VictorJuiz/thesis huggingface dataset:
Ultrafast-laser investigations of radiation damage in nuclear materials

Ionizing radiation—in the form of fission fragments, gamma rays, and alpha particles—is a major source of damage in nuclear materials. Energy from sources of ionizing radiation is deposited primarily into the electronic subsystem of a material over timescales shorter than that required for electron-phonon coupling. This induces highly non-equilibrium conditions that causes damage on femtosecond-to-nanosecond timescales. These short timescales have, thus far, made the direct observation of ionizing radiation damage impossible. Furthermore, the non-equilibrium conditions intrinsic to the damage process are difficult to model. This dissertation reports on the use of ultrafast lasers to better understand the process of ionizing radiation damage in nuclear materials. Studies of nuclear materials subjected to static high-pressure conditions were also conducted in order to better understand the interplay between induced defects and material properties and behavior. Structural phase transformations driven by ultrafast laser irradiation in ZrO2 and Gd2O3 were compared to previous results from swift heavy ion irradiation. Identical phase transformations were induced by the two irradiation techniques. This observation served as an experimental proof-of-concept for the use of ultrafast lasers to investigate ionizing radiation damage. The separation of thermal- and pressure-affected zones in ultrafast laser-irradiated materials provided the basis for understanding the phase transformation mechanism in Ln2O3 (Ln = Er-Lu). Although all investigated Ln2O3 compositions underwent the same transformation, their transformation mechanisms were found to differ. The transformation was pressure-driven for Ln = Tm--Lu, consistent with the material's phase behavior under equilibrium conditions. However, the transformation was thermally driven for Ln = Er, revealing that the non-equilibrium conditions of ionizing radiation can lead to unexpected transformation pathways. The use of ultrafast lasers also allowed time-resolved experiments to be performed, permitting the process of ionizing radiation damage to be observed in real-time. Investigations into the response of UO2 revealed a long-lived GHz-scale mode, which had not been observed under equilibrium conditions. This mode was attributed to the production of a photogenerated species. This species was found to originate in the dynamical magnetic structure of UO2. Investigation of the pressure-dependent mode behavior revealed coupling to the electronic structure of UO2. This non-equilibrium mode provides the basis for understanding the anomalously high resistance of UO2 to ionizing radiation damage.

Keywords: Geological Sciences.
Advisor: Ewing Rodney C. 
Mao Wendy (Wendy Li-wen) 
Gleason Arianna
Uncovering the Hidden Role of the Ribosome in Mammalian Development

The ribosome, far from being a constitutive molecular machine, exerts
developmental control as apparent in the prevalence of ribosomopathies that manifest in remarkably specific developmental defects. However, much is unknown regarding the mechanism of how the ribosome affects and interacts with developmental processes. In order to further understand this role of the ribosome, we investigated a specific ribosomal protein, RPS6, deficient mouse model that presented with limb defects. Using skeletal staining and in situ hybridization with cartilage markers, we first characterized the defect as exhibiting radial underdevelopment (hypoplasia and aplasia). In order to assess if ribosomal protein reduction led to a perturbation of patterning, we explored candidate pathways and genes involved with proximal distal and anterior posterior patterning by performing in situ hybridization with candidate genes.
We identified perturbations in two genes important for limb patterning and development, namely Gli1 and Shh, indicating that ribosomal protein reduction may be interacting with these pathways. Finally, we sought to establish, beyond merely correlation, if candidate genes and pathways genetically interact with the phenotype of radial hypoplasia and aplasia. In order to do this, we reduced Shh expression and assessed if the phenotype is rescued or amplified. We found that reducing Shh expression resulted in a more severe mutant phenotype in the RPS6 deficient mouse, ascertaining the genetic interaction of RPS6 reduction and the Shh pathway. Thus, we concluded that ribosomes play a role in limb development, indicating that ribosomes are involved with the general process of patterning and development. We investigated the unknown mechanisms through which ribosomes regulate development, thus providing a potential foundation for understanding how ribosomal defects can manifest in tissue specific phenotypes.

