VictorJuiz/thesis huggingface dataset:
Convergence of Molecular, Nanoscale, and Data-driven Technologies for Therapeutics and Diagnostics
Keywords: Biosensors
Cell Sorting
Drug Discovery
Electrochemistry
Machine Learning
Microfluidics

The next revolution in healthcare will require the convergence of many traditional healthcare sectors to create therapeutic drugs and diagnostics tools that can target challenging diseases. Solutions which utilize molecular processes of cells and proteins, devices which use nanoscale physical principles for precision operation, and data-driven solutions which provide insights beyond current knowledge will all significantly impact clinical practices. In this thesis we aim to explore three main convergent fields: microfluidics for therapeutics research, machine learning for drug discovery, and biosensors for wearable disease management. With microfluidics, we develop a label-free cell sorting platform to sort whole populations of cells from the eye to isolate retinal stem cells, a potential therapeutic target that could reverse blindness. Deterministic lateral displacement cell sorting is implemented with notched microstructures, increasing sorting resolution by limiting cell deformation. Retinal stem cells are sorted and profiled based on size, purifying the population for downstream photoreceptor differentiation experiments. Next with machine learning, we develop an in silico pipeline, kCellect, for therapeutic antibody candidate selection from in vitro phage display biopanning against proteins associated with various cancers. We utilize k-means clustering for motif-based selection of antibody candidate sequences and identify cluster correlations with in vitro sequence frequency data. We discover novel antibodies against CD151 and FZD7 with similar or greater affinity to first-in-class antibodies, and doing so more time and computationally efficiently compared to previous methods. Finally, with biosensors, we design molecular pendulum sensors which consist of rigid DNA, an antibody recognition element to target proteins, and a redox reporter. Under an applied electric field, the pendulums fall, and the kinetics of motion between bound and unbound probes are measured through a surrogate redox electron transfer. We demonstrate the detection of multiple biomarkers of chronic disease in numerous biological fluids, and the continuous monitoring of biomarkers in situ in a mouse’s mouth. The three projects described in this thesis showcase biomedical convergence for the development of advanced therapeutics and diagnostics. The strategies utilized are sensitive to small quantities of biologicals, rapid in processing and development time, and versatile to many challenging disease targets.
Design of Organic Carbonyl Materials for Lithium-ion Batteries
Keywords: batteries
organic electrodes
organic rechargeable batteries
redox materials

Redox-active organic compounds have great potential as electrodes for lithium-ion batteries due to their structural tunability, high natural abundance, and ability to attain high capacity. Among others, carbonyl-containing organic cathode materials have demonstrated promising results, since they have fast redox kinetics and structural diversity in design. However, these electrodes still exhibit intrinsic drawbacks, such as solubility in battery electrolytes, low conductivity or poor cycling stability. In this thesis, I present the design of new carbonyl compounds to address specific challenges of organic electrode materials. In chapters 2 and 3, I explored triptycene-based small molecules and frameworks that can produce multi-redox reactions. Triptycene-based quinones have unique rigid structures that impart high stability in electrolytes, which practically provide a high specific capacity and cycling performance. Chapter 2 explores a unique synthetic route to prepare triptycene-based frameworks and small molecules consisting of dual redox-active units at both the core building blocks and linkers. This work provides a direction for new design in developing highly stable cathode materials for lithium-ion batteries. Chapter 3 describes synthetic strategies to produce triptycene-based small molecules for organic electrodes. Series of triptycene ortho-quinones and triptycene dimers with sulfides are explored. Importantly, this work emphasizes the advantage of using a rigid triptycene scaffold in the design of lithium-ion batteries; the scaffold offers a new molecular platform with minimal non-active mass for achieving high performing organic cathode materials. Chapter 4 studied the rational molecular design of redox-active and ionically conducting oxanorbornene pendant polymers that can increase Li-ion mobility. The additional oxygen contained within the repeat unit of poly(oxanorbornene) serves as an ionic-conducting functionality that facilitates the transport of Li-ions into the bulk materials. Poly (oxanorbornene) results in superior electrochemical performance, including a high rate capability, cycling stability and ionic conductivity. Lastly, Chapter 5 summarizes the thesis and potential future works of the above projects, and provide an overview of carbonyl-based electrodes.
A General Interfacial Capacitance Model for Nanomaterial Supercapacitors: Insights from Computational Quantum Mechanics
Keywords: density functional theory
energy storage
graphene
molecular dynamics
nanomaterial
supercapacitor

