VictorJuiz/thesis huggingface dataset:
Electron Microscopy for the Study of Defect Development in Nanomaterials

Precisely controlled synthesis of nanostructures is heavily emphasized in the field of nanoscience, in large part due to the desire to control the size, shape, and terminating facets of nanoparticles for applications in catalysis, optics, and medicine. Direct control of the size and shape of solution-grown nanoparticles relies on an understanding of how synthetic parameters alter nanoparticle structures during synthesis. Synthesis conditions rarely yield uniform particles, but the heterogeneity in these populations is hard to quantify especially in respect to atomic structure. Defects are a particularly challenging element to quantify the influence of synthesis on due to the limited number of methods which can give insight into these features. In this thesis we analyze the development of defect structures in multiply twinned metal nanoparticles. We find that non-classical growth mechanisms occur during standard colloidal growth. In order to further study the conditions which cause these non-classical growth modes we analyze methods to automate the analysis of defects in nanoparticles. To this end we propose a machine learning based pipeline for the segmentation and stacking fault classification in nanoparticles. We demonstrate a flexible two step pipeline for analysis of high resolution transmission electron microscopy data, which uses a U-Net for segmentation followed by a random forest for detection of stacking faults. Our trained U-Net is able to segment nanoparticle regions from amorphous background with a Dice coefficient of 0.8 and significantly outperforms traditional image segmentation methods. Using these segmented regions, we are then able to classify whether nanoparticles contain a visible stacking fault with 86% accuracy. We further explore the role of architecture in achieving better segmentation of HRTEM micrographs. These studies in turn illuminate the challenges in generating a large enough dataset for neural network training in electron microscopy. To solve this we evaluate methods to minimize labelling requirements by using automated labeling methods. This strategy recasts the classification problem as a label noise problem, with the fraction of label noise problem. We analyze the impact of label noise rate and training method on the classification error of neural networks for SEM data. We compare the noise resistance of three training methods: standard network training, pretraining with a tiny, label noise-free dataset, and co-teaching. We find that the pretraining approach yields the most accurate results across label error rates. These developments will help in enabling automated analysis of nanomaterials.

Advisor: Scott, Mary C
A Synapse-level Characterization of Gustatory Receptor Neurons in Drosophila melanogaster

Animals utilize the rich variety of chemicals in their surroundings to identify and evaluate food sources, communicate with conspecifics and detect predators. These vital functions have led to the evolution of sophisticated sensory systems for the detection of volatiles and dissolved chemicals in the form of olfactory and gustatory systems, respectively. In a testament to the universality of many of these evolutionary pressures, remarkably diverse animals have adopted similar schemes to most efficiently extract relevant chemosensory information, even though key details such as receptor identity and mechanism vary widely. In this work, we provide an extensive overview of the sensory organs, chemoreceptors and sensory coding strategies underlying taste and smell in common model organisms. An especially attractive model to study gustation is the fruit fly Drosophila melanogaster. Not only does Drosophila detect a wide range of food compounds with a relatively small number of easily accessible sensory neurons, but there is also a vast array of sophisticated tools available to the fly neurobiology community. One such resource is a recent electron microscopy dataset of the entire adult fly brain. We used this dataset to reconstruct the majority of gustatory sensory neurons of the outer mouthparts. We further annotated all synaptic sites on these neurons to elucidate their connectivity. We utilized a combination of our anatomical and connectivity data to identify which taste modality reconstructed sensory neurons are likely to detect. We found that connectivity between sensory neurons is a common circuit motif and occurs mostly between neurons of the same type. We further explored the significance of these direct synaptic connections of neurons of the same and different modalities using a combination of calcium and voltage imaging. While we were not able to show functional connectivity between sensory neurons of different taste modalities, we did find preliminary evidence suggesting that excitatory connections exist between neurons of the same subclass. Lastly, we identified a second order gustatory neuron in the EM dataset and characterized its taste response profile, which was congruent with our assignment of its inputs’ taste modality identities. Our studies provide a valuable gateway to the further exploration of the gustatory circuitry of Drosophila at synaptic detail.

