The novel gene asb11:a regulator of the size of the neural progenitor compartment

From a differential display designed to isolate genes that are down-regulated upon differentiation of the central nervous system in Danio rerio embryos, we isolated d-asb11 (ankyrin repeat and suppressor of cytokine signaling box–containing protein 11). Knockdown of the d-Asb11 protein altered the expression of neural precursor genes sox2 and sox3 and resulted in an initial relative increase in proneural cell numbers. This was reflected by neurogenin1 expansion followed by premature neuronal differentiation, as demonstrated by HuC labeling and resulting in reduced size of the definitive neuronal compartment. Forced misexpression of d-asb11 was capable of ectopically inducing sox2 while it diminished or entirely abolished neurogenesis. Overexpression of d-Asb11 in both a pluripotent and a neural-committed progenitor cell line resulted in the stimulus-induced inhibition of terminal neuronal differentiation and enhanced proliferation. We conclude that d-Asb11 is a novel regulator of the neuronal progenitor compartment size by maintaining the neural precursors in the proliferating undifferentiated state possibly through the control of SoxB1 transcription factors

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

d-Asb11 is an essential mediator of canonical Delta-Notch signalling

In canonical Delta-Notch signalling, expression of Delta activates Notch in neighbouring cells, leading to downregulation of Delta in these cells. This process of lateral inhibition results in selection of either Delta-signalling cells or Notch-signalling cells. Here we show that d-Asb11 is an important mediator of this lateral inhibition. In zebrafish embryos, morpholino oligonucleotide (MO)-mediated knockdown of d-Asb11 caused repression of specific Delta-Notch elements and their transcriptional targets, whereas these were induced when d-Asb11 was misexpressed. d-Asb11 also activated legitimate Notch reporters cell-non-autonomously in vitro and in vivo when co-expressed with a Notch reporter. However, it repressed Notch reporters when expressed in Delta-expressing cells. Consistent with these results, d-Asb11 was able to specifically ubiquitylate and degrade DeltaA both in vitro and in vivo. We conclude that d-Asb11 is a component in the regulation of Delta-Notch signalling, important in fine-tuning the lateral inhibition gradients between DeltaA and Notch through a cell non-autonomous mechanism