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A review is given on the highlights of a scatter-shot approach of developing machine-learning methods and artificial neural networks based fast predictors for the application to fusion exhaust. The aim is to enable and facilitate optimized and improved modeling allowing more flexible integration of physics models in the light of extrapolations towards future fusion devices. The project encompasses various research objectives: (a) developments of surrogate model predictors for power & particle exhaust in fusion power plants; (b) assessments of surrogate models for time-dependent phenomena in the plasma-edge; (c) feasibility studies of micro–macro model discovery for plasma-facing components surface morphology & durability; and (d) enhancements of pedestal models & databases through interpolators and generators exploiting uncertainty quantification. Presented results demonstrate useful applications for machine-learning and artificial intelligence in fusion exhaust modeling schemes, enabling an unprecedented combination of both fast and accurate simulation.

The representation of the natural-density, heterogeneous connectivity of neuronal network models at relevant spatial scales remains a challenge for Computational Neuroscience and Neuromorphic Computing. In particular, the memory demands imposed by the vast number of synapses in brain-scale network simulations constitute a major obstacle. Limiting the number resolution of synaptic weights appears to be a natural strategy to reduce memory and compute load. In this study, we investigate the effects of a limited synaptic-weight resolution on the dynamics of recurrent spiking neuronal networks resembling local cortical circuits and develop strategies for minimizing deviations from the dynamics of networks with high-resolution synaptic weights. We mimic the effect of a limited synaptic weight resolution by replacing normally distributed synaptic weights with weights drawn from a discrete distribution, and compare the resulting statistics characterizing firing rates, spike-train irregularity, and correlation coefficients with the reference solution. We show that a naive discretization of synaptic weights generally leads to a distortion of the spike-train statistics. If the weights are discretized such that the mean and the variance of the total synaptic input currents are preserved, the firing statistics remain unaffected for the types of networks considered in this study. For networks with sufficiently heterogeneous in-degrees, the firing statistics can be preserved even if all synaptic weights are replaced by the mean of the weight distribution. We conclude that even for simple networks with non-plastic neurons and synapses, a discretization of synaptic weights can lead to substantial deviations in the firing statistics unless the discretization is performed with care and guided by a rigorous validation process. For the network model used in this study, the synaptic weights can be replaced by low-resolution weights without affecting its macroscopic dynamical characteristics, thereby saving substantial amounts of memory., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Dasbach, Tetzlaff, Diesmann and Senk.)

We assessed the nature and frequency of genome alterations in Staphylococcus aureus during chronic lung infection in patients with cystic fibrosis (CF) and during colonization of the nares in healthy individuals. Only individuals harboring the same S. aureus clone on consecutive samplings were included in the present study. Clone definition was based on pulsed-field gel electrophoresis (PFGE) analysis. Minor fragment variations in consecutive clones were interpreted as genome alterations. The frequency of genome alterations was significantly higher in S. aureus derived from patients with CF (mean time, 1.03 years) than in isolates derived from healthy individuals (mean time, 13.4 years). In total, 19 S. aureus strain pairs showing genome alterations were available for molecular analysis to clarify the nature of recombinational events in the host environment. In 8 cases, genome alteration could be linked to phage mobilization. Phage conversion of beta-toxin production was evident in 7 pairs. In 1 strain pair, changes in the PFGE pattern were accompanied by deletion of a phage similar to ETA. Obviously, phage mobilization plays an important role in vivo. During long-term lung infection in patients with CF, the specific host response and/or the regular exposure to antibiotics exercises strong selective pressure on the pathogen. Genome plasticity may facilitate the adaptation to various host conditions.