Genetically encoded fluorescent indicators (GEFI) change fluorescence level under microscope following a conformational change when bound to a target molecule, and can be used to visualize spatiotemporally specific biological processes involving a targeted molecule. Various imaging analysis tools exist to analyze the non-temporal fluorescent cell data, however there was no industry standard for the pipeline used to analyze molecular dynamics when imaged with GEFI in time-series experiments. This project aimed to develop a computational pipeline that analyzed the fluorescent readout of single cells in spatiotemporal experiments that utilized GEFI. The pipeline included both segmentation of cells, utilizing Cellpose, an existing deep learning-based generalizable and highly efficient segmentation program, and tracking of single cells across all frames. I personally contributed to the design, implementation, and testing of the tracking component of the pipeline. The tracking algorithm was designed using unsupervised machine learning, specifically k-means clustering with convolutional neural network feature extraction techniques. The pipeline was implemented using Python and made available and open source, accessible through Google Colaboratory for a more user friendly version, as well as Github for more thorough documentation and generalizability. Ultimately, this project aimed to minimize bias to result in more accurate and efficient high-throughput investigation of molecular dynamics when using fluorescent probes for dynamic cell imaging. Preliminary results demonstrated the effectiveness of the pipeline in tracking cells across various time points and provided a foundation for future optimizations and applications.

Microelectromechanical (MEMS) resonators are small scale devices with well-defined vibrational characteristics that make them useful tools for sensing and useful platforms to study many problems in classical and quantum physics. However, nanoscale and molecular structures can exist in a more complex regime. Polymers such as DNA are highly influenced by thermal fluctuations from their surroundings which can completely alter their mechanical and biological properties. While the impact of these interactions in overdamped environments such as liquids, is well understood the behavior of thin elastic objects in underdamped environments is greatly unexplored. Our goal is to study single-molecule resonators to better understand the mechanical and vibrational behavior of semiflexible polymers in underdamped environments in a broad range of temperatures. Understanding and controlling molecular systems will help clarify important questions related to quantum dissipation and decoherence and may inspire a class of novel integrated sensors. In our experiment, we suspend single molecules of DNA between two nanosized pillars, much the strings on a guitar. Fluctuations from the molecule’s local environment cause the DNA string to vibrate. We place our system into a cryostat that gives us precise control of the system’s surrounding temperature. We measure the vibrations of the DNA with a specialized photonic crystal cavity, a device that allows us to read out the position of a section of the molecule due to an electric field in the cavity that changes in response to the molecule’s motion. Overall, we show progress on sub-micron scale interfacing of a suspended DNA string with a nanoscale photonic crystal cavity to show broadband, real-time detection of the molecule’s thermal fluctuations. We expect that these fluctuations and thermally driven nonlinear effects will lead to changes in the vibrational spectrum of the DNA molecule by inducing strong coupling between resonance modes and significantly effecting the resonance mode’s decay widths.

The aim of this study is to assess the ecological response of an urban dam removal in Seattle, WA. As we work to decrease our environmental footprint and dam removals become more common, it is essential to learn the extent of the biological impact post-dam removal and assess future removal techniques to alleviate disturbances. The biological condition of the Hidden Lake dam removal was assessed using benthic macroinvertebrates as bio-indicators of stream health through a before-after-control-impact (BACI) study design. Two downstream and upstream sites were established in relation to the impoundment and sampled for invertebrates before removal and two and fourth months post-removal. Samples were also taken in the newly established stream after the draining of Hidden Lake and restoration of Boeing Creek. The bugs were sorted and identified in order to calculate the Benthic Index of Biotic Integrity (B-IBI) metric for each site. B-IBI offers a quantitative method for determining and comparing the biological health of a stream. Paired T-test were run on the upstream and downstream sites with insignificant results downstream but significant results upstream. The community composition of taxa was also evaluated and compared using non-metric dimensional scaling. The non-significant result of B-IBI scores downstream indicate little to no ecological impacts as a result of dam removal. The taxa composition of macroinvertebrates in the new segment of Boeing Creek indicates the establishment of drifters and crawlers in the new system. Overall, this study shows encouraging results for the future of dam removals and the recovery of freshwater systems after these disturbances.

Complex numbers are often introduced in classical theories as a convenient trick to simplify math, such as the complex representation of electromagnetic fields. However, quantum mechanics introduces the wave function as an inherently complex-valued function. This raises a fundamental question: are complex numbers required for a complete theory of quantum mechanics? Recently, Renou et al. developed a multiparty Bell experiment that can probe the predictive power of a theory of real quantum mechanics compared to a complex one. In this talk, we describe our implementation of the Complex Bell Test on a publicly available IBM quantum computer and test the robustness of the experiment. To address the noise inherent in the system, we implement read-out error mitigation techniques, allowing our experiment to achieve a result over the upper limit allowed by real quantum mechanics. We provide an analysis of the physical system considering the different error mitigation and suppression techniques employed to show their varying effectiveness. Ultimately, we conclude that this test is valid and a successful refutation of the real number formulation of quantum mechanics up to 12 standard deviations. This project demonstrates the promise of running quantum information experiments in the noisy intermediate scale quantum (NISQ) era.

Two-dimensional van der Waals materials are a class of materials that can be exfoliated into thin layers. Exotic properties can emerge in these thin-layer materials, such as electric polarization. In this presentation, we report the observation of irregular piezoelectric domains in natural flakes of thin-layer tungsten disulfide, a transition metal dichalcogenide (TMD), detected with piezoresponse force microscopy (PFM). These domains also exhibit different surface potential when analyzed with kelvin probe force microscopy, which is consistent with our PFM observation. We attribute the emergence of these intriguing domains to the formation of opposite R-stacked regions with inversion symmetry breaking, as opposed to inversion-symmetric H-stacked layers. To investigate this further, we performed reflectance measurements in a dual gated device with strong position dependent hysteresis, indicating different built-in potentials of the domains. Our work provides a new avenue to engineer electric polarization in thin-layer materials, which will contribute to applications such as information storage.

With increasing temperatures and changing ocean conditions, it is important to measure the effects felt on both a species specific and ecosystem level, to better understand the consequences of this change. To investigate this issue specifically off of the Northeast US Atlantic Coast, I worked collaboratively with the National Oceanic and Atmospheric Administration, using both bottom temperature and sea surface temperature as oceanographic variables to examine whether the changes observed have influenced fish consumption over time across seventeen prominent fish species. We calculated average annual fish consumption per species from 1993-2018, where I then compared this to both sea surface temperature and bottom temperature using generalized additive models. Additionally, we plotted the above variables independently using generalized linear models and linear models to analyze their respective trends. I also created a sea surface temperature model to compare the extreme temperature changes the ecosystem was experiencing. Overall, increasing trends in both sea surface temperature and bottom temperature were detected, and within species’ consumption trends, four species showed significant increases in consumption (buckler dory (Zenopsis conchifer), fourspot flounder (Hippoglossina oblonga), longhorn sculpin (Myoxocephalus octodecemspinosus), striped searobin (Prionotus evolans)) whereas two indicated significant decreases in consumption (Atlantic cod (Gadus morhua), thorny skate (Amblyraja radiata)). When compared to sea surface and bottom temperature, three species' consumption rates were found to be significantly influenced by these variables (longhorn sculpin, thorny skate, spiny dogfish (Squalus acanthias)). Given these results, it is likely that the adaptability of species and their respective mobility will influence the degree of impact by changing ocean conditions, constituting both winners and losers in this changing time period. Therefore, we recommend further analysis to better understand how various related biological factors influenced by climate change will be impacted in the future to develop a more thorough understanding of the consequences of this change.