Schistosomes are parasitic flatworms causing one of the most prevalent infectious diseases from which millions of people are currently suffering. These parasites have high fecundity and their eggs are both the transmissible agents and the cause of the infection-associated pathology. Given its biomedical significance, the schistosome germline has been a research focus for more than a century. Nonetheless, molecular mechanisms that regulate its development are only now being understood. In particular, it is unknown what balances the fate of germline stem cells (GSCs) in producing daughter stem cells through mitotic divisions versus gametes through meiosis. Here, we perform single-cell RNA sequencing on juvenile schistosomes and capture GSCs during de novo gonadal development. We identify a genetic program that controls the proliferation and differentiation of GSCs. This program centers around onecut, a homeobox transcription factor, and boule, an mRNA binding protein. Their expressions are mutually dependent in the schistosome male germline, and knocking down either of them causes over-proliferation of GSCs and blocks germ cell differentiation. We further show that this germline-specific regulatory program is conserved in the planarian, schistosome’s free-living evolutionary cousin, but the function of onecut has changed during evolution to support GSC maintenance.
AbstractSchistosomes are parasitic flatworms causing one of the most prevalent infectious diseases from which millions of people are currently suffering. Their germline outputs many fertilized eggs, which are both the transmissible agents and the cause of the infection-associated pathology. Given its significance, the schistosome germline has been a research focus for more than a century. Nonetheless, little is known about the molecular mechanisms that regulate its development. Here, we construct a transcriptomic cell type atlas of juvenile schistosomes. This allows us to capture germline stem cells (GSCs) during de novo gonadal development. We identify a genetic program that balances the fate of GSC between proliferation and differentiation. This program is controlled by onecut, a homeobox transcription factor, and boule, an mRNA binding protein. Evaluating this genetic program in schistosome’s free-living evolutionary cousin, the planarian, shows that this germline-specific regulatory program is conserved but its function has changed significantly during evolution.
Autonomous on-demand services, such as GOGOX (formerly GoGoVan) in Hong Kong, provide a platform for users to request services and for suppliers to meet such demands. In such a platform, the suppliers have autonomy to accept or reject the demands to be dispatched to him/her, so it is challenging to make an online matching between demands and suppliers. Existing methods use round-based approaches to dispatch demands. In these works, the dispatching decision is based on the predicted response patterns of suppliers to demands in the current round, but they all fail to consider the impact of future demands and suppliers on the current dispatching decision. This could lead to taking a suboptimal dispatching decision from the future perspective. To solve this problem, we propose a novel demand dispatching model using deep reinforcement learning. In this model, we make each demand as an agent. The action of each agent, i.e., the dispatching decision of each demand, is determined by a centralized algorithm in a coordinated way. The model works in the following two steps. (1) It learns the demand’s expected value in each spatiotemporal state using historical transition data. (2) Based on the learned values, it conducts a Many-To-Many dispatching using a combinatorial optimization algorithm by considering both immediate rewards and expected values of demands in the next round. In order to get a higher total reward, the demands with a high expected value (short response time) in the future may be delayed to the next round. On the contrary, the demands with a low expected value (long response time) in the future would be dispatched immediately. Through extensive experiments using real-world datasets, we show that the proposed model outperforms the existing models in terms of Cancellation Rate and Average Response Time.
Abstract-A neural network-based scheme for decision directed edgeadaptive Kalman filtering is introduced in this work. A backpropagation neural network makes the decisions about the orientation of the edges based on the information in a window centered at the current pixel being processed. Then based upon the neural network output an appropriate image model which closely matches the local statistics of the image is chosen for the Kalman filter. This prevents the oversmoothing of the edges, which would have otherwise been caused by the standard Kalman filter. Simulation results are presented which show the effectiveness of the proposed scheme.
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