VictorJuiz/thesis huggingface dataset:
Adipose-Derived Stem Cell-Seeded Hydrogels Increase Endogenous Progenitor Cell Recruitment and Functionality

Chronic wounds afflict over 6.5 million individuals and cost over $25 billion annually in the United States alone. These nonhealing wounds are caused in large part by impairments in neovascularization, the formation of new blood vessels. Stem cell therapies have emerged as a potentially viable approach to treat chronic wounds. In particular, adipose-derived mesenchymal stem cells (ASCs) are promising due to their ease of harvest and production of pro-regenerative molecules, including those that promote neovascularization. However, a challenge with stem cell therapies is maintaining cell survival in the harsh wound environment. Therefore, stem cell delivery to the wound must be optimized to make cell-based therapies for wound healing translatable. Previously, the Gurtner laboratory found that delivering ASCs by seeding these cells in a hydrogel composed of collagen and the polysaccharide pullulan and topically applying the ASC-seeded hydrogel to a cutaneous wound accelerated wound closure. This project focused on further elucidating how ASC-seeded hydrogels have this therapeutic effect. Specifically, we tested the hypothesis that ASC-seeded hydrogels increase the recruitment and heighten the functionality of endogenous progenitor cells that promote neovascularization. We used techniques including a model in vivo to study cell recruitment to cutaneous wounds, fluorescence activated cell sorting (FACS) of recruited cells, and in vitro measurement of recruited cell proliferation, migration, and expression of provascular genes. We found that application of ASC-seeded hydrogels to wounds in vivo, compared to injected ASCs or a saline control, increased the recruitment of circulating bone marrow-derived mesenchymal progenitor cells (BM-MPCs, p < 0.05), a population we previously found plays a role in neovascularization. In vitro, we found an increase in expression of genes related to angiogenesis, as well as BM-MPC proliferation, migration, and tubule formation, all of which play a role in neovascularization (p < 0.05 for all assays). These data support our hypothesis, demonstrating that treating cutaneous wounds with ASCs seeded in hydrogel increases recruitment of progenitor cells that contribute to neovascularization and improves recruited cell provascular functionality. Overall, this study demonstrates that our pullulan-collagen hydrogel can optimize delivery of ASCs to cutaneous wounds, underscoring the potential of ASC-seeded hydrogels to be used clinically in cell-based therapies for chronic wound healing.

Keywords:  Department of Biology
Biology - Wound Healing
Thesis
Advisor: Gurtner Geoffrey
Shen Kang
Advanced applications of multilinear maps in cryptography [electronic resource]

We study two new cryptographic primitives inspired by recent advances in multilinear maps: private constrained pseudorandom functions (PRFs) and order-revealing encryption (ORE). We show how these primitives have direct applications in searchable symmetric encryption, watermarking, deniable encryption, private information retrieval, and more. To construct private constrained PRFs, we first demonstrate that our strongest notions of privacy and functionality can be achieved using indistinguishability obfuscation. Then, for our main constructions, we build private constrained PRFs for bit-fixing constraints and for puncturing constraints from concrete algebraic assumptions over multilinear maps. We also construct the first implementable ORE scheme that provides what is known as ``best-possible'' semantic security. In our scheme, there is a public algorithm that given two ciphertexts as input, reveals the order of the corresponding plaintexts and nothing else. Our constructions are inspired by obfuscation techniques, but do not use obfuscation. Finally, we also show how to build efficiently implementable ORE from PRFs, achieving a simulation-based security notion with respect to a leakage function that precisely quantifies what is leaked by the scheme.

