VictorJuiz/thesis huggingface dataset:
So that it might be different for you : the intersection of adolescents/ce, legal culture, and sexual violence in young adult literature
Keywords: Humanities and Sciences.
Modern Thought and Literature.

This dissertation investigates the question of how--and to what end--law is represented in contemporary young adult literature ("YA Lit") and graphic narratives. With a focus on laws surrounding sexual violence, it investigates these works' unique expression of law as part of an adolescent legal culture--via the inclusion of legal actors, spaces, procedures, and the language of and references to laws themselves--to adolescent readers, who are not taught law in school, though remain subject to it. By contextualizing law as it relates to adolescents, and specifically adolescent sexual violence, I argue that these works mediate the transitional gap space of adolescence, filling a necessary educative niche by providing vital insight for young people into the complex legal structure governing the United States, particularly as it relates to individual and sexual rights. Thus, these works function not only as entertainment, but as important tools for necessary intergenerational conversations on sexual violence specifically, and law more generally. The result is an unprecedented space for young people to ask questions of and engage in conversations for which there currently is no clear, consistent space in the school or home. In this way, this dissertation argues, the counterpublics stemming from these YA Lit narratives provide a unique and unprecedented space for adolescents to ask tough questions, to have difficult conversations, to talk about what the issues of the day may mean, and to provide each other support and guidance, thereby encouraging an essential, broadscale, adolescent-focused space for deliberative democracy, critical citizenship, and participatory politics.
Social media sensitivity : probing heterogeneity across people, places, platforms, types of use and time
Keywords: Humanities and Sciences.
Communication.

Recent research suggests that the relationship between social media use and wellbeing is heterogeneous and multifaceted. This dissertation examines five potential sources of this heterogeneity: user's psychological dispositions (e.g., tendency for psychological vulnerability), the physical and social context of use (e.g., where and around whom use is occurring), the platform that is being used (e.g., Instagram, YouTube), the type of use that is occurring (e.g., passive active) and time domains (e.g., hours, weeks and months). I deploy random-slope multilevel models to analyze two large corpuses of experience sampling data collected from young adults (N=3,233; Observations= 219,027). Results reveal small but persistent associations between social media use and wellbeing outcomes that are moderated by a range of variables. Psychologically vulnerable people tend to feel worse after using social media as compared to psychologically healthy people. Using social media in social locations and around others is associated with negative wellbeing outcomes, but these results differ across platforms. Both passive and active social media use is associated with increased wellbeing outcomes at the within-person level but decreased wellbeing outcomes at the between-person level. Lastly, some evidence suggests that social media's effects on wellbeing accumulate steadily over time instead of "drenching" users in the moments after use has occurred. Theoretically, my dissertation provides empirical support for three key theories -- Digital Wellbeing as a Dynamic Construct, Differential Susceptibility to Media Effects Model and The Complementarity-Interference Framework. More broadly, results from dissertation suggest that blanket legislation on social media companies might be less effective than promoting tools that bolsters' users agency over their social media use.
Social Network Optimization to Reduce Opinion Polarization Using a Graph Pooling Policy Network
Keywords: sociopolitical polarization, polarization, computational sociology, social networks
Text
Thesis

In recent years there has been a substantial increase in sociopolitical polarization—it is clear that our society does not agree on issues in politics, healthcare, education, and beyond. Evidence of polarization in politics has been found in the increasingly partisan voting patterns of the members of Congress (Poole and Rosenthal (1984), Poole and Rosenthal (1991)) and widespread partisan sorting (Baldassarri and Gelman (2008)). Indeed, McCarty et al. (2016) claim via rigorous analysis that America is polarized in terms of political attitudes and beliefs.Drawing on the computational sociology approach presented by Macy and Willer (2002), this research operationalizes a sociotechnical perspective where agent-based modeling assists in simulating the social dynamics underpinning observed trends. The study incorporates the interplay of individual actors within the social network structure, moving beyond conventional statistical analyses to an agent-centered understanding of polarization phenomena.Empirical studies have argued that that homophily, i.e., greater interaction between like-minded individuals, results in polarization (Gilbert et al. (2009), Baron et al. (1996), Sunstein (2002)). Butas Dandekar et al. (2013) show, homophily alone, without biased assimilation, is not sufficient to polarize society. Indeed, research has shown that biases lead to more network homophily and, overtime, more homophily leads to actions that put more weight on biases and less weight on expert opinion (Hakobyan and Koulovatianos (2019)). This interplay between homophily plus confirmation and assimilation bias result in opinion polarization.Counterintuitively, the increase polarization has been accompanied by the growth of social media platforms where individuals are sharing more information more than ever before and are able to connect with others across a wider range of communities (Silver et al. (2019)). Despite the ability to connect with a greater diversity of people, individual’s beliefs have become more ideologically constrained and extreme in some areas (Kozlowski and Murphy (2021)). Research into social networks and the role of bias, as well as “echo chambers,” have indicated that the structure of social networks can make consensus harder to reach, potentially preventing exposure to other opinions,even despite increases in the total number of connections in a network. As such, modifications to asocial network through purposeful addition of edges may enable less polarization.
The social structure of knowledge innovation, diffusion, and practice
Keywords: Business.

