Proliferating neural stem cells and intermediate progenitors persist in the ventricular-subventricular zone (V-SVZ) of the adult mammalian brain. This extensive germinal layer in the walls of the lateral ventricles is the site of birth of different types of interneurons destined for the olfactory bulb. The cell cycle dynamics of stem cells (B1 cells), intermediate progenitors (C cells), and neuroblasts (A cells) in the V-SVZ and the number of times these cells divide remain unknown. Using whole mounts of the walls of the lateral ventricles of adult mice and three cell cycle analysis methods using thymidine analogs, we determined the proliferation dynamics of B1, C, and A cells in vivo. Achaete-scute complex homolog (Ascl)1(+) C cells were heterogeneous with a cell cycle length (T(C)) of 18-25 h and a long S phase length (T(S)) of 14-17 h. After C cells, Doublecortin(+) A cells were the second-most common dividing cell type in the V-SVZ and had a T(C) of 18 h and T(S) of 9 h. Human glial fibrillary acidic protein (hGFAP)::GFP(+) B1 cells had a surprisingly short Tc of 17-18 h and a T(S) of 4 h. Progenitor population analysis suggests that following the initial division of B1 cells, C cells divide three times and A cells once, possibly twice. These data provide essential information on the dynamics of adult progenitor cell proliferation in the V-SVZ and how large numbers of new neurons continue to be produced in the adult mammalian brain.

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.