Advisor: Boneh Dan
Valiant Gregory advisor. Thesis advisor
Williams Ryan (Richard Ryan)
Advanced computing and automation in radiation therapy treatment planning [electronic resource]

\prefacesection{Abstract} The Boltzmann transport equation describes the macroscopic behavior of radiation particles such as neutrons, photons, and electrons as they travel through and interact with matter. The widely known Monte Carlo (MC) method is a general approach to obtaining open form solutions to the linearized Boltzmann transport equation. Monte Carlo algorithm is thte most accurate way to predict how dose is delivered inside a patient. However, the computation time inhibits its routine use in the clinic. For a patient, MC carlo simulation takes anywhere from 10 hours to several days. In this dissertation, we use the state-of-the-art cloud computing technology to accelerate Monte Carlo methods. The approach is evaluated for pencil beams and broad beams of high-energy electrons and photons. The cloud-based Monte Carlo simulation is compared to single-threaded implementation and demonstrates a 47x speed up. Current clinical treatment planning requires multiple trial-and-error adjustments of system model parameters. Producing a treatment plan is time consuming. A team of physician, dosimetrist, and physicist manually adjust parameters in a commercial planning environment. In this dissertation, an autonomous treatment planning technique is implemented in a clinical platform. An outer-loop decision function interacts on-the-fly with an inner-loop clinical treatment planning system (TPS). The approach is applied to 3 head and neck volumetric modulated arc therapy (VMAT) cases and one prostate intensity-modulated radiation therapy (IMRT) case. A strategy of using population-based prior patient data was explored. An upper and lower bound for the dose-volume segments are derived from a group of previously treated patients. The bounds are then used for new case to provide a dosimetric range for acceptability. An heuristic algorithm adjusts the constraints for the optimization using a stochastic approach. Rather than setting a deterministic value for each dose-volume segments, the constraint is changed during each iteration of the outer-loop optimization. The proposed algorithm is applied to a head and neck VMAT case and a prostate IMRT case on a clinical treatment planning system. Results obtained show a comparable dose volume histogram (DVH) compared to manual planning.

Keywords: Electrical Engineering.
Advisor: Xing Lei primary advisor. Thesis advisor
Pauly John (John M.) advisor. Thesis advisor
Ye Yinyu advisor. Thesis advisor
Advances in CRISPR-Cas9 genome engineering technologies in saccharomyces cerevisiae and tests for cis-regulatory evolution [electronic resource]

This thesis is organized into two primary areas of research: cis-regulatory evolution and genome engineering. It is now known that noncoding regions comprise the vast majority of genomic regions under selective constraint in mammals. Despite this, the ability to detect natural selection on noncoding regions has lagged behind the ability to detect selection on coding regions. In Chapter 1, we introduce a new test to detect selection on cis-regulatory elements, and demonstrate its utility on three mammalian transcriptional enhancers. In Chapter 2, we investigate the evolution of resistance to the mycotoxin citrinin by comparing two closely related species of budding yeast, Saccharomyces paradoxus and Saccharomyces cerevisiae. Applying a genome-wide test for selection on cis-regulation, we identified five genes involved in resistance in S paradoxus, four of which are necessary for resistance and increase resistance in S cerevisiae when over-expressed. In the second half of this dissertation, I discuss my work with genome engineering and CRISPR-Cas9. In Chapter 2, we build an improved CRISPR activator (CRISPRa) to simultaneously induce overexpression (in S cerevisiae) of the four genes identified to confirm their role in citrinin resistance. In Chapter 3, we build an inducible CRISPR interference (CRISPRi) system and and designed gRNA libraries to determine design rules for CRISPRi gRNA design in S cerevisiae. We determined that gRNAs targeted to a region with low nucleosome occupancy and high chromatin accessibility within a window of 0 to 200bp upstream of the transcription start site (TSS) are most likely to be effective. In Chapter 4 we confirm and refine these rules in a much larger library consisting of ~9000 unique strains. Additionally, we present a novel method for parsing complex oligonucleotide libraries into single, sequence verified DNA sequences using high throughput sequencing and yeast synthetic biology. We utilize this technology to create and characterize a collection of ~9000 individual inducible CRISPRi strains to the vast majority of essential and respiratory essential genes in S cerevisiae. In Chapter 5, we compare mismatch tolerance for Cas9 in vitro and in vivo (in S cerevisiae) and test a variety of truncated and full-length gRNAs (with 17, 18, and 20 nucleotides of complementarity sequence). We observed notable differences between in vitro and in vivo Cas9 cleavage specificity profiles, with in vivo cleavage being more sensitive/less tolerant to mismatches.

