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<jats:p>We demonstrate that the chemical-feature model described in our original paper is distinguishable from the nongeneralizable models introduced by Chuang and Keiser. Furthermore, the chemical-feature model significantly outperforms these models in out-of-sample predictions, justifying the use of chemical featurization from which machine learning models can extract meaningful patterns in the dataset, as originally described.</jats:p>

<jats:title>Development of human brain neurons</jats:title> <jats:p> The earliest stages of human brain development are very difficult to monitor, but using induced pluripotent stem cells (iPSCs) can help to elucidate the process. Real <jats:italic>et al.</jats:italic> transplanted neural progenitors derived from human iPSCs into the brains of adult mice. They used intravital imaging to visualize how resulting neurons grew and connected. The human cells produced neurons that integrated and developed synaptic networks with oscillatory activity. Dendritic pruning was observed and involved a process of branch retraction, not degeneration. Cells derived from individuals with Down syndrome, upon transplantation into the mouse brain, produced neurons that grew normally but showed reduced dendritic spine turnover and less network activity. </jats:p> <jats:p> <jats:italic>Science</jats:italic> , this issue p. <jats:related-article xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="doi" related-article-type="in-this-issue" xlink:href="10.1126/science.aau1810">eaau1810</jats:related-article> </jats:p>