VictorJuiz/thesis huggingface dataset:
The Structural and Functional Connectivity of the Cerebellum in Autism Spectrum Disorder

The cerebellum coordinates motor, cognitive, and affective function in the brain, largely via its connectivity to the cerebral cortex. Perinatal injury to the cerebellum and mutations to genes integral for cerebellar development are strongly associated with Autism Spectrum Disorder (ASD), a highly prevalent neurodevelopmental disorder with an unclear etiology and heterogeneous behaviour presentation. Disrupted cerebellar development results in changes to its coordinated growth and neurophysiological activity with other brain regions, heretofore referred to as structural and functional connectivity. Recurrent patterns in cerebellar-cerebral connectivity in the context of atypical neurodevelopment have been observed. However, much is still unknown regarding the extent to which these patterns correspond to particular ASD-related behaviours and genetic mutations. In chapter 2, behaviour-correlated functional connectivity profiles - measured using resting-state functional magnetic resonance imaging (MRI) - were identified using a neurodevelopmental disorder (NDD) imaging and behaviour dataset. The findings were then assessed for replication using an independent dataset from a second consortium. Two functional connectivity components were observed in the original and replication dataset: a first component maximally correlated to obsessive-compulsive behaviour and a second component characterized by social communication deficit contrasted against attention deficit. Statistically stable features of these components mainly included nodes pertaining to the attentional, central executive, and default mode networks. Having identified functional connectivity profiles correlated to ASD-related behaviours, structural connectivity profiles were then characterized and compared to those of clustered mouse models, where each model bore a genetic mutation associated with ASD. Structural connectivity was defined as correlations between regional brain volumes measured via MRI. Two mouse model clusters were significantly similar to human clusters following False Discovery Rate correction: the cluster with elevated protein product expression in the cerebellum versus the cerebrum, and the cluster with genes implicated mTOR pathway biomolecule synthesis. Structural correlations were most similar between mice and humans for correlations between cerebellar Crus II or vermal lobule VI and all cerebral regions. Similar to the findings of chapter 2, large structural correlation differences mainly involved regions pertaining to the attentional, central executive, and default-mode networks.

Keywords: autism
cerebellum
connectivity
fMRI
MRI
Advisor: Lerch, Jason P
Reduction of Manganese Ore by Methane

Manganese is a critical element in steelmaking and most manganese ores mined today are consumed as some form of ferromanganese (FeMn) or silicomanganese (SiMn) alloying material for steel. The production of FeMn and SiMn is energy and carbon intensive, typically requiring thousands of kilowatt-hours of electrical energy and hundreds of kilograms of coke per tonne of alloy produced in the conventional submerged arc furnace (SAF) process, resulting in substantial CO2 emissions. As demand for these FeMn and SiMn materials is expected to grow significantly with the widespread adoption of next generation high-manganese steel grades, a less energy- and carbon-intensive processing strategy is required. A methane-based gas-solid reaction scheme has previously been proposed for the reduction of manganese oxides and ores. Using this approach, a combination of methane and hydrogen gases are used to develop a high activity adsorbed carbon phase on the surface of the manganese oxide, enabling the reduction of the oxide at high temperatures. The basic feasibility of this approach has been demonstrated for manganese oxide and ore powders in systems not limited by bulk diffusion of the gases. The present work considered the reduction of manganese ore briquettes with the intent of informing the scale-up of this methane-based reaction scheme. The reaction rate was observed to be sensitive to the partial pressure of methane in the system, consistent with prior investigations. However, a significant impact of gas diffusion within the ore briquette was observed, resulting in a ten-fold decrease in the apparent rate of reaction under the conditions assessed. This decreased rate of reaction is in part attributed to the sintering of the oxide phase and partial melting of metallic phases, both of which present a critical limitation on the reaction temperature. Additional technical barriers include challenges associated with the thermal decomposition of methane in the temperature range of interest for this reduction scheme and a strong sensitivity to the partial pressure of carbon monoxide in the system. As a result of these barriers, it is unlikely that a methane-based reduction scheme will be a technically or economically viable alternative to the conventional SAF processing route.

