Browsing by Subject "Stochastic processes"

Now showing 1 - 3 of 3
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    A bright monomeric green fluorescent protein derived from Branchiostoma lanceolatum
    (Springer Nature, 2013) Shaner, Nathan C.; Lambert, Gerard G.; Chammas, Andrew; Ni, Yuhui; Cranfill, Paula J.; Baird, Michelle A.; Sell, Brittney R.; Allen, John R.; Day, Richard N.; Israelsson, Maria; Davidson, Michael W.; Wang, Jiwu; Cellular and Integrative Physiology, School of Medicine
    We report a monomeric yellow-green fluorescent protein, mNeonGreen, derived from a tetrameric fluorescent protein from the cephalochordate Branchiostoma lanceolatum. mNeonGreen is the brightest monomeric green or yellow fluorescent protein yet described to our knowledge, performs exceptionally well as a fusion tag for traditional imaging as well as stochastic single-molecule superresolution imaging and is an excellent fluorescence resonance energy transfer (FRET) acceptor for the newest cyan fluorescent proteins.
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    A nonparametric Bayesian perspective for machine learning in partially-observed settings
    (2014-07-31) Akova, Ferit; Dundar, Mehmet Murat; Qi, Yuan Alan
    Robustness and generalizability of supervised learning algorithms depend on the quality of the labeled data set in representing the real-life problem. In many real-world domains, however, we may not have full knowledge of the underlying data-generating mechanism, which may even have an evolving nature introducing new classes continually. This constitutes a partially-observed setting, where it would be impractical to obtain a labeled data set exhaustively defined by a fixed set of classes. Traditional supervised learning algorithms, assuming an exhaustive training library, would misclassify a future sample of an unobserved class with probability one, leading to an ill-defined classification problem. Our goal is to address situations where such assumption is violated by a non-exhaustive training library, which is a very realistic yet an overlooked issue in supervised learning. In this dissertation we pursue a new direction for supervised learning by defining self-adjusting models to relax the fixed model assumption imposed on classes and their distributions. We let the model adapt itself to the prospective data by dynamically adding new classes/components as data demand, which in turn gradually make the model more representative of the entire population. In this framework, we first employ suitably chosen nonparametric priors to model class distributions for observed as well as unobserved classes and then, utilize new inference methods to classify samples from observed classes and discover/model novel classes for those from unobserved classes. This thesis presents the initiating steps of an ongoing effort to address one of the most overlooked bottlenecks in supervised learning and indicates the potential for taking new perspectives in some of the most heavily studied areas of machine learning: novelty detection, online class discovery and semi-supervised learning.
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    Persistence as an Optimal Hedging Strategy
    (Elsevier, 2021) Browning, Alexander P.; Sharp, Jesse A.; Mapder, Tarunendu; Baker, Christopher M.; Burrage, Kevin; Simpson, Matthew J.; Medicine, School of Medicine
    Bacteria invest in a slow-growing subpopulation, called persisters, to ensure survival in the face of uncertainty. This hedging strategy is remarkably similar to financial hedging, where diversifying an investment portfolio protects against economic uncertainty. We provide a new, to our knowledge, theoretical foundation for understanding cellular hedging by unifying the study of biological population dynamics and the mathematics of financial risk management through optimal control theory. Motivated by the widely accepted role of volatility in the emergence of persistence, we consider several models of environmental volatility described by continuous-time stochastic processes. This allows us to study an emergent cellular hedging strategy that maximizes the expected per capita growth rate of the population. Analytical and simulation results probe the optimal persister strategy, revealing results that are consistent with experimental observations and suggest new opportunities for experimental investigation and design. Overall, we provide a new, to our knowledge, way of conceptualizing and modeling cellular decision making in volatile environments by explicitly unifying theory from mathematical biology and finance.