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• W3046128520 abstract "Abstract We demonstrate a statistical modeling technique to recognize T cell responses to different external environmental conditions using membrane distributions of T cell receptors. We transformed fluorescence images of T cell receptors from each T cell into estimated model parameters of a partial differential equation. The model parameters enabled the construction of an accurate classification model using linear discrimination techniques. We further demonstrated that the technique successfully differentiated immobilized T cells on non-activating and activating surfaces. Compared to machine learning techniques, our statistical technique relies upon robust image-derived statistics and achieves effective classification with a limited sample size and a minimal computational footprint. The technique provides an effective strategy to quantitatively characterize the global distribution of membrane receptors under various physiological and pathological conditions." @default.
• W3046128520 created "2020-08-03" @default.
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• W3046128520 date "2020-07-29" @default.
• W3046128520 modified "2023-10-18" @default.
• W3046128520 title "Discerning cellular response using statistical discrimination of fluorescence images of membrane receptors" @default.
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• W3046128520 doi "https://doi.org/10.1101/2020.07.28.225144" @default.
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