In this dissertation, I ask how social structure--patterned regularity and configuration of social life--shape the production and dissemination of knowledge and practice in society. Just as social structures permeate social life at every conceivable level of analysis, from the macroscopic array of institutions and fields to the microscopic units of turns in everyday conversation, this dissertation spans a similarly diverse array of contexts such as political speeches, judicial decisions, quarterly earnings calls, and labor markets. I integrate recent methodological advances in natural language processing and deep learning with rich sociological theories to demonstrate the inseparability of social structure and knowledge
Some algorithms and hardness results for the planted clique problem
Keywords: Engineering.
Electrical Engineering.

One fascinating aspect of trying to solve modern high-dimensional statistical tasks is the rich interplay between the twin requirements of computational as well as statistical tractability. The planted clique problem is a prototypical task that exhibits this interplay. As such, it has become a testbed for theoretical computer scientists interested in developing tools to understand and predict the computational intractability of statistically feasible problems. In this thesis, we discuss some algorithmic and hardness results for the planted clique problem, particularly using the lens of sublinear-time algorithms, query complexity, and memory complexity.
Some results in high dimensional statistics : iterative algorithms and regression with missing data
Keywords: Engineering.
Electrical Engineering.

We study two problems at the interface between statistics and computation. In the first part of this thesis, we consider the problem of fitting a model to data by applying an iterative algorithm to perform empirical risk minimization. While the choice of iterative algorithm determines both the speed with which a fitted model is produced as well as the statistical quality of the produced model, the process of algorithm selection is often performed in a heuristic manner: via either expensive trial-and-error or appeal to (potentially) conservative worst case efficiency estimates. Motivated by this, we develop a framework---based on Gaussian comparison inequalities---to rigorously compare and contrast different iterative procedures to perform empirical risk minimization in an average case setting. In turn, we use this framework to demonstrate concrete separations in the convergence behavior of several iterative algorithms as well as to reveal some nonstandard convergence phenomena. In the second part of this thesis, we turn to studying parametric models with missing data in high dimensions. While this problem is well understood in the classical fixed dimension and infinite sample scaling, the situation changes drastically when the data is high dimensional. First, the high dimensional nature of the problem---which ensures that most samples contain missing entries---removes a complete case analysis from the statistician's toolbox. Second, the log-likelihood may be nonconcave in which case maximum likelihood estimation may prove computationally infeasible. Motivated by these issues, we study imputation-based methodology. In particular, we show that conditional mean imputation followed by a complete data method yields minimax optimal estimators in the linear model. We then show that the situation is quite different in the logistic model: in a stylized setting, conditional mean imputation can yield inconsistent estimators which nonetheless match the (Bayes) optimal prediction error
Some variations of Khovanov homology for null homologous links in RP³
Keywords: Humanities and Sciences.
Mathematics.

Asaeda, Przytycki and Sikora extended Khovanov homology to links in I-bundles over surface. In particular, their construction works for links in the real projective space RP³. In this thesis, we will present two variations of their construction for null homologous links in RP³. First, we introduce Khovanov-type homologies which depend on an extra algebraic input [alpha] = (V₀, V₁, f, g), consisting of two graded vectors spaces and two maps between them. With some specific choice of [alpha] = [alpha] [subscript APS], we recover a reduced version of the Asaeda-Przytycki-Sikora construction. With another choice of [alpha] = [alpha] [subscript HF], we construct a spectral sequence from our theory converging to the Heegaard Floer homology of the even branched double cover of RP³, extending the usual Ozsváth-Szabó spectral sequence for links in S³. Second, we introduce Bar-Natan homology for null homologous links in RP³ over the field of two elements. It is a deformation of the Asaeda-Przytycki-Sikora construction, in the sense that the associated graded complex with respect to the quantum grading gives their chain complex. We also define an s-invariant from this deformation following the same recipe as for links in S³, and establish certain genus bounds based on its properties
Sparsification, online optimization, and an almost-linear time algorithm for maximum flow
Keywords: Humanities and Sciences.
Mathematics.

