VictorJuiz/thesis huggingface dataset:
Advances on chip-in-cell wireless platform for continuous monitoring of physiological parameters in single cells

Cells are the smallest functional unit of the human body, which contains 37 trillion cells on average. Current technology for intracellular sensing does not yet allow us to follow biological processes inside living intact cells on a continuous, real-time basis. Information about such processes is invaluable for understanding complex diseases, such as cancer, immune-pathological, and cardiovascular disorders. For example, the acidity in the micro-environment inside cancer cells can undergo transitory stages that last up to a few hours and are difficult to detect by non-continuous detection methods. However, most of today's diagnostic tests and medical monitoring operate at the body, organ, or tissue level. Continuous detection of cellular activities can significantly advance our knowledge of biology and disease and enable new discoveries in early diagnostics and tailored therapeutics. This dissertation presents the exploratory work on a new approach toward intracellular sensing with chip-in-cell (CHIC) devices. Each CHIC device has a wireless front-end to communicate data and a sensor interface responsive to a specific stimulus. We present the design, fabrication, and characterization of the wireless front-end with a radio-frequency identification (RFID) and a corresponding external detector. The RFID diameter is 22 μm and can be naturally internalized by certain classes of living cells. This is the world's first cellular-implantable RFID. The detector can identify RFID presence and distinguish RFID capacitance changes via resonance frequency shifts. Requirements on system-level integration with biological microfluidic platforms are included. To detect biologically meaningful parameters, a pH-responsive hydrogel is integrated as an example prototype CHIC sensor interface. The hydrogel is fabricated onto interdigitated electrodes. Capacitance shifts at gigahertz range are characterized in physiologically relevant pH ranges from 6 to 9, with steps as small as 0.25 pH identified. Overall, the CHIC sensors are addressable to individual cells, bio-compatible for long-term observation, and sensitive to specific chemical changes; a solid step toward continuous real-time intra-cellular sensing.

Keywords: Electrical Engineering.
Advisor: Wong Hon-Sum Philip
Akin Demir 
Poon Ada Shuk Yan
Aftershock : aid, Ebola, and civil society in West Africa

Despite significant international aid to local and national organizations and their role in community education and mobilization, they are often marginalized during crisis. International humanitarian organizations cite the lack of technical capacity and the political nature of local organizations, as reasons to not work with them. Concurrently, the nonprofit sector experiences increased demands for metrics and specialization, assumed to reflect organizational capacity. As a result, nonprofit organizations dependent on foreign aid slowly move away from the grassroots, and towards professionalization and specialization. However, does the professionalization of civil society organizations, through an over-reliance on metrics, blueprints, and specialization, restrict organizational capacity to generate civic engagement? In this study, I explore a major paradox in terms of how international NGOs work with local organizations in developing countries by examining the 2013-2016 West Africa Ebola response. I address the lack of data on domestic organizations in Sierra Leone and Guinea by collecting my own unique data through extensive fieldwork. As a consequence of the expansion of bureaucracy by international organizations, I find that organizational professionalism potentially restricts broader civic engagement in two aid-dependent countries with different levels of foreign funding, Guinea and Sierra Leone. The evidence for these conclusions comes from a combination of interviews with domestic organizations in Sierra Leone and Guinea, automated text analysis of international humanitarian organization strategy reports, and a dataset merging cross-national survey data on social capital and WHO Ebola mortality data.

