Spatio-textual indexing for geographical search on the web

Many web documents refer to specific geographic localities and many people include geographic context in queries to web search engines. Standard web search engines treat the geographical terms in the same way as other terms. This can result in failure to find relevant documents that refer to the place of interest using alternative related names, such as those of included or nearby places. This can be overcome by associating text indexing with spatial indexing methods that exploit geo-tagging procedures to categorise documents with respect to geographic space. We describe three methods for spatio-textual indexing based on multiple spatially indexed text indexes, attaching spatial indexes to the document occurrences of a text index, and merging text index access results with results of access to a spatial index of documents. These schemes are compared experimentally with a conventional text index search engine, using a collection of geo-tagged web documents, and are shown to be able to compete in speed and storage performance with pure text indexing

Detectors could spot plagiarism in research proposals

Having all been involved in proposal evaluation, we believe the studies indicate that a text matching analysis of research proposals could reduce plagiarism in subsequent publications. For instance, when European Commission evaluators have met in the past to evaluate research proposals, they received printed copies which had to be returned before the panel members left, and had no computer access during deliberations. A plagiarism detector using text-mining methods could be used instead of the current security measures. Such a system could, in principle, detect similarities to previous submissions and uncited sources using advanced document segmentation. Only official agencies have access to confidential proposals and the funds to experiment with automated plagiarism-detectors. It is important that they should investigate these approaches to reducing the possibility of scientific misconduct

Text Messaging in the Patient-Centered Medical Home to Improve Glucose Control and Retinopathy Screening.

Purpose: To evaluate the effectiveness of a text messaging program (TMP) to improve glucose control, retinopathy screening (RS) rates, and self-care behaviors in patients with uncontrolled type 2 diabetes. Methods: A single-group design with a quasi-systematic random sample (n=20) received educational/exhortational text messages on their cellular phones for 3 months. Subjects, 12 of whom identified as a minority ethnicity, were mostly male, aged 27-73 years. Results: Glucose control and RS rates improved significantly. Subjects (\u3e70%) reported changes in self-care behaviors. Conclusion: Leveraging ubiquitous technology, a TMP for patients with limited access to healthcare education, holds promis

Large-scale event extraction from literature with multi-level gene normalization

Text mining for the life sciences aims to aid database curation, knowledge summarization and information retrieval through the automated processing of biomedical texts. To provide comprehensive coverage and enable full integration with existing biomolecular database records, it is crucial that text mining tools scale up to millions of articles and that their analyses can be unambiguously linked to information recorded in resources such as UniProt, KEGG, BioGRID and NCBI databases. In this study, we investigate how fully automated text mining of complex biomolecular events can be augmented with a normalization strategy that identifies biological concepts in text, mapping them to identifiers at varying levels of granularity, ranging from canonicalized symbols to unique gene and proteins and broad gene families. To this end, we have combined two state-of-the-art text mining components, previously evaluated on two community-wide challenges, and have extended and improved upon these methods by exploiting their complementary nature. Using these systems, we perform normalization and event extraction to create a large-scale resource that is publicly available, unique in semantic scope, and covers all 21.9 million PubMed abstracts and 460 thousand PubMed Central open access full-text articles. This dataset contains 40 million biomolecular events involving 76 million gene/protein mentions, linked to 122 thousand distinct genes from 5032 species across the full taxonomic tree. Detailed evaluations and analyses reveal promising results for application of this data in database and pathway curation efforts. The main software components used in this study are released under an open-source license. Further, the resulting dataset is freely accessible through a novel API, providing programmatic and customized access (http://www.evexdb.org/api/v001/). Finally, to allow for large-scale bioinformatic analyses, the entire resource is available for bulk download from http://evexdb.org/download/, under the Creative Commons -Attribution - Share Alike (CC BY-SA) license

Delivering 3D advertising to mobile phones.

Directing advertising to mobile phones currently is limited to commercial text messages, short-code text-back messages, two dimensional (2D) images, or wireless access protocol (WAP) clickable push links. All of these traditional methods do not facilitate advertising approach were consumers can interact with prospective purchases. In this paper we introduce a novel and highly interactive location- and permission-based advertising system that allows 3D product adverts to be displayed on users' mobile phones. The paper provides a thorough discussion of the system covering its performance, implementation structure, platform-dependent optimizations and suggestions for future work. With mobile phones and 3D interactive tools, advertising becomes more engaging, rewarding and entertaining and provides marketing executives with new means of directing their campaigns to a more specific target audience