Invasion by non-native annual grasses poses a serious threat to native vegetation in California, and interference with native ecosystems may be facilitated through interaction with wildfires. Our work is the first attempt to use the coupled fire-atmosphere model, WRF-Fire, to investigate how shifts from native, shrub-dominated vegetation to invasive grasses could have affected a known wildfire event in southern California. We simulate the Mountain Fire, which burned >11,000 ha in July 2013, under idealized fuel conditions that represent varying extents of grass invasion. Expanding grass to double its observed coverage causes fire to intensify and spread faster due to increased wind speed. Beyond this, further grass expansion reduces the simulated spread rate and intensity because the relatively low fuel loads of grasses partially offset the positive wind-speed effect. Overall, our simulations suggest that grass expansion would generally promote larger, faster spreading wildfires in southern California, motivating continued efforts to reduce the spread of invasive annual grasses in this region.

Functional precision medicine (FPM) in cancer is an emerging treatment paradigm involving exposure of patient-derived tumor material to drugs to assess their efficacy and to guide therapeutic selection. Tumor organoids, in addition to being versatile disease models for basic and translational research, are of particular interest for FPM applications. However, improved approaches for high-throughput drug screening of three-dimensional (3D) tumor organoids, capable of resolving both population-level and single organoid–level data and allowing for consideration of heterogeneity in drug screening readouts, are needed. Quantitative phase imaging (QPI) is a label-free microscopy technique for imaging optically transparent biological samples and quantifying various biophysical properties such as cell biomass, mass density, and growth. This thesis demonstrates a new application of high-speed live cell interferometry (HSLCI), a high-throughput QPI platform, for screening 3D-cultured organoid models of cancer, and describes the development of this method. The method presented combines bioprinting, HSLCI, and machine learning technologies to enable accurate, label-free, and highly time-resolved biomass measurements of thousands of organoids in parallel, and rapid identification of drug sensitivity and resistance with temporal monitoring and single-organoid resolution. The complete QPI-based organoid screening pipeline can be leveraged for fundamental studies of disease biology, in addition to FPM studies aiming to establish clinical correlations and ultimately improve treatment selection for patients with solid cancers.

Unveiling the nanoscale structural mechanism is believed to be key to understanding the nature of biology. In the last 15 years, the advancement of super-resolution microscopy, especially STochastic Optical Remonstration Microscopy (STORM) or Single Molecule Localization Microscopy (SMLM), equivalently brought the resolution of optical microscopy down to ~10nm and thus enabled many disrupting biological discoveries. In this dissertation, I initially show how the spatial resolution of optical microscopy could be largely increased through the development of photo-switchable fluorophores and a single molecule localization algorithm. Then, I use two interesting examples to show how STORM could provide a new angle in fundamental cell biology research. First, I illustrate the discovery of a novel structural model of the tubular endoplasmic reticulum as well as its regulation mechanism by curvature formation protein, Rtn4, and luminal bridge, Climp63. Second, I apply STORM to demonstrate the actin-associated vesicle scission mechanism of clathrin-coated pits during eukaryote endocytosis. In the last Chapter, I make some future outlook the in the recent development of functional super-resolution microscopy, which not only achieves higher spatial resolution but also encode useful physicochemical insight in the image.