VictorJuiz/thesis huggingface dataset:
Measuring physical and chemical properties of biological nanoparticles in solution with interferometric scattering in an active feedback trap
Keywords: Humanities and Sciences.
Applied Physics.

Single-particle studies can unveil the heterogeneity and dynamics of not only single biomolecules and proteins, but also larger protein assemblies and biological nanoparticles. Previous work has developed the anti-Brownian electrokinetic (ABEL) trap that uses fluorescence to track the position of a single diffusing particle, and applies feedback in the form of electric fields to suppress the Brownian motion and allow for long observation times for mobile particles. This dissertation presents my work on developing an ABEL trap that uses interferometric scattering for tracking single nanoparticles, and on making physical and chemical measurements on those nanoparticles. I begin in Chapter 1 by describing the benefits and limitations of the ABEL trap, and some recent developments in interferometric scattering that motivate us to use it for tracking particles. I then sketch out the concept of an interferometric scattering ABEL trap (or ISABEL trap), including some of the new studies which it should enable. I also describe biological nanoparticles from autotrophic bacteria called carboxysomes, which we study with the ISABEL trap. These self-assembled particles create a distinct chemical environment for the efficient action of the enzyme rubsico to fix organic carbon and are responsible for 10-25% of CO2 fixation on Earth. I discuss the theoretical aspects of interferometric scattering as relevant to our experiments in Chapter 2, describing the image formation process and the sources of noise. I then utilize this information in Chapter 3, where I describe the experimental implementation of the ISABEL trap. I describe three different setups that iteratively improved the sensitivity and capabilities of the instrument, broken down into the different components of the experimental setup. I then describe our experiments on carboxysomes in the ISABEL trap, and the physical and chemical information we can now access. Chapter 4 describes our work on measuring the redox environment inside individual carboxysomes with a redox sensitive fluorescent protein using ratiometric readout. This work takes advantage of the independent fluorescence channels opened up by interferometric scattering detection. Chapter 5 describes our work on measuring the mass of single carboxysomes, including measurements of single-particle scattering cross-sections, and the use of a simultaneous fluorescence channel to infer the mass distribution inside carboxysomes. This work takes advantage of the quantitative aspects of the scattering measurement, and its label-free nature. Finally, in Chapter 6, I describe the outlook for the ISABEL trap, including comparing the current and possible capabilities to other experiments, looking at other samples of interest, and mentioning future capabilities that show promise if developed
Measuring the unseen universe with statistical vision : strong lensing as a probe of small-scale structure
Keywords: Humanities and Sciences.
Physics.

For decades, modern cosmology has held that the majority of the matter in our Universe is cold, collisionless dark matter (CDM). Many of our dark matter theories impose scales at which the CDM paradigm breaks down, mainly by changing the distribution of collapsed structures (halos) at low masses. An investigative priority of modern astrophysics has been searching for these violations of CDM predictions. Probing dark matter at these scales is challenging; dark matter halos are traditionally traced by the galaxies they host, but at low masses, we do not fully understand the connection between halos and galaxies. However, strong gravitational lenses are sensitive to low-mass halos even if they host no galaxies. In this thesis, I develop the statistical tools that allow us to use strong lenses to measure the small-scale, dark matter structure underlying our Universe. I present work that leverages neural networks to produce posterior distributions for the parameters underlying strong gravitational lensing images. This work includes the development of a hierarchical inference framework that corrects for the implicit prior encoded into the network by the training distribution. After showing that we can use the technique to constrain the population statistics of lenses without low-mass halos, I present work that extends the methodology to measurements of the subhalo mass function (SHMF). With the aid of new simulation tools, the results demonstrate that we can reliably infer the SHMF across disparate configurations of hundreds of lenses. I then discuss the improvements that can be made as the methodology is extended to the data. I conclude by outlining the physics that can be measured with this new set of tools. I argue that the advances in this thesis serve as a foundation for turning strong gravitational lenses into a sensitive probe of dark matter physics
Mechanical and biomaterials engineering optimization of complex cardiovascular operations and innovative therapeutics
Keywords: Engineering.
Bioengineering.