Keywords: Genetics.
Advisor: Davis Ronald W. (Ronald Wayne)
Fire Andrew Zachary advisor. Thesis advisor
Sherlock Gavin advisor. Thesis advisor
Steinmetz Lars advisor. Thesis advisor
Advances in flight safety analysis for commercial space transportation [electronic resource]

The diversity in the kinds of vehicles that are appearing in the commercial space transportation sector raises questions regarding the applicability of the licensing procedures and methodologies that are in place to protect public safety. These licensing procedures are designed to limit risks to public safety in case of a space vehicle explosion. Concerns arise because the methods currently used are derived from expendable launch vehicles (ELVs) developed during the Space Shuttle era, and thus they might not be fully applicable to future vehicles, which include new types of ELVs, suborbital vehicles, reusable launch vehicles (RLVs), and a number of hybrid configurations. This dissertation presents a safety analysis tool, called the Range Safety Assessment Tool (RSAT), that quantifies the risks to people on the ground due to a space vehicle explosion or breakup. This type of problem is characterized by the complexity and uncertainty in the physical modeling. RSAT has been used to analyze both launch and reentry scenarios and can be applied to many possible vehicle configurations. The Space Shuttle Columbia accident was modeled with RSAT, and the results were compared with simulations performed by the Columbia Accident Investigation Board (CAIB). A methodology to perform sensitivity and optimization studies is also presented. This methodology leverages previous work done in active subspaces and Gaussian process regression to generate surrogate models. The proposed sensitivity and optimization methodologies were used to analyze a commercial ELV. The results show that the methodology can handle a large number of stochastic inputs and identify opportunities to decrease risk.

Keywords: Aeronautics and Astronautics.
Advisor: Alonso Juan José
Kochenderfer Mykel J.
Senesky Debbie advisor. Thesis advisor
Advances in structure-based modeling of turbulent flows [electronic resource]

Structure-based modeling, introduced by Kassinos and Reynolds (1995), uses one-point tensors to provide a more detailed description of the turbulent fluctuations. This thesis describes further developments to two structure-based models, namely the Algebraic Structure-based Model (ASBM), which belongs to the RANS category, and the Interacting Particle Representation Model (IPRM), which can been interpreted as a PDF model. The ASBM is an engineering model of turbulence for wall-bounded flows. A new variant of the model has been formulated, which exhibits (1) a segregated near-wall correction that leads to a new paradigm for model development and comparison, (2) a set of fully-explicit equations that replaces the original formulation and reveals the highly nonlinear nature of the ASBM, and (3) a new coupling with transport equations that improves the accuracy of the model. The original and newer variants of the ASBM are then applied in the simulation of separated flows, so as to obtain a comprehensive assessment of their predictive capabilities. This is then followed by a thorough study on the ability of the model to provide well-converged solutions. The IPRM is a stochastic structure-based model of homogeneous turbulence. This thesis documents a new formulation based on an Eulerian reference frame that replaces the original Lagrangian framework and thus avoids the slow convergence and bias of statistical estimators. The derivation of the Eulerian formulation, its solution through radial basis functions, and a comparison against the original solution methods are reported in detail. Taken together, the work performed on both models advances the applicability and understanding of structure-based modeling for turbulent flows.

