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   The past 15–20 years has seen a remarkable shift in our understanding of astrocyte contributions to central nervous system (CNS) function. Astrocytes have emerged from the shadows of neuroscience and are now recognized as key elements in a broad array of CNS functions. Astrocytes comprise a substantial fraction of cells in the human CNS. Nevertheless, fundamental questions surrounding their basic biology remain poorly understood. While recent studies have revealed a diversity of essential roles in CNS function, from synapse formation and function to blood brain barrier maintenance, fundamental mechanisms of astrocyte development, including their expansion, migration, and maturation, remain to be elucidated. The coincident development of astrocytes and synapses highlights the need to better understand astrocyte development and will facilitate novel strategies for addressing neurodevelopmental and neurological dysfunction. In this review, we provide an overview of the current understanding of astrocyte development, focusing primarily on mammalian astrocytes and highlight outstanding questions that remain to be addressed. We also include an overview of Drosophila glial development, emphasizing astrocyte-like glia given their close anatomical and functional association with synapses. Drosophila offer an array of sophisticated molecular genetic tools and they remain a powerful model for elucidating fundamental cellular and molecular mechanisms governing astrocyte development. Understanding the parallels and distinctions between astrocyte development in Drosophila and vertebrates will enable investigators to leverage the strengths of each model system to gain new insights into astrocyte function. 

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   The framework of database repairs and consistent answers to queries is a principled approach to managing inconsistent databases. We describe the first system able to compute the consistent answers of general aggregation queries with the COUNT( A ), COUNT (*), and SUM operators, and with or without grouping constructs. Our system uses reductions to optimization versions of Boolean satisfiability (SAT) and then leverages powerful SAT solvers. We carry out an extensive set of experiments on both synthetic and real-world data that demonstrate the usefulness and scalability of this approach. 

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