VictorJuiz/thesis huggingface dataset:
A Syringe Tumbler for Ink Resuspension (STIR)
Keywords: 3D bioprinting
cell sedimentation
cell settling
ink resuspension
Text
Thesis

Embedded 3D bioprinting is advancing the field of regenerative medicine through the ability to create patient-specific tissues and organs that can fulfill the organ donor shortage. However, the process of 3D bioprinting is currently limited by cell settling within bioinks since printing of human-scale organs can take hours–or even days–to complete. To address the problem of cell settling, I designed and developed Syringe Tumbler for Ink Resuspension (STIR), a mixing system with a Zero-Dead-Volume (ZDV) magnetic tumbler that can prevent settling and maintain high cell viability throughout long-duration prints. In this thesis, cell settling at various mixing speeds and patterns are characterized by mixing of fluorescent beads that resemble embryoid bodies–a particularly challenging object for cellular mixing due to their large size. True cellular mixing is further characterized by the use of fibroblast cells. The effects of mixing are analyzed through cell concentration analysis and staining to ascertain cell viability while using the device. From the system characterization, I identified back-and-forth mixing as the most consistent method as it allows for improved ink homogeneity at lower mixing speeds, thus reducing the shear stress induced on cells. More specifically, by defining the degree of mixing through thresholding in image analysis, the back-and-forth mixing improved the homogeneity of a 10 mg/mL fibrinogen ink embedded with fluorescent beads by 33.68% over a 15.6 second window, whereas settling reduced the ink homogeneity by 48.84% over the same time frame. Similarly, for long-term cellular prints with and without STIR, a reduced variance in cell concentration was experienced. This suggests that STIR can yield more predictable tissue compositions for both short-duration and long-duration prints. To test the effects of tissue composition, I designed a tissue in the form of a flower, where each petal was to be printed with a cellular layer. The flower-shaped tissue was successfully printed without cells, and two leaflets were printed with fibroblast cells. Evidence of compaction was detected for the cellular constructs, highlighting the first successful tissue created with STIR. Future works will include repeating the tests developed in this thesis in order to confirm my initial findings.
A Systematic Analysis of Model Sensitivity: Investigating the Effect of Wildfire Smoke PM2.5 on Mortality
Keywords: Particulate matter
Wildfires
Mortality
Text
Thesis

Wildfire smoke-related fine particulate matter pollution (PM2.5) is a health hazard known to affect human health and has made up an increasingly large burden of overall PM2.5 pollution in the US over the past two decades. Previous literature studying PM2.5 and mortality has primarily relied on cohort studies and daily time series, with a smaller subset utilizing annually aggregated time series data. We identified several papers that used coarser, ecological data and two-way fixed effects (TWFE) models to study the association between PM2.5 and mortality. Within that literature, there are a variety of different preprocessing and modeling choices. These “researcher degrees of freedom” can lead to a “garden of forking paths”, in which reasonable a priori decisions can produce qualitatively different research findings. To investigate the potential sensitivity of these researcher decisions, we utilized county-month level all-cause mortality data from the CDC’s Multiple Cause of Death files along with new smoke PM2.5 estimates to systematically vary parameters within the TWFE modeling approach used by previous studies. We tested 75 models, 56 of which had negative point estimates. 59 of the models reported statistically significant results when using IID-based standard errors (SEs) while 8 were statistically significant when using robust SEs. The results of our systematic analysis suggest that applying TWFE models to non-daily time series data produces estimates that are minimally robust to different specifications and too wide to detect an effect when robust SEs are used. Based on our replication of Ma et al. 2023 and comparison of the `gnm` and `fixest` packages in R, we believe it is likely that studies finding significant associations between PM2.5 and mortality using non-daily data rely on the IID assumption, and that their confidence intervals may be insufficiently conservative. We recommend that future research studies in this area clearly define the assumptions behind their modeling choices, carefully choose appropriately conservative standard errors, and publish their code to maximize replicability and transparency.
A Tale of Two Policies: Qualitative Perspectives on Efforts to Improve Emergency Department-to-Specialty Referrals in the Context of EMTALA and Local Insurance Networks
Keywords: Medicine
Qualitative research
Health equity
Emergency Medical Treatment and Active Labor Act (United States)
Health insurance
Referral processes
Text
Thesis

