When a landslide impacts a river, it may form a dam that blocks the flow of water and builds up a lake. These lakes are prone to sudden outbursts, where they rapidly drain and catastrophically flood downstream areas. Recent UW research has estimated how susceptible rivers in the Oregon Coast Range are to landslide dam formation. However, where these outburst floods would be most dangerous for humans remains unknown. In this project, we ask in which Oregon Coast Range drainage basins are the flood risk and vulnerability the highest. In other words, where would a landslide dam cause the most harm? To answer this, we follow a GIS based methodology for computing flood risk for the Oregon Coast Range. We assess the magnitude of the flood risk in the study area. We define risk as the amount of people (or building footprints) that may be exposed to future flooding hazards. We will also be assessing flood vulnerability, which we calculate using population demographic data. Using these results, we will analyze flood risk and vulnerability in concert with the probability of landslide dam flooding to determine which areas should be highlighted for further detailed study and possible mitigation planning.
 

The placenta is a crucial fetal organ providing oxygen and nutrients to the developing infant. Placental cell models, which are derived from immortalized or placental cancer cells, are typically used to study the organ. The use of placental cell models is important because human samples are difficult to obtain, and placental physiology is highly species-specific. However, our understanding of these models and how they compare to placental tissue samples is limited. This project aims to determine which placental cell model most directly reflects the gene expression of the human placenta. We obtained twenty-eight RNA sequencing datasets from the HTR-8/SVneo, JEG-3, JAR and BeWo placental cell models as well as human placental villous explants and primary trophoblast cells using the Gene Expression Omnibus database. Fetal sex was determined by quantifying expression of the Y-chromosome for each of the models. From this analysis we identified that HTR-8/Svneo was of female origin, while JEG-3, JAR and BeWo was of male origin. A clustering analysis was also conducted which identified groups of genes that showed similar expression profiles across the groups of cell lines and placenta tissue. This was subsequently used within a pathway analysis to identify which biological pathway defined the cluster. Pathways are chains of reactions leading to products or changes in a cell. The analysis showed at 22 of the 53 clusters were enriched for 1 or more pathways, which helps provide insight into the biological functions of these clusters and indicates biological processes that may be different between these models. With this information we have created an interactive web application. This site allows users to search a given gene and identify the expression data across all the models. This tool aims to provide a resource to the placental biology research community in further investigations of the placenta.

 In geomorphology, computer simulations of synthetic landscapes can help us understand the dynamics of tectonics and surface processes. However, there are always limitations to the applicability of these models to real-world observations. Thus, contrasting computer-derived synthetic versus natural measurements is key to validating our model-derived hypotheses. The main goal of this project is to compare geomorphological markers from synthetic topography from landscape evolution models of strike-slip faults to topographic observations derived from photogrammetric techniques of the Salar Grande Fault in Northern Chile. To accomplish this goal, the project involves four steps. First, the generation of a high-resolution digital elevation model (DEM) from drone images collected in the Salar Grande Fault using Agisoft Metashape software. The second step involves using the constructed DEM to measure and quantify geological markers near the Salar Grande Fault through ArcGIS. Third, the quantification and measurement of the same markers in synthetic topography. And finally, the comparison between our observations. Our results will consist of offset channels and valley spacing measurements to reveal if features from arid landscapes such as the Salar Grande Fault are consistent with model predictions for slow-slipping faults. This project helps to test insights from models and impacts our understanding of how to use geomorphic indicators to study strike-slip faults in arid environments that develop under sporadic erosional processes.

Vertical velocities are a fundamental component of ocean flow and are vital to characterizing global circulation. However, vertical velocities are small compared to horizontal velocities and are thus difficult to measure. Previous studies attempting to estimate them ignore the impacts of topography, mesoscale eddies, internal waves, and spatial variability. Novel estimates from the Argo float array allow for direct estimates of vertical velocities. This project will focus on comparing these new Argo estimates with vertical velocity observations from moorings in the Southern Ocean. The Southern Ocean is an important site of vertical volume transport for mass ocean circulation with global implications, particularly the Antarctic Circumpolar Current which dynamically links many of these interactions. We expect vertical velocity characterized by moorings to maintain coherency with Argo float estimates. Differences may occur, however, due to mismatches in spatial resolution between Argo-based estimates and mooring-based estimates, which rely on mass conservation across larger scales. In comparing novel Argo datasets to known mooring values, we gain a more complete understanding of vertical velocities in the Southern Ocean which have direct implications for data assimilation in models and parameterization of energy pathways.

In order to design effective countermeasures against HIV, we must first understand the forces that drive it to evolve resistance within hosts. While linkage patterns in genetic data are potentially a powerful tool to quantify the relative contributions of multiple evolutionary forces (mutation, recombination, selection) acting during an HIV infection, the severe viral population bottlenecks accompanying drug therapy complicate these patterns. To interpret genetic linkage in the context of such major changes in population structure (which are themselves driven by specific mutations), we develop a simulation framework for viral evolution in which genetics and population structure influence each other. This framework overcomes limitations from both dynamical modeling, in which patterns of linked variation are ignored, and from population genetic modeling, in which population structure is predetermined. Using few parameters, we are able to reproduce linkage patterns and population bottlenecks that broadly conform to those observed in vivo. As a case study to demonstrate this model’s utility, we consider a recent hypothesis that viral recombination is suppressed during population bottlenecks due to diminished opportunities for coinfection. In simulating populations with and without recombination suppression during population contraction, we show that this effect measurably changes genetic diversity in rebounding populations, but is less visible when examining simulated viral loads or resistance mutations alone. Then, using this model as a null expectation for linkage patterns, we assess if the linkage structure in HIV populations treated with bNAbs is consistent with density-dependent recombination in vivo. Collectively, our work demonstrates that, by generating realistic null expectations of linkage under complex changes in population structure, we can employ linkage patterns as a powerful source of information for evaluating viral evolutionary hypotheses.