VictorJuiz/thesis huggingface dataset:
Translatome analysis reveals microglia and astrocytes to be distinct regulators of inflammation in the hyperacute and acute phases after stroke
Keywords: Medicine.

Ischemic stroke is a leading cause of death and long-term disability. Neuroin-flammation after stroke can significantly affect stroke outcomes¬--it can clear dead tissue and aid neuroplasticity but also exacerbate cell death. However, the exact astrocytic and microglial signaling pathways initiating neuroinflammation after stroke are unresolved and will be critical for developing immunomodulatory stroke therapies. In Chapter 1, I give background on the progression of stroke, status quo of treatments, and the genetic tools used to profile gene expression changes in microglia and astrocytes. Furthermore, I lay out the key gaps in knowledge and give an overview of how this dissertation fills those gaps. In Chapter 2, to parse the astrocytic and microglial response from that of infil-trating cells in the brain after stroke, we used the RiboTag technique to separately obtain astrocyte and microglia-derived transcriptional responses. By crossing floxed RiboTag mice with Aldh1l1-CreER or Cx3cr1-CreER mice, we tagged astrocytic or microglial ribo-somes, respectively, and then isolated cell-specific mRNA from ribosomes immunopre-cipitated from brain tissue. We performed RNA-sequencing on astrocyte or microglia-specific transcripts obtained from peri-stroke cortex in male and female mice 4 hours and 3 days after distal middle cerebral artery occlusion or sham surgery. Few sex differences were observed. Microglia initiated a rapid response to stroke at 4 hours by adopting a proinflammatory profile associated with the recruitment of immune cells while the astro-cyte profile was dominated by stress response, as well as proinflammatory genes. By 3 days, microglia have dampened their inflammatory response in exchange for a prolifera-tive, phagocytic state. At this timepoint, astrocytes have enhanced their pro-inflammatory response and may be doing this through transcriptional regulation. Taken together this data comprehensively describes the astrocyte and microglia-specific translatome re-sponse in the hyperacute and acute period after stroke and identifies pathways critical for initiating neuroinflammation. Chapter 3 summarizes the findings, provides additional discussion, and comments on future directions to be taken
TrkA-ing pain : an optobiological approach for probing the NGF/TrkA pathway in relation to pain
Keywords: Humanities and Sciences.
Chemistry.

Chronic pain is a prevalent problem that plagues modern society, and there is a need for better understanding its mechanisms to produce alternative therapeutics for managing pain. Nerve Growth Factor (NGF) and its primary receptor, Tropomyosin Receptor Kinase A (TrkA) are known to be potent mediators of chronic pain, but there is a lack of established methods for precisely perturbing the NGF/TrkA signaling pathway in the study of pain and nociception. Optobiological tools that leverage light-induced protein-protein interactions allow for precise spatial and temporal control. Previously, our lab reported a blue-light activated version of TrkA generated using the improved Light inducible Dimer (iLiD) system, opto-iTrkA, and demonstrated its ability to activate similar downstream survival and differentiation pathways to native NGF/TrkA signaling in a cellular model. This work aims to extend the use of opto-iTrkA as a tool to studying nociceptive pathways \textit{in vitro} and pain \textit{in vivo}. We show that opto-iTrkA activation orthogonal to NGF/TrkA activation is able to sensitize the Transient Receptor Potential Vanilloid 1 (TRPV1) channel in cellular models, cause the transduction of phospho-ERK signals from axonal terminals to the soma in cultured sensory neurons, and also induce mechanical sensitization in mice expressing opto-iTrkA in the peripheral nervous system. We hope that this demonstrates the utility of opto-iTrkA as a tool to expand the toolbox for dissecting nociceptive pathways and pain.
Trust, Sovereignty, and Social Lives of Displacement in Iraq
Keywords: Humanities and Sciences.
Anthropology.

How can those who have experienced violence establish a system of order and survival when the state authority fails? This dissertation explores the motivations and moral beliefs of internally displaced Iraqi Arabs who make informal sponsorship agreements with Iraqi Kurds to ensure protection, residency, jobs, and resources for themselves, their families, and acquaintances in the Kurdistan Region of Iraq [KRI]. It challenges the common belief that civil wars lead to the breakdown of neighborliness. Instead, this dissertation demonstrates that these sponsorship agreements enable displaced Iraqi Arabs to maintain a mutual sense of obligation and trust with the Iraqi Kurdish community while remaining separate from an interior domain of Kurdish life. It argues that by redefining colonial and state power, Iraqis who enter into such sponsorship agreements create a less-explored form of sovereignty, in which the founding sovereign act is not the exclusion of bare life, but the conditional integration of the ethnic other into social life. Drawing from two years of ethnographic fieldwork and archival research in the Sulaymaniyah Governorate of the Kurdistan Region and the adjacent disputed territories, the dissertation weaves together stories of overlapping narratives of displacement in peri-urban neighborhoods, narratives of everyday encounters with authorities in checkpoints, theological ruminations of Kurdish tribal leaders around compassion and engaged humanitarianism, the bitter nihilism and disengagement of young Kurdish men, deeply exhausting travails of fugitive displaced Iraqis falsely accused of terrorism, and the remarkable agency exercised by displaced women in keeping their families together, among others. At a monumental time when social hierarchies are deeply in flux, it provides a lively portrait of how the fragmentation of authority is experienced, imagined, and narrated by Iraqis.
Truth-vetting & mathematics : sense-making practices in an inequitable world
Keywords: Education.

