SOX2 expression levels distinguish between neural progenitor populations of the developing dorsal telencephalon

The HMG-Box transcription factor SOX2 is expressed in neural progenitor populations throughout the developing and adult central nervous system and is necessary to maintain their progenitor identity. However, it is unclear whether SOX2 levels are uniformly expressed across all neural progenitor populations. In the developing dorsal telencephalon, two distinct populations of neural progenitors, radial glia and intermediate progenitor cells, are responsible for generating a majority of excitatory neurons found in the adult neocortex. Here we demonstrate, using both cellular and molecular analyses, that SOX2 is differentially expressed between radial glial and intermediate progenitor populations. Moreover, utilizing a SOX2 mouse line, we show that this differential expression can be used to prospectively isolate distinct, viable populations of radial glia and intermediate cells for analysis. Given the limited repertoire of cell-surface markers currently available for neural progenitor cells, this provides an invaluable tool for prospectively identifying and isolating distinct classes of neural progenitor cells from the central nervous system.► SOX2 is differentially expressed between radial glia and intermediate progenitors. ► Distinct progenitor populations can be isolated based upon SOX2 expression levels. ► SOX2-High cells generate larger neurospheres . ► SOX2-High cells have increased capacity to generate secondary neurospheres

Improving the Validity of Decision Trees as Explanations

In classification and forecasting with tabular data, one often utilizes tree-based models. This can be competitive with deep neural networks on tabular data [cf. Grinsztajn et al., NeurIPS 2022, arXiv:2207.08815] and, under some conditions, explainable. The explainability depends on the depth of the tree and the accuracy in each leaf of the tree. Here, we train a low-depth tree with the objective of minimising the maximum misclassification error across each leaf node, and then ``suspend'' further tree-based models (e.g., trees of unlimited depth) from each leaf of the low-depth tree. The low-depth tree is easily explainable, while the overall statistical performance of the combined low-depth and suspended tree-based models improves upon decision trees of unlimited depth trained using classical methods (e.g., CART) and is comparable to state-of-the-art methods (e.g., well-tuned XGBoost)

“Runx”ing towards Sensory Differentiation

Somatosensory stimuli are encoded by molecularly and anatomically diverse classes of dorsal root ganglia (DRG) neurons. In this issue of Neuron, three papers demonstrate that the Runx transcription factors, Runx1 and Runx3, respectively regulate the molecular identities and spinal terminations of TrkA+ nociceptive neurons and TrkC+ proprioceptive neurons. These findings emphasize the importance of intrinsic genetic programs in generating the diversity of DRG neurons and specifying the circuits into which they incorporate

Cleft Palate in a Mouse Model of SOX2 Haploinsufficiency

Objective—While SEX-determining region Y-Box 2 (SOX2) mutations are typically recognized as yielding ocular and central nervous system abnormalities, they have also been associated with other craniofacial defects. To elucidate the genesis of the latter, Sox2 hypomorphic (Sox2HYP) mice were examined, with particular attention to secondary palatal development. Results—Clefts of the secondary palate were found to be highly penetrant in Sox2HYP mice. The palatal clefting occurred in the absence of mandibular hypoplasia and resulted from delayed or failed shelf elevation. Conclusions—Sox2 hypomorphism can result in clefting of the secondary palate, an effect that appears to be independent of mandibular hypoplasia and is thus expected to result from an abnormality that is inherent to the palatal shelves and/or their progenitor tissues. Further clinical attention relative to SOX2 mutations as a basis for secondary palatal clefts appears warranted

SOX2 Functions to Maintain Neural Progenitor Identity

Neural progenitors of the vertebrate CNS are defined by generic cellular characteristics, including their pseudoepithelial morphology and their ability to divide and differentiate. SOXB1 transcription factors, including the three closely related genes Sox1, Sox2, and Sox3, universally mark neural progenitor and stem cells throughout the vertebrate CNS. We show here that constitutive expression of SOX2 inhibits neuronal differentiation and results in the maintenance of progenitor characteristics. Conversely, inhibition of SOX2 signaling results in the delamination of neural progenitor cells from the ventricular zone and exit from cell cycle, which is associated with a loss of progenitor markers and the onset of early neuronal differentiation markers. The phenotype elicited by inhibition of SOX2 signaling can be rescued by coexpression of SOX1, providing evidence for redundant SOXB1 function in CNS progenitors. Taken together, these data indicate that SOXB1 signaling is both necessary and sufficient to maintain panneural properties of neural progenitor cells

Is ensemble classifier needed for steganalysis in high-dimensional feature spaces?

International audienceThe ensemble classifier, based on Fisher Linear Discriminant base learners, was introduced specifically for steganalysis of digital media, which currently uses high-dimensional feature spaces. Presently it is probably the most used method to design supervised classifier for steganalysis of digital images because of its good detection accuracy and small computational cost. It has been assumed by the community that the classifier implements a non-linear boundary through pooling binary decision of individual classifiers within the ensemble. This paper challenges this assumption by showing that linear classifier obtained by various regularizations of the FLD can perform equally well as the ensemble. Moreover it demonstrates that using state of the art solvers linear classifiers can be trained more efficiently and offer certain potential advantages over the original ensemble leading to much lower computational complexity than the ensemble classifier. All claims are supported experimentally on a wide spectrum of stego schemes operating in both the spatial and JPEG domains with a multitude of rich steganalysis feature sets