We examined how asparagine-linked glycans within and adjacent to the V3 loop (C2 and C3 regions) and within the immunologically silent face (V4, C4, and V5 regions) of the human immunodeficiency virus (HIV) SF612 envelope affect the viral phenotype. Five of seven potential glycosylation sites are utilized when the virus is grown in human peripheral blood mononuclear cells, with the nonutilized sites lying within the V4 loop. Elimination of glycans within and adjacent to the V3 loop renders SF162 more susceptible to neutralization by polyclonal HIV ؉ -positive and simian/human immunodeficiency virus-positive sera and by monoclonal antibodies (MAbs) recognizing the V3 loop, the CD4-and CCR5-binding sites, and the extracellular region of gp41. Importantly, our studies also indicate that glycans located within the immunologically silent face of gp120, specifically the C4 and V5 regions, also conferred on SF162 resistance to neutralization by anti-V3 loop, anti-CD4 binding site, and anti-gp41 MAbs but not by antibodies targeting the coreceptor binding site. We also observed that the amino acid composition of the V4 region contributes to the neutralization phenotype of SF162 by anti-V3 loop and anti-CD4 binding site MAbs. Collectively, our data support the proposal that the glycosylation and structure of the immunologically silent face of the HIV envelope plays an important role in defining the neutralization phenotype of HIV type 1.

To recognize an object in an image, an algorithm must identify not only the object pixels, but also non-object clutter pixels. Non-object pixels can be assessed with a priori clutter models that account for the varying terrain and cultural objects. Radar clutter models have been well developed; however, these models typically incorporate a single distribution to capture background effects. In this paper, we propose to use a fusion of distributions through mixture modeling to characterize various background clutter information so as to more accurately develop a clutter model useful for object recognition. In a radar example, we show a fused-distribution using a Rayleigh and Pareto model describing the average and heavy tail clutter characteristics.