Keywords: Manganese
Methane
Reduction
Advisor: Barati, Mansoor
Twisted Eleven-dimensional Supergravity and Infinite Dimensional Exceptional Simple Super-Lie Algebras

We study a class of formal moduli problems associated to eleven-manifolds with a rank 6 trans- versely holomorphic foliation and a transverse Calabi-Yau structure. On R × C5, the (−1)-shifted tangent complex of this formal moduli problem is L∞-equivalent to a Lie-2 extension of an in- finite dimensional exceptional simple super-Lie algebra called E(5|10). In the first part of this thesis, we equip this formal moduli problem with the structure of a perturbative classical field the- ory in the Batalin-Vilkovisky formalism. Conjecturally, this theory describes the minimal twist of eleven-dimensional supergravity. We present strands of evidence for this conjecture by computing dimensional reductions and comparing with expected descriptions of twists of supergravity in lower dimensions, and by identifying the residual symmetries of the putative twist of eleven-dimensional supergravity within the symmetries of our theory. In the second half of the thesis, we construct particular backgrounds for our theory which we conjecture are twisted avatars of the AdS4 × S7 and AdS7 × S4 backgrounds of eleven-dimensional supergravity. To justify this conjecture, we study spaces of supergravity states on these backgrounds. We find that their characters match with prior expressions enumerating multi-gravitons in AdS4 × S7 and AdS7 × S4 respectively, and also admit specializations recovering generating functions of representation theoretic and enumerative significance, such as the MacMahon function. We study a decomposition of the state spaces we construct that exhibits them as direct sums of modules for two other infinite dimensional exceptional simple super-Lie algebras, E(1|6) and E(3|6) respectively. We conclude with some speculations about how our results can be used as input for holographic techniques.

Advisor: Costello, Kevin
Gualtieri, Marco
Causal Inference with Multiple Outcomes

Unobserved confounding presents a major threat to the validity of causal inference from observational studies. In this thesis, we introduce a novel framework that leverages the information in multiple outcomes for identification and estimation of causal effects. We first showed nonparametric identifiability of the causal effects with at least three outcomes. The key assumption in our approach is the conditional independence among multiple outcomes given the treatment and (both measured and unmeasured) confounders. Our identifiability results apply to both categorical and continuous treatment and outcomes. The theoretical derivation relies on the eigendecomposition of a matrix in the categorical case, and the spectral decomposition of a linear operator in the continuous case. We provide simulation results and a real data example for the discrete case. To accommodate high-dimensional data, we further develop parametric identification and estimation tools under a set of linear structural equation models. By assuming sparsity in the causal effects and exploiting the factor model structure of the outcomes, we are able to find the outcomes with zero causal effects, and use these outcomes to help estimate all other non-zero causal effects. We evaluate this proposal through synthetic and real data analyses, where we study the effect of smoking on laboratory test results. Moreover, we demonstrate that causal inference is possible with two parallel outcomes under a set of linear structural equation models. We invent two identification strategies by introducing a secondary outcome. Specifically, we utilize the symmetry and asymmetry properties of distributions of random variables to achieve identification. We develop accompanying estimating procedures and evaluate their finite sample performance through simulations and a data application studying the causal effect of the tau protein level on behavioral deficits in Alzheimer's disease. Finally, we explore some possible ways to relax the conditional independence assumption in this framework and discuss some possible directions for future research.