Keywords: Education.
Advisor: Ramirez Francisco O. 
Carnoy Martin 
Wotipka Christine
Aggregates in datalog

Datalog is a logic programming language that is often used as a query language for deductive databases, with many practical applications, ranging from declarative networking to data integration to computational law. While Datalog is highly expressive and easy to understand, there is no agreed-upon way of representing aggregate queries, i.e., queries that provide summary values of sets of data. Over the years, Datalog has been extended in various ways to address this deficiency. Unfortunately, these extensions are either not expressive enough or do not deal with incremental updates or folding of aggregate queries in a general setting. We present a Datalog extension, called DatalogA, that provides a representation for aggregates while eliminating the above weaknesses. We show that DatalogA is more expressive than prior aggregate extensions of Datalog. Keys to the increased expressivity in DatalogA are the inclusion of lists and sets as first class citizens in the language, a general aggregation operator that allows us to characterize sets of objects with specified properties, and the use of Prolog-like rules to define aggregation functions on these sets (e.g., count, sum). We present an algorithm to incrementally maintain materialized DatalogA views in response to changes to the underlying data. We show that the taken to incrementally maintain DatalogA views can be decreased by materializing additional views and tracking the number of tuple derivations. We also present an algorithm to fold DatalogA queries using views. We characterize two classes of query folding problems for which our algorithm generates maximally contained query answers.

Advisor: Genesereth Michael R.
Ré Christopher 
Waldinger Richard
Algorithms and generalization for large-scale matrices and tensors

Algorithms research has undergone a paradigm shift in the past decade, from the challenge of processing data to understanding data. The need to process large-scale data raises the question of designing simple and efficient algorithms, that can be adaptive to a rich variety of settings. Once the computational challenge is addressed, we are faced with the next question of how to understand, or learn from the data. A fundamental question for learning from the data is generalization, i.e. the ability to make predictions on unknown data. My thesis focuses on studying formats of data including graphs, matrices and tensors, that are rich enough to model a large family of real world settings, yet analytically tractable. In the first part, we study the generalization performance of commonly used gradient-based optimization methods, motivated by their huge success in learning neural networks. First, we study gradient based optimization methods and their generalization performance (or sample efficiency) in over-parameterized matrix models. Our result highlights the role of the optimization algorithm in explaining generalization when there are more trainable parameters than the size of the dataset. Next, we consider the problem of predicting the missing entries of high-dimensional tensor data. We show an interesting representation-sample trade-off in the choice of tensor models for fitting the data. In the second part, we study algorithms for processing large-scale social and information networks. Lots of work has been dedicated to dealing with large graphs in the theory community. However this line of work has typically focused on worst-case analysis. On the other hand, real world networks enjoy many interesting structures as discovered over the past two decades. My thesis has combined ideas from both sides, by incorporating network structures into the algorithm design process. I will present several results on static or dynamic data structures for shortest path and personalized PageRank computation, as well as modeling trust on decentralized networks with expansion properties.

Advisor: Goel Ashish 
Valiant Gregory 
Charikar Moses 
Ma Tengyu
All-cause Mortality in Danish Multiple Sclerosis Patients Following a New Diagnosis of Cancer: A National Registry Analysis

Multiple sclerosis (MS), a chronic, autoimmune neurological disease, typically has a prolonged natural course, conferring only a modest decline in life expectancy in treated patients. As such, MS patients remain susceptible to death from other diseases, including cancer. Although MS patients appear to be at a slightly decreased risk of being diagnosed with cancer, the prognosis of MS patients following a new cancer diagnosis has not been fully explored. Immunological mechanisms, interference with adequate treatment, co-morbidities, and diagnostic delays could all confer a worse prognosis compared to cancer patients without MS. In this population-based study using linked administrative datasets from Denmark, we compared the all-cause mortality of MS patients with a set of matched population controls, following a first-time diagnosis of cancer. We identified 2,178 patients diagnosed with MS between 1975-2015 in the Danish National Patient Registry and matched to 18,589 non-MS patients (up to 10 matches per MS case) on age, sex, year of cancer diagnosis, type of cancer, and cancer stage. All-cause mortality in a Cox regression model adjusting for all of the above factors demonstrated increased mortality of MS patients compared to the matched controls (HR 1.38, 95% CI 1.28-1.47). When stratifying the Cox regression models according to type of cancer, the greatest mortality differences were observed for melanoma and other immunologic- or infectious-related cancers. No mortality difference was observed for hematological cancers or for lung cancer. We conclude that MS patients have increased mortality following cancer diagnosis compared to non-MS patients. We hypothesize that immunological mechanisms that promote tumor progression or interfere with immune-based treatments may contribute to poorer cancer prognosis in MS patients, although we cannot exclude that increased deaths associated with MS may have impacted the observed survival differences.

