VictorJuiz/thesis huggingface dataset:
Microfabricated hydrogel platforms to compare single cells in 2D vs 3D engineered microenvironments

I developed novel platforms to investigate the effects of 2D vs 3D controlled microenvironments on cells. Cells can sense and adapt to their local mechanical environment. In the body, cells exist in 3D environments, highly structured for the cell's given purpose. I wanted to investigate the effects of 3D microenvironments on single cells in a very controlled manner, thus allowing comparisons between 2D and 3D. The motivation of this work is to eventually study heart muscle cells, or cardiomyocytes, though the platforms I developed could be applied to numerous biological models. In this thesis, I provide an introduction to cardiomyocytes (Chapter 1) and engineered 2D and 3D microenvironments that have been developed by the field (Chapter 2). I then introduce my platforms to constrain cells to 2D or 3D microenvironments and provide characterization of the geometry and stiffness (Chapter 3). I next show simple mechanical models that provide insight into the mechanical loading environment for cells in each of the platforms (Chapter 4). Next, I show that 3D microenvironments can drastically affect cell cytoskeletal structure and nuclear shape. I also describe the development of a different platform, which enables patterning two proteins on 3D platforms using only soft lithography techniques (Chapter 6). Last, I provide conclusions and future directions for the work, providing ideas for how my controlled 2D and 3D microenvironments could be implemented to gain more understanding of single cells (Chapter 7).

Keywords: Mechanical Engineering.
Advisor: Chaudhuri Ovijit 
Pruitt Beth 
Bernstein Daniel
Microglial restoration and systemic rejuvenation to treat age-related neurodegenerative disease

A rapidly aging population and a limited therapeutic toolbox urgently necessitate new approaches to treat neurodegenerative diseases. Brain aging involves complex cellular and molecular processes that eventually result in cognitive decline. While cell-intrinsic defects in neurons, the effector cells of behavior, memory, and consciousness, may partially explain this decline, cell-extrinsic changes in the local environment, mediated by glia, and the systemic environment, mediated by blood, have recently been shown to drive chronic inflammation and subsequent brain dysfunction with age. Here, I describe my efforts to understand systemic mediators of brain aging and rejuvenation, to restore brain-resident immune cell function, and to develop next-generation glia-directed therapies for neurodegenerative disease. In the first case, I review the current understanding of how systemic factors interact with the brain sequentially, starting with known sources of systemic factors, conduits and barriers to entry, cellular targets in the brain, and clearance mechanisms. Next, I elucidate a mechanism of age-related microglial impairment and a strategy to restore homeostasis in the aging brain. Finally, I propose a microglia-directed treatment to ameliorate cognitive decline in Alzheimer's disease. Taken together, this work illuminates the roles of systemic and local brain inflammation on age-related cognitive dysfunction and provides new microglia-directed therapeutic strategies to restore homeostasis in the aging brain and to treat neurodegenerative diseases.

Advisor: Wyss-Coray Anton 
Bassik Michael 
Monje-Deisseroth Michelle
Microlocal Analysis and Geodesic X-Ray Transform Problems

We restate main results on microlocal analysis including the definition and properties of scattering symbols and pseudodifferential operators. We also provide a brief overview on topics including microlocalization, operators on manifolds and propagation of singularity. Next, we discuss an application of microlocal analysis on geodesic X-ray transform problems. We will look at the scalar and one form cases of the problem and provide a relatively self contained treatment, with general curve families in the place of geodesics. Using a back projection operator and theory of scattering pseudodifferential operators, we show that for manifolds satisfying certain convexity requirements, the geodesic X-ray transform on scalars is injective and the geodesic X-ray transform on one forms is injective on functions satisfying the normal gauge.

Keywords: Department of Mathematics
School of Humanities and Sciences
Stanford University
microlocal analysis
geodesic X-ray transform
pseudodifferential operators
Thesis
Advisor: Vasy Andras
Microribbon-based hydrogels for cartilage regeneration : a LEGO®-building approach

Articular cartilage (AC) is the smooth tissue that lines the ends of joints and enables painless, frictionless movement. Damage to AC occurs quite frequently, and due to the avascular nature of cartilage, AC will not intrinsically heal once damaged. Often, AC damage progresses into osteoarthritis, a debilitating disease that currently affects 30 M Americans and costs the US healthcare >

Keywords: Bioengineering.
Advisor: Yang Fan (Bioengineering researcher and teacher) 
Bhutani Nidhi
Smith R. A. (Geologist)
Microtubule Organization and Function in C. elegans Intestinal Epithelium and Body Wall Muscle

