VictorJuiz/thesis huggingface dataset:
Strategies for Pharmacological Modulation of Mitophagy
Keywords: mitophagy
Parkinson's disease

Mitochondrial damage and impaired mitophagy underly the pathogenesis of Parkinson’s disease (PD). Amongst the loci implicated with onset of familial PD are several genes that encode proteins which mediate a common goal: the removal of damaged mitochondria. Mitochondria, like other organelles, are subject to damage, which may affect lipids, DNA or proteins. The level of damage the cell can endure before its survival is compromised exists on a spectrum and there are systems the cell employs to mitigate mitochondrial damage. Pathogenic mutations in PINK1 and PRKN cause early-onset familial PD because they compromise mitophagy, a pathway employed by dopaminergic neurons to handle mitochondrial damage. Since the accumulation of dysfunctional mitochondria produces reactive oxygen species, this further perpetuates damage to lipids, DNA, and proteins in the mitochondrial milieu. Once a critical threshold of damage is crossed, a cell death program is executed leading to loss of dopaminergic neurons in the substantia nigra, the defining pathology in PD. The goal for this work is the identification of small molecules and molecular mechanisms that potentiate the mitophagy pathway, which may be advantageous for dopaminergic neurons susceptible to mitochondrial damage in the context of PD. Importantly, our strategies sought to identify molecules which function without induction of mitochondrial damage and/or apoptosis. The hope is that by bolstering mitophagy, loss of dopaminergic neurons will be slowed, leading to reduced PD-related phenotypes that arise consequently. Fortuitously, several model organisms which reflect PD pathology and systemic characteristics of the disease are available. Molecules that enhance mitophagy were tested in preclinical PD models of mitochondrial damage. This general pipeline led to the identification of ROCK inhibitors and probucol as compounds that enhanced mitophagy and these compounds demonstrably reduced PD-related phenotypes in model organisms reflecting mitochondrial damage. In addition, dissection of the mechanisms through which the molecules function illuminated new aspects of mitophagy and confirmed the idea that mitophagy enhancers may be a viable strategy for the development of PD therapeutics. Additionally, rhomboid-7 negatively regulates basal mitophagy in the dopaminergic neurons of flies. This supports the idea of PARL inhibitors as another class of mitophagy agonists and potential PD therapeutics. Using a robust eye degeneration phenotype, rhomboid-7’s possible epistatic relationships were investigated.
Energy Level Alignment at Organic Semiconductor Interfaces
Keywords: energy level alignment
molecular orientations
organic electronics
organic heterojunctions

Organic semiconductors are promising materials for future generations of light-weight and flexible electronic devices. Several distinct functional organic semiconducting layers are usually stacked together to create a variety of devices such as organic light emitting diodes and organic solar cells. The relative alignment of each layer’s frontier molecular orbital energies with those in its adjacent layers is critical in dictating a device’s performance and functionality. With a rapidly increasing number of new functional organic semiconducting layers in modern devices, the energy level alignment (ELA) at organic-organic interfaces is becoming an increasingly crucial aspect in material selections and device design. Despite of this, there is still lack of a general scientific guideline in charting interface energy levels at such interfaces. In this thesis, a comprehensive experimental study is conducted on several dozens of organic heterointerfaces by using photoelectron spectroscopy. Broad sets of molecules having a diverse range of electronic and geometric structures were carefully selected. After correcting variables such as molecular orientation-dependent ionization energies, a general energy level alignment rule for charting interface energy levels (energy offsets and charge-transfer dipoles) over three distinct regions was found. This universal energy level alignment rule covers a wide achievable range of frontier orbital energies at organic heterojunctions, and is expected to provide a master guide in selection of new materials for fabricating future generations of superior organic semiconductor devices.
Practice Patterns and Health Outcomes of Adjuvant Oxaliplatin Chemotherapy for Colorectal Cancer
Keywords: chemotherapy
colorectal cancer
neuropathy
non-inferiority
oxaliplatin
practice variation

