The objective was to assess whether changes of cartilage oligomeric matrix protein (COMP) serum levels can predict the development of osteoarthritis following traumatic knee injury. Sera and synovial fluids were acquired at surgery (T0) and postoperatively during the first (T1) and second (T2) year from 30 knee-injured patients. COMP levels and anti-COMP autoantibodies were quantified by ELISA. Radiographs and patient questionnaires were used to assess outcomes. At T0, compared with controls (1.6 +/- 1.6 micrograms/ml), the serum COMP concentration was significantly elevated (6.5 +/- 2.8 micrograms/ml) with a tendency to further increase (T0 vs. T1, P = 0.076) and subsequently decrease (T1 vs. T2, P = 0.074). However, individual variations are observed, e.g. persistently high (8/30) or increasing (T0 to T2, 8/30) serum COMP. Ten of these patients have elevated COMP at T2 that increased from T0. COMP levels in serum and synovial fluid correlated significantly (P = 0.012). Interestingly, some patients who revealed increasing serum levels of COMP from T0 to T2 displayed anti-COMP autoantibodies. These data suggest that local immune response could contribute to further joint damage. The subgroup of 10 patients (33%) with elevated and increasing serum COMP levels and in particular the patients with antibodies against cartilage matrix molecules appear at increased risk for developing posttraumatic osteoarthritis.

A cornerstone in Domain-Specific Modeling is the definition of modeling languages. A widely used method to formalize domain-specific languages is the metamodeling approach. There are a huge number of metamodeling languages. The choice of a suitable metamodeling approach is a challenging task because there is often a lack of knowledge about the selection criteria and the offered metamodeling features. In this paper, we analyze a set of metamodeling languages (ARIS, Ecore, GME, GOPPRR, MS DSL Tools, and MS Visio), define a comparison framework, and compare the selected meta-metamodels. The comparison forms a first foundation for solving the selection problem.

In this article, we present the prototype of a modeling tool that applies graph‐based rules for identifying problems in business process models. The advantage of our approach is twofold. First, it is unnecessary to compute the complete state space of the model to find errors. Second, our technique can even be applied to incomplete business process models. Thus, the modeler can be supported by direct feedback during the model construction. This feedback not only reports problems, but also identifies their reasons and gives suggestions for improvement. Copyright © 2010 John Wiley & Sons, Ltd.

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Abstract. In this paper, we demonstrate the prototype of a modelling tool that applies graph-based rules for identifying problems in business process models. The advantages of our approach are twofold. Firstly, it is not necessary to compute the complete state space of the model in order to find errors. Secondly, our technique can even be applied to incomplete business process models. Thus, the modeller can be supported by direct feedback during the model construction. This feedback does not only report problems, but it also identifies their reasons and makes suggestions for improvements.