Electrochemical energy storage devices play a crucial role in the drive towards a sustainable energy future. They are ubiquitously utilized in electric vehicles, distributed renewable energy systems, and are increasingly considered for utility-scale power applications. High-power operation, however, present unique challenges to these devices at the physical and chemical level. Despite decades of experimentally driven research progress, theoretical and computational models of energy storage devices, such as batteries and supercapacitors, are built upon a foundation that was laid more than 150 years ago. The electrode/electrolyte interface is the region of most interest. This is where charge transfer reactions take place in batteries and ion adsorption/polarization take place in supercapacitors. The physicochemical phenomena occurring at the interface depend on a wide variety of quantum mechanical effects which are not accounted for within traditional continuum models based on partial differential equations. While there have been many efforts to address this knowledge gap through traditional methods, they are either limited in applicability or entail simplifying assumptions which exclude whole classes of devices such as those based on low-dimensional nanomaterials. Graphene- based supercapacitors are especially sensitive to quantum interactions since the electrode’s capacitance can be influenced by the presence of substrates, dopants, and/or electrolyte species. Conventional quantum-capacitance-based series models of graphene capacitors assume no electronic interaction between graphene and its interfacial neighbours, which leads to an overestimation of electrode capacitance by an order of magnitude. To address these limitations, we have developed a General Interfacial Capacitance Model (GICM) for nanomaterial-based supercapacitors. The GICM is based on first-principles computational quantum mechanics in the frameworks of Density Functional Theory (DFT) and Ab-Initio Molecular Dynamics (AIMD) combined with the principles of microscopic polarization theory. Three case studies for graphene-based interfaces are presented as applications of the GICM: bilayer graphene on a silica substrate, nitrogen-doped graphene on a copper substrate, and an interface between water and copper-supported graphene. The aim of this work is to improve current understanding of the performance of nanomaterial-based electrochemical supercapacitors and guide their design at the atomic level.
Single-cell Profiling of Molecular and Cellular Features that Underlie Myeloma Disease Progression
Keywords: myeloma
single cell RNA-sequencing

Multiple myeloma (MM) is an incurable cancer of bone marrow (BM) plasma cells, consistently preceded by a premalignant disease stage. However, the drivers of progression are incompletely understood owing to extensive genomic heterogeneity and a complex microenvironmental ecosystem. To address this gap, I profiled the malignant and non-malignant tumour compartment of a transgenic mouse model of myeloma progression throughout disease evolution using a longitudinal single cell RNA-sequencing (scRNA-seq) approach. This included 104,880 cells isolated from 18 mice at 3 separate time points. Using scRNA-seq data from 7,160 plasma cells, I found that while the malignant compartment contains copy number-driven subclones at all disease stages, transcriptional divergence emerges late. Importantly, I showed that certain late-stage subclones reflect previously dominant molecular programs from early disease, which represents a novel concept in precursor myeloma evolution. This notably included a stress response-related subclonal program universally present in MM mice that I subsequently implicated in precursor progression using publicly-available patient data and established as a novel therapeutic vulnerability in myeloma. Turning to the non-malignant population, I next analyzed 97,720 BM cells and found that immune-related changes occurred prior to the development of active MM. This notably included early emergence of a dysfunctional T cell population that co-expressed LAG3 and PD-1. I then demonstrated that early combinatorial blockade of these proteins delays progression, supporting that immune dysregulation plays a causal role in precursor progression. Finally, to understand the mechanisms underlying response to this regimen, I performed paired scRNA-seq and TCR-profiling on 41,705 cells isolated from the BM of mice with varying responses to LAG3/PD-1 blockade. In doing so, I found that response variability is associated with differential enrichment of expanded T cells along a continuous dysfunctional gradient, which may be related to underlying differences in the TCR repertoire. Moreover, I determined that these expanded T cells derive from a TCF1+ progenitor exhausted T cell subset. Collectively, this work advances our understanding of molecular processes that underlie myeloma progression and nominates novel mechanisms associated with transition from precursor to active MM that may inform new approaches to the identification and treatment of high-risk precursor patients.
The ATRX Chromatin Remodelling/Helicase Protein Maintains Telomere Integrity Through its Protein Associations
Keywords: ALT
ATRX
chromatin
chromatin remodelling
proteomics
telomeres

