VictorJuiz/thesis huggingface dataset:
Supramolecular chemical biology for applications in protein modification and nanomedicine

Supramolecular chemistry is a diverse field that encompasses host-guest interactions, mechanical bond formation, metal coordination, and other noncovalent chemistries. The design strategies of supramolecular chemistry have been implemented in a diverse set of applications, but has seen limited use in the field of chemical biology. By combining supramolecular chemistry with chemical biology, new discoveries and advances could be made that would not be possible without a supramolecular approach. This work therefore sought to combine supramolecular strategies and chemical biology for applications in protein modification and nanomedicine. By using cucurbituril host-guest chemistry, a supramolecular-promoted copper-free click chemistry reaction was developed for protein modification. Cucurbituril host-guest chemistry was additionally explored as a selective protecting group for either the N terminus in the case of cucurbit[7]uril (CB7), or as a lysine protecting group in the case of cucurbit[6]uril (CB6). Supramolecular interactions were further utilized in the design of new turn-on diagnostic probes for 129Xe NMR/MRI detection of cancer, via the rotaxane-mediated suppression, and subsequent activation of Xe-CB6 host-guest interactions. Lastly, self-assembled protein-based nanomaterials were explored for applications in chemotherapeutic drug delivery to glioblastomas, with a special focus on how the morphology of protein-based nanomaterials can affect their biodistributions and drug delivery efficacies. Throughout this work, supramolecular interactions are interwoven with applications and techniques in chemical biology to achieve new advances that would not be possible otherwise.

Advisor: Francis, Matthew B
Challenging the dominant narratives of a Digital Financial Inclusion

‘Financial Inclusion’ has over the years assumed a central role in poverty alleviation by connecting the materially poor with formal financial services. Access to these formal financial services is expected to help the poor manage their erratic income streams and thus manage their day-to-day and critical expenses. In this way, the financial inclusion narrative assumes that the poor around the world are a homogeneous population that suffers from a universal condition of ‘financial exclusion’. In this scenario, informal finance is considered to be sub-optimal, expensive, and unpredictable. Yet researchers have begun to challenge this formal/informal, and consequently the inclusion/exclusion (false) dichotomies. I maintain and extend this intellectual tradition in this dissertation within the context of a digital financial inclusion paradigm./nWith the growing popularity of mobile money in the developing world, and its ability to connect the poor to financial services, the financial inclusion agenda is becoming increasingly digitized. Mobile money now constitutes a type of formal financial access that preserves and further normalizes the assumptions made about ‘formal’ and ‘modern’ financial services and their ability to help the poor. I interrogate these simplistic assumptions of a digitized financial inclusion paradigm. In general, the mobile money narrative prioritizes its technological innovations and its ability to create and sustain a cashless society while sidelining the broader infrastructure(s) that helps accomplish the challenging task of managing the poor’s unique and precarious cash flows. This narrative also renders invisible the human work that goes into making financial inclusion work for the poor. I challenge these prioritizations and focus instead on the less glamorous aspects of mobile money and how it can help the poor manage their precarity. I also demonstrate the politics of financial inclusion and how misguided policies in its name can fail to accomplish their stated goals, or worse have unintended consequences that actually hurt the poor rather than help them. The 2016 Demonetization event in India provides the ideal research context for this. Eventually, the goal of this dissertation is to develop a more circumspect, and therefore meaningful, analysis of financial inclusion, especially when considering the persistent precarity of poverty.

Advisor: Burrell, Jenna
Phase Transitions in Nuclear Organization and Function

Compartmentalization is a theme used throughout all kingdoms of biology to create functionally distinct units within a complex cellular environment. In addition to membrane-bound organelles, compartments can exist in the cell that are not membrane bound, yet are still physically distinct from surrounding space. The cell contains many of these membraneless organelles (nucleoli, stress granules, PML bodies, etc.), which are thought to be formed by liquid-liquid phase separation. We find that in early Drosophila embryos, a distinct chromatin domain (heterochromatin) forms by nucleating multiple Heterochromatin Protein 1a (HP1a) foci that grow individually, then fuse together into the final domain. This formation process is reminiscent of liquid-like fusion of nucleoli, which led us to comprehensively study whether heterochromatin could also be a phase-separated system within the nucleus. We utilized Fluorescence Correlation Spectroscopy (FCS) methods to investigate protein diffusion dynamics and determine if heterochromatin displays characteristics associated with phase separation. We find that, similar to other membraneless organelles, the heterochromatin domain is indeed capable of liquid-like fusion, is selectively permeable, and the hetero-euchromatic interface is a barrier to protein diffusion. Additionally, Drosophila HP1a protein is capable of liquid demixing in vitro and mediates domain formation in vivo. This work is the first to demonstrate that the heterochromatin domain is subject to phase separation principles, which suggests that phase interaction, rather than steric hindrance due to chromatin compaction, defines accessibility of heterochromatic areas. It has been suggested that phase separation could be a general organizing property for many membraneless organelles; therefore this model has broad implications for understanding the mechanism of nuclear and genome organization.