Advisor: Wang, Linbo
Kong, Dehan
Exploring the Role that SR Kinases Play in Controlling Viral Infections with a Focus on HIV-1

RNA processing plays a critical role in gene expression, enhancing protein diversity. SR proteins are essential components of this regulation whose activity and localization are modulated by their phosphorylation by SR kinases (CLK1-4, DYRK1A, SRPKs). HIV-1 relies on host RNA processing for viral protein production/replication, making the modulation of this process a potential target to regulate HIV-1 replication. This thesis examines the discrete roles of SR kinases in controlling HIV-1 gene expression. Depleting CLK1 enhanced HIV-1 gene expression, while CLK2 or SRPK1 depletion reduced it. CLK3 or DYRK1A depletion had no impact on HIV-1 expression. The opposing effects of CLK1 vs CLK2 depletion were due to action at distinct steps; CLK1 depletion increased HIV-1 promoter activity while depletion of CLK2 affected steps post-transcript initiation. RNA-Seq on cells depleted of each SR kinase revealed moderate but very discrete roles of the kinases in regulating host gene expression. While >80% DYRK1A depletion had the least impact on the host transcriptome, a two-fold reduction in CLK2 impacted the greatest number of genes at all levels. In most instances, CLK1 or CLK2 depletion had opposite effects on splicing indicating antagonistic roles of the two kinases. BioID-mass spectrometry (MS) of the CLKs identified a total of 287 potential interacting partners common and unique to each enzyme, many previously identified as affecting HIV-1 expression. Besides the known substrates of CLKs (SRs and related proteins), BioID-MS also detected factors that function at the level of transcription initiation, elongation, polyadenylation, splicing, and export. The demonstration that modulating SR kinase levels/activity can be used to alter HIV-1 expression, led me to identify small molecule SR kinase inhibitors, 1H3, 2E3, and Harmine with potent anti-HIV-1 activity. The compounds also modulated CLK1 and CLK2 function and altered their abundance. Harmine was unique among the SR kinase inhibitors tested in suppressing coronavirus replication, including SARS-CoV-2. At doses required to suppress HIV-1, Harmine had limited impact on the host gene expression as assessed by RNA-Seq. Together, my studies demonstrate the feasibility of targeting host RNA processing to inhibit a range of viruses with minimal impact on the host cell.

Keywords: Coronaviruses
HIV
RNA processing
SR kinases
Therapeutics
Advisor: Cochrane, Alan W
Physical Mechanisms Behind the Midlatitude Atmospheric Energy Transport Response to Imposed Arctic sea ice loss

The extratropical storm tracks play a major role in shaping midlatitude climate and account for most of the atmospheric heat transport (AHT) in the extratropics. The storm tracks are expected to intensify and shift poleward, in part due to a tug-of-war between low-latitude warming and strong Arctic warming. In this thesis, we focus on the role of Arctic warming through sea ice loss on the midlatitude storm tracks and on AHT. We present a multi-model analysis of the AHT responses to Arctic sea ice loss and low-latitude warming, employing numerical experiments from the Polar Amplification Model Intercomparison Project (PAMIP). A moist isentropic circulation framework allows separation of the contributions of mass transport from those of stratification driving changes in AHT. In PAMIP's atmospheric general circulation model experiments, prescribed sea ice loss reduces high-latitude AHT by warming the returning moist isentropic circulation, while prescribed warming of the ocean surface increases AHT by strengthening eddy mass transport. Ocean-atmosphere coupling has been shown to contribute significantly to the response to Arctic sea ice. To assess the role of ocean coupling on the storm tracks and AHT, we carry out the centennial coupled sea ice loss experiments from. We present a novel sea ice constraining method that grants better control over the partitioning of sea ice concentration and sea ice thickness, in comparison with existing methods, while conserving total water. The development of a novel technique is required because of the relatively weak sea ice melt in the PAMIP targets. Using the coupled PAMIP simulations, we build an ocean-atmosphere coupling hierarchy in order to characterize the role of ocean-atmosphere interactions on the storm-track response to Arctic sea ice. These simulations reveal that ocean-atmosphere coupling is necessary to capture the weakening of the storm tracks in response to Arctic sea ice loss, and that surface turbulent heat flux and ocean dynamics have complementary effects on the response. From the isentropic diagnostic perspective, most of the weakening of the storm tracks and its associated changes in AHT arise from a weakening of the transient eddy mass flux.

