Spreading Relation Annotations in a Lexical Semantic Network Applied to Radiology

International audienceDomain specific ontologies are invaluable but their development faces many challenges. In most cases, domain knowledge bases are built with very limited scope without considering the benefits of including domain knowledge to a general ontology. Furthermore, most existing resources lack meta-information about association strength (weights) and annotations (frequency information like frequent, rare ... or relevance information like pertinent or irrelevant). In this paper, we are presenting a semantic resource for radiology built over an existing general semantic lexical network (JeuxDeMots). This network combines weight and annotations on typed relations between terms and concepts. Some inference mechanisms are applied to the network to improve its quality and coverage. We extend this mechanism to relation annotation. We describe how annotations are handled and how they improve the network by imposing new constraints especially those founded on medical knowledge. 1 Introduction For more than two decades, medical practice and bio-medical research have benefited from the availability of biomedical ontologies (Bodenreinder, 2008). These resources are used for semantic analysis such as entity recognition (i.e., the identification of biomedical entities in texts as name of genes, disease, etc.), and relation extraction (i.e., the identification of semantic relationships among biomedical entities like for instance interaction between proteins). In the framework of the UMLS project, which interrelates some 60 controlled vocabularies, an upper-level ontology, the UMLS semantic network (Lomax, 2004) has been built. In the field of radiology, such a semantic network is used to facilitate or automate the analysis of radiologist reports in order to extract recommended courses of action or to trigger warning systems to improve patient management (Yetisgen-Yildiz and al., 2013). There exist reference on-tologies in biomedical domain (UMLS), but they might not be suited to a particular domain like radiology because result sets are too large and too complex (Mejino 2008). To solve this problem, the Radiology Society of North America (RSNA) has created reference ontology for radiology RadLex (Rubin, 2008). RadLex and its derivatives rely on English and are not considered medically complete (Hong, 2012)

An ultra fast detection method reveals strain-induced Ca2+ entry via TRPV2 in alveolar type II cells

A commonly used technique to investigate strain-induced responses of adherent cells is culturing them on an elastic membrane and globally stretching the membrane. However, it is virtually impossible to acquire microscopic images immediately after the stretch with this method. Using a newly developed technique, we recorded the strain-induced increase of the cytoplasmic Ca2+ concentration ([Ca2+]c) in rat primary alveolar type II (ATII) cells at an acquisition rate of 30ms and without any temporal delay. We can show that the onset of the mechanically induced rise in [Ca2+]c was very fast (<30 ms), and Ca2+ entry was immediately abrogated when the stimulus was withdrawn. This points at a direct mechanical activation of an ion channel. RT-PCR revealed high expression of TRPV2 in ATII cells, and silencing TRPV2, as well as blocking TRPV channels with ruthenium red, significantly reduced the strain-induced Ca2+ response. Moreover, the usually homogenous pattern of the strain-induced [Ca2+]c increase was converted into a point-like response after both treatments. Also interfering with actin/myosin and integrin binding inhibited the strain-induced increase of [Ca2]c. We conclude that TRPV2 participates in strain-induced Ca2+ entry in ATII cells and suggest a direct mechanical activation of the channel that depends on FAs and actin/myosin. Furthermore, our results underline the importance of cell strain systems that allow high temporal resolution