During development, neural progenitors are temporally patterned to sequentially generate a variety of neural types. In Drosophila neural progenitors called neuroblasts, temporal patterning is regulated by cascades of Temporal Transcription Factors (TTFs). However, known TTFs were mostly identified through candidate approaches and may not be complete. In addition, many fundamental questions remain concerning the TTF cascade initiation, progression, and termination. In this work, we use single-cell RNA sequencing of Drosophila medulla neuroblasts of all ages to identify a list of previously unknown TTFs, and experimentally characterize their roles in temporal patterning and neuronal specification. Our study reveals a comprehensive temporal gene network that patterns medulla neuroblasts from start to end. Furthermore, the speed of the cascade progression is regulated by Lola transcription factors expressed in all medulla neuroblasts. Our comprehensive study of the medulla neuroblast temporal cascade illustrates mechanisms that may be conserved in the temporal patterning of neural progenitors.
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http://dx.doi.org/10.1038/s41467-022-28915-3 | DOI Listing |
Sci Rep
December 2024
Computer Science Department, Saarland University, Saarbrücken, Germany.
Estimating the numbers and whereabouts of internally displaced people (IDP) is paramount to providing targeted humanitarian assistance. In conflict settings like the ongoing Russia-Ukraine war, on-the-ground data collection is nevertheless often inadequate to provide accurate and timely information. Satellite imagery may sidestep some of these challenges and enhance our understanding of the IDP dynamics.
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December 2024
Department of Biology, University of Oxford, Mansfield Road, Oxford, OX1 3SZ, UK.
Long-distance migrants must optimise their timing of breeding to capitalise on resources at both breeding and over-wintering sites. In species with protracted breeding seasons, departing earlier on migration might be advantageous, but is constrained by the ongoing breeding attempt. Here we investigated how breeding timing affects migratory strategies in the Manx shearwater (Puffinus puffinus), a trans-hemispheric migratory seabird with large temporal variation in the onset of breeding.
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December 2024
Department of Civil Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
Deep learning models are widely used for traffic forecasting on freeways due to their ability to learn complex temporal and spatial relationships. In particular, graph neural networks, which integrate graph theory into deep learning, have become popular for modeling traffic sensor networks. However, traditional graph convolutional networks (GCNs) face limitations in capturing long-range spatial correlations, which can hinder accurate long-term predictions.
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December 2024
Department of Computing and Information Systems, Sunway University, 47500, Petaling Jaya, Selangor Darul Ehsan, Malaysia.
Urban mobility prediction is crucial for optimizing resource allocation, managing transportation systems, and planning urban development. We propose a novel framework, GeoTemporal LSTM (GT-LSTM), designed to address the intricate spatiotemporal dynamics of urban environments. GT-LSTM integrates temporal dependencies with geographic information through a multi-modal approach that combines attention mechanisms and Recurrent Neural Networks (RNNs).
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December 2024
BAOBAB Unit, NeuroSpin center, CEA, Université Paris-Saclay, Gif-sur-Yvette, France.
Decoding states of consciousness from brain activity is a central challenge in neuroscience. Dynamic functional connectivity (dFC) allows the study of short-term temporal changes in functional connectivity (FC) between distributed brain areas. By clustering dFC matrices from resting-state fMRI, we previously described "brain patterns" that underlie different functional configurations of the brain at rest.
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