Microtubules are key components of the cytoskeleton and facilitate a number of cellular processes such as intracellular transport, structural support, and mitosis. In dividing animal cells, microtubules are organized around the centrosome. Upon differentiation in some tissues such as epithelia and muscle, microtubules are reorganized around a different site including the apical membrane or nuclear envelope, respectively. These non-centrosomal microtubule organizing centers (ncMTOCs) are found in plant and most differentiated animal cell types, yet little is understood about how cells establish an ncMTOC and the importance of non-centrosomal microtubules for cell function. C. elegans is an ideal system to study ncMTOCs, because in all differentiated cells thus far investigated, the centrosome is inactivated and an ncMTOC is designated. We sought to find a tissue that displays a marked phenotype when microtubules are disrupted in order to study general mechanisms of microtubule organization and function. We began by investigating microtubule function in epithelial tissue by overexpressing the microtubule depolymerizing enzyme SPAS-1 in the embryonic intestinal epithelium. SPAS-1 overexpression resulted in microtubule degradation and slowed total organismal growth. However, SPAS-1 expression and/or perturbation of microtubules was not consistent in this tissue. We therefore also characterized microtubule organization in C. elegans body wall muscle (BWM). Muscle cell microtubules are distinctly organized around another ncMTOC, the nuclear envelope, in most species. However, visualizing fluorescently tagged microtubule-associated proteins revealed two distinct populations of BWM microtubules in larval and adult worms. These results demonstrate that C. elegans BWM cells possess a unique microtubule organization. Furthermore, preliminary data suggest that BWM and sex muscle microtubules can be perturbed by SPAS-1 overexpression. Our combined results indicate that BWM is a strong candidate for future microtubule studies.

Keywords:  Department of Biology
Biology
Microtubule
MTOC
ncMTOC
SPAS-1
Auxin
TIR1
C. elegans
Intestine
Body Wall Muscle
Thesis
Advisor: Feldman Jessica
Fire Andrew
Migrant Youth Disadvantages in China: How Teacher Biases Shape Student Outcomes

Over the past three decades, rural to urban migration in China has had unprecedented implications for millions of migrant children. Many of them have been excluded from urban public schools due to their Hukou (residence) status. Those fortunate enough to enter public schools often encounter stereotyping and subtle discrimination from peers and teachers, which may lead to lower academic outcomes. This paper uses a value-added model to analyze the correlation between teachers’ perceptions towards migrant students and their respective Chinese and math test performances in public middle school settings. With a nationally representative panel dataset, we find that rural migrant students are performing at least as well as their local peers on 8th grade midterm exams. However, having a teacher with negative biases against migrant students’ is harmful to their academic outcomes, while having a teacher with a preference for teaching local students over migrant students is not. We conclude by discussing the implications of our findings.

Keywords: Migrant Students
Teacher Perceptions
Stereotype Threat
Value-added Model
China
Public Schools
Stanford Graduate School of Education International Education Policy Analysis
Thesis
Mixed-signal processing for machine learning

Recent advancements in machine learning algorithms, hardware, and datasets have led to the successful deployment of deep neural networks (DNNs) in various cloud-based services. Today, new applications are emerging where sensor bandwidth is higher than network bandwidth, and where network latency is not tolerable. By pushing DNN processing closer to the sensor, we can avoid throwing data away and improve the user experience. In the long term, it is foreseeable that such DNN processors will run on harvested energy, eliminating the cost and overhead of wired power connections and battery replacement. A significant challenge arises from the fact that DNNs are both memory and compute intensive, requiring millions of parameters and billions of arithmetic operations to perform a single inference. This thesis presents circuit and architecture techniques that leverage the noise tolerance and parallel structure of DNNs to bring inference systems closer to the energy-efficiency limits of CMOS technology. In the low signal to noise ratio (SNR) regime where DNNs operate, thermally-limited analog signal processing circuits are more energy-efficient than digital. However, the massive scale of DNNs favors circuits compatible with dense digital memory. Mixed-signal processing allows us to integrate analog efficiency with digital scalability, but close attention must be paid to energy consumed at the analog-digital interface and in memory access. Binarized neural networks minimize this overhead, and hence operate closer to the analog energy limit. This thesis describes a mixed-signal binary convolutional neural network (CNN) processor for embedded inference applications that achieves 3.8 uJ/classification at 86% accuracy on the CIFAR-10 image classification dataset. A weight-stationary, parallel-processing architecture amortizes memory access across many computations, leaving wide vector summation as the remaining energy bottleneck. This design features an energy-efficient switched-capacitor neuron that addresses this challenge, employing a 1024-bit thermometer-coded capacitive DAC section for summing point-wise products of CNN filter weights and activations and a 9-bit binary-weighted section for adding the filter bias. The design occupies 6 mm^2 in 28 nm CMOS, contains 328 KB of on-chip SRAM, operates at 237 frames/s (FPS), and consumes 0.9 mW from 0.6 V/0.8 V supplies. The corresponding energy per classification (3.8 uJ) amounts to a 40x improvement over the previous low energy benchmark on CIFAR-10, achieved by both architectural specialization and mixed-signal processing. The switched-capacitor neuron array is 12.9x more energy efficient than a synthesized digital implementation, which amounts to a 4x advantage in system-level energy per classification at the same classification accuracy. We provide an apples-to-apples comparison of the mixed-signal, hand-designed digital, and synthesized digital implementations of this architecture.

