VictorJuiz/thesis huggingface dataset:
Conformational Dynamics of Actin Family Proteins

The actin family of cytoskeletal proteins is essential to the physiology of virtually all archaea, bacteria, and eukaryotes. While X-ray crystallography and electron microscopy have revealed structural homologies among actin-family proteins, these techniques cannot probe molecular-scale conformational dynamics. Here, we use long- time scale, all-atom molecular dynamic simulations to reveal conserved dynamical behaviors in four prokaryotic actin homologs: MreB, FtsA, ParM, and crenactin. We demonstrate that the majority of the conformational dynamics of prokaryotic actins can be explained by treating the four subdomains as rigid bodies. MreB, ParM, and FtsA monomers exhibited nucleotide-dependent dihedral and opening angles, while crenactin monomer dynamics were nucleotide-independent. We further determine that the opening angle of ParM is sensitive to a specific interaction between subdomains. Steered molecular dynamics simulations of MreB, FtsA, and crenactin dimers revealed that changes in subunit dihedral angle lead to intersubunit bending or twist, suggesting a conserved mechanism for regulating filament structure. Taken together, our results provide molecular-scale insights into the nucleotide and polymerization dependencies of the structure of prokaryotic actins. Using these results, we propose a proof of concept design workflow, where we use properties of atomistic MreB simulations to design mutants with tuned dihedral and opening angles.

Keywords:  Department of Bioengineering
actin
molecular dynamics
simulation
Thesis
Advisor: Huang Kerwyn
Colavin Alexandre
Confronting America's Opioid Crisis: Measuring the Impacts of State Laws on Opioid Misuse

This paper offers some of the first long-run evidence on important laws addressing America's opioid crisis. Naloxone access laws make the opioid-overdose antidote naloxone more widely available; drug-overdose Good Samaritan laws provide legal immunity to overdose victims and witnesses. I exploit the staggered introduction of these laws across states as well as differences among the same type of law. Enactment of both laws is followed by a 4% increase in illicit drug use. Good Samaritan laws are associated with significantly fewer subsequent opioid deaths, while the effect of naloxone laws is negligible. Naloxone laws are associated with more opioid-related emergency room visits, while Good Samaritan laws are associated with fewer visits. These findings suggest that naloxone and Good Samaritan laws may encourage opioid use.

Keywords:  Department of Economics
Department of Economics
opioid
drug overdose
naloxone
good samaritan
risk compensation
Thesis
Advisor: Einav Liran
Connect: Coaching College Students to Build Relationships and Launch New Opportunities

College students need to network with a range of people to reach their academic and career goals. But they often struggle with networking. Reaching out to people for help makes them uncomfortable, often because they do not have the skills and tools to do it effectively. They don’t know who to reach out to, their purpose for reaching out, or how to effectively reach out.
Connect is a web application that prompts students to reflect on their immediate goals. Having identified areas of their lives they could benefit from the help of others, Connect provides a collection of just-in-time prompts and suggestions. From effective emails to alumni or setting up an informational interview to writing a meaningful thank-you note, students are able to learn from videos, templates, and articles. Reminders with customized tips are sent based on tasks and deadlines students set for themselves. Connect coaches college students to make the moves that will launch their next opportunities.
Learner studies using a map resource included in the app showed positive outcomes for the learners’ reframing of how they structure and think about who they are currently connected to and who they would benefit from reaching out to. Positive results were also observed for knowledge of networking techniques and attitudes toward networking after participating students explored a collection of resources included in the app.

Keywords: networking
college students
social capital
Thesis
The contribution of innate mechanical properties of epithelian in tissue homeostasis [electronic resource]

Epithelial tissues are subject to stresses either from external or internal environments, as is the case with the epidermis and intestinal epithelia, respectively. One of the stresses epithelia are subject to is mechanical force. Mechanobiology is the study of how tissues, cells and their proteins respond and adapt to mechanical forces. For my thesis work, I have dedicated my research to investigating the effect of mechanical shear force on epithelial collective behavior by fabricating novel biocompatible devices to apply various types of mechanical forces on cell monolayers. Collective behaviors within tissues require cell-cell junctions and studies have shown that epithelial cell-cell junctions are mechanically responsive. However, it is poorly understood how the innate mechanical properties of cells and their junctions contribute to regaining homeostasis when an external force is applied. By applying a localized shear force to the mid-plane of an epithelial monolayer I have revealed that epithelia resist acute forces through their innate mechanical property but also behave as an active material over longer periods of time to regain a balance of force.

