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Nicholas Thomas

Researcher at GE Healthcare

Publications -  30
Citations -  583

Nicholas Thomas is an academic researcher from GE Healthcare. The author has contributed to research in topics: Cell cycle & Cell. The author has an hindex of 11, co-authored 30 publications receiving 548 citations. Previous affiliations of Nicholas Thomas include General Electric.

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Spatio-temporal cell cycle phase analysis using level sets and fast marching methods.

TL;DR: The ability to simultaneously track cell cycle phase and cell motion at the single cell level is demonstrated in a model-based approach to characterize the four phases of the cell cycle G1, S, G2, and M.
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High-Throughput Multi-Parameter Profiling of Electrophysiological Drug Effects in Human Embryonic Stem Cell Derived Cardiomyocytes using Multi-Electrode Arrays

TL;DR: This study is the first to apply multi-parameter phenotypic profiling and clustering techniques commonly used for high-content imaging and microarray data to the analysis of electrophysiology data obtained by multi-electrode array (MEA) analysis of hESC-CM.
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DMSO efficiently down regulates pluripotency genes in human embryonic stem cells during definitive endoderm derivation and increases the proficiency of hepatic differentiation.

TL;DR: A novel approach to rapidly and efficiently down regulate pluripotency genes during initiation of differentiation to DE cells by addition of dimethyl sulfoxide (DMSO) to Activin A-based culture medium is presented and its effects on the downstream differentiation to hepatocyte-like cells are reported.
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High-Content Screening: A Decade of Evolution

TL;DR: High-content screening has become a highly developed approach to obtaining richly descriptive quantitative phenotypic data using automated microscopy and is now widely recognized as providing an efficient and effective approach to large-scale programs investigating cell biology in situ and in context.
Proceedings ArticleDOI

Spatio-temporal cell cycle analysis using 3D level set segmentation of unstained nuclei in line scan confocal fluorescence images

TL;DR: A set of image analysis methods designed to automatically segment nuclei in 2D time-lapse images by segmenting the spatio-temporal volume using level sets is described.