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

Diet, vitamin A and K, genetics and orofacial clefts, 2010

Several factors have been shown to promote orofacial clefting e.g. genetic factors, teratogenes, maternal alcohol and cigarette use, herbicides and nutrient intake like folate and other B-vitamins. The primary aim of the project is to study the association between maternal dietary intakes of the first three months of pregnancy and the risk of orofacial clefts in the offspring. This proposal builds on a unique data set including 574 Norwegian case-parents and 763 randomly selected control-parents that have been collected through a population-based case-control study of Norwegian babies born in the period 1996-2001. NSD1844-1: Datafile on nutrients (næringsstoffer, 2) NSD1844-2: Datafile on fatty acids (fettsyrer, 2) NSD1844-3: Datafile on coffe, tea and caffeine (Kaffe te koffein, 2) NSD1844-4: Datafile on food group 1 (matvaregrupper 1, 2) NSD1844-5: Datafile on food group 2 (matvaregrupper 2, 2

Impaired Adipocyte SLC7A10 Promotes Lipid Storage in Association With Insulin Resistance and Altered BCAA Metabolism

Context The neutral amino acid transporter SLC7A10/ASC-1 is an adipocyte-expressed gene with reduced expression in insulin resistance and obesity. Inhibition of SLC7A10 in adipocytes was shown to increase lipid accumulation despite decreasing insulin-stimulated uptake of glucose, a key substrate for de novo lipogenesis. These data imply that alternative lipogenic substrates to glucose fuel continued lipid accumulation during insulin resistance in obesity. Objective We examined whether increased lipid accumulation during insulin resistance in adipocytes may involve alter flux of lipogenic amino acids dependent on SLC7A10 expression and activity, and whether this is reflected by extracellular and circulating concentrations of marker metabolites. Methods In adipocyte cultures with impaired SLC7A10, we performed RNA sequencing and relevant functional assays. By targeted metabolite analyses (GC-MS/MS), flux of all amino acids and selected metabolites were measured in human and mouse adipose cultures. Additionally, SLC7A10 mRNA levels in human subcutaneous adipose tissue (SAT) were correlated to candidate metabolites and adiposity phenotypes in 2 independent cohorts. Results SLC7A10 impairment altered expression of genes related to metabolic processes, including branched-chain amino acid (BCAA) catabolism, lipogenesis, and glyceroneogenesis. In 3T3-L1 adipocytes, SLC7A10 inhibition increased fatty acid uptake and cellular content of glycerol and cholesterol. SLC7A10 impairment in SAT cultures altered uptake of aspartate and glutamate, and increased net uptake of BCAAs, while increasing the net release of the valine catabolite 3- hydroxyisobutyrate (3-HIB). In human cohorts, SLC7A10 mRNA correlated inversely with total fat mass, circulating triacylglycerols, BCAAs, and 3-HIB. Conclusion Reduced SLC7A10 activity strongly affects flux of BCAAs in adipocytes, which may fuel continued lipogenesis during insulin resistance, and be reflected in increased circulating levels of the valine-derived catabolite 3-HIB.Peer reviewe

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