VictorJuiz/thesis huggingface dataset:
Designing a Clinical Tool for Visualizing Variation in Pediatric Perioperative Opioid Use
Keywords:  Department of Biology
Anesthesiology
Stanford University Department of Biology
Stanford University
Decision support systems
Clinical informatics
Machine learning
Pediatrics
Thesis

Anesthesiologists integrate diverse sets of variables to determine appropriate opioid doses for surgery that sufficiently manage patient nociception and pain during the operation, while minimizing adverse effects. Understanding and reducing unnecessary variation in opioid administration is key to achieving optimal patient safety and health outcomes. However, relevant data is currently disparate, and direct comparisons amongst anesthesiologists are complicated by case-specific patient demographics and surgical factors. We engaged healthcare specialists associated with surgical care at a pediatric hospital to develop end-user requirements for an opioid utilization data dashboard and identified patient characteristics and surgical factors that may impact opioid administration. After aggregating and cleaning 5.33 years of surgical case data, mathematical modeling determined variable importance and overall percent of variation explained (R2) by all variables currently available. Finally, we worked with stakeholders to integrate modeling results to iteratively build and deploy an opioid utilization dashboard. Modeling identified patient weight, patient age, and surgery type to be most responsible for opioid-use variability. Machine learning algorithms using a comprehensive set of the available patient and surgical variables explained 58.6% of variation in intraoperative opioid dose delivered to children. This suggests that a large portion of variation results from uncaptured data, such as patient vital signs and difficult to capture data, including random and potentially unnecessary clinical variation between similar cases. The iterative dashboard design process identified two mechanisms to enable comparative visualizations with meaningful case-mix adjustment: an interactive data filtration tool and machine learning-based normalization. When deployed in real practice, our dashboard identified a number of low opioid use outliers who effectively reduced utilization with opioid alternatives while maintaining patient outcomes. These results demonstrate one way in which the dashboard can help physicians learn from historical practice. We developed algorithms and an interactive interface that applies contemporary data informatics to provide anesthesiologists with timely data on their practice patterns while adjusting for case-mix differences. Normalized comparative visualizations can empower physicians to track changes and variation in opioid administration over time and to understand their practice relative to their peers. Access to transparent, interpretable data may help reduce unnecessary variation in surgical care opioid utilization and thereby improve patient health outcomes.
Designing and understanding high-energy fast-charging lithium batteries
Keywords: Materials Science and Engineering.

Secondary battery systems based on lithium (Li)-ion chemistries have achieved great success with their broad applications in portable electronics, electric vehicles and grid storage during the past few decades. However, current Li-ion battery technology requires urgent improvements in two key aspects: fast charging capability and energy density. Fast charging of electric vehicles could significantly improve the recharging experience, but it is currently impossible to fully charge within 10 minutes without undermining cycle life. Further improvement in energy density could enhance vehicle range, but it calls for transition in chemistry to, for example, Li metal batteries that show intrinsically fast capacity decay. Therefore, researches have been focusing on understanding the failure mechanism during Li-ion battery fast charging, as well as pro-long the cycle life of higher energy Li metal battery systems. In Chapter 1, background will be introduced about the current status of efforts to high specific energy, fast charging Li batteries. In Chapter 2, the temperature dependence of equilibrium potential is revealed to impact the Li plating pattern on graphite anodes, directing potential designs to enable the extreme fast charging of Li-ion batteries. In chapters 3 and 4, designs of artificial "host" frameworks are introduced to stabilize the volume of Li metal anodes during cycling, improving the cycle life. In chapter 5 to 7, molecular designs of novel solvent molecules are discussed to enable highly stable liquid electrolytes with practical Li metal battery cycling performances. The design principles and working mechanisms of these new electrolytes will also be elaborated. Finally, future directions of EV battery developments will be outlooked
Designing hysteresis free high-valent redox cathode materials for electrochemical applications
Keywords: Materials Science and Engineering.

Advances in electrochemical devices such as batteries, fuel cells, and water-splitting membranes are making global transition towards clean and renewable energy more possible than ever. Foundational to (electro)chemical and catalytic transformations in these devices are a stable and reversible high-valent redox couples. In particular, the phenomenon of high-valent oxygen redox (anionic redox) in lithium- and sodium-ion positive electrodes has the potential to significantly improve cell energy density by providing additional high voltage capacity beyond that of most transition metal redox couples. However, the additional capacity from (anionic redox) has come at the expense of reduced reversibility in the form of voltage hysteresis and voltage fade. As a result, high valent redox couples have been historically avoided. After providing historical context of lithium- and sodium-ion battery technologies in Chapter 1, my thesis, will outline the mechanism and the framework for understanding the source of poor electrochemical reversibility in high-valent redox (Chapter 2 and 3). In Chapter 4 and 5, I will demonstrate mechanisms and design rules where structural disorder and voltage hysteresis can either be completely avoided or mitigated, respectively. Finally, I will conclude by summarizing the main findings and their implications to the future of (electro)chemistry, solid-state chemistry and condensed matter physics (Chapter 6)
Designing multi-joint exoskeleton assistance
Keywords: Mechanical Engineering.

