VictorJuiz/thesis huggingface dataset:
Expanding therapeutic targets for ALS : a novel CRISPR screen for regulators of ALS protein Ataxin-2

Neurodegenerative diseases are devastating for all involved, with no cures and few treatments. One feature of neurodegeneration is the accumulation of misfolded proteins in the brain, and strategies to eliminate these proteins are emerging. For amyotrophic lateral sclerosis (ALS), TDP-43 is the major protein aggregate, presenting in ~97% of patient brains. In this context, ataxin-2 (encoded by the gene ATXN2) has become a compelling therapeutic target for ALS due to 1) its ability to modify TDP-43 aggregation and toxicity in vitro and in vivo, and 2) the discovery of mutations in ATXN2 that cause heightened risk for ALS. Genetic reduction of ataxin-2 ameliorates motor impairments and profoundly extends lifespan in a mouse model of TDP-43 proteinopathy, and anti-sense oligonucleotides (ASOs) targeting ATXN2 are now being tested in humans in an ongoing phase 1 clinical trial. My doctoral dissertation involves taking a genes-to-small molecule therapy approach in a project aiming to identify additional ways to therapeutically reduce ataxin-2 levels. I begin by establishing a genome-wide screening strategy in human cells using CRISPR-Cas9 and applying this approach to uncover modifiers of ataxin-2 protein levels. Using antibody staining in fixed cells and fluorescence as a read-out via fluorescence-activated cell sorting (FACS), I identified numerous previously unknown modifiers of endogenous ataxin-2 levels, with many acting in the same biological pathways or encoding subunits of a single protein complex. After individually validating numerous hit genes, I honed in on one class of hits—genes encoding components of the lysosomal v-ATPase—which can be targeted by multiple FDA-approved small molecule inhibitors. Using both genetic and pharmacologic inhibition, I found that perturbing the v-ATPase results in decreased ataxin-2 protein levels in human and mouse neurons, as well as in vivo in the brains of mice upon oral administration of one of the drugs—Etidronate. These results demonstrate not only the tractability and efficacy of a FACS-based genetic screening approach in discovering regulators of proteins of interest, but also the potential to identify and repurpose existing drugs for diseases impacted by protein levels. This screening platform and overall target discovery approach may also be useful in uncovering regulators of many other disease-associated genes, to empower the discovery of novel therapeutic targets and normal cell biological mechanisms in contexts not limited to ALS

Advisor: Gitler Aaron D.
Bassik Michael
Reimer Richard J.
Wyss-Coray Anton
Experience dependent neural codes in the hippocampus

As animals navigate through the world, their survival depends on their ability to maintain a memory for episodes that occurred in different locations. During such memory-guided navigation, sparse sets of neurons in the mammalian hippocampus show striking correlations with an animal's instantaneous location in an environment. These neurons, called 'place cells', are maximally active when an animal occupies one or few restricted spatial locations and collectively form a neural map of the external environment. Across distinct environments, place cells will turn on, turn off, or move their location of peak firing—a set of phenomena collectively referred to as 'remapping'. Moreover, the activity of place cells is hypothesized to provide the neural basis for episodic memory and support the ability of animals to navigate both physical and conceptual spaces. During my graduate work, I used a combination of in vivo two photon imaging, virtual reality behaviors, genetic and viral manipulations, and computational and statistical modeling to address several open questions regarding how these striking neural representations are formed and recalled. In the first set of experiments, I address how the hippocampus chooses which place cell representation to recall when entering an environment. Uniting decades of conflicting findings, I found that the hippocampus activates the representation for the most likely environment given the animal's past experience and the available sensory information. I demonstrate that the animal's prior experience with the different environments can even be reconstructed in an unsupervised manner from the neural activity alone. In the second set of experiments, I investigated the cellular mechanisms that give rise to these hippocampal computations. Surprisingly, I found that the most well studied form of synaptic plasticity in the hippocampus, long term potentiation, is not necessary for forming or maintaining contextual or spatial codes in the output region of the hippocampus, CA1. Instead, long term potentiation in CA1 is specifically involved in novelty and reward processing. These data and the associated model help to disentangle the influences of inherited neural dynamics and plasticity in driving place cell representations and behavior. In the final set of experiments, I helped develop and test a novel genetically encoded voltage indicator for use in behaving animals. We show that with minimal modifications to a commercial two photon microscope, we can record intracellular voltage fluctuations from dozens of cells simultaneously. Combined, my thesis work identified a central mnemonic computation performed by the hippocampus and began to untangle how this and other computations of the hippocampus arise from the components of the biology

