Lubrication at physiological pressures by polyzwitterionic brushes

The very low sliding friction at natural synovial joints, which have friction coefficients of mu < 0.002 at pressures up to 5 megapascals or more, has to date not been attained in any human-made joints or between model surfaces in aqueous environments. We found that surfaces in water bearing polyzwitterionic brushes that were polymerized directly from the surface can have m values as low as 0.0004 at pressures as high as 7.5 megapascals. This extreme lubrication is attributed primarily to the strong hydration of the phosphorylcholine-like monomers that make up the robustly attached brushes, and may have relevance to a wide range of human-made aqueous lubrication situations

Information Preserving Component Analysis: Data Projections for Flow Cytometry Analysis

Flow cytometry is often used to characterize the malignant cells in leukemia and lymphoma patients, traced to the level of the individual cell. Typically, flow cytometric data analysis is performed through a series of 2-dimensional projections onto the axes of the data set. Through the years, clinicians have determined combinations of different fluorescent markers which generate relatively known expression patterns for specific subtypes of leukemia and lymphoma -- cancers of the hematopoietic system. By only viewing a series of 2-dimensional projections, the high-dimensional nature of the data is rarely exploited. In this paper we present a means of determining a low-dimensional projection which maintains the high-dimensional relationships (i.e. information) between differing oncological data sets. By using machine learning techniques, we allow clinicians to visualize data in a low dimension defined by a linear combination of all of the available markers, rather than just 2 at a time. This provides an aid in diagnosing similar forms of cancer, as well as a means for variable selection in exploratory flow cytometric research. We refer to our method as Information Preserving Component Analysis (IPCA).Comment: 26 page

Geometric and Photometric Data Fusion in Non-Rigid Shape Analysis

In this paper, we explore the use of the diffusion geometry framework for the fusion of geometric and photometric information in local and global shape descriptors. Our construction is based on the definition of a diffusion process on the shape manifold embedded into a high-dimensional space where the embedding coordinates represent the photometric information. Experimental results show that such data fusion is useful in coping with different challenges of shape analysis where pure geometric and pure photometric methods fai

Orthogonal inactivation of influenza and the creation of detergent resistant viral aggregates: towards a novel vaccine strategy

<p>Abstract</p> <p>Background</p> <p>It has been previously shown that enveloped viruses can be inactivated using aryl azides, such as 1-iodo-5-azidonaphthalene (INA), plus UVA irradiation with preservation of surface epitopes in the inactivated virus preparations. Prolonged UVA irradiation in the presence of INA results in ROS-species formation, which in turn results in detergent resistant viral protein fractions.</p> <p>Results</p> <p>Herein, we characterize the applicability of this technique to inactivate influenza. It is shown that influenza virus + INA (100 micromolar) + UVA irradiation for 30 minutes results in a significant (<it>p </it>< 0.05) increase in pelletablehemagglutinin after Triton X-100 treatment followed by ultracentrifugation. Additionally, characterization of the virus suspension by immunogold labeling in cryo-EM, and viral pellet characterization via immunoprecipitation with a neutralizing antibody, shows preservation of neutralization epitopes after this treatment.</p> <p>Conclusion</p> <p>These orthogonally inactivated viral preparations with detergent resistant fractions are being explored as a novel route for safe, effective inactivated vaccines generated from a variety of enveloped viruses.</p

A theoretical and numerical study of a phase field higher-order active contour model of directed networks.

We address the problem of quasi-automatic extraction of directed networks, which have characteristic geometric features, from images. To include the necessary prior knowledge about these geometric features, we use a phase field higher-order active contour model of directed networks. The model has a large number of unphysical parameters (weights of energy terms), and can favour different geometric structures for different parameter values. To overcome this problem, we perform a stability analysis of a long, straight bar in order to find parameter ranges that favour networks. The resulting constraints necessary to produce stable networks eliminate some parameters, replace others by physical parameters such as network branch width, and place lower and upper bounds on the values of the rest. We validate the theoretical analysis via numerical experiments, and then apply the model to the problem of hydrographic network extraction from multi-spectral VHR satellite images