HIV-1-containing internal compartments are readily detected in images of thin sections from infected cells using conventional transmission electron microscopy, but the origin, connectivity, and 3D distribution of these compartments has remained controversial. Here, we report the 3D distribution of viruses in HIV-1-infected primary human macrophages using cryo-electron tomography and ion-abrasion scanning electron microscopy (IA-SEM), a recently developed approach for nanoscale 3D imaging of whole cells. Using IA-SEM, we show the presence of an extensive network of HIV-1-containing tubular compartments in infected macrophages, with diameters of ∼150–200 nm, and lengths of up to ∼5 µm that extend to the cell surface from vesicular compartments that contain assembling HIV-1 virions. These types of surface-connected tubular compartments are not observed in T cells infected with the 29/31 KE Gag-matrix mutant where the virus is targeted to multi-vesicular bodies and released into the extracellular medium. IA-SEM imaging also allows visualization of large sheet-like structures that extend outward from the surfaces of macrophages, which may bend and fold back to allow continual creation of viral compartments and virion-lined channels. This potential mechanism for efficient virus trafficking between the cell surface and interior may represent a subversion of pre-existing vesicular machinery for antigen capture, processing, sequestration, and presentation.

We describe algebraic methods for creating implicit surfaces using linear combinations of radial basis interpolants

We describe algebraic methods for creating implicit surfaces using linear combinations of radial basis interpolants

The National Institutes of Health (NIH) has launched the NIH 3D Print Exchange, an online portal for discovering and creating bioscientifically relevant 3D models suitable for 3D printing, to provide both researchers and educators with a trusted source to discover accurate and informative models. There are a number of online resources for 3D prints, but there is a paucity of scientific models, and the expertise required to generate and validate such models remains a barrier. The NIH 3D Print Exchange fills this gap by providing novel, web-based tools that empower users with the ability to create ready-to-print 3D files from molecular structure data, microscopy image stacks, and computed tomography scan data. The NIH 3D Print Exchange facilitates open data sharing in a community-driven environment, and also includes various interactive features, as well as information and tutorials on 3D modeling software. As the first government-sponsored website dedicated to 3D printing, the NIH 3D Print Exchange is an important step forward to bringing 3D printing to the mainstream for scientific research and education.

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ÐPhotographic volumes present a unique, interesting challenge for volume rendering. In photographic volumes, voxel color is predetermined, making color selection through transfer functions unnecessary. However, photographic data does not contain a clear mapping from the multivalued color values to a scalar density or opacity, making projection and compositing much more difficult than with traditional volumes. Moreover, because of the nonlinear nature of color spaces, there is no meaningful norm for the multivalued voxels. Thus, the individual color channels of photographic data must be treated as incomparable data tuples rather than as vector values. Traditional differential geometric tools, such as intensity gradients, density, and Laplacians, are distorted by the nonlinear nonorthonormal color spaces that are the domain of the voxel values. We have developed different techniques for managing these issues while directly rendering volumes from photographic data. We present and justify the normalization of color values by mapping RGB values to the CIE L à u à v à color space. We explore and compare different opacity transfer functions that map three channel color values to opacity. We apply these many-to-one mappings to the original RGB values as well as to the voxels after conversion to L à u à v à space. Direct rendering using transfer functions allows us to explore photographic volumes without having to commit to an a priori segmentation that might mask fine variations of interest. We empirically compare the combined effects of each of the two color spaces with our opacity transfer functions using source data from the Visible Human Project. Index TermsÐVolume rendering, transfer functions, photographic data.

No abstract

No abstract