VictorJuiz/thesis huggingface dataset:
Idiographic prediction of short-term suicidal ideation

Despite decades-long efforts devoted to enhancing the understanding, prediction, and prevention of death by suicide, suicide rates have continued to rise both in the United States specifically and in many countries worldwide. Although the advent of machine learning techniques has improved our ability to predict suicidal thoughts and behaviors (STBs), few studies have focused on the short-term prediction of these phenomena. Furthermore, the increasing recognition of the individual- and time-varying nature of STBs necessitates the use of individualized predictive models to detect short-term STBs person-by-person with greater precision. In the present study, I used ecological momentary assessment (EMA) data collection methods with idiographic analytic approaches to better describe and predict short-term suicidal ideation and its risk factors on a person-by-person basis. Key factors measured via these EMA methods included variables related to several prominent theories of suicidal behavior, including the interpersonal, hopelessness, and three-step theories of suicide, and key emotions previously demonstrated to predict suicidal thinking. I also employed a series of machine learning techniques to examine whether these approaches could enhance individualized prediction models for short-term suicidal ideation. With a largely female sample of ten (N = 10) individuals that reported at least one suicide attempt in the past year or reported intense ideation for more days than not over the past month. These individuals were also largely Caucasian and identified as sexual minorities. Results demonstrated that short-term suicidal ideation and its risk factors displayed considerable variability over time. Further, results also indicate that individualized models can produce reliable predictions of short-term suicidal ideation—and that these predictions could be further improved by employing machine learning techniques. Furthermore, both auto-regressive and machine learning-based models, on average, outperformed individualized models derived from the interpersonal, hopelessness, and three-step theory of suicide. Taken together, the present findings may represent a first step toward developing a more precise and individualized approach for understanding and preventing death by suicide through better modeling and predicting short-term suicidal ideation and its risk factors. Specifically, these findings suggest that the combination of EMA methodology, idiographic modeling, and machine learning can be used to effectively identify sets of risk factors related to promiment theories of suicide and past research to predict short-term suicidal ideation person-by-person with high precision.

Advisor: Fisher, Aaron J
How does power affect emotion regulation? Power roles, social norm concerns, and the regulation of anger and sadness

Asymmetries in power, or the control over valued resources and outcomes, exist in social relations across a broad range of contexts. Power influences motivation, behavior, and emotional expression, and in turn can impact social interactions both positively and negatively. However, little is known about how power specifically influences emotion regulation. The present research examined whether experimentally manipulated power roles (e.g., being a boss versus an employee) affect regulation of anger and sadness, and how social norm concerns – beliefs about appropriateness, closeness, and power concerns – might explain these effects. Using a within-subjects design, one exploratory study (Study 1, N = 207) varied hypothetical power roles (i.e., high, equal, and low power vis-à-vis another person) and assessed their impact on emotion regulation (suppression, acceptance, and reappraisal). Participants reported more suppression, less acceptance, and more reappraisal in the high- and low-power roles compared to the equal-power role. These effects were consistent across anger and sadness scenarios and male and female gender. A pre-registered study (Study 2, N = 447) replicated Study 1 while also exploring social norm concerns as a potential mechanism driving the effects of power roles on emotion regulation. Study 2 replicated Study 1’s effects and also suggested that social norm concerns partially statistically mediated the power-role effects on suppression, acceptance, and reappraisal. A pre-registered study (Study 3, N = 291) replicated and confirmed Study 1 and Study 2’s main effects and Study 2’s mediation. A pre-registered study (Study 4, N = 471) then assessed a boundary condition (i.e., the other person in the power role scenario as the source of the to-be-regulated emotion vs. a bystander), and replicated main and mediation effects from Studies 1-3 in the low-power (but not high-power) compared to equal-power role, supporting the idea that the source of the to-be-regulated emotion matters when individuals possessing different power roles regulate negative emotions. A final between-subjects study (Study 5, N = 116) expanded previous study designs by experimentally manipulating power roles vis-à-vis an interaction partner who, in the course of the interaction, was the source of the to-be-regulated negative emotion. Power roles did not directly affect emotion regulation, but indirect effects via social norm concerns on suppression and acceptance seen in Study 4 were replicated. Overall, the findings suggest that different power roles differentially influence the regulation of anger and sadness and that social norm concerns partially explain these effects. The effects of social norm concerns depend in part on the source of the to-be-regulated emotion. This research provides novel insights and explanations for how and why individuals occupying different power roles manage their negative emotions.

