5,241 research outputs found
Innovative in silico approaches to address avian flu using grid technology
The recent years have seen the emergence of diseases which have spread very
quickly all around the world either through human travels like SARS or animal
migration like avian flu. Among the biggest challenges raised by infectious
emerging diseases, one is related to the constant mutation of the viruses which
turns them into continuously moving targets for drug and vaccine discovery.
Another challenge is related to the early detection and surveillance of the
diseases as new cases can appear just anywhere due to the globalization of
exchanges and the circulation of people and animals around the earth, as
recently demonstrated by the avian flu epidemics. For 3 years now, a
collaboration of teams in Europe and Asia has been exploring some innovative in
silico approaches to better tackle avian flu taking advantage of the very large
computing resources available on international grid infrastructures. Grids were
used to study the impact of mutations on the effectiveness of existing drugs
against H5N1 and to find potentially new leads active on mutated strains. Grids
allow also the integration of distributed data in a completely secured way. The
paper presents how we are currently exploring how to integrate the existing
data sources towards a global surveillance network for molecular epidemiology.Comment: 7 pages, submitted to Infectious Disorders - Drug Target
Asynchronous Remote Medical Consultation for Ghana
Computer-mediated communication systems can be used to bridge the gap between
doctors in underserved regions with local shortages of medical expertise and
medical specialists worldwide. To this end, we describe the design of a
prototype remote consultation system intended to provide the social,
institutional and infrastructural context for sustained, self-organizing growth
of a globally-distributed Ghanaian medical community. The design is grounded in
an iterative design process that included two rounds of extended design
fieldwork throughout Ghana and draws on three key design principles (social
networks as a framework on which to build incentives within a self-organizing
network; optional and incremental integration with existing referral
mechanisms; and a weakly-connected, distributed architecture that allows for a
highly interactive, responsive system despite failures in connectivity). We
discuss initial experiences from an ongoing trial deployment in southern Ghana.Comment: 10 page
Grid Databases for Shared Image Analysis in the MammoGrid Project
The MammoGrid project aims to prove that Grid infrastructures can be used for
collaborative clinical analysis of database-resident but geographically
distributed medical images. This requires: a) the provision of a
clinician-facing front-end workstation and b) the ability to service real-world
clinician queries across a distributed and federated database. The MammoGrid
project will prove the viability of the Grid by harnessing its power to enable
radiologists from geographically dispersed hospitals to share standardized
mammograms, to compare diagnoses (with and without computer aided detection of
tumours) and to perform sophisticated epidemiological studies across national
boundaries. This paper outlines the approach taken in MammoGrid to seamlessly
connect radiologist workstations across a Grid using an "information
infrastructure" and a DICOM-compliant object model residing in multiple
distributed data stores in Italy and the UKComment: 10 pages, 5 figure
Real-world Machine Learning Systems: A survey from a Data-Oriented Architecture Perspective
Machine Learning models are being deployed as parts of real-world systems
with the upsurge of interest in artificial intelligence. The design,
implementation, and maintenance of such systems are challenged by real-world
environments that produce larger amounts of heterogeneous data and users
requiring increasingly faster responses with efficient resource consumption.
These requirements push prevalent software architectures to the limit when
deploying ML-based systems. Data-oriented Architecture (DOA) is an emerging
concept that equips systems better for integrating ML models. DOA extends
current architectures to create data-driven, loosely coupled, decentralised,
open systems. Even though papers on deployed ML-based systems do not mention
DOA, their authors made design decisions that implicitly follow DOA. The
reasons why, how, and the extent to which DOA is adopted in these systems are
unclear. Implicit design decisions limit the practitioners' knowledge of DOA to
design ML-based systems in the real world. This paper answers these questions
by surveying real-world deployments of ML-based systems. The survey shows the
design decisions of the systems and the requirements these satisfy. Based on
the survey findings, we also formulate practical advice to facilitate the
deployment of ML-based systems. Finally, we outline open challenges to
deploying DOA-based systems that integrate ML models.Comment: Under revie
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