Pericytes are vascular mural cells embedded in the basement membrane of blood microvessels. They extend their processes along capillaries, pre-capillary arterioles, and post-capillary venules. The central nervous system (CNS) pericytes are uniquely positioned within the neurovascular unit between endothelial cells, astrocytes, and neurons. They integrate, coordinate, and process signals from their neighboring cells to generate diverse functional responses that are critical for CNS functions in health and disease including regulation of the blood-brain barrier permeability, angiogenesis, clearance of toxic metabolites, capillary hemodynamic responses, neuroinflammation, and stem cell activity. Here, we examine the key signaling pathways between pericytes and their neighboring endothelial cells, astrocytes, and neurons that control neurovascular functions. We also review the role of pericytes in different CNS disorders including rare monogenic diseases and complex neurological disorders such as Alzheimer's disease and brain tumors. Finally, we discuss directions for future studies.

Nitric oxide (NO) produced by vascular endothelial cells is a potent vasodilator and an antiinflammatory mediator. Regulating production of endothelial-derived NO is a complex undertaking, involving multiple signaling and genetic pathways that are activated by diverse humoral and biomechanical stimuli. To gain a thorough understanding of the rich diversity of responses observed experimentally, it is necessary to account for an ensemble of these pathways acting simultaneously. In this article, we have assembled four quantitative molecular pathways previously proposed for shear-stress-induced NO production. In these pathways, endothelial NO synthase is activated 1), via calcium release, 2), via phosphorylation reactions, and 3), via enhanced protein expression. To these activation pathways, we have added a fourth, a pathway describing actual NO production from endothelial NO synthase and its various protein partners. These pathways were combined and simulated using CytoSolve, a computational environment for combining independent pathway calculations. The integrated model is able to describe the experimentally observed change in NO production with time after the application of fluid shear stress. This model can also be used to predict the specific effects on the system after interventional pharmacological or genetic changes. Importantly, this model reflects the up-to-date understanding of the NO system, providing a platform upon which information can be aggregated in an additive way.

A multi-staged system for off-line handwritten numeral recognition is presented here. After scanning, the digitized binary bitmap image of the source document is passed through a preprocessing stage which performs segmentation, thinning and rethickening, normalization, and slant correction. The recognizer is a three-layered neural net trained with back-propagation algorithm. While a few systems that use three-layered nets for recognition have been presented in the literature, the contribution of our system is based on two aspects: elaborate preprocessing based on structural pattern recognition methods combined with a neural net based recognizer; and integration of neural net based and structural pattern recognition methods to produce high accuracies.