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

Biobjective Optimization Algorithms Using Neumann Series Expansion for Engineering Design

In this paper, two novel algorithms are designed for solving biobjective optimization engineering problems. In order to obtain the optimal solutions of the biobjective optimization problems in a fast and accurate manner, the algorithms, which have combined Newton’s method with Neumann series expansion as well as the weighted sum method, are applied to deal with two objectives, and the Pareto optimal front is achieved through adjusting weighted factors. Theoretical analysis and numerical examples demonstrate the validity and effectiveness of the proposed algorithms. Moreover, an effective biobjective optimization strategy, which is based upon the two algorithms and the surrogate model method, is developed for engineering problems. The effectiveness of the optimization strategy is proved by its application to the optimal design of the dummy head structure in the car crash experiments

The Synergistic Priming Effect of Exogenous Salicylic Acid and H2O2 on Chilling Tolerance Enhancement during Maize (Zea mays L.) Seed Germination

Chilling stress is an important constraint for maize seedling establishment in the field. To examine the role of salicylic acid (SA) and hydrogen peroxide (H2O2) in response to chilling stress, we investigated the effects of seed priming with SA, H2O2, and SA+H2O2 combination on maize resistance under chilling stress (13°C). Priming with SA, H2O2, and especially SA+H2O2 shortened seed germination time and enhanced seed vigor and seedling growth as compared with hydropriming and non-priming treatments under low temperature. Meanwhile, SA+H2O2 priming notably increased the endogenous H2O2 and SA content, antioxidant enzymes activities and their corresponding genes ZmPAL, ZmSOD4, ZmAPX2, ZmCAT2, and ZmGR expression levels. The α-amylase activity was enhanced to mobilize starch to supply metabolites such as soluble sugar and energy for seed germination under chilling stress. In addition, the SA+H2O2 combination positively up-regulated expressions of gibberellic acid (GA) biosynthesis genes ZmGA20ox1 and ZmGA3ox2, and down-regulated GA catabolism gene ZmGA2ox1 expression; while it promoted GA signaling transduction genes expressions of ZmGID1 and ZmGID2 and decreased the level of seed germination inhibitor gene ZmRGL2. The abscisic acid (ABA) catabolism gene ZmCYP707A2 and the expressions of ZmCPK11 and ZmSnRK2.1 encoding response receptors in ABA signaling pathway were all up-regulated. These results strongly suggested that priming with SA and H2O2 synergistically promoted hormones metabolism and signal transduction, and enhanced energy supply and antioxidant enzymes activities under chilling stress, which were closely relevant with chilling injury alleviation and chilling-tolerance improvement in maize seed.Highlights:Seed germination and seedling growth were significantly improved under chilling stress by priming with SA+H2O2 combination, which was closely relevant with the change of reactive oxygen species, metabolites and energy supply, hormones metabolism and regulation

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 science. © The Author(s) 2019. Published by Oxford University Press