A5075754914- Model Reduction and Neural Networks
- Fluid Dynamics and Turbulent Flows
- Erythrocyte Function and Pathophysiology
- Erythropoietin and Anemia Treatment
- Computer Graphics and Visualization Techniques
- Single-cell and spatial transcriptomics
- Meteorological Phenomena and Simulations
- Magnetic Properties and Applications
Universität Hamburg
2020-2023
Hamburg University of Technology
2023
University Medical Center Hamburg-Eppendorf
2020
Abstract Erythropoietin (EPO), named after its role in hematopoiesis, is also expressed mammalian brain. In clinical settings, recombinant EPO treatment has revealed a remarkable improvement of cognition, but underlying mechanisms have remained obscure. Here, we show with novel line reporter mice that cognitive challenge induces local/endogenous hypoxia hippocampal pyramidal neurons, hence enhancing expression and receptor (EPOR). High-dose administration, amplifying auto/paracrine EPO/EPOR...
Iterative parallel-in-time algorithms like Parareal can extend scaling beyond the saturation of purely spatial parallelization when solving initial value problems. However, they require user to build coarse models handle inevitably serial transport information in time.This is a time consuming and difficult process since there still only limited theoretical insight into what constitutes good efficient model. Novel approaches from machine learning solve differential equations could provide...
Parallel-in-time algorithms provide an additional layer of concurrency for the numerical integration models based on time-dependent differential equations. Methods like Parareal, which parallelize across multiple time steps, rely a computationally cheap and coarse integrator to propagate information forward in time, while parallelizable expensive fine propagator provides accuracy. Typically, method is using lower resolution, reduced order or simplified model. Our paper proposes use...