Talking about colds and flu: The lay diagnosis of two common illnesses among older British people

This paper reports on a study of the ways in which 54 older people in South Wales (UK) talk about the symptoms and causes of cold and influenza (flu). The study was designed to understand why older people might reject or accept the offer of seasonal flu vaccine, and in the course of the interviews respondents were also asked to express their views about the nature and causes of the two key illnesses. The latter are among the most common infections in human beings. In terms of the biomedical paradigm the common cold is caused by numerous respiratory viruses, whilst flu is caused by the influenza virus. Medical diagnosis is usually made on clinical grounds without laboratory confirmation. Symptoms of flu include sudden onset of fever and cough, and colds are characterized by sneezing, sore throat, and runny nose, but in practice the symptoms often overlap. In this study we examine the degree by which the views of lay people with respect to both diagnosis and epidemiology diverge with that which is evident in biomedical discourse. Our results indicate that whilst most of the identified symptoms are common to lay and professional people, the former integrate symptoms into a markedly different observational frame from the latter. And as far as causation is concerned it is clear that lay people emphasize the role of 'resistance' and 'immunity' at least as much as 'infection' in accounting for the onset of colds and flu. The data are analyzed using novel methods that focus on the co-occurrence of concepts and are displayed as semantic networks. As well as reporting on its findings the authors draw out some implications of the study for social scientific and policy discussions concerning lay diagnosis, lay expertise and the concept of an expert patient.

Using Graph Components Derived from an Associative Concept Dictionary to Predict fMRI Neural Activation Patterns that Represent the Meaning of Nouns

In this study, we introduce an original distance definition for graphs, called the Markov-inverse-F measure (MiF). This measure enables the integration of classical graph theory indices with new knowledge pertaining to structural feature extraction from semantic networks. MiF improves the conventional Jaccard and/or Simpson indices, and reconciles both the geodesic information (random walk) and co-occurrence adjustment (degree balance and distribution). We measure the effectiveness of graph-based coefficients through the application of linguistic graph information for a neural activity recorded during conceptual processing in the human brain. Specifically, the MiF distance is computed between each of the nouns used in a previous neural experiment and each of the in-between words in a subgraph derived from the Edinburgh Word Association Thesaurus of English. From the MiF-based information matrix, a machine learning model can accurately obtain a scalar parameter that specifies the degree to which each voxel in (the MRI image of) the brain is activated by each word or each principal component of the intermediate semantic features. Furthermore, correlating the voxel information with the MiF-based principal components, a new computational neurolinguistics model with a network connectivity paradigm is created. This allows two dimensions of context space to be incorporated with both semantic and neural distributional representations.

Contextual Intrusion Alerts for SCADA Networks

The complexity of modern SCADA networks and their associated cyber-attacks requires an expressive but flexible manner for representing both domain knowledge and collected intrusion alerts with the ability to integrate them for enhanced analytical capabilities and better understanding of attacks. This paper proposes an ontology-based approach for contextualized intrusion alerts in SCADA networks. In this approach, three security ontologies were developed to represent and store information on intrusion alerts, Modbus communications, and Modbus attack descriptions. This information is correlated into enriched intrusion alerts using simple ontology logic rules written in Semantic Query-Enhanced Web Rules (SQWRL). The contextualized alerts give analysts the means to better understand evolving attacks and to uncover the semantic relationships between sequences of individual attack events. The proposed system is illustrated by two use case scenarios.