VictorJuiz/thesis huggingface dataset:
Quantitative models of RNA thermodynamics for the design of riboswitches

RNA has recently emerged as the ideal candidate as a building block to create the next generation of molecular medicines, with the potential to specifically disable or manipulate the genes involved in disease. RNA's functional versatility is exemplified by the development of myriad novel RNA-based synthetic biology tools, including virus-detecting RNA devices, CRISPR/Cas9 genome editing, and RNA silencing. The ability to design RNA elements is central to gaining precise control over these systems and customizing them for use as RNA-based medicines. However, doing so relies upon a thorough understanding of the energetics of RNA interactions and computational models has proven to be inadequate to quantitatively account for the biophysical behavior of RNA molecules. This lack of predictive models has been a major barrier to the advancement of RNA-based technologies. Fortunately, modern high-throughput experimental techniques have enabled us to collect massive datasets of biophysical parameters, and recent innovations in deep learning have shown unprecedented power in the extraction of relevant features from such datasets. Herein, I present work that combines high-throughput datasets with deep learning models to advance quantitative models that enable better riboswitch design. First, I introduce a method that enables the design of a diverse range of riboswitches, allowing for varied ligand types and flexible constraints. Second, I describe a recurrent neural network model that quantitatively models the energetics governing RNA-RNA interactions to facilitate evaluation of candidate riboswitch sequences. Finally, I demonstrate a convolutional neural network model for directly modeling RNA motif energies to enable generalization to all biophysical predictions of RNA's behavior. Together, these results demonstrate new computational approaches to modeling the thermodynamics of RNA and enable new routes to the design of riboswitches for a new generation of RNA biotechnological and biomedical techniques.

Advisor: Das Rhiju
Altman Russ 
Greenleaf William James
Quantum acoustics with lithium niobate nanostructures

Phononic crystals are today the most sophisticated technology for manipulating acoustic waves on the surface of a chip. As such, ordinarily these devices can be described by the classical physics of elasticity; accessing a regime where their behavior is manifestly quantum mechanical has so far remained elusive. One way of reaching this regime would be to use a superconducting qubit to monitor and control the acoustics. Such `quantum acoustic' devices would have wide-ranging applications to quantum transduction, sensing, and information processing. In this thesis I present a collection of works that lay the groundwork for realizing this vision. First I introduce a technique for calculating coupling strengths between superconducting circuits (which include, but are not restricted to, transmon qubits) and arbitrary piezoelectric nanostructures. I then present the first demonstration of direct, resonant coupling between a superconducting circuit and a phononic-crystal-defect resonator (PCDR). This required developing a fabrication process that combines superconducting microwave circuits and suspended lithium niobate nanostructures in a fully integrated, on-chip platform. I then present results from a later generation of devices, including the demonstration of a transmon qubit and an array of PCDRs in the strong coupling regime. Finally, using these same devices I show we can generate a strong dispersive interaction that results in phonon-number-dependent splitting of the qubit spectroscopic line. This constitutes the first observation of the quantized energy levels of a nanomechanical oscillator, taking us back to the original scientific motivation of directly observing the quantum mechanical nature of a (confined) acoustic wave. Needless to say, such dispersive physics also has applications in quantum sensing and information processing. I conclude with a discussion on future experiments along that direction, including quantum nondemolition measurements of single phonons and generation of coherent state superpositions (a.k.a. cat states) of mechanical motion.

Keywords: Applied Physics.
Advisor: Safavi-Naeini Amir H. 
Fejer Martin M. (Martin Michael) 
Hayden Patrick
Quantum engineering with solid state nanophotonic systems

Photonics and optics are ubiquitous in our daily lives. By exploiting the quantum mechanical nature of light and matter, quantum optics holds promise to revolutionize communication, computing, metrology, and sensing. One class of quantum matter that interacts strongly with light is solid state color centers. These color centers are optically active lattice defects hosted in large bandgap materials such as diamond, which can serve as individual quantum nodes interacting in a quantum network through the emitted photons. In this dissertation, we explore a type of color center in diamond called silicon-vacancy (SiV) center, which presents a promising platform for implementation of quantum technologies. In particular, we will introduce the background on the photo-physics of SiV centers in diamond, and then walk through our journey studying this color center. We start by studying the optical properties of SiVs in nanodiamonds, and created hybrid diamond-silicon carbide (SiC) platforms to take advantage of the material properties of both diamond and SiC. Next, we discuss optical coherent control of optical transition of a single SiV center in a nanopillar array platform, which is a step towards scalable, on-chip quantum systems. Lastly, we discuss our efforts to create SiV-photon interface by embedding single SiV centers in diamond optical resonators. Using this platform, we demonstrate strong Purcell enhancement and cavity-enhanced Raman emission from a single color center, thereby achieving a large frequency tuning range of 100 GHz for Raman photon emission.

