Registering Scientific Information Sources for Semantic Mediation. In: Spaccapietra S, March ST, Kambayashi Y, editors. Conceptual Modeling — ER 2002. Berlin: Springer Berlin Heidelberg; 2003. pp. 182-98. [DOI: 10.1007/3-540-45816-6_23]

Magnesium transporter AtMGT9 is essential for pollen development in Arabidopsis

Magnesium (Mg(2+)) is abundant in plant cells and plays a critical role in many physiological processes. A 10-member gene family AtMGT (also known as AtMRS2) was identified in Arabidopsis, which belongs to a eukaryote subset of the CorA superfamily, functioning as Mg(2+) transporters. Some family members (AtMGT1 and AtMGT10) function as high-affinity Mg(2+) transporter and could complement bacterial mutant or yeast mutant lacking Mg(2+) transport capability. Here we report an AtMGT family member, AtMGT9, that functions as a low-affinity Mg(2+) transporter, and is essential for pollen development. The functional complementation assay in Salmonella mutant strain MM281 showed that AtMGT9 is capable of mediating Mg(2+) uptake in the sub-millimolar range of Mg(2+). The AtMGT9 gene was expressed most strongly in mature anthers and was also detectable in vascular tissues of the leaves, and in young roots. Disruption of AtMGT9 gene expression resulted in abortion of half of the mature pollen grains in heterozygous mutant +/mgt9, and no homozygous mutant plant was obtained in the progeny of selfed +/mgt9 plants. Transgenic plants expressing AtMGT9 in these heterozygous plants can recover the pollen phenotype to the wild type. In addition, AtMGT9 RNAi transgenic plants also showed similar abortive pollen phenotype to mutant +/mgt9. Together, our results demonstrate that AtMGT9 functions as a low-affinity Mg(2+) transporter that plays a crucial role in male gametophyte development and male fertility.

Mediator infrastructure for information integration and semantic data integration environment for biomedical research

This paper presents current progress in the development of semantic data integration environment which is a part of the Biomedical Informatics Research Network (BIRN; http://www.nbirn.net) project. BIRN is sponsored by the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH). A goal is the development of a cyberinfrastructure for biomedical research that supports advance data acquisition, data storage, data management, data integration, data mining, data visualization, and other computing and information processing services over the Internet. Each participating institution maintains storage of their experimental or computationally derived data. Mediator-based data integration system performs semantic integration over the databases to enable researchers to perform analyses based on larger and broader datasets than would be available from any single institution's data. This paper describes recent revision of the system architecture, implementation, and capabilities of the semantically based data integration environment for BIRN.

Semantic enrichment for medical ontologies

The Unified Medical Language System (UMLS) contains two separate but interconnected knowledge structures, the Semantic Network (upper level) and the Metathesaurus (lower level). In this paper, we have attempted to work out better how the use of such a two-level structure in the medical field has led to notable advances in terminologies and ontologies. However, most ontologies and terminologies do not have such a two-level structure. Therefore, we present a method, called semantic enrichment, which generates a two-level ontology from a given one-level terminology and an auxiliary two-level ontology. During semantic enrichment, concepts of the one-level terminology are assigned to semantic types, which are the building blocks of the upper level of the auxiliary two-level ontology. The result of this process is the desired new two-level ontology. We discuss semantic enrichment of two example terminologies and how we approach the implementation of semantic enrichment in the medical domain. This implementation performs a major part of the semantic enrichment process with the medical terminologies, with difficult cases left to a human expert.