University of Windsor

About: University of Windsor is a education organization based out in Windsor, Ontario, Canada. It is known for research contribution in the topics: Population & Argumentation theory. The organization has 10654 authors who have published 22307 publications receiving 435906 citations. The organization is also known as: UWindsor & Assumption University of Windsor.

Preparation of asymmetric phospholipid vesicles for use as cell membrane models.

Abstract: Freely suspended liposomes are widely used as model membranes for studying lipid–lipid and protein–lipid interactions. Liposomes prepared by conventional methods have chemically identical bilayer leaflets. By contrast, living cells actively maintain different lipid compositions in the two leaflets of the plasma membrane, resulting in asymmetric membrane properties that are critical for normal cell function. Here, we present a protocol for the preparation of unilamellar asymmetric phospholipid vesicles that better mimic biological membranes. Asymmetry is generated by methyl-β-cyclodextrin-catalyzed exchange of the outer leaflet lipids between vesicle pools of differing lipid composition. Lipid destined for the outer leaflet of the asymmetric vesicles is provided by heavy-donor multilamellar vesicles containing a dense sucrose core. Donor lipid is exchanged into extruded unilamellar acceptor vesicles that lack the sucrose core, facilitating the post-exchange separation of the donor and acceptor pools by centrifugation because of differences in vesicle size and density. We present two complementary assays allowing quantification of each leaflet’s lipid composition: the overall lipid composition is determined by gas chromatography–mass spectrometry, whereas the lipid distribution between the two leaflets is determined by NMR, using the lanthanide shift reagent Pr3+. The preparation protocol and the chromatographic assay can be applied to any type of phospholipid bilayer, whereas the NMR assay is specific to lipids with choline-containing headgroups, such as phosphatidylcholine and sphingomyelin. In ~12 h, the protocol can produce a large yield of asymmetric vesicles (up to 20 mg) suitable for a wide range of biophysical studies. Improved and robust procedures for a cyclodextrin-mediated preparation of asymmetric large unilamellar vesicles of diverse lipid compositions and the characterization of their degree of asymmetry and individual leaflet compositions with NMR and GC.

Tree2Vector: Learning a Vectorial Representation for Tree-Structured Data

Abstract: The tree structure is one of the most powerful structures for data organization. An efficient learning framework for transforming tree-structured data into vectorial representations is presented. First, in attempting to uncover the global discriminative information of child nodes hidden at the same level of all of the trees, a clustering technique can be adopted for allocating children into different clusters, which are used to formulate the components of a vector. Moreover, a locality-sensitive reconstruction method is introduced to model a process, in which each parent node is assumed to be reconstructed by its children. The resulting reconstruction coefficients are reversely transformed into complementary coefficients, which are utilized for locally weighting the components of the vector. A new vector is formulated by concatenating the original parent node vector and the learned vector from its children. This new vector for each parent node is inputted into the learning process of formulating vectorial representation at the upper level of the tree. This recursive process concludes when a vectorial representation is achieved for the entire tree. Our method is examined in two applications: book author recommendations and content-based image retrieval. Extensive experimental results demonstrate the effectiveness of the proposed method for transforming tree-structured data into vectors.

Moving Cast Shadows Detection Using Ratio Edge

Abstract: Moving objects segmentation plays a very important role in real-time image analysis. However, as one of the common parts in the natural scenes, shadows severely interfere with the accuracy of moving objects detection in video surveillance. In this paper, we present a novel method for moving cast shadows detection. Based on the analysis of the physical model of moving shadows, we prove that the ratio edge is illumination invariant. The distribution of the ratio edge is discussed and a significance test is performed to classify each moving pixel into foreground object or moving shadow. Intensity constraint and geometric heuristics are imposed to further improve the performance. Experiments on various typical scenes exhibit the robustness of the proposed method. Extensively quantitative evaluation and comparison demonstrate that the proposed method significantly outperforms state-of-the-art methods.