinson and Svensson, 2013). It also found that most research has tended to be locally focussed (i.e. limited to one jurisdiction) with very few comparative studies. This article reports on the application of a visual method in a small-scale comparative study. Practitioners in five European countries participated and took photographs of the places and spaces where offender supervision occurs. The aims of the study were two-fold: firstly to explore the utility of a visual approach in a comparative context; and secondly to provide an initial visual account of the environment in which offender supervision takes place. In this article we address the first of these aims. We describe the application of the method in some depth before addressing its strengths and weaknesses. We conclude that visual methods provide a useful tool for capturing data about the environments in which offender supervision takes place and potentially provide a basis for more normative explorations about the practices of offender supervision in comparative contexts.

Knowledge Management Techniques to Augment Textual CBR (PhD Thesis)

The past decade had witnessed an unprecedented growth in the amount of available digital content, and its volume is expected to continue to grow the next few years. Unstructured text data generated from web and enterprise sources form a large fraction of such content. Many of these contain large volumes of reusable data such as solutions to frequently occurring problems, and general know-how that may be reused in appropriate contexts. In this work, we address issues around leveraging unstructured text data from sources as diverse as the web and the enterprise within the Case-based Reasoning framework. Case-based Reasoning (CBR) provides a framework and methodology for systematic reuse of historical knowledge that is available in the form of problemsolution
pairs, in solving new problems. Here, we consider possibilities of enhancing Textual CBR systems under three main themes: procurement, maintenance and retrieval. We adapt and build upon the stateof-the-art techniques from data mining and natural language processing in addressing various challenges therein. Under procurement, we investigate the problem of extracting cases (i.e., problem-solution pairs) from data sources such as incident/experience
reports. We develop case-base maintenance methods specifically tuned to text targeted towards retaining solutions such that the utility of the filtered case base in solving new problems is maximized. Further, we address the problem of query suggestions for textual case-bases and show that exploiting the problem-solution partition can enhance retrieval effectiveness by prioritizing more useful query suggestions. Additionally, we illustrate interpretable clustering as a tool to drill-down to domain specific text collections (since CBR systems are usually very domain specific) and develop techniques for improved similarity assessment in social media sources such as microblogs. Through extensive empirical evaluations, we illustrate the improvements that we are able to
achieve over the state-of-the-art methods for the respective tasks.

Comprehensive molecular pathology analysis of small bowel adenocarcinoma reveals novel targets with potential for clinical utility

Small bowel accounts for only 0.5% of cancer cases in the US but incidence rates have been rising at 2.4% per year over the past decade. One-third of these are adenocarcinomas but little is known about their molecular pathology and no molecular markers are available for clinical use. Using a retrospective 28 patient matched normal-tumor cohort, next-generation sequencing, gene expression arrays and CpG methylation arrays were used for molecular profiling. Next-generation sequencing identified novel mutations in IDH1, CDH1, KIT, FGFR2, FLT3, NPM1, PTEN, MET, AKT1, RET, NOTCH1 and ERBB4. Array data revealed 17% of CpGs and 5% of RNA transcripts assayed to be differentially methylated and expressed respectively (p < 0.01). Merging gene expression and DNA methylation data revealed CHN2 as consistently hypermethylated and downregulated in this disease (Spearman -0.71, p < 0.001). Mutations in TP53 which were found in more than half of the cohort (15/28) and Kazald1 hypomethylation were both were indicative of poor survival (p = 0.03, HR = 3.2 and p = 0.01, HR = 4.9 respectively). By integrating high-throughput mutational, gene expression and DNA methylation data, this study reveals for the first time the distinct molecular profile of small bowel adenocarcinoma and highlights potential clinically exploitable markers.

Evidence for the reliability and validity, but not the practical utility of the two-factor Consideration of Future Consequences Scale-14

Researchers have proposed 1-factor, 2-factor, and bifactor solutions to the 12-item Consideration of Future Consequences Scale (CFCS-12). In order to overcome some measurement problems and to create a robust and conceptually useful two-factor scale the CFCS-12 was recently modified to include two new items and to become the CFCS-14. Using a University sample, we tested four competing models for the CFCS-14: (a) a 12-item unidimensional model, (b) a model fitted for two uncorrelated factors (CFC-Immediate and CFC-Future), (c) a model fitted for two correlated factors (CFC-I and CFC-F), and (d) a bifactor model. Results suggested that the addition of the two new items has strengthened the viability of a two factor solution of the CFCS-14. Results of linear regression models suggest that the CFC-F factor is redundant. Further studies using alcohol and mental health indicators are required to test this redundancy.

Utility of US Norms for the Bayley Scales of Infant Development (III) in UK and Irish Children

The aim of this paper is to explore the utility of the United States norms for United Kingdom and Republic of Ireland populations. The Bayley Scales of Infant Development (BSID III) is a globally used developmental assessment for typically developing and clinical samples of children aged 1 to 42 months. A UK norming exercise (REF) confirmed the suitability of US norms for UK based research and practice. However, debate has continued concerning the utility of the US norms in other countries. This paper further explores the utility of the US norms for the UK and ROI populations using BSID III developmental outcome data from two samples of over one thousand typically developing children.