Cardiovascular disease is the leading cause of death globally. Every year, close to 18 million people succumb to complications of cardiovascular disease. Coronary artery disease and valvular diseases, two of the main types of cardiovascular disease, are both highly prevalent and frequently treated. For valvular heart disease in particular, guidelines continue to evolve to advocate for earlier intervention. Overall, this represents a significant economic burden with over
Mechanisms of Ferroptosis Evasion Promoted by Extracellular Metabolites
Keywords:  Department of Biology
Ferroptosis
Cysteine
Albumin
Lysosome
Monounsaturated Fatty Acids
Spheroid
Biology
Text
Thesis

Cancer cells require nutrients to grow and proliferate. The availability of the amino acid cysteine impacts whether cancer cells die by an iron-dependent, non-apoptotic form of cell death executed by lipid peroxidation called ferroptosis. Ferroptosis can be induced if the availability of the amino acid cysteine is limiting or by inhibiting the phospholipid hydroperoxidase glutathione peroxidase 4 (GPX4). Extracellular metabolites such as monounsaturated fatty acids (MUFAs) and cysteine can protect from ferroptosis. However, it is unclear how cancer cells acquire cysteine to evade ferroptosis or what MUFAs they can use to evade ferroptosis. Using human-derived cancer cell lines in both adherent (2D) and suspension (3D) culture, this study demonstrates that the cysteine-rich extracellular protein albumin protects from ferroptosis caused by cysteine deprivation. Albumin in combination with inhibition of the nutrient and energy sensing kinase, mammalian target of rapamycin complex 1 (mTORC1), is required to robustly protect from cysteine deprivation-induced ferroptosis in 2D. This combination treatment protects from ferroptosis by increasing intracellular levels of cysteine. Furthermore, this process is dependent on the catabolism of albumin into its amino acid constituents. Additionally, albumin protects from cystine deprivation-induced ferroptosis in 3D culture. This study reveals that a variety of exogenous MUFAs that have not previously been characterized as ferroptosis-protective metabolites protect HT-1080 cells in monolayer and HT-1080 and Panc-1 spheroids from GPX4 inhibition-induced ferroptosis. These MUFAs exhibit different protective abilities in adherent compared to spheroid culture and this suggests growth-context dependent differences in MUFA protection. These results describe novel metabolite-mediated strategies of ferroptosis evasion in 2D and 3D culture. Understanding how cells utilize exogenous protective metabolites such as albumin and MUFAs in death-inducing conditions can reveal novel ways to induce more complete cancer cell death by curtailing alternative avenues of protection. Targeting these mechanisms to induce more effective cell death in cancer cells reveals potential therapeutic relevance of this work.
Mechanisms of postnatal mammalian skin regeneration
Keywords: Medicine.

Scars following trauma, burn, and surgeries cumulatively impose an enormous biomedical burden for hundreds of millions of patients each year. In contrast, skin regeneration, with complete recovery of normal dermal architecture, appendages (hair follicles, glands), and mechanical strength equal to that of unwounded skin, would represent a vastly superior outcome. This dissertation explores skin regeneration through the lens of fibroblast mechanobiology (i.e., how mechanical forces in the wound environment alter fibroblast behavior). Chapter I describes skin regeneration, demonstrated for the first time in postnatal mammalian wounds, following targeted modulation of Engrailed-1 (En-1) lineage-negative fibroblasts with the Yes-associated protein (YAP) inhibitor, verteporfin. Chapter II concerns the specific mechanisms of skin regeneration following YAP inhibition, as compared to scarring (PBS control), studied using single cell RNA-sequencing, timsTOF shotgun proteomics, and a novel AI ultrastructural algorithm (described in detail in Chapter IV) to profile regenerating (verteporfin) and scarring (PBS) wounds at multiple time points of healing (post-operative [POD] days 0, 2, 7, 14, and 30). Chapter III reports the translation of the above work to red Duroc pigs, an animal model featuring human-like skin architecture and mechanical properties. Chapter IV describes an image processing and machine learning pipeline I developed to profile extracellular matrix features in solid organ fibroses, which I then utilize to profile matrix architectures in pancreatic ductal adenocarcinoma (PDAC) desmoplasia. In summary, these studies highlight the central functional role of mechanical forces in skin fibrosis after injury and support mechanotransduction inhibition as a promising translational approach to promote regenerative healing outcomes
Mechanoluminescent nanoparticles as an energy relay for minimally invasive light delivery in vivo
Keywords: Humanities and Sciences.
Chemistry.