Keywords: Atmospheric energy transport
Sea ice loss
Storm tracks
Advisor: Kushner, Paul J
Affinity-Based Approaches for Controlling Release of Therapeutic Proteins from Biopolymer Hydrogels

Proteins are powerful therapeutic agents but are challenging to effectively administer to the body. Their fragile 3D structures enable them to interact with other molecules in precise ways to regulate biochemical pathways. Most are administered parenterally, but natural clearance mechanisms limit the duration of their effect. Conventional controlled-release strategies typically used for small-molecule drugs often require harsh processing conditions that can cause irreversible protein denaturation. Affinity-based release systems incorporate therapeutic proteins into delivery vehicles under mild conditions, enabling higher bioactive drug loading. The rate of release is governed by diffusion and non-covalent interactions with the delivery vehicle. We designed a series of release strategies enabling flexible delivery from biopolymer-based hydrogels. Src homology 3 (SH3) binding peptides enabled the sustained release of rod-derived cone viability factor long (RdCVFL) fused with an SH3 domain from a hyaluronan methylcellulose-based hydrogel. Using finite element analysis, we modelled drug distribution to inform the delivery vehicle formulation. We learned that higher excess concentrations of binding peptide led to subtherapeutic levels of protein in the tissue, while lower concentrations did not extend the effective time of release. This strategy could enable effective concentrations of RdCVFL-SH3 for 35 days. To enable multifactor release, we developed unique affibody binding partners through directed evolution that specifically bound to insulin-like growth factor 1 (IGF-1) and pigment epithelium-derived factor (PEDF). By varying the concentration of these protein binding partners, tunable and simultaneous release of both IGF-1 and PEDF from biopolymer hydrogels were achieved over the course of 7 days. This strategy enables the delivery of multicomponent therapies that are vital to the clinic. To streamline the process of developing binding partners, we utilized ROSETTA to create a binding peptide for vascular endothelial derived growth factor (VEGF). We isolated a 10 amino acid sequence within the complementarity-determining region by analyzing the crystal structure of the complex between bevacizumab and VEGF. We further validated the interaction using biolayer interferometry. This approach enables the rapid discovery of binding peptides for affinity-based release. Overall, we demonstrated that affinity-based release systems effectively sustain the delivery of therapeutic proteins, and specific binding partners enable multicomponent controlled release strategies.

Keywords: affibodies
affinity-based release
biopolymer hydrogels
controlled release
directed evolution
Advisor: Shoichet, Molly S
Validation of Existing Methods for Determining Protein and Amino Acid Requirements

The validity of protein and amino acid (AA) requirement estimates obtained using the minimally invasive 1-day indicator amino acid oxidation (IAAO) method have been questioned. Critics argue that there is no evidence linking 1-day IAAO-derived protein requirements to beneficial health outcomes in humans. Another concern is that the 1-day IAAO protocol is too short, potentially leading to inaccurate estimates of AA requirements due to incomplete adaptation. To address these concerns, this thesis conducted the following research: 1) a randomized cross-over trial measuring erythrocyte glutathione (GSH) kinetics in healthy adults receiving graded proteins intakes (0.6 to 1.5 gkg-1d-1) for 3 days; 2) a scoping review on adaptation and AA oxidation methods, identifying knowledge gaps to inform the planning of study 3 and; 3) a repeated measures design using IAAO to determine whether 1, 3, or 7 days of adaptation to experimental diets altered the threonine requirement in adult men. The first study found that a protein intake of 1.0 gkg-1d-1 (95% CI= 0.63, 1.39) maximized GSH fractional synthesis rates in healthy adults. The scoping review indicated that adaptation periods exceeding 7 days violate the conditions of successful adaptation, but further research is needed to directly compare 1-day and 7-day adaptations in AA requirement studies. This led to the purpose of study 3. In study 3, the mean threonine requirements (95% CI) for days 1, 3, and 7 were 10.5 (5.7, 15.9), 10.6 (7.5, 13.7), and 12.1 (9.2, 15.0 mgkg-1d-1), respectively. These requirements were not statistically different. In healthy young adults, maximum antioxidant synthesis was achieved at a protein intake of 1.0gkg-1d-1, which closely aligns with the 1-day IAAO-derived requirement of 0.93 gkg-1d-1. This suggests that 1-day IAAO-derived protein requirements are indeed reflective of favourable health outcomes. Furthermore, the short 1-day IAAO protocol allows for reliable estimation of AA requirements, as it was not different from those obtained after 3 or 7 days of adaptation. Overall, the 1-day IAAO method proves suitable for studying protein and AA requirements in humans. Moreover, its minimally invasive nature makes it particularly appropriate for investigating the nutritional needs of vulnerable populations that have been scarcely studied.