Advisor: Chen, Serena
Quiet Acquisition: The Politics of Justification in Military Capability Trajectories

Why do states choose different military technology procurement strategies when attempting to develop their security capacity? In this dissertation I examine under what conditions middle-power states choose to develop capabilities indigenously, purchase from allies, or pursue “middle ground” strategies like production under license. How states choose to acquire capabilities, as well as what they choose to develop indigenously, sends important signals about the structure of domestic politics as well as decisionmaker perceptions of the alliance. I address these questions through a close case study of Japanese procurement strategies for key defense technologies acquired since the end of the Second War. Japanese military acquisition is constrained in ways no other middle powers face. Despite this, there is significant variation in how Japanese policymakers seek to procure defense technologies. Drawing on archival research, direct observation, and interviews, I discuss the process of negotiating acquisitions trajectories in cases including Japanese information-gathering satellites, the Patriot missile program, maritime patrol aircraft, and radar. I compare my findings in these cases to patterns in South Korea and Taiwan./nTwo key takeaways emerge from this analysis. First, I argue that defense technology acquisition patterns reflect domestic political balancing between state and business actors. When government and business interests align, acquisition patterns are consistent over time. Second, I argue that in cases where Japanese business and government interests diverge, policymakers tend to opt for production under license as a procurement strategy. I claim that this is because a divergence in business and state interest in domestic production tends to be based on what I term the “justifiability” of capabilities./nDecisionmakers are concerned about their image, especially in democracies. Government actors want to be perceived as both effective and legitimate. When indigenous development of a capability is seen as “difficult to justify” because it is seen as ineffective or illegitimate, government actors are unlikely to support domestic development in the short term. However, “justifiability” is based on public understanding of the legitimacy of technology and is therefore mutable. For this reason, where Japanese business and government interests diverge due to concerns regarding whether indigenization of the capability can be justified, production under license is a likely procurement strategy. This approach offers a middle ground for businesses and government in which businesses are granted some, but not all, benefits of domestic production. This allows an implicit promise to businesses that when capabilities become “justifiable” domestic production will be sought. In cases where public understanding of capabilities changes over time, the shift is made to indigenize. When justifiability stays constant, so too does licensed production.

Advisor: Hassner, Ron
Investigation of the Regulation and Functional Roles of MuERV-L in Murine Preimplantation Development

Murine preimplantation embryogenesis is characterized by the onset of totipotency, and the gradual restriction of cellular developmental potency. Intriguingly, due to the erasure of repressive epigenetic marks, endogenous retroviruses (ERVs) are transcriptionally active in a manner that depends on the ERV family and developmental stage being considered. It has been accepted that the ERV, MERVL, is associated with the expansion of cell fate as its expression marks totipotent blastomeres in vivo and correlates with the augmented developmental potential observed in MERVL+ embryonic stem cells (ESCs). Additionally, ERVs ORR1A0 and ORR1A1 share a similar expression profile with MERVL, both in vivo and in vitro, establishing a cohort of markers of expanded cell fate. Transcription factor Klf5’s motif is highly enriched in all three of these ERVs. Consistently, overexpression of Klf5 in ESCs expands the scope of their differentiation to include extraembryonic lineages. Remarkably, a single ESC overexpressing Klf5 can contribute to placenta, yolk sac and the embryo proper, whereas control ESCs invariably only contribute to embryonic tissues. Further, Klf5 is required for lineage formation in vivo through its direct regulation of trophectoderm specification genes and its cooperative action with Klf4 during the establishment of the inner cell mass. Beyond simply marking totipotent cells, I describe a role for MERVL’s Gag protein during the one cell to two cell transition. Altogether, this work describes a novel regulator of ERVs that mark totipotency and details the requirement of MERVL-Gag during cellular abscission in murine preimplantation development.