Keywords: Electrical Engineering.
Advisor: Murmann Boris 
Arbabian Amin 
Mitra Subhasish
Mixotrophy and polyhydroxyalkanoates in type II methanotrophic bacteria

There is worldwide concern about pollution caused by non-biodegradable petroleum-based plastics and carbon emissions in the atmosphere. Specifically, the natural abundance of cheap methane-rich mixtures such as biogas (CH4 and CO2) and natural gas (primarily CH4 and C2H6) has increased atmospheric greenhouse gas (GHG) emissions. Type II methanotrophs are organisms that can potentially address both of these problems by metabolizing these methane-rich mixtures to sequester the carbon into produce high-value biodegradable plastics in the polyhydroxyalkanoate (PHA) family such as polyhydroxybutyrate (PHB) and polyhydroxybutyrate-co-hydroxyvalerate (PHBV). Through this dissertation, I demonstrate that Type II methanotrophs can sequester CO2 when using CH4 as the sole electron donor during cell growth and PHB accumulation, and I also provide evidence of CO2 incorporation into the PHB and PHBV biopolymer. To establish the effects of natural gas, I show PHB production from ethane alone in Type II methanotroph, Methylocystis parvus OBBP. The dissertation culminations with an evaluation of the impacts of CH4 and C2H6 on Methylocystis parvus OBBP cell growth and PHB accumulation. Overall, the findings in this dissertation contribute to our understanding about the metabolic versatility of Type II methanotrophs which can be harnessed in order to address significant global problems related to climate change, and plastic pollution.

Advisor: Criddle Craig 
Waymouth Robert M. 
Welander Paula
Modeling and interpreting molecular kinetics from simulation data

Atomistic simulations provide detailed information about a molecular system's dynamics at finer time and length scales than experiments can access. However, the modeling and interpretation of simulation datasets in an unbiased and statistically sound way require dedicated algorithms. One analysis method is the so-called variational approach to conformational dynamics, which introduces an objective framework for the ranking of models. One such class of models are Markov state models (MSMs), which separate the configuration space explored by a simulated molecule, such as a protein, into discrete, disjoint states between which the transitions can be modeled as Markovian. In Chapter 1, I summarize the essentials of MSM construction and the variational approach. In Chapters 2 and 3, I describe systematic studies in varying MSM parameters, in pursuit of general trends for variationally optimal models of proteins, with a focus on clustering into states. In Chapters 4 and 5, I present algorithmic advances in comparing multiple related models and in coarse-graining MSMs, also using clustering. In Chapter 6, I summarize the current state of the art in MSM methods and identify frontiers in their application and methods development. Finally, in Chapters 7 and 8, I apply clustering to different problems, such as in fluid dynamics. I discuss the possibility of extending the methods motivated here to a broader class of dynamical systems in Chapter 9. Overall, the methods presented in this work focus on interpretability and statistical robustness.

Keywords: Chemistry.
Advisor: Martinez Todd J. (Todd Joseph)
Pande Vijay 
Zare Richard N.
Modeling global strategies for control and elimination of helminth infections

Schistosomiasis and soil-transmitted helminthiasis (STH) are helminth diseases that affect 1.5 billion people in the world's poorest regions. Given over a decade of experience on the public health control of these helminth infections, new strategies are now necessary to push towards more ambitious goals of optimal morbidity control and eventual elimination of transmission. In this thesis, I apply methods of mathematical epidemiology, health economics, and causal inference to evaluate new strategies to improve global control and elimination of these helminth infections. In chapter 2-3, I model transmission dynamics and cost-effectiveness to define optimal programmatic mass drug administration (MDA) against schistosomiasis and STH with reference to World Health Organization (WHO) guidelines. I find that expanding MDA from the status quo of only school-aged children to entire communities would avert substantial disease morbidity across all age groups, reduce reinfection rates, and be highly cost-effective. The strategy of annual MDA against schistosomiasis was highly cost-effective in treatment of school-aged children above 5% prevalence (WHO recommends 50%) and entire communities above 15%. Annual MDA against STH was highly cost-effective in treatment of school-aged children above 20% prevalence (WHO recommends 20%) and entire communities above 60%. Using cost-effective MDA recommendations, I estimate that treatment needs in sub-Saharan Africa would be 6-fold higher than current guidelines for praziquantel and 2-fold higher for albendazole. In chapter 4, I evaluate urine-pooling strategies for schistosomiasis surveillance to inform where MDA is needed using simulation modeling and primary epidemiologic data. I find that urine-pooling strategies can reduce the number of tests and cost for rapid Schistosoma prevalence estimation compared with traditional stool microscopy. In chapter 5, I perform a global empirical evaluation of relationships between deworming and health outcomes in over 320,000 pre-school age children using observational data from 45 countries. I detect a robust and consistent association between deworming and reduced stunting, with additional evidence for reduced anemia in sub-Saharan Africa. This dissertation provide rigorous epidemiologic analyses utilized by the international community to inform the global strategy against schistosomiasis and STH, and highlight further opportunities to avert the substantial disease burden caused by these neglected tropical diseases.

