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Photodynamic therapy (PDT) is an appealing modality for cancer treatments. However, the limited tissue penetration depth of external-excitation light makes PDT impossible in treating deep-seated tumors. Meanwhile, tumor hypoxia and intracellular reductive microenvironment restrain the generation of reactive oxygen species (ROS). To overcome these limitations, a tumor-targeted self-illuminating supramolecular nanoparticle T-NPCe6-L-N is proposed by integrating photosensitizer Ce6 with luminol and nitric oxide (NO) for chemiluminescence resonance energy transfer (CRET)-activated PDT. The high H2O2 level in tumor can trigger chemiluminescence of luminol to realize CRET-activated PDT without exposure of external light. Meanwhile, the released NO significantly relieves tumor hypoxia via vascular normalization and reduces intracellular reductive GSH level, further enhancing ROS abundance. Importantly, due to the different ROS levels between cancer cells and normal cells, T-NPCe6-L-N can selectively trigger PDT in cancer cells while sparing normal cells, which ensured low side effect. The combination of CRET-based photosensitizer-activation and tumor microenvironment modulation overcomes the innate challenges of conventional PDT, demonstrating efficient inhibition of orthotopic and metastatic tumors on mice. It also provoked potent immunogenic cell death to ensure long-term suppression effects. The proof-of-concept research proved as a new strategy to solve the dilemma of PDT in treatment of deep-seated tumors.

The study of disease ecology aims to understand the complex interactions among hosts, environments, and pathogens which result in a final disease outcome. An area of research that has been expanded within this field in recent years is the impact of climate change and global warming. Climate change impacts are of particular concern as the alterations of a host or pathogen’s physiology to more variable or warm environments have been found to be highly influential of disease outcomes in many disease systems. To understand the influence of climate change on disease systems, researchers have assessed the thermal responses of a given pathogen or host in constant laboratory conditions, which may be difficult to relate to more complex, natural environments, or variable field conditions that may be difficult to disentangle direct cause and effect of individual environmental factors on physiological traits. A primary focus of this dissertation is to incorporate the complexities of variable temperatures predicted with climate change conditions in experimental evolution that can assess the implications of climate change on a pathogen known as Batrachochytrium dendrobatidis (Bd) and the resulting disease outcomes within the chytridiomycosis system. In the first chapter of this dissertation, I conduct a literature review of the impact climate change may have on disease systems and the role that temperature has on the thermal biology and adaptive potential of pathogens and hosts within a given disease system. In the second chapter, I assess and establish the characteristics of thermal biology for multiple isolates of Bd that will be used in later chapters. In the third chapter, I use the knowledge of the thermal biology of the isolate from New Mexico to understand patterns of seasonal infection intensity observed in the field. In the last chapter, I assess the physiological responses and adaptive potential of previously studied isolates within this dissertation when experimentally evolved to climate change simulations.

We develop an explicit second order staggered finite difference discretization scheme for simulating the transport of highly heterogeneous gas mixtures through pipeline networks. This study is motivated by the proposed blending of hydrogen into natural gas pipelines to reduce end use carbon emissions while using existing pipeline systems throughout their planned lifetimes. Our computational method accommodates an arbitrary number of constituent gases with very different physical properties that may be injected into a network with significant spatiotemporal variation. In this setting, the gas flow physics are highly location- and time- dependent, so that local composition and nodal mixing must be accounted for. The resulting conservation laws are formulated in terms of pressure, partial densities and flows, and volumetric and mass fractions of the constituents. We include non-ideal equations of state that employ linear approximations of gas compressibility factors, so that the pressure dynamics propagate locally according to a variable wave speed that depends on mixture composition and density. We derive compatibility relationships for network edge domain boundary values that are significantly more complex than in the case of a homogeneous gas. The simulation method is evaluated on initial boundary value problems for a single pipe and a small network, is cross-validated with a lumped element simulation, and used to demonstrate a local monitoring and control policy for maintaining allowable concentration levels.