Turbulence in Saturn's Magnetosphere and Forward Modeling of Reduced Spectra from Three-Dimensional Wave Vector Space

In the first part of this thesis, we analyze the statistical properties of magnetic field fluctuations measured by the Cassini spacecraft inside Saturn's magnetosphere. We introduce Saturn's magnetosphere as a new laboratory for plasma turbulence, where the background magnetic field is strong (5\,$nT$\,\leq B_0\leq75\,$nT), fluctuations are weak ($\left=0.07$) and the ion plasma$\beta_i$is smaller than one. We conduct a case study of the second orbit of Cassini and find the statistics of the fluctuations on MHD scales to be characterized by large scale non-stationary processes. The spectral index on these scales varies between$0.8$and$1.7$. At higher frequencies, we observe a steeper spectrum with nearly constant power-law exponent. A spectral break on ion scales separates the two frequency ranges. We carry out a statistical study of the high frequency, kinetic range, fluctuations using the first seven orbits of Cassini. To account for the changing plasma conditions in the magnetosphere, we use power spectral densities transformed to wave number space normalized to ion scales. At radial distances greater than$9\,R_\mathrm{s}$, we observe an average slope of$2.6$on kinetic scales, but closer to Saturn the spectral indices tend to get shallower. Within error limits, these results are in accordance with a critically balanced cascade of kinetic Alfv\'en waves. Probability density functions of the fluctuations have increasingly non-Gaussian tails with increasing frequency. The flatness grows with frequency like a power-law indicating intermittency and formation of coherent structures. We show that the dissipation of magnetic field fluctuations has important implications for Saturn's magnetosphere. We estimate the total energy flux along the turbulent cascade as$140{-}160\,$GW, which is ultimately dissipated as heat. For Saturn's magnetosphere, this turbulent heating mechanism is introduced for the first time. It provides energy on the same order of magnitude as needed to explain the large plasma temperatures measured at Saturn. In an extended data set of$42$orbits, we further analyze the local time and longitude asymmetries. We observe significantly stronger fluctuations in the pre-noon sector of the outer magnetosphere and the midnight sector close to the planet. The spectral energy and the turbulent heating rate are enhanced in a longitude range that coincides with regions of denser plasma. In the second part of this thesis, we present a numerical model to evaluate one-dimensional reduced power spectral densities from arbitrary energy distributions in wave vector space. We assume axisymmetry and approximate the poloidal fluctuations to be passively cascaded by Alfv\'enic fluctuations. The diagonal elements of the spectral tensor can be calculated separately and we are able to analyze the implications of the measurement geometry. Based on a critically balanced turbulent cascade, we construct an energy distribution in three dimensional$\mathbf{k}$-space from MHD to electron scales. We investigate the power spectra in detail and focus on the spectral slope as a function of field-to-flow angle$\theta$and of outer scale. We show for the first time that critically balanced turbulence develops toward a$\theta$-independent cascade with a quasi-perpendicular spectral slope. This occurs at a frequency$f_\mathrm{max}$, which is analytically estimated and is controlled by the outer scale, the critical balance exponent and the field-to-flow angle. We also discuss anisotropic damping terms acting on the$\mathbf{k}$-space distribution of energy and their effects on the PSD. Here, the dominating parameter is the electron temperature, which controls the onset of damping. We calculate synthetic spectra for given measurement geometries and plasma parameters in the solar wind and compare them to recent observations that are interpreted in terms of a critically balanced turbulent cascade. A qualitatively successful reproduction of the observations indicates that the results are indeed in agreement with a critically balanced cascade of (kinetic) Alfv\'en waves. However, we find that the addition of a damping term is substantial to obtain a smooth transition of spectral slopes from small to large field-to-flow angles. In order to corroborate our interpretation of turbulence at Saturn, we model magnetospheric power spectral densities using data presented in the first part of this thesis. We qualitatively reproduce the location of the spectral break and the spectral slopes on MHD and kinetic scales for a selected spectrum discussed in the case study. Further, we model the observed radial distribution of spectral indices and find that damping on scales of the hot electrons might explain the shallower spectral slopes inside$9\,R_\mathrm{s}$. These results indicate that the energy transferred along the turbulent cascade is predominantly deposited into the hot electron population

