Abschlussbericht des Verbundprojekts Tools4BPEL

Unternehmensübergreifende Geschäftsprozesse werden zunehmend nach dem Paradigma der Services organisiert. Dabei stellen sich Fragen nach der Komponierbarkeit, Fehlerbehandlung, sowie der Rücksetzbarkeit (Kompensation) im Fehlerfall. In diesem Vorhaben werden Methoden und Werkzeuge zum Umgang mit solchen Fragen entwickelt und am Beispiel der Geschäftsprozess-Modellierungssprache BPEL und im Modellierungswerkzeug der Firma MEGA international erprobt. Es wurde zum einen der Übersetzer BPEL2oWFN entwickelt, der anhand einer Petrinetzsemantik für BPEL einen BPEL-Prozess in ein (offenes) Petrinetz transformiert. Zum anderen wurden Korrektheitskriterien (wie Bedienbarkeit und Verhaltenskompatibilität) für Services erarbeitet, Algorithmen zu ihrer Überprüfung entworfen und in Fiona implementiert. Die Algorithmen sind Petrinetz-basiert. Damit spielen Übersetzung und Analyse eng zusammen und ein vorhandener BPEL-Prozess kann auf bspw. Bedienbarkeit hin untersucht werden. In diesem Vorhaben wurden die Modellierungssprache BPEL4Chor, Choreographie-Erweiterungen für BPMN entwickelt, sowie die Transformation von BPMN nach BPEL angepasst, um den Weg von BPMN nach BPEL4Chor zu unterstützen. Weiterhin wurden Konzepte entwickelt, wie sich partner-übergreifende Fehlerbehandlung, Rücksetzbarkeit, sowie die Autonomie der Partner mittels BPEL4Chor darstellen lassen. BPEL4Chor kann als Standardsprache zur Spezifikation von Protokollen, die zwischen mehreren Partnern ablaufen, verwendet werden. Durch seine enge Verbindung mit BPEL kann BPEL4Chor sehr gut als Startpunkt für eine Webservice-Lösung verwendet werden

Assessing airway inflammation in clinical practice – experience with spontaneous sputum analysis

<p>Abstract</p> <p>Background</p> <p>The assessment of airway inflammation for the diagnosis of asthma or COPD is still uncommon in pneumology-specialized general practices. In this respect, the measurement of exhaled nitric oxide (NO), as a fast and simple methodology, is increasingly used. The indirect assessment of airway inflammation, however, does have its limits and therefore there will always be a need for methods enabling a direct evaluation of airway inflammatory cell composition. Sampling of spontaneous sputum is a well-known, simple, economic and non-invasive method to derive a qualitative cytology of airway cells and here we aimed to assess today's value of spontaneous sputum cytology in clinical practice.</p> <p>Methods</p> <p>Three pneumologists provided final diagnoses in 481 patients having sputum cytology and we retrospectively determined posterior versus prior probabilities of inflammatory airway disorders. Moreover, in a prospective part comprising 108 patients, pneumologists rated their confidence in a given diagnosis before and after knowing sputum cytology and rated its impact on the diagnostic process on an analogue scale.</p> <p>Results</p> <p>Among the 481 patients, 45% were diagnosed as having asthma and/or airway hyperresponsiveness. If patients showed sputum eosinophilia, the prevalence of this diagnosis was elevated to 73% (n = 109, p < 0.001). The diagnosis of COPD increased from 40 to 66% in patients with neutrophilia (n = 29, p < 0.01).</p> <p>Thirty-three of the 108 patients were excluded from the prospective part (26 insufficient samples, 7 incomplete questionnaires). In 48/75 cases the confidence into a diagnosis was raised after knowing sputum cytology, and in 15/75 cases the diagnosis was changed as cytology provided new clues.</p> <p>Conclusion</p> <p>Our data suggest that spontaneous sputum cytology is capable of assisting in the diagnosis of inflammatory airway diseases in the outpatient setting. Despite the limitations by the semiquantitative assessment and lower sputum quality, the supportive power and the low economic effort needed can justify the use of this method, particularly if the diagnosis in question is thought to have an allergic background.</p

Searching for a Stochastic Background of Gravitational Waves with LIGO

The Laser Interferometer Gravitational-wave Observatory (LIGO) has performed the fourth science run, S4, with significantly improved interferometer sensitivities with respect to previous runs. Using data acquired during this science run, we place a limit on the amplitude of a stochastic background of gravitational waves. For a frequency independent spectrum, the new limit is $\Omega_{\rm GW} < 6.5 \times 10^{-5}$. This is currently the most sensitive result in the frequency range 51-150 Hz, with a factor of 13 improvement over the previous LIGO result. We discuss complementarity of the new result with other constraints on a stochastic background of gravitational waves, and we investigate implications of the new result for different models of this background.Comment: 37 pages, 16 figure