Advisor: Karpen, Gary H
Influences On The Effectiveness Of Information Technology Innovations In Primary Health Care

Health information technology (HIT) represents one of the largest areas of innovation in health care in recent history. While earlier theoretical and empirical work has focused mostly on understanding the factors that lead health care providers or patients to use an HIT innovation, less is understood about whether and how this use leads to improved healthy behaviors or health outcomes. The first paper of this dissertation studies the relationship between the ease of use of electronic health records (EHR) and relational coordination among primary care team members. Support is found for the hypothesis that ease of use contributes to better team coordination through use of the EHR. The second paper is a mixed-methods study of a diabetes care management text- messaging intervention for low-income, Latino patients. We examine both the implementation and impact of the program, and find some evidence of improvements to blood glucose (HbA1c) among participants. The third paper studies the relationship between a text-messaging intervention for pregnant women in Samoa and antenatal care attendance. The findings suggest that the text-messaging intervention did not encourage, and might have even discouraged, antenatal care attendance. Together with findings on implementation barriers and facilitators, these findings are discussed in the context of other current literature on mHealth programs for maternal health. The three papers provide support for an overall conceptual model that draws upon earlier theoretical and empirical literature, and helps us to better understand the effectiveness of HIT innovations in a broad range of settings.

Advisor: Rodriguez, Hector P
Multisymplectic Geometry with Boundaries

Geometric approaches form the foundation of modern classical mechanics. The prototypical example of a geometric method in mechanics is symplectic geometry applied to the Hamiltonian formulation of a system of particles. Extending this approach to field theories leads to unattractive features, such as an infinite-dimensional phase space and loss of manifest covariance. These deficiencies are particularly glaring in general relativity, where manifest covariance is closely tied to the fundamental symmetries of the theory./nRecent progress on covariant Hamiltonian approaches for field theories has led to the development of multisymplectic geometry. Multisymplectic geometry generalizes the symplectic geometry of particle systems to covariant fields, producing a finite-dimensional phase space and retaining manifest covariance. The symplectic 2-form common to symplectic geometry generalizes to the multisymplectic 5-form. The Euler-Lagrange equations for the field can be written in geometric language using the 5-form in a way that is formally identical to the geometric form of Hamilton's equations in particle mechanics. The resulting approach is a powerful geometric tool for understanding classical field theories./nIn this dissertation, we improve upon the current approach to performing a 3+1 decomposition (also known as space-time split) of multisymplectic geometry. We clarify the relationship between multisymplectic geometry, its 3+1 decomposition, and the traditional symplectic approach to field theory. The key observation is that there exist two intermediate phase spaces between the multisymplectic phase space and the traditional symplectic phase space. We show how a proper understanding of the geometry of these intermediate spaces clarifies aspects of the traditional symplectic formulation. Our improved 3+1 decomposition allows us to easily handle the case when the spatial manifold (in our space-time split) has a boundary. By careful consideration of what happens to the theory at the boundary, we can arrive at appropriate boundary conditions and boundary modifications to various 3+1 quantities. This is the first such decomposition of the multisymplectic phase space with boundaries in the literature./nLastly, we develop a multisymplectic formalism for general relativity. Our approach here is new, and gives great insight into the geometric structure of the theory. In the course of developing multisymplectic general relativity, we introduce local Lorentz transformations as an additional gauge symmetry. We show how reducing by this symmetry after 3+1 decomposition leads to the usual symplectic approach to general relativity.

