An Introduction to Google Scholar

This handout discusses the pros and cons of using Google Scholar to find books and journal articles. It tells how to connect Google Scholar to our Jerry Falwell Library subscription databases. It also discusses Google Books

Proteomic analysis of Glossina pallidipes salivary gland hypertrophy virus virions for immune intervention in tsetse fly colonies

Many species of tsetse flies (Diptera: Glossinidae) can be infected by a virus that causes salivary gland hypertrophy (SGH). The viruses isolated from Glossina pallidipes (GpSGHV) and Musca somestica (MdSGHV) have recently been sequenced. Tsetse flies with SGH have a reduced fecundity and fertility which cause a serious problem for mass rearing in the frame of sterile insect technique (SIT) programs to control and eradicate tsetse populations in the wild. A potential intervention strategy to mitigate viral infections in fly colonies is neutralizing of the GpSGHV infection with specific antibodies against virion proteins. Two major GpSGHV virion proteins of about 130 kDa and 50 kDa, respectively, were identified by Western analysis using polyclonal rabbit antibody raised against whole GpSHGV virions. The proteome of GpSGHV, containing the antigens responsible for the immune-response, was investigated by liquid chromatography tandem mass spectrometry (LC-MS/MS) and 61 virion proteins were identified by comparison with the genome sequence. Specific antibodies were produced in rabbits against seven candidate proteins including the ORF10 / C-terminal fragment, ORF47 and ORF96 as well as proteins involved in peroral infectivity PIF-1 (ORF102), PIF-2 (ORF53), PIF-3 (ORF76) and P74 (ORF1). Antiserum against ORF10 specifically reacted to the 130 kDa protein in a Western blot analysis and to the envelope of GpSGHV using immunogold-EM. This result suggests that immune intervention of viral infections in colonies of G. pallidipes is a realistic optio

Answering Complex Questions by Joining Multi-Document Evidence with Quasi Knowledge Graphs

Direct answering of questions that involve multiple entities and relations is a challenge for text-based QA. This problem is most pronounced when answers can be found only by joining evidence from multiple documents. Curated knowledge graphs (KGs) may yield good answers, but are limited by their inherent incompleteness and potential staleness. This paper presents QUEST, a method that can answer complex questions directly from textual sources on-the-fly, by computing similarity joins over partial results from different documents. Our method is completely unsupervised, avoiding training-data bottlenecks and being able to cope with rapidly evolving ad hoc topics and formulation style in user questions. QUEST builds a noisy quasi KG with node and edge weights, consisting of dynamically retrieved entity names and relational phrases. It augments this graph with types and semantic alignments, and computes the best answers by an algorithm for Group Steiner Trees. We evaluate QUEST on benchmarks of complex questions, and show that it substantially outperforms state-of-the-art baselines