Exoskeletons could make walking easier for people including military personnel, first responders, older adults, and people with disabilities. These devices have shown some improvements by reducing the energetic cost of walking, but we still don't know what assistance strategies are best or how effective they can be. Which leg joints should exoskeletons assist? What torques should they apply? What's the greatest improvement we could expect? In what conditions can they be most effective? To investigate these questions, we designed a bilateral lower-limb exoskeleton emulator to study walking assistance. We then optimized hip-knee-ankle exoskeleton assistance for each joint individually, for two-joint combinations, and for the whole-leg. We found that assisting the whole leg reduced the energy cost of walking by 50%, double the state-of-the-art. However, while assisting the whole-leg was most effective, we found assisting a single joint to be more efficient in terms of percent reduction per joint assisted. To study how this optimal assistance could change in new environments, we then optimized assistance for walking on inclines up to 15 degrees. We found similar improvements of at least 50%, demonstrating whole-leg assistance can be beneficial across a wide range of inclines. The optimized assistance strategies we found could be used to inform the design of future exoskeletons, bringing them closer to the goal of product-like devices for those who would benefit from walking assistance
Designing the gas, liquid, and electron pathways for electrocatalytic gas-involving systems
Keywords: Chemistry.

Developing sustainable techniques to produce fuels and chemicals is indispensable in reducing carbon dioxide emissions while providing chemical feedstocks for daily use. Large-scale electrocatalytic applications for energy conversion with high cell efficiency are promising candidates but suffer from various problems, of which elucidating and mitigating electrode mass transport issues and system resistance are two crucial aspects. In this dissertation, I first introduce the background and problems of gas-harnessing and gas-generating electrocatalytic reactions, along with discussions about current strategies for enabling their use. Next, I focus on an electrode design to improve the mass transport of gas reactants to enhance the overall electrocatalytic performance by mimicking the mammalian inhalation process. Then, I tackle the gas product bubble-induced resistance by mimicking the mammalian exhalation process to enhance electrocatalytic gas evolution performance. By constructing bubble-free gas evolution electrodes, I conduct comprehensive investigations of the causes of bubble generation in porous membrane systems and identified three key parameters—membrane permeability, membrane hydrophobicity, and electrical conductivity. Finally, I demonstrate a 3D 3-phase electrode design that considers three pathways--electron, ion, and gas--as well as effective catalytic active sites including both quality (activity) and quantity (loading). Using a hydrogen model system with platinum catalysts, this 3D continuous 3-phase matrix electrode exhibits superior on-site hydrogen generation, separation, and reusage performance
Determining causal relationships between the microbiome and immune system
Keywords: Bioengineering.

The gut microbiome is a diverse ecosystem of ~100 trillion microbes deeply embedded in multiple aspects of human immune and metabolic function. Defining the causal mechanisms underlying the gut-immune axis is imperative to understand dysregulation during disease and the correction necessary for therapeutic development. This work presents three different strategies to determine the language by which microbes communicate with our human physiology: 1) conduct interventional human studies to determine how microbiome-targeted interventions affect the immune profile, 2) characterize microbial and metabolic profiles indicative of a healthy and diseased state, and 3) identify circulating small molecules that are both are microbially derived and causal for immune modulation
Developing a giant magnetoresistive (GMR)-based assay for quantification of the human immune response to influenza infection and vaccination
Keywords: Bioengineering.