Keywords: Biology.
Advisor: Nelson W. J. (W. James)
Cyert Martha S.
Oro Anthony
Stearns Tim advisor. Thesis advisor
Control, calibration, and correction of a PET system with cubic millimeter detector spatial resolution

Positron Emission Tomography (PET) is a functional medical modality that derives its contrast from the selective uptake of a radioactively label compound. There has been considerable interest in designing specialized PET systems to selectively image organs or regions of the body. We are constructing a two-panel system PET system dedicated to locoregional cancer imaging; focusing primarily on the breast as well as the head and neck regions. The system contains a large number of detectors with a spatial resolution of 1mm3. This design presents several challenges which require careful consideration. We describe here the methods used to control the operation of this system, calibrate the output from each of the detectors to provide accurate position, energy, and time information for every interaction, and correct this information in order to provide an accurate image for clinical use. We implement a control system to maintain a constant temperature at the detectors. This control maintains the target temperature within ±0.04C over a period of 8.66 h. We demonstrate an L1-norm minimization method for deriving the precise time of interactions within the detector that is robust both to random coincidences and low statistics cases. The L1-norm method achieves a timing resolution of 15.63 ± 0.02 ns FWHM for this system. We extend a Monte Carlo simulator for PET systems, called Gray, to include radioactive decay and a model of the electronics of PET systems to use in correction of the data from the system. We validate this electronics model by simulating the NEMA NU-2 sensitivity and scatter phantom measurements in a Siemens Biograph mCT system; estimating the peak noise-equivalent count rate within 0.3%. We then adapt several methods in literature to reduce the number of free variables in the sensitivity correction for image reconstruction from 2.4e9, one for each unique detector pair in the system, to 3.3e6; demonstrating a 33.2 % improvement in contrast-to-noise ratio.

Keywords: Electrical Engineering.
Advisor: Levin Craig 
Nishimura Dwight George 
Su Dong
Control of directionality of light with active and passive silicon nanostructures

Semiconductor nanoantennas have recently emerged as very promising platforms for light manipulation at the nanoscale. More specifically, Silicon has become very popular among researchers thanks to its strong light-matter interaction features, mature fabrication technology availabilities, potential for serving simultaneous optical and electronic purposes, and significantly lower losses compared to its metallic competitors. In this thesis, we employed two main architectures that help us achieve our ultimate goal of controlling the light emission and scattering properties in the nanoscale. In the first architecture, we utilize Si nanowires as our nanoantennas to control the emission directionality of 2D monolayer MoS2 emitters, where our choice of emitter material was based on the attractive properties and recent popularity of 2D materials emitting in the visible range of the spectrum. In this Si- MoS2 configuration, we demonstrate more than 25 top-to-bottom emission ratio enhancement with Si NWs compared to bare MoS2 emission. By using NWs of different radii, we demonstrated such directionality by two different mechanisms which were fundamentally different in terms of the excited resonances in the NW. While targeting directionality, we also observed strong polarization selectivity and spectral modification of the operation principles which gave us the power to tune the spectral and polarization properties of the dominant top emission of this system. As the next step, we aimed to demonstrate an active and on-demand control of directionality of scattering of plane-waves. To achieve this, we designed, fabricated and characterized a Si MEMS architecture that enabled us to observe more than 30° active control of angular emission with the application of 5 V potential.

Keywords: Electrical Engineering.
Advisor: Brongersma Mark L. 
Fan Shanhui
Vuckovic Jelena
Controlling the microenvironment to investigate single cell mechanobiology, adhesion, and function

I developed hydrogel platforms mimicking physical properties of body tissues to study how cells sense and exert physical forces. Cells respond to mechanical cues at contacts with the extracellular matrix and other cells by altering their behavior and structure. Thus, hydrogel platforms mimicking rigidity and spatial organization of the in vivo cell microenvironment enable critical experiments to probe mechanosignaling pathways. I have advanced hydrogel platforms for single cell biomechanics by characterizing hydrogel materials, optimizing methods to determine cell-generated tractions on compliant substrates, and co-developing a technique to create high-fidelity protein patterns on hydrogel platforms. Our protein patterning method achieves high protein transfer efficiency, pattern accuracy, and pattern yield. With collaborators, I have applied my contributions to study how fibroblasts alter their contractility in response to different extracellular matrix ligands and how epithelial cells regulate their cell-cell adhesion based on the rigidity of a cell-cell adhesion protein, E-cadherin. In summary, the tools I advanced during my PhD enable new mechanobiology studies to identify and characterize the force-dependent properties of junctional proteins and examine how pathways are activated by manipulating cell shape and size.

