MOTIVATION: Annotation databases are widely used as public repositories of biological knowledge. However, most of these resources have been developed by independent groups which used different designs and different identifiers for the same biological entities. As we show in this article, incoherent name spaces between various databases represent a serious impediment to using the existing annotations at their full potential. Navigating between various such name spaces by mapping IDs from one database to another is a very important issue which is not properly addressed at the moment. RESULTS: We have developed a web-based resource, Onto-Translate (OT), which effectively addresses this problem. OT is able to map onto each other different types of biological entities from the following annotation databases: Swiss-Prot, TrEMBL, NREF, PIR, Gene Ontology, KEGG, Entrez Gene, GenBank, GenPept, IMAGE, RefSeq, UniGene, OMIM, PDB, Eukaryotic Promoter Database, HUGO Gene Nomenclature Committee and NetAffx. Currently, OT is able to perform 462 types of mappings between 29 different types of IDs from 17 databases concerning 53 organisms. Among these, over 300 types of translations and 15 types of IDs are not currently supported by any other tool or resource. On average, OT is able to correctly map between 96 and 99% of the biological entities provided as input. In terms of speed, sets of approximately 20 000 IDs can be translated in <30 s, in most cases. AVAILABILITY: OT is a part of Onto-Tools, which is freely available at http://vortex.cs.wayne.edu/Projects.html

We have witnessed the increasing demand for pervasive Internet access from public area wireless networks (PAWNs). The diverse service requirements from end users necessitate an efficient service differentiation mechanism, which should satisfy two goals: end-user fairness and maximizing the utilization of wireless link. However, we found that the existing best-effort based service model is not enough to satisfy either goal. We have proposed an application-aware service differentiation mechanism which takes both application semantics and user requirements into consideration. The results show that our proposed method outperforms two other bandwidth allocation approaches, best effort and static allocation, in terms of both client fairness and wireless link bandwidth utilization, especially in heavy load environments.