Seasonal and pandemic strains of influenza viruses remain among the most common and deadly respiratory viral pathogens. The gold standard for influenza testing is reverse transcription polymerase chain reaction (RT-PCR), in which amplification of influenza viral RNA is performed and quantified. However, the primers for RT-PCR testing are designed to detect specific viral sequences and the diagnostic efficacy can be reduced when a new strain emerges in the population. While detection of viral RNA is important for disease diagnosis, exploring differences in host gene expression during the time course of infection could provide useful clinical information, such as dose and duration of antiviral medications. An analysis of publicly available transcriptional data identified an eleven-gene host transcriptional signature, the Influenza Meta Signature (IMS), that could distinguish individuals infected with influenza from those with bacterial or other respiratory viral infections. Detecting and quantifying the host transcriptional response to influenza virus infection can serve as a real-time diagnostic tool for clinical management. Moreover, measuring differences in transcript levels after influenza vaccination may predict vaccine responsiveness. We have employed the multiplexing capabilities of GMR sensors to develop a novel assay based on the influenza metasignature (IMS), which can classify influenza infection and influenza vaccine responsiveness based on transcript levels. After characterizing GMR sensors to detect and quantify a single transcript, we show that the assay can reliably detect eleven IMS transcripts and distinguish patients with influenza infection from those with other symptomatic viral infections(AUC 0.93, 95% CI: 0.84-1.00). The assay can also predict a likely vaccine non- responder by measuring IMS expression levels before and after vaccination (paired t-test p< 0.0001). We demonstrate that a portable GMR biosensor can be used as a tool to diagnose influenza infection by measuring the host response, simultaneously highlighting the power of immune system metrics and advancing the field of gene expression-based diagnostics
Developing and applying quantitative high-throughput microfluidics to elucidate enzyme functional architecture
Keywords: Biochemistry.

Protein enzymes are Nature's most proficient catalysts, accelerating rates of chemical transformation to the timescales of life. They are also central to achieving clean and efficient industrial processes and to realizing next-generation precision health. Our ability to target and engineer enzymes, and predict how their variation alters biological function and dysfunction, ultimately requires that we describe them quantitatively and at massive scale. However, classical methods of enzymology are too laborious and time consuming to make measurements at the required scale. Here, I detail the development of High-Throughput Microfluidic Enzyme Kinetics (HT-MEK), a technology meeting this need through the integrated expression, purification, and quantitative measurement of enzymes at a hundred-fold the scale and at one-hundredth the time of classical methods. HT-MEK can be generally applied to any enzyme with a fluorescent readout of function. We used HT-MEK to deeply study mutations at each position in an efficient phosphatase, PafA, measuring > 6,000 kinetic and thermodynamic constants for > 1000 mutants using multiple substrates, inhibitors, and under varied conditions. We found that positions with mutational effects on different functions compose discrete regions, PafA's functional architecture, extending to surfaces that may be harnessed for rational engineering. We also discovered widespread misfolding to inactive states, a potentially-common feature among highly-stable enzymes. Fundamentally, HT-MEK provides ground truth physical constants, a basis for accurate next-generation enzyme function prediction algorithms. These constants are also needed to understand how enzyme disfunction manifests in human disease. Towards this, I present preliminary work extending HT-MEK to the large-scale characterization of variants of the human phosphatase SHP2, some of which can cause cancer. Measurements of biophysical constants describing catalysis, stability, and inhibition for more than 100 SHP2 variants (> 200 total mutants) revealed discrete regions of activation and destabilization relevant for explaining known in vivo effects and predicting new ones. HT-MEK and the described studies of PafA and SHP2 advance classical enzymology into the post-genomic era, and are a basis for new efforts to engineer enzymes, realize precision medicine by broadly describing allelic variation, and more deeply understand Nature
Developing data efficient algorithms in artificial intelligence
Keywords: Management Science and Engineering.

In the past few years there has been an enormous amount of progress in machine learning, and one of the biggest contributing factors, especially for deep learning, is the vast amount of data that we have been able to collect, due to digitization and the internet. Harder and more ambitious problems in general artificial intelligence that that will enable agents to learn on their own and to act autonomously in the environment remain largely open. Initial breakthroughs include training an agent to play a complicated board game, or training agent to drive a car demonstrate that these problems require a lot of data even more data, even more compute than ever before, and possibly more than what we currently have available. This motivates several algorithmic challenges, namely how do we design algorithms that make the best use of the data that is available, and how do we design algorithms that are empirically and theoretically effective on the kinds of data that we often see in practice, for example, data with temporal dependencies and data that follow distributions that are hard to describe. This thesis proposes and analyzes a few algorithmic solutions along this theme, which is an important step to more reliably deploying general artificial intelligence into society
Diagnostic Raman spectroscopy : from liquid phase bacterial identification to co-incubation free antibiotic susceptibility testing
Keywords: Bioengineering.