Keywords: Bioengineering.
Advisor: Pruitt Beth 
Heilshorn Sarah 
Nelson William
Coordinated exploration in concurrent reinforcement learning

Consider a farm of robots operating concurrently, learning to carry out a task, and sharing data with one another in real time. There are clear benefits to scale, since a larger number of robots can gather and share more data in the same amount of time that enables each robot in the group to learn faster than it would learn on its own. However, the benefits of parallelization can be greatly magnified if the robots explore the environment in a coordinated fashion, diversifying their experience and adapting appropriately as data is gathered. We identify three properties which are necessary for efficient coordinated exploration -- adaptivity, commitment, diversity -- and demonstrate that straightforward extensions of statistically efficient optimistic and posterior sampling approaches from single-agent to concurrent reinforcement learning fail to satisfy them. As an alternative, we propose seed sampling, which extends posterior sampling in a manner that meets these requirements. We proceed to design concurrent reinforcement learning algorithms that achieve the three properties of efficient coordinated exploration in real systems with potentially enormous state spaces, to which tabular methods do not scale. This is achieved by coupling the seed sampling concept with learning general value function or policy function representations that allow the agents to generalize and operate in arbitrarily large scale environments. Overall, this dissertation presents a concept that enables rapid learning of effective policies in the presence of complex goals, when we have multiple agents who learn to operate in parallel in a common, unknown environment and collaborate in real time. There is a multitude of applications that can benefit from this algorithmic framework, ranging from recommendation systems, to robot automation, to autonomous vehicles.

Keywords: Management Science and Engineering.
Advisor: Van Roy Benjamin 
Athey Susan 
Brunskill Emma
Corrupting politicians : evidence from Kenya

Although voters dislike politician corruption, the misuse of public funds by elected officials remains rampant in emerging democracies. This dissertation investigates why politician corruption persists in these democracies through the lens of Kenyan politics. It uses politician interview data, survey data, audit data, and administrative data to develop three main findings. First, high levels of voter poverty and low levels of voter information create an environment of voter pessimism that requires politicians to adopt a less honest representational style in order to cater to constituent demands. Second, although voters dislike politician corruption, a pessimistic electorate expects for politicians to provide the safety net that the public sector fails to provide through charitable assistance. Third, voter pessimism creates apathy towards corruption, and the electorate does not reward candidates for being clean. Together, the findings explain why politician corruption is pervasive in new democracies, and illustrates how voter pessimism enables dishonest politicians to thrive at the expense of honest ones.

Keywords: Political Science.
Advisor: Laitin David D. 
Miguel Edward 
Finan Frederico
Fiorina Morris P. 
Rodden Jonathan
Counting votes at scale : what computational models teach us about election polls, voter fraud, and electoral change

The last decade of American politics has been marked by an increasingly polarized electorate and a subsequent ideological schism in political discourse. In this climate, it is imperative for research to focus on quantitative methods, and to provide inherently objective views on topics under public scrutiny. In this study, we explore various ways in which modern computational models can help in examining political topics and presenting impartial conclusions. We begin by investigating the accuracy of election polls through an empirical analysis of polls conducted during the final three weeks of the campaigns. We establish that the average survey error is approximately twice as large as that implied by most reported margins of error. Using a Bayesian hierarchical model, we find that average absolute election-level bias is about 2 percentage points, indicating that polls for a given election often share a common component of error. We discuss how these results can help to explain polling failures in the 2016 U.S. presidential election. Next, we focus on voter fraud and show how a probabilistic birthdate model can be used to estimate the number of double votes in U.S. presidential elections. Refuting commonly asserted allegations of rampant double voting, we estimate that fewer than 0.03% of votes cast in 2012 were double votes, and we show that almost all of these cases can be explained by a plausible rate of error in marking registration records. Finally, we leverage a variant of model-based post-stratification (MP) to measure the effects of sentiment and turnout on electoral change between two elections. We use this model to explain the Republican surge in the House in 2010 and 2014, and to critique the narrative that young people have tilted Republican in these two elections. The findings of our study reveal the presence of polling error, the negligibility of voter fraud, and the advantage of using an MP-based approach to investigate electoral change, and reinforce the necessity of applying computational models to difficult topics in political science.

Keywords: Management Science and Engineering.
Advisor: Goel Sharad
Saberi Amin 
Ugander Johan
Creating Worlds through Music: Creative Music-Making as an Identity-Making Process in Contemporary Japanese American Community

This thesis examines the ways in which creative musicians of Japanese descent living in America have negotiated and continue to make sense of their ethnic, racial, and national identities. The research focuses on interviews with 7 musicians of Japanese descent from a variety of family migration histories who describe their work as “creative” or “improvisational.” Using my own experiences as a musician and as a child of post-WWII Japanese immigrants to guide my understanding, I explore the ways in which creative music-making also serves as an identity-making process. I argue that creative music-making makes it necessary for artists to navigate identity labels such as Shin-Nikkei, Nikkei, Japanese, American, and Japanese American, as well as intersectional identities such as gender and sexuality. These artists demonstrate that identity is a living, non-linear process, and that recognizing differences is an essential part of building connections and inviting people into community.