Keywords:  Department of Biology
Biology
Development
Ribosome
Translation
Thesis
Advisor: Barna Maria
Red-Horse Kristy
Uncovering therapeutic strategies for TDP-43 proteinopathy

Amyotrophic Lateral Sclerosis (ALS) is a progressive paralytic disorder that leads to death usually within 3-5 years of onset. Mutations in over 40 genes have been associated with an increased risk of ALS, however, approximately 90% of patients have no family history of disease and no known disease-associated mutations. Despite this genetic diversity, one unifying characteristic of ALS is that 97% of patients have aggregates of the RNA-binding protein TDP-43 specifically in dying populations of neurons. Rare mutations in the gene encoding TDP-43 can cause ALS, suggesting that this protein has a pathogenic role in neurodegeneration. TDP-43 overexpression induces toxicity in a diverse range of model systems, including yeast, cultured human cells, and mouse neurons in vivo. Through unbiased, genome-wide screens, our lab previously discovered the yeast homolog of ataxin-2 is a dose-dependent modulator of toxicity induced by TDP-43 expression in yeast. To test whether this interaction holds true for mammalian neurons in vivo, I crossed ataxin-2 knockout mice to TDP-43 transgenic mice that overexpress human, wildtype TDP-43 specifically in neurons. I found that ataxin-2 reduction dramatically improves lifespan and ameliorates motor impairment in TDP-43 transgenic mice. We also demonstrated that an antisense oligonucleotide (ASO) drug that lowers ataxin-2 also improves lifespan and motor function. Additionally, ataxin-2 reduction decreased neuronal TDP-43 inclusions. These findings and additional work in cultured cells suggest that lowering ataxin-2 mitigates TDP-43 toxicity by reducing its propensity to aggregate. This work also suggests that ataxin-2 reduction could be an effective therapeutic intervention for ALS. To find novel modifiers of TDP-43 toxicity, I also performed a genome-wide CRISPRi screen in human cells. CRISPR technology has now made it possible to do genome-scale screens that were previously only feasible in yeast. We believe this screen will uncover bona fide modulators of TDP-43 proteinopathy that could inform new therapeutic strategies for ALS. I hypothesize that targeting the formation and physical properties of TDP-43 inclusions will be the key to protecting cells from TDP-43 toxicity.

Advisor: Gitler Aaron D. 
Bassik Michael 
Reimer Richard J. 
Wyss-Coray Anton
Understanding and improving the energy efficiency of DRAM

Human created data has been growing exponentially for decades and there are many reports stating 90% of all data were created in the past couple of years [1]. Moreover, this exceptionally high data growth rate is predicted to continue in the future. Increased data volume then drives storage capacity growth, and also places high demands on high bandwidth and large capacity DRAM [2]. However, growing capacity and memory performing also grows memory power making DRAM power consumption one of the most important component to optimize in power limited modern computer systems. This power issue led to the development of specialized DRAMs, such as Low Power Double Data Rate (LPDDR) and High Bandwidth Memory (HBM), that are much more energy efficient than conventional DDR DRAM. While minimizing memory power is a popular research topic, the DRAM design space is highly constrained by the heavy cost-per-bit optimization done in DRAM. Surprisingly, these constraints are difficult to find as those information are known only within the small community of DRAM circuit designers. To address this issue, we worked with former and current DRAM designers to build an open-sourced DRAM modeling framework named DramDSE, short for DRAM Design Space Exploration. The modeling framework incorporates crucial design constraints posed by modern DRAM and provides the area, detailed power breakdown, and current values that are needed to explore the design space of DRAM efficiently. We used DramDSE to create the first public domain models of two state-of-the-art DRAMs, LPDDR4 and HBM, for the first time in public domain and validated the correctness of our model using measured data collected from mass-produced LPDDR4 and HBM DRAMs. With a better understanding of DRAM gained from DramDSE, we propose three DRAM energy reduction schemes. The first two are built on top of a unique cell array design that can access only half of the page. Half Page DRAM reduces the row energy due to the row buffer overfetch problem in multi-core systems, which yields 38% row energy savings without any bandwidth loss. Charge Recycling Refresh reduces up to 32% of refresh energy by recycling charges from fully refreshed half page rows to the other half page rows that are to be refreshed. Each of them can be implemented in a modern DRAM with less than 1.5% area overhead and when both schemes are used together, the total power consumption is reduced by 15% on average across various workload. This is equivalent to the power savings achieved by scaling the DRAM technology node from 20 nm to a 10 nm class [3]. Finally, we propose Smart Refresh to further reduce refresh energy with negligible area overheads by utilizing the retention time distribution of the cells. Reducing refresh energy becomes more important as DRAM technology scales and memory density increases. Our two refresh energy reduction schemes also work in self-refresh mode, where refresh energy consumption is even more significant.

