The grid: computing without bounds

COVID-19 prevention and control in China: grid governance

The coronavirus disease 2019 (COVID-19) pandemic has spread worldwide and caused negative economic and health effects. China is one of the most seriously affected countries, and it has adopted grid governance measures at the basic level of society, which include city lockdown, household survey and resident quarantine. By the end of April, China had basically brought the pandemic under control within its own borders, and residents' lives and factory production gradually began to return to normal. In referring to the specific cases of different communities, schools, and enterprises in the four cities of Anhui, Beijing, Shenzhen and Zibo, we analyze grid-based governance measures and we summarize the effectiveness and shortcomings of these measures and discuss foundations and future challenges of grid governance. We do so in the expectation (and hope) that the world will gain a comprehensive understanding of China's situation and introduce effective measures that enable the prevention and control of COVID-19.

Supporting genotype-to-phenotype association studies with grid-enabled knowledge discovery workflows

Web Services and Grid-enabled scientific workflows are of paramount importance for the realization of efficient and secure knowledge discovery scenarios. This paper presents a Grid-enabled Genotype-to-Phenotype discovery scenario (GG2P), which is realized by a respective scientific workflow. GG2P supports the seamless integration of SNP genotype data sources, and the discovery of indicative and predictive genotype-to-phenotype association models - all wrapped around custom-made Web Services. GG2P is applied on a whole-genome SNP-genotyping experiment (breast cancer vs. normal/control phenotypes). A set of about 100 indicative SNPs are induced with very high classification performance. The biological relevance of the findings is supported by the relevant literature.

A semantically aware platform for the authoring and secure enactment of bioinformatics workflows

Recent advances in the field of bioinformatics present a number of challenges in the secure and efficient management and analysis of biological data resources. Workflow technologies aim to assist scientists and domain experts in the design of complex, long running, data and computing intensive experiments that involve many data processing and analysis tasks with the objective of generating new knowledge or formulate new hypothesis. In this paper we present a bioinformatics workflow authoring and execution environment that intends to greatly facilitate the whole lifecycle of such experiments. Emphasis is given on the security and ethical requirements of these scenarios and the corresponding technological response. In addition we present our semantic framework used for supporting specific user-requirements related to the reasoning and inference capabilities of the environment.

Grid infrastructures for computational neuroscience: the neuGRID example

Neuroscience is increasingly making use of statistical and mathematical tools to extract information from images of biological tissues. Computational neuroimaging tools require substantial computational resources and the increasing availability of large image datasets will further enhance this need. Many efforts have been directed towards creating brain image repositories including the recent US Alzheimer Disease Neuroimaging Initiative. Multisite-distributed computing infrastructures have been launched with the goal of fostering shared resources and facilitating data analysis in the study of neurodegenerative diseases. Currently, some Grid- and non-Grid-based projects are aiming to establish distributed e-infrastructures, interconnecting compatible imaging datasets and to supply neuroscientists with the most advanced information and communication technologies tools to study markers of Alzheimer’s and other brain diseases, but they have so far failed to make a difference in the larger neuroscience community. NeuGRID is an Europeon comission-funded effort arising from the needs of the Alzheimer’s disease imaging community, which will allow the collection and archiving of large amounts of imaging data coupled with Grid-based algorithms and sufficiently powered computational resources. The major benefit will be the faster discovery of new disease markers that will be valuable for earlier diagnosis and development of innovative drugs. The initial setup of neuGRID will feature three nodes equipped with supercomputer capabilities and resources of more than 300 processor cores, 300 GB of RAM memory and approximately 20 TB of physical space. The scope of this article is highlights the new perspectives and potential for the study of the neurodegenerative disorders using the emerging Grid technology.

Database-managed grid-enabled analysis of neuroimaging data: the CNARI framework

Functional magnetic resonance imaging (fMRI) has led to an enormous growth in the study of cognitive neuroanatomy, and combined with advances in high-field electrophysiology (and other methods), has led to a fast-growing field of human neuroscience. Technological advances in both hardware and software will lead to an ever more promising future for fMRI. We have developed a new computational framework that facilitates fMRI experimentation and analysis, and which has led to some rethinking of the nature of experimental design and analysis. The Computational Neuroscience Applications Research Infrastructure (CNARI) incorporates novel methods for maintaining, serving, and analyzing massive amounts of fMRI data. By using CNARI, it is possible to perform naturalistic, network-based, statistically valid experiments in systems neuroscience on a very large scale, with ease of data manipulation and analysis, within reasonable computational time scales. In this article, we describe this infrastructure and then illustrate its use on a number of actual examples in both cognitive neuroscience and neurological research. We believe that these advanced computational approaches will fundamentally change the future shape of cognitive brain imaging with fMRI.

Ontologies and semantic data integration

The increased generation of data in the pharmaceutical R&D process has failed to generate the expected returns in terms of enhanced productivity and pipelines. The inability of existing integration strategies to organize and apply the available knowledge to the range of real scientific and business issues is impacting on not only productivity but also transparency of information in crucial safety and regulatory applications. The new range of semantic technologies based on ontologies enables the proper integration of knowledge in a way that is reusable by several applications across businesses, from discovery to corporate affairs.

Sharing neuroimaging studies of human cognition

After more than a decade of collecting large neuroimaging datasets, neuroscientists are now working to archive these studies in publicly accessible databases. In particular, the fMRI Data Center (fMRIDC), a high-performance computing center managed by computer and brain scientists, seeks to catalogue and openly disseminate the data from published fMRI studies to the community. This repository enables experimental validation and allows researchers to combine and examine patterns of brain activity beyond that of any single study. As with some biological databases, early scientific, technical and sociological concerns hindered initial acceptance of the fMRIDC. However, with the continued growth of this and other neuroscience archives, researchers are recognizing the potential of such resources for identifying new knowledge about cognitive and neural activity. Thus, the field of neuroimaging is following the lead of biology and chemistry, mining its accumulating body of knowledge and moving toward a 'discovery science' of brain function.

The national pediatric trauma registry: a legacy of commitment to control of childhood injury

The National Pediatric Trauma Registry represents almost 15 years of effective collaboration among hospitals committed to improving care for the injured child. Its design of providing a "physiologic snapshot" of the injured child on presentation has supported numerous studies that have helped define the epidemiology of childhood injury and refine principles of management. Global analysis of the 103,434 records included in this database suggest that mortality is significantly higher in the very young, that vehicular injury remains a major pediatric public health challenge, and that shock is just as devastating in the child as the adult. Based on this foundation of collaborative commitment, future versions of a pediatric trauma database must harness the emerging internet technology that combines information accrual with human thought, and must extend this effort to include all the children of our world.

3D-GENOMICS: a database to compare structural and functional annotations of proteins between sequenced genomes

The 3D-GENOMICS database (http://www.sbg.bio. ic.ac.uk/3dgenomics/) provides structural annotations for proteins from sequenced genomes. In August 2003 the database included data for 93 proteomes. The annotations stored in the database include homologous sequences from various sequence databases, domains from SCOP and Pfam, patterns from Prosite and other predicted sequence features such as transmembrane regions and coiled coils. In addition to annotations at the sequence level, several precomputed cross- proteome comparative analyses are available based on SCOP domain superfamily composition. Annotations are available to the user via a web interface to the database. Multiple points of entry are available so that a user is able to: (i) directly access annotations for a single protein sequence via keywords or accession codes, (ii) examine a sequence of interest chosen from a summary of annotations for a particular proteome, or (iii) access precomputed frequency-based cross-proteome comparative analyses.