Deconvolving molecular signatures of interactions between microbial colonies

Motivation: The interactions between microbial colonies through chemical signaling are not well understood. A microbial colony can use different molecules to inhibit or accelerate the growth of other colonies. A better understanding of the molecules involved in these interactions could lead to advancements in health and medicine. Imaging mass spectrometry (IMS) applied to co-cultured microbial communities aims to capture the spatial characteristics of the colonies’ molecular fingerprints. These data are high-dimensional and require computational analysis methods to interpret

ON LEARNING COMPOSABLE AND DECOMPOSABLE GENERATIVE MODELS USING PRIOR INFORMATION

Within the field of machining learning, supervised learning has gained much success recently, and the research focus moves towards unsupervised learning. A generative model is a powerful way of unsupervised learning that models data distribution. Deep generative models like generative adversarial networks (GANs), can generate high-quality samples for various applications. However, these generative models are not easy to understand. While it is easy to generate samples from these models, the breadth of the samples that can be generated is difficult to ascertain. Further, most existing models are trained from scratch and do not take advantage of the compositional nature of the data. To address these deficiencies, I propose a composition and decomposition framework for generative models. This framework includes three types of components: part generators, composition operation, and decomposition operation. In the framework, a generative model could have multiple part generators that generate different parts of a sample independently. What a part generator should generate is explicitly defined by users. This explicit ”division of responsibility” provides more modularity to the whole system. Similar to software design, this modular modeling makes each module (part generators) more reusable and allows users to build increasingly complex generative models from simpler ones. The composition operation composes the parts from the part generators into a whole sample, whereas the decomposition operation is an inversed operation of composition. On the other hand, given the composed data, components of the framework are not necessarily identifiable. Inspired by other signal decomposition methods, we incorporate prior information to the model to solve this problem. We show that we can identify all of the components by incorporating prior information about one or more of the components. Furthermore, we show both theoretically and experimentally how much prior information is needed to identify the components of the model. Concerning the applications of this framework, we apply the framework to sparse dictionary learning (SDL) and offer our dictionary learning method, MOLDL. With MOLDL, we can easily include prior information about part generators; thus, we learn a generative model that results in a better signal decomposition operation. The experiments show our method decomposes ion mass signals more accurately than other signal decomposition methods. Further, we apply the framework to generative adversarial networks (GANs). Our composition/decomposition GAN learns the foreground part and background part generators that are responsible for different parts of the data. The resulting generators are easier to control and understand. Also, we show both theoretically and experimentally how much prior information is needed to identify different components of the framework. Precisely, we show that we can learn a reasonable part generator given only the composed data and composition operation. Moreover, we show the composable generators has better performance than their non-composable generative counterparts. Lastly, we propose two use cases that show transfer learning is feasible under this framework.Doctor of Philosoph

11th International Coral Reef Symposium Abstracts

https://nsuworks.nova.edu/occ_icrs/1001/thumbnail.jp

Size-related Isotopic Heterogeneity in Lipids from the Marine Water Column

Microbes, including Bacteria, are globally important mediators of elemental transformations in the marine water column, but not until recently has their biomass been suggested to contribute significantly to carbon export flux. Here I characterize lipid and carbon isotopic signatures in marine particulate organic matter (POM) explicitly at microbial size scales, and I quantitatively explore how these signatures are transferred down the water column. In the North Pacific Subtropical Gyre (NPSG) an isotopically-enriched pool of submicron POM appears to dominate export to mesopelagic depths, supporting recent observations that bacterioplankton communities contribute to export flux in proportion to their biological abundance. In the Eastern Tropical North Pacific (ETNP) complex pathways emerge for the flux of POM to the deep ocean. I use the largest data set to date for natural $^{13}C$ signatures of individual water column lipids to reveal that submicron and larger-size suspended POM size classes are isotopically distinct. Results point to de novo production of lipids above and within the oxygen minimum zone. I develop quantitative models to deconvolve the signatures of sinking and in situ sources of these lipids. Results converge on a best-fit model for downward flux in the ETNP that includes both surface-derived and sub-photic zone lipids. Overall results from the modern ocean suggest that approximately half of total suspended POM is submicron in size, much of it is bacterial in origin, and despite the small size of this material, it participates dynamically in water column export flux. These results also suggest some revised interpretations of organic matter signatures in the geologic record. I formulate a quantitative model of marine microbial production and degradation, and reproduce "inverse" isotopic signatures found in lipids and organic matter preserved in Proterozoic sedimentary rocks. Results suggest that the disappearance of this inverse $^{13}C$ pattern was a consequence of the shift from Bacteria to Eukarya as dominant producers of marine autotrophic biomass. Together, results of this thesis reveal that heterogeneity in the isotopic signatures of marine suspended POM is associated with particle size, and by extension, must be a function of the composition of the total planktonic community.Earth and Planetary Science