INTRODUCTION: Oxaliplatin-containing adjuvant chemotherapy improves survival for stage III colon cancer. Yet it is not without harms, most commonly dose-dependent peripheral neuropathy. The 2018 International Duration Evaluation of Adjuvant Chemotherapy (IDEA) trial sought to determine the optimal duration of adjuvant therapy by evaluating the non-inferiority (NI) of 3 versus 6 months of treatment. The 3-year disease-free survival probability was 74.6% in the 3-month group versus 75.5% in the 6-month group (HR 1.07, 95% CI 1.00-1.15; pNI = 0.11). There remain open questions regarding how these findings changed practice, whether these outcomes are obtained in the general population, and – given oxaliplatin-induced neuropathy – the relationship between treatment duration and post-treatment falls. METHODS: We leveraged linked administrative and population-based datasets held at ICES to conduct three retrospective cohort studies of patients with stage III colon cancer in Ontario, Canada. First, we employed interrupted time series regression and mixed models to evaluate the association between IDEA’s publication and prescribed treatment durations. Second, we utilized overlap propensity score-weighted survival models to determine the relationship between treatment duration and mortality. Third, we again used weighted survival models to examine the association between treatment duration and fall-related injury. RESULTS: IDEA’s publication was associated with a 16.4% (95% CI 12.5-20.3%) absolute increase in patients treated with ≤50% of a 6-month course of therapy. Between-prescriber variation strongly influenced treatment duration in the post-IDEA period (median odds ratio 2.10 [95% CI 1.73-3.18] for short-duration treatment). Treatment with 50% of a 6-month course of therapy was associated with a HR of 1.13 (95% CI 0.88-1.47) for overall mortality versus >85% of a 6-month course of therapy. Meanwhile, treatment with >85% of a 6-month course of therapy conferred a HR of 0.84 (95% CI 0.62-1.13) for fall-related injury. CONCLUSION: Although IDEA could not conclude non-inferiority of 3 to 6 months of adjuvant therapy, the trial rapidly shifted clinical practice while introducing greater provider-level practice variation. While intuitive concerns about the relationship between oxaliplatin and fall-related injury are not borne out in the data, evidence from routine practice solidifies that treatment duration is a preference-sensitive decision involving a tradeoff between survival and toxicity.
Online Learning and Optimization in Communication Networks
Keywords: Distributed Computation
Mobile Communications
Networked Systems
Online Learning
Stochastic Optimization

In this thesis, I propose new online learning and optimization approaches to evaluate and design communication networks by investigating unknown system variation, feedback delay, and communication efficiency over time. The results of this thesis provide new analytical insights and design guidelines that will help to improve future communication networks. In the first part of this thesis, we consider periodic decision updates for constrained Online Convex Optimization (OCO). This is motivated by many practical wireless communication systems, which only permit a fixed decision update over multiple time slots, while the environment changes between the decision epochs. We propose an efficient online algorithm, which employs a periodic virtual queue together with aggregated gradient descent for decision updates. We evaluate the performance of theproposed algorithm in a large-scale multi-antenna system shared by multiple wireless service providers. In the second part of this thesis, we study OCO with long-term constraints in the presence of multi-slot feedback delay. We propose an efficient online algorithm, which uses a double regularization together with a penalty mechanism on the long-term constraint violation, to tackle the asynchrony between information feedback and decision updates. We apply the proposed algorithm to solve a general network resource allocation problem. In the third part of this thesis, we exploit over-the-air computation to jointly optimize the training of the global model and the analog aggregation of the local models over time for federated learning (FL). We propose an efficient algorithm to adaptively update the local and global models based on the time-varying communication environment. The trained model is both channel- and power-aware, and it is in closed form incurring low computational complexity. We derive performance bounds on both the computation and communication performance metrics. In the last part of this thesis, we encourage temporal similarity in the decision sequence over time to control the communication overhead in online distributed optimization. We propose an efficient algorithm, which uses a tunable virtual queue together with a modified Lyapunov drift analysis to jointly consider computation and communication over time. We apply the proposed algorithm to enable communication-efficient FL.
The Real Subalgebras of $\mathfrak{so}_4(\mathbb{C})$ and $G_{2(2)}$
Keywords: conjugacy
representations
subalgebras

Classifying the subalgebras of a simple Lie algebra is a pursuit that stretches back to the work of Cartan on representations of simple Lie algebras. Mal'cev, in classifying orthogonal and symplectic representations of simple Lie algebras also found the semisimple subalgebras of $B_n, C_n,$ and $D_n$. Following that, Dynkin and Minchenko classified the semisimple subalgebras of the complex exceptional Lie algebras. We investigate the real subalgebras of a number of rank 2 Lie algebras: $\mathfrak{so}_4(\mathbb{C})$, its real forms, and the split real form of $G_2$, $G_{2(2)}$. In this thesis, we classify the real subalgebras of these Lie algebras up to inner automorphism (i.e., up to the adjoint action of the corresponding Lie group). For the matrix algebras above, we largely proceed with the help of copious amounts of linear algebra. For $G_{2(2)}$, we take advantage of the Cartan decomposition $G_{2(2)} = \mathfrak{k} \oplus \mathfrak{p}$, where $\mathfrak{k}$ is a compact subalgebra, to identify the semisimple and Levi-decomposable subalgebras of $G_{2(2)}$. To find the solvable subalgebras, we use the classifications of semisimple and nilpotent elements of $G_{2(2)}$, as well as our own classification of Jordan elements, to build nilpotent and solvable subalgebras.
Developing a Complex Intervention to Improve Knee Osteoarthritis Care in Persons with Type 2 Diabetes
Keywords: Implementation Science
Osteoarthritis
Type 2 Diabetes