Keywords: Aeronautics and Astronautics.
Advisor: Iaccarino Gianluca primary advisor. Thesis advisor
Alonso Juan José
Duraisamy Karthikeyan
Lele Sanjiva K. (Sanjiva Keshava)
Advances on chip inside a cell for monitoring physiological cell parameters [electronic resource]

Continuous monitoring of physiological parameters inside a living cell will lead to major advances in understanding of biology and complex diseases, such as cancer. It also enables us to develop new medical diagnostics and therapeutics. Here we propose a technique to measure the pH level inside a cell using a wireless implantable passive sensor, which consists of capacitive and inductive components. Even a slight change of cellular pH level can reveal substantial information about the health of a cell. Only a highly sensitive pH sensor can detect this slight variation. Building such a wireless implantable sensor platform is the main goal of this thesis. Our designed and fabricated pH sensor is basically an extended gate ISFET (Ion Sensitive Field Effect Transistor) structure. It is compatible with CMOS (Complementary Metal Oxide Silicon) fabrication processes and can be miniaturized as an implantable device. To fully understand the parameters that give rise to high pH sensitivity, we have investigated the effect of solution molecular size on enhancing the sensitivity of this type of sensor. Our simulation and experimental results show that buffered solutions with larger counter ion size can induce significantly higher hydrogen ion activity at the sensor surface. The larger buffer counter ion size results in higher externally measured sensitivity, which even exceeds the Nernst limit (59mV/pH). For each pH buffer solution there is a specific size of the counter ion, which is necessary to achieve this high sensitivity. In addition, progress in nanofabrication and wireless communication has opened up the potential of making our wireless chip small enough that it can be wholly inserted into a cell. To investigate how the chip could be internalized into the cell and how the chip would affect cell physiology, we designed and fabricated a series of 3D multilayer structures with different sizes as a potential RFID (Radio Frequency IDenentification) cell tracker. Our experiments show that the chips with smaller sizes than 21 µm × 9 µm × 1.5 µm can be easily internalized by various types of living cells, such as macrophages, cancer cells, and normal/healthy cells. The incubated cells with internalized chips stayed alive during the 7 days of monitoring. Also, we observed successful cell division from these incubated cells. These results are the first steps towards long-term, wireless, intracellular physiologic monitoring.

Keywords: Electrical Engineering.
Advisor: Wong Hon-Sum Philip
McConnell Michael
Poon Ada Shuk Yan
Advancing motor neural prosthesis robustness and neuroscience [electronic resource]

The frontier challenges that must be solved before brain-machine interfaces (BMIs) can be used as clinically useful motor prostheses differ depending on the degree of function being restored. Two-dimensional cursor control (i.e., for communication) has recently reached high levels of peak performance in pre-clinical studies, but translation is hampered by less than reliable performance due to unstable neural signals. Meanwhile, control of robotic arms remains poor, despite some impressive glimpses at what the future could be, because we lack fundamental understanding of how the brain incorporates the BMI into its motor schema. This hampers our ability to accurately decode intended arm movements. My dissertation focused on both sets of problems in pre-clinical macaque BMI studies. Chapters 2 and 3 provide solutions for improving BMI robustness. I first describe a machine learning approach to building decoder algorithms that are robust to the changing neural-to-kinematic mappings that plague translational BMI efforts. We developed a multiplicative recurrent neural network decoder that could exploit the large quantities of data generated by a chronic BMI — data that has heretofore gone unused. I then describe a neural engineering approach for increasing the device lifespan by providing high performance control even after losing spike signals. I developed a method for decoding local field potentials (LFPs) as a longer-lasting alternative or complimentary BMI control signal. This led to the highest-performing LFP-driven BMI and the first 'hybrid' BMI which decoded kinematics from spikes and LFPs together. Chapter 4 looks ahead to challenges that will be encountered when BMI-controlled limbs operate in the physical world by describing how error signals impact ongoing BMI control. I perturbing the kinematics of monkeys performing a BMI cursor task and found that visual feedback drove responses starting 70 ms later in the same motor cortical population driving the BMI. However, this initial response did not cause unwanted BMI output because it was limited to a decoder null space in which activity does not affect the BMI. When activity changed in output-potent dimensions starting 115 ms after perturbation, it caused corrective BMI movement. This elegant arrangement may hint at a broader computational strategy by which error processing is separated from output.