Increasing students' performance in school-based mathematics is a constant focus that is fueled by differences in students' performance outcomes. Research that examines students' experiences with learning mathematics has shown that students take on different ways of knowing that either align or are at odds with the type of classroom instruction they receive. The research has shown that these preferences tend to fall along gendered lines. This study uses student interview data to argue that knower types (ways of knowing) fall more generally along social identity lines, and that this phenomenon is influenced by the interaction of identity and social construction. The framework and findings of this project also provide a new lens for understanding and interrogating "gaps" in students' math performance and differences in math trajectories.
Ultrafast dynamics beyond the average response : a case study of KTaO3 and SrTiO3
Keywords: Humanities and Sciences.
Applied Physics.

Ultrafast light pulses offer a powerful means to drive materials into non-equilibrium states, unlocking novel properties that are often inaccessible in equilibrium conditions. In strongly coupled systems, such as the perovskite materials, macroscopic states such as paraelectricity, ferromagnetism, ferroelectricity, multiferroicity, and even superconductivity emerge, often accompanied by associated or competing structural transitions. Using ultrafast light pulses it is possible to dynamically modify the properties of these materials, aiming to drive them towards energetically related states. Recent advancements in free electron lasers enable the characterization of these states at all relevant length scales, which enables the development of techniques to dynamically control materials from the mesoscopic to the atomic scale. I first discuss the case of KTaO3 , which exhibits a ferroelectric instability as a soft mode at low temperatures, but quantum fluctuations prevent the occurrence of a ferroelectric transition. By dynamically modifying the electronic structure of KTaO3 through above-gap optical excitation, I then employ X-ray pulses to observe the resulting modified structure. My observations reveal the stabilization of the paraelectric phase and a notable hardening of the acoustic branch. Next, I investigate SrTiO3 , which also demonstrates a similar ferroelectric instability at low temperatures. Here, I used a single cycle THz pulse with the intention of driving the zone center ferroelectric soft mode and observing the emergence of ferroelectricity in SrTiO3 using X-ray pulses. Surprisingly, I do not observe an average unit cell response and instead observe nanoscale dynamics. Even more unusually, this nanoscale response is polar and inverts with the direction of the THz field. Overall, this research demonstrates the exciting potential of ultrafast light pulses in driving material transitions and exploring the dynamic properties of strongly coupled systems. The observed modifications and emergent phenomena in KTaO3 and SrTiO3 offer valuable insights into the nonequilibrium physics of materials, which serve as a foundation for the future design and development of novel materials.
Uncertainty-aware control, planning, and learning for reliable robotic autonomy
Keywords: Engineering.
Aeronautics and Astronautics.

As autonomous systems take on increasingly challenging tasks in safety-critical settings such as autonomous driving and aerospace, their ability to explicitly account for uncertainty becomes critical for ensuring their robustness. However, effectively accounting for uncertainty from various factors remains challenging. In particular, there is a pressing need for more efficient algorithms for (1) precisely propagating uncertainty through the decision making stack, (2) quickly selecting actions that optimize objectives and satisfy constraints under uncertainty, and (3) safely gathering informative data to adapt and reduce uncertainty. The first part of this thesis tackles the problem of reachability analysis, which entails characterizing all states that a dynamical system can reach. We introduce efficient sampling-based techniques and study their theoretical properties with random set theory, geometry, and optimal control, guiding the design of accurate reconstruction algorithms. We use these sampling-based methods to design robust control and planning algorithms. The second part of this thesis focuses on stochastic programming and trajectory optimization under uncertainty. We study the sample average approximation (SAA) approach and identify a limitation of the method for problems with expected value equality constraints. We formulate an alternative SAA, prove its asymptotic optimality under mild assumptions, and demonstrate it on challenging risk-averse trajectory optimization problems. The third part of this thesis presents a sequential exploration-exploitation framework for safe and active dynamics learning and control. This approach enables robots to autonomously learn their dynamics while satisfying constraints, until uncertainty levels are small enough to safely achieve desired tasks. The approach is validated on a robotic platform emulating spacecraft dynamics.
Uncovering and inducing interpretable causal structure in deep learning models
Keywords: Humanities and Sciences.
Linguistics.

