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 increasing complexity of the power grid and the continuous integration of volatile renewable energy systems on all aspects of it have made more precise forecasts of both energy supply and demand necessary for the future Smart Grid. Yet, the ever increasing volume of tools and services makes it difficult for users (e.g., energy utility companies) and researchers to obtain even a general sense of what each tool or service offers. The present contribution provides an overview and categorization of several energy‐related forecasting tools and services (specifically for load and volatile renewable power), as well as general information regarding principles of time series, load, and volatile renewable power forecasting. WIREs Data Mining Knowl Discov 2018, 8:e1235. doi: 10.1002/widm.1235 This article is categorized under: Application Areas > Business and Industry Application Areas > Data Mining Software Tools Technologies > Prediction
The Matlab toolbox SciXMiner is designed for the visualization and analysis of time series and features with a special focus to classification problems. It was developed at the Institute of Applied Computer Science of the Karlsruhe Institute of Technology (KIT), a member of the Helmholtz Association of German Research Centres in Germany. The aim was to provide an open platform for the development and improvement of data mining methods and its applications to various medical and technical problems.SciXMiner bases on Matlab (tested for the version 2017a). Many functions do not require additional standard toolboxes but some parts of Signal, Statistics and Wavelet toolboxes are used for special cases. The decision to a Matlab-based solution was made to use the wide mathematical functionality of this package provided by The Mathworks Inc. MATLAB R and Simulink R are registered trademarks of The MathWorks, Inc.SciXMiner is controlled by a graphical user interface (GUI) with menu items and control elements like popup lists, checkboxes and edit elements. This makes it easier to work with SciXMiner for inexperienced users. Furthermore, an automatization and batch standardization of analyzes is possible using macros. The standard Matlab style using the command line is also available.
Dispatchability of renewable energy sources and inflexible loads can be achieved using a volatility-compensating energy storage. However, as the future power outputs of the inflexible devices are uncertain, the computation of a dispatch schedule for such aggregated systems is non-trivial. In the present paper, we propose a novel scheduling method that enforces the feasibility of the dispatch schedule with a pre-determined probability based on a description of the operation of the system as a two-stage decision process. Thereby, a crucial point is the use of probabilistic forecasts, in terms of cumulative density function, of the inflexible energy consumption/production profile. Then, for the sake of comparison, we introduce a second scheduling method based on state-of-the-art scenario optimization, where, unlike the proposed method, the focus is on the minimization of the expected final cost. We draw upon simulations based on real consumption and production data to compare the methods and illustrate our findings.
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