Keywords: Electrical Engineering.
Advisor: Horowitz Mark (Mark Alan) 
Kozyrakis Christoforos
Wong S.
Understanding climatic and terrestrial drivers of lake phytoplankton blooms using high resolution satellite imagery

The global expansion of harmful algal blooms (HABs) over the past half-century poses a serious threat to human health and ecosystem services in lake systems. HABs dominated by toxic cyanobacteria reduce shoreline recreational opportunities, damage vital fisheries and other aquaculture, and elevate the risk of dangerous toxins in drinking water supplies. While it is well known that phosphorus loading is the main driver of algal blooms in lakes, the apparent proliferation of HABs has prompted deeper examination of the factors contributing to HAB formation and occurrence. Past studies have been limited by the paucity of long-term in situ data in individual lakes, and few have leveraged new opportunities made possible by continental-scale lake surveys to understand drivers of algal blooms in large numbers of lake systems. The studies in this dissertation leverage advances in satellite remote sensing and cloud-based parallel computing over the past decade to study the long-term drivers of lake phytoplankton blooms at regional and global scales. I examine whether different monitoring approaches lead to ambiguity over basic questions of HAB occurrence, spatial extent, and timing, motivating the need for consistent long-term data for understanding HABs. I then describe a novel methodological approach for evaluating satellite remote sensing algorithms that is used to identify an algorithm to detect phytoplankton blooms in Lake Erie, one of the Laurentian Great Lakes. This algorithm is used to hindcast historical algal blooms in Lake Erie since 1984, and the historical record is then used to explore long-term drivers of phytoplankton blooms. The algorithm is also applied to dozens of lakes across six continents in order to characterize long-term trends in historical phytoplankton bloom intensity globally and to examine both climatic and terrestrial drivers at the global scale. In the final component of the dissertation, I explore the hypothesized mechanisms by which climate change is expected to impact harmful algal blooms, doing so by leveraging over twelve hundred summertime lake observations from across the continental U.S. Overall, the findings suggest that management efforts should consider the impacts of global climate change as well as the long-term effects of cumulative nutrient loading in designing mitigation strategies. The research as a whole highlights how knowledge of HABs is being expanded beyond local understanding in a few well-studied systems to a more global understanding based on many systems, and informs strategies to protect water quality within a changing climate.

Advisor: Davis Jenna (Professor)
Michalak Anna M.
Boehm Alexandria
Understanding degradation and improving stability of solution-processed organic and perovskite solar cells

