Dual Quorum Quenching Capsules: Disrupting two bacterial communication pathways that lead to virulence

Healthcare Associated Infections (HAIs) in the United States, are estimated to cost nearly $10 billion annually. And, while device-related infections have decreased, the 60% attributed to pneumonia, gastrointestinal pathogens and surgical site infections (SSIs) remain prevalent. Furthermore, these are often complicated by antibacterial resistance that ultimately cause 2 million illnesses and 23,000 deaths in the US annually. Antibacterial resistance is an issue increasing in severity as existing antibiotics are losing effectiveness, and fewer new antibiotics are being developed. As a result, new methods of combating bacterial virulence are required. Modulating communications of bacteria can alter phenotype, such as biofilm formation and toxin production. Disrupting these communications provides a means of controlling virulence without directly interacting with the bacteria of interest, a strategy contrary to traditional antibiotics. Inter- and intra-species bacterial communication is commonly called quorum sensing because the communication molecules have been linked to phenotypic changes based on bacterial population dynamics. By disrupting the communication, a method called ‘quorum quenching’, bacterial phenotype can be altered. Virulence of bacteria is both population and species dependent; each species will secrete different toxic molecules, and total population will affect bacterial phenotype9. Here, the kinase LsrK and lactonase SsoPox were combined to simultaneously disrupt two different communication pathways with direct ties to virulence leading to SSIs, gastrointestinal infection and pneumonia. To deliver these enzymes for site-specific virulence prevention, two naturally occurring polymers were used, chitosan and alginate. Chitosan, from crustacean shells, and alginate, from seaweed, are frequently studied due to their biocompatibility, availability, self-assembly and biodegrading properties and have already been verified in vivo for wound-dressing. In this work, a novel functionalized capsule of quorum quenching enzymes and biocompatible polymers was constructed and demonstrated to have dual-quenching capability. This combination of immobilized enzymes has the potential for preventing biofilm formation and reducing bacterial toxicity in a wide variety of medical and non-medical applications.

CAUSAL INFERENCE WITH A CONTINUOUS TREATMENT AND OUTCOME: ALTERNATIVE ESTIMATORS FOR PARAMETRIC DOSE-RESPONSE FUNCTIONS WITH APPLICATIONS.

Causal inference with a continuous treatment is a relatively under-explored problem. In this dissertation, we adopt the potential outcomes framework. Potential outcomes are responses that would be seen for a unit under all possible treatments. In an observational study where the treatment is continuous, the potential outcomes are an uncountably infinite set indexed by treatment dose. We parameterize this unobservable set as a linear combination of a finite number of basis functions whose coefficients vary across units. This leads to new techniques for estimating the population average dose-response function (ADRF). Some techniques require a model for the treatment assignment given covariates, some require a model for predicting the potential outcomes from covariates, and some require both. We develop these techniques using a framework of estimating functions, compare them to existing methods for continuous treatments, and simulate their performance in a population where the ADRF is linear and the models for the treatment and/or outcomes may be misspecified. We also extend the comparisons to a data set of lottery winners in Massachusetts. Next, we describe the methods and functions in the R package causaldrf using data from the National Medical Expenditure Survey (NMES) and Infant Health and Development Program (IHDP) as examples. Additionally, we analyze the National Growth and Health Study (NGHS) data set and deal with the issue of missing data. Lastly, we discuss future research goals and possible extensions.

Tensor Completion for Multidimensional Inverse Problems with Applications to Magnetic Resonance Relaxometry

This thesis deals with tensor completion for the solution of multidimensional inverse problems. We study the problem of reconstructing an approximately low rank tensor from a small number of noisy linear measurements. New recovery guarantees, numerical algorithms, non-uniform sampling strategies, and parameter selection algorithms are developed. We derive a fixed point continuation algorithm for tensor completion and prove its convergence. A restricted isometry property (RIP) based tensor recovery guarantee is proved. Probabilistic recovery guarantees are obtained for sub-Gaussian measurement operators and for measurements obtained by non-uniform sampling from a Parseval tight frame. We show how tensor completion can be used to solve multidimensional inverse problems arising in NMR relaxometry. Algorithms are developed for regularization parameter selection, including accelerated k-fold cross-validation and generalized cross-validation. These methods are validated on experimental and simulated data. We also derive condition number estimates for nonnegative least squares problems. Tensor recovery promises to significantly accelerate N-dimensional NMR relaxometry and related experiments, enabling previously impractical experiments. Our methods could also be applied to other inverse problems arising in machine learning, image processing, signal processing, computer vision, and other fields.