Routine habitat switching alters the likelihood and persistence of infection with a pathogenic parasite

Animals switch habitats on a regular basis, and when habitats vary in suitability 21 for parasitism, routine habitat switching alters the frequency of parasite exposure 22 and may affect post-infection parasite proliferation. However, the effects of 23 routine habitat switching on infection dynamics are not well understood. 24 2. We performed infection experiments, behavioural observations, and field 25 surveillance to evaluate how routine habitat switching by adult alpine newts 26 (Ichthyosaura alpestris) influences infection dynamics of the pathogenic parasite, 27 Batrachochytrium dendrobatidis (Bd). 28 3. We show that when newts are exposed to equal total doses of Bd in aquatic 29 habitats, differences in exposure frequency and post-exposure habitat alter 30 infection trajectories: newts developed more infections that persisted longer when 31 doses were broken into multiple, reduced-intensity exposures. Intensity and 32 persistence of infections was reduced among newts that were switched to 33 terrestrial habitats following exposure. 34 4. When presented with a choice of habitats, newts did not avoid exposure to Bd, 35 but heavily infected newts were more prone to reduce time spent in water. 36 5. Accounting for routine switching between aquatic and terrestrial habitat in the 37 experiments generated distributions of infection loads that were consistent with 38 those in two populations of wild newts. 39 6. Together, these findings emphasize that differential habitat use and behaviours 40 associated with daily movement can be important ecological determinants of 41 infection risk and severity. 4

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Representing and operating with model differences

Models and metamodels play a cornerstone role in Model-Driven Software Development (MDSD). Models conform to metamodels, which usually specify domain-specific languages that allow to represent the various facets of a system in terms of models. This paper discusses the problem of calculating differences between models conforming to arbitrary metamodels, something essential in any MDSD environment for dealing with the management of changes and evolution of software models. We present a metamodel for representing the differences as models, too, following the MDSD “everything is a model” principle. The Difference Metamodel, together with the difference and other related operations (do, undo and composition) presented here have been specified in Maude and integrated in an Eclipse-developed environment

A manifesto for semantic model differencing

Abstract. Models are heavily used in software engineering and together with their systems they evolve over time. Thus, managing their changes is an important challenge for system maintainability. Existing approaches to model differencing concentrate on heuristics matching between model syntactic level. While showing some success, these approaches are inherently limited to comparing syntactic structures. This paper is a manifesto for research on semantic model differencing. We present our vision to develop semantic diff operators for model comparisons: operators whose input consists of two models and whose output is a set of diff witnesses, instances of one model that are not instances of the other. In particular, if the models are syntactically different but there are no diff witnesses, the models are semantically equivalent. We demonstrate our vision using two concrete diff operators, for class diagrams and for activity diagrams. We motivate the use of semantic diff operators, briefly discuss the algorithms to compute them, list related challenges, and show their application and potential use as new fundamental building blocks for change management in model-driven engineering.