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

Phenotypic and Biomass Yield Variations in Natural Populations of Prairie Cordgrass (Spartina pectinata Link) in the USA

Prairie cordgrass (Spartina pectinata Link) is a productive warm-season, C4 perennial grass native to most of North America having tolerance to wet, cold, and saline growing conditions. Excellent stress tolerance, along with high biomass yields, makes prairie cordgrass a good candidate as a dedicated energy crop on marginal land. However, there is little information available on genetic variation, including yield potential, of native populations in the USA. The objectives of this study were to evaluate biomass yield and to identify the nature and extent of genetic variation in natural populations of prairie cordgrass by comparing endemic strains collected throughout the USA. Forty-two prairie cordgrass populations were collected from prairie-remnant sites in 13 states and evaluated at the University of Illinois in Urbana, IL. The 4-year field study of prairie cordgrass revealed extensive variations in biomass yield and phenotypic traits associated with biomass yield among these populations. Strong correlations were observed between the phenotypic values and origins of the populations. Path coefficient analysis indicated that tiller mass, tiller density, heading date, plant height, and phytomer number positively affected biomass yield directly or indirectly. However, the phenotypic traits including biomass yield showed significant variation among years except for phytomer number and heading date. With the extensive genetic variability and high biomass yield potential demonstrated in this experiment, prairie cordgrass could become a highly productive bioenergy crop by developing a well-planned breeding program