Advisor: Giocomo Lisa
Ding Jun (Jun B.)
Ganguli Surya
Raymond Jennifer L.
Experimental characterization of genetic variants causing hypertrophic cardiomyopathy

Hypertrophic cardiomyopathy (HCM) is a common inherited heart condition, affecting one in 500 people. It is most often caused by variants in the genes that encode the cardiac sarcomere, including the gene encoding beta cardiac myosin, MYH7. However, hundreds of such variants exist, and most are very rare. This presents an obstacle to variant pathogenicity assignment, and population genetic studies are limited in their utility because they will never capture every possible variant. In this dissertation, I discuss the diagnostic challenges presented by uncharacterized variation in disease genes like those associated with HCM. I then describe a combination of experimental approaches that provide insight into the pathogenicity of HCM-associated genetic variants. I performed high-throughput mutagenesis MYH7 in induced pluripotent stem cells (iPSCs) using two methods, the in vitro PCR-based strategy POPcode and the direct genomic mutagenesis method CRISPR-X, comparing efficacy and comprehensiveness. To study the impacts of changes to MYH7 copy number, which is altered by some mutagenesis strategies, I both knocked out a copy of MYH7 and inserted a copy of cDNA. I also developed two methods for pooled disease phenotyping of cells differentiated to iPSC-cardiomyocytes (iPSC-CMs). The first sorts cells by size; hypertrophy is the pathognomonic feature of HCM. The second utilizes a fluorescent reporter for a gene, NPPB, that is consistently up-regulated in HCM-affected cardiac tissue. I used both of these methods to sort mutagenized cells and identify variants that may cause cardiomyocyte hypertrophy. Part of the missing heritability of HCM may be explained by variation in non-sarcomeric genes. I also share a study where we identified a novel variant in the Wnt signaling regulator WTIP in a family with HCM, and characterize its phenotypic impacts in multiple models. Together, these strategies can be combined to generate functional phenotypic data across variants in MYH7 and other HCM-associated genes, providing us with a deeper biological understanding of the structure-function relationships of the variant proteins that cause HCM and informing patient diagnosis in the clinic

Keywords: Genetics.
Advisor: Ashley Euan A.
Bassik Michael
Bhatt Ami (Ami Siddharth)
Fire Andrew Zachary
Experimental design and decision-making in marketplace platforms

Online platforms often rely on experiments to aid decision-making. When considering a new change, they test the intervention on a subset of the users before deciding whether to launch platform-wide. However, in the setting of marketplace platforms, prior work shows that treatment effect estimates can be biased. Users in a market interact with each other, which violates the Stable Unit Treatment Value Assumption (SUTVA), creates biased estimates, and may impact the resulting decisions made from these experiments. We develop models to capture market dynamics and investigate the effect of interference on different designs and estimators. In particular, we are able to highlight and formalize the relationship between the magnitude of the treatment effect bias in commonly run experiments and the level of supply and demand imbalance in the market. Building on these insights, we propose a novel class of experimental designs and estimators using two-sided randomization (TSR), as a method to reduce bias. In addition, we show that the commonly used standard error estimates are also biased in these marketplace settings. We analyze the impact of the statistical biases on the resulting decisions based on the experiment, show that both forms of biases interact to negatively impact decision-making, and propose practical methods to mitigate such biases

Keywords: Management Science and Engineering.
Advisor: Johari Ramesh
Weintraub Gabriel
Ugander Johan
Experiments in learning and transfer of inquiry strategies using short instructional videos

Science simulations enable students to conduct rapid experimentation. Many students conduct sub-optimal experiments. Consequently, they may not discover the underlying scientific regularities. The current research examined whether simple instructional videos can help students learn more effective inquiry strategies such that they will spontaneously use them in a new situation. Five experiments with 591 eighth-graders in Colombia compared the effectiveness of five instructional conditions and a Control condition. Instructional conditions varied in the number of pedagogical features hypothesized to be helpful. At the simplest level -- Show Effective -- an instructional video simply showed an effective inquiry strategy for a simulation after the students had already tried the simulation themselves. At the most complex level -- Expanded -- students were shown the effective strategy along with an explanation. They were also shown a common ineffective (default) strategy and an explanation of why this strategy is an alluring default. Importantly, the Expanded instruction also told students that the effective strategy is more cognitively demanding in the short run but better than the tempting default strategy in the long run. Several additional conditions partied the relative effect of the different elements added to the Expanded instruction. Students engaged in simulations as a pre-test and a transfer task roughly two weeks later. We analyzed changes in their inquiry patterns because of the intervening instructional videos. The experiments were conducted under the highly variable school conditions brought about by COVID-19, as well as sampling across very different types of schools. No experiment yielded definitive results. However, when aggregating across the studies, the Expanded instructional model led to significantly greater transfer of the effective strategy while reducing the default strategy. This work provides initial evidence that adding a cue to resist the lure of a poor strategy plus recognizing the effort of the effective strategy improves transfer over simply explaining the effective strategy and alerting students to a common ineffective strategy