Keywords: Applied Physics.
Advisor: Vuckovic Jelena 
Fan Shanhui
Melosh Nicholas A.
Qubits and chaos : on the power of quantum packing lemma, universal codes and random circuits

This thesis explores two major topics, quantum Shannon theory and many-body quantum chaos. The first two chapters present results in quantum Shannon theory: a quantum multiparty packing lemma and universal quantum codes for the compound quantum channel. The last chapter uses random quantum circuits and numerical methods to study the onset of random matrix behavior in many-body quantum chaotic systems.

Keywords: Physics.
Advisor: Susskind Leonard 
Hayden Patrick (Patrick M.)
Shenker Stephen Hart
Racial/Ethnic Disparities in Severe Postpartum Depression

Importance: A recent report shows upward trends of maternal hospitalizations for depression. Given the expanding non-white population and two major national obstetric priorities in the United States to improve maternal mental health and reduce disparities in maternal care, there is a pressing need to examine whether racial/ethnic disparities exist in severe postpartum depression.
Objective: To determine whether the risk of hospital based care for postpartum depression differs according to maternal race and ethnicity.
Design: A retrospective cohort study
Setting: Analysis was based on the linked data containing hospital discharge and birth certificate data from the Office of Statewide Health Planning and Development (OSHPPD) in California.
Participants: Primiparous mothers aged 15-50 years who delivered in California hospitals between 2008 and 2012.
Exposure: Race and ethnicity
Main Outcomes and Measures: Hospital encounters for postpartum depression in the first 9 months after delivery
Results: Our cohort comprises 984,167 first-time mothers. The overall cumulative incidences over the first 9 months after delivery for a principal diagnosis and a principal or secondary diagnosis of depression are 15.9 and 53.6 per 10,000 deliveries respectively. Non-Hispanic black women had the highest incidence of postpartum admission for depression and non-Hispanic Asian women had the lowest incidence. When compared to the non-Hispanic white, the risk was 2-fold higher in black women while Asian and Hispanic women had a lower risk by 20-60%. In our sensitivity analyses, similar findings were observed after excluding women with antepartum depression, or under different scenarios where women underwent postpartum hospitalization or assessment only in the emergency room. Mediation analyses revealed maternal education and insurance status may contribute to the differences up to 80%.
Conclusions and Relevance: Non-Hispanic black women may be one of the most susceptible groups that warrant more attention to monitoring and/or treating the depressive symptoms at the community setting. Further research is needed to determine causative factors for these disparities and identify ways to improve mental health care and support for African-American women after delivery.

Keywords: Racial and ethnic disparities
Postpartum depression
Major depression after delivery
Hospital care for postpartum depression
Postpartum hospitalization for depression
Department of Health Research &amp; Policy Epidemiology &amp; Clinical Research School of Medicine Stanford University
Thesis
Advisor: Butwick Alexander James
Popat Rita Ashok
Radio-frequency penetrable positron emission tomography (PET) insert for simultaneous PET and magnetic resonance imaging

Combining medical imaging modalities that measure complementary, multi-parametric information such as anatomy and biochemistry about a patient's disease has been proven to provide tremendous clinical value. In recent years, hybrid positron emission tomography (PET)/magnetic resonance imaging (MRI) has risen to the cutting edge of multi-modality medical imaging technology. Unfortunately, the cost of purchasing a commercially-available integrated PET/MRI scanner is unaffordable for most healthcare providers, therefore limiting the dissemination of PET/MRI and its long-term potential. This dissertation presents the development of a radio-frequency (RF)-penetrable PET insert that can be placed into any existing MRI system to acquire simultaneous PET and MRI images. The insert approach can reduce the cost barrier for an existing MRI site to offer PET/MRI scans by leveraging its existing infrastructure. A brain-sized prototype PET insert with a 2.8-cm axial field-of-view (FoV) has been developed and its performance (energy, timing, spatial, system stability, and count rate) evaluated. Tomographic images of resolution and 3-D Hoffman brain phantoms have been acquired with the PET insert both outside and inside and simultaneous running with a 3T MR system. A second-generation RF-penetrable PET insert with an extended axial field-of-view (16 cm) and a coincidence timing resolution (< 300 picosecond full-width-at-half-maximum) capable of time-of- flight (TOF) measurement is currently under development. Two out of a total of sixteen detector modules have been developed and their performance evaluated. A novel PET detector design based on a phoswich configuration capable of both excellent coincidence timing resolution and photon depth-of-interaction measurements has also been developed and tested to improve spatial resolution uniformity of the insert.