Knee osteoarthritis is a leading cause of disability, frequently co-occurs in people with type 2 diabetes, and the presence of knee osteoarthritis-related chronic joint pain and functional impairment can challenge optimal type 2 diabetes management. It is thus important to effectively identify and treat knee osteoarthritis in these individuals to improve type 2 diabetes outcomes and quality of life, in addition to osteoarthritis outcomes. Through four inter-related projects and guided by the Medical Research Council framework, this thesis describes the development of a complex intervention to improve the diagnosis and evidence-based treatment of knee osteoarthritis in people with type 2 diabetes. It involved a partnership with Arthritis Society Canada for intervention delivery. The first three projects used qualitative methods to understand determinants of behaviour in stakeholder groups using the Theoretical Domains Framework. The first project identified barriers and enablers to seeking and engaging in osteoarthritis care in people with type 2 diabetes. The second project identified barriers and enablers of diabetes health professionals’ assessment and treatment of knee osteoarthritis in their patients. The third project identified barriers and enablers of arthritis therapists from Arthritis Society Canada considering type 2 diabetes when formulating an osteoarthritis management plan. The fourth project comprehensively described the multi-step process of complex intervention development that combined theories of behaviour change, stakeholder involvement, and existing evidence. Relevant domains identified from stakeholder interviews were mapped to behavioural change techniques to identify potential intervention components. Stakeholder meetings ascertained acceptability and feasibility of proposed intervention components, and a program theory was constructed to inform the implementation of the intervention and its evaluation. The intervention components, incorporating a range of behavioural change techniques at the patient, health professional, and arthritis therapist level, intend to identify persons with knee osteoarthritis within type 2 diabetes care and refer them to Arthritis Society Canada for delivery of evidence-based longitudinal knee osteoarthritis management. Diverse stakeholder input throughout the development allowed co-design of an intervention that appears feasible and acceptable to target users. This thesis lays the foundation for subsequent feasibility testing and evaluation, and ultimately seeks to inform improved care and outcomes for individuals with type 2 diabetes and knee osteoarthritis.
Satisfying Model Validity for Robust Clinical Machine Learning for Time-Series
Keywords: Dataset Shift
Healthcare
Machine Learning
Neural Networks
Robustness
Time-series

Many healthcare data are endogenously produced as a by-product of care. The chronologically recorded healthcare system interactions naturally become time-series data. Machine learning models built to infer meaningful information from these data have the ability to capture the complex dynamics of human health leading to an improvement in care without an escalation in cost. However, these models must be safe and effective in order to have an impact on healthcare. Safety and efficacy stem from models which are robust to perturbations. This thesis investigates machine learning robustness under three prominent dataset shift settings. First, this thesis identifies common design pitfalls that lead machine learning models to immediately violate internal validity. Such models will not be expected to generalise to deployment scenarios. The effects of these design decisions are highlighted and remedied. Next, internal validity violations due to dataset shift are explored. In an attempt to preserve privacy in de-identified datasets, models developed for electronic health record prediction tasks have been blinded to damaging covariate shift. In COVID-19 detection tasks using wearable data, failure to address prior probability shift leads to inflated expectations of performance. By addressing feature representation and study design, this thesis reports performance that could be reasonably expected during deployment. Finally, necessary external validity violations due to data scarcity are explored. With endogenously collected data, especially health data, it is not feasible to invest in collecting more supervised labels. Yet label-scarce datasets are abundant, whether it be for emergent disease detection, rare disease identification, or from data-scarce institutions. Pre-training using multi-task learning is discovered as a superior method to masked imputation for target-label scarce tasks. A large EHR domain transfer experiment reveals that pretraining on external datasets only confers benefits in the few-shot transfer setting. Preference is given to participate in data warehouses rather than sharing pre-trained models. This thesis illustrates how to design effective machine learning models for endogenously produced clinical time-series, how to ensure they are robust to inevitable distribution shifts, and how to share knowledge between domains. It is essential to overcome these bottlenecks to realise the potential of machine learning for clinical time-series data.
Analysis of Learning Algorithms via Diffusion Limits
Keywords: Deep Learning Theory
Infinite-Depth-and-Width Limit
Langevin Algorithm

