Angiopoietin-2 (Ang2) exhibits broad expression in the remodeling vasculature of human tumors but very limited expression in normal tissues, making it an attractive candidate target for antiangiogenic cancer therapy. To investigate the functional consequences of blocking Ang2 activity, we generated antibodies and peptide-Fc fusion proteins that potently and selectively neutralize the interaction between Ang2 and its receptor, Tie2. Systemic treatment of tumor-bearing mice with these Ang2-blocking agents resulted in tumor stasis, followed by elimination of all measurable tumor in a subset of animals. These effects were accompanied by reduced endothelial cell proliferation, consistent with an antiangiogenic therapeutic mechanism. Anti-Ang2 therapy also prevented VEGF-stimulated neovascularization in a rat corneal model of angiogenesis. These results imply that specific Ang2 inhibition may represent an effective antiangiogenic strategy for treating patients with solid tumors.

We introduce two appearance-based methods for clustering a set of images of 3-D objects, acquired under varying illumination

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Previous work has demonstrated that the image variation of many objects (human faces in particular) under variable lighting can be effectively modeled by low dimensional linear spaces, even when there are multiple light sources and shadowing. Basis images spanning this space are usually obtained in one of three ways: A large set of images of the object under different lighting conditions is acquired, and principal component analysis (PCA) is used to estimate a subspace. Alternatively, synthetic images are rendered from a 3D model (perhaps reconstructed from images) under point sources, and again PCA is used to estimate a subspace.Finally, images rendered from a 3D model under diffuse lighting based on spherical harmonics are directly used as basis images.In this paper, we show how to arrange physical lighting so that the acquired images of each object can be directly used as the basis vectors of a low-dimensional linear space, and that this subspace is close to those acquired by the other methods. More specifically, there exist configurations of k point light source directions, with k typically ranging from 5 to 9, such that by taking k images of an object under these single sources, the resulting subspace is an effective representation for recognition under a wide range of lighting conditions. Since the subspace is generated directly from real images, potentially complex and/or brittle intermediate steps such as 3D reconstruction can be completely avoided; nor is it necessary to acquire large numbers of training images or to physically construct complex diffuse (harmonic) light fields. We validate the use of subspaces constructed in this fashion within the context of face recognition.

Abstract