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RISS 인기검색어
- 검색키워드
- 저자 : Charlot, Noeloikeau (검색결과 2 건)
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Automation in Image Cytometry: Continuous HCS and Kinetic Image Cytometry
Charlot, David J University of California, San Diego 2012 해외박사(DDOD)
Image based high content analysis (HCA) and high content screens (HCS) are powerful tools used predominately in the study of cellular and molecular dynamics, function, and structure and for cDNA, RNAi and compound library screens. Analyses and screens based on fully automated image cytometry create a vast wealth of information in a hands free, unbiased manner. Large-scale screens of tens of thousands to millions of compounds and potential clinical diagnostic applications would benefit from increased image acquisition speeds. Section I of this dissertation (Chapters 1--6) discusses the development of an HT-HCS instrument that uses TDI image capture and dynamic autofocus. Current instruments typically scan at peak speeds of 20,000 to 50,000 wells/day (1--4 image(s)/well), whereas many define "high throughput" screening as > 100,000 wells/day. Progress is reported about routine continuous scanning with time-delay-and-integrate (TDI) 3-color fluorescence imaging at ∼70,000 wells/day (20x 0.75 NA Nikon objective, 384-well plate, 8--10 usable images/well depending on wall thickness and edge effects). Image cytometry quality (e.g., precision, accuracy and dynamic range of measurements) is directly linked to the contrast and resolution (detail) available in each image, and is thus dependent on the quality of autofocus. The system was validated using a synthetic assay and compared with the Perkin Elmer Opera using an assay screened by the Sanford-Burnham Center for Chemical Genomics (Chapter 4). Section II of this dissertation discusses the development of an HCS instrument capable of automatically stimulating, monitoring, and analyzing kinetic activity in cells. The Kinetic Image Cytometer or KIC will electrically stimulate (or pace) the cells, record the resulting Ca++ transients from cells in microtiter plates (e.g., with 96 wells), and automatically quantify characteristics such as the duration of the Ca++ waves on a cell-by-cell basis in a fully automated manner on large scale screens. Chapters 1 and 2 are a summation of research to develop the technology using cardiomyoctes as the control model. Chapters 3 and 4 are a summation of research to use the KIC to perform dose response assays of different chemical compounds on human cardiomyocytes from different sources, including hESC---derived cardiomyocytes (hESC---CMs) and hiPSC---derived cardiomyocytes (hiPSC---CMs).
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Applications of Complex Network Dynamics in Ultrafast Electronics
Charlot, Noeloikeau ProQuest Dissertations & Theses The Ohio State Uni 2022 해외박사(DDOD)
The success of modern digital electronics relies on compartmentalizing logical functions into individual gates, and controlling their order of operations via a global clock. In the absence of such a timekeeping mechanism, systems of connected logic gates can quickly become chaotic and unpredictable -- exhibiting analog, asynchronous, autonomous dynamics. Such recurrent circuitry behaves in a manner more consistent with neural networks than digital computers, exchanging and conducting electricity as quickly as its hardware allows. These physics enable new forms of information processing that are faster and more complex than clocked digital circuitry. However, modern electronic design tools often fail to measure or predict the properties of large recurrent networks, and their presence can disrupt other clocked architectures.In this thesis, I study and apply the physics of complex networks of self-interacting logic gates at sub-ns timescales. At a high level, my unique contributions are: 1. I derive a general theory of network dynamics and develop open-source simulation libraries and experimental circuit designs to re-create this work; 2. I invent a best-in-class digital measurement system to experimentally analyze signals at the trillionth-of-a-second (ps) timescale; 3. I introduce a network computing architecture based on chaotic fractal dynamics, creating the first `physically unclonable function' with near-infinite entropy.In practice, I use a digital computer to reconfigure a tabletop electronic device containing millions of logic gates (a field-programmable gate array; FPGA) into a network of Boolean functions (a hybrid Boolean network; HBN). From within the FPGA, I release the HBN from initial conditions and measure the resulting state of the network over time. These data are transferred to an external computer and used to study the system experimentally and via a mathematical model.Existing mathematical theories and FPGA simulation tools produce incorrect results when predicting HBNs, and current FPGA-based measurement tools cannot reliably capture the ultrafast HBN dynamics. Thus I begin by generalizing prior mathematical models of Boolean networks in a way that reproduces extant models as limiting cases. Next I design a ps-scale digital measurement system (Waveform Capture Device; WCD). The WCD is an improvement to the state-of-the-art in FPGA measurement systems, having external application in e.g. medical imaging and particle physics. I validate the model and WCD independently, showing that they reproduce each-other in a self-consistent manner. I use the WCD to fit the model parameters and predict the behavior of simple HBNs on FPGAs.I go on to study chaotic HBN. I find that infinitesimal changes to the model parameters -- as well as uncontrollable manufacturing variations inherent to the FPGAs - cause near-identical HBNs to differ exponentially. The simulations predict that fractal patterns separate infinitesimally distinct networks over time, motivating the use of HBN dynamics as 'digital fingerprints' (Physically Unclonable Functions; PUFs) for hardware security. I conclude by rigorously analyzing the experimental properties of HBN-PUFs on FPGAs across a variety of statistical metrics, ultimately discovering super-exponential entropy scaling -- a significant improvement to the state-of-the-art.
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