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using a data-driven landscape model. PNAS 109,19262-19267 Bhadriraju K, et al. (2016) Large-scale time-lapse microscopy of Oct4 expression in human embryonic stem cell colonies. Stem Cell Research (17
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applied to large biopharmaceutical products or platforms, and change the way scientists fundamentally deal with these types of data and biological systems. key words biotechnology; drug products; neutron
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scaled up to handle large numbers of samples in massively parallel, low-cost analysis systems. Before such systems can be realized, the electromagnetic response of biochemical samples must be understood in
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301.975.3507 Description Recent developments in Artificial Intelligence (AI) have allowed machine learning models to solve certain complex problems in natural language processing and other areas at large scales
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development, microfabricated device design and development, measurement of samples with ultrahigh throughput sequencing and microarrays, and bioinformatic/biostatistical data analysis of the large data sets
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of protein therapeutics, because NMR spectra are sensitive to molecular shape and intermolecular interactions as well as chemical structure, and NMR can reproducibly probe this information at atomic resolution
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, pixelated detection system. Taking full advantage of this cutting edge technology will require the development of new methods for collecting, processing, and interpreting the large amounts of data we now have
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large portion of that is due to a lack of understanding of the underlying mechanisms of corrosion, which would provide guidance to mitigation technologies. And this corrosion problem is likely to
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Description Electromagnetic techniques can provide a means for rapidly analyzing or processing biochemical samples in a manner that can be readily scaled up to handle large numbers of samples in massively
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Development of magnetism-based future electronics is fueled by demand for large memory capacity and high data processing rates. New technologies such as hard drives with bit-patterned media and magnetic memory