A faithful and interpretable explanation of an AI model's behavior and internal structure is a high-level explanation that is human-intelligible but also consistent with the known, but often opaque low-level causal details of the model. We argue that the theory of causal abstraction provides the mathematical foundations for the desired kinds of model explanations. In the analysis mode, we uncover causal structure using interventions on model-internal states to assess whether an interpretable high-level causal model is a faithful description of a deep learning model. In the training mode, we induce interpretable causal structure using interventions during model training to simulate counterfactuals in the deep learning model's activation space. We show how to uncover and induce causal structures in a variety of case studies on deep learning models that reason over language and/or images.
Uncovering novel therapeutic solutions for antimalarial drug resistance
Keywords: Medicine.
Biochemistry.

Emergence and spread of drug resistance against all currently used antimalarial compound classes continues to prevent eradication of malaria, one of the world's deadliest infectious diseases. To aid the effort to discover new antimalarials, we performed a phenotypic chemical screen of compound libraries provided by Medicines for Malaria: Malaria, Pathogen, Pandemic response, and Covid boxes and identified 3 compounds for follow-up. First, a non-bisphosphonate compound, MMV019313 was discovered previously in the lab to target bifunctional P. falciparum farnesyl/geranylgeranyl diphosphate synthase (PfFPPS/GGPPS), a prenyltransferase of the isoprenoid pathway. PfFPPS/GGPPS is a validated, high-priority antimalarial drug target. Unfortunately, current bisphosphonate drugs that inhibit PfFPPS/GGPPS show poor bioavailability and selectivity against the human enzymes. We generated a series of MMV019313 analogs which contained compounds that were potent, selective, and with drastically improved in vitro ADME profiles. Second, we identified a picomolar antimalarial inhibitor, MMV026468, that specifically targets the isoprenoid precursor pathway in blood-stage Plasmodium falciparum. MMV026468's action on the isoprenoid precursor pathway was validated by chemical rescue, microscopy, and western blot. Metabolite analysis demonstrated an overall decrease in isoprenoid precursor pathway intermediates. Additionally, we generated highly resistant, stable, and selective parasites against MMV026468 in three different P.falciparum strains (3D7, W2, Dd2) which underwent whole genome sequencing. Altogether we identified a promising, highly potent, pathway-specific lead compound for development of new antimalarials to combat multidrug resistant parasites. Finally, we identified MMV1580853 (previously BPH-1358), a potent and exceedingly fast-acting blood-stage antimalarial. MMV1580853's rate-of-kill mimics artemisinin in that it kills parasites equally as fast in a 1 hr pulse as treatment for a full 72 hr parasite replication cycle . MMV1580853 has been annotated as a prenyltransferase inhibitor in both humans and bacteria but did not inhibit any of the three annotated P. falciparum prenyltransferases. We did not observed buildup of ubiquitinated proteins, eIF2a phosphorylation, and hemoglobin in MMV1580853-treated parasites, indicating MMV1580853 does not have the same mechanism-of-action as either chloroquine or artemisinin, two fast-acting antimalarials used clinically. MMV1580853 also demonstrated very low mammalian cell cytotoxicity and artemisinin-resistant Kelch13 strains were equally as susceptible to MMV1580853 treatment as non-drug resistant strains. Overall, these properties make MMV150853 extremely favorable as a new lead antimalarial compound for clinical development.
Uncovering the connectomic and functional components of neurodegenerative disease spread using dynamic network modeling
Keywords: Bioengineering.

An emerging view regarding neurodegenerative diseases is that discreet seeding of misfolded proteins leads to widespread pathology. However, the mechanisms by which misfolded proteins seed distinct brain regions and cause differential whole-brain pathology remain elusive. Using whole-brain tissue clearing and high-resolution imaging, I longitudinally mapped pathology in an α-synuclein preformed fibril injection model of Parkinson's disease. Cleared brains at different time points of disease progression were quantitatively segmented and registered to a standardized atlas, revealing distinct phases of spreading and decline. I then developed a computational network model with parameters that represent α-synuclein pathological spreading, aggregation, decay, and gene expression pattern to this longitudinal dataset. This model generalized to predicting α-synuclein spreading patterns from several distinct brain regions and could also estimate their origins. Altogether, these results empower a more mechanistic understanding and accurate prediction of neurodegenerative disease progression
Uncovering the genetic, developmental and enzymatic logic of plant natural product biosynthesis
Keywords: Humanities and Sciences.
Chemistry.