Reproducible Neural Network Simulations: Statistical Methods for Model Validation on the Level of Network Activity Data

Computational neuroscience relies on simulations of neural network models to bridge the gap between the theory of neural networks and the experimentally observed activity dynamics in the brain. The rigorous validation of simulation results against reference data is thus an indispensable part of any simulation workflow. Moreover, the availability of different simulation environments and levels of model description require also validation of model implementations against each other to evaluate their equivalence. Despite rapid advances in the formalized description of models, data, and analysis workflows, there is no accepted consensus regarding the terminology and practical implementation of validation workflows in the context of neural simulations. This situation prevents the generic, unbiased comparison between published models, which is a key element of enhancing reproducibility of computational research in neuroscience. In this study, we argue for the establishment of standardized statistical test metrics that enable the quantitative validation of network models on the level of the population dynamics. Despite the importance of validating the elementary components of a simulation, such as single cell dynamics, building networks from validated building blocks does not entail the validity of the simulation on the network scale. Therefore, we introduce a corresponding set of validation tests and present an example workflow that practically demonstrates the iterative model validation of a spiking neural network model against its reproduction on the SpiNNaker neuromorphic hardware system. We formally implement the workflow using a generic Python library that we introduce for validation tests on neural network activity data. Together with the companion study (Trensch et al., 2018), the work presents a consistent definition, formalization, and implementation of the verification and validation process for neural network simulations

Cassini observations of aperiodic waves on Saturn’s magnetodisc

The location and motion of Saturn’s equatorial current sheet is the result of an interplay between a quasi-static deformation that varies in radial distance and local time, impulsive perturbations that produce large-scale displacements, quasi-periodic perturbations near the planetary rotation period, and wave-like structures on shorter timescales. This study focuses on the latter, aperiodic wave pulses with periods from 1-30 minutes, that are unrelated to the quasi-periodic ‘flapping’ with a period near that of Saturn’s rotation. Cassini magnetometer data were surveyed for these aperiodic structures and then fitted to a simple model in order to estimate the properties of the waves.The model consists of a modified Harris current sheet model deformed by a Gaussian pulse wave function. This then allows for the extraction of wave parameters and current sheet properties. In particular we show an increase in current sheet scale height with radial distance from Saturn, an increase in the wave amplitude with radial distance, and the resolution of propagation directions using the wave vector fitted by the model. The dominant propagation direction is found to be radially outwards from Saturn

Stability and diffusion of Hg implanted YBa2_{2}Cu3_{3}O6+x_{6+x}

The radioactive isotope $^{197m}$Hg was implanted at 60 keV with low fluences (10$^{13}$ ions/cm$^{2}$ ) into YBa$_{2}$Cu$_{3}$O$_{6+x}$ (YBCO) superconducting thin films at ISOLDE/CERN. We report on the Hg dynamics and stability inside the YBCO lattice as a function of annealing temperature up to 890 K in vacuum or O$_{2}$ atmosphere. The perturbed angular correlation (PAC) technique was used for probing the Hg behavior at the atomic scale, while by monitoring the sample's activity in situ the Hg outdiffusion was studied. We found that Hg ions occupy unique lattice sites and that Hg should be bound to two apical oxygens. Hg diffusion occurs only for annealing temperatures above 653 K, in vacuum. The Hg migration energy was estimated to be EM = 1.58 $\pm$ 0.15 eV

