VictorJuiz/thesis huggingface dataset:
Exploiting Diverse Chemical Collections to Uncover Compounds with Novel Activity Against Human Fungal Pathogens
Keywords: Candida
Chemical genetics
Drug discovery
Drug resistance
Fungal biology

Fungal pathogens are an underappreciated contributor to infectious disease related deaths, infecting billions and killing 1.6 million people annually. The rise in drug resistance amongst pathogenic fungi paired with the limited arsenal of antifungals available is an imminent threat to our medical system. To address this, I screened two distinct compound libraries to identify novel strategies to expand the antifungal armamentarium. The first collection, from Boston University, was screened to identify two types of compounds with activity against the highly drug-resistant pathogen Candida auris: those that were bioactive alone, and those that enhanced the efficacy of fluconazole, the most commonly administered systemic antifungal. Through these efforts I identified the rocaglates, a chemical class exhibiting single agent fungicidal activity. The rocaglates were found to inhibit translation initiation in C. auris but not in its close pathogenic relative Candida albicans. This species-specific activity was contingent on a single amino acid difference in the drug target, the translation initiation factor Tif1. Strikingly, rocaglate-mediated translation inhibition activated cell death in C. auris, but not in a rocaglate-sensitized C. albicans strain. From the fluconazole combination screen, I discovered a compound, subsequently termed azoffluxin, that enhanced fluconazole efficacy against C. auris through increasing its intracellular accumulation. This activity was through the inhibition of efflux pumps, indicating azoffluxin targets a key resistance mechanism. Furthermore, azoffluxin significantly reduced fungal burden alone and in combination with fluconazole in a mouse model of C. auris disseminated infection. The second chemical library screened, the RIKEN Natural Product Depository, was evaluated for antifungal activity against four major human fungal pathogens: C. auris, C. albicans, Candida glabrata, and Cryptococcus neoformans. One molecule, NPD6433, was prioritized for having broad-spectrum antifungal activity and minimal mammalian cytotoxicity. NPD6433 was determined to inhibit the enoyl reductase domain of the essential fungal enzyme fatty acid synthase 1, Fas1. Treatment with NPD6433 inhibited various virulence traits in multiple fungal species and rescued mammalian cell growth in a co-culture model with C. auris. Overall, this work identifies compounds with bioactivity against fungal pathogens, revealing important biology and paving the way for the critical development of new therapeutic strategies.
Quantum Dynamics and Topology of the Kitaev Materials
Keywords: frustrated magnetism
Kitaev material
Kondo lattice
quantum spin liquid
superconductivity
topological magnon

Spin-orbit coupled Mott insulators on trivalent lattices open up a new arena of exotic correlated quantum phases by realizing frustrating bond-directional interactions. These frustrated quantum magnets, so called the Kitaev materials, provide the platform for the long-sought quantum spin liquid with Majorana fermions and Z2 gauge fields, as well as various noncollinear magnetic orders with topological magnons. In this thesis, we study the topological properties of proximate superconducting and magnetic phases of the Kitaev materials and provide theory of a novel spectroscopic experiment for sharper characterization of quantum spin liquids. We first investigate topological nature of the emergent superconductors born out of the Kitaev spin liquid strongly Kondo coupled to conduction electrons. We discover a ferromagnetic topological superconductor carrying non-Abelian anyons and a nonmagnetic Z2 topological superconductor, whose symmetry and topology are heavily influenced by the emergent gauge structure of the parent spin liquid. Next, we discuss the nonsymmorphic symmetry protected topological nodal magnons in the three dimensional Kitaev materials. We discovered stable magnon band crossings in the field-induced canted zigzag order of the hyperhoneycomb β-Li2IrO3 and showed that the magnetic glide symmetries and the non-Hermitian nature of the bosonic magnons lead to unique topological protection that is different from the case of their fermionic counterparts. Finally, we propose a theory of two-dimensional spectroscopy, where nonlinear dynamical responses provide insight into the low-energy excitations, for quantum spin liquids. By considering the exactly solvable Kitaev honeycomb model as an archetype example, we theoretically demonstrate that the two-dimensional Fourier transform of the nonlinear dynamical response functions can reveal clear signatures of the itinerant Majorana fermions and gapped Z2 fluxes of the Kitaev spin liquid.
Machine Learning and Computer Vision Approaches for Phenotypic Profiling in Yeast
Keywords: Computational Biology
Computer Vision
Genomics
Machine Learning
Systems Biology

