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dc.contributor.authorLannin, Timothy Burke
dc.date.accessioned2017-04-04T20:27:58Z
dc.date.available2017-04-07T06:00:31Z
dc.date.issued2017-01-30
dc.identifier.otherLannin_cornellgrad_0058F_10026
dc.identifier.otherhttp://dissertations.umi.com/cornellgrad:10026
dc.identifier.otherbibid: 9906082
dc.identifier.urihttps://hdl.handle.net/1813/47835
dc.description.abstractAdvances in rare cell capture technology have made possible the interrogation of circulating tumor cells (CTCs) captured from patient blood. However, manually locating captured cells in the device bottlenecks data processing by being tedious (hours per sample) and compromises the results by being inconsistent and prone to user bias. In the first aim of my thesis, I employed machine learning algorithms to locate and classify thousands of possible cells in a few minutes rather than a few hours, representing an order of magnitude increase in processing speed performance. Optimal algorithm selection depends on the peculiarities of the individual dataset, indicating the need of a careful comparison and optimization of algorithms for individual image classification tasks. The capture of circulating tumor cells via immuno-affinity may be compromised by reduced antigen expression associated with acquired resistance to chemotherapy, deprivation of growth factors when entering circulation, and the epithelial-to-mesenchymal transition. Dielectrophoresis (DEP), however, could enhance the capture of these rare cells by attracting cells to the antibody-coated surface. In order to reliably use dielectrophoresis, cancer cell crossover frequencies must remain lower than those of white blood cells. For my second aim, I used automated electrorotation to measure the cytoplasmic permittivity, cytoplasmic conductivity, and specific membrane capacitance of pancreatic cancer cells under three treatments: 1) acquired resistance to gemcitabine, 2) serum starvation, and 3) induced epithelial-to-mesenchymal transition. I found that the median computed crossover frequency for cancer cells under all treatments remains significantly below that of blood cells, indicating that DEP is a promising technique for enhancing capture. Algae are a promising feedstock for biofuels, and there is a critical need for a rapid, inexpensive, and label-free measurement of lipid accumulation in algae cells. Measuring the electrical properties of algae has shown promise in the literature for monitoring lipid accumulation because it correlates with a decrease in effective cytoplasmic conductivity. Previous models, however, have assumed a constant cytoplasmic permittivity through the lipid accumulation process, and that assumption must be validated. For my third aim, I used automated electrorotation to measure properties of Chlamydomonas reinhardtii cells undergoing lipid accumulation.
dc.language.isoen_US
dc.subjectImage Processing
dc.subjectMechanical engineering
dc.subjectAlgal Biodiesel
dc.subjectCancer Cells
dc.subjectDielectrophoresis
dc.subjectElectrorotation
dc.subjectmachine learning
dc.titleTWO TOOLS FOR THREE CHARACTERIZATIONS OF CELLS: MACHINE LEARNING FOR AUTOMATED CANCER CELL IDENTIFICATION AND ELECTROROTATION FOR CANCER CELL AND ALGAE CELL ELECTRICAL PROPERTY MEASUREMENT
dc.typedissertation or thesis
thesis.degree.disciplineMechanical Engineering
thesis.degree.grantorCornell University
thesis.degree.levelDoctor of Philosophy
thesis.degree.namePh. D., Mechanical Engineering
dc.contributor.chairKirby, Brian
dc.contributor.committeeMemberSingh, Ankur
dc.contributor.committeeMemberFeigenson, Gerald W
dcterms.licensehttps://hdl.handle.net/1813/59810
dc.identifier.doihttps://doi.org/10.7298/X4Q23X6G


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