MOTIVATION: Finding protein-protein interaction (PPI) information from literature is challenging but an important issue. However, keyword search in PubMed(®) is often time consuming because it requires a series of actions that refine keywords and browse search results until it reaches a goal. Due to the rapid growth of biomedical literature, it has become more difficult for biologists and curators to locate PPI information quickly. Therefore, a tool for prioritizing PPI informative articles can be a useful assistant for finding this PPI-relevant information. RESULTS: PIE (Protein Interaction information Extraction) the search is a web service implementing a competition-winning approach utilizing word and syntactic analyses by machine learning techniques. For easy user access, PIE the search provides a PubMed-like search environment, but the output is the list of articles prioritized by PPI confidence scores. By obtaining PPI-related articles at high rank, researchers can more easily find the up-to-date PPI information, which cannot be found in manually curated PPI databases. AVAILABILITY: http://www.ncbi.nlm.nih.gov/IRET/PIE/.

Recently, various research efforts have been conducted to develop strategies for accelerating multi-dimensional query processing using the Graphics Processing Units (GPUs). Well-known multidimensional access methods such as R-tree, B-tree and their variants, however, are hardly applicable to GPUs in practice, mainly due to the characteristics of a hierarchical structure. More specifically, the structure not only causes frequent transfers of small volume of data but also gives limited opportunities to exploit GPUs' advanced data parallelism. To address the problems, we propose an approach which uses GPUs as a buffer. Main idea is that object entries in recently visited leaf nodes are buffered in the global memory of the GPU and are processed by massive threads of GPUs in parallel. In this paper, we presented both a single buffer strategy and a multiple buffer strategy since a single buffer strategy has problems deteriorating query performance seriously. Through the extensive performance studies, we observed that the proposed approach achieved up to 5 times higher query performance than the original CPU-based R-trees.

With the rapid advances of wireless communications and positioning techniques, tracking the positions of moving objects is becoming increasingly feasible and necessary. Traditional spatial index structures are not suitable for storing these positions because of numerous update operations. To reduce the number of update operations, many existing approaches use a linear function to describe the movements of objects. In many real applications, however, the movements of objects are too complicated to be represented as a simple linear function. In this case, such approaches based on a linear function cannot reduce update cost efficiently. In this paper we propose a novel R-tree based indexing technique called LUR-tree. This technique updates the structure of the index only when an object moves out of the corresponding MBR (minimum bounding rectangle). If a new position of an object is in the MBR, it changes only the position of the object in the leaf node. It can update the position of the object quickly and reduce update cost greatly. Since it is based on the R-tree, the LUR-tree also uses the same algorithms to process various types of queries as the R-tree. We present the experimental results which show that our technique outperforms other techniques.