In spite of the importance of plant chemistry, studying plant compounds and how they are made within plants remains challenging, limiting our ability to harness the power of these molecules. In my dissertation, I explore how plants utilize intricate genomic, enzymatic and developmental strategies to produce some of the world's most important bioactive compounds. First, I present a novel long-read genome sequencing approach that enhances our ability to predict plant natural product biosynthetic pathways. This approach uncovers new biosynthetic gene clusters in non-model medicinal plants with no genome sequences previously reported, and enables the genome-guided discovery of novel plant biosynthetic pathways. Second, I explore the developmental coordination of plant secondary metabolite biosynthesis in geophytes, a diverse group of plants with underground food-storage organs that comprise various species of important edible, ornamental and medicinal crops. I show that while clinically-relevant plant specialised metabolites like the Amaryllidaceae alkaloids produced by plants like daffodils accumulate to high levels in many tissues in their producer plants, their biosynthesis is localized to nascent, growing tissue at the base of leaves. A similar trend is found for the production of steroidal alkaloids (e.g. cyclopamine) in corn lily. Taken together, my work sheds light on the developmental logic of toxin biosynthesis in daffodil and corn lily and more broadly, it suggests a paradigm for biosynthesis regulation in monocot geophytes where plants are protected from herbivory through active charging of newly formed cells with potent eukaryotic toxins that persist as aboveground tissue develops. Third, with this understanding of the developmental logic of medicinal alkaloid biosynthesis in daffodils, I identify the genetic basis of medicinal alkaloid biosynthesis in daffodils and engineer their de novo production in heterologous hosts. My work led to the elucidation of a complete set of biosynthetic genes for the production of the FDA-approved Alzheimer's disease drug galantamine and the eukaryotic ribosome inhibitor haemanthamine, and revealed that a set of closely related cytochromes P450s differentially tailor a common intermediate to generate three scaffold types with distinct biological activities. This work enables the reconstitution of the de novo production of these toxins in a heterologous host, Nicotiana benthamiana (tobacco).
Underlying physics of mixing efficiency of stably stratified turbulence
Keywords: Engineering.

Global and regional ocean simulations rely on eddy viscosities and diffusivities to represent the unresolved turbulent mixing of momentum and scalars. The simulated flow and the transport of quantities such as heat and carbon are quite sensitive to how the turbulence is modeled. Particularly, the eddy diffusivity model of Osborn (1980) is widely used to represent the vertical buoyancy flux, which requires accurate knowledge of the mixing coefficient—defined as the ratio of the dissipation rates of available turbulent potential energy (TPE) and turbulent kinetic energy (TKE). While a constant value of 0.2 is often prescribed for the mixing coefficient, there is significant evidence for parameterizing it as a function of dimensionless numbers that characterize the state of the turbulence. Using direct numerical simulations, we studied stably stratified turbulence under three different sets of forcing: (i) linear axisymmetric forcing; (ii) three types of shear forcing; and (iii) combined momentum and buoyancy forcing. By analyzing the budgets of the normal Reynolds stresses and the vertical buoyancy flux, we observed that terms involving the pressure field (i.e., pressure-strain correlations and pressure scrambling) exhibited significant changes as the turbulent mixing became more efficient. Each of these three sets of flows exhibited quantitative physical differences in their mixing characteristics. Our findings suggested the need for improved models of the turbulent mixing in stratified flows, which we achieved by revising existing scaling relationships for the mixing coefficient and exploring anisotropic model forms for the turbulent momentum and scalar fluxes
Understanding and recapitulating blood development for safe cell therapies
Keywords: Medicine.

Human pluripotent stem cells (hPSCs) resemble some of the earliest cells in the developing human embryo, and they have immense potential to be used for regenerative medicine because they can differentiate into any cell type in the body. While promising, this potential is dependent on the development of better methods to differentiate hPSCs into therapeutic cell types and better tools to ensure the safety of those cells when given to patients. In this thesis, I will provide an overview of hPSC culture and differentiation, outlining many of the challenges associated with differentiating hPSCs and various strategies for overcoming these challenges. One cell type that has proven to be difficult to generate is the hematopoietic stem cell (HSC), which is of great therapeutic interest because it gives rise to all the cells that make up the blood and immune system for life, and HSCs can be used to treat or cure a variety of blood and immune diseases. Often, the most significant roadblock in differentiating a given cell type, including HSCs, is a lack of understanding in how that cell normally develops in the growing embryo. Thus, I set out to better characterize where HSCs come from by identifying the intermediate cell progenitors that give rise to HSCs during development. Applying these developmental lessons to hPSC differentiation, I developed a protocol to efficiently generate HSC-like cells that closely resemble primary HSCs. As expected, the HSC-like cells have the potential to give rise to all the major blood cell types including erythroid, myeloid, and lymphoid cells -- all of which could be utilized for regenerative medicine applications. Finally, I developed a hPSC line engineered with two orthogonal genetic safety switches to address the two primary safety risks associated with hPSC-based therapies. Ultimately, the goal of this body of work is to further advance the use of hPSCs for regenerative medicine