Convergence on layer-adapted meshes and anisotropic interpolation error estimates of non-standard higher order finite elements

Abstract For a general class of finite element spaces based on local polynomial spaces E with P p ⊂ E ⊂ Q p we construct a vertex-edge-cell and point-value oriented interpolation operators that fulfil anisotropic interpolation error estimates. Using these estimates we prove ε-uniform convergence of order p for the Galerkin FEM and the LPSFEM for a singularly perturbed convection-diffusion problem with characteristic boundary layers

Towards an Ontology-Based Phenotypic Query Model

Clinical research based on data from patient or study data management systems plays an important role in transferring basic findings into the daily practices of physicians. To support study recruitment, diagnostic processes, and risk factor evaluation, search queries for such management systems can be used. Typically, the query syntax as well as the underlying data structure vary greatly between different data management systems. This makes it difficult for domain experts (e.g., clinicians) to build and execute search queries. In this work, the Core Ontology of Phenotypes is used as a general model for phenotypic knowledge. This knowledge is required to create search queries that determine and classify individuals (e.g., patients or study participants) whose morphology, function, behaviour, or biochemical and physiological properties meet specific phenotype classes. A specific model describing a set of particular phenotype classes is called a Phenotype Specification Ontology. Such an ontology can be automatically converted to search queries on data management systems. The methods described have already been used successfully in several projects. Using ontologies to model phenotypic knowledge on patient or study data management systems is a viable approach. It allows clinicians to model from a domain perspective without knowing the actual data structure or query language

Mid-Term Clinical Outcome and Reconstruction of Posterior Tibial Slope after UKA

Unicompartmental knee arthroplasty (UKA) has gained growing popularity over the last decades. The posterior tibial slope (PTS) has been shown to play a significant role for knee biomechanics and is thought to be crucial for clinical function of the UKA. We evaluated the clinical outcome at mid-term follow-up after UKA. Furthermore, the reconstruction of the individual PTS was analyzed. A total of 91 consecutive patients undergoing medial UKA for osteoarthritis were included. Patients were contacted by telephone for a survival analysis at a minimum of 30 months after surgery. Patient-oriented questionnaires and Knee Osteoarthritis Outcome Score (KOOS) were obtained. A retrospective chart review and radiological analysis of component alignment were performed for all patients before and at 6 weeks after surgery. Of 91 patients (93 knees) undergoing UKA, 69 patients (70 knees) were available for clinical follow-up after a mean of 56.0 (range 31-81) months post-surgery. The clinical results of the examined patients in the present study showed mean subscale scores of the KOOS andWestern Ontario and McMaster Universities Osteoarthritis Index between 71 and 91%. Overall 7 of 91 patients were revised during the course of follow-up period and underwent total knee arthroplasty. A Kaplan-Meier analysis showed a survival rate for UKA of 90.5% after 48 months. Calculated implant survival was 75.9 months (95% confidence interval 72.3-79.6) at the mean. The radiographic analysis of pre-and postoperative PTS showed no differences (p = 0.113). UKA for osteoarthritis of the medial knee compartment shows encouraging clinical results at mid-term follow-up. The individual PTS could be reconstructed within acceptable ranges. This is a retrospective therapeutic study with Level IV

Towards an Ontology-Based Phenotypic Query Model

Clinical research based on data from patient or study data management systems plays an important role in transferring basic findings into the daily practices of physicians. To support study recruitment, diagnostic processes, and risk factor evaluation, search queries for such management systems can be used. Typically, the query syntax as well as the underlying data structure vary greatly between different data management systems. This makes it difficult for domain experts (e.g., clinicians) to build and execute search queries. In this work, the Core Ontology of Phenotypes is used as a general model for phenotypic knowledge. This knowledge is required to create search queries that determine and classify individuals (e.g., patients or study participants) whose morphology, function, behaviour, or biochemical and physiological properties meet specific phenotype classes. A specific model describing a set of particular phenotype classes is called a Phenotype Specification Ontology. Such an ontology can be automatically converted to search queries on data management systems. The methods described have already been used successfully in several projects. Using ontologies to model phenotypic knowledge on patient or study data management systems is a viable approach. It allows clinicians to model from a domain perspective without knowing the actual data structure or query language