The Emergency Medical Treatment and Labor Act (EMTALA) supports a basic minimum access to equitable emergency care for all patients but has led to well-documented unintended consequences including a high financial burden on hospitals and crowding of Emergency Departments. These consequences reach beyond the Emergency Department including affecting hospital capacity. However, it is less well understood how local hospital, regional/state, and insurance network policies impact health equity within the Emergency Department-to-outpatient referral processes. This qualitative study aimed to explore health equity impacts at the intersection of national policy (EMTALA) and local network considerations on Emergency Department-to-specialty referral processes. The context for this work was a quality improvement project piloted between our Emergency and Neurology Departments, which was identified as one of the highest volumes recipient (n = 1069 in 2022) of Emergency Department referrals in our single-site, high volume, academic medical system. We conducted 18 semi-structured interviews with key stakeholders involved in this quality improvement project to specifically explore how policy and insurance network considerations impacted the process. Data were analyzed using rapid matrix analysis and keyword-in-context approaches. Our results found four main themes that represent barriers to equitable healthcare: 1) lack of health insurance and insurance network restrictions thwart people interfacing with the healthcare system: staff, patients, and physicians; 2) mistrust in the healthcare system; 3) inefficient communication between departments; and 4) limited outpatient access capacity. We organized our themes according to Fortney’s five access dimensions model and identified the need for simultaneous initiatives at various dimension levels to improve the issue of access. This work adds to existing literature by determining opportunities for health equity and access improvement in Emergency Department referral processes to specialty departments.
Abstractions for scaling stateful cloud applications
Keywords: Engineering.

As the scale of both computing and data grows, developers are increasingly building distributed stateful systems in the cloud. However, these systems are challenging to build at scale because they must provide fault tolerance and consistency for stateful computations while managing both compute and data resources. Thus, we need new high-level abstractions that hide the complexity of distributed state management from developers. This dissertation proposes three such abstractions at multiple levels of the stack of a stateful cloud application. The first part of this dissertation targets cloud application developers, proposing Apiary, a database-oriented transactional function-as-a-service (FaaS) platform for stateful cloud applications. FaaS is an increasingly popular programming model because it abstracts away resource management concerns and reduces the complexity of cloud deployment, but existing FaaS platforms struggle to efficiently or reliably serve stateful applications. Apiary solves this problem by tightly integrating function execution with data management, improving FaaS performance on stateful applications by 2-68x while providing fault tolerance and strong transactional guarantees. The second part of this dissertation targets developers of the data management systems on which stateful cloud apps depend, proposing data-parallel actors (DPA), a framework for scaling data management systems. DPA targets an increasingly important class of data management systems called query serving systems, which are characterized by data-parallel, low-latency computations and frequent bulk data updates. DPA allows developers to construct query serving systems from purely single-node components while automatically providing critical properties such as data replication, fault tolerance, and update consistency. We use DPA to build a new query serving system, a simplified data warehouse based on MonetDB, and port existing ones, such as Druid, Solr, and MongoDB, enhancing them with new features such as a novel parallelism-optimizing data placement policy that improves query tail latency by 7-64%. The third part of this dissertation targets application developers utilizing multiple data management systems, proposing Epoxy, a protocol for providing ACID transactions across diverse data stores. Such applications are increasingly common because developers often use multiple data stores to manage heterogeneous data, for example doing transaction processing in Postgres and text search in Elasticsearch while storing image data in a cloud object store like AWS S3. To provide transactional guarantees for these applications, Epoxy adapts multi-version concurrency control to a cross-data store setting. We implement Epoxy for five data stores: Postgres, Elasticsearch, MongoDB, Google Cloud Storage, and MySQL, finding it outperforms existing distributed transaction protocols like XA while providing stronger guarantees and supporting more systems
Accountability or Appeasement? An Exploration of the World Bank's Inspection Panel
Keywords: World Bank
World Bank. Inspection Panel
Accountability
Responsibility
Financial institutions, International
Text
Thesis

The World Bank’s Inspection Panel was the first accountability mechanism of its kind at an international finance institution. The Panel was created in response to civil society protests regarding the human rights violations that were increasing in frequency because of Bank-funded projects. Since its inception in 1993, the Inspection Panel has received over 160 complaints citing concerns about project implementation and design. The central question of this thesis surfaced when it became apparent that, despite the Panel’s promises to address the concerns of communities adversely affected by Bank-funded projects, the Panel has taken decisive action in a concerningly low number of cases. There is a clear gap between the Panel’s mandate and the actions that it took. But why? As such, the question emerged: What determines how cases move through the World Bank’s Accountability Mechanism complaint process? Through a combination of case studies and Panel data, supplemented by interviews with Accountability Counsel staff members and a former Inspection Panel member, I argue that both time and civil society organization (CSO) involvement were significant factors that influenced a complaint’s mobility through the Panel’s process. The thesis found that, while these factors could play a role, ultimately, it boils down to the whims of the World Bank’s Management, who are heavily involved in the Inspection Panel process despite the Panel’s design as an independent body. Management’s overt involvement in the Panel’s process severely undermines the ability of the Panel to practice its mandate and blurs the necessary lines of separation between the Panel and the Bank. This major finding raises concerns on the practice of accountability at these institutions and highlights the need for the Panel, and the Bank, to amend the ways in which communities can seek redress within its walls. Evidently, the Inspection Panel still has a long way to go in the way of meeting its original mandate.
Achieving order with two-photon lithography : colloidal self-assembly and direct laser writing
Keywords: Engineering.
Mechanical Engineering.

