Human Cortex Spheroid with a Functional Blood Brain Barrier for High-Throughput Neurotoxicity Screening and Disease Modeling.

The integral selectivity characteristic of the blood brain barrier (BBB) limits therapeutic options for many neurologic diseases and disorders. Currently, very little is known about the mechanisms that govern the dynamic nature of the BBB. Recent reports have focused on the development and application of human brain organoids developed from neuro-progenitor cells. While these models provide an excellent platform to study the effects of disease and genetic aberrances on brain development, they may not model the microvasculature and BBB of the adult human cortex. To date, most in vitro BBB models utilize endothelial cells, pericytes and astrocytes. We report a 3D spheroid model of the BBB comprising all major cell types, including neurons, microglia and oligodendrocytes, to recapitulate more closely normal human brain tissue. Spheroids show expression of tight junctions, adherens junctions, adherens junction-associated proteins and cell specific markers. Functional assessment using MPTP, MPP+ and mercury chloride indicate charge selectivity through the barrier. Junctional protein distribution was altered under hypoxic conditions. Our spheroid model may have potential applications in drug discovery, disease modeling, neurotoxicity and cytotoxicity testing.

Kinetic scale turbulence and dissipation in the solar wind: key observational results and future outlook.

Turbulence is ubiquitous in the solar wind. Turbulence causes kinetic and magnetic energy to cascade to small scales where they are eventually dissipated, adding heat to the plasma. The details of how this occurs are not well understood. This article reviews the evidence for turbulent dissipation and examines various diagnostics for identifying solar wind regions where dissipation is occurring. We also discuss how future missions will further enhance our understanding of the importance of turbulence to solar wind dynamics.

Permutation entropy and statistical complexity analysis of turbulence in laboratory plasmas and the solar wind.

The Bandt-Pompe permutation entropy and the Jensen-Shannon statistical complexity are used to analyze fluctuating time series of three different turbulent plasmas: the magnetohydrodynamic (MHD) turbulence in the plasma wind tunnel of the Swarthmore Spheromak Experiment (SSX), drift-wave turbulence of ion saturation current fluctuations in the edge of the Large Plasma Device (LAPD), and fully developed turbulent magnetic fluctuations of the solar wind taken from the Wind spacecraft. The entropy and complexity values are presented as coordinates on the CH plane for comparison among the different plasma environments and other fluctuation models. The solar wind is found to have the highest permutation entropy and lowest statistical complexity of the three data sets analyzed. Both laboratory data sets have larger values of statistical complexity, suggesting that these systems have fewer degrees of freedom in their fluctuations, with SSX magnetic fluctuations having slightly less complexity than the LAPD edge I(sat). The CH plane coordinates are compared to the shape and distribution of a spectral decomposition of the wave forms. These results suggest that fully developed turbulence (solar wind) occupies the lower-right region of the CH plane, and that other plasma systems considered to be turbulent have less permutation entropy and more statistical complexity. This paper presents use of this statistical analysis tool on solar wind plasma, as well as on an MHD turbulent experimental plasma.

Ensemble downscaling in coupled solar wind-magnetosphere modeling for space weather forecasting.

Advanced forecasting of space weather requires simulation of the whole Sun-to-Earth system, which necessitates driving magnetospheric models with the outputs from solar wind models. This presents a fundamental difficulty, as the magnetosphere is sensitive to both large-scale solar wind structures, which can be captured by solar wind models, and small-scale solar wind "noise," which is far below typical solar wind model resolution and results primarily from stochastic processes. Following similar approaches in terrestrial climate modeling, we propose statistical "downscaling" of solar wind model results prior to their use as input to a magnetospheric model. As magnetospheric response can be highly nonlinear, this is preferable to downscaling the results of magnetospheric modeling. To demonstrate the benefit of this approach, we first approximate solar wind model output by smoothing solar wind observations with an 8 h filter, then add small-scale structure back in through the addition of random noise with the observed spectral characteristics. Here we use a very simple parameterization of noise based upon the observed probability distribution functions of solar wind parameters, but more sophisticated methods will be developed in the future. An ensemble of results from the simple downscaling scheme are tested using a model-independent method and shown to add value to the magnetospheric forecast, both improving the best estimate and quantifying the uncertainty. We suggest a number of features desirable in an operational solar wind downscaling scheme. KEY POINTS: Solar wind models must be downscaled in order to drive magnetospheric models Ensemble downscaling is more effective than deterministic downscaling The magnetosphere responds nonlinearly to small-scale solar wind fluctuations.

