Motivation: Event extraction using expressive structured representations has been a significant focus of recent efforts in biomedical information extraction. However, event extraction resources and methods have so far focused almost exclusively on molecular-level entities and processes, limiting their applicability.Results: We extend the event extraction approach to biomedical information extraction to encompass all levels of biological organization from the molecular to the whole organism. We present the ontological foundations, target types and guidelines for entity and event annotation and introduce the new multi-level event extraction (MLEE) corpus, manually annotated using a structured representation for event extraction. We further adapt and evaluate named entity and event extraction methods for the new task, demonstrating that both can be achieved with performance broadly comparable with that for established molecular entity and event extraction tasks.Availability: The resources and methods introduced in this study are available from http://nactem.ac.uk/MLEE/.Contact: pyysalos@cs.man.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.

BackgroundWe report the Gene Normalization (GN) challenge in BioCreative III where participating teams were asked to return a ranked list of identifiers of the genes detected in full-text articles. For training, 32 fully and 500 partially annotated articles were prepared. A total of 507 articles were selected as the test set. Due to the high annotation cost, it was not feasible to obtain gold-standard human annotations for all test articles. Instead, we developed an Expectation Maximization (EM) algorithm approach for choosing a small number of test articles for manual annotation that were most capable of differentiating team performance. Moreover, the same algorithm was subsequently used for inferring ground truth based solely on team submissions. We report team performance on both gold standard and inferred ground truth using a newly proposed metric called Threshold Average Precision (TAP-k).ResultsWe received a total of 37 runs from 14 different teams for the task. When evaluated using the gold-standard annotations of the 50 articles, the highest TAP-k scores were 0.3297 (k=5), 0.3538 (k=10), and 0.3535 (k=20), respectively. Higher TAP-k scores of 0.4916 (k=5, 10, 20) were observed when evaluated using the inferred ground truth over the full test set. When combining team results using machine learning, the best composite system achieved TAP-k scores of 0.3707 (k=5), 0.4311 (k=10), and 0.4477 (k=20) on the gold standard, representing improvements of 12.4%, 21.8%, and 26.6% over the best team results, respectively.ConclusionsBy using full text and being species non-specific, the GN task in BioCreative III has moved closer to a real literature curation task than similar tasks in the past and presents additional challenges for the text mining community, as revealed in the overall team results. By evaluating teams using the gold standard, we show that the EM algorithm allows team submissions to be differentiated while keeping the manual annotation effort feasible. Using the inferred ground truth we show measures of comparative performance between teams. Finally, by comparing team rankings on gold standard vs. inferred ground truth, we further demonstrate that the inferred ground truth is as effective as the gold standard for detecting good team performance.

No abstract

Mineral trioxide aggregate (MTA) cement is an attractive material in endodontic dentistry. The purpose of this study was to produce calcium silicate, which is a major component of MTA, from waste materials. A dental alginate impression gel and used chalks were selected and mixed in a suitable ratio (Code: EXP). As a control, CaCO3 and a commercial diatomite were used (Code: CON). Each powder was heated to 850˚C and 1000˚C, and then kneaded with water. TG-DTA, compressive tests, SEM observations, elemental mapping analyses, and XRD analyses were performed. TG-DTA indicated that weight reduction of CaCO3 started at 600˚C, and it completely decomposed on heating at 850˚C. The strength was affected by the temperature. After heating, CaCO3 was transformed into CaO and/or Ca2SiO4, and Ca(OH)2 was formed by mixing with water. There were no differences between EXP and CON. These data suggested that recycled wastes might be promising MTA sources.