Modern machine learning algorithms are often very difficult to analyze, as discrete units of scaling and time typically do not lead to very clean closed form representations. For example, updates can be in discrete time, and neural networks have finitely many distinct neurons per layer. In this thesis, we study the computational properties of two such algorithms through their continuous scaling limits. In both cases, the limiting object has a rich mathematical structure, is significantly more tractable to analyze, yet remains a good approximation of the finite discrete algorithms. In particular, we will see that both algorithms converges to a diffusion limit described by stochastic differential equations. In the first project of this thesis, we study a discretization of the Langevin diffusion on a product of spheres manifold. We provide a convergence guarantee in terms of KL-divergence under the assumption of a logarithmic Sobolev inequality. Furthermore, we also establish a Poincar\'e and logarithmic Sobolev inequality in the presence of saddle points, where we introduce a new escape time based approach. In the second project, we consider ResNets --- neural networks with skip connections --- at initialization. We show that the infinite-depth-and-width limit for a single input marginal is a log-normal distribution with adjusted parameters. In particular, we provide a new approach of analyzing of this limiting result by identifying the norm of each layer as a key structure, and hence avoiding previous combinatorial arguments. In the final project of this thesis, we consider the joint distribution of neural networks over many input data points. We show that upon a modification of the activation functions, the infinite-depth-and-width limit is described by a stochastic differential equation for the covariance matrix. In particular, the limit remains a great approximation for finite size networks, and we already gain significant tractability over a simple example for finite explosions.
Harmonic Measures for Random Walks on Cocompact Fuchsian Groups
Keywords: Fuchsian groups
harmonic measures
random walks
singularity conjecture

The singularity conjecture states that for an admissible finite-range random walk on a cofinite Fuchsian group $\Gamma$, the hitting measure is singular with respect to the Lebesgue measure on $S^1$. It has been known to hold for finite-range random walks on cofinite non-cocompact Fuchsian groups; however, none of the existing methods immediately generalize to the cocompact case. In this thesis we present a method which allows us to prove the singularity conjecture for a wide variety of finite-range random walks on cocompact Fuchsian groups, using the Blach\`ere-Ha\"{i}ssinsky-Mathieu criterion. In particular, we are able to prove the conjecture for a class of random walks on cocompact Fuchsian groups generated by the side-pairing translations identifying opposite sides of a centrally symmetric polygon. This includes a family of non-nearest-neighbour random walks supported on powers of side-pairing translations. Finally, we generalize our results to a similarly defined class of random walks on Coxeter groups, showing that the hitting measures for the Coxeter groups generated by reflections with respect to the sides of centrally symmetric polygons are singular in the nearest-neighbor case.
Unconventional Superconductivity in Spin-Orbit Coupled Systems
Keywords: interorbital pairing
spin-orbit coupling
spin-triplet
strontium ruthenate
superconductivity

Following the exciting discovery of the high-temperature superconducting cuprates, resulting in a rapid rise in the highest known transition temperatures, the condensed matter community broadened its search for superconductivity tonon-cuprate compounds with similar structures. Much interest and intrigue surrounded the discovery of superconductivity in Sr2RuO4, thought to be a leading candidate for chiral p-wave superconductivity, a possible building block of fault tolerant quantum computers. However, 20-year-old Knight shift results, once taken as evidence of this p-wave state, were recently overturned, reinvigorating the debate on the superconducting symmetry. Despite receiving a vast amount of attention, the pairing state remains shrouded in mystery with various seemingly contradictory experimental results. Throughout this thesis we attempt to reconcile some of these experimental results within one coherent picture. Using an interorbital even-parity spin-triplet pairing state, we show that the pseudospin-singlet nature of this state, arising due to spin-orbit coupling, results in a drop of the Knight shift in the superconducting state, consistent with experiments. We also consider how the microscopic details of the spin-orbit coupling allow for a multi-component, nodal, time-reversal symmetry breaking superconducting state as required by various experiments. Further, we use the underlying interorbital nature of this pairing state to explain phase-sensitive Josephson junction experiments, thought to be indicative of an odd-parity pairing state, despite the even-parity pairing presenthere.Using the same model which results in superconductivity in Sr2RuO4, we consider the closely related material, Sr3Ru2O7, and show that this same model captures both the lack of superconductivity, as well as the observed spin-density wave order, helping to justify our model. Finally, as the presence of p-wave superconductivity in Sr2RuO4 appears less and less likely, we consider how a p-wave superconductor may instead be engineered via distortions. In the related material, Sr2IrO4, rotations and tilting of the oxygen octahedra alter the Fermi surface as well as effective interaction terms, which work in combination to supress d-wave pairing and reveal p-wave pairing as the leading instability.