Virus-mediated gene delivery is restricted by the infectivity profile of the chosen vector. Targeting the vascular endothelium via systemic delivery has been attempted using peptides isolated in vitro (using either phage or vector display) and implicit reliance on target receptor expression in vivo. This has limited application since endothelial cells in vitro and in vivo differ vastly in receptor profiles and because of the existence of complex endothelial "zip codes" in vivo. We therefore tested whether in vivo phage display combined with adeno-associated virus (AAV) capsid modifications would allow in vivo homing to the endothelium residing in defined organs. Extensive in vivo biopanning in rats identified four consensus peptides homing to the lung or brain. Each was incorporated into the VP3 region of the AAV-2 capsid to display the peptide at the virion surface. Peptides that conferred heparan independence were shown to retarget virus to the expected vascular bed in vivo in a preferential manner, determined 28 days post-systemic injection by both virion DNA and transgene expression profiling. Our findings significantly impact the design of viral vectors for targeting individual vascular beds in vivo.

Semantic matching of schemas in heterogeneous data

No abstract

Peer-to-peer (P2P) systems show numerous advantages over centralized systems, such as load balancing, scalability, and fault tolerance, and they require certain functionality, such as search, repair, and message and data transfer. In particular, structured P2P networks perform an exact search in logarithmic time proportional to the number of peers. However, keyword similarity search in a structured P2P network remains a challenge. Similarity search for service discovery can significantly improve service management in a distributed environment. As services are often described informally in text form, keyword similarity search can find the required services or data items more reliably. This paper presents a fast similarity search algorithm for structured P2P systems. The new algorithm, called P2P fast similarity search (P2PFastSS), finds similar keys in any distributed hash table (DHT) using the edit distance metric, and is independent of the underlying P2P routing algorithm. Performance analysis shows that P2PFastSS carries out a similarity search in time proportional to the logarithm of the number of peers. Simulations on PlanetLab confirm these results and show that a similarity search with 34,000 peers performs in less than three seconds on average. Thus, P2PFastSS is suitable for similarity search in large-scale network infrastructures, such as service description matching in service discovery or searching for similar terms in P2P storage networks. Abstract-Peer-to-peer (P2P) systems show numerous advantages over centralized systems, such as load balancing, scalability, and fault tolerance, and they require certain functionality, such as search, repair, and message and data transfer. In particular, structured P2P networks perform an exact search in logarithmic time proportional to the number of peers. However, keyword similarity search in a structured P2P network remains a challenge. Similarity search for service discovery can significantly improve service management in a distributed environment. As services are often described informally in text form, keyword similarity search can find the required services or data items more reliably. This paper presents a fast similarity search algorithm for structured P2P systems. The new algorithm, called P2P Fast Similarity Search (P2PFastSS), finds similar keys in any distributed hash table (DHT) using the edit distance metric, and is independent of the underlying P2P routing algorithm. Performance analysis shows that P2PFastSS carries out a similarity search in time proportional to the logarithm of the number of peers. Simulations on PlanetLab confirm these results and show that a similarity search with 34,000 peers performs in less than three seconds on average. Thus, P2PFastSS is suitable for similarity search in large-scale network infrastructures, such as service description matching in service discovery or searching for similar terms in P2P storage networks.