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Single Particle Tracking (SPT) plays an important role in the study of physical and dynamic properties of biomolecules moving in their native environment. To date, many algorithms have been developed for localization and parameter estimation in SPT. Though the performance of these methods is good when the signal level is high and the motion model simple, they begin to fail as the signal level decreases or model complexity increases. In addition, the inputs to the SPT algorithms are sequences of images that are cropped from a large data set and that focus on a single particle. This motivates us to seek machine learning tools to deal with that initial step of extracting data from larger images containing multiple particles. This thesis makes contributions to both data extraction question and to the problem of state and parameter estimation. First, we build upon the Expectation Maximization (EM) algorithm to create a generic framework for joint localization refinement and parameter estimation in SPT. Under the EM-based scheme, two representative methods are considered for generating the filtered and smoothed distributions needed by EM: Sequential Monte Carlo - Expectation Maximization (SMC-EM), and Unscented - Expectation Maximization (U-EM). The selection of filtering and smoothing algorithms is very flexible so long as they provide the necessary distributions for EM. The versatility and reliability of EM based framework have been validated via data-intensive modeling and simulation where we considered a variety of influential factors, such as a wide range of {\color{red}Signal-to-background ratios (SBRs)}, diffusion speeds, motion blur, camera types, image length, etc. Meanwhile, under the EM-based scheme, we make an effort to improve the overall computational efficiency by simplifying the mathematical expression of models, replacing filtering/smoothing algorithms with more efficient ones {\color{purple} (trading some accuracy for reduced computation time)}, and using parallel computation and other computing techniques. In terms of localization refinement and parameter estimation in SPT, we also conduct an overall quantitative comparison among EM based methods and standard two-step methods. Regarding the U-EM, we conduct transformation methods to make it adapted to the nonlinearities and complexities of measurement model. We also extended the application of U-EM to more complicated SPT scenarios, including time-varying parameters and additional observation models that are relevant to the biophysical setting. The second area of contribution is in the particle detection and extraction problem to create data to feed into the EM-based approaches. Here we build Particle Identification Networks (PINs) covering three different network architectures. The first, \PINCNN{}, is based on a standard Convolutional Neural Network (CNN) structure that has previously been successfully applied in particle detection and localization. The second, \PINRES, uses a Residual Neural Network (ResNet) architecture that is significantly deeper than the CNN while the third, \PINFPN{}, is based on a more advanced Feature Pyramid Network (FPN) that can take advantage of multi-scale information in an image. All networks are trained using the same collection of simulated data created with a range of SBRs and fluorescence emitter densities, as well as with three different Point Spread Functions (PSFs): a standard Born-Wolf model, a model for astigmatic imaging to allow localization in three dimensions, and a model of the Double-Helix engineered PSF. All PINs are evaluated and compared through data-intensive simulation and experiments under a variety of settings. In the final contribution, we link all above together to create an algorithm that takes in raw camera data and produces trajectories and parameter estimates for multiple particles in an image sequence.

With a focus on the psychological process of identification with media characters, this thesis builds upon existing research about the various representations of familial structures in fictional television and their effect on audience members. Using survey data to inquire about how modern television’s evolving definitions of family could impact viewer responses to accessible programming allows for further exploration of the role that the familial aspects and nuances which are portrayed on television may play in the way that viewers experience identification with these characters. After performing a quantitative and qualitative analysis of survey responses, given modest results it can be concluded that similarity between the viewer’s family and the family of an on-screen character is a predictor of identification between the viewer and that character. We gain, through this research, a deeper understanding of trends in how participants experience identification with fictional families and individuals. Furthermore, we can better understand how audiences could be influenced by seeing (or not seeing) families that resemble one’s own in entertainment media. / Media & Communication

