Clinical and Serologic Manifestations of Autoimmune Disease in MRL-lpr/lpr Mice Lacking Nitric Oxide Synthase Type 2

Nitric oxide (NO) is an important mediator of the inflammatory response. MRL–lpr/lpr mice overexpress inducible nitric oxide synthase (NOS2) and overproduce NO in parallel with the development of an autoimmune syndrome with a variety of inflammatory manifestations. In previous studies, we showed that inhibiting NO production with the nonselective nitric oxide synthase (NOS) inhibitor NG-monomethyl–arginine reduced glomerulonephritis, arthritis, and vasculitis in MRL–lpr/lpr mice. To define further the role of NO and NOS2 in disease in MRL–lpr/lpr mice, mice with targeted disruption of NOS2 were produced by homologous recombination and bred to MRL–lpr/lpr mice to the N4 generation. MRL–lpr/lpr littermates homozygous for disrupted NOS2 (−/−), heterozygous for disrupted NOS2 (+/−), or wildtype (+/+) were derived for this study. Measures of NO production were markedly decreased in the MRL-lpr/lpr (−/−) mice compared with MRL-lpr/lpr (+/+) mice, with intermediate production by the MRL-lpr/lpr (+/−) mice. There was no detectable NOS2 protein by immunoblot analysis of the spleen, liver, kidney, and peritoneal macrophages of the (−/−) animals, whereas that of (+/+) was high and (+/−) intermediate. The (−/−) mice developed glomerular and synovial pathology similar to that of the (+/−) and (+/+) mice. However, (−/−) mice and (+/−) mice had significantly less vasculitis of medium-sized renal vessels than (+/+) mice. IgG rheumatoid factor levels were significantly lower in the (−/−) mice as compared with (+/+) mice, but levels of anti-DNA antibodies were comparable in all groups. Our findings show that NO derived from NOS2 has a variable impact on disease manifestations in MRL-lpr/lpr mice, suggesting heterogeneity in disease mechanisms

BMQ

BMQ: Boston Medical Quarterly was published from 1950-1966 by the Boston University School of Medicine and the Massachusetts Memorial Hospitals

Neural Network Reduction for Efficient Execution on Edge Devices

As the size of neural networks increase, the resources needed to support their execution also increase. This presents a barrier for creating neural networks that can be trained and executed within resource limited embedded systems. To reduce the resources needed to execute neural networks, weight reduction is often the first target. A network that has been significantly pruned can be executed on-chip, that is, in low SWaP hardware. But, this does not enable either training or pruning in embedded hardware which first requires a full-sized network to fit within the restricted resources. We introduce two methods of network reduction that allows neural networks to be grown and trained within edge devices, Artificial Neurogenesis and Synaptic Input Consolidation