Bacterial bloodstream infections account for over 40% of deaths in hospitals and are one of the most expensive medical conditions in the US. Globally, a child dies every 20 seconds due to infections. Current diagnostic methods are slow and costly, due to the long bacterial culturing step required for detection, identification, and antibiotic susceptibility testing. My work utilized Raman spectroscopy for rapid culture-free, sensitive, and specific bacterial identification and antibiotic susceptibility testing. Despite such promise, Raman's clinical translation has lagged due to reproducibility issues, bulky spectroscopy equipment needs and challenges in clinically suited sample preparation. This thesis demonstrates three major milestones to address these issues by using machine learning and nanophotonics. First, we develop a novel and robust liquid well setup for clinical sample handling with uniform Raman spectral enhancement using gold nanorods. Second, we demonstrate promising antibiotic susceptibility prediction on 100 patient derived E. coli samples with diverse response profiles to 15 major antibiotics. Third, we enable design of lower resolution more affordable spectrometers by implementing feature recognition approaches to isolate bands of spectra that are key for the accurate classifications. This work opens the door for clinical translation of novel spectroscopy based diagnostic tools for identifying bacterial infections, viral infections such as the current COVID-19 virus, early cancer detection and drug susceptibility testing by merging machine learning and nanophotonics
Differentiable and bilevel optimization for control in robotics
Keywords: Aeronautics and Astronautics.

In this dissertation, we investigate an up-and-coming class of mathematical programs, bilevel optimization, and how it can be leveraged to tackle the most pressing algorithmic challenges of control in robotics. In this dissertation, we give an overview of our work on bilevel optimization, where two mathematical programs are nested into one another, and our progress on leveraging this class of problems to move us closer to computationally tractable control of nonlinear systems. Specifically, we demonstrate how it is possible to design novel solution methods that utilize advances in automatic differentiation while retaining the benefits of state of the art constrained nonlinear optimization solvers. We also demonstrate how particularly challenging problems of nonlinear control such as planning through contact, adversarial learning of value functions, and Lyapunov synthesis can all surprisingly be tackled by explicitly addressing them as bilevel optimization problems
The differential roles of monocytes and neutrophils in inflammation-induced neurological dysfunction
Keywords: Immunology.

Patients with inflammatory disorders frequently experience neurological complications including epilepsy, depression, attention deficit disorders, migraines, and dementia. However, the mechanistic basis for these associations is unknown. Given that many patients are unresponsive to existing medications or experience debilitating side effects, novel therapeutics that target the underlying pathophysiology of these conditions are urgently needed. There are many hypotheses as to why peripheral inflammation and neurological conditions co-occur; however, there have been few mechanistic studies demonstrating a causal relationship, and investigations of these mechanisms have yielded contradictory results. Further, neuroinflammation can exist in patients with neurological disorders who do not have a diagnosed autoinflammatory disease. Because the vast majority of studies investigating the occurrence and impact of neuroinflammation on brain function have utilized models of primary neurological disease, it is difficult to uncouple the role of the primary neurological pathology from that of inflammation. To more clearly understand the mechanisms by which peripheral inflammatory disease causes neurological symptoms, it is therefore necessary to employ models of peripheral inflammation in which the brain is not initially involved. In this thesis, I utilize a murine colitis model in which colitic mice demonstrate enhanced seizure susceptibility to untangle the differential role of monocytes and neutrophils on neurological dysfunction in the absence of a primary neurological disorder. I further present investigations into the neuroinflammatory phenotype of patients with epilepsy and pediatric acute-onset neuropsychiatric syndrome (PANS)
A dipolar quantum liquid trapped in quasi-one dimension
Keywords: Applied Physics.

Highly excited eigenstates of interacting quantum systems are generically thermal; regardless of initial conditions, physical observables behave as they would in thermal equilibrium at times past the intrinsic dynamical time scale. Systems in which thermalization is absent are of great fundamental interest because they violate equilibrium statistical mechanics, and of technological interest because some quantum information encoded in these states evades decoherence. For the purpose of tackling this line of inquiry, ultracold neutral atoms in dilute gas offers a tabletop platform that is highly configurable, readily scalable, and well isolated from the environment. In this dissertation, I summarize our efforts of creating non-thermal states in a bosonic quantum gas of dysprosium, the most magnetic element, confined in quasi-one-dimensional waveguides. With long-range dipole-dipole repulsion that is two orders of magnitude stronger than alkali atoms, a highly excited super-Tonks-Girardeau gas is stabilized against collapse and thermalization. Stiffness and energy-per-particle measurements indicate that the system is dynamically stable regardless of contact strength. This metastability enables the cycling of contact interaction from weakly to strongly repulsive, then strongly attractive, and finally weakly attractive via two neighboring magnetically tuned collisional resonances. Iterating this quantum holonomy cycle constitutes an energy-space topological pump that creates a hierarchy of increasingly excited prethermal states. In addition to being the first systematic experimental investigation of a dipolar Luttinger liquid, the result opens up an unexplored regime of quantum control and may have wide-ranging implications for understanding the onset of chaos in near-integrable systems