Abstract. The focus of contemporary Web information retrieval systems has been to provide efficient support for the querying and retrieval of relevant documents. More recently, information retrieval over semantic metadata extracted from the Web has received an increasing amount of interest in both industry and academia. In particular, discovering complex and meaningful relationships among this metadata is an interesting and challenging research topic. Just as ranking of documents is a critical component of today's search engines, the ranking of complex relationships will be an important component in tomorrow's Semantic Web analytics engines. Building upon our recent work on specifying and discovering complex relationships in RDF data, called Semantic Associations, we present a flexible ranking approach which can be used to identify more interesting and relevant relationships in the Semantic Web. Additionally, we demonstrate our ranking scheme's effectiveness through an empirical evaluation over a real-world dataset.

Public and private organizations have access to vast amount of internal, deep Web and open Web information. Transforming this heterogeneous and distributed information into actionable and insightful information is the key to the emerging new class of business intelligence and national security applications. Although role of semantics in search and integration has been often talked about, in this paper we discussed semantic approaches to support analytics on vast amount of heterogeneous data. In particular, we bring together novel academic research and commercialized Semantic Web technology. The academic research related to semantic association identification, is built upon commercial Semantic Web technology for semantic metadata extraction. A prototypical demonstration of this research and technology is presented in the context of an aviation security application of significance to national security.

Abstract. Discovering and composing individual Web Services into more complex yet new and more useful Web Processes is an important challenge. In this paper, we present three techniques for (semi) automatically composing Web Services into Web Processes by using their ontological descriptions and relationships to other services. In Interface-Matching Automatic composition technique, the possible compositions are obtained by checking semantic similarities between interfaces of individual services. Then these compositions are ranked considering their Quality of Services (QoS) and an optimum composition is selected. In Human-Assisted composition the user selects a service from a ranked list at certain stages. We also address automatic compositions in a Peer-to-Peer network.

In this article, we demonstrate the applicability of semantic techniques for detection of Conflict of Interest (COI). We explain the common challenges involved in building scalable Semantic Web applications, in particular those addressing connecting-the-dots problems. We describe in detail the challenges involved in two important aspects on building Semantic Web applications, namely, data acquisition and entity disambiguation (or reference reconciliation). We extend upon our previous work where we integrated the collaborative network of a subset of DBLP researchers with persons in a Friend-of-a-Friend social network (FOAF). Our method finds the connections between people, measures collaboration strength, and includes heuristics that use friendship/affiliation information to provide an estimate of potential COI in a peer-review scenario. Evaluations are presented by measuring what could have been the COI between accepted papers in various conference tracks and their respective program committee members. The experimental results demonstrate that scalability can be achieved by using a dataset of over 3 million entities (all bibliographic data from DBLP and a large collection of FOAF documents).

The booming nanotech industry has raised public concerns about the environmental health and safety impact of engineered nanomaterials (ENMs). High-throughput assays are needed to obtain toxicity data for the rapidly increasing number of ENMs. Here we present a suite of high-throughput methods to study nanotoxicity in intact animals using Caenorhabditis elegans as a model. At the population level, our system measures food consumption of thousands of animals to evaluate population fitness. At the organism level, our automated system analyzes hundreds of individual animals for body length, locomotion speed, and lifespan. To demonstrate the utility of our system, we applied this technology to test the toxicity of 20 nanomaterials under four concentrations. Only fullerene nanoparticles (nC60), fullerol, TiO2, and CeO2 showed little or no toxicity. Various degrees of toxicity were detected from different forms of carbon nanotubes, graphene, carbon black, Ag, and fumed SiO2 nanoparticles. Aminofullerene and UV irradiated nC60 also showed small but significant toxicity. We further investigated the effects of nanomaterial size, shape, surface chemistry, and exposure conditions on toxicity. Our data are publicly available at the open-access nanotoxicity database www.QuantWorm.org/nano.

Abstract. Precisely identifying entities in web documents is essential for document indexing, web search and data integration. Entity disambiguation is the challenge of determining the correct entity out of various candidate entities. Our novel method utilizes background knowledge in the form of a populated ontology. Additionally, it does not rely on the existence of any structure in a document or the appearance of data items that can provide strong evidence, such as email addresses, for disambiguating person names. Originality of our method is demonstrated in the way it uses different relationships in a document as well as from the ontology to provide clues in determining the correct entity. We demonstrate the applicability of our method by disambiguating names of researchers appearing in a collection of DBWorld posts using a large scale, realworld ontology extracted from the DBLP bibliography website. The precision and recall measurements provide encouraging results.