Keywords: Multiple sclerosis > Epidemiology
Cancer > Epidemiology
Text
Thesis
Advisor: Henderson Victor
Tamang Suzanne
Alone on The Ridge: Life in Paradise after the Camp Fire

The Camp Fire took 86 lives and 14,000 homes in a town nicknamed “The Ridge” that sits in the foothills of the Sierra Nevada Mountain range. Thousands of people lost everything and are trying to find ways to start over whether it’s through rebuilding, moving far away or finding a home in a neighboring town. But the Camp Fire also left people and structures behind. About 1 percent of the homes in Paradise remain standing, most of which are reoccupied, said Paradise Mayor Jody Jones. The fire caused over 1,000 businesses to immediately close for months, but the Chamber of Commerce estimates 150 have now reopened.
This story paints a picture of what life is like in a town trying to clean up and rebuild. It has four sections that detail this reality: a couple who live in a mostly-empty neighborhood, a business owner fighting to stay open, a church that has become a community center and Paradise Intermediate’s experience finishing out the school year in an emptied hardware store and plans to return to Paradise next August.
Those left face a struggle against undrinkable water. They fight to get insurance coverage for their homes since almost every major insurance company has stopped reinsuring houses in Paradise. They drive through the charred landscape getting stuck in the construction or losing their way without the familiar landmarks to guide their paths. Everything has changed on The Ridge.

Keywords: Camp Fire
environment
MA Journalism
Stanford
Paradise
water
insurance
fire
natural disasters
Thesis
Advisor: Zacharia Janine
Migielicz Geri
Alveolar stem cells and niches in aging, injury and evolution

The lung is the primary organ of respiration and the site of diseases that cause 7 million deaths annually. Although relatively understudied at the turn of the century, the field of lung stem cell biology is experiencing a renaissance due to advances in the ability to monitor and genetically mark and manipulate cells in mice. This dissertation focuses on two major topics in the field. In the first part, I focus on the identification of the alveolar stem, its niche and the signals regulating alveolar stem cell identity. I show that alveolar stem cells display active Wnt signaling, which imparts upon them the ability to proliferate and inhibits their transdifferentiation. Wnt signaling is activated by a juxtacrine signal from a single, neighboring fibroblast. I also describe how alveolar stem cells react to injury and the increased need for regeneration. I show that quiescent AT2 cells are recruited as emergency stem cells during injury through a transient autocrine source of Wnt signaling. I then describe the probing by single cell transcriptomics of the molecular underpinnings of alveolar stem cells and how they differ from other alveolar epithelial cells. Last, I describe a cellular transplantation assay developed for future study of these cells. The second part of my dissertation focuses on creating molecular cell atlases of mouse and human lungs. As a member of the Tabula Muris consortium, I led a team that captured lung tissues and analyzed data for the lung, spleen and kidney. I then describe my collaborative effort to create a molecular cell atlas of the human lung by single cell RNA sequencing (scRNAseq), for which we sequenced > 70,000 cells and molecularly characterized 58 molecular cell types including 14 novel populations. I then use these data to analyze mammalian lung evolution at single cell resolution. Finally, I describe my efforts to discover the molecular program responsible for the unique flat morphology of AT1 cells, which comprise one side of the gas exchange surface and enables efficient exchange of gases.

