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Secretory products from HIV-1-infected immune-competent mononuclear phagocytes (MP) damage neuronal dendritic arbor (Zheng et al., 2001). The mechanism behind neuronal injury and whether it is species and/or viral strain dependent is not fully understood. To these ends, we investigated whether HIV-1-infected and lipopolysaccharide (LPS)-activated MDM elicit neuronal injury in primary human neurons. Neuronal damage was compared to that seen in rat neurons. Utilizing a spectrum of HIV-1 strains to infect human monocyte-derived macrophages (MDM), productive viral replication proved necessary, but not sufficient, for neuronal injury. Neuronal demise was induced by virion-free HIV-1-infected and immune-activated MDM culture supernatants. Maximal alterations in glutamate mediated neuronal signaling, resulted from exposure to secretory products from HIV-1-infected and immune-activated MDM. Apoptosis was the predominant mechanism of cell death induced by HIV-1-infected and LPS-treated MDM. Importantly, neuronal injury and increases in calcium influx mediated by HIV-1-infected and immune-activated MDM culture supernatants was partially blocked by the N-methyl D-aspartate (NMDA) receptor antagonist, MK 801. These data support a primary role for immune-activation in MP neurotoxic activities. The upregulation of NMDA receptor sensitive soluble factors and neuronal apoptosis by HIV-1-infected and immune-activated MDM provide unique insights into links between soluble factors, produced as a consequence of MP immunity, and neuronal demise in HAD.

Over the past decade, the number of Earth orbiters and deep space probes has grown dramatically and is expected to continue in the future as miniaturization technologies drive spacecraft to become more numerous and more complex. This rate of growth has brought a new focus on autonomous and self-preserving systems that depend on fault diagnosis. Although diagnosis is needed for any autonomous system, current approaches are almost uniformly "ad-hoc," inefficient, and incomplete. Systematic methods of general diagnosis exist in literature, but they all suffer from two major drawbacks that severely limit their practical applications. First, they tend to be large and complex and hence difficult to apply. Second and more importantly, in order to find the minimal diagnosis set, i.e., the minimal set of faulty components, they rely on algorithms with exponential computational cost and hence are highly impractical for application to many systems of interest.Ib this paper, we propose a two-fold approach to overcome these two limitations. Then we report the details of a new and powerful tool, Diagnosis Engine version 1.0, we have developed based on these techniques. First, we propose a novel and compact reconstruction of General Diagnosis Engine (GDE), as one of the most fundamental approaches to model-based diagnosis. We then present a novel algorithmic approach for calculation of minimal diagnosis set. Using a powerful yet simple representation of the calculation of minimal diagnosis set, we map the problem onto two well-known problems, that is, the Boolean Satisfiability and 011 Integer Programming problems. The mapping onto Boolean Satisfiability enables the use of very efficient algorithms with a super-polynomial rather than an exponential complexity for the problem. The mapping onto 0/1 Integer Programming problem enables the use of a variety of algorithms that can efficiently solve the problem for up to several thousand components. These new algorithms significantly improve over the existing ones, enabling efficient diagnosis of large complex systems. In addition, the latter mapping allows, for the first time, determination of the bound on the solution, i.e., the minimum number of faulty components, before solving the problem. This is a powerful insight that can be exploited to develop yet more efficient algorithms for the problem.At the end, we report the results of validating and benchmarking of our engine based on this technology.