STORM - a novel information fusion and cluster interpretation technique

Analysis of data without labels is commonly subject to scrutiny by unsupervised machine learning techniques. Such techniques provide more meaningful representations, useful for better understanding of a problem at hand, than by looking only at the data itself. Although abundant expert knowledge exists in many areas where unlabelled data is examined, such knowledge is rarely incorporated into automatic analysis. Incorporation of expert knowledge is frequently a matter of combining multiple data sources from disparate hypothetical spaces. In cases where such spaces belong to different data types, this task becomes even more challenging. In this paper we present a novel immune-inspired method that enables the fusion of such disparate types of data for a specific set of problems. We show that our method provides a better visual understanding of one hypothetical space with the help of data from another hypothetical space. We believe that our model has implications for the field of exploratory data analysis and knowledge discovery.

"The post-antibiotic apocalypse" and the "war on superbugs": catastrophe discourse in microbiology, its rhetorical form and political function

Discourses evoking an antibiotic apocalypse and a war on superbugs are emerging just at a time when so-called "catastrophe discourses" are undergoing critical and reflexive scrutiny in the context of global warming and climate change. This article combines insights from social science research into climate change discourses with applied metaphor research based on recent advances in cognitive linguistics, especially with relation to "discourse metaphors." It traces the emergence of a new apocalyptic discourse in microbiology and health care, examines its rhetorical and political function and discusses its advantages and disadvantages. It contains a reply by the author of the central discourse metaphor, "the post-antibiotic apocalypse," examined in the article.