Advisor: Littlejohn, Robert
Profiling antibody immune markers using nucleic acid systems

Technology to profile nucleic acid materials has been greatly advanced over the past decade. Nowadays, tools such as real-time quantitative PCR (qPCR) and next generation sequencing (NGS) are standard workhorses for biomedical laboratory. However, in contrast to great advance in nucleic acid science, assays used to quantify proteins and antibodies immune markers remain much stagnant. Traditional assays immunoassays such as enzyme linked immunosorbent assays (ELISA) and western blots, invented almost half a century ago, are still widely used. These assays face limitations including low sensitivity, low specificity, limited multiplex power and requirement of large sample volume. To this end, I have thought to create a methodology that allows researchers to leverage advanced nucleic acid tools to profile protein and antibody immune markers. In this thesis, I will provide an overview on current landscapes of nucleic acid-based assays for protein and antibody immune markers, and discuss how the new nucleic acid-based assay that I developed differentiates and complements other nucleic acid tools. Then, I will describe in details about our new nucleic acid assays including the creation of protein-DNA conjugates and use of these reagents to transform protein/antibody identities into quantifiable nucleic acid signals. Furthermore, I will describe potential impact of such methods in research and clinical settings. Finally, I will discuss potential development that is required to make this assays widely deployable in research and clinical communities.

Advisor: Bertozzi, Carolyn R
Wemmer, David E
Evaluating the Impact of a Cross-Group Friendship Intervention on Early Adolescents

The rapidly changing ethnic composition of the United States has resulted in a heightened need to create inclusive environments within schools. Research suggests that intergroup contact, specifically cross-group friendships, can positively impact students in diverse school contexts. Cross-group friendships have been associated with less peer victimization, better leadership skills, and heightened levels of psychosocial safety among youth. In this study, an existing adult friendship intervention (the Fast Friends Task) was adapted for adolescents and implemented in the school context. The effects of participation in the intervention on interpersonal closeness, ethnocultural empathy, and outgroup attitudes were examined. Middle school participants (N = 139) were paired with same-sex partners from different ethnic backgrounds and assigned to the control group or intervention group. Data were collected through pre- and post-surveys. Research questions were (a) does the adapted version of the Fast Friends Task generate interpersonal closeness, (b) does participation in the adapted Fast Friends Task increase empathy, and is empathy a mediator of the relationship between interpersonal closeness and outgroup attitudes, and (c) does participation in the adapted Fast Friends Task improve outgroup attitudes and increase early adolescents’ desire to interact with outgroup members? Results of a series of two-way mixed level analyses of variance indicated interactions between intervention and time on interpersonal closeness and desire to interact with outgroup members. The intervention did not result in changes in ethnocultural empathy, thoughts or warmth towards the outgroup. Implications and limitations of the study are discussed.

Advisor: Worrell, Frank C
Piezoelectric Micromachined Ultrasonic Transducers for Ultrasonic Fingerprint Sensors

A variety of physical mechanisms have been exploited to capture electronic images of a human fingerprint, including optical, capacitive, pressure, and acoustic mechanisms. Compared to other technologies, ultrasonic fingerprint sensors have two major advantages (1) they are insensitive to contamination and moisture on the finger (2) they have the ability to measure images at multiple depths hundreds of microns from the sensor surface. With the maturity of the thin film piezoelectric materials technology and MEMS-CMOS eutectic wafer-bonding process, piezoelectric micromachined ultrasonic transducers (PMUTs) arrays with electrical addressing to individual pixel have been proposed and developed for ultrasonic fingerprint sensors./nThis research focuses on the modeling and characterization of the PMUT-based ultrasonic fingerprint sensors. First of all, a 51.7% fill-factor, 110 x 56 rectangular PMUTs array is demonstrated with improved pressure output, receive sensitivity, and image resolution. With a customized CMOS design, time-gated images collected at two imaging depths was demonstrated to construct overlaid patterns separated axially by 127 um. Using another 65 x 42 circular PMUTs array ultrasonic fingerprint sensor design, transmit beamforming is implemented. The measured TX pressure output with beamforming is 25 kPa and the 3 dB beam-width is 50 um, a 1.6 x increase in pressure and 6.4 x decrease in beam-width relative to non-beamformed measurements. Beamforming increases the receive voltage by a factor of 1.4 and the SNR by 7 dB. On the other hand, the mode shapes of the dense rectangular PMUTs array with shared mechanical anchors are more complicated than a single-MUT dynamic model. Finite element model was used to model the mode shapes exhibited by the entire array, while experimental measurements of the mode-shape via laser Doppler vibrometry (LDV) was used to compute the volume velocity, which correlates to the measured far field pressure. In order to have an accurate prediction of the 2D near field pressure output from dense transducer arrays, time domain acoustic simulation using k-wave MatLab toolbox and vibration displacement measurements using LDV were utilized. Effects of mechanical crosstalk, acoustic surface waves, and anchor motions were also characterized. To further improve the fingerprint sensors performance, equivalent circuit model of circular PMUTs operating at the same resonant frequency in air is formulated. PMUT displacement is proved to be inversely proportional to the device thickness. Sub-micro thick piezoelectric layer devices were designed and fabricated to demonstrate the thickness scaling benefits.