Advisor: Shenoy Krishna V. (Krishna Vaughn) primary advisor. Thesis advisor
Newsome William T. advisor. Thesis advisor
Norcia Anthony Matthew
Raymond Jennifer L. advisor. Thesis advisor
Advancing multidimensional chemical mapping of RNA structure through next-generation sequencing [electronic resource]

RNAs are an essential and versatile category of biopolymers, fulfilling roles from relaying genetic information to catalysis to regulation of gene expression. The formation of and interconversion between conformational states is crucial to the biological functions of RNAs. Chemical mapping methods have proven valuable in providing solution-state, per-nucleotide structural information for RNAs, and computational prediction methods, which can incorporate chemical mapping data, have permitted modeling of both secondary and tertiary RNA structures. However, certain techniques have faced limitations: high-confidence secondary structure prediction through '2D' chemical mapping of systematic mutants (mutate-and-map, M2) is limited by RNA size, and tertiary structure inference through analysis of spatially localized hydroxyl radicals (multiplexed •OH cleavage analysis with gel electrophoresis, MOHCA-gel) is limited by imprecise readouts of proximal residue pairs. Next-generation sequencing (NGS) has greatly enhanced the throughput of chemical mapping, allowing for estimation of chemical reactivities for viral genomes and cellular transcriptomes, but has not yet been leveraged to alleviate the limitations on M2 and MOHCA. In this dissertation, I present the application of NGS readouts to advance a multidimensional chemical mapping (MCM) pipeline for probing and modeling RNA secondary and tertiary structure. First, I demonstrate that a sequencing-based MOHCA-seq method allows for precise detection of tertiary proximities throughout target non-coding RNA (ncRNA) domains. Using constraints from MOHCA-seq data improves Rosetta modeling with experimentally inferred secondary structures to 1-nm accuracy, which allows both visualization of complex ncRNA states, such as the ligand-free states of cyclic-di-GMP, glycine, and adenosylcobalamin riboswitch aptamers, and blind structure predictions of a 188-nt lariat capping ribozyme and a Hox internal ribosome entry site domain. Second, I apply MCM to a bioengineered, directionally switchable RNA-protein motor and demonstrate that it is able to detect distinct sets of tertiary proximities in alternative conformations of a multistate RNA system. Finally, I show that the length and throughput limits of M2 can be overcome by deriving 2D data from high-modification mutational profiling experiments, which permit readout of multiple modifications in single RNA molecules, and explore the basis of the 2D signal. These results improve the applicability and usability of the MCM pipeline and enable structural characterization of previously intractable RNAs.

Keywords: Biochemistry.
Advisor: Das Rhiju
Herschlag Daniel advisor. Thesis advisor
Straight Aaron
Advertiser Retaliation Against Media: Evidence from South Korea

In this paper, I present evidence on financial retaliation by advertisers in the newspaper market in South Korea. Specifically, I study whether the coverage of negative news stories about the advertisers affects their advertising expenditure. I obtained data on daily advertising spending by the top 50 advertisers in South Korea, as of 2007, across seven national newspapers during the period 2007-2011 I also retrieved daily newspaper coverage of negative news about those top 50 advertisers for the same seven national newspapers during that period. I then performed a difference in differences analysis comparing advertising spending before and after the negative news was published, and between media outlets responsible for breaking negative news and the rest to see an average differential effect. I also interacted the difference in differences model with the date variable to observe the acute effect of publishing negative news about advertisers on ads amount. In order to identify each negative story, I manually selected events that attracted nationwide interest. Then, I separated outlets responsible for breaking news from those that did not, based on both quantitative and qualitative measures.

Keywords:  Department of Economics
Stanford Department of Economics
Thesis
Advisor: Gentzkow Matthew