A Flow Trace Generator using Graph-based Traffic Classification Techniques

We propose a novel methodology to generate realistic network flow traces to enable systematic evaluation of network monitoring systems in various traffic conditions. Our technique uses a graph-based approach to model the communication structure observed in real-world traces and to extract traffic templates. By combining extracted and user-defined traffic templates, realistic network flow traces that comprise normal traffic and customized conditions are generated in a scalable manner. A proof-of-concept implementation demonstrates the utility and simplicity of our method to produce a variety of evaluation scenarios. We show that the extraction of templates from real-world traffic leads to a manageable number of templates that still enable accurate re-creation of the original communication properties on the network flow level.

RGD motif of lipoprotein T, involved in adhesion of Mycoplasma conjunctivae to lamb synovial tissue cells

Lipoprotein T (LppT), a membrane-located 105-kDa lipoprotein of Mycoplasma conjunctivae, the etiological agent of infectious keratoconjunctivitis (IKC) of domestic sheep and wild Caprinae, was characterized. LppT was shown to promote cell attachment to LSM 192 primary lamb joint synovial cells. Adhesion of M. conjunctivae to LSM 192 cells is inhibited by antibodies directed against LppT. The RGD (Arg-Gly-Asp) motif of LppT was found to be a specific site for binding of M. conjunctivae to these eukaryotic host cells. Recombinant LppT fixed to polymethylmethacrylate slides binds LSM 192 cells, whereas LppT lacking the RGD site is deprived of binding capacity to LSM 192, and LppT containing RGE rather than RGD shows reduced binding. Synthetic nonapeptides derived from LppT containing RGD competitively inhibit binding of LSM 192 cells to LppT-coated slides, whereas nonapeptides containing RAD rather than RGD do not inhibit. RGD-containing, LppT-derived nonapeptides are able to directly inhibit binding of M. conjunctivae to LSM 192 cells by competitive inhibition, whereas the analogous nonapeptide containing RAD rather than RGD or the fibronectin-derived RGD hexapeptide has no inhibitory effect. These results reveal LppT as the first candidate of a RGD lectin in Mycoplasma species that is assumed to bind to beta integrins.

Graph-Based Multi-Surface Segmentation of OCT Data Using Trained Hard and Soft Constraints

Optical coherence tomography (OCT) is a well-established image modality in ophthalmology and used daily in the clinic. Automatic evaluation of such datasets requires an accurate segmentation of the retinal cell layers. However, due to the naturally low signal to noise ratio and the resulting bad image quality, this task remains challenging. We propose an automatic graph-based multi-surface segmentation algorithm that internally uses soft constraints to add prior information from a learned model. This improves the accuracy of the segmentation and increase the robustness to noise. Furthermore, we show that the graph size can be greatly reduced by applying a smart segmentation scheme. This allows the segmentation to be computed in seconds instead of minutes, without deteriorating the segmentation accuracy, making it ideal for a clinical setup. An extensive evaluation on 20 OCT datasets of healthy eyes was performed and showed a mean unsigned segmentation error of 3.05 ±0.54 μm over all datasets when compared to the average observer, which is lower than the inter-observer variability. Similar performance was measured for the task of drusen segmentation, demonstrating the usefulness of using soft constraints as a tool to deal with pathologies.