<p> Until a little over a decade ago, the regulatory roles of small RNAs (sRNAs) in prokaryotes were largely undetected. Since then, there has been an explosion in the discovery of novel sRNA sequences and we have begun to understand their functions and mechanisms of regulation. The identification and characterization of sRNAs from different organisms have largely been achieved through computational and experimental approaches that focus on sequence elements in intergenic regions. Based on these previously established techniques, we have developed and applied a new bioinformatics approach to search for highly conserved sequences in unannotated intergenic regions from several bacterial genomes, which may contain new sRNA sequences. Through this search, we have identified seven candidate sequences that are conserved at the primary sequence level, and some of the secondary structure motifs are also conserved among multiple bacteria genomes. When we examined those seven candidates experimentally, it was found that when the expression of one mutated candidate (rUIG0803 _ 4D) was induced at the RNA level, minor morphological changes and a delayed lethal phenotype was elicited. The expression of the RNA also may result in the altered expression of kanamycin kinase and glycerol kinase, as indicated by the mass spectrometry data. Experimental characterizations of eight previously identified sRNAs from literature with functions unknown have also been performed but no apparent phenotypic phenomenon was observed in this project, which indicated that all or some of those 8 sRNAs might not play any regulatory roles in cells, or their roles need to be characterized through other genetic screens. To further search for RNA sequences with regulatory functions, we created a library of random DNA transcript using the Lambda Phage genomic DNA. Preliminary screening efforts show that three of the 192 clones screened could trigger reduced cell growth when their RNA was overexpressed. This study marks the first use of a bioinformatics approach that uses primary sequence and secondary structure information to search for sRNAs in the unannotated intergenic region. Moreover it also marks the first time that the effects of introducing random lambda phage RNA in an E. coli host. </p> / Thesis / Master of Science (MSc)

In this thesis I propose methods and strategies for the design of advanced model predictive control designs. The contributions are in the areas of data-driven model based MPC, model monitoring and explicit incorporation of closed-loop response considerations in the MPC, while handling issues such as plant-model mismatch, constraints and uncertainty. In the initial phase of this research, I address the problem of handling plant-model mismatch by designing a subspace identification based MPC framework that includes model monitoring and closed-loop identification components. In contrast to performance monitoring based approaches, the validity of the underlying model is monitored by proposing two indexes that compare model predictions with measured past output. In the event that the model monitoring threshold is breached, a new model is identified using an adapted closed-loop subspace identification method. To retain the knowledge of the nominal system dynamics, the proposed approach uses the past training data and current input, output and set-point as the training data for re-identification. A model validity mechanism then checks if the new model predictions are better than the existing model, and if they are, then the new model is utilized within the MPC. Next, the proposed MPC with re-identification method is extended to batch processes. To this end, I first utilize a subspace-based model identification approach for batch processes to be used in model predictive control. A model performance index is developed for batch process, then in the case of poor prediction, re-identification is triggered to identify a new model. In order to emphasize on the recent batch data, the identification is developed in order to increase the contribution of the current data. In another direction, the stability of data driven predictive control is addressed. To this end, first, a data-driven Lyapunov-based MPC is designed, and shown to be capable of stabilizing a system at an unstable equilibrium point. The data driven Lyapunov-based MPC utilizes a linear time invariant (LTI) model cognizant of the fact that the training data, owing to the unstable nature of the equilibrium point, has to be obtained from closed-loop operation or experiments. Simulation results are first presented demonstrating closed-loop stability under the proposed data-driven Lyapunov-based MPC. The underlying data-driven model is then utilized as the basis to design an economic MPC. Finally, I address the problem of control of nonlinear systems to deliver a prescribed closed-loop behavior. In particular, the framework allows for the practitioner to first specify the nature and specifics of the desired closed-loop behavior (e.g., first order with smallest time constant, second order with no more than a certain percentage overshoot, etc.). An optimization based formulation then computes the control action to deliver the best attainable closed loop behavior. To decouple the problems of determining the best attainable behavior and tracking it as closely as possible, the optimization problem is posed and solved in two tiers. In the first tier, the focus is on determining the best closed-loop behavior attainable, subject to stability and tracking constraints. In the second tier, the inputs are tweaked to possibly improve the tracking of the optimal output trajectories given by the first tier. The effectiveness of all of the proposed methods are illustrated through simulations on nonlinear systems. / Dissertation / Doctor of Philosophy (PhD)