Structural or spatial order at the nanometer/micron regime is an avenue to improve material properties. The field of photonics and metamaterials have shown that size-effects at these regimes, in combination with purposefully designed architected structures, can enhance mechanical and optical performance. A common approach to achieve these types of ordered structures is through colloidal self-assembly or direct laser writing of 3D structures. In this work, I propose using direct laser writing to fabricate colloidal particles and to fabricate complex 3D structures that have enhanced mechanical properties. In the first part of my work, I focus on colloidal self-assembly as a method to achieve order. Due to the limited chemistries and shapes of colloids available to self-assemble, a large majority of self-assembled structures remain elusive. I propose using two-photon lithography to fabricate micron-sized particles and experimentally study the effect of shape (both concave and convex) on the final self-assembled structure. This method allows for highly monodisperse fabrication of colloidal particles which can then be imaged using optical techniques due to their micron size. I fabricate colloidal conical shapes that self-assemble under entropic conditions (depletants) and tune the degree of assembly by changing the effective driving force through size. I then use a custom machine learning framework to identify these assembled structures (columnar grains) and recover self-assembly trends in which larger particles show a higher degree of self-assembly. Building upon this work, convex particles, specifically the Archimedean truncated tetrahedron, are also fabricated using the same framework and studied under another entropic condition (hard-particle interaction). These particles assemble in a six-fold symmetry upon interaction with an interface and transition to a three-fold symmetry upon application of a potential field. Analytical and computational results show that the six-fold symmetry state is a quasi-stable state and upon additional energy input, a transition occurs to achieve the lower energy state. In the second part of my work, I use two-photon lithography in conjunction with nanoclusters to enhance the direct laser writing process and improve the mechanical properties. I fabricate lattices with micron sized features and test them mechanically. The resulting nanocomposite lattices shows high stiffness and best-of-class energy absorbance by suppressing layer by layer collapse that is commonly seen with these types of structures
Adapting SnapDx Platform For Low-Cost, Electricity-Free Molecular Amplification Based Detection of Schistosomiasis in LMIC Settings
Keywords: Global Health
Bioengineering > Research
Molecular Amplification
Schistosomiasis > Diagnosis
Point-of-care testing
Text
Thesis

Schistosomiasis, caused by parasitic worms, is a neglected tropical disease affecting nearly 240 million people globally. The World Health Organization released a Target Product Profile in 2021 calling for the development of new diagnostics for monitoring and evaluation of Schistosomiasis, to move towards elimination. Traditional diagnostics using microscopy are reliant on laboratory infrastructure and technician skill. They also lack sufficient sensitivity and specificity. This thesis develops a loop-mediated isothermal amplification (LAMP) direct from urine Schistosomiasis assay for translation onto SnapDx platform, an electricity-free, point-of-care nucleic-acid diagnostics platform developed in the Prakash Lab for respiratory infections like Sars-CoV-2 detection. Here we demonstrate that the current version of the assay with genomic DNA from Schistosomiasis spiked in human urine in controlled lab settings displays a sensitivity of 77.50% at 1 ng/uL and 62.50% at 100 pg/uL and a specificity of 97.5% - all read via a simple colorimetric readout, in Eppendorf tubes. We further test the impact of various sample preparation methods on the assay. The lowest limit of detection reached on simulated urine samples is 1 pg/uL using samples purified with MidiPrep prior to the introduction of genomic DNA. We further find 11% of all the reactions run showing differing results between fluorescence and colorimetric readout. The mismatch is found to be greater at lower sample concentrations. Next we report on testing the functionality of the assay with wet bath inactivation and run, as required in the electricity-free SnapDx usage in field context. In future work, we intend to investigate different primer combinations, urine-based reaction inhibitors, and compact, low-cost sample preparation methods (such as urine egg concentration) are required to further optimize the assay. Next, reliability, sensitivity, specificity and limit of detection will need to be evaluated with real patient samples. Our work lays the foundation for the future implementation of this assay in field conditions without electricity, opening the door to molecular based surveillance approaches , which satisfies the usability and infrastructure requirements of the World Health Organization’s Target Product Profile. It also contributes to a body of work aiming to develop SnapDx applications for a wide range of infectious diseases, compatible with urine, saliva, blood, and other biological fluids.
Adapting Whisker Sensors to Deep Sea Environment For Ocean One Robot
Keywords: Mechanical Engineering
Whiskers
Underwater exploration
Academic theses
Text
Thesis