Live Cell Interferometry: A Novel Quantitative Phase Imaging Technique for Rapid Characterizations of Tumor Heterogeneity, Drug Resistance, Cell Fates and Biophysical Properties

Cell mass is an important biophysical property that provides a crucial link between external physical measurements and internal cellular processes such as cell cycle progression, division, differentiation and cell death. Advancements in quantitative phase imaging (QPI) techniques have enabled many in-depth studies of cell mass in the context of basic and translational research. This thesis describes the development and implementation of live cell interferometry (LCI) as a novel QPI technique that is high speed, high throughput, precise and label-free. It was validated as a powerful tool in dissecting tumor heterogeneity and drug resistance in patient derived melanoma cell lines. LCI analysis of cell fate in response to mitotic inhibitors provided valuable insights in cancer drug development and dose selection. Furthermore, it was utilized to characterize many other biophysical responses in fundamental research such as cardiomyocyte hypertrophy in cardiac wound healing responses. These studies showcased the unique capabilities and advantages of LCI in applications from bench to bedside

Predicting Responses of Geo-ecological Carbonate Reef Systems to Climate Change: A Conceptual Model and Review

[Chapter Abstract] 230Coral reefs provide critical ecological and geomorphic (e.g. sediment production for reef-fronted shoreline maintenance) services, which interact in complex and dynamic ways. These services are under threat from climate change, requiring dynamic modelling approaches that predict how reef systems will respond to different future climate scenarios. Carbonate budgets, which estimate net reef calcium carbonate production, provide a comprehensive ‘snap-shot’ assessment of reef accretionary potential and reef stability. These budgets, however, were not intended to account for the full suite of processes that maintain coral reef services or to provide predictive capacity on longer timescales (decadal to centennial). To respond to the dual challenges of enhancing carbonate budget assessments and advancing their predictive capacity, we applied a novel model elicitation and review method to create a qualitative geo-ecological carbonate reef system model that links geomorphic, ecological and physical processes. Our approach conceptualizes relationships between net carbonate production, sediment transport and landform stability, and rates knowledge confidence to reveal major knowledge gaps and critical future research pathways. The model provides a blueprint for future coral reef research that aims to quantify net carbonate production and sediment dynamics, improving our capacity to predict responses of reefs and reef-fronted shorelines to future climate change.https://nsuworks.nova.edu/occ_facbooks/1116/thumbnail.jp

Predicting Responses of Geo-ecological Carbonate Reef Systems to Climate Change: A Conceptual Model and Review

Coral reefs provide critical ecological and geomorphic (e.g. sediment production for reef-fronted shoreline maintenance) services, which interact in complex and dynamic ways. These services are under threat from climate change, requiring dynamic modelling approaches that predict how reef systems will respond to different future climate scenarios. Carbonate budgets, which estimate net reef calcium carbonate production, provide a comprehensive ‘snap-shot’ assessment of reef accretionary potential and reef stability. These budgets, however, were not intended to account for the full suite of processes that maintain coral reef services or to provide predictive capacity on longer timescales (decadal to centennial). To respond to the dual challenges of enhancing carbonate budget assessments and advancing their predictive capacity, we applied a novel model elicitation and review method to create a qualitative geo-ecological carbonate reef system model that links geomorphic, ecological and physical processes. Our approach conceptualizes relationships between net carbonate production, sediment transport and landform stability, and rates knowledge confidence to reveal major knowledge gaps and critical future research pathways. The model provides a blueprint for future coral reef research that aims to quantify net carbonate production and sediment dynamics, improving our capacity to predict responses of reefs and reef-fronted shorelines to future climate change