Amyloid beta protein (Abeta) is the principal component of neuritic plaques in Alzheimer's disease (AD). Abeta is derived from beta amyloid precursor protein (APP) by beta- and gamma-secretases. Beta-site APP cleaving enzyme 1 (BACE1) has been identified as the major beta-secretase. BACE2 is the homolog of BACE1. The BACE2 gene is on chromosome 21 and has been implicated in the pathogenesis of AD. However, the function of BACE2 in Abeta generation is controversial. Some studies have shown that BACE2 cleaved APP at the beta-site whereas other studies showed it cleaved around the alpha-secretase site. To elucidate the involvement of BACE2 in AD pathogenesis, we compared BACE2 and BACE1 gene regulation and their functions in Abeta generation. We cloned and functionally characterized the human BACE2 promoter. The BACE2 gene is controlled by a TATA-less promoter. Though Sp1 can regulate both BACE1 and BACE2 genes, comparative sequence analysis and transcription factor prediction showed little similarity between the two promoters. BACE1 increased APP cleavage at the beta-site and Abeta production whereas BACE2 did not. Overexpression of BACE2 significantly increased sAPP levels in conditioned media but markedly reduced Abeta production. Knockdown of BACE2 resulted in increased APP C83. Our data indicate that despite being homologous in amino acid sequence, BACE2 and BACE1 have distinct functions and transcriptional regulation. BACE2 is not a beta-secretase, but processes APP within the Abeta domain at a site downstream of the alpha-secretase cleavage site. Our data argue against BACE2 being involved in the formation of neuritic plaques in AD.

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Abstract-Smart grid is delay-sensitive and requires the techniques that can identify and react on the abnormal changes (i.e. system fault, attacker, shortcut, etc.) in a timely manner. In this paper, we propose a real-time detection scheme against false data injection attack in smart grid networks. Unlike the classical detection test, the proposed algorithm is able to tackle the unknown parameters with low complexity and process multiple measurements at once, leading to a shorter decision time and better detection accuracy. The objective is to detect of adversary as quickly as possible while satisfy certain detection error constraints. A Markov chain based analytical model is constructed to systematically analyze the proposed scheme. With the analytical model, we are able to configure the system parameters for guaranteed performance in terms of false alarm rate, average detection delay, and missed detection ratio under a detection delay constraint. The Simulations are conducted with MATPOWER 4.0 package for different IEEE test systems.

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Web service architectures have gained popularity in recent years within the scientific grid research community. One reason for this is that web services allow software and services from various organizations to be combined easily to provide integrated and distributed applications. However, most applications developed and used by scientific communities are not web-service-oriented, and there is a growing need to integrate them into grid applications based on service-oriented architectures. In this paper, we describe a framework that allows scientists to provide a web service interface to their existing applications as web services without having to write extra code or modify their applications in any way. In addition, application providers do not need to be experts in web services standards, such as Web Services Description Language, Web Services Addressing, Web Services Security, or secure authorization, because the framework automatically generates these details. The framework also enables users to discover these application services, interact with them, and compose scientific workflows from the convenience of a grid portal.

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