Stanford’s Ocean One robot has been paving the way in human robot collaboration and underwater archaeology. The humanoid form factor of this scuba diving robot makes it so humans can easily control it remotely to perform fine manipulation with its 3 fingered hands. Currently, Ocean One’s wrists measure forces and torque and convey it back to the user using haptic feedback. It may seem strange to send a robot in place of a human, but explorations, such as Ocean One’s exploration of King Louis XIV’s sunken ship (La Lune), require diving 100 meters below. While humans can technically dive to 100 meters, these are dangerous depths for humans due to factors like the pressure, so 40 meters is the recreational diving limit. Therefore, Ocean One makes going deeper much safer, while still allowing human-like interaction with the environment.
Similarly to human scuba divers, the Oceans One robot has an Achilles heel. While exploring uncharted underwater territories, it could get hooked on a piece of ship wreckage. Even for a human, it can be difficult to unhook oneself, but it is even harder for the Oceans One robot since it does not yet have integrated sensors signaling how it is caught. Without sensors, it is difficult to convey what is stuck, further complicating the job for remote teammates helping it break free.
Adding sensors could not only make it easier for the robot to stay clear and get out of tangles, but it could also provide helpful information on currents and surroundings. Sensors on the arms would provide helpful feedback on obstacles for Ocean One to avoid before getting caught at its shoulders. My first quarter of research involved defining sensor requirements, looking at various sensor possibilities, and narrowing the scope to whisker sensors. My second quarter involved developing code to filter ocean noise. My final quarter involved adapting the existing whisker sensors to high pressure underwater conditions. After testing against sensor requirements, I concluded that sensitivity, robustness, and resolution of the whisker sensor, make it a promising option to improve the Ocean One’s safety and sensing as it navigates cluttered underwater environments.
My project highlights the potential of underwater whisker sensors to address the issue of sensing objects before getting caught and ultimately extend the lifespan of the Ocean One Robot.
Adaptive algorithms for data science and computational genomics
Keywords: Engineering.
Electrical Engineering.

Recent years have seen a sustained exponential growth in the volume of data generated within the domains of data science and biology. This dramatic pace of data generation has outstripped the growth in computational power predicted by Moore's Law, creating a computational bottleneck. Existing algorithmic techniques are computationally insufficient, and cannot scale to meet this demand. My research has focused on developing new algorithmic techniques to handle this massive scale of data, using probabilistic modeling to design algorithms that adapt to the input dataset's structure. In this thesis, I present my work on data-adaptive algorithms, highlighting their application in computational genomics. The first part of this thesis focuses on how we can utilize methods from multi-armed bandits to construct computationally adaptive methods for data-scientific problems. We show that adaptivity can be used to design algorithms that are more efficient than their brute-force counterparts, while still providing theoretical guarantees discarded by common heuristic approaches. These methods leverage the fact that practical problems have structured data that is generated by nature, so classical worst-case analyses are overly pessimistic. To this end, we develop a general framework for the use of adaptivity in a class of computational tasks arising in data-science, including in clustering and randomized numerical linear algebra. Tackling each separate problem highlights the modularity of this framework, which we show can be further improved to exploit the structure and constraints of the specific task, which sometimes yield new multi-armed bandit settings requiring the development of new bandit algorithms. The second part of this thesis focuses on the application of these ideas to computational genomics. Nowhere is the computational bottleneck of exploding data more apparent than in genomics, where the rapidly decreasing cost of sequencing has fallen orders of magnitude faster than Moore's Law. The first problem I discuss is that of estimating pairwise sequence similarity, a common first step in genome assembly, for which we develop a novel statistical measure of similarity which adapts to the overall sequence similarity in the dataset, and a computationally efficient adaptive SVD to estimate it. The second problem I highlight is that of reference-free inference, analyzing sequencing data without the use of a reference genome. For this task we develop a novel method for analyzing sequencing data without a reference genome, and a novel finite-sample valid test on contingency tables. In both of these problems, we highlight the importance of improved probabilistic modeling and show how it can be used to develop new algorithms that adapt to structure in the data and are both statistically and computationally efficient.