Functional genomics research aims to systematically discover the functions of all genes in an organism. Developing computational tools to facilitate gene function discoveries has been the overarching goal of my thesis work. I have developed computational pipelines for the automated detection of abnormal phenotypes in single cell images of the budding yeast, Saccharomyces cerevisiae. My collaborators in the Boone and Andrews labs have used methods that automate yeast genetics to produce genome-wide arrays of yeast mutants, each carrying a defined perturbation in a single gene, and expressing fluorescent markers for various subcellular compartments. The resulting large-scale image dataset consists of millions of single cells each harbouring a single gene perturbation and a single fluorescently labelled subcellular compartment for around 6000 yeast genes and 13 compartments. To develop my image analysis for discovering mutant phenotypes in single-cell images of arrayed mutants, I first focused on comparing different combinations of methods for performing outlier detection to automatically detect cells with abnormal morphologies. This analysis allowed me to quantify the percentage of cells in a mutant population with abnormal phenotypes and thus the penetrance of phenotypes associated with a specific genetic perturbation. In the second data chapter of my thesis, I combined outlier detection and a neural network-based image analysis of single cells to quantify the phenotypic variability within a population of cells. As a model system, I focused on the genes that influence the architecture of four subcellular compartments of the endocytic pathway and identified 17 distinct abnormal phenotypes that are associated with perturbation of many genes. Nearly half of these perturbed populations displayed multiple phenotypes, suggesting that morphological pleiotropy is prevalent. In the final data chapter of my thesis, I describe my work to automate the clustering of cells with abnormal morphology from a dataset of images of yeast cells expressing fluorescent markers of 13 subcellular compartments into distinct abnormal phenotypes to understand the global morphology of the yeast cell. My analyses have facilitated the identification of connections between discrete biological processes, the prediction of novel gene function, and a visual representation of basic eukaryotic cell biology.
Investigating the Dynamic CFTR Interactome using the Mammalian Membrane Two-hybrid (MaMTH) platform
Keywords: Cystic Fibrosis
High-throughput screening
Integrative computational biology
Interactome
Mammalian Membrane Two-Hybrid
Protein-protein interactions

Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) is a chloride channel found in secretory epithelia with a plethora of known interacting proteins found throughout its life cycle. Over 2,100 mutations have been reported in the CFTR gene associated with Cystic Fibrosis (CF), a complex disease that leads to progressive respiratory illness and other complications of phenotypic variance resulting from perturbations of its protein interaction network. To date, four FDA-approved drugs have provided promising clinical effects on modulating functional expression of the most common CFTR mutations. However, treatments that can modulate one type of mutation in CFTR have been shown not to work for others, leading to diverse classification of CFTR modulators. Furthermore, high variance in drug response within patients of the same genotype, including those who are refractory to the treatment, has been attributed to the presence of secondary genetic modifiers and environmental factors. For these reasons, therapies that solely target the CFTR protein itself have not had therapeutic payoffs matching research input. Studying the collection of CFTR interacting proteins and the differences between the interactomes of mutant and wildtype CFTR may be an effective way to address this problem, providing a comprehensive view of the disease and identifying novel drug targets that may allow for more patient-specific therapeutic regimens. Using the Mammalian Membrane Two-Hybrid (MaMTH) protein fragment complementation assay developed in our lab, my thesis focuses on systematic, high-throughput identification of CFTR-interacting proteins to provide additional insight into CFTR regulation and function. Through a collaborative initiative involving clinicians, biochemists and bioinformaticians, we developed a High-Throughput Screening pipeline (MaMTH-HTS) to identify and functionally validate numerous novel interactors of CFTR in multiple disease models including patient-derived intestinal organoid cultures. In parallel to these studies, we applied the MaMTH assay as part of a drug discovery platform (MaMTH-DS) and screened multiple small molecule libraries to identify compounds that can stabilize CFTR’s interaction with our most promising interaction partner 14-3-3 gamma. Lastly, we performed proof-of-concept screening of a comprehensive set of SLC transporters against CFTR and discovered an unusually high number of interactions suggesting an intricate network of PPIs that remains to be discovered.
Second Language Writing: A Study of the Learning and Teaching of Academic Writing in a Networked Culture
Keywords: Contrastive rhetoric
L2 writing pedagogies
Lived experiences
Networked contrastive rhetoric
Networked culture
Second language writing

