Phenotypic cell-based high-throughput screenings play a central role in drug discovery and toxicology. The main tendency in cell screenings is the increase of the throughput and decrease of reaction volume in order to accelerate the experiments, reduce the costs, and enable screenings of rare cells. Conventionally, cell-based assays are performed in microtiter plates, which exist in 96- to 1536-wells formats and cannot be further miniaturized. In addition, performing screenings of suspension cells is associated with risk of losing cell content during the staining procedures and incompatibility with high-content microscopy. Here, we evaluate the Droplet-Microarray screening platform for culturing, screening, and imaging of suspension cells. We demonstrate pipetting-free cell seeding and proliferation of cells in individual droplets of 3–80 nL in volume. We developed a methodology to perform parallel treatment, staining, and fixation of suspension cells in individual droplets. Automated imaging of live suspension cells directly in the droplets combined with algorithms for pattern recognition for image analysis is demonstrated. We evaluated the developed methodology by performing a dose–response study with antineoplastic drugs. We believe that the DMA screening platform carries great potential to be adopted for broad spectrum of screenings of suspension cells.

The inherent storage of plug-in electric vehicles is likely to foster the integration of intermittent generation from renewable energy sources into existing power systems. To the end of achieving dispatchability of a system composed of plug-in electric vehicles and intermittent generation, we propose a three-stage scheme. The main difficulties in dispatching such a system are the uncertainties inherent to intermittent generation and the time-varying aggregation of vehicles. We propose to address the former by means of probabilistic forecasts, while we approach the latter with separate stage-specific models. Specifically, we first compute a dispatch schedule, using probabilistic forecasts together with an aggregated dynamic model of the system. The power output of the single devices are set subsequently using deterministic forecasts and device-specific models. We draw upon a simulation study based on real data of generation and vehicle traffic to validate our findings.