There is now abundant evidence of rapid evolution in natural populations, but the genetic mechanisms of these changes remain unclear. One possible route to rapid evolution is through changes in the expression of genes that influence traits under selection. We examined contemporary evolutionary gene expression changes in plant populations responding to environmental fluctuations. We compared genome-wide gene expression, using RNA-seq, in two populations of Brassica rapa collected over four time points between 1997 and 2014, during which precipitation in southern California fluctuated dramatically and phenotypic and genotypic changes occurred. By combining transcriptome profiling with the resurrection approach, we directly examined evolutionary changes in gene expression over time. For both populations, we found a substantial number of differentially expressed genes between generations, indicating rapid evolution in the expression of many genes. Using existing gene annotations, we found that many changes occurred in genes involved in regulating stress responses and flowering time. These appeared related to the fluctuations in precipitation and were potentially adaptive. However, the evolutionary changes in gene expression differed across generations within and between populations, indicating largely independent evolutionary trajectories across populations and over time. Our study provides strong evidence for rapid evolution in gene expression, and indicates that changes in gene expression can be one mechanism of rapid evolutionary responses to selection episodes. This study also illustrates that combining resurrection studies with transcriptomics is a powerful approach for investigating evolutionary changes at the gene regulatory level, and will provide new insights into the genetic basis of contemporary evolution.

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.