Keywords: Applied Physics.
Advisor: Levin Craig 
Miller D. A. B. 
Glover Gary H.
Radiomic Biopsies: Segmentation Invariant Feature Extraction for Quantitative Imaging

Radiomics aims to radically improve cancer detection through automating the evaluation of potentially cancerous masses in medical images. Early evaluation of cancer is crucial to treatment decisions and to eventual survival outcomes. Radiomics approaches compute quantitative descriptors (shape, size, intensity, texture, etc.) of regions of interest in images and then build predictive statistical models from these numerical data. Computational techniques require radiologists to define regions of interest (ROI) in a process called segmentation. Radiologists often disagree on the exact outline of ROIs, which leads to unreliable data and poor models. We propose a new paradigm called “radiomic biopsies” that focuses on a spherical sample of the ROI. For intensity and texture features robust to variation in segmentation, radiomic biopsies produce samples of these features without the laborious segmentation process. Radiomic biopsies have proven to produce reliable features, but their application to predictive modeling has never been tested. To explore the application of radiomic biopsies to different disease types, we conducted a simulated case study in predicting non-small-cell lung carcinoma recurrence from chest CT scans. We then designed and implemented a novel 3D Radiomic Biopsy Tool for differentiating oncocytoma and chromophobe renal cell carcinoma. Across both studies, we showed that radiomic biopsies perform similarly to full segmentations on proven predictive tasks and can successfully differentiate subtypes in novel tasks.

Keywords:  Department of Biology
radiomics
radiomic biopsy
machine learning
chromophobe renal cell carcinoma
oncocytoma
non-small-cell lung carcinoma
recurrence
Biology
Thesis
Advisor: Napel Sandy
Gozani Or
Raman Spectroscopy Deconvolution Using Machine Learning

Proper characterization of a tumor is essential for informing treatment and assessing prognosis for the patient. The concentration of certain biomarkers, presence or absence of specific mutations, and even the pattern of distribution of biomarkers throughout the tumor can be extremely important in determining the aggressiveness of the tumor. For example, tumors that are more homogenous tend to be less aggressive and have a better prognosis than those that are more heterogenous. Therefore, we seek to image multiple biomarkers in vivo using targeted dyes.
However, imaging the tumor multiple times in series without an invasive biopsy is much too time consuming. Usually, for these targeted dyes, imaging occurs 5-7 days after the imaging agent is administered, meaning that imaging any more than one or two biomarkers would be prohibitively lengthy, possibly affecting timely treatment of the tumor. Therefore, we can utilize unique surface-enhanced resonance Ra- man scattering (SERRS) particles to target distinct biomarkers. In order to determine the concentration of each particle at each point, the individual spectra need to be deconvolved from the multiplexed spectra.
Conventional methods for separating the spectra, nonnegative least squares (NNLS), have been successful for low numbers of spectra. However, NNLS must calculate the pseudoinverse, and as the number of spectra increases, the condition number for that matrix increases quickly. Thus, past five spectra or so, using NNLS to deconvolute the spectra becomes untenable. Thus, we aim to use machine learning as an alternative to NNLS, with the potential to expand spectral deconvolution to more spectra accurately and quickly during run time.

Keywords: Raman spectroscopy
Convolutions (Mathematics) > Data processing
Machine learning > Mathematical models
Text
Thesis
Rational design of ribozyme switches through big data and machine learning

RNA switches are a broad class of functional RNAs that support conditional regulation of gene expression. These genetic switches have broad application in creating biological systems responsive to changes in their environment - a situation that occurs frequently in the fields of metabolic engineering, environmental sensing, and therapeutics. These switches gain their function from both their structural conformation and their sequence of functional regions, implying that an improved elucidation of relationships between structure, sequence, and function can be used to improve our capacities for de novo design. One type of RNA switch is the ribozyme switch, which changes its self-cleavage activity based on the presence of the target ligand. Here we leverage sequencing data and machine learning to relate the sequence and structure of biological molecules to their function and develop models that predict the function of novel ribozyme switches. In the course of our work, we generated data on the activity of hundreds of thousands of ribozyme switch sequences. Using automated structural analysis and machine learning, we leveraged this wealth of sequence data to develop predictive models that forecast the in vivo activity of a functional RNA sequence using the sequence of functional regions. We have used these models for the de novo design of novel ribozyme switches that exhibit changes in expression activity upon introduction of a target ligand. Our work demonstrates how to use computational tools to turn RNA sequences into quantitative features that represent important sequence and structural motifs and how to use those features to develop models that can learn how the interactions of these motifs translates to device function. We have integrated the model for predicting switch activity into design software that will provide a broadly accessible tool for designing custom ribozyme switches.

