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SummaryNeural progenitor cells (NPCs) in the adult subventricular zone (SVZ) are associated with ependymal and vasculature niches, which regulate stem cell self-renewal and differentiation. Activated Type B stem cells and their progeny, the transit-amplifying type C cells, which express EGFR, are most highly associated with vascular cells, indicating that this niche supports lineage progression. Here, we show that proliferative SVZ progenitor cells home to endothelial cells in a stromal-derived factor 1 (SDF1)- and CXC chemokine receptor 4 (CXCR4)-dependent manner. We show that SDF1 strongly upregulates EGFR and α6 integrin in activated type B and type C cells, enhancing their activated state and their ability to bind laminin in the vascular niche. SDF1 increases the motility of type A neuroblasts, which migrate from the SVZ toward the olfactory bulb. Thus, differential responses to SDF1 can regulate progenitor cell occupancy of and exit from the adult SVZ vascular niche.

EMAMAP is a program for the data acquisition phase of maximum-likelihood analysis of electron microscope autoradiographs. This program is written in C and has been implemented on a Masscomp MC-500 which supports a graphics processor and a digitizing tablet. The image analysis is automated at a low level: the program operator outlines the edges of the structures of interest using the digitizing tablet, while contiguous regions formed by closed contours are automatically filled by the software. The resulting image is compressed for efficient storage by a quadtree encoding technique for which data compression ratios of greater than 25:1 have been achieved. In practical terms, this implies that the data from a typical experiment of 50 autoradiographs could be stored on a single floppy disk. The system is currently in use for acquiring actual biological experimental data.

Essential to the solution of ill posed problems in vision and image processing is the need to use object constraints in the reconstruction. While Bayesian methods have shown the greatest promise, a fundamental difficulty has persisted in that many of the available constraints are in the form of deterministic rules rather than as probability distributions and are thus not readily incorporated as Bayesian priors. In this paper, we propose a general method for mapping a large class of rule-based constraints to their equivalent stochastic Gibbs' distribution representation. This mapping allows us to solve stochastic estimation problems over rule-generated constraint spaces within a Bayesian framework. As part of this approach we derive a method based on Langevin's stochastic differential equation and a regularization technique based on the classical autologistic transfer function that allows us to update every site simultaneously regardless of the neighbourhood structure. This allows us to implement a completely parallel method for generating the constraint sets corresponding to the regular grammar languages on massively parallel networks. We illustrate these ideas by formulating the image reconstruction problem based on a hierarchy of rule-based and stochastic constraints, and derive a fully parallelestimator structure. We also present results computed on the AMT DAP500 massively parallel digital computer, a mesh-connected 32x32 array of processing elements which are configured in a Single-Instruction, Multiple Data stream architecture.

A general event-based Monte Carlo approach is presented for computationally performing quantitative and qualitative analyses relevant to the propagation of light through turbid media such as smoke mixtures and fog. The application of interest is the visualization of lighted safety-related signs through fire-caused smoke. The proposed approach is totally numerical in character. The scattering profiles for individual particles are obtained using a laser-based instrument. These are used to numerically model realistic complex, inhomogeneous, and time-varying media for which analytic methods are unavailable and/or intractable. The present approach results in high-quality visualizations, instead of the usual set of extinction coefficients. The computations are parallelizable on a massive scale, and are performed rapidly using a Connection Machine with 32768 processing elements. Experimental results are presented to demonstrate the effectiveness and usefulness of the overall approach. >

Electron microscopic autoradiography is of growing importance as a technique for the quantification of radiolabelled biochemicals in subcellular structures. In this paper, we present the results of a series of experiments that evaluate the Maximum-Likelihood (ML) method of Miller et al. and compare its performance with the well-known crossfire (CF) method.We used the ML and CF methods to analyze a common set of 48 different simulated micrographs (Fig.1) at 5 different grain-counts and 3 different Half-Distances (HDs) for a point-spread modelling H. The CF method was implemented ideally to correspond to the use of an uncountably infinite number of mask points. The count-levels and HDs were chosen to span the range of realistic values. The experiments were repeated a large number of times and the ensemble signal-to-noise ratio (ESNR), measured in decibels (dB) was computed and used as an average performance measure. A 3dB performance improvement in ESNR amounts to a doubling of accuracy.

Meeting abstracts T cells genetically modified to enforce expression of a chimeric antigen receptor (CAR) have shown considerable promise in clinical trials even in tumors refractory to all other treatment methods. In particular, the use of CAR T cells rendered specific for CD19 demonstrated