This thesis designs efficient algorithms for combinatorial and continuous optimization problems. By combining new techniques developed in this thesis pertaining to iterative methods, dynamic algorithms, and high-dimensional geometry, we give an almost-linear-time algorithm that computes an exact maximum flow and minimum cost flows in directed graphs. Our insights lead to algorithms for broader classes of combinatorial and continuous optimization problems including online discrepancy minimization, regression, and dynamic maxflow. In the first part of the thesis we study iterative methods, which are procedures which solve an optimization problem through a sequence of simpler subproblems. By understanding the geometry of the underlying optimization problem, we give iterative methods with improved convergence rates and runtimes for the problems of $\ell_p$ regression, maximum flow, and mincost flow. In the second part of the thesis we study dynamic algorithms and online optimization which naturally arise from studying iterative methods. In particular, we give the improved algorithms that maintain an approximate maximum flow in dynamic graphs undergoing edge insertions, and also nearly-optimal algorithms for minimizing the discrepancy of vectors arriving online. In the third part of the thesis, we study sparsification problems motivated by high-dimensional geometry. The goal of sparsification is to reduce the size of an objective while approximately maintaining the desired properties, such as the value on all inputs. We give improved and nearly-optimal size bounds for sparsification of several objectives including spectral hypergraph energies and sums of symmetric submodular functions. Finally, we give nearly-linear size bounds for sparsification of generalized linear models satisfying natural bounds on their growth, which imply nearly-optimal algorithms for solving $\ell_p$-regression for \in (1, 2]$. This thesis contains material from the papers \cite{KLS20, ALS20, LSS21, CKLPPS22, JLS22, JLS23, BLS23, JLLS23, JLLS23b}
Spatial and molecular analysis of growth patterns in the lung adenocarcinoma microenvironment
Keywords: Medicine.

Non-small cell lung cancer adenocarcinoma tumors are composed of heterogeneous cell populations that contribute to the development and dissemination of malignancies. Lung adenocarcinoma tumors have been observed to progress through a well-defined series of histologic growth patterns, including lepidic, acinar, papillary, and solid designations. These patterns are routinely utilized clinically and have been shown to correlate with clinical prognosis. Fibroblast cells are among the most prevalent within this microenvironment, but their relationship to growth patterns in lung adenocarcinoma is understudied. Despite the close association of lung adenocarcinoma growth patterns with prognosis and growing interest in the roles of pathological fibroblasts, the predominant fibroblast subtypes and their interactions within the context of each lung adenocarcinoma growth pattern are poorly defined. To understand the many cell types present in the lung adenocarcinoma tumor microenvironment, with a focus on resident lung fibroblasts, I generated an imaging dataset comprising whole-slide images of 8 lung adenocarcinoma tumors and analyzed spatially bound growth pattern regions annotated by an expert pathologist. I initialized a novel multiplexed immunofluorescence microfluidics instrument, developed a tumor marker panel for use in said instrument, and collected an in-house lung tumor tissue sample bank. I utilized this pipeline and methods to investigate specific biological hypotheses in lung adenocarcinoma fibroblast subtype localization with other cell types in varying histological growth patterns. In particular, I observed that colocalization of fibroblasts expressing the surface marker CD90 with CD3+ T cells are more associated with invasive acinar than noninvasive lepidic growth patterns. I utilized an independent lung tumor microarray dataset to perform concurrent analyses and found that this spatial cell association signature is correlated with prognosis and survival. I also leveraged multiple sources of imaging in these samples, revealing the potential of extracellular matrix features in predicting nodal status of individual samples. I observed differential changes in the localization of specific cell types and calculated extracellular matrix features in lung adenocarcinoma samples depending on nodal involvement. By defining pathological lung fibroblast subtypes through their shared markers, interactions with other cells, and functions, I shed light on these crucial components of the tumor microenvironment and ultimately define fibroblast- and microenvironment-based treatment targets for lung adenocarcinoma. These studies contribute to the field's understanding of spatial fibroblast interactions with the lung adenocarcinoma microenvironment and offer a conceptual framework for future investigations into the tumor microenvironment
Spatially-resolved multiomic characterization of the maternal-fetal interface
Keywords: Medicine.
Microbiology and Immunology.

