In the aftermath of a natural disaster, reconnaissance is typically conducted by teams of engineers tasked with tagging buildings according to their damage state. Tagging (red, yellow, or green) conveys information about the condition of the building (unsafe, needs further evaluation, or safe, respectively). While thorough, the process can take several days to weeks to be completed. Automated assessment is an attractive alternative to manual inspection but requires deploying a dense network of sensors at the granularity of each structure. Such a network was deemed to be impractical with respect to cost or deployment time. However, with the advent of the Internet of things (IoT) era, a massive network of citizen-owned smart devices such as tablets and smart-phones that contain vibration sensors (e.g. accelerometers) is already deployed. The objective of this work is to develop a framework that can crowd-source relatively low-quality readings from distributed smart citizen owned devices and distill that information into actionable information. This information can be provided to public safety personnel within minutes of an event, in the form of a disaster map, with buildings tagged by their most likely damage state. This paper reports on the development of an application running on a mobile phone to collect readings and coupled to a cloud-based server used to generate the necessary tags.

Particulate Matter (PM) is a complex mixture of organic and inorganic chemicals, which can trigger systemic health effects including chronic obstructive pulmonary disease (COPD), lung cancer, cardiovascular dysfunction, obesity, and diabetes. The exact mechanisms by which disease progression occurs, however, remain unclear. Therefore, proper chemical characterization of PM and their effects on the development of diseases are required to fully understand PM-induced health effects. In this dissertation, we investigated the toxicological responses and disease progression pathways through transcriptomic analysis to probe the potential molecular mechanisms leading to PM-induced health outcomes. First, the toxicological potency of PM emitted from a modern vehicle equipped with a gasoline direct injection (GDI) engine was examined using eight different fuel blends with varying aromatic hydrocarbon and ethanol contents. Second, the potential health impacts of dimethyl selenide (DMSe)-derived secondary organic aerosols (SOA) were investigated by RNA sequencing (RNA-seq). Third, the lncRNA-mRNA coexpression analysis was conducted to investigate the role of lncRNAs in altered gene expression following DMSe-SOA exposure. Results from these studies indicate that gasoline exhaust particles from eight different fuel blends imbalance the gene expression related to oxidative stress and inflammation. RNA-seq data reveal major biological pathways perturbed by DMSe-derived SOA associated with elevated genotoxicity, DNA damage, and p53-mediated stress responses, as well as downregulated glycolysis and interleukin IL-4/IL-13 signaling that regulate diabetogenesis and allergic airway inflammation, respectively. In addition, we found that four trans-acting lncRNAs known to be associated with human carcinogenesis, including PINCR, PICART1, DLGAP1-AS2, and LINC01629, also differentially expressed in human airway epithelial cells treated with DMSe-derived SOA. Overall, using toxicogenomic approaches, this dissertation contributes to an improved understanding of potential biomarkers in early biological responses to PM exposure derived from traffic and natural sources.

Dinoflagellate bioluminescence provides a near-instantaneous reporter of cell response to flow. Although both fluid shear stress and acceleration are thought to be stimulatory, previous studies have used flow fields dominated by shear. In the present study, computational and experimental approaches were used to assess the relative contributions to bioluminescence stimulation of shear stress and acceleration in a laminar converging nozzle. This flow field is characterized by separate regions of pronounced acceleration away from the walls, and shear along the wall. Bioluminescence of the dinoflagellates Lingulodinium polyedrum and Ceratocorys horrida, chosen because of their previously characterized different flow sensitivities, was imaged with a low-light video system. Numerical simulations were used to calculate the position of stimulated cells and the levels of acceleration and shear stress at these positions. Cells were stimulated at the nozzle throat within the wall boundary layer where, for that downstream position, shear stress was relatively high and acceleration relatively low. Cells of C. horrida were always stimulated significantly higher in the flow field than cells of L. polyedrum and at lower flow rates, consistent with their greater flow sensitivity. For both species, shear stress levels at the position of stimulated cells were similar to but slightly greater than previously determined response thresholds using independent flow fields. L polyedrum did not respond in conditions where acceleration was as high as 20 g. These results indicate that shear stress, rather than acceleration, was the stimulatory component of flow. Thus, even in conditions of high acceleration, dinoflagellate bioluminescence is an effective marker of shear stress.

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