In both academia and the pharmaceutical industry, large-scale assays for drug discovery are expensive and often impractical, particularly for the increasingly important physiologically relevant model systems that require primary cells, organoids, whole organisms, or expensive or rare reagents. We hypothesized that data from a single high-throughput imaging assay can be repurposed to predict the biological activity of compounds in other assays, even those targeting alternate pathways or biological processes. Indeed, quantitative information extracted from a three-channel microscopy-based screen for glucocorticoid receptor translocation was able to predict assay-specific biological activity in two ongoing drug discovery projects. In these projects, repurposing increased hit rates by 50- to 250-fold over that of the initial project assays while increasing the chemical structure diversity of the hits. Our results suggest that data from high-content screens are a rich source of information that can be used to predict and replace customized biological assays.

[1] Massively parallel numerical simulations of magnetic reconnection are presented in this study. Electromagnetic full-particle implicit code iPIC3D is used to study the dynamics and 3-D evolution of reconnection outflows. Such features as Hall magnetic field, inflow and outflow, and diffusion region formation are very similar to 2-D particle-in-cell (PIC) simulations. In addition, it is well known that instabilities develop in the current flow direction or oblique directions. These modes could provide for anomalous resistivity and diffusive drag and can serve as additional proxies for magnetic reconnection. In our work, the unstable evolution of reconnection transient front structures is studied. Reconnection configuration in the absence of guide field is considered, and it is initialized with a localized perturbation aligned in the cross-tail direction. Our study suggests that the instabilities lead to the development of finger-like density structures on ion-electron hybrid scales. These structures are characterized by a rapid increase of the magnetic field, normal to the current sheet (B z ). A small decrease in the magnetic field component parallel to the reconnection X line and the component perpendicular to the current sheet is observed in the region ahead of the front. The instabilities form due to fact that the density gradient inside the front region is opposite to the direction of the acceleration Lorentz force. Such density structures may possibly further develop into larger-scale earthward flux transfer events during magnetotail reconnection. In addition, oscillations mainly in the magnetic and electric fields and the electron density are observed shortly before the arrival of the main front structure which is consistent with recent THEMIS observations.

1] Acquiring quantitative metrics-based knowledge about the performance of various space physics modeling approaches is central for the space weather community. Quantification of the performance helps the users of the modeling products to better understand the capabilities of the models and to choose the approach that best suits their specific needs. Further, metrics-based analyses are important for addressing the differences between various modeling approaches and for measuring and guiding the progress in the field. In this paper, the metrics-based results of the ground magnetic field perturbation part of the Geospace Environment Modeling 2008-2009 Challenge are reported. Predictions made by 14 different models, including an ensemble model, are compared to geomagnetic observatory recordings from 12 different northern hemispheric locations. Five different metrics are used to quantify the model performances for four storm events. It is shown that the ranking of the models is strongly dependent on the type of metric used to evaluate the model performance. None of the models rank near or at the top systematically for all used metrics. Consequently, one cannot pick the absolute "winner": the choice for the best model depends on the characteristics of the signal one is interested in. Model performances vary also from event to event. This is particularly clear for root-mean-square difference and utility metric-based analyses. Further, analyses indicate that for some of the models, increasing the global magnetohydrodynamic model spatial resolution and the inclusion of the ring current dynamics improve the models' capability to generate more realistic ground magnetic field fluctuations.

In this paper the metrics‐based results of the inner magnetospheric magnetic field part of the 2008–2009 GEM Metrics Challenge are reported. The Metrics Challenge asked modelers to submit results for four geomagnetic storm events and five different types of observations that can be modeled by statistical or climatological or physics‐based (e.g., MHD) models of the magnetosphere‐ionosphere system. We present the results of 12 model settings that were run at the Community Coordinated Modeling Center and at the institutions of various modelers for these events. To measure the performance of each of the models against the observations, we use direct comparisons between the strength of the measured magnetic field (B), the sine of the elevation angle Θxz (τ), and the spectral power of fluctuations for both quantities. We find that model rankings vary widely by type of variable and skill score used. None of the models consistently performs best for all events. We find that empirical models perform well for weak storm events, and physics‐based (magnetohydrodynamic) models are better for strong storm events. Within a series of runs of the same model we find that higher resolution may not always improve results unless more physics of the inner magnetosphere, such as the kinetic description of the ring current, is included.

A novel adaptive technique for electromagnetic Particle In Cell (PIC) plasma simulations is presented here. Two main issues are identified in designing adaptive techniques for PIC simulation: first, the choice of the size of the particle shape function in progressively refined grids, with the need to avoid the exertion of self-forces on particles, and, second, the necessity to comply with the strict stability constraints of the explicit PIC algorithm. The adaptive implementation presented responds to these demands with the introduction of a Multi Level Multi Domain (MLMD) system (where a cloud of self-similar domains is fully simulated with both fields and particles) and the use of an Implicit Moment PIC method as baseline algorithm for the adaptive evolution. Information is exchanged between the levels with the projection of the field information from the refined to the coarser levels and the interpolation of the boundary conditions for the refined levels from the coarser level fields. Particles are bound to their level of origin and are prevented from transitioning to coarser levels, but are repopulated at the refined grid boundaries with a splitting technique. The presented algorithm is tested against a series of simulation challenges.