Advisor: He, Lin
Sustaining Inclusive Education in K–12 Schools: A Framework for Design, Implementation and Analysis of Inclusive Practices

This dissertation research aimed to examine the processes and practices of inclusive education for students with diverse disabilities in a school district in northern California. The first part is a theoretical articulation of a conceptual framework that school districts and local education agencies can use to design, implement, and sustain inclusive educational practices. This framework has three central tenets, which relate to (a) how students access general education, (b) meaningful ways in which they can participate alongside their peers, and (c) support structures that can undergird effective implementation. The second part of this dissertation consists of an empirical study that evaluated the deployment of this framework in implementing inclusive education in the kindergarten-through-12th-grade context. It brought under scrutiny the system-level processes and programmatic structures that were in place or being developed at this school district. Facilitators and barriers to inclusion were examined as they related to access, meaningful participation, and creation and provision of supports for all participants within their sociocultural context. I make visible the ableist and disablist discourses and practices that sometimes frame the construction of disability and what it means to teach or parent a child with a disability within those confines. In this way, this dissertation untangles the oppressive ideologies that are prevalent in narratives about the disabled and destabilizes any claims made to a normative ideal, while affirming that inclusion and receiving education in the least restrictive environment is a civil right for disabled students./n

Advisor: Sterponi, Laura
The Eugenic University

This dissertation examines the university and institutions of schooling as technologies of power imbricated in the state violences they purport to be separate from or the solution to. I examine the logics of the university within the assemblages of policing, settlement, and empire of the U.S. state and its racial capitalist regime. I use methodological practices of “curation” to draw together disjunctive moments, theories, and analytic techniques in order to highlight new analyses and openings for contestation. I explore Northern California universities in the Progressive Era as a case study of these assemblages, examining key university administrators, professors, and researchers who were involved in the burgeoning eugenics movement. These include August Vollmer, the “father of modern policing” and the founder of the first university criminology department; Leo Stanley, chief surgeon and researcher at San Quentin State Prison; David Starr Jordan, first president of Stanford University and preeminent philosopher of eugenics; and David Prescott Barrows, president of UC Berkeley, phrenologist, and architect of the public school system in the Philippines. Data sources include Bancroft Library Archives, including the major collections of the August Vollmer Papers and David P. Barrows Papers; the Marin County Free Library archives, including the David Starr Jordan Papers, Leo L. Stanley Papers, and archives related to the Panama-Pacific International Exhibition; and the published works available online of Vollmer, Stanley, Jordan, and Barrows. The first chapter interrogates writings of August Vollmer and Leo L. Stanley with a discussion of prisons and policing in the context of racial capitalism. The second chapter brings theorizations of David Starr Jordan together with an analysis of democratization of land and education in the context of settler colonialism. The third chapter analyzes the work of David P. Barrows and colonial and international education in the context of U.S. imperialism. Together, these chapters discuss the technologies and logics of education, schooling, and universities in order to curate a broader critique of the institutions and the nature and structure of the United States.

Advisor: Gutiérrez, Kris
Interpretable and efficient statistical approaches for biomedical data

Statistics and machine learning have achieved remarkable successes in solving data problems including driving new biomedical discoveries. In particular, prediction and hypothesis testing are two important applications of statistics and machine learning to biomedical data. In this dissertation, we will investigate how appropriate interpretations of prediction algorithms, and scrutiny of efficiency of hypothesis testing techniques can help us extend the capability of statistical and machine learning approaches in biomedical science./nChapter 1 of this dissertation provides an overview of the topics covered, as well as the background information for the rest of the dissertation. Chapters 2 and 3 introduce the applications of an interpretable machine learning prediction pipeline for two biomedical problems: drug response prediction, and molecular partner prediction in clathrin-mediated endocytosis.In the drug response prediction task, our predictive and stability-driven pipeline achieves the state-of-the-art performance in identifying stable, predictive -omics features for drug response. In the molecular partner prediction task, we developed a interpretable deep learning model that achieves state-of-the-art accuracy in predicting whether a clathrin-coated pit is abortive or valid./nChapter 4 focuses on the interpretation of a specific algorithm: random forest. Random forest has witnessed numerous applications in biomedical sciences, and its interpretation has become an important topic of research. We derived the first finite sample bound on the bias of Mean Decrease Impurity, one of the most widely used measure of feature importance.To reduce this bias, we proposed a new feature importance measure, called MDI-oob. MDI-oob achieves state-of-the-art performance in feature selection from random forest in biology inspired simulations./nChapters 5 and 6 aim to provide a more comprehensive understanding of some of the most popular high dimensional tests for biomedical data. Of particular interest is the comparison between special-purpose tests with the Bonferroni correction (or the closely associated max test in global testing), a simple and transparent test whose Type-I error (false positive) is robust to arbitrary dependence between $p$-values of univariate null hypotheses. In the context of global testing, we showed that the max test is optimal for detecting sparse signals, provided that the distribution of the signals has Gaussian or heavier tails. We also derived the first general negative results for knockoff methods. We give realistic conditions on the covariance matrix of the design matrix under which the true positive rate of the best achievable knockoff method must converge to zero, even when the true positive rate of Bonferroni correction converges to 1.

