MDSSF: a federated architecture for product procurement

In the AEC (Architecture / Engineering / Construction) industry, large construction projects are tackled by consortia of companies and individuals, who work collaboratively for the duration of the project. The consortia include design teams, product suppliers, contractors and inspection teams who must collaborate and conform to predefined scheduling constraints and standards. These projects are unique, complex and involve many participants from a number of organisations. Construction projects require consortia to procure supplies such as building materials and furniture from product suppliers. In large AEC projects, procurement of products, services and construction materials is an important and time consuming activity. Materials are sourced on a global basis from a large number of suppliers. The scale of the purchases made in large projects show that their procurement is a non-trivial exercise. Therefore, consortia members or the contractors require access to a large body of information about products or material information to aid procurement decision making. Web based communication and network technologies play an increasingly important role in supporting collaboration in AEC projects. However collaborative working in the construction industry is still restricted by the current limitations of network and communication technologies and their system architectures which are usually client/server based. The construction industry has been examining how the advancements in distributed computing technologies such as the Grid computing can remove some of the existing limitations and enhance collaboration. This research investigated how the procurement challenges such as accessing up-to-date product information available from a large number of products suppliers in an integrated manner using standard means could be addressed. A novel solution to the procurement challenges in the form of a distributed information sharing architecture is presented. The architecture uses the concepts of federated databases such as distribution of data and autonomy of databases and couples it with Grid computing to facilitate information exchange in a collaborative, coherent and integrated way to address the product procurement challenges

Genetic Evidence Implicates the Immune System and Cholesterol Metabolism in the Aetiology of Alzheimer's Disease

Background 1Late Onset Alzheimer's disease (LOAD) is the leading cause of dementia. Recent large genome-wide association studies (GWAS) identified the first strongly supported LOAD susceptibility genes since the discovery of the involvement of APOE in the early 1990s. We have now exploited these GWAS datasets to uncover key LOAD pathophysiological processes. Methodology We applied a recently developed tool for mining GWAS data for biologically meaningful information to a LOAD GWAS dataset. The principal findings were then tested in an independent GWAS dataset. Principal Findings We found a significant overrepresentation of association signals in pathways related to cholesterol metabolism and the immune response in both of the two largest genome-wide association studies for LOAD. Significance Processes related to cholesterol metabolism and the innate immune response have previously been implicated by pathological and epidemiological studies of Alzheimer's disease, but it has been unclear whether those findings reflected primary aetiological events or consequences of the disease process. Our independent evidence from two large studies now demonstrates that these processes are aetiologically relevant, and suggests that they may be suitable targets for novel and existing therapeutic approaches

Common variants at ABCA7, MS4A6A/MS4A4E, EPHA1, CD33 and CD2AP are associated with Alzheimer's disease

We sought to identify new susceptibility loci for Alzheimer's disease through a staged association study (GERAD+) and by testing suggestive loci reported by the Alzheimer's Disease Genetic Consortium (ADGC) in a companion paper. We undertook a combined analysis of four genome-wide association datasets (stage 1) and identified ten newly associated variants with P ≤ 1 × 10−5. We tested these variants for association in an independent sample (stage 2). Three SNPs at two loci replicated and showed evidence for association in a further sample (stage 3). Meta-analyses of all data provided compelling evidence that ABCA7 (rs3764650, meta P = 4.5 × 10−17; including ADGC data, meta P = 5.0 × 10−21) and the MS4A gene cluster (rs610932, meta P = 1.8 × 10−14; including ADGC data, meta P = 1.2 × 10−16) are new Alzheimer's disease susceptibility loci. We also found independent evidence for association for three loci reported by the ADGC, which, when combined, showed genome-wide significance: CD2AP (GERAD+, P = 8.0 × 10−4; including ADGC data, meta P = 8.6 × 10−9), CD33 (GERAD+, P = 2.2 × 10−4; including ADGC data, meta P = 1.6 × 10−9) and EPHA1 (GERAD+, P = 3.4 × 10−4; including ADGC data, meta P = 6.0 × 10−10)

Correction: genetic evidence implicates the immune system and cholesterol metabolism in the aetiology of Alzheimer's disease.

[This corrects the article on p. e13950 in vol. 5.]. Background: Late Onset Alzheimer's disease (LOAD) is the leading cause of dementia. Recent large genome-wide association studies (GWAS) identified the first strongly supported LOAD susceptibility genes since the discovery of the involvement of APOE in the early 1990s. We have now exploited these GWAS datasets to uncover key LOAD pathophysiological processes. Methodology: We applied a recently developed tool for mining GWAS data for biologically meaningful information to a LOAD GWAS dataset. The principal findings were then tested in an independent GWAS dataset. Principal Findings: We found a significant overrepresentation of association signals in pathways related to cholesterol metabolism and the immune response in both of the two largest genome-wide association studies for LOAD. Significance: Processes related to cholesterol metabolism and the innate immune response have previously been implicated by pathological and epidemiological studies of Alzheimer's disease, but it has been unclear whether those findings reflected primary aetiological events or consequences of the disease process. Our independent evidence from two large studies now demonstrates that these processes are aetiologically relevant, and suggests that they may be suitable targets for novel and existing therapeutic approaches

Actor provenance capture with Ganglia

Provenance is generally defined as the documentation of a process that leads to some result, and has long been recognised as being fundamental to the development of problem solving mechanisms within Grid environments. The knowledge of how a particular result has been derived is just as important as the result itself within many e-Science experiments. We present the concept of "actor" provenance, which provides detailed information concerning the state of an actor at a particular time. We also demonstrate how actor provenance differs from "interaction" provenance between actors. We describe how actor provenance may be represented and recorded using monitoring tools such as ganglia. This is explained using a number of use cases in a Bio-Diversity application