Text documents often embed data that is structured in nature, and we can expose this structured data using information extraction technology. By processing a text database with information extraction systems, we can materialize a variety of structured "relations," over which we can then issue regular SQL queries. A key challenge to process SQL queries in this text-based scenario is efficiency: information extraction is timeconsuming, so query processing strategies should minimize the number of documents that they process. Another key challenge is result quality: in the traditional relational world, all correct execution strategies for a SQL query produce the same (correct) result; in contrast, a SQL query execution over a text database might produce answers that are not fully accurate or complete, for a number of reasons. To address these challenges, we study a family of select-project-join SQL queries over text databases, and characterize query processing strategies on their efficiency andcritically-on their result quality as well. We optimize the execution of SQL queries over text databases in a principled, cost-based manner, incorporating this tradeoff between efficiency and result quality in a user-specific fashion. Our large-scale experimentsover real data sets and multiple information extraction systemsshow that our SQL query processing approach consistently picks appropriate execution strategies for the desired balance between efficiency and result quality.

Nuclear factor (erythroid-derived 2)-like 2 (Nrf2; encoded in humans by the NFE2L2 gene) is a transcription factor that regulates the gene expression of a wide variety of cytoprotective phase II detoxification and antioxidant enzymes through a promoter sequence known as the antioxidant-responsive element (ARE). The ARE is a promoter element found in many cytoprotective genes; therefore, Nrf2 plays a pivotal role in the ARE-driven cellular defense system against environmental stresses. Agents that target the ARE/Nrf2 pathway have been tested in a wide variety of disorders, with at least one new Nrf2-activating drug now approved by the US Food and Drug Administration. Examination of in vitro and in vivo experimental results, and taking into account recent human clinical trial results, has led to an opinion that Nrf2-activating strategies – which can include drugs, foods, dietary supplements, and exercise – are likely best targeted at disease prevention, disease recurrence prevention, or slowing of disease progression in early stage illnesses; they may also be useful as an interventional strategy. However, this rubric may be viewed even more conservatively in the pathophysiology of cancer. The activation of the Nrf2 pathway has been widely accepted as offering chemoprevention benefit, but it may be unhelpful or even harmful in the setting of established cancers. For example, Nrf2 activation might interfere with chemotherapies or radiotherapies or otherwise give tumor cells additional growth and survival advantages, unless they already possess mutations that fully activate their Nrf2 pathway constitutively. With all this in mind, the ARE/Nrf2 pathway remains of great interest as a possible target for the pharmacological control of degenerative and immunological diseases, both by activation and by inhibition, and its regulation remains a promising biological target for the development of new therapies.

Abstract-The rapid growth of Web communities has motivated many solutions for building community data portals. These solutions follow roughly two approaches. The first approach (e.g., Libra, Citeseer, Cimple) employs semi-automatic methods to extract and integrate data from a multitude of data sources. The second approach (e.g., Wikipedia, Intellipedia) deploys an initial portal in wiki format, then invites community members to revise and add material. In this paper we consider combining the above two approaches to building community portals. The new hybrid machine-human approach brings significant benefits. It can achieve broader and deeper coverage, provide more incentives for users to contribute, and keep the portal more up-to-date with less user effort. In a sense, it enables building "community wikipedias", backed by an underlying structured database that is continuously updated using automatic techniques. We outline our ideas for the new approach, describe its challenges and opportunities, and provide initial solutions. Finally, we describe a real-world implementation and preliminary experiments that demonstrate the utility of the new approach.

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