Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Development and characterization of microsatellite markers for the Brazil nut tree Bertholletia excelsa Humb. & Bonpl. (Lecythidaceae)

Twelve polymorphic microsatellite markers were developed for the Brazil nut (Bertholletia excelsa), one of the most valuable non-timber forest products from the Amazon, based on enrichment protocol. Six to 18 (mean 10.4) alleles per locus were identified and the expected heterozygosity ranged from 0.663 to 0.923 based on a screen of 40 individuals from one population of B. excelsa. The combined probabilities of genetic identity (8.39 × 10-17) and paternity exclusion (0.999999) indicated that multilocus genotypes are likely to be unique allowing precise analyses of genetic structure, gene flow, and mating system of this economically important species. © 2009 Blackwell Publishing Ltd

Genetic analysis of forest species Eugenia uniflora L. through of newly developed SSR markers

Nine microsatellite loci for genetic analysis of three populations of the tropical tree Eugenia uniflora L. (pitanga or Brazilian cherry) from fragments of semideciduous forest were developed. We used the technique of building a (GA) n and (CA) n microsatellite-enriched library by capture with streptavidin-coated magnetic beads. We assessed the polymorphism of seven microsatellites in 84 mature trees found in three areas (Ribeirão Preto, Tambaú and São José do Rio Pardo), highly impacted by the agricultural practices, in a large region among Pardo river and Mogi-Guaçu river basins, in state of São Paulo, Brazil. All loci were polymorphic, and the number of alleles was high, ranging from 6 to 24, with a mean of 14.4. All stands showed the same high level of genetic diversity (mean H E = 0.83) and a low genetic differentiation (mean F ST = 0.031), indicating that genetic diversity was higher within rather than among populations. Seven of the nine loci were highly variable, and sufficiently informative for E. uniflora. It was concluded that these new SSR markers can be efficiently used for gene flow studies.Ronai Ferreira-Ramos, Prianda R. Laborda, Melissa de Oliveira Santos, Matheus S. Mayor, Moacyr A. Mestriner, Anete P. de Souza, Ana Lilia Alzate-Mari