Keywords: Biochemistry.
Advisor: Krasnow Mark
Artandi Steven E. 
Desai Tushar
Rohatgi Rajat
Amino Acid Withdrawal Engages ATF4-mediated Transsulfuration and Protects Cancer Cells from Oxidative Death

Ferroptosis is a form of regulated cell death which can be triggered by starvation of the amino acid cysteine. Its role in human physiology is poorly understood, but evidence suggests some tumor suppressors act by restricting cellular import of cysteine, thus inducing ferroptosis and preventing cancer growth. Here, I show that the supraphysiologic concentration of arginine in culture medium promotes sensitivity to ferroptosis, and that tumor-physiologic levels of arginine do not permit ferroptosis induction by cysteine starvation. This was observed in a broad panel of cancer cell lines, but not in two non-cancer cell lines. Mechanistically, arginine withdrawal induces Activating Transcription Factor 4 (ATF4) expression, which drives de novo cysteine biosynthesis via the transsulfuration pathway. Treatment with the transsulfuration inhibitor propargylglycine abrogated the protective effects of arginine starvation and direct ATF4 stimulation with thapsigargin also suppressed ferroptosis. Thus, cysteine interconversion from methionine downstream of cystine import suppresses ferroptosis. These data suggest a mechanism by which ATF4 permits cancer cell evasion of tumor suppressive, non-apoptotic cell death. Inhibitors of ATF4 or transsulfuration should be evaluated for their ability to slow tumor growth and induce cancer cell ferroptosis in in vivo models.

Keywords:  Department of Biology
Biology
ferroptosis
cell death
tumor suppression
amino acids
mTOR
ATF4
transsulfuration
Thesis
Advisor: Dixon Scott
Gozani Or
An analysis of energy loads using machine learning to examine zero net energy and all-electric communities that have solar and energy storage

Twenty-first century technology is driving profound changes in the electric utility business model. At the same time, the threat of global climate change necessitates the adoption of these technologies. California and the Netherlands have been at the forefront of the adoption of solar generation and energy storage, and, for this reason, these two regions present a unique opportunity to study the adoption of these new technologies and their impact on the existing utility grid. In the research presented here, I examine two prototype communities that have integrated solar generation and energy storage. Furthermore, we apply a cutting-edge unsupervised machine learning statistical analytics platform called VISDOM-REx to gain particular insight into the differences between these communities and a standard community. By examining the net electrical consumption and production of each individual house over each hour of a year, I present a number of common electricity consumption profiles. These load profiles make it apparent when solar generation or energy storage provides a distinct advantage to the consumer or the grid operator. Certain inefficiencies in the current system are also presented and methods to overcome these are suggested. In sum, by applying machine learning to the initial data coming out of the first advanced energy communities, I have provided electric grid operators a much clearer picture of how the rollout of solar and energy storage on a much larger scale might proceed.

Advisor: Fischer Martin
Weyant John P. (John Peter) 
Rajagopal Ram
Analyzing the Impact of Advanced Maternal Age and Inflammation on Pregnancy Outcomes

Advanced maternal age (AMA; ≥ 35 years in humans) is associated with an increased risk of severe pregnancy complications, including miscarriages and stillbirths. These risks are also elevated in circumstances of infection and elevated inflammatory signaling in the mother. Given that older individuals frequently develop chronic inflammation that worsens with age, it is possible that the immune system may contribute to poor pregnancy outcomes in AMA mothers. This study characterized the immunological profiles of young and aged pregnant mice in order to identify the role of inflammation on AMA pregnancy outcomes. Older pregnant mice (aged 6–8 months) exhibited prolonged and elevated cytokine levels compared to young controls (aged 2–4 months) following mid-gestation challenges of either saline or lipopolysaccharide (LPS), a bacterial mimetic. Analyses of embryonic development from these pregnancies later demonstrated that the percentage of inviable, hemorrhaged or otherwise affected fetuses was significantly increased in litters from AMA dams. This effect tended to be exacerbated following a mild immune challenge by LPS. Finally, embryonic brains from AMA litters tended to exhibit reduced neural stem cell proliferation in the neocortex compared to those from control litters, and placentas from AMA dams tended to display greater degrees of necrosis than placentas from young dams. These findings not only highlight inflammation as a potentially relevant risk factor in AMA pregnancies but also implicate maternal age as a possible confounder in studies on maternal immune activation.