This thesis explores the intricate dynamics of pregnancy, focusing on the dynamics of trophoblast invasion and the remodeling of uterine spiral arteries. Through a cutting-edge multiomic approach employing spatial proteomics and transcriptomics, we construct a comprehensive spatio-temporal atlas of the maternal-fetal interface in early pregnancy and expand this to examine pertubations in preeclampsia. Our findings shed light on cellular interactions between fetal and maternal cells, the evolution of this process with gestational age and spiral artery remodeling, and the mechanisms behind EVT invasion. Our work offers new insights into the regulation of maternal-fetal tolerance in healthy and pathological pregnancy and providse a framework for elucidating etiological perturbations in pregnancy complications. Our research can also serve as a model for comprehending the abnormal recruitment and combination of immune tolerance, tissue remodeling, and angiogenesis during the advancement of tumors.
Spatial-omics approaches to dissect host-disease interaction
Keywords: Medicine.
Microbiology and Immunology.

The spatial interaction between different biological agents and the host immune system plays a critical role in maintaining tissue homeostasis and influencing disease outcomes. These interactions can be characterized by their complexity and diversity, encompassing various scenarios: Harmonized Interaction, for example, the mutualistic relationship between the microbiota and the immune system is vital for maintaining tissue integrity and immune homeostasis. Hostile Interaction, for example when a viral infection occurs, it triggers a local immune response aimed at eliminating the pathogen. This immune response can result in systemic retaliation, leading to inflammation and other systemic effects. Dynamic Interaction, for example within the tumor microenvironment, there exists a dynamic interplay between tumor cells and immune cells. This complex interaction influences the efficacy of immunotherapy and can determine the outcome of the disease. However, studying these interactions requires methodologies that go beyond traditional single-domain approaches. To comprehensively understand these complex phenomena, we need methods that can capture and preserve spatio-temporal information, identify cross-domain feature responses in a high-plex format, and harmonize biological knowledge across different modalities and biological entities. This thesis work attempts to bridge this gap. Chapter 1 provides a background on tissue biology and the methodologies developed so far, and the unmet need in this field. Chapter 2 introduces MicroCart, an experimental assay for studying host-microbiome interactions, highlighting its design, validation process, and compatibility with different modalities. Chapter 3 presents Redsea, a computational method addressing lateral signal contamination in imaging, emphasizing its significance in improving imaging quality. Chapter 4 discusses Mario, a data integration algorithm enabling the matching and integration of data from different single-cell proteomic and multi-modal methods. Chapter 5 focuses on MaxFuse, a data integration algorithm designed for 'weakly-linked' modalities, facilitating the creation of a spatial-trimodality dataset, showcasing its benefits and applications
Spatiotemporal dynamics of plant-soil-microbe interactions
Keywords: Humanities and Sciences.
Biology.

Understanding how communities assemble across space and time remains a fundamental ecological question. In this dissertation, I investigate how plants, soil, and microbes interact to shape the outcome of aboveground and belowground communities. To understand temporal dimensions of microbially mediated plant-soil feedback, I quantified how the strength of conspecific and heterospecific feedback changes over time in greenhouse experiments. Using molecular sequencing, I show that plant-associated microbial communities changes within a plant generation, depending on the initial microbial source. I then show that microbial community turnover between generations affects the coexistence outcomes of plant communities driven by natural gaps in growing seasons and litter decomposition. In the spatial dimension, I use spatially-explicit field data to show how plant and microbial community distribution varies in relation to soil nutrients. Using observations from forest dynamics plots, I show that mycorrhizal association drives the aggregation patterns of trees across a gradient of soil fertility, interacting with the dispersal mode of different species and age structure within each species. All together, my dissertation explores various temporal and spatial structures of plant--soil--microbe interactions and their role in shaping species diversity above- and belowground.
Spatiotemporal gene expression mapping of brain aging in mice
Keywords:  Department of Biology
Hahn, Oliver
Aging
Brain regions
Spatial transcriptomics
Gene expression
Text
Thesis

In aging individuals, the brain is a vulnerable organ and at higher risk of neurodegenerative disease, yet mechanisms underlying the aging process in the brain are not fully understood. Previous studies have shown that the impact of aging on the brain involves a multitude of biological processes which differ between brain regions, however a comprehensive understanding of age-related gene expression dynamics in many brain regions across multiple ages remains to exist. This research aims to characterize spatiotemporal gene expression patterns associated with aging across several brain regions in mice. 10X Visium spatial transcriptomes of healthy mice aged 6, 18, and 21 months are analyzed to investigate region-specific expression changes over time. An interactive web application is available to enable access to the spatial transcriptomic dataset. The findings suggest that aging across the brain occurs in a heterogenous fashion, affecting different brain regions at different magnitudes and through a variety of gene pathways. Due to their accelerated rates of aging, white matter tracts, areas in the brain made up of myelinated axons, may be of interest as “hotspots” of aging.