The Computer Science Ontology: A Large-Scale Taxonomy of Research Areas

Ontologies of research areas are important tools for characterising, exploring, and analysing the research landscape. Some fields of research are comprehensively described by large-scale taxonomies, e.g., MeSH in Biology and PhySH in Physics. Conversely, current Computer Science taxonomies are coarse-grained and tend to evolve slowly. For instance, the ACM classification scheme contains only about 2K research topics and the last version dates back to 2012. In this paper, we introduce the Computer Science Ontology (CSO), a large-scale, automatically generated ontology of research areas, which includes about 26K topics and 226K semantic relationships. It was created by applying the Klink-2 algorithm on a very large dataset of 16M scientific articles. CSO presents two main advantages over the alternatives: i) it includes a very large number of topics that do not appear in other classifications, and ii) it can be updated automatically by running Klink-2 on recent corpora of publications. CSO powers several tools adopted by the editorial team at Springer Nature and has been used to enable a variety of solutions, such as classifying research publications, detecting research communities, and predicting research trends. To facilitate the uptake of CSO we have developed the CSO Portal, a web application that enables users to download, explore, and provide granular feedback on CSO at different levels. Users can use the portal to rate topics and relationships, suggest missing relationships, and visualise sections of the ontology. The portal will support the publication of and access to regular new releases of CSO, with the aim of providing a comprehensive resource to the various communities engaged with scholarly data

Identifying work related injuries: comparison of methods for interrogating text fields

Background: Work-related injuries in Australia are estimated to cost around $57.5 billion annually, however there are currently insufficient surveillance data available to support an evidence-based public health response. Emergency departments (ED) in Australia are a potential source of information on work-related injuries though most ED’s do not have an ‘Activity Code’ to identify work-related cases with information about the presenting problem recorded in a short free text field. This study compared methods for interrogating text fields for identifying work-related injuries presenting at emergency departments to inform approaches to surveillance of work-related injury.---------- Methods: Three approaches were used to interrogate an injury description text field to classify cases as work-related: keyword search, index search, and content analytic text mining. Sensitivity and specificity were examined by comparing cases flagged by each approach to cases coded with an Activity code during triage. Methods to improve the sensitivity and/or specificity of each approach were explored by adjusting the classification techniques within each broad approach.---------- Results: The basic keyword search detected 58% of cases (Specificity 0.99), an index search detected 62% of cases (Specificity 0.87), and the content analytic text mining (using adjusted probabilities) approach detected 77% of cases (Specificity 0.95).---------- Conclusions The findings of this study provide strong support for continued development of text searching methods to obtain information from routine emergency department data, to improve the capacity for comprehensive injury surveillance

Discounting in Economic Evaluations

Appropriate discounting rules in economic evaluations have received considerable attention in the literature and in national guidelines for economic evaluations. Rightfully so, as discounting can be quite influential on the outcomes of economic evaluations. The most prominent controversies regarding discounting involve the basis for and height of the discount rate, whether costs and effects should be discounted at the same rate, and whether discount rates should decline or stay constant over time. Moreover, the choice for discount rules depends on the decision context one adopts as the most relevant. In this article, we review these issues and debates, and describe and discuss the current discounting recommendations of the countries publishing their national guidelines. We finish the article by proposing a research agenda

Curcumin Prevents Palmitoylation of Integrin β4 in Breast Cancer Cells

We thank Dr. Tak yee Aw for technical assistance and critical discussion of the manuscript. This study is supported by American Cancer Society (RSG-09-091-01-CSM: JC) and NIH-NCI (R01CA163657-01A1: JC).Curcumin has been shown to mitigate cancer phenotypes such as invasive migration, proliferation, and survival by disrupting numerous signaling pathways. Our previous studies showed that curcumin inhibits integrin β4 (ITG β4)-dependent migration by blocking interaction of this integrin with growth factor receptors in lipid rafts. In the current study, we investigated the possibility that curcumin inhibits ITG β4 palmitoylation, a post-translational modification required for its lipid raft localization and signaling activity. We found that the levels of ITG β4 palmitoylation correlated with the invasive potential of breast cancer cells, and that curcumin effectively reduced the levels of ITG β4 palmitoylation in invasive breast cancer cells. Through studies of ITG β4 palmitoylation kinetics, we concluded curcumin suppressed palmitoylation independent of growth factor-induced phosphorylation of key ITG β4 Ser and Tyr residues. Rather, curcumin blocked autoacylation of the palmitoyl acyltransferase DHHC3 that is responsible for ITG β4 palmitoylation. Moreover, these data reveal that curcumin is able to prevent the palmitoylation of a subset of proteins, but not indiscriminately bind to and block all cysteines from modifications. Our studies reveal a novel paradigm for curcumin to account for much of its biological activity, and specifically, how it is able to suppress the signaling function of ITG β4 in breast cancer cells.Yeshttp://www.plosone.org/static/editorial#pee