Optogenetics has revolutionized neuroscience research and has quickly become a ubiquitous tool for the interrogation of neural circuits and the elucidation of the neural underpinnings of behavior. With optogenetics, researchers are able to activate or silence targeted neurons in a rapid and repeatable manner using light as the input signal to control cells expressing light-gated ion channels termed "opsins". The use of light as the input affords excellent temporal control for driving neural activity with millisecond-timescale precision. However, opsins must be activated by light in the visible spectrum, which suffers from extremely poor penetration depths in biological tissues. Therefore, visible light is ordinarily delivered through macroscopic light sources such as optical fibers or LEDs implanted directly into the brain. Along with the acute trauma associated with the highly invasive implantation procedure, the presence of these large foreign bodies in neural tissue elicits chronic immune responses and perturbs endogenous neural physiology. Herein, I detail a novel strategy for in vivo light delivery through the use of intravenously administered mechanoluminescent nanoparticles coupled with focused ultrasound which exhibits exceptional transmission through biological tissues. Mechanoluminescent nanoparticles in the blood circulation are charged with photoexcitation light while passing through superficial blood vessels and store the energy from the absorbed light until application of focused ultrasound, which then triggers the release of the stored energy in the form of visible light emission. This process of charging and discharging can be repeated indefinitely while the nanoparticles circulate through the body, allowing for controllable, repetitive visible light emission in the brain. In this way, mechanoluminescent nanoparticles act as an energy relay for light delivery through the blood circulatory system. Due to the densely distributed cerebral vasculature and microvasculature, the nanoparticles are able to illuminate any region of the brain controlled by the location of ultrasound focus, while the application of ultrasound is accomplished through the intact scalp and skull to minimize any damage to the brain tissue. Therefore, compared to the conventional method of light delivery for optogenetics, this strategy allows for a much less invasive interface for risk-averse optogenetic neuromodulation.
Media, celebrity, and personality from the beats to the New York school
Keywords: Humanities and Sciences.

The history of mid-20th century American poetry should be understood alongside a history of personal recording media. Between the end of WWII and the late 1970s, three developments refashioned the relationship between the lyric poetic "speaker" and her implied listener: the invention of portable tape recorders in the late 40s; the increased affordability of photographic film through the 50s and 60s; and the advent of television and video recording through the 60s and 70s. As continuous self-recording became, for the first time in history, an option for ordinary people, new lyric modes emerged that registered the presence of recording machines—revealing their function as literal, guaranteed, or perfected audiences, even for relatively unknown and self-consciously unfamous poets. As they became cheaper and more powerful, personal recorders were recruited by more obscure writers to launder the social difference between ordinary and famous individuals—which is to say recording media have functioned as what we now call "social media" for at least a half century
Men, Money, and Mistrust: The Story Behind South Korea’s Conservative Uprising
Keywords:  Stanford Global Studies
Stanford Global Studies
East Asian Studies
Policy
Asia
Korea (South)
Gender
Politics and government
Elections
Voting
Polarization (Social sciences)
Partisanship
Text
Thesis

Studies carried out in previous decades saw women as agents of change for a gender gap in electoral behavior. The recent observation is that the partisanship gap is mainly a function of women’s swing to the left. Here, it is examined whether this phenomenon of the modern gender gap, with women more left-leaning, has become apparent in South Korea. The paper draws on evidence from the 2003-2021 Korean General Social Survey (KGSS) to investigate the pattern of gender differences in party identification. This study establishes that gender differences in partisanship in South Korea have been realigning, with men shifting toward the right, and delves into the reason for this development, especially focusing on the role of unemployment. Through logistic models of party preferences, this article concludes that unemployment is positively correlated with male conservatism in South Korea after 2016.
Merging ultrafast gas-phase diffraction experiment, theory, and machine learning for a new look at molecular dynamics
Keywords: Humanities and Sciences.
Physics.

Understanding fundamental molecular behavior from first principles of quantum mechanics is crucial in developing a complete understanding of molecular dynamics and eventually chemical reactions. Chemistry is often defined by a change in molecular structure. Ultrafast gas phase diffraction is a primary probe of such fundamental molecular structure dynamics. This technique provides sufficient spatio-temporal resolution to produce "molecular movies" of the quantum dynamics critical in chemical reactions. Directly inverting these measured diffraction patterns for the time-dependent molecular geometry directly has been intractable. This results in a so-called inverse problem. Traditionally, to explain the dynamics we employ complex \textit{ab initio} molecular dynamics simulations which we validate through comparisons to our measurement. This traditional methodology limits the capabilities of ultrafast gas-phase diffraction to the domain of dynamics that can be simulated, keeping much of the interesting and crucial molecular behavior out of reach. In this defense, I illustrate the strengths and weaknesses of traditional analysis methodologies through an investigation into the dissociative dynamics of nitrobenzene. This experiment was done at the SLAC MeV ultrafast electron diffraction facility. After, I introduce an analysis technique to retrieve the molecular geometry distribution $[vertical line]\Psi(\mathbf{R}, t)[vertical line] 2$ by statistically re-framing this measurement and employing fundamental machine learning techniques. I then illustrate this method's capabilities to determine the molecular structure probability distribution with simulated NO$_2$ and measured N$_2$O data from the SLAC MeV ultrafast electron diffraction facility. From measured N$_2$O, we observe 40~m\AA{} coordinate-space resolution from 3.75~\AA$^{-1}$ reciprocal space range and poor signal-to-noise, a 50X improvement over traditional Fourier transform methods. In simulated NO$_2$, typical to high signal-to-noise levels predict 100--1000X resolution improvements, down to 0.1~m\AA. This method's greatest potential is to transform ultrafast gas-phase diffraction into a discovery-oriented technique capable of probing molecular dynamics that are intractable to current simulation methods.
Metacommunity Dynamics in Consumer Resource Models
Keywords: Physics
metacommunities, ecology
Text
Thesis