The sun provides the most abundant source of clean energy and has the potential to provide three orders of magnitude more energy than the world energy demand. However, while state of the art commercial technology is capable of harvesting solar energy with 25% power conversion efficiency (PCE), it does so at a high cost. In order to incentivize the use of solar generated electricity and thus reduce our global carbon footprint, it is therefore necessary to develop more affordable and comparably efficient technologies. Organic bulk-heterojunction and perovskite solar cells are two promising solution-processed technologies with single junction record efficiencies of 14% and 22.7%, respectively. Nevertheless, stability is the biggest concern limiting commercialization of these technologies. The first part involves developing new design criteria for stabilizing organic solar cells. Solution-processed small molecules were used as a model system because of their narrow polydispersity index, ease of purification, and PCE exceeding 10%. The operational stability of six different high performance small molecule solar cells were assessed under 1 sun intensity at maximum power in an inert environment. Two types of degradation typically seen in organic solar cells -- burn-in and linear -- were observed and resulted in a TS80 stabilized lifetime of 2-3 years. Burn-in degradation was induced by heat and light, while linear degradation was caused only by heat. From these results, we concluded that the stability of small molecule solar cells can be improved by designing molecules with higher molecular weight for thermal stability and higher crystallinity for photostability. The second part covers optimization of perovskite solar cell encapsulation for environmental stability. While perovskite solar cells are efficient, they are known to decompose in the presence of heat and moisture. They are also mechanically fragile because of mismatches in the thermal expansion coefficients between multiple layers. I will provide my insights on a holistic design of glass-glass encapsulation utilizing commercially available materials, which for the first time enabled perovskite solar cells to pass the IEC standard's damp heat, temperature cycling, and UV-exposure tests. Having demonstrated that properly packaged perovskite solar cells can be environmentally stable, I pave the way for long-term stability of perovskites in the field and bring solution-processed perovskite solar cells one step closer to commercialization.

Keywords: Materials Science and Engineering.
Advisor: McGehee Michael
Salleo Alberto
Dauskardt R. H. (Reinhold H.)
Understanding early vision : from white noise to natural scenes

The retina constitutes the first stages of vision, an exposed part of our nervous system that transduces light into a sophisticated and efficient binary code conveying everything we can know about the visual world. This thesis details attempts to understand these neural computations. First I present theoretical work showing how the retina constructs an efficient yet diverse cell population, providing an explanation for how and why parallel inhibitory pathways generate the retinal ganglion cell classical receptive field. This first study builds on more than a century of using artificial stimuli to understand the retina. However, the normal function of the retina is to convey information about the natural visual world, and yet we currently lack methods to understand retinal computations in their native regime. In the second part I present a way forward using modern deep learning techniques to learn accurate models of the retina under natural conditions. These artificial neural network models of the retina allow unprecedented insight into the structure and function of the retina---we find that they reproduce known retinal phenomena, have internal units that are highly correlated with interneuron responses, and allow for the discovery of new retinal phenomena and mechanisms. These models provide a powerful and transparent framework for testing hypotheses and understanding sensory computations in their natural setting. The thesis concludes with how principles learned from the computations of biological vision can lead to better artificial neural networks for computer vision.

Advisor: Baccus Stephen A. 
Ganguli Surya
Clandinin Thomas R. (Thomas Robert)
Guibas Leonidas J. 
Newsome William T.
Understanding human behavior using interactions with online content [electronic resource]

People often interact with one another on online applications through liking, viewing, and sharing each other's content. Instead of these applications being comprised only of a network of individuals, the content itself often plays a primary role. One can view such applications in terms of a content interaction graph: a bipartite graph with users on one side, content on the other, and edges representing interactions with content connecting the two. These content interactions reflect user intentions, behavioral patterns, and biases. Understanding these behaviors enables improved content recommendation and user interface flows, which in turn provides increased utility to users of content-driven online applications. In this thesis, we study in various ways how the content interaction graph can be used to better understand the behavioral mechanisms through which humans interact with content. First, we examine individual user intentions by studying users' purchasing intent, i.e. inclination to make a monetary purchase, on Pinterest, a content discovery application. We show how user activity information, the way users interact with content, and content itself can be used as signals of rising purchasing intent over time. We next focus on content saving interactions, by studying the way users save content into collections over time. We characterize the growth of these individual collections, and show how signals obtained from modeling the inter-event times of saving interactions can be used to predict collection size and lifespan. Finally, we broaden the scope of "users" in content interaction graphs to media outlets (e.g. CNN) to show how such a graph can uncover media bias in the coverage of Obama's presidency. We discuss NIFTY, a large-scale system to efficiently identify and track "memes", or quoted content, that mutate over time. We then show how clusters of quoted content can be encoded in a low-rank space that reveals dimensions of media bias in an unsupervised manner. Put together, this work demonstrates how content interaction graph analysis is a powerful tool that can, in the future, be used to infer an even broader range of user behaviors on online applications.