Keywords: Education.
Advisor: Schwartz Daniel L.
Bernstein Michael
Levine Sarah L. (Sarah Loewenberg)
Wieman C. E. (Carl Edwin)
Exploring Associative Memory Performance of Confocal Cavity QED Network

Driven-dissipative systems have emerged as featured models of study between the domains of physics, mathematics, and computer science, notably atomic molecular and optical (AMO) physics and quantum information. The ability of these systems to interact with their external environment heralds applications in quantum computing, quantum communication, metrology, and sensing via phenomena such as natural error correction. We explore a candidate experimental framework for achieving driven-dissipative computation in the form of an associative memory, a neural network capable of pattern storage and recollection. This platform is realized by a confocal cavity QED apparatus, where the modes of this optical cavity behave as synapses, mediating interactions between a network of spinful bosonic ensembles which function as neurons. In this thesis we investigate the capabilities of this multimode cavity system to perform memory retrieval where the intracavity spin relaxation dynamics are modeled according to a mean-field treatment. We confirm (from previous works by Marsh et. all) that the system minimizes energy according to deterministic steepest descent dynamics instead of Glauber dynamics, in concert with previous results which do not utilize a mean field approach. Furthermore, these dynamics were predicted to occur at faster timescales than the previous non mean-field treatment. We also explore the efficiency of memory recollection over expanded ranges of input errors when the system is subjected to auxiliary longitudinal fields. We observed that these longitudinal fields performed state preparation effectively, and provided a slight improvement in memory recall contingent upon the appropriate tuning.

Keywords: Physics
associative memory
cavity
mean-field
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Thesis
Exploring structure-function relationships in knee cartilage and meniscus using medical imaging and soft tissue biomechanics techniques

Knee cartilage and meniscus have important roles in supporting loading in the knee, and their functional ability to support these loads is in large part due to their structure. Degeneration of these tissues through the process of aging, traumatic injury, and diseases like osteoarthritis can drastically change the structure and function of knee cartilage and meniscus. Because the structure and function of cartilage and meniscus are inherently linked, changes in one impact the other. To evaluate the effects of changes in functional loading of cartilage on its structure, non-invasive medical imaging techniques were used to analyze pre- and post-run scans to look for post-exercise changes in the quantitative magnetic resonance imaging parameter T2 relaxation time to better design future studies monitoring cartilage health. To evaluate the effects of changes in cartilage and meniscus structure on their function, mechanical testing techniques were used to examine the specific use case of the impact of imaging contrast agent solution exposure on meniscus and cartilage structure to develop a road map for designing safer contrast agent solutions. Finally, a combination of function and structural measurements were leveraged to characterize material properties of previously understudied pediatric meniscus specimens. Insights from this line of investigation will inform future studies focused on developing treatments and therapies targeted specifically for pediatric meniscus tissue. The work presented in this thesis advances our scientific understanding of the structure-function relationship of knee cartilage and meniscus and will provide a foundation for research supporting the development of treatments, interventions, and therapies for injury and disease in the knee for people of all ages

Keywords: Mechanical Engineering.
Advisor: Gold Garry E.
Levenston Marc Elliot
Hargreaves Brian Andrew
Exploring Vietnamese American Identity and Political Affiliation: An Auto-Ethography on Experiences of Loss and Belonging

In this work, I, a second generation Vietnamese American, conduct an auto-ethnography of myself, my mother, and my grandfather on their political beliefs to gather qualitative data on shifting Vietnamese American political attitudes between generations. Through a series of interviews, I aim to determine to what extent Vietnamese American ethnic background has on individual political beliefs, if at all. Ultimately, I have found that it is not ethnic and racial identity that uniformly influence political affiliation; rather, it is the range of experiences in immigration and assimilation that affect said beliefs. I divide my findings into three core themes: the refugee experience, the Americanization experience, and the racial consciousness experience.