Keywords: Center for Comparative Studies in Race and Ethnicity
Creative Music
Japanese American
Nikkei
Shin-Nikkei
identity
intersectionality
intersectional identities
nationality
ethnicity
race
gender
queerness
interview-based
qualitative research
Thesis
Critical Civic Praxis Within Digital Media Use Among Historically Marginalized College Students

We examine the ways in which historically marginalized students at Stanford University engage in critical civic practices on social media platforms. We look at specific practices across digital platforms, the development of critical consciousness in the social media context, and the most significant contributing factors to participants’ learning and involvement in social justice work. To do so, we conducted an online survey and an in-person, artifact-based interview with a subset of the survey respondents, using their devices and social media accounts as artifacts to guide the conversation. We find that participants leverage social media platforms as a mediatory tool for amplifying the power and knowledge that they already hold within their lived experiences, physical communities, and academic studies. Specific practices vary between individuals and platforms, but overarching objectives include staying informed and educated, self-reflection, and building solidarity in connected networks. The affordances of digital platforms can increase and equalize access, challenge dominant discourse through subversive internet culture, and provide anonymity for safe healing and self-expression. However, just as --- if not more so --- important are existing resources students leverage in the physical world: offline students’ in-person connections, physical communities, and academic studies; the powerful practices that appear unique to social media platforms are ultimately manifestations of students’ whole education and resources on and offline. Finally, looking at an interesting insight from participants, we suggest future work be done to further examine how students navigate the problems caused by technology companies themselves and leverage their products as platforms to reclaim power.

Keywords: critical civic praxis
critical consciousness
civic education
digital media
social media
social justice learning
activism
higher education
historically marginalized students
online survey
artifact-based interview
Stanford University
Graduate School of Education
Thesis
Advisor: Garcia Antero
Willinsky John
Lam Cindy
Cybersecurity Magic: Parallel Structures of Design by Hackers and Magicians

Almost half of the American public has been a victim of a major cyber-hacking incident and cyber-crime now tops the Gallup Poll’s list as the crime Americans worry about the most. (Pagliery 2014; Riffkin 2017) Yet for many Americans, their vulnerability to hacking seems mysterious. How can so much cyber-crime occur when so many technical efforts are devoted to preventing it? One possible answer to this question might be that cyber-hackers take advantage of common human psychological traits that make us all vulnerable to deception and misdirection. The ability to take advantage of such human vulnerabilities is part of the art of deception in many realms, such as espionage, warfare, politics, theatre, and especially in performance magic. What are the similarities and differences between the process used by cyber-criminals to design their hacking attacks and the process used by magicians to design their magic tricks? I investigated these connections by collecting primary sources and interviewing a small number of magicians, “white hat hackers,” and individuals from the national security intelligence community to discover how they design their activities to take advantage of individuals’ common human vulnerabilities and new technology vulnerabilities. I present multiple case studies and then compare specific magic tricks with specific cybersecurity exploits, demonstrating how the design processes used by hackers have inherent structural similarities to those used by magicians. Furthermore, this research demonstrates the value in demystifying the design processes of hackers and magicians to produce better cybersecurity.

Keywords: magic
cybersecurity
hacking
white hat hackers
magician
technology
deception
sleight of hand
Thesis
Data science for human well-being

The popularity of wearable and mobile devices, including smartphones and smartwatches, has generated an explosion of detailed behavioral data. These massive digital traces provide us with an unparalleled opportunity to realize new types of scientific approaches that enable novel insights about our lives, health, and happiness. However, gaining actionable insights from these data requires new computational approaches that turn observational, scientifically "weak" data into strong scientific results and can computationally test domain theories at scale. In this dissertation, we describe novel computational methods that leverage digital activity traces at the scale of billions of actions taken by millions of people. These methods combine insights from data mining, social network analysis, and natural language processing to improve our understanding of physical and mental well-being: (1) We show how massive digital activity traces reveal unknown health inequality around the world, and (2) how personalized predictive models can support targeted interventions to combat this inequality. (3) We demonstrate that modeling the speed of user search engine interactions can improve our understanding of sleep and cognitive performance. (4) Lastly, we describe how natural language processing methods can help improve counseling services for millions of people in crisis.

Advisor: Leskovec Jurij 
Delp Scott 
Jurafsky Dan