Alignment and scaling of large-scale fluctuations in the solar wind.

We investigate the dependence of solar wind fluctuations measured by the Wind spacecraft on scale and on the degree of alignment between oppositely directed Elsasser fields. This alignment controls the strength of the nonlinear interactions and, therefore, the turbulence. We find that at scales larger than the outer scale of the turbulence the Elsasser fluctuations become on average more antialigned as the outer scale is approached from above. Conditioning structure functions using the alignment angle reveals turbulent scaling of unaligned fluctuations at scales previously believed to lie outside the turbulent cascade in the "1/f range." We argue that the 1/f range contains a mixture of a noninteracting antialigned population of Alfvén waves and magnetic force-free structures plus a subdominant population of unaligned cascading turbulent fluctuations.

Anisotropy of imbalanced Alfvénic turbulence in fast solar wind.

We present the first measurement of the scale-dependent power anisotropy of Elsasser variables in imbalanced fast solar wind turbulence. The dominant Elsasser mode is isotropic at lower spacecraft frequencies but becomes increasingly anisotropic at higher frequencies. The subdominant mode is anisotropic throughout. There are two distinct subranges exhibiting different scalings within what is normally considered the inertial range. The low Alfvén ratio and the different scaling of the Elsasser modes suggests an interpretation of the observed discrepancy between the velocity and magnetic field scalings, the total energy is dominated by the latter. These results do not appear to be fully explained by any of the current theories of incompressible imbalanced MHD turbulence.

Scale-free texture of the fast solar wind.

The higher-order statistics of magnetic field magnitude fluctuations in the fast quiet solar wind are quantified systematically, scale by scale. We find a single global non-Gaussian scale-free behavior from minutes to over 5 h. This spans the signature of an inertial range of magnetohydrodynamic turbulence and a ~1/f range in magnetic field components. This global scaling in field magnitude fluctuations is an intrinsic component of the underlying texture of the solar wind and puts a strong constraint on any theory of solar corona and the heliosphere. Intriguingly, the magnetic field and velocity components show scale-dependent dynamic alignment outside of the inertial range.

Anisotropy of solar wind turbulence between ion and electron scales.

The anisotropy of turbulence in the fast solar wind, between the ion and electron gyroscales, is directly observed using a multispacecraft analysis technique. Second order structure functions are calculated at different angles to the local magnetic field, for magnetic fluctuations both perpendicular and parallel to the mean field. In both components, the structure function value at large angles to the field S{⊥} is greater than at small angles S{∥}: in the perpendicular component S{⊥}/S{∥}=5±1 and in the parallel component S{⊥}/S{∥}>3, implying spatially anisotropic fluctuations, k{⊥}>k{∥}. The spectral index of the perpendicular component is -2.6 at large angles and -3 at small angles, in broad agreement with critically balanced whistler and kinetic Alfvén wave predictions. For the parallel component, however, it is shallower than -1.9, which is considerably less steep than predicted for a kinetic Alfvén wave cascade.

Mutual information as a tool for identifying phase transitions in dynamical complex systems with limited data.

We use a well-known model [T. Vicsek, Phys. Rev. Lett. 15, 1226 (1995)] for flocking, to test mutual information as a tool for detecting order-disorder transitions, in particular when observations of the system are limited. We show that mutual information is a sensitive indicator of the phase transition location in terms of the natural dimensionless parameters of the system which we have identified. When only a few particles are tracked and when only a subset of the positional and velocity components is available, mutual information provides a better measure of the phase transition location than the susceptibility of the data.