Keywords: Vietnamese
Vietnamese Americans
Vietnam War (1961-1975)
asian american
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Thesis
Extensions and applications of deep probabilistic inference for generative models

Despite the growth of data size, many applications for which we would like to apply learning algorithms to are limited by data quantity and quality. Generative models propose a framework to naturally combine prior beliefs with real world data. Core to the generative approach is the challenge of probabilistic inference, or estimating latent variables given observations. This challenge has led to a rich field of research spanning many statistical techniques. More recently, deep learning methods have been used to solve inference queries, aptly named deep inference. In my dissertation I will explore extensions to deep inference in response to real world challenges of sparsity and efficiency. I will present case studies of practical applications where deep inference achieves considerable improvements upon prior work. This dissertation is centered around three parts. We present the background for generative models and deep inference with an emphasis on modern variational methods. The first part will present new algorithms for generalizing inference to be robust to different notions of sparsity, such as multimodal data, missing data, or computational constraints. Second, we study meta-amortized inference, or "inferring how to infer". A doubly-amortized inference algorithm would be cheaply able to solve inference queries for a novel generative model. We will show a new algorithm to re-purpose masked language modeling to do just this. Third, we present two real-world applications of deep inference in education: (a) estimating student abilities under item response theory and related psychometric models, and (b) inferring educational feedback for students learning to solve programming questions. Together, these contributions showcase the richness and utility of deep inference in education, and more broadly in real world contexts

Advisor: Goodman Noah (Noah D.)
Ermon Stefano
Finn Chelsea
F4graph : an API for camera scheduling and heterogeneous image processing on mobile devices

Mobile device photography is in the midst of a transformation, with a proliferation of new hardware for both image capture and processing. However, the camera APIs for mobile operating systems rarely expose the full capabilities of the hardware, because these depend on low-level code or platform-specific details. This work introduces F4graph, an application model and software interface which opens up many more capabilities of the imaging hardware to userspace applications. Within F4graph, captures on multiple image sensors or other hardware can be linked together to produce complex sequences of actions using a straightforward constraint-based scheduling model. The system provides strong guarantees by checking as many constraints as possible before committing actions to hardware and by only breaking constraints at well-defined points. Additionally, F4graph allows the application to define the processing pipeline along with the capture sequence so image data can be streamed directly to a reconfigurable image processor or other hardware. This includes the ability to stream continuously, to process multiple images from a stream, and to create feedback cycles for metering and similar applications. This thesis also describes how F4graph can be implemented in a real system, using a Xilinx Zynq Ultrascale board as a prototyping platform. Our demonstration system uses the Zynq's FPGA fabric as a reconfigurable image processing accelerator and its real-time core to orchestrate timing-critical events while the application processor cores run a normal multitasking Linux OS. Using the F4graph API on our prototype, userspace applications are able to schedule and execute actions with a temporal resolution under 20 microseconds. The complexity of this heterogeneous hardware system is managed by a build system which generates the entire hardware/software stack from the application code and a short human-readable system description file. This makes it possible to develop complete applications that use precise capture sequencing and hardware-accelerated image processing without the need to write platform-specific or real-time code

Keywords: Electrical Engineering.
Advisor: Horowitz Mark (Mark Alan)
Hanrahan P. M. (Patrick Matthew)
Levoy Marc
Far from home : understanding pleiotropic effects of adaptive mutations in non-home environments

Understanding how organisms adapt to their environment and determining the fitness consequences of such adaptation is a major goal of evolutionary biology. It is unknown how mutations that are adaptive in one environment fare in a new environment. The fitness consequences of mutations in many different environments is what we define as pleiotropy. In this thesis, I shed light on adaptive mutations in the environment they evolved in (or "home" environment) and their effects in novel environments ("non-home" environments). First, I characterize components of the morphological adaptation of the dynamic pyrenoid in the algae, Chlamydomonas reinhardtii in both low carbon concentrations ("home environment") and high carbon concentrations ("non-home environment"). Second, I leverage barcode lineage tracking to analyze individual adaptive mutations from both haploid and diploid evolved populations and estimate their fitness effects in both their home and non-home environments. Lastly, I identified adaptive mutations and constructed homozygous and heterozygous mutant strains to gain insight into the roles of dominance and pleiotropy in adaptation. I find that environment is not the sole driver of pleiotropy, but ploidy, dominance, and target genes contribute to fitness in other environments. Together, this study of adaptive mutations reveals a diversity of forces driving pleiotropy that are hidden and cannot be revealed by the mutation's fitness in its home environment alone

Keywords: Biology.
Advisor: Sherlock Gavin
Cyert Martha S.
Good Benjamin H.
Petrov Dmitri Alex