APPLICATIONS OF MACHINE LEARNING IN MICROBIAL FORENSICS

Microbial ecosystems are complex, with hundreds of members interacting with each other and the environment. The intricate and hidden behaviors underlying these interactions make research questions challenging – but can be better understood through machine learning. However, most machine learning that is used in microbiome work is a black box form of investigation, where accurate predictions can be made, but the inner logic behind what is driving prediction is hidden behind nontransparent layers of complexity. Accordingly, the goal of this dissertation is to provide an interpretable and in-depth machine learning approach to investigate microbial biogeography and to use micro-organisms as novel tools to detect geospatial location and object provenance (previous known origin). These contributions follow with a framework that allows extraction of interpretable metrics and actionable insights from microbiome-based machine learning models. The first part of this work provides an overview of machine learning in the context of microbial ecology, human microbiome studies and environmental monitoring – outlining common practice and shortcomings. The second part of this work demonstrates a field study to demonstrate how machine learning can be used to characterize patterns in microbial biogeography globally – using microbes from ports located around the world. The third part of this work studies the persistence and stability of natural microbial communities from the environment that have colonized objects (vessels) and stay attached as they travel through the water. Finally, the last part of this dissertation provides a robust framework for investigating the microbiome. This framework provides a reasonable understanding of the data being used in microbiome-based machine learning and allows researchers to better apprehend and interpret results. Together, these extensive experiments assist an understanding of how to carry an in-silico design that characterizes candidate microbial biomarkers from real world settings to a rapid, field deployable diagnostic assay. The work presented here provides evidence for the use of microbial forensics as a toolkit to expand our basic understanding of microbial biogeography, microbial community stability and persistence in complex systems, and the ability of machine learning to be applied to downstream molecular detection platforms for rapid and accurate detection

Contributions to electrocardiographic science

This thesis reports original theoretical and experimental studies related to the measurement and interpretation of the electrical activity of the heart. The relevant literature and clinical practice are reviewed at length. Part I is a review of the science of electrocardiography. Included in the review are the electrophysiology of the heart, the potential theory which relates the electrocardiogram (E.C.G.) to its source, the various schemes used to measure and interpret the E.C.G. and the use of computer modelling to aid in E.C.G. interpretation. The effects of body shape and internal conductivity inhomogeneities on the E.C.G. are studied by means of a computer model. A simple form of the model has a piecewise homogeneous interior with spherical boundaries and a surface admittance is invoked to model changes in the surface shape. An extended form of the model allows the boundaries to be irregular and it is solved by means of an integral equation and the extended boundary condition. Representative numerical results are presented, illustrating the practical utility of the model. The sensitivity of the E.C.G. to certain types of inhomogeneity and surface shape changes is established. An experimental study, supported by a computer model based on the techniques outlined above, of the non-invasive detection of the signals from the ventricular specialised conduction system is reported. Thirty-five subjects were studied using a measurement system with a frequency response extending from 0.1 Hz to 500 Hz (-3 dB points) and using a pair of chest electrodes (similar to Lead CM1), Signal averaging was performed on groups of approximately 50 beats, using the onset of the QRS wave as a timing reference. The signals were detected with certainty in 85% of the subjects studied. The typical measured signal waveform is remarkably similar to that predicted by the aforementioned computer modelling technique. Two features are identified: an initial positive deflection (which probably represents the initial activation of the bundle branches) and a notch approximately 10 msec later (which may represent the passage of the activation into the bundle branches)

Transient morphing and optimal shape design of synthetic and natural active structures

Living organisms often display shape morphing capabilities allowing them to efficiently perform tasks that are fundamental for survival. Understanding the way biological activity is exploited to perform shape changes has a deep impact both on natural sciences and technology, often through a process of reverse engineering. In this thesis, we examine four instances of shape morphing both in synthetic and natural, active structures. In the first Chapter, we analyze the transient shaping of a linear poroelastic plate and investigate how mechanical parameters, strains, and stresses influence the swelling dynamics. We obtain an approximate analytical solution for the case of stress-free evolutions and investigate the effect of stresses in the case of an axisymmetric plate. We show that compressive stresses promote faster swelling with respect to the stress-free case, and vice-versa. In the the second Chapter, we address the question of devising efficient morphing strategies for the attainment of specific shape changes in active structures. We set up an optimal control problem which selects, among the activation patterns producing a prescribed shape change, the one minimizing an objective functional, designed to quantify the complexity of the activation. We provide analytical insights for the case of affine shape changes and, with the aid of numerics, we explore the outcome of different objective functionals in a more general context. Chapter 3 is devoted to the study of active reconfigurations in axons, slender cylindrical structures of neurons, which are responsible for the transmission of electro-chemical signals. Axons are able to actively regulate their thickness trough a contractile coating, named cortex, surrounding the cytoplasm (axoplasm). Here, we develop a continuum model describing the interplay between the cortex contractility and the axoplasm elastic response inherited by a network of microtubules. The validity of our modelling assumptions are supported by an excellent match between numerical simulations and experiments. Finally, in the last Chapter, we develop a teleological model to interpret leaves morphogenesis by accounting for the simultaneous growth of both the venation pattern and the blade. Inspired by previous works in the relevant literature, we develop a continuum model by which leaves growth is driven by a gradient flow maximizing the net power absorbed by the leaf. The numerical solution of the ensuing equations provides preliminary results showing some qualitative agreement with features of existing leaves