Multidimensional flow mapping for proportional valves

Inverse, multidimensional input-output flow mapping is very important for use of valves in precision motion control applications. Due to the highly nonlinear characteristic and uncertain model structure of the cartridge valves, it is hard to formulate the modelling of their flow mappings into simple parameter estimation problems. This contribution conducts a comprehensive analysis and validation of three- and four-dimensional input-output-mapping approaches for a proportional pilot operated seat valves. Therefore, a virtual and a physical test-rig setup are utilized for initial measurement, implementation and assessment. After modeling and validating the valve under consideration, as a function of flow, pressure and temperature different mapping methods are investigated. More specifically, state of the art approaches, deep-learning methods and a newly developed approach (extPoly) are examined. Especially ANNs and Polynomials show reasonable approximation results even for more than two inputs. However, the results are strongly dependent on the structure and distribution of the input data points. Besides identification effort, the invertibility was investigated

Adaptive feature selection for classification of microscope images

For high-throughput screening of genetically modified plant cells, a system for the automatic analysis of huge collections of microscope images is needed to decide whether the cells are infected with fungi or not. To study the potential of feature based classification for this application, we compare different classifiers (kNN, SVM, MLP, LVQ) combined with several feature reduction techniques (PCA, LDA, Mutual Information, Fisher Discriminant Ratio, Recursive Feature Elimination). We achieve a significantly higher classification accuracy using a reduced feature vector instead of the full length feature vector

The influence of polymorbidity, revascularization, and wound therapy on the healing of arterial ulceration

Joerg Tautenhahn1, Ralf Lobmann2, Brigitte Koenig3, Zuhir Halloul1, Hans Lippert1, Thomas Buerger11Department of General, Visceral and Vascular Surgery; 2Department of Endocrinology and Metabolism; 3Institute for Medical Microbiology, Medical School, Otto-von-Guericke University, Magdeburg, GermanyObjective: An ulcer categorized as Fontaine’s stage IV represents a chronic wound, risk factor of arteriosclerosis, and co-morbidities which disturb wound healing. Our objective was to analyze wound healing and to assess potential factors affecting the healing process.Methods: 199 patients were included in this 5-year study. The significance levels were determined by chi-squared and log-rank tests. The calculation of patency rate followed the Kaplan-Meier method.Results: Mean age and co-morbidities did not differ from those in current epidemiological studies. Of the patients with ulcer latency of more than 13 weeks (up to one year), 40% required vascular surgery. Vascular surgery was not possible for 53 patients and they were treated conservatively. The amputation rate in the conservatively treated group was 37%, whereas in the revascularizated group it was only 16%. Ulcers in patients with revascularization healed in 92% of cases after 24 weeks. In contrast, we found a healing rate of only 40% in the conservatively treated group (p < 0.001). Revascularization appeared more often in diabetic patients (n = 110; p < 0.01) and the wound size and number of infections were elevated (p = 0.03). Among those treated conservatively, wound healing was decelerated (p = 0.01/0.02; χ² test).Conclusions: The success of revascularization, presence of diabetes mellitus, and wound treatment proved to be prognostic factors for wound healing in arterial ulcers.Keywords: arterial leg ulcer, wound management, risk factors, revascularizatio

Arteriovenous Blood Metabolomics: A Readout of Intra-Tissue Metabostasis.

The human circulatory system consists of arterial blood that delivers nutrients to tissues, and venous blood that removes the metabolic by-products. Although it is well established that arterial blood generally has higher concentrations of glucose and oxygen relative to venous blood, a comprehensive biochemical characterization of arteriovenous differences has not yet been reported. Here we apply cutting-edge, mass spectrometry-based metabolomic technologies to provide a global characterization of metabolites that vary in concentration between the arterial and venous blood of human patients. Global profiling of paired arterial and venous plasma from 20 healthy individuals, followed up by targeted analysis made it possible to measure subtle (<2 fold), yet highly statistically significant and physiologically important differences in water soluble human plasma metabolome. While we detected changes in lactic acid, alanine, glutamine, and glutamate as expected from skeletal muscle activity, a number of unanticipated metabolites were also determined to be significantly altered including Krebs cycle intermediates, amino acids that have not been previously implicated in transport, and a few oxidized fatty acids. This study provides the most comprehensive assessment of metabolic changes in the blood during circulation to date and suggests that such profiling approach may offer new insights into organ homeostasis and organ specific pathology

Highly sensitive feature detection for high resolution LC/MS

<p>Abstract</p> <p>Background</p> <p>Liquid chromatography coupled to mass spectrometry (LC/MS) is an important analytical technology for e.g. metabolomics experiments. Determining the boundaries, centres and intensities of the two-dimensional signals in the LC/MS raw data is called feature detection. For the subsequent analysis of complex samples such as plant extracts, which may contain hundreds of compounds, corresponding to thousands of features – a reliable feature detection is mandatory.</p> <p>Results</p> <p>We developed a new feature detection algorithm <it>centWave </it>for high-resolution LC/MS data sets, which collects regions of interest (partial mass traces) in the raw-data, and applies continuous wavelet transformation and optionally Gauss-fitting in the chromatographic domain. We evaluated our feature detection algorithm on dilution series and mixtures of seed and leaf extracts, and estimated recall, precision and F-score of seed and leaf specific features in two experiments of different complexity.</p> <p>Conclusion</p> <p>The new feature detection algorithm meets the requirements of current metabolomics experiments. <it>centWave </it>can detect close-by and partially overlapping features and has the highest overall recall and precision values compared to the other algorithms, <it>matchedFilter </it>(the original algorithm of <it>XCMS</it>) and the centroidPicker from <it>MZmine</it>. The <it>centWave </it>algorithm was integrated into the Bioconductor R-package <it>XCMS </it>and is available from <url>http://www.bioconductor.org/</url></p