Genomic investigations of unexplained acute hepatitis in children

Since its first identification in Scotland, over 1,000 cases of unexplained paediatric hepatitis in children have been reported worldwide, including 278 cases in the UK1. Here we report an investigation of 38 cases, 66 age-matched immunocompetent controls and 21 immunocompromised comparator participants, using a combination of genomic, transcriptomic, proteomic and immunohistochemical methods. We detected high levels of adeno-associated virus 2 (AAV2) DNA in the liver, blood, plasma or stool from 27 of 28 cases. We found low levels of adenovirus (HAdV) and human herpesvirus 6B (HHV-6B) in 23 of 31 and 16 of 23, respectively, of the cases tested. By contrast, AAV2 was infrequently detected and at low titre in the blood or the liver from control children with HAdV, even when profoundly immunosuppressed. AAV2, HAdV and HHV-6 phylogeny excluded the emergence of novel strains in cases. Histological analyses of explanted livers showed enrichment for T cells and B lineage cells. Proteomic comparison of liver tissue from cases and healthy controls identified increased expression of HLA class 2, immunoglobulin variable regions and complement proteins. HAdV and AAV2 proteins were not detected in the livers. Instead, we identified AAV2 DNA complexes reflecting both HAdV-mediated and HHV-6B-mediated replication. We hypothesize that high levels of abnormal AAV2 replication products aided by HAdV and, in severe cases, HHV-6B may have triggered immune-mediated hepatic disease in genetically and immunologically predisposed children

Adaptive Multimedia Retrieval: Identifying, Summarizing, and Recommending Image and Music

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Security and Safety Considerations for the DECOS Core OS

Abstract. This paper presents safety and security considerations for the Core Operating System (COS) of the Encapsulated Execution Environment (EEE) developed in DECOS (Dependable Embedded Components and Systems), an integrated project within the Sixth Framework Programme of the European Commission. It is shown that security and safety is well considered in the COS and a high level of security and safety can be achieved when systems using the COS are designed properly

Generative Adversarial Networks: A Primer for Radiologists

Artificial intelligence techniques involving the use of artificial neural networks (ie, deep learning techniques) are expected to have a major effect on radiology, and some of the most exciting applications of deep learning in radiology make use of generative adversarial networks. Artificial intelligence techniques involving the use of artificial neural networks—that is, deep learning techniques—are expected to have a major effect on radiology. Some of the most exciting applications of deep learning in radiology make use of generative adversarial networks (GANs). GANs consist of two artificial neural networks that are jointly optimized but with opposing goals. One neural network, the generator, aims to synthesize images that cannot be distinguished from real images. The second neural network, the discriminator, aims to distinguish these synthetic images from real images. These deep learning models allow, among other applications, the synthesis of new images, acceleration of image acquisitions, reduction of imaging artifacts, efficient and accurate conversion between medical images acquired with different modalities, and identification of abnormalities depicted on images. The authors provide an introduction to GANs and adversarial deep learning methods. In addition, the different ways in which GANs can be used for image synthesis and image-to-image translation tasks, as well as the principles underlying conditional GANs and cycle-consistent GANs, are described. Illustrated examples of GAN applications in radiologic image analysis for different imaging modalities and different tasks are provided. The clinical potential of GANs, future clinical GAN applications, and potential pitfalls and caveats that radiologists should be aware of also are discussed in this review

Generative Adversarial Networks: A Primer for Radiologists

Artificial intelligence techniques involving the use of artificial neural networks—that is, deep learning techniques—are expected to have a major effect on radiology. Some of the most exciting applications of deep learning in radiology make use of generative adversarial networks (GANs). GANs consist of two artificial neural networks that are jointly optimized but with opposing goals. One neural network, the generator, aims to synthesize images that cannot be distinguished from real images. The second neural network, the discriminator, aims to distinguish these synthetic images from real images. These deep learning models allow, among other applications, the synthesis of new images, acceleration of image acquisitions, reduction of imaging artifacts, efficient and accurate conversion between medical images acquired with different modalities, and identification of abnormalities depicted on images. The authors provide an introduction to GANs and adversarial deep learning methods. In addition, the different ways in which GANs can be used for image synthesis and image-to-image translation tasks, as well as the principles underlying conditional GANs and cycle-consistent GANs, are described. Illustrated examples of GAN applications in radiologic image analysis for different imaging modalities and different tasks are provided. The clinical potential of GANs, future clinical GAN applications, and potential pitfalls and caveats that radiologists should be aware of also are discussed in this review