Advisor: Scott, Kristin
Numerical Algorithms based on the Immersed Boundary Method for Modeling the Dynamics of Semiflexible Filaments and the Mechanics of Actin Cortex

The mechanical behavior of cortical actin cytoskeleton, a network of cross-linked semiflexible actin filaments (F-actin), is of interest to biologists and engineers since it plays a key role in many fundamental cellular properties and processes, such ascell shape and motility. The dynamics of flexible filaments in low-Reynolds number viscous shear flow has also gained much interest in the last decades in a wide variety of applications involving biological systems like DNA, polymers and proteins. In this dissertation, two numerical algorithms based on the Immersed Boundary Method (IBM) are proposed and implemented for the study of the actin cortex and the dynamics of semiflexible filaments immersed in low-Reynolds flows./nA new, modified, and more computationally efficient version of the IBM that combines the Coarse-Graining Method (CGM) with IBM is developed for modeling of inextensible filaments in shear flow at low Reynolds numbers. The various two-dimensional orbit regimes of flexible filaments are studied and the results of the proposed method are validated with theoretical results and previous works, numerical and experimental, showing excellent agreement. They are subsequently used to develop a prediction model using Artificial Neural Networks (ANN) to effectively forecast the orbit regime of a filament in shear flow with different parameters./nAn extension of the traditional IBM to include a stochastic stress tensor is also proposed in order to model the thermal fluctuations in the cytoplasmic fluid surrounding the actin cortex. The theoretical values for time-averaged contraction for a single inextensible filament under hydrodynamic thermal fluctuations are verified through numerical simulation. The mechanical behavior of the actin cortex and its elasticity when subjected to shear flow is investigated, illustrating a stiffening of the cross-linked network with increasing strain under shear flow, as other experimental and numerical studies have shown. By implementing the proposed extension of the IBM, the behavior and interaction of passive F-actin under thermal fluctuations is also studied in the current work, where a trend of filaments to spread out is observed.

Advisor: Papadopoulos, Panayiotis
Millimeter-wave Impedance Spectroscopy for High Sensitivity and Selectivity Biomolecular Detection

An integrated biosensor is a miniaturized device which performs biochemical analysis normally handled by a laboratory equipment. By incorporating signal processing electronics, it automates the massive processing of biological samples, avoids noise and interferences from wiring and interfacing with other systems and enables real-time detection of biochemical reactions. With these numerous benefits, integrated biosensors are poised to change the face of chemical and biological analysis and eventually revolutionize the pharmaceutical industry./nAmong different types of biosensing techniques, impedance spectroscopy is peculiarly attractive for the purpose of integration. It is fully electronic in nature, since the sensing frontend can be simply an electrode pair or an inductor coil, which are native to the standard CMOS processes and can be readily adapted into various IC building blocks. Recent years have seen a tremendous advancement in integrated impedance biosensing, a handful of which have already pervaded people's daily lives. Nevertheless, the majority of the impedance biosensors focus on dc-MHz frequency range even though the transistor cut off frequency is approaching half terahertz./nTo fulfill the frequency gap, this thesis aims to develop mmWave integrated impedance spectrometers for biomolecular sensing. Operating at mmWave frequencies offers additional advantages. For example, biomolecules interact with EM fields very differently at mmWave frequencies, which allows the discovery of new biochemical fingerprints. In the first part of the thesis, an oscillator-based dielectric sensor is developed which allows the characterization of biomolecules around 40 GHz with sub-ppm sensitivity. It is integrated with single-photon-avalanche-diode based optical sensors to allow highly selective sensing by creating a high-dimensional sample dataset. The second part of the thesis presents an integrated electron paramagnetic resonance spectrometer for in vivo applications. Several design challenges related to in vivo mmWave frequency sensing and detection are discussed and addressed.

Advisor: Niknejad, Ali M.
Backgrounds in LUX and LZ: Extending the Sensitivity of LUX to Low-mass Dark Matter

Cosmological and astrophysical phenomena indicate the existence of a class of particles that are dark in the sense that they interact only very weakly with the Standard Model (SM). A strong set of candidates are Weakly Interacting Massive Particles (WIMPs) with masses of $m_{\chi}\in10$--$10,000$~GeV. The Large Underground Xenon (LUX) experiment utilized a dual-phase xenon time projection chamber (TPC) to search for this type of dark matter (DM), setting competitive limits on the spin-independent DM-nucleon scattering cross section for $m_{\chi}\gtrsim5$~GeV. It's successor, LUX-ZEPLIN (LZ), is currently being commissioned and will improve upon these results. This dissertation presents work extending the sensitivity of both detectors to rare events above and below the energy range of WIMP scattering. It does so, first, through precise management of backgrounds that can hide or mimic higher-energy signals of interest. A particularly challenging set of backgrounds comes from radon and its daughters. The LUX data were used to characterize the distribution of these isotopes throughout the xenon and place limits on the rate at which radon-related surface contamination washes off of TPC walls. To make this work possible, strategies were developed to accurately calibrate the detector and select high-energy decays by mitigating spurious low-energy signals that accompany the primary events. Another significant source of backgrounds is intrinsic contamination of detector components by primordial nuclides and cosmogenically activated isotopes. To best-design the LZ detector, a new facility named the Black Hills Underground Campus (BHUC) was built to house high-purity germanium (HPGe) detectors. These were used to assays potential construction materials and the lowest-activity materials were selected for use in the LZ detector. In addition, LUX components were assayed to refine the background model of this experiment. This dissertation also presents work lowering the LUX energy threshold to gain sensitivity to low-mass DM. This was done by incorporating events containing only ionization signals which remain robust at very-low energies where there are usually no detectable scintillation signals. In this regime, that data are plagued by backgrounds from radiogenic surface contamination of the grid wires and additional grid electron emissions. A novel machine learning technique was used to mitigate these events based on ionization pulse shape. It was applied to an effective $5$~tonne$\cdot$day exposure from the 2013 LUX science operation to place strong limits on the scattering of low-mass DM particles with $m_{\chi} \in 0.15$--$10$~GeV.