Keywords: Bioengineering.
Advisor: Smolke Christina D. 
Altman Russ 
Das Rhiju
Ready, Set, Bio (Jet): A Policy Recommendation for Indian Commercial Aviation

India is a country with tremendously rich culture, history and economic growth. India’s aviation industry is the fastest growing in the world. However, it is also responsible for a large share of overall greenhouse gas emissions – an especially relevant problem in a country where a many people are dying due to air pollution every year. This proposal contains a cost-benefit analysis of adopting biofuel, also known as Sustainable Aviation fuel (SAF), in the commercial aviation sector as a replacement to Aviation Turbo Fuel (ATF) which is currently used. A thorough investigation shows that the adoption of biofuel will significantly reduce the aviation sector’s contribution to greenhouse gas emissions, specifically CO2, HC, CO and SOx emissions, under certain taxation assumptions. This will allow India’s aviation industry to play a role in reducing air pollution and ensuring that the country’s population remains healthy and is able to contribute to India’s economic growth. The spillover benefits of adopting biofuels also exceed the costs, therefore contributing positively to work-force participation and productivity.

Keywords:  Public Policy Program
Stanford University
Humanities and Sciences
Public Policy Program
biofuels
civil aviation
emissions
traditional fuel
routes
short haul
medium haul
long haul
taxation structure
jatropha
Thesis
Advisor: Bruce Cain
The real-alternative school paradox : a California continuation high school's struggle to be something different in a universe of real schools

Though they serve upwards of 100,000 students in a given school year, California continuation high schools remain marginal to both educational research and practice. As California's primary dropout prevention response for "at-risk" high school youth, continuation high schools allow students acquired into persistent school failure a second chance at obtaining a high school diploma. They have served various functions throughout their one-hundred-year existence, but remain largely invisible, acting as convenient depositories for the problems created by an American schooling system rooted in competition and social sorting. My dissertation examines the paradoxical question of what it means to be a "real-alternative" school. It pursues answers via the presentation of an institutional history of the California continuation high school system, through theoretical examination of what it means—in the words of Mary Metz (1989)—to be and not be "real school, " and via a descriptive case study of Bridgeview High School, a continuation school whose staff attempts to balance the pull of being "real" with the promise of being "alternative" through the creation and practice of social justice inspired project-based learning. The study concludes with practical recommendations for educational researchers, state and district-level policymakers, and an impractical recommendation for teachers: to teach absurdly.

Keywords: Education.
Advisor: Labaree David F.
Gordon Leah N.
McDermott Ray (Raymond Patrick)
Reciprocity and legislative organization

I explore the implications of a novel assumption about legislators' preferences: that they, like most human beings, have a preference for reciprocity. That is, they like to help those who have helped them in the past and harm those who have harmed them in the past for its own sake. The first chapter highlights that the preference for reciprocity implies congressional parties face a tradeoff between giving their leaders the resources necessary to produce collective goods and ensuring that ineffective leaders can be removed from office. The second chapter shows that the legislature faces a tradeoff between ensuring allocative efficiency by using discretionary authority and minimizing costly interpersonal conflict through deference to rules. The third chapter provides empirical evidence for the preference for reciprocity by studying how legislators behave immediately before they exit the chamber.

Keywords: Business.
Advisor: Krehbiel Keith
Broockman David E.
Moe Terry M.
Reconsidering the electron-phonon problem

One of the most important interactions in a solid is that between electrons and the vibrational modes of the underlying lattice (phonons). This interaction determines many of the familiar features of conventional metals, e.g. resistivity at elevated temperatures, as well as their superconducting transition temperature, Tc. From a theoretical standpoint, the electron-phonon problem was considered to be solved 60 years ago, thanks to the pioneering work of Migdal and Eliashberg. We have revisited this problem using a combination of numerical and analytic techniques, and I will show that this "solved" problem harbors basic features that have not been properly understood. I will show that the Migdal-Eliashberg approach works remarkably well for sufficiently weak coupling but breaks down entirely as the coupling strength is increased (even while the nominal condition for its validity remains intact), giving way to a qualitatively new regime of strong coupling physics. I will discuss the implications of these results for superconductivity and argue they suggest an approximate, universal upper bound on Tc in conventional metals.

Keywords: Physics.
Advisor: Kivelson Steven 
Hartnoll Sean
Raghu Srinivas