Keywords:  Department of Biology
Advanced maternal age (AMA)
maternal immune activation
pregnancy complications
inflammation
Thesis
Advisor: Palmer Theo
McConnell Susan
Anisotropic full-waveform inversion with pore pressure constraints

Anisotropy can arise as an intrinsic property of rocks (for example shales) and minerals (for example clay), but most commonly as an effective property of layered or fractured media. Anisotropy provides a better description of the subsurface and seismic wavefields. To include anisotropy in Full-Waveform Inversion (FWI), however, one needs to extend the search space and invert for not only a single velocity model but also anisotropic parameters that describe how velocity varies with direction. Sadly, there is often an ambiguity in determining whether a change in the seismic data recorded on the surface comes from a perturbation in velocity or anisotropic parameters in the subsurface. This means there may be a number of subsurface anisotropic models that, for a given objective function, t the observed data equally well. To address this uncertainty problem in anisotropic FWI, I constrain the inversion with bounds derived from a rock physics workflow that models the relationship between velocity and pore pressure. This workflow combines various sources of data such as well logs, drilling data, basin history, and shale diagenesis to build velocity templates which are consistent with our rock physics models and understanding of pore pressure behavior in the subsurface. In a 3D eld data application, enforcement of the derived constraints improves the final seismic image in terms of focusing and continuity of reflectors and better flattens the common image gathers we use to assess velocities. In addition, the resulting anisotropic velocity models are more physically plausible and better suited for subsequent exploration workflows such as pore pressure prediction and geo-hazard prevention.

Keywords: Geophysics.
Advisor: Biondi Biondo
Levin Stewart A.
Mukerji Tapan
The application of Bayesian inference to identify the energy saving potential for energy efficiency measures through electric utility interval data and weather information

Utility ratepayer-funded energy efficiency programs are the prevalent method for reducing the energy consumption of single-family homes. These programs rely on estimating the electric energy efficiency savings potential of various EEMs (e.g., added insulation or weatherization) to determine their cost-effectiveness, which is the main criterion for determining the success of a program. Program managers lack a cost efficient and evidence-driven method to infer the energy savings potential of a specific energy efficiency measure in a single-family home. This dissertation presents a cost-efficient and empirical evidence-driven method for inferring the energy savings potential of an energy efficiency measure by analyzing moments when the inefficiency of the relevant characteristics is most prominent. This Inefficiency Estimation Method selects homes using only readily available information, specifically 15-minute electric utility data (i.e., interval data) and hourly ambient weather data. The cornerstone of this method is the application of an objective Bayesian model. The use of Bayesian statistics has several benefits over classical statistical methods. One benefit is the ability to improve the accuracy of the analysis by adding new information into the model through informed objective priors. I compared the results of my Inefficiency Estimation Method to selecting homes based on annual energy consumption and selecting homes based on annual A/C energy usage, the currently available empirical methods. The comparative metrics for the methods were energy savings and cost-effectiveness. The inefficiency estimation method calculated 2X more energy savings and 30% better cost-effectiveness for A/C unit replacement than the existing methods. For selecting homes that need insulation, the inefficiency estimation method calculated 18% more energy savings and approximately the same cost-effectiveness as compared to the A/C energy usage method. These results show that it is feasible to target homes by inferring the energy savings potential of specific energy efficiency measures with interval data and weather information using Bayesian inference. This selection method provides energy efficiency program managers an empirical evidence-based way for household level targeting that has the potential to increase such programs' energy savings and cost-effectiveness. Future work can investigate the ability of the Inefficiency Estimation Method to infer the energy savings potential of other energy efficiency measures.

Advisor: Fischer Martin
Jain Rishee 
Rajagopal Ram