Advisor: Lesko, Kevin T
Jacobsen, Robert G
Investigating how murine cytomegalovirus downregulates ERAAP and modulates non-classical T cell responses

The endoplasmic reticulum aminopeptidase associated with antigen presentation (ERAAP) plays a crucial role in shaping the peptide-MHC I repertoire and maintaining proper immune surveillance. While murine cytomegalovirus (MCMV) has multiple strategies for manipulating the antigen processing pathway to evade immune responses, the host has also developed ways to combat viral immune evasion. In this report, we describe how MCMV modulates ERAAP and induces a novel non-classical Qa-1b restricted anti-viral T cell response. Specifically, ERAAP downregulation during infection led to presentation of a self-peptide FL9 on non-classical Qa-1b, thereby eliciting non-classical QFL T cells to proliferate in infected mice and control infection. This report also investigates the mechanism of ERAAP downregulation during MCMV infection. By testing several MCMV mutants and screening all MCMV genes for ERAAP downregulation, we identified several candidate viral genes that are potentially involved in targeting ERAAP. In addition, through a CRISPR-Cas9 screen, we found and confirmed a host protein, ERp44, that is essential for ERAAP retention in the ER and showed that this interaction may be disrupted during viral infection. Specifically, we found that MCMV reduces the intracellular oxidation state of cells, which may disrupt disulfide bond formation between ERp44-ERAAP, leading to ERAAP secretion. Altogether, our study investigates how MCMV infection impacts ERAAP function and highlights the consequences of ERAAP downregulation on non-classical T cell responses during viral infection. These findings could be applied to studies of HCMV, which also downregulates ERAAP, and provide ideal targets for use in immunotherapies and vaccines against CMV infection.

Advisor: Coscoy, Laurent
Dysflective Cones in Healthy and Retinal Disease Eyes

Robust, sensitive measures of foveal health and cone loss, particularly ones that rely on structural measures rather than subjective and time consuming psychophysical measures, could facilitate measurement of disease progression. The research described in this document uses multimodal high-resolution imaging techniques to study the relationship between structure of cone photoreceptors and visual function in both healthy and diseased eyes. Visualizing cone structure in living eyes offers a way to detect and monitor retinal disease and assess the health of cone photoreceptors. It is generally the case that regions that lack normal reflections from the photoreceptors correspond to regions that lack functional cones. However, areas of hyporeflective cones (cones that reflect significantly less than normal) even over large areas have been reported to have function, and these are defined as dysflective cones. The research described in this document focuses on determining the presence of these dysflective cones in healthy subjects, the timeline of dysflective cone reflectivity changes, and the sensitivity differences between healthy appearing cones and dysflective cones. Healthy dysflective cones were found to have similar function to surrounding areas, while dysflective areas in participants with retinal disease were found to have worse function, elevated thresholds, compared to surrounding areas.

