The balance between self-renewal and differentiation of neural progenitor cells is an absolute requirement for the correct formation of the nervous system. Much is known about both the pathways involved in progenitor cell self-renewal, such as Notch signaling, and the expression of genes that initiate progenitor differentiation. However, whether these fundamental processes are mechanistically linked, and specifically how repression of progenitor self-renewal pathways occurs, is poorly understood. Nuclear factor I A (Nfia), a gene known to regulate spinal cord and neocortical development, has recently been implicated as acting downstream of Notch to initiate the expression of astrocyte-specific genes within the cortex. Here we demonstrate that, in addition to activating the expression of astrocyte-specific genes, Nfia also downregulates the activity of the Notch signaling pathway via repression of the key Notch effector Hes1. These data provide a significant conceptual advance in our understanding of neural progenitor differentiation, revealing that a single transcription factor can control both the activation of differentiation genes and the repression of the self-renewal genes, thereby acting as a pivotal regulator of the balance between progenitor and differentiated cell states.
Signs of cerebellar dysfunction combined with signs suggestive of shunt malfunction developed in three children with obstructive hydrocephalus. Shunt function was normal. In all cases, the cerebellar signs persisted and computerized tomography scans revealed enlargement of the fourth ventricle. Shunting of the fourth ventricle returned the patients to normal function.
We propose a simple and complete method to determine the needed rights for roles in a system. We make use of the concept of use cases, commonly used to determine requirements in object-oriented system development. We extend use cases with rights specifications and we determine all of a role's rights from the collection of all use cases for the system. This method is in strict accordance with the least privilege principle.
This paper presents a composite multi-layer classifier system for predicting the subcellular localization of proteins based on their amino acid sequence. The work is an extension of our previous predictor PProwler v1.1 which is itself built upon the series of predictors SignalP and TargetP. In this study we outline experiments conducted to improve the classifier design. The major improvement came from using Support Vector machines as a "smart gate" sorting the outputs of several different targeting peptide detection networks. Our final model (PProwler v1.2) gives MCC values of 0.873 for non-plant and 0.849 for plant proteins. The model improves upon the accuracy of our previous subcellular localization predictor (PProwler v1.1) by 2% for plant data (which represents 7.5% improvement upon TargetP).
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