Keywords: Amino acid
Glutathione
Oxidation
Protein
Requirements
Advisor: Courtney-Martin, Glenda
Human Factors in Data-Driven Healthcare

Artificial Intelligence (AI) has fueled advances in many fields including healthcare, busi- ness and engineering. However, AI methods such as deep learning treat decision making as a black box where inputs are converted into outputs using a large number of parameter- ized features, with the reasoning behind decisions being largely opaque. It is difficult for humans to trust decisions when they don’t know the reasoning behind them. The goal of human-centered AI is to build reliable and safe AI systems. Human-in-the-Loop (HITL) systems build on earlier human factors approaches to complex aviation and nuclear plant interfaces, where trust is increased by integrating human supervision and expertise into the automation/AI system. Human factors engineering seeks to reduce human error, increase productivity, and enhance safety and comfort with a specific focus on the interaction be- tween the human and the automation/AI. In the research reported in this dissertation I merged human-computer interaction approaches to user experience design with human fac- tors approaches. In the research reported in this dissertation I show how human factors can be applied in the field of data-driven healthcare. I present empirical findings concerning the value of human experts in improving machine learning clinical prediction based on medi- cal data. I also report on the design and development of tools and approaches for making machine learning prediction models explainable and usable in the context of data-driven healthcare. Since highly skilled data scientists and machine learning experts will always be scarce relative to the ever increasing needs to use large data sets to improve decision mak- ing, the present research points the way towards human factors tools/systems that can allow users (such as physicians) without strong machine learning or data mining backgrounds, to use AI-based clinical decision support systems that they can trust.

Advisor: Chignell, Mark
Essays in Econometrics

This thesis collects three essays, each in the form of a chapter, on two independent topics in econometrics. The first two chapters address the problem of uniformly valid inference for set-identified linear functionals in models with linear moment inequalities. The identified set for the linear functional is characterized by value functions of two linear programs parameterized by the underlying probability distribution which generates the data. Chapter 1 exploits the generic nature of well-behaved linear programs to show that a randomly perturbed bootstrap-based confidence set delivers a computationally tractable inference procedure that is uniformly asymptotically valid over a large class of data-generating processes. Importantly, it is established that the validity of the method does not rely on high-level assumptions that are difficult to verify in practice. In Chapter 2, in a subclass of linear moment inequality models, an alternative method is developed to obtain a never-empty confidence set with similar properties. This is motivated by the fact that many moment inequality models have a large number of moment constraints relative to the dimension of the structural parameters, in which case confidence sets based on an optimization framework are often empty. Chapter 3 considers an independent problem of identification and estimation in measurement error models. Unlike in a classical measurement error setting where independent measurements are observed, economic data is often truncated by ranking and thus necessarily correlated. A notable example is data on ascending auctions with independent private values where only drop-out bids are observed. This raises the question of whether the underlying data-generating process is nonparametrically identified when the ranks of the order statistics are known. It is shown that the model is identified under some tail conditions on the distribution of measurement errors and a uniformly consistent nonparametric estimator is proposed.

Advisor: Luo, Yao
Wan, Yuanyuan