Telomeres are repetitive genetic elements at chromosomal ends that span up to 14 kb and protect subtelomeric genes from degradation and damage. Due to the inherent nature of DNA replication, telomeres shorten with each cell division, with short telomeres ultimately triggering senescence. Cancer cells subsequently elongate telomeres to achieve replicative immortality. Typically, this is achieved through reactivation of the telomere-extension enzyme, telomerase. However, a subset of cancers instead utilizes the alternative lengthening of telomeres (ALT) pathway. ALT strongly correlates with a loss of the ATRX protein. ATRX is an ATP-dependent chromatin remodeller/helicase that associates with the DAXX histone chaperone to deposit histones at repetitive DNA elements, including telomeres. This maintains nucleosome density and transcriptional silencing. Because the ATRX-DAXX interaction imparts the histone deposition capabilities of ATRX, I hypothesized that other ATRX protein interactions confer additional molecular functions, and that these are needed to maintain telomere stability. Using proximity-dependent biotinylation (BioID), I identified 36 proximal ATRX associations, including known interactors such as DAXX. To assess the breadth of their roles, I centered on the proteins that remained enriched over the most stringent controls, with a particular focus on the novel DNA damage response association, SLF2. The ATRX and SLF2 proteins together play roles in telomeric stability by suppressing aberrant telomeric exchanges and influencing proteins at telomeres. Together, these functions may inhibit or prevent ALT. Altogether, I have expanded the known ATRX protein associations, thereby expanding its functions beyond histone deposition.
Unification Educators and the Enactment of Educational Policy for North Korean Refugee Students in South Korea
Keywords: Education Policy enactment
North Korean refugee students
North Korean youth education in South Korea
Refugee education
South Korea education policy
Unification Educators

Educational injustice is prevalent in the education of North Korean Refugee Students (NKRSs) in South Korean schools. This qualitative study critically examines the policies regarding the education of NKRSs, and elicits the views of 18 paraeducators, known as Unification Educators (UEs), who are also North Korean refugees and former NK teachers. They are appointed at schools with more than ten NKRSs to assist NKRSs to ensure a more just education. This thesis concerns the students known as North Korean Defector Students, herein NKRSs. Based on Freire’s (2007) pedagogy of oppression, the NKRSs are viewed as individuals oppressed within the South Korean educational system. The work of Levinson, Sutton, and Winsted (2009) contextualizes the UEs as policy actors who must implement their mandates within the oppressive system. The Ministry of Education policies are reviewed along with prior research on the education of the NKRSs. The present research adopts a qualitative research design founded on Ball, Maguire, and Braun (2011) using in-depth interviews with the UEs to inquire about their practices in relation to the policy stakeholder, i.e., NKRSs and their parents, school administrators, and teachers, and their lived experiences as the primary policy enactors. Through the voices of the UEs, this study confirms that the educational well-being of the NKRSs has been threatened due to the lack of economic, social, and cultural capitals from their family and support within the school system. Likewise, UEs suffer from oppression, and their role as policy actors is limited by bureaucracy and prejudice. The thesis offers recommendations to include UEs in the drafting of educational policy, to improve relationships with the Ministry of Education and with educational staff to achieve educational equity.
A Multimodal Developmental Atlas for Cerebellar Molecular Layer Interneurons
Keywords: Brain development
Cerebellum
Interneurons
Morphology
Pseudotime
Transcriptomics

The central nervous system is comprised of an extraordinary diversity of neuronal cell-types. Classification of neurons into types and subtypes enables systematic studies of nervous system organization and function in health and disease. Defining cell types is complicated by the need to consider multiple modalities of classification including morphology, gene expression and function. Additionally, neuronal cell types diverge from common progenitors during development through protracted and dynamic processes. Studies at maturity have shown that multimodal approaches are needed to delineate the repertoire of neuronal cell-type diversity. However, similar methods have not been extended to interrogate the steps by which neuronal progenitors are programmed to acquire their adult forms and functions. In this thesis, I present a multimodal diversification map of the cerebellar molecular layer interneurons (MLIs), a heterogeneous inhibitory interneuron population that derives from a common progenitor pool. Using genetic, histological and computational approaches, I first define the diversity of mature MLIs along morphological and transcriptional axes. My analyses reveal examples of both discrete and continuous heterogeneity within each modality. Interestingly, I did not find direct correlation between MLI morphological and transcriptional identities. Instead, I identify graded transitions in gene expression profiles between morphologically discrete neurons, as well as graded transitions in neuronal morphology within transcriptionally discrete neurons. I demonstrate that neither morphology nor gene expression alone can be used to fully annotate interneuron diversity. I further define MLI subtypes by tracking the emergence of distinct identities during development using a novel application of pseudotime trajectory inference to morphological reconstructions. MLI subtype-specific morphological phenotypes emerge early during neuronal migration within the molecular layer, challenging prevailing views that MLI subtype specification is dependent on positional cues. In a complementary approach, I use in situ single molecule RNA imaging to visualize changes in marker gene expression during the timeframe of MLI migration and maturation. I find that MLIs do not establish the full repertoire of subtype-specific expression signatures until days after the completion of neuronal migration. Altogether, these studies present a multimodal map of MLI diversification and offer a framework that is broadly applicable for defining the developmental trajectory of interneuron populations.
The Physical World of the Dog
Keywords: Animal behaviour
Canine science
Comparative cognition
Domestic dog
Physical inference