Advisor: Roorda, Austin
Physically Aware Design of Generated Systems-on-Chip

Generator-based integrated-circuit design flows are crucial for meeting the aggressive system-on-chip development timelines demanded by rapidly changing modern workloads. Generators allow chip designers to develop complex solutions to classes of problems rather than individual instances, allowing significant design changes late in the development cycle and enabling incremental improvements to existing solutions. The philosophies of welcoming changing requirements and steady, incremental development have been embraced by the software development community for some time, but have only recently been incorporated into hardware development. While this adoption of proven software development philosophies has decreased the turnaround time of systems-on-chip, productivity has been limited by hard-to-automate tasks like physical design. Each generated design instance requires a new human-generated floorplan or other changes to the physical design flow, limiting the throughput of design space exploration by the available engineering resources. Automation of physical design is therefore critical for state-of-the-art generator-based system-on-chip design./nThis work describes a series of generator-based integrated circuits manufactured in 28nm FD-SOI and 16nm FinFET, outlines the physical design challenges encountered in their development, and presents a physical design methodology purpose-built to solve these challenges. The integrated circuits presented include an 8192-point digital spectrometer in 28nm FD-SOI, a dual-core RISC-V vector processor with on-chip fine-grain power management in 28nm FD-SOI, a dual-lane RISC-V vector processor with a dedicated on-chip power management core in 28nm FD-SOI, an eight-core RISC-V vector machine in 16nm FinFET, and a 21-core RISC-V vector machine with a systolic array accelerator in 16nm FinFET. The eight-core chip achieves a state-of-the-art energy efficiency of 209.5 GFLOPS/W on a half-precision matrix multiplication (GEMM) kernel./nThe physical design methodology presented uses a framework, Hammer, to provide reusable physical design deliverables by decoupling the design-specific, tool-specific, and technology-specific aspects of back-end design along with a novel floorplan generation framework for Chisel designs. This physical design methodology has been incorporated into the Chipyard framework, an open-source RISC-V system-on-chip development platform leveraging the Chisel hardware construction language. The floorplan generation framework allows Chisel programs, which generate RTL, to specify composable floorplans without modifying the original source code. The flow solves common challenges associated with floorplanning generated RTL, such as SRAM mapping and placement, demonstrating the efficacy of floorplan generation in reducing the overhead and cycle times of generator-based design.

Advisor: Nikolić, Borivoje
Development of Strategies for Targeting Voltage-Sensitive Fluorescent Dyes to Subcellular Organelles

Membrane potential is a crucial signaling factor in many biological processes and understanding these processes requires measurements of membrane potential in a normal physiological environment. Voltage-sensitive fluorophores provide a non-invasive tool for high throughput optical measurements of membrane potential. In the Miller lab, we focus on a class of voltage-sensitive fluorophores known as VoltageFluors which rapidly modulate their fluorescence in response to changes in membrane potential via changes in the rate of photoinduced electron transfer from an electron-rich aniline. Prior to this work, VoltageFluors had only been targeted to the extracellular surface of plasma membranes. This work reports the development of new strategies for targeting of VoltageFluors to specific membranes including those of subcellular organelles. Chapter 1 provides background material on methods of targeting fluorescent probes to organelles. Chapter 2 describes the targeting and methods for use of a novel VoltageFluor, SPIRIT RhoVR, that localizes to the mitochondrial inner membrane via electrostatic accumulation followed by unmasking of an ester for retention in mitochondria. Chapter 3 describes a tetrazine-transcyclooctene click chemistry strategy for targeting of a novel VoltageFluor, LUnAR RhoVR, to multiple cellular locations with the primary focus being the endoplasmic reticulum (ER). Using LUnAR RhoVR, we were able to monitor ER membrane potential during calcium release and establish the directionality of ER-plasma membrane electrical coupling. Chapter 4 focuses on the physics behind energy transfer in fluorescent dyes, provides a computational description of VoltageFluor sensitivity, and proposes an approach for using VoltageFluors as ratiometric sensors of membrane potential. This dissertation follows this main body of work with two appendices describing initial steps towards synthesizing water-soluble and enzymatically uncageable dyes that would enhance targeting of fluorescent dyes to specific membranes in complex samples.

Advisor: Miller, Evan W
Provable and Efficient Algorithms for Federated, Batch and Reinforcement Learning

We propose and analyze iterative algorithms that are computationally efficient, statistically sound and adaptive (in some settings). We consider three different frameworks in which data is presented to the learner. First, we consider the Federated (Distributed) Learning (FL) setup, where data is only available at the edge, and a center machine learns various models via iteratively interacting with the edge nodes. Second, we study the canonical setting of supervised batch learning, where all the data and label pairs are available to the learner at the beginning. Third, we examine the framework of online learning, where data is presented in a streaming fashion. In particular, we focus on specific settings like Bandit and Reinforcement Learning (RL)./nIn the Federated Learning (FL) framework, we address the canonical problems of device heterogeneity, communication bottleneck and adversarial robustness for large scale high dimensional problems. We propose efficient and provable first and second order algorithms, and use ideas like quantization of information and apply several robust aggregation schemes to address the above-mentioned problems, while retaining the optimal statistical rates simultaneously. For the (supervised) batch learning framework, we use an efficient and statistically sound algorithm, namely Alternating Minimization (AM) and address the problem of max-affine regression; a non convex problem that generalizes the classical phase retrieval and closely resembles convex regression. We give convergence guarantees of AM, with near optimal statistical rate. Finally, in the online learning setup, we address the problem of adaptation (model selection) for contextual bandits (linear and beyond) and later extend these techniques to Reinforcement Learning (RL). Our algorithms here are efficient, provable and more importantly adaptive to the problem complexity.

Advisor: Ramchandran, Kannan
Guntuboyina, Aditya