SDN Controller Mechanisms for Flexible and Customized Networking

Software-Defined Networking (SDN) is seen as the most promising networking technology today. The spread of a new technology depends on the acceptance of the engineers implementing the networks. Typically, when engineers start the conceptualization of new network devices that work with a new paradigm, and that should provide expected business values, they must identify and utilize technical enablers for the defined business use cases. This paper tries to summarize essential SDN applications and defines the technical enablers for advanced and efficient SDN networking. To this end, we identify the core technical mechanisms, expecting to provide a useful analysis for the design of new SDN networks

State of the field: digital history

Computing and the use of digital sources and resources is an everyday and essential practice in current academic scholarship. The present article gives a concise overview of approaches and methods within digital historical scholarship, focussing on the question: How have the Digital Humanities evolved and what has that evolution brought to historical scholarship? We begin by discussing techniques in which data are generated and machine searchable, such as OCR/HTR, born-digital archives, computer vision, scholarly editions, and Linked Data. In the second section, we provide examples of how data is made more accessible through quantitative text and network analysis. We close with a section on the need for hermeneutics and data-awareness in digital historical scholarship. The technologies described in this article have had varying degrees of effect on historical scholarship, usually in indirect ways. For example, technologies such as OCR and search engines may not be directly visible in a historical argument; however, these technologies do shape how historians interact with sources and whether sources can be accessed at all. It is with this article that we aim to start to take stock of the digital approaches and methods used in historical scholarship which may serve as starting points for scholars to understand the digital turn in the field and how and when to implement such approaches in their work

Perivascular epitheloid cell tumour (PEComa) of the retroperitoneum – a rare tumor with uncertain malignant behaviour: a case report

<p>Abstract</p> <p>Introduction</p> <p>Perivascular epitheloid cell tumours are rare mesenchymal neoplasms characterized by a proliferation of perivascular cells with an epitheloid phenotype and expression of myomelanocytic markers.</p> <p>Case presentation</p> <p>Here we present the case of a cystic perivascular epitheloid cell tumour of the retroperitoneum associated with multifocal lung lesions. A 27-year-old woman underwent laparotomy to remove a 10 × 6 × 4 cm sized retroperitoneal mass. The resected specimen was subjected to frozen and permanent histological sections with conventional and immunohistochemical stains, including antibodies against HMB45. The tumour displayed the typical morphological and immunohistochemical features of a perivascular epitheloid cell tumour. Focal necrosis and a proliferative index of 10% suggested a malignant potential. Moreover, postoperative computed tomography scans demonstrated multiple lung lesions, which were radiologically interpreted as being most likely compatible with lymphangioleiomyomatosis.</p> <p>Conclusion</p> <p>Since lymphangioleiomyomatosis, an otherwise benign condition, belongs to the family of perivascular epitheloid cell tumours, it cannot be excluded that the lung lesions in this case in fact represent metastases from the retroperitoneal perivascular epitheloid cell tumour rather than independent neoplasms. More experience with this new and unusual tumour entity is clearly needed in order to define reliable criteria for benign or malignant behaviour.</p

Bayesian Model Selection Applied to the Analysis of Fluorescence Correlation Spectroscopy Data of Fluorescent Proteins in Vitro and in Vivo

Fluorescence correlation spectroscopy (FCS) is a powerful technique to investigate molecular dynamics with single molecule sensitivity. In particular, in the life sciences it has found widespread application using fluorescent proteins as molecularly specific labels. However, FCS data analysis and interpretation using fluorescent proteins remains challenging due to typically low signal-to-noise ratio of FCS data and correlated noise in autocorrelated data sets. As a result, naive fitting procedures that ignore these important issues typically provide similarly good fits for multiple competing models without clear distinction of which model is preferred given the signal-to-noise ratio present in the data. Recently, we introduced a Bayesian model selection procedure to overcome this issue with FCS data analysis. The method accounts for the highly correlated noise that is present in FCS data sets and additionally penalizes model complexity to prevent over interpretation of FCS data. Here, we apply this procedure to evaluate FCS data from fluorescent proteins assayed in vitro and in vivo. Consistent with previous work, we demonstrate that model selection is strongly dependent on the signal-to-noise ratio of the measurement, namely, excitation intensity and measurement time, and is sensitive to saturation artifacts. Under fixed, low intensity excitation conditions, physical transport models can unambiguously be identified. However, at excitation intensities that are considered moderate in many studies, unwanted artifacts are introduced that result in nonphysical models to be preferred. We also determined the appropriate fitting models of a GFP tagged secreted signaling protein, Wnt3, in live zebrafish embryos, which is necessary for the investigation of Wnt3 expression and secretion in development. Bayes model selection therefore provides a robust procedure to determine appropriate transport and photophysical models for fluorescent proteins when appropriate models are provided, to help detect and eliminate experimental artifacts in solution, cells, and in living organisms.National Science Foundation (U.S.). Physics of Living Systems ProgramNational Institute of Mental Health (U.S.) (Award U01MH106011