Equine proliferative enteropathy--a review of recent developments.

Equine proliferative enteropathy (EPE) is a disease of foals caused by the obligate intracellular organism Lawsonia intracellularis. This emerging disease affects mainly weanling foals and causes fever, lethargy, peripheral oedema, diarrhoea, colic and weight loss. The diagnosis of EPE may be challenging and relies on the presence of hypoproteinaemia, thickening of segments of the small intestinal wall observed upon abdominal ultrasonography, positive serology and molecular detection of L. intracellularis in faeces. Although the clinical entity, diagnostic approach and treatment of EPE are well established and described, the epidemiology for this disease has remained largely unaddressed. This article focuses on new developments in the field of EPE, including epidemiology, pathophysiology, clinical signs, diagnosis, treatment and prevention

Autoentity: automated entity detection from massive text corpora

Entity detection is one of the fundamental tasks in Natural Language Processing and Information Retrieval. Most existing methods rely on human annotated data and hand-crafted linguistic features, which makes it hard to apply the model to an emerging domain. In this paper, we propose a novel automated entity detection framework, called AutoEntity, that performs automated phrase mining to create entity mention candidates and enforces lexico-syntactic rules to select entity mentions from candidates. Our experiments on real-world datasets in different domains and multiple languages have demonstrated the effectiveness and robustness of the proposed method

Identifying Conflicting Requirements in Systems of Systems

A System of Systems (SoS) is an arrangement of useful and independent sub-systems, which are integrated into a larger system. Examples are found in transport systems, nutritional systems, smart homes and smart cities. The composition of component sub-systems into an SoS enables support for complex functionalities that cannot be provided by individual sub-systems on their own. However, to realize the benefits of these functionalities it is necessary to address several software engineering challenges including, but not limited to, the specification, design, construction, deployment, and management of an SoS. The various component sub-systems in an SoS environment are often concerned with distinct domains; are developed by different stake-holders under different circumstances and time; provide distinct functionalities; and are used by different stakeholders, which allow for the existence of conflicting requirements. In this paper, we present a framework to support management of emerging conflicting requirements in an SoS. In particular, we describe an approach to support identification of conflicts between resource-based requirements (i.e. requirements concerned with the consumption of different resources). In order to illustrate and evaluate the work, we use an example of a pilot study of an IoT SoS ecosystem designed to support food security at different levels of granularity, namely individuals, groups, cities, and nations

What's unusual in online disease outbreak news?

Background: Accurate and timely detection of public health events of international concern is necessary to help support risk assessment and response and save lives. Novel event-based methods that use the World Wide Web as a signal source offer potential to extend health surveillance into areas where traditional indicator networks are lacking. In this paper we address the issue of systematically evaluating online health news to support automatic alerting using daily disease-country counts text mined from real world data using BioCaster. For 18 data sets produced by BioCaster, we compare 5 aberration detection algorithms (EARS C2, C3, W2, F-statistic and EWMA) for performance against expert moderated ProMED-mail postings. Results: We report sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), mean alerts/100 days and F1, at 95% confidence interval (CI) for 287 ProMED-mail postings on 18 outbreaks across 14 countries over a 366 day period. Results indicate that W2 had the best F1 with a slight benefit for day of week effect over C2. In drill down analysis we indicate issues arising from the granular choice of country-level modeling, sudden drops in reporting due to day of week effects and reporting bias. Automatic alerting has been implemented in BioCaster available from http://born.nii.ac.jp. Conclusions: Online health news alerts have the potential to enhance manual analytical methods by increasing throughput, timeliness and detection rates. Systematic evaluation of health news aberrations is necessary to push forward our understanding of the complex relationship between news report volumes and case numbers and to select the best performing features and algorithms