An Application of a Service-oriented System to Support ArrayAnnotation in Custom Chip Design for Epigenomic Analysis

We present the implementation of an application using caGrid, which is the service-oriented Grid software infrastructure of the NCI cancer Biomedical Informatics Grid (caBIGTM), to support design and analysis of custom microarray experiments in the study of epigenetic alterations in cancer. The design and execution of these experiments requires synthesis of information from multiple data types and datasets. In our implementation, each data source is implemented as a caGrid Data Service, and analytical resources are wrapped as caGrid Analytical Services. This service-based implementation has several advantages. A backend resource can be modified or upgraded, without needing to change other components in the application. A remote resource can be added easily, since resources are not required to be collected in a centralized infrastructure

GridIMAGE: A Novel Use of Grid Computing to Support Interactive Human and Computer-Assisted Detection Decision Support

This paper describes a Grid-aware image reviewing system (GridIMAGE) that allows practitioners to (a) select images from multiple geographically distributed digital imaging and communication in medicine (DICOM) servers, (b) send those images to a specified group of human readers and computer-assisted detection (CAD) algorithms, and (c) obtain and compare interpretations from human readers and CAD algorithms. The currently implemented system was developed using the National Cancer Institute caGrid infrastructure and is designed to support the identification of lung nodules on thoracic computed tomography. However, the infrastructure is general and can support any type of distributed review. caGrid data and analytical services are used to link DICOM image databases and CAD systems and to interact with human readers. Moreover, the service-oriented and distributed structure of the GridIMAGE framework enables a flexible system, which can be deployed in an institution (linking multiple DICOM servers and CAD algorithms) and in a Grid environment (linking the resources of collaborating research groups). GridIMAGE provides a framework that allows practitioners to obtain interpretations from one or more human readers or CAD algorithms. It also provides a mechanism to allow cooperative imaging groups to systematically perform image interpretation tasks associated with research protocols

Dorian: Grid Service Infrastructure for Identity Management and Federation

Identity management and federation is becoming an ever present problem in large multi-institutional environments. By their nature, Grids span multiple institutional administration boundaries and aim to provide support for the sharing of applications, data, and computational resources in a collaborative environment. One underlying problem is to enable participating institutions to manage the identities of their own members by leveraging existing institutional identity management systems, while at the same time facilitating the participation in larger Grids through the deployment of grid-wide user credentials. Those grid-wide identities are used for features such as single sign-on, secure communication, and are the basis for authorization decisions. In this paper we will present the design and implementation of Dorian, a grid service infrastructure component that enables the federation of users across the collaboration. 1

Study of subgrid-scale velocity models for reacting and nonreacting flows

A study is conducted to identify advantages and limitations of existing large-eddy simulation (LES) closures for the subgrid-scale (SGS) kinetic energy using a database of direct numerical simulations (DNS). The analysis is conducted for both reacting and nonreacting flows, different turbulence conditions, and various filter sizes. A model, based on dissipation and diffusion of momentum (LD-D model), is proposed in this paper based on the observed behavior of four existing models. Our model shows the best overall agreements with DNS statistics. Two main investigations are conducted for both reacting and nonreacting flows: (i) an investigation on the robustness of the model constants, showing that commonly used constants lead to a severe underestimation of the SGS kinetic energy and enlightening their dependence on Reynolds number and filter size; and (ii) an investigation on the statistical behavior of the SGS closures, which suggests that the dissipation of momentum is the key parameter to be considered in such closures and that dilatation effect is important and must be captured correctly in reacting flows. Additional properties of SGS kinetic energy modeling are identified and discussed

Enabling the Provisioning and Management of a Federated Grid Trust Fabric

In order to authenticate and authorize users and other peer-services, Grid services need to maintain a list of authorities that they trust as a source for issuing credentials. Grids inherently span multiple institutional administration domains and aim to support the sharing of applications, data, and computational resources in a collaborative environment. In this environment there may exist hundreds of certificate authorities, each issuing hundreds if not thousands of certificates. In such a dynamic multi-institutional environment with tens of thousands of users, credentials will be issued and revoked frequently, and new authorities will be added regularly. Clearly a Grid-wide mechanism is needed for maintaining and provisioning trusted certificate authorities, such that Grid services and users may make authentication and authorizations decisions against the most up-to-date trust information. In this paper we present the design and implementation of the Grid Trust Service (GTS), a federated framework for creating and managing a Grid trust fabric, enabling the provisioning of certificate authority information. 1

A Distributed Data Management Middleware for Data-Driven Application Systems

A key challenge in supporting data-driven scientific applications is the storage and management of input and output data in a distributed environment. In this paper, we describe a distributed storage middleware, based on a data and metadata management framework, to address this problem. In this middleware system, applications define the structure of their input and output data using XML schemas. The system provides support for 1) registration, versioning, management of schemas, and 2) management of storage, querying, and retrieval of instance data corresponding to the schemas in distributed databases. We carry out an experimental evaluation of the system on a set of PC clusters connected over wide- and local-area networks

Grid-based management of biomedical data using an XML-based distributed data management system

This paper presents the application of a generic, XML-based distributed data management system for Grid-enabled man-agement and integration of biomedical data, including im-age, molecular, and outcome data. We discuss the use of this system in three inter-related application scenarios: Manage-ment of large-scale image data, access to data from Internet-based bioinformatic data repositories, and integrating clini-cal data stored in an enterprise information warehouse into translational research. 1

A Grid-Based Image Archival and Analysis System

Here the authors present a Grid-aware middleware system, called GridPACS, that enables management and analysis of images in a massive scale, leveraging distributed software components coupled with interconnected computation and storage platforms. The need for this infrastructure is driven by the increasing biomedical role played by complex datasets obtained through a variety of imaging modalities. The GridPACS architecture is designed to support a wide range of biomedical applications encountered in basic and clinical research, which make use of large collections of images. Imaging data yield a wealth of metabolic and anatomic information from macroscopic (e.g., radiology) to microscopic (e.g., digitized slides) scale. Whereas this information can significantly improve understanding of disease pathophysiology as well as the noninvasive diagnosis of disease in patients, the need to process, analyze, and store large amounts of image data presents a great challenge