Plants utilize cell surface protein receptors to recognize insect herbivory through the detection of Herbivore Associated Molecular Patterns (HAMPs) Following the detection of HAMPs, plants initiate specific immune responses, often measured by the increased production of the hormone ethylene gas and by Reactive Oxygen Species (ROS). The Inceptin Receptor (INR), which is specific to legume plants, recognizes the HAMP Inceptin11 (In11). The binding of In11 to INR initiates a signaling cascade, leading to an immune response. However, the signaling mechanism activated by INR is unknown. The Receptor-Like Cytoplasmic Kinase (RLCK) gene Herbivore Induced Kinase 1 (HIK1) is upregulated by In11 treatment in the bean species Phaseolus vulgaris. The goal of this research is to determine if HIK1 and other RLCKs are downstream proteins required for INR signaling. Because of the genetic intractability of P. vulgaris, I transform Arabidopsis thaliana with RLCK genes using Agrobacterium tumefaciens infiltration. Isolated genetic lines are then used to analyze the effect each RLCK has on immune signaling. Transgenic plants are treated with bacterial associated molecular patterns to trigger an immune response, then tissue samples of the leaves are measured for ROS and ethylene gas production. Results are then compared with ROS and ethylene gas production of wildtype plants. If the studied RLCKs are involved in downstream INR signaling, the transgenic plants will have increased ROS and ethylene gas production. I anticipate HIK1 to have the strongest increase in ROS and ethylene gas production due to the upregulation of HIK1 after In11 treatment in P. vulgaris. Understanding the INR signaling pathway is vital for engineering of plants that are resistant to insect herbivory without the use of pesticides.

While survival rates from breast cancer have notably improved, survivors still face long-term health complications due to the side effects of chemotherapy. A significant concern is that patients can develop lasting cardiovascular problems that are exacerbated by physical inactivity during treatment. This study hopes to address this by identifying the optimal fitness intervention for improved cardiorespiratory fitness (CRF) to enhance the cardiovascular health of breast cancer survivors, particularly in those who showed reduced CRF after breast cancer treatment. This randomized control trial enrolled thirty breast cancer survivors into an aerobic exercise, resistance exercise, or control group. Over six-months, the participants performed their designated training, and MRI scans were taken before and after the training program. I am analyzing the depots of fat, including visceral and subcutaneous fat from MRI images of the thigh and abdomen in order to determine the association with cardiorespiratory fitness. Statistical analysis utilized a paired t-test with an intent-to-treat approach to determine the difference in fat levels and to mitigate bias from non-compliance and dropouts. I expect a marginal difference in levels of overall fat between the separate fitness interventions, however, I anticipate a significant difference between the control and both fitness groups. The interpretation of this data provides a first step towards understanding how exercise interventions can be tailored to breast cancer survivors, potentially improving post-treatment survivorship.

The measurement problem is the challenge of reconciling the probabilistic and deterministic aspects of Quantum Mechanics. In this study, our primary aim is to unravel the measurement problem by investigating the possibility that quantum collapse occurs according to the probabilities presented by Born's Rule due to a perturbation on the system, appearing during the measurement process. Our group employs a multifaceted approach, where we examine the interplay between time dynamics and classical limits, alongside the influence of time-(in)dependent perturbations. Computational simulations serve as our primary tool in this exploration. One part of our group worked with a 3-well system with a time-independent perturbation, another part looked at a 2-well system with a time-dependent perturbation, and the last part saw what a time-independent perturbation does to a 2D-well system. We anticipate that our investigations will uncover critical parameters that are expected to yield probabilities consistent with Born's Rule, a foundational principle of Quantum Mechancis. This research points towards a potential reconsideration of quantum collapse as a dynamical, affected by the perturbation introduced by measurement. While preliminary, these findings contribute to the ongoing discourse in Quantum Mechanics and may offer insights for future theoretical developments and applications.

Cannabidiol (CBD), a non-psychoactive cannabinoid compound found in cannabis, has been reported to attenuate morphine tolerance and can potentially be used as an alternative to opioids in treating chronic pain. Previous work has established connections between morphine tolerance and Reactive Oxygen Species (ROS) production through JNK-mediated Peroxiredoxin 6 (PRDX6) activation. Excess ROS production promotes desensitization of opioid receptors, which in turn leads to opioid tolerance. CBD administration is associated with decreasing pain-related Reactive Oxygen Species (ROS) production, and it was hypothesized that CBD directly interacts with JNK, blocking JNK’s activities. This project aims to investigate the connections between CBD administration and ROS production to determine CBD’s effects on JNK-mediated ROS production. To quantify ROS production through fluorescence imaging, I will transfect wild-type HEK 293 cells with oROS, a genetically encoded sensor, which fluoresces proportionally to ROS production. Coverslips of HEK 293 cells expressing oROS are treated with buffer (control) and CBD before administration of Tumor Necrosis Factor alpha (TNFα), a known activator for JNK released during pain states. After imaging with oROS, I will quantify ROS production and compare this between groups with and without CBD pretreatment to determine CBD’s activity on inhibiting JNK-mediated pro-inflammatory pathways. I predict that relative to the control, cells treated with CBD will have significantly less ROS production. If the results are consistent with this prediction, CBD could be a potentially promising co-treatment with opioids in managing chronic pain as it can potentially attenuate opioids' side effects like tolerance. 