The traditional notions of contrastive rhetoric conceive L2 writers of English as separate group of people having diverse cultural and rhetorical backgrounds, which interfere with their writing in English. It is assumed that their cultural thought patterns get expressed in their writing in English, thereby making their writing different from that of English writers. However, given the impact of new media and technology and its globalizing spread, L2 writers cannot be fully defined by a culture because they live in a networked culture. So, the relevance of contrastive rhetoric studies needs to be examined to suit the teaching situation, which is both cultural and social product of a given time. Building on the critical studies (Connor, 2002, 2004; Kubota Lehner, 2004; Petric, 2005), this research consolidates the existing dissonances with the traditional notions of contrastive rhetoric by examining the lived experience of graduate students. I also propose networked contrastive rhetoric (NCR) that better theorizes and operationalizes L2 writing theory and pedagogy. Using NCR as a conceptual framework that combines contrastive rhetoric (CR), L1 and L2 similarities and differences, L2 writing pedagogy, genre and academic conventions, discourse community, and networked culture (and their impact on L2 skills development and the familiarization with academic genre), this research does an empirical qualitative study by triangulating some research methods such as L2 student survey questionnaires and semi-structured faculty and L2 student interviews using survey research methodology and proposes some L2 writing pedagogical approaches. The findings show that a) L2 writers have challenges with both linguistic and rhetorical competences; b) they would benefit from more appropriate collegiality and lack institutional supports; c) L2 writers’ performance cannot be generalized as it depends on a lot of factors; d) the lived experiences of the faculty members and students problematize L2 writing as problems assumption and the notion of the expression of cultural thought patterns and transfer theory because networked culture has impacted L2 writing. Incorporating the findings of this research in future teaching may introduce a paradigm shift in L2 writing pedagogies in the ways L2 writing is taught in the classroom because it can have serious pedagogical implications in the multilingual, multiethnic, and cross-cultural context of present L2 writing.
Exploring the Synthesis and Reactivity of Phosphorus/Boron Frustrated Lewis Pairs and Nitrogen-based Lewis Acids
Keywords: Frustrated Lewis Pair Chemistry
Main Group Chemistry
Nitrogen Lewis Acids

The definition of frustrated Lewis pairs has been broadened from combinations of sterically encumbered Lewis acids and bases to stable classical Lewis adducts, which opens a new avenue as FLPs in challenging small molecule activation and catalysis. The objective for this thesis is to explore the design and fundamental reactivity of Lewis acid and base pairs featuring group 13 and 15 elements within the realm of frustrated Lewis pair chemistry.An FLP strategy was used in the interception of intermediates in mesityl-N-oxide-mediated phosphine oxidation with stable P/B-based Lewis adducts. The FLP 1,3-addition products were shown to react with strong bases allowing the phosphine oxidation to proceed, which provided experimental data supporting the related phosphine oxidation mechanism proposed by Cummins. Highly electrophilic hydridoboranes, H2B(C6F5)·SMe2 and HB(C6F5)2 were demonstrated to yield bicyclic and cyclic boron species when exposed to carboxylic acids or phosphinic acid. The subsequent reactivity of many of these heterocycles with Lewis bases was investigated. Oxygen-bridging P/B molecules featuring a weak Lewis acid centre were prepared and the FLP reactivity of these species was systematically explored. Such POB-containing intramolecular FLPs were found to effect in the capture of N2-surrogate, diazomethanes, which resulted in the formation of unexpected stable heterocycles. Isomeric nitrenium Lewis acids were prepared from alkylation of spiro[fluorene‐9,3’‐indazole] with tBuCl. The chemical properties of the cations were examined and their reactivity with phosphorus and carbon nucleophiles was explored.
Knowledge Representation and Artificial Intelligence for Management of Socio-Technical Risks in Megaprojects
Keywords: Artificial Intelligence
Bayesian
Knowledge Representation
Megaprojects
Ontology
Socio-Technical Risks

Megaprojects are probabilistically dependent systems prone to progressive failures that are undertaken in significantly incentivized economic and political domains. Current processes in definition, estimation, and financing of these large projects often exclude relevant sources of socio-technical risks with overarching effects on the project. Applications of the artificial intelligence methods in forecasting project performance and outcomes considering socio-technical sources of risks have been mainly ad-hoc solutions catered to specific needs, project types, and data availability. This research proposes a high-level framework for dealing with socio-technical risks by utilizing available sources of data, expert knowledge, and the most applicable analytics methodologies. The framework consists of representation, quantification, and inference connected through a loop of dynamic learning. The representation part defines various sources of risk in an expandable data format and with universal semantics, considering the nature of risk factors as to when and how they should be collected, in conjunction with project performance measures. An ontology is developed for such representation using the linked data and the semantic web format. The quantification aims to measure such sources of risk, which could vary from machine learning algorithms' applications over remote sensing data to pure qualitative judgments in discrete scales. The quantification was exemplified by creating a remoteness risk index, called Nighttime Remoteness Index (NIRI) for risk and resilience assessment of remote projects. The remoteness index takes nighttime satellite imagery as input and produces a remoteness index using machine learning algorithms. The index was validated based on two census bases remoteness indices of Australia and Canada. The inference part aims to incorporate the effects and dependencies of socio-technical risk factors on project outcome variables by defining an expandable object-oriented Bayesian Network (OOBN). The models can be trained based on the collected data from knowledge representation or based on expert knowledge, and different extents of their combinations. The employed methodologies allow for evergreen process of data collection and process optimization in the previous three sections. The framework creates a systematic approach to capturing and modeling project socio-technical risks, enables quality controls across the process, and can be applied to various risk sources such as the Environmental, Social, and Governance (ESG) factors.
Application of Molecular Tools to Determine Effects of Key Growth Factors on Cultivated Algal Biofilm Communities
Keywords: Algae
Biofilms
Bioreactors
Community
qPCR
Wastewater