Microbial communities are critical to sustaining a wide variety of ecosystems, including the gut microbiome. The composition and diversity of gut microbiota at different taxonomic levels have been linked to different health outcomes. Individual gut microbiota are typically colonized by a few strains of a given species, despite extensive strain level diversity in the larger metapopulation. At a theoretical level, it is not well understood how and why the difference in diversity at these different scales emerges. In this project, we study how biodiversity arises in a simple toy model of a metacommunity called a consumer resource model. We present the full migration-diversification-drift-balance equation, governing the distribution of strain frequencies in a single deme in the metapopulation. We demonstrate through analytical calculations and simulations that in the weak migration limit, strains are subject to competitive exclusion at the metapopulation level. Finally, we show that introducing environmental heterogeneity between demes can rescue coexistence at the metapopulation level.
Meta-reinforcement learning : algorithms and applications
Keywords: Engineering.

Reinforcement learning from scratch often requires a tremendous number of samples to learn complex tasks, but many real-world applications demand learning from only a few samples. For example, an effective news recommendation system must be able to adapt to the tastes of a new user after only observing the results of only a few recommendations. To meet the demands of such applications that require quickly learning or adapting to new tasks, this thesis focuses on meta-reinforcement learning (meta-RL). Specifically we consider a setting where the agent is repeatedly presented with new tasks, all drawn from some related task family. The agent must learn each new task in only a few shots, formalized as a few episodes of interaction with the task. How the agent spends its few shots critically determines whether it will be able to subsequently solve the task, but learning to effectively use the few shots is challenging because there is no direct supervision. In this thesis, we argue that effectively leveraging the few shots — and hence, learning to quickly solve new tasks — requires carefully decoupling learning to spend the few shots from learning to solve the task. Concretely, we show that existing meta-RL algorithms that do not decouple the two struggle to learn complex strategies for spending the few shots due to a chicken-and-egg problem, where learning to effectively spend the few shots depends on having already learned to solve the task and vice-versa. We then address this problem with a new algorithm called Dream that decouples the two. Additionally, we also study how to leverage pre-collected offline data in this setting. We show that popular approaches for extracting skills from the offline data to quickly learn new tasks use an underspecified objective with degenerate solutions, and address this with an auxiliary objective that makes the optimization problem well-specified. Our algorithms enable previously unexplored applications with meta-RL. Specifically, we show that (1) Dream enables a new paradigm for language learning without large text datasets by learning language in the process of solving tasks that do not necessarily require language. For example, in our experiments, Dream learns to read building floor plans with language descriptions in the process of learning to navigate to particular offices in various buildings; and (2) Dream can help automatically grade interactive computer science assignments that typically require significant manual grading. We deployed Dream to assist with grading the Breakout assignment in Stanford's introductory computer science course and found that it sped up grading by 28%, corresponding to about 10 hours, without sacrificing accuracy
Methods and systems for targeted evaluations of clinical machine learning models on the deployment population
Keywords: Medicine.
Biomedical Informatics.

The development of clinical machine learning applications frequently follows a sequential path from ideation to deployment — where each step is only considered after completion of the prior. A common consequence is that the distribution of data used to validate a model and permit eventual translation (the evaluation population) fails to reflect the distribution of data on which the model is served (the deployment population). In this dissertation I discuss methods and systems for enabling targeted evaluations of clinical machine learning models on the deployment population. I detail the implications of label selection (censoring) on the evaluation of binary classifiers, showing that traditional weighted estimators from causal inference literature recover performance over the deployment population when selection probabilities are properly specified. I emphasize the importance of silent prospective evaluations of machine learning models to appraise performance in the intended production environment. Silent trial evaluations require integrated deployment infrastructure with modern electronic medical record vendors — for which I develop and blueprint. I finally discuss the implications of feedback effects, where use of deployed models induce differences between the deployment population and the population that is prospectively observed. I provide a taxonomy of feedback effects, and recommend feedback aware model monitoring strategies. Instead of following a linear path from model ideation, to development and eventual deployment, researchers and machine learning practitioners should first consider the deployment population, and use it to contextualize model development and evaluation throughout its entire life-cycle.