Neuronal differentiation of embryonic stem cells

AbstractNeuronal differentiation from totipotent precursors in vitro, is thought to require two signals: first a biophysical state (cellular aggregation) followed by a biochemical signal (retinoic acid treatment). In investigating the properties of retinoic acid-differentiated embryonic stem cell lines. However, we noted that retinoic acid treatment without prior aggregation, is sufficient to induce expression of the neuronal markers GAP-43 and NF-165. In agreement, immunohistochemistry revealed the presence of GAP-43 positive cells in these embryonic stem cell monolayers after three days of retinoic acid (RA) treatment. Furthermore an NF-165 positive subpopulation of cells was clearly observed after 4–5 days of RA treatment. The expression of these neuronal markers coincided with the appearance of electrically excitable cells, as assayed with whole cell patch clamp recording. We conclude that for neuronal differentiation of totipotent embryonic stem cells in vitro, one biochemical signal, i.e. retinoic acid treatment, is sufficient

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