Cultivating microalgae as a biofilm may overcome some of the challenges of conventional suspended cultivation for the production sustainable biofuels and bioproducts. However, strategies to enhance the growth of product-producing species within biofilms should be investigated to optimize the profitability of algal bioprocesses. The objective of this thesis was to investigate how biofilm attachment material, wastewater, and inoculation strategy influence the accumulation of algal biofilm biomass and the composition of the community. To achieve specific and accurate quantification of target algal species, quantitative polymerase chain reaction (qPCR) assays were designed to estimate the cell abundance of Chlorella vulgaris and Scenedesmus obliquus. Additionally, a workflow was developed to simultaneously estimate absolute abundance of bacterial and algal taxa by targeting 16S rRNA genes. These tools were used to determine how manipulating the growth factors influence the biomass and composition of biofilms cultivated in a parallel-plate air-lift reactor. Pretreating reactors with wastewater and growing biofilms on cotton increased biofilm biomass by up to a factor of 5.6, and inoculating S. obliquus into cotton attachment materials increased its abundance in biofilms by up to a factor of 13.5. Depending on inoculation strategy or presence of wastewater, biofilm community compositions were distinct from that in their corresponding suspensions and that in the inoculum. In conclusion, the interactions between wastewater, attachment material, and inoculation strategy can have significant, species-specific influences on algal biofilm growth, thus may be exploited to simultaneously increase biomass and the growth of target species in cultivated biofilms. The experimental work described in this thesis contributes to the suite of molecular tools available for the analysis of species in algal biofilms and demonstrates the utility of qPCR and amplicon sequencing for evaluating the performance of algal biofilm cultivation systems, specifically determining the abundance of desired species in biofilms. The developed tools and findings in this thesis can be used to inform, not only the optimization of biofilm cultivation systems for the commercialization of algal products, but also the study of mixed algal communities in the environment.
Role of Inferior Frontoparietal Structures in Motor Control and Freezing of Gait in Parkinson’s Disease
Keywords: Angular gyrus
Freezing of gait
Frontoparietal
Inferior frontal gyrus
Motor control
Parkinson's disease

Freezing of gait (FOG) is a disabling symptom of Parkinson’s disease (PD) in which patients are unable to step forward when desired. Abnormal brain circuits responsible for FOG in PD patients are unclear and experimental paradigms are needed to address this disabling symptom. We hypothesized that motor interruptions of lower limb movements while seated may be related to FOG and may indicate abnormal brain activities. In Study 1, we tested 19 PD patients and 20 healthy controls and showed that motor interruption or cessation of bilateral stepping while sitting correlated with the presence and severity of FOG and this effect persisted in the “on” dopaminergic medication state. In Study 2, we tested how these “lower limb motor blocks” or LLMB are related to changes in 4-30 Hz cortical activities recorded from 64-channel scalp electroencephalography. In 17 PD patients, we found that sporadic LLMB differed from typical steps in the right angular gyrus (RAG, F=154.6, P=0.0185, before and during LLMB>typical steps). LLMB also differed from cued stops in the right inferior frontal gyrus (F=32.0, P=0.0005, before LLMB>cued stops; F=42.0, P=0.0005, during LLMB
The Relation between Payroll and Income Tax Avoidance
Keywords: Employee Classification
Non-Income Taxes
Tax Planning

Payroll taxes, such as contributions mandated through the Federal Insurance Contributions Act (FICA), represent a considerable expense for businesses and are a large source of government revenue. Considering that employers pay approximately 50% of FICA, payroll taxes have a material impact on firm profitability. Despite the large cost, little is known about the determinants of payroll tax avoidance. By misclassifying employees as independent contractors, a firm can avoid their portion of FICA contributions and a variety of other employee related costs. This paper utilizes publicly available compliance action data from the Wage and Hour Division (i.e., WHD) of the United States Department of Labor (i.e., DOL) to identify employee misclassification and to empirically identify whether firms that avoid income taxes also avoid payroll taxes. This paper documents two main results. First, income tax avoidance, proxied by CashETR, is positively related to the likelihood of a successful discovery of a Fair Labor Standards Act (i.e., FLSA) violation during a Wage and Hour Division audit. Second, firms reduce their income tax avoidance following the discovery of FLSA violations.