Advisor: Horsley, David A
Lin, Liwei
Inferring Structural Models of Travel Behavior: An Inverse Reinforcement Learning Approach

Large volumes of digital human trajectories at high spatiotemporal resolution have become increasingly available to researchers and public entities. Derived from anonymized cellular records and social network postings, fine-grained mobility traces present exciting opportunities for longitudinal studies of daily travel and activity planning decisions. Through the application of automated and efficient data-mining techniques, researchers in machine learning, transportation engineering, and related disciplines have been able to use these movement microdata to model and forecast daily traffic conditions at metropolitan scales with unprecedented accuracy (González and Hidalgo, 2008; Lin Z. et al., 2017; Widhalm et al., 2015; Yin, M. et al., 2017)./nHowever, state-of-the-art machine-learning and discrete-choice frameworks do not consider the dynamics of daily mobility decisions at the individual level. Existing methods also do not take into account strategic, interdependent interactions between representative agents, complicating the cost-benefit analysis of innovative decentralized policy instruments such as induced peer-to-peer influence. Interpretable structural models that can provide consistent and disaggregate estimates of replanning behavior are needed in order to evaluate the impacts of these novel regulatory measures./nTherefore, in order to take better advantage of future and emerging technologies as tools to forge cooperative and sustainable relationships between citizens, governments, and the built environment, this thesis develops a framework for data-driven city management that bridges established travel demand planning practices with innovations in big data, reinforcement learning, and strategic decision-making. The work described herein is comprised of three major components. First, we develop a two-stage game theoretic model of peer pressure to investigate feedback between social, geographic, and temporal dimensions of agent choices in a hyper-realistic microsimulation of urban travel behavior. Second, in order to learn representations of dynamic agent utility functions, we extend \textit{inverse reinforcement learning} (IRL) algorithms to novel activity and travel planning environments and estimate associated structural parameters. Finally, we investigate the strength of modern high-dimensional imitation learning techniques to train flexible and accurate models of schedule composition and activity duration. Results from applications of the empirical methods developed herein suggest that our contributions could effectively complement the microsimulation and discrete choice modeling techniques used in disaggregate urban infrastructure planning frameworks such as activity-based transportation demand models.

Advisor: Pozdnukhov, Alexey
Program Synthesis for Systems Biology

Cell signaling controls basic cellular activities and coordinates cell actions, such as cell differentiation, division and growth. Consequently, errors in cellular signaling are responsible for diseases such as cancer, autoimmunity, and diabetes./nExecutable biology describes mechanistic models of biological processes in a formal language that is dynamic and executable by a computer. Models in executable biology are able to capture complex behaviors of biological systems, such as time and concurrency. In addition, discrete modeling enables efficient algorithms to exhaustively explore spaces of models./nThis thesis introduces tools to automatically infer executable models at different levels of abstraction from varied types of experimental data. In each case, we investigate identifiability of models when the provided experimental evidence and prior knowledge are varied. We make the following individual contributions:/n* TPS:A framework for the automated inference of signed directed graphs modeling protein signaling networks, using time series data./n* SBL: A modeling language embedded in Scala for the automated synthesis of concurrent programs modeling cell fate decision using mutation experiments./n* Karme: A framework for investigating identifiability of asynchronous Boolean network models from single-cell gene expression data./nTo evaluate our work, we apply our tools to in vivo, in vitro, and in silico data sets on cellular differentiation and protein signaling. We show that, through explicit characterization of ambiguities in input specifications, our approaches make unambiguous predictions supported by experimental evidence, and suggest new experiments that help disambiguate alternative explanations. Applied to epidermal growth factor signaling response data, TPS exhaustively explores all models that are consistent with the input, and makes predictions that are unambiguous across the model space. These predictions are supported by further experimental validation. Using SBL, we synthesize valid models of cell fate decision in C. elegans vulval precursor cells, fixing a bug in previous modeling. We show the existence of internally different models that are behaviorally equivalent under all mutation experiments. One of the inferred models expresses a previously unknown biological hypothesis. Finally, we use Karme to synthesize models of myeloid cell differentiation from simulated noisy single-cell data, and demonstrate that experimental design can reveal key genes in the system.

Advisor: Bodik, Rastislav
Yosef, Nir