Advisor: Yu, Bin
Fithian, William
Resolving Ecological, Evolutionary, and Global Change Effects on Biodiversity Dynamics: A Review and Empirical Study

Understanding why we find different types and numbers of species in different places has been a long-standing question for biologists. Ecological communities provide a conceptual and literal framework for unraveling generalizations about the composition, distribution, and dynamics of biodiversity. Communities assemble via ecological and evolutionary mechanisms that lead to the accumulation of species and the development of species interactions. Insights into the dynamics of community assembly have relied extensively on theoretical work or studies with limited spatiotemporal scale. Directly studying community structure through time using long-term time-series data offers an exciting opportunity to link evolutionary relatedness across multiple taxa and the ecological attributes of the communities in which they occur, within a dynamic framework. Oceanic archipelagos provide a chance to sample communities from discrete time points during community assembly. Present-day communities face threats, such as climate change and invasive species, demanding more holistic approaches that consider all species in an ecosystem and the interactions between them. Advancements in molecular techniques, bioinformatics, and data science have led to the possibility of a more comprehensive understanding of the structural properties of communities and of biodiversity overall. The research presented here explores the interplay of ecological and evolutionary processes on biodiversity dynamics over deep time. I employ multiple research approaches including field collections, laboratory work with environmental DNA and museum specimens, macroecological theory, and network thinking to create a novel synthesis of information for understanding the evolution of ecological communities. First, I highlight the singular importance of insular systems for studying fundamental questions in ecology and evolutionary biology. By recording patterns of species accumulation and genetic distances in multiple arthropod lineages, I explore patterns of community assembly over time and how they differ between lineages. Additionally, I investigate systematic deviations in observed species abundance distributions from predictions of theory for understanding community steady state. Last, I demonstrate the implications of shifting network architecture metrics for understanding ecosystem change. My work makes critical advances in understanding biodiversity, community assembly, and the consequences of environmental perturbations on ecological communities.

Advisor: Gillespie, Rosemary G
Tailoring the Mechanical Behavior of Architected Materials through the Strategic Arrangement of Defects and the Tactical Coalescence of Lattice Members

Architected materials are considered the state of the art of engineering ingenuity. Specifically, mechanical metamaterials have been accentuated due to their unconventional and augmented responses. They have been gingerly investigated under the context of ultralight-ultrastiff structures for aerospace applications, tailored buckling mechanisms for energy storage, soft robotics and controlled wave propagation and designed anisotropy for tissue engineering./nAlbeit the plethora of remarkable results promulgating this subject, the analysis of architected materials has many questions that need to be addressed. There is no rigorous explanation for the selection of specific 3D designs that have been thoroughly utilized in the literature (regarding the selection of specific design variables and cost functions). Consequently, in practice specific structures are repeatedly used, without any explanation whether further search of the design space could not provide a substantially improved result. Therefore, the lack of understanding of the design space and the inherent physical phenomena has not elucidated the tools to obtain a globally optimal design. Thus, tailoring mechanical metamaterials is extremely arduous and has led to an obstacle in the progress of this field./nThis thesis aims to provide an analysis for the design of architected materials by illuminating the physical mechanisms and how to model and optimize such problems. The structure of this thesis is comprised of two main themes. The first method aims to control the mechanical performance through interconnected beam members that enhance the densification of the structure and impede catastrophic failure. The second method is related to geometrical defects that dictate the localized failure and anisotropic behavior. Furthermore, the optimization of specific design examples will be presented, employing low computational power for large design spaces and demonstrate how such design problems can be addressed, setting the framework for the systematic design and characterization of architected materials.

Advisor: Grigoropoulos, Costas P