In the context of computer-aided design, researchers have studied how to reconstruct an input geometry in CAD by decomposing it into CAD primitives. Such reconstruction is useful for creating CAD designs for manufacturing applications. What we want to study is also object decomposition but towards a different goal: understanding object affordances and interactability. For example, a handle of a basket can be grasped or hung from a sticky hook, and we recognize this affordance or functionality because it has a certain shape (e.g. hook or rod). Prior research has identified eight types of shape primitives that are common in everyday objects, but the existing tagging process requires a high degree of modeling expertise. We aim to create a more automatic and easy-to-use tagging tool. Our proposed research is to develop user-in-the-loop methods for tagging shape primitives given an object geometry. This takes advantage of human intuition for how objects function and interact. We start with building an interface, where users sketch over the input mesh to indicate the region for fitting and select the type of primitive to be fit. On top of this, we plan to crop the selected mesh data to generate a reduced mesh that encompasses only the area selected by the user. Finally, we utilize differentiable rendering techniques to automatically optimize the shape parameters of user-selected primitives to fit our reduced mesh data. With this tagging tool, we can enable more people without modeling expertise to tag objects. Data generated with this tool can support future research that studies object affordances with learning, as well as improve applications in robotics, product design, and assembly design like FabHacks.

Mobile genes are commonly found in bacteria, and they are capable of being transferred between unrelated bacterial cells via a process termed horizontal gene transfer (HGT). Mobile genes that undergo HGT can evolve in various “host” bacteria and thereby evolve in several different genomic backgrounds. In a recent publication, my lab constructed a mathematical model to assess the effects of HGT (i.e “host-switching”) on mobile gene evolution. I have built upon this work by probing additional factors that may influence mobile gene evolution. In phase one of my project, I compared the original evolutionary model used in the publication to a newly expanded, more ecologically realistic model in which growth rates of the bacteria depend on resource availability. To compare the models, I ran simulations with both models on a set of bacterial-host pairs and compared the resulting evolutionary outcomes from both models. I found that the categorical effect of HGT occurring (i.e. HGT confers a higher, lower, or neutral change in host fitness) was the same across both models, indicating that ecological factors are less predictive of mobile gene evolutionary outcomes. For the second phase of my experiment, I assessed the effect of variable HGT rate on mobile gene evolutionary outcomes. I ran simulations using a set of bacterial host-pairs while varying the HGT rate along a biologically relevant range, and found a positive correlation between HGT rate and the magnitude of positive fitness effects conferred by a mobile gene that has undergone HGT. This indicates that HGT rates play an important role in governing the evolutionary outcomes of mobile genes. Using evolutionary simulations has allowed us to gain insight into the predictive factors governing mobile gene evolution and thereby mobile gene-containing bacterial evolution. This is especially important, as many genes conferring antibiotic resistance are mobile.

Local Group galaxies are the closest ones we can study in detail to decipher the processes that shape the universe around us. An interesting property of these galaxies is their star formation history (SFH), which provides a fossil record of when stars were formed in a galaxy. The process by which this occurs is a complex interplay between the gas, the interstellar medium (ISM), and the energy from newly formed stars. By pairing SFH measurements with data on the galaxy’s gas content, we can investigate the timescales on which young massive stars affect the structure of the surrounding gas in the (ISM) as well as its ability to form more stars. Furthermore, since star formation is closely linked to the properties of the gas in a galaxy, such as metallicity and extinction, the SFH also probes these properties. By utilizing resolved stellar photometry from the Hubble Space Telescope (HST), I measured the SFH for four Local Group galaxies (IC10, IC1613, WLM, and NGC 6822) that already have detailed imaging of their gas content from radio observations. First I measured the colors and brightnesses of resolved stars in each galaxy from the HST imaging. Next, I generated and processed a set of artificial stars using the same photometry pipeline as the real observations to provide statistical measures of our data quality. With the processed artificial stars and the original photometry, I then fitted a series of model Hess diagrams for a range of ages and metallicities to obtain each galaxy’s SFH. These measurements allow us to pair this SFH with other observational data. For example, we can map star formation and compare it with observations such as supernovae locations, and we can explore links between the star formation and the ISM as measured through emission from neutral and ionized hydrogen.

The expected launch of the Nancy Grace Roman Space Telescope (Roman), a next-generation space-based infrared observatory, will soon allow us to observe fields in minutes that would previously have taken months to cover. As such, it will advance our knowledge of galactic structure and evolution at a rapid rate. To better leverage the influx of science that will come out of the launch and commissioning of Roman, we are developing a pipeline that simulates observational images taken by Roman and then performs photometry on the images. As a first step, we are testing methods for recovering dwarf galaxies from photometry catalogs that contain both a dwarf and a surrounding stellar halo. In order to produce mock Roman observations, we use the Space Telescope Science Institute's Space Telescope Image Product Simulator (STScI-STIPS) software tools, which are able to add background galaxies, realistic background levels, and noise along with source catalogs to produce simulated images. Our early testing uses input catalogs designed to simulate typical dwarf galaxy characteristics added to stellar catalogs generated from numerical simulations that mimic the nearby spiral galaxy M81. The ultimate goal of this pipeline software is to determine the observational strategy to resolve structures in extended stellar halos of nearby galaxies. Such measurements would allow us to distinguish between formation and evolution scenarios for these halos. This work is part of the Roman Infrared Nearby Galaxies Survey (RINGS), a large Roman Wide-Field Science (WFS) program funded by NASA under grant 80NSSC24K0084.