This article describes an extension of the OpenModelica Compiler that translates regular Modelica models into a simpler language, called Micro-Modelica (µ-Modelica), that can be understood by the recently developed stand-alone Quantized State Systems (QSS) solvers. These solvers are very efficient when simulating systems with frequent discontinuities. Thus, strongly discontinuous Modelica models can be simulated noticeably faster than with the standard discrete time solvers.The simulation of two discontinuous models is analyzed in order to demonstrate the correctness of the proposed implementation as well as the advantages of using the QSS stand-alone solvers.
This study describes the current implementation of an interface that automatically translates a discontinuous model described using the Modelica language into the Discrete Event System Specification (DEVS) formalism. More specifically, the interface enables the automatic simulation of a Modelica model with discontinuities in the PowerDEVS environment, where the Quantized State Systems (QSS) integration methods are implemented. Providing DEVS-based simulation algorithms to Modelica users should extend significantly the tools that are currently available in order to efficiently simulate several classes of largescale real-world problems, e.g. systems with heavy discontinuities. In this work both the theoretical design and the implementation of the interface are discussed. Furthermore, simulation results are provided that demonstrate the correctness of the proposed implementation as well as the superior performance of QSS methods when simulating discontinuous systems.
In this study we aimed to establish an unbiased automatic quantification pipeline to assess islet specific features such as β-cell area and density per islet based on immunofluorescence stainings. To determine these parameters, the in vivo protein expression levels of TMEM27 and BACE2 in pancreatic islets of 32 patients with type 2 diabetes (T2D) and in 28 non-diabetic individuals (ND) were used as input for the automated pipeline. The output of the automated pipeline was first compared to a previously developed manual area scoring system which takes into account the intensity of the staining as well as the percentage of cells which are stained within an islet. The median TMEM27 and BACE2 area scores of all islets investigated per patient correlated significantly with the manual scoring and with the median area score of insulin. Furthermore, the median area scores of TMEM27, BACE2 and insulin calculated from all T2D were significantly lower compared to the one of all ND. TMEM27, BACE2, and insulin area scores correlated as well in each individual tissue specimen. Moreover, islet size determined by costaining of glucagon and either TMEM27 or BACE2 and β-cell density based either on TMEM27 or BACE2 positive cells correlated significantly. Finally, the TMEM27 area score showed a positive correlation with BMI in ND and an inverse pattern in T2D. In summary, automated quantification outperforms manual scoring by reducing time and individual bias. The simultaneous changes of TMEM27, BACE2, and insulin in the majority of the β–cells suggest that these proteins reflect the total number of functional insulin producing β–cells. Additionally, β–cell subpopulations may be identified which are positive for TMEM27, BACE2 or insulin only. Thus, the cumulative assessment of all three markers may provide further information about the real β–cell number per islet.
Abstract. It is estimated that in 2010 more than 220 million people will be affected by type 2 diabetes mellitus (T2DM). Early evidence indicates that specific markers for alpha and beta cells in pancreatic islets of Langerhans can be used for early T2DM diagnosis. Currently, the analysis of such histological tissues is manually performed by trained pathologists using a light microscope. To objectify classification results and to reduce the processing time of histological tissues, an automated computational pathology framework for segmentation of pancreatic islets from histopathological fluorescence images is proposed. Due to high variability in the staining intensities for alpha and beta cells, classical medical imaging approaches fail in this scenario.The main contribution of this paper consists of a novel graph-based segmentation approach based on cell nuclei detection with randomized tree ensembles. The algorithm is trained via a cross validation scheme on a ground truth set of islet images manually segmented by 4 expert pathologists. Test errors obtained from the cross validation procedure demonstrate that the graph-based computational pathology analysis proposed is performing competitively to the expert pathologists while outperforming a baseline morphological approach.
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