Advisor: Andrews Jason Randolph 
Bendavid Eran
Basu Sanjay
Parsonnet Julie
Popat Rita
Modeling the optical response of monolayer molybenum disulfide in a cavity

As semiconductor-based systems face heat dissipation and signal distribution issues, the focus has shifted to on-chip nano-photonics as a promising replacement. Unlike the traditional architectures, photonic resonators such as optical cavities are controlled by electromagnetic radiation instead of electrical voltage. The key challenge lies in generating a nonlinear input-output relationship. To this end, one popular solution is to couple the resonator to a saturable absorber. Here, we will focus on the prospect of using monolayer molybdenum disulfide (MoS2) as the saturable absorber, with the material placed inside a Fabry-Perot cavity at low temperature. The strength of the interaction between the excitons of monolayer MoS2 and the electromagnetic field is calculated by deriving the electronic band structure of the material and superposing transition amplitudes between valence and conduction band states. Unlike a multi-atom cavity, the interaction between different excitons has a significant effect on the input-output relationship of the cavity. We will show the theoretical model required to calculate the exciton-photon coupling coefficient and the exciton-exciton annihilation rate, eventually culminating in the use of a Lindblad-Hamiltonian formalism to derive the optical nonlinearity for the cavity-material system.

Keywords: Physics.
Advisor: Mabuchi Hideo 
Heinz Tony F. 
Lev Benjamin L.
Modeling the structures and energetics of RNA and RNA-protein complexes

RNA molecules perform numerous important biological functions including sensing small molecules, regulating gene expression, and catalyzing reactions in all living systems. For many of these processes, RNAs bind to proteins to form RNA-protein assemblies (RNPs) that carry out processes such as translation of RNA to proteins and splicing of pre-mRNA. The structures and energetics of these RNAs and RNA-protein complexes dictate their sophisticated behavior, but often elude rich experimental characterization. Predictive models of these features would enable powerful interpretation of sparse experimental data and efficient design of new RNAs and RNPs. My work aims to create the first computational tools to calculate RNA-protein binding landscapes, build three-dimensional structures of RNA-protein complexes, automatically model RNA coordinates into cryo-electron microscopy (cryo-EM) density maps, and to accelerate reliable RNA structure determination. My colleagues and I have rigorously assessed the predictive power of these methods through blind tests on functionally and structurally diverse RNAs and RNA-protein complexes. This work has culminated in the determination of several novel three-dimensional structures of RNA molecules including ribozymes, riboswitches, and computationally designed molecules. These results suggest that predictive models of RNA and RNP structure and energetics can now enable accelerated, unbiased structure determination when combined with rapidly acquired experimental data, and will be useful for understanding the complex biological functions of RNAs and RNA-protein complexes.

Advisor: Das Rhiju
Fordyce Polly 
Puglisi Joseph D.
Models of microbial dynamics : from genetic exchange to ecological interactions

How can physics approaches illuminate evolutionary and ecological dynamics? This thesis contains two physics-style toy models in these areas. Fine-scale diversity within traditionally defined species is observed to be maintained over long timespans. How can ecological interactions lead to such coexistence? Previous ap- proaches have required conditions less reasonable for closely-related strains. Chapter 1 introduces a model in which structured interactions allow such strains to coexist in a state of spatio-temporal chaos with broad distributions of abundances. The dynamics of many interacting strains are understood using methods from statistical physics. Genetic exchange in microbial populations can be rare enough that evolution is almost entirely asexual. What is the benefit of such rare genetic exchange? In Chapter 2, a minimal model is presented to understand how the speed of evolution depends quantitatively on the rate of genetic exchange. The rates needed for initial speed ups are found to decrease as a power of the population size and the evolution exhibits spontaneous oscillations.

Keywords: Physics.
Advisor: Fisher Daniel S. 
Sherlock Gavin 
Silverstein Eva