Advisor: Leskovec Jurij primary advisor. Thesis advisor
Garcia-Molina Hector advisor. Thesis advisor
Jurafsky Dan
Understanding human disease through integrated molecular and clinical analyses

Traditional biomedical experiments are designed to study a single cohort for a single disease using a single technology. I show that studying disease with a narrow lens has lead researchers to make discoveries that are not reproducible because they are not representative of the real heterogeneity of disease. I developed and implemented methods for integrating datasets to identify robust disease signatures. By integrating data from over 40 studies and 7,000 patients, we establish a robust signature of systemic lupus erythematosus which correlates with disease activity and persists across blood, tissue, and sorted cell populations. We compare relationships of 104 diseases based on molecular and clinical manifestations from 41,000 gene expression samples and 2 million patient records. Finally, we contextualize single-cell RNA-seq data with bulk gene expression profiles to understand the relationships of novel cell subsets to known cell populations and human disease. By integrating biomedical datasets, my work has enabled an unbiased and multi-scale understanding of disease.

Keywords: Biomedical Informatics.
Advisor: Khatri Purvesh
Utz Paul 
Altman Russ
Understanding morphology evolution in printed organic solar cells

Polymer-based organic photovoltaics (OPVs) have emerged as a promising renewable energy candidate suitable for inexpensive and scalable production, being lightweight, flexible, and amenable to low-energy solution processing. However, despite having surpassed 10% power conversion efficiency (PCE) - widely held as the threshold for commercial viability - OPVs are still mostly constrained to lab-scale devices fabricated by spin coating. Efforts to translate to scalable roll-to-roll printing trail significantly in efficiency, commonly by an order of magnitude, highlighting the need to better understand the processing-morphology-performance relationship in the context of linear printing methods. The work presented will focus on two aspects of OPV development: 1) process control to translate from spin coating to printing in order to achieve scalable high-performance devices, and 2) application of improved tools for nanoscale morphological characterization. To the former, a thermodynamic model of phase separation is presented for a model polymer:fullerene system. Next we investigate a high-performance system which has demonstrated > 10% PCE via spincoating but only exhibits 1% PCE when roll-to-roll printed due to differences in drying dynamics and phase separation. OPV bulk heterojunctions are characterized using synchrotron X-ray scattering techniques, elucidating the impact of a critical residual chemical additive on the phase-separated morphology. It is discovered that excessive additive residence time within the semi-dry film gives rise to a hierarchal morphology that severely degrades device performance. Using the understanding gained in this study, we are able to achieve a printed OPV with 5.33% PCE, which is among the highest performing roll-to-roll OPVs to date. To the latter, we address the fact that commonly used microscopy techniques suffer from significant shortcomings for imaging OPVs. We demonstrate the first application of a technique known as Photo-induced Force Microscopy (PiFM) for imaging OPVs with nanoscale chemical specificity. Results from image processing are corroborated with established synchrotron methods and photovoltaic device performance, revealing excellent quantitative agreement. Further, we demonstrate that images from atomic force microscopy (AFM) and PiFM show poor correlation, highlighting the need to move beyond standard AFM for morphology characterization of bulk heterojunctions. We emphasize that PiFM is high-throughput, lab-scale, ambient, and requires no special sample preparation, filling an important underserved role in imaging of OPVs.

Keywords: Chemical Engineering.
Advisor: Bao Zhenan 
Salleo Alberto
Toney Michael Folsom