BackgroundThe disease caused by Haemonchus contortus, a blood-feeding nematode of small ruminants, is of major economic importance worldwide. The infective third-stage larva (L3) of this gastric nematode is enclosed in a cuticle (sheath) and, once ingested with herbage by the host, undergoes an exsheathment process that marks the transition from the free-living (L3) to the parasitic (xL3) stage. This study explored changes in gene transcription associated with this transition and predicted, based on comparative analysis, functional roles for key transcripts in the metabolic pathways linked to larval development.ResultsTotals of 101,305 (L3) and 105,553 (xL3) expressed sequence tags (ESTs) were determined using 454 sequencing technology, and then assembled and annotated; the most abundant transcripts encoded transthyretin-like, calcium-binding EF-hand, NAD(P)-binding and nucleotide-binding proteins as well as homologues of Ancylostoma-secreted proteins (ASPs). Using an in silico-subtractive analysis, 560 and 685 sequences were shown to be uniquely represented in the L3 and xL3 stages, respectively; the transcripts encoded ribosomal proteins, collagens and elongation factors (in L3), and mainly peptidases and other enzymes of amino acid catabolism (in xL3). Caenorhabditis elegans orthologues of transcripts that were uniquely transcribed in each L3 and xL3 were predicted to interact with a total of 535 other genes, all of which were involved in embryonic development.ConclusionThe present study indicated that some key transcriptional alterations taking place during the transition from the L3 to the xL3 stage of H. contortus involve genes predicted to be linked to the development of neuronal tissue (L3 and xL3), formation of the cuticle (L3) and digestion of host haemoglobin (xL3). Future efforts using next-generation sequencing and bioinformatic technologies should provide the efficiency and depth of coverage required for the determination of the complete transcriptomes of different developmental stages and/or tissues of H. contortus as well as the genome of this important parasitic nematode. Such advances should lead to a significantly improved understanding of the molecular biology of H. contortus and, from an applied perspective, to novel methods of intervention.

Genetic screens have been widely applied to uncover genetic mechanisms of movement disorders. However, most screens rely on human observations of qualitative differences. Here we demonstrate the application of an automatic imaging system to conduct a quantitative screen for genes regulating the locomotive behavior in Caenorhabditis elegans. Two hundred twenty-seven neuronal signaling genes with viable homozygous mutants were selected for this study. We tracked and recorded each animal for 4 min and analyzed over 4,400 animals of 239 genotypes to obtain a quantitative, 10-parameter behavioral profile for each genotype. We discovered 87 genes whose inactivation causes movement defects, including 50 genes that had never been associated with locomotive defects. Computational analysis of the high-content behavioral profiles predicted 370 genetic interactions among these genes. Network partition revealed several functional modules regulating locomotive behaviors, including sensory genes that detect environmental conditions, genes that function in multiple types of excitable cells, and genes in the signaling pathway of the G protein Gαq, a protein that is essential for animal life and behavior. We developed quantitative epistasis analysis methods to analyze the locomotive profiles and validated the prediction of the γ isoform of phospholipase C as a component in the Gαq pathway. These results provided a system-level understanding of how neuronal signaling genes coordinate locomotive behaviors. This study also demonstrated the power of quantitative approaches in genetic studies.gene network | high-content screening | locomotion A number of neuronal signaling genes are known to regulate locomotive behaviors of animals. For example, disruption of the heterotrimeric G protein subunit Gαq in neurons caused movement disorders in Caenorhabditis elegans and mice (1, 2). The Gαq signaling pathway is composed of proteins and lipids conserved in all animals (3-5). The main target of Gαq is phospholipase C (PLC), which converts phosphatidylinositol 4,5-bisphosphate to the second messengers, diacyl glycerol (DAG) and inositol trisphosphate (3-5). In C. elegans excitatory motor neurons, DAG promotes ACh release, necessary for locomotion.Despite the wealth of information on individual signaling genes and pathways, a system-level understanding remains missing on how these genes coordinate animal behavior. For example, among all neuronal signaling genes, which ones are involved in regulating a specific stereotyped behavior? How do these genes interact with each other to form networks that process information? A successful method to uncover large-scale gene networks in metazoans is high-content phenotypic profiling. Using binary parameters to score presence and absence of multiple phenotypic details, this approach enabled computational approaches such as hierarchical clustering to infer interactions among development genes (6-8). Behavioral phenotypes such as movement disorders are intrinsically quantitative. Therefore, a quantitative m...