Domestic dogs are primarily recognized for their social cognition and skillful interactions with humans, but comparatively little is known about how they make inferences about physical objects or recognize cause-and-effect relationships in the environment. How do dogs perceive the world around them and what cognitive processes are involved in playing a game of fetch or finding a ball that has rolled under the couch? While these might seem like trivial scenarios, the ability to track objects (even toys) in one’s environment and make inferences about how they will behave in object interactions are mental abilities that support a wide variety of behaviours from play to hunting to understanding social interactions. Sparse previous scholarship has probed dogs’ physical and causal cognition and findings are contradictory, resulting in a poor understanding of how dogs represent the physical world. I address this knowledge gap with empirical studies of dogs’ physical and causal cognition, examining their ability to make inferences about object interactions and recognize underlying causal structures in the world. My work expands our understanding of cognitive abilities in dogs, and sheds light on the evolutionary origins of fundamental cognitive processes by exploring how dogs view and behave with objects in their environment.
Ramsey Degree Theory of Ordered and Directed Sets
Keywords: Graphs
Orders
Ramsey Theory
Set Theory

We will study various Ramsey degree problems pertaining to categories of structures under variousembedding types. Using a technique originally penned by Laver, we show that the class of Aronszajn lines ordered under the embedding relation is a better quasi-order when we assume PFA. As a corollary, we deduce one-dimensional Ramsey degrees for Aronszajn lines. We also devise a colouring algorithm to colour Borel graphs coded by better quasi-orders on countable sets. We apply our algorithm to various types of well established better quasi-orders and deduce that there is a whole class of better quasi-orders that exists without a single known constructible example. We will conclude with some results pertaining to the Kechris-Pestov-Todorcevic correspondence. This will include a categorical notion of precompact expansion, which will prove to be more versatile in computing Ramsey degrees, as well as a weaker notion of the Ramsey property which also corresponds to fixed point properties of automorphism groups of ultrahomogeneous structures. This includes an application to trees under various embedding types, including the foundational strong embedding types studied by Milliken.
Computational dissection of the sequence elements defining human cleavage and polyadenylation sites and their role in determining gene ends
Keywords: Logistic Regression
Machine learning
mRNA
polyadenylation
Random Forest
systems biology

After decades of research, significant progress towards understanding how living organisms decode their genomes has been made. Eukaryotes utilize protein factors as well as DNA sequence elements to define eukaryotic gene starts and gene ends. During 3′-end gene definition, when pre-mRNA is being produced, it has to go through several steps of maturation, including cleavage and polyadenylation (CPA), when the pre-mRNA gets cleaved and a string of ~200 adenine nucleotides gets added at 3′ end of the transcript. Mechanistically, CPA is thought to utilize five key sequence recognition elements that reside in pre-mRNA 3′-UTRs: the upstream UGUA, polyadenylation signal (PAS), U-rich sequences, the CA/UA dinucleotide where cleavage occurs, and GU-rich downstream elements (DSE). It remains contentious whether these sequences are sufficient to delineate CPA sites, however. To date, no studies have explicitly attempted to assess how well the established features differentiate CPA sites from the remainder of the genes. Furthermore, it is not clear what role these sequences play in defining human geneends. Here, I expand on previous work by dissecting the contributions of numerous individual sequence features to CPA site selection. Using classical discriminative approaches, I show that computational models can be constructed that accurately identify constitutive CPA sites, relative to the entire pre-mRNA sequence, for 41% of all genes. U1-hybridizing sequences, which have been described as one of the strong additional cryptic CPA regulators, have surprisingly little impact on model performance, suggesting their limited role in regulating human CPA. Addition of all known RBP motifs to the model, however, increases this figure to 49%, with feature scores that highlight known and suspected CPA regulators, as well as many potential new factors. Together, these results suggest that while the established features have a higher specificity in defining human CPA sites than expected, the existing knowledge of sequence elements contributing to CPA definition does not account for the full complexity of CPA regulation.