Stability studies of Hg implanted YBa2_{2}Cu3_{3}O6+x_{6+x}

High quality YBa$_{2}$Cu$_{3}$O$_{6+x}$ (YBCO) superconducting thin films were implanted with the radioactive $^{197m}$Hg (T$_{1/2}$ = 24 h) isotope to low fluences of 10$^{13}$ atoms/cm$^{2}$ and 60 keV energy. The lattice location and stability of the implanted Hg were studied combining the Perturbed Angular Correlation (PAC) and Emission Channeling (EC) techniques. We show that Hg can be introduced into the YBCO lattice by ion implantation into unique regular sites. The EC data show that Hg is located on a highly symmetric site on the YBCO lattice, while the PAC data suggests that Hg occupies the Cu(1) site. Annealing studies were performed under vacuum and O$_{2}$ atmosphere and show that Hg starts to diffuse only above 653 K

The Micro-Pillar Shear-Stress Sensor MPS3 for Turbulent Flow

Wall-shear stress results from the relative motion of a fluid over a body surface as a consequence of the no-slip condition of the fluid in the vicinity of the wall. To determine the two-dimensional wall-shear stress distribution is of utter importance in theoretical and applied turbulence research. In this article, characteristics of the Micro-Pillar Shear-Stress Sensor MPS3, which has been shown to offer the potential to measure the two-directional dynamic wall-shear stress distribution in turbulent flows, will be summarized. After a brief general description of the sensor concept, material characteristics, possible sensor-structure related error sources, various sensitivity and distinct sensor performance aspects will be addressed. Especially, pressure-sensitivity related aspects will be discussed. This discussion will serve as ‘design rules’ for possible new fields of applications of the sensor technology

The discovAIR project:a roadmap towards the Human Lung Cell Atlas

The Human Cell Atlas (HCA) consortium aims to establish an atlas of all organs in the healthy human body at single-cell resolution to increase our understanding of basic biological processes that govern development, physiology and anatomy, and to accelerate diagnosis and treatment of disease. The lung biological network of the HCA aims to generate the Human Lung Cell Atlas as a reference for the cellular repertoire, molecular cell states and phenotypes, and the cell-cell interactions that characterise normal lung homeostasis in healthy lung tissue. Such a reference atlas of the healthy human lung will facilitate mapping the changes in the cellular landscape in disease. The discovAIR project is one of six pilot actions for the HCA funded by the European Commission in the context of the H2020 framework program. DiscovAIR aims to establish the first draft of an integrated Human Lung Cell Atlas, combining single-cell transcriptional and epigenetic profiling with spatially resolving techniques on matched tissue samples, as well as including a number of chronic and infectious diseases of the lung. The integrated Lung Cell Atlas will be available as a resource for the wider respiratory community, including basic and translational scientists, clinical medicine, and the private sector, as well as for patients with lung disease and the interested lay public. We anticipate that the Lung Cell Atlas will be the founding stone for a more detailed understanding of the pathogenesis of lung diseases, guiding the design of novel diagnostics and preventive or curative interventions

An integrated cell atlas of the lung in health and disease

Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP1 + profibrotic monocyte-derived macrophages in COVID-19, pulmonary fibrosis and lung carcinoma. Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas. </p

Chronic lung diseases are associated with gene expression programs favoring SARS-CoV-2 entry and severity

Patients with chronic lung disease (CLD) have an increased risk for severe coronavirus disease-19 (COVID-19) and poor outcomes. Here, we analyze the transcriptomes of 611,398 single cells isolated from healthy and CLD lungs to identify molecular characteristics of lung cells that may account for worse COVID-19 outcomes in patients with chronic lung diseases. We observe a similar cellular distribution and relative expression of SARS-CoV-2 entry factors in control and CLD lungs. CLD AT2 cells express higher levels of genes linked directly to the efficiency of viral replication and the innate immune response. Additionally, we identify basal differences in inflammatory gene expression programs that highlight how CLD alters the inflammatory microenvironment encountered upon viral exposure to the peripheral lung. Our study indicates that CLD is accompanied by changes in cell-type-specific gene expression programs that prime the lung epithelium for and influence the innate and adaptive immune responses to SARS-CoV-2 infection