VictorJuiz/thesis huggingface dataset:
A longitudinal analysis of the human immune repertoire [electronic resource]

The human immune system is crucial for maintaining our health throughout our entire lives. In spite of its importance, we still do not fully understand 1) what a healthy immune baseline is, 2) how this baseline changes over time, 3) how the healthy baseline responds to natural infection, and 4) how class switching plays in to creating and maintaining this healthy baseline. To this end, I analyzed the immune repertoire dynamics of a healthy individual. I accomplished this by profiling the B cell receptors of a relatively healthy individual through two periods of infection to better understand how a healthy immune repertoire responds to everyday life versus an infectious challenge. I found that a healthy immune repertoire fluctuates within predetermined patterns during periods of infection and that longitudinal profiling allows the determination of a personal baseline immune repertoire that can be used as both a personal standard for future medical care and a public resource for future studies.

Keywords: Genetics.
Advisor: Snyder Michael
Fire Andrew Zachary advisor. Thesis advisor
Salit Marc L. advisor. Thesis advisor
Long-range sensitization in the vertebrate retina

The natural visual world is highly structured. Physical objects and the speeds at which they commonly move induce strong correlations in both space and time. A growing body of recent work suggests that the retinal ganglion cells (RGCs), the output of the retina, act as a set of detection channels, each tuned to specific and ecologically-relevant visual features. In this view, classical theories of signal detection indicate that a feature detector striving for optimality ought to use the long-range information inherent in natural scenes, as a feature's presence in one location provides statistical expectations about its presence elsewhere. One common example of such a visual stimulus is an object with similar texture across its extent. In this work, I show that RGCs change their processing of local stimuli in response to stimulation in the periphery of the visual field. I first describe experiments which show that strong visual input in the periphery, far outside the classical receptive field, increases the local sensitivity of RGCs, here called long-range sensitization. This in- creased sensitivity also supports improved discrimination of similar stimuli, as well as detection of low-contrast stimuli. I then show that this process exhibits a simple form of pattern-specificity: RGCs increase their local sensitivity more when the peripheral and central stimuli share spatial frequency statistics, as is the case in fine textures. Using a combination of pharmacology and modeling, I then show that long-range sensitization requires input from wide-field, nonlinear inhibitory interneurons. Lastly, I present evidence that long-range sensitization is seen in a a novel perceptual effect in humans, in which strong surround stimulation improves the detection of a small central stimulus. The results presented here establish that the retina uses long-range statistics of the visual world to better encode local visual features, and present how such improved stimulus encoding may play a role in perception.

Advisor: Baccus Stephen A. 
Druckmann Shaul 
Ganguli Surya
Norcia Anthony Matthew
The low-mass limit : dark matter detectors with eV-scale energy resolution

The SuperCDMS SNOLAB experiment will be a 20-kg scale Si and Ge direct dark matter detection experiment designed to probe down to 300 MeV in dark matter (DM) mass through DM-nucleus scattering and 500 keV in DM electron scattering. In order to reach these low masses with appreciable sensitivity to dark matter, it needs to achieve very low energy resolution (≤ 10 ev) for nuclear recoils in both detector materials, which will be achieved using a new detector design and operating mode, CDMS HV. This detector is designed to operate at a bias of 100V to convert charges liberated in our detector targets to into phonon energy in order to resolve individual electron-hole pairs. This has never before been achieved in a kg-scale detector. In this thesis, I cover three elements of the design of the CDMS HV detectors. I discuss the detector physics controlling how charges and phonons are generated in our detector crystals, com- paring theory to results of recent experiments carried out at Stanford. I move on to describe the operating principles of our phonon-mediated charge readout, as well as the design of the CDMS HV detector. I then describe the performance tests of early CDMS HV prototypes in conjunction with the SuperCDMS SNOLAB electronics, and discuss the path towards achieving single electron-hole pair resolving detectors at the kg-scale given the performance obtained thus far. As a result of these tests, we were able to refine our noise and sensor dynamics models, and develop new metrics for diagnosing non-ideal sources of noise to aid in reducing coupling of the external environment to our detectors. In order to study the microphysics of phonon and charge production in our target crystals, we fabricated a number of gram-scale devices with various sensor designs in order to separate sensor and environmental effects from intrinsic crystal properties. These devices provided the first successful demonstrating of using voltage to amplify charge energy by production of phonons (the Neganov-Trofimov-Luke effect) in order to resolve electron-hole pairs, and opened up a new regime of dark matter and photon science at the gram-scale that we are just beginning to explore. A first dark matter search was carried out with one of these gram-scale devices, producing world-leading limits on electron-recoiling dark matter between 0.5 and 5 MeV in dark matter mass for multiple form factors. This device achieved a phonon resolution of 10 eV, allowing a single gram-day of exposure to rival kg-days of exposure in the competing liquid-noble based electron-recoil search.

Keywords: Physics.
Advisor: Cabrera Blas 
Partridge Richard
Graham Peter (Peter Wickelgren) 
Gratta Giorgio
Low-power and highly-stable encapsulated MEMS-based clocks

In today's economy, timing references are everywhere from smart-phones to cars. Timing references or clocks provide a stable signal that serves as a reference for communication between digital systems from processors within a smart-phone to cellphones on a wireless network. In the past decades, quartz clocks have dominated the field of portable timing references, and their size and power consumption have limited the design specifications of electronics and other devices. Recently, encapsulated MEMS-based clocks have entered this market, due to their small size, low cost and low power consumption. The main challenge of MEMS-based clocks is that they are fabricated with silicon, which has elastic properties with a strong linear temperature dependence that contributes to lower frequency stability over temperature. Previous research aimed to solve this problem with temperature compensation techniques including passive methods such as changes in the wafer doping and resonator orientation, and active methods such as ovenization. These methods aimed to reduce the linear relationship between the frequency and temperature characteristic of silicon resonators. These efforts are expanded in this work to demonstrate novel dual-mode MEMS-based clocks with in-chip device layer micro-oven that controls the temperature of the resonators with 10x reduced power consumption and provides more than 30x improved frequency stability when compared to recent results with a micro-oven embedded in the encapsulation layer. This device layer micro-oven enables the correction of ambient temperature fluctuations and achieves long-term frequency stability over temperature of +/- 1.5ppb, which is better than the stability of TCXOs and competes with state-of-the-art quartz OCXOs and miniature atomic clocks, while requiring much less power. This work also explored techniques to improve the yield of devices with micro-ovens embedded in the device layer and the thermal effects of ovenized devices on other sensors located in the same chip.

Keywords: Mechanical Engineering.
Advisor: Kenny Thomas William 
Howe Roger Thomas 
Senesky Debbie
Lymphocyte differentiation trajectories in human health and cancer [electronic resource]

Cellular differentiation is a continuous and coordinated process that integrates outputs from complex cell signaling networks to make decisions about cell identity, proliferation, and death. Traditionally, cellular differentiation has been described as a series of discrete populations. More recently, emerging technologies for deep single-cell phenotyping, such as mass cytometry by time-of-flight (CyTOF), have enabled us to describe cellular differentiation as a continuous process, where individual cells are aligned onto a trajectory based on their phenotypes. In Chapter 2, I build on prior work from our and other labs to develop experimental and computational methods in order to: (1) build a single-cell map of human naïve T-cell differentiation during expansion for adoptive cell transfer therapy for cancer as a model system; (2) discern division state-dependent from time-dependent processes on this map; (3) examine regulatory signaling on this map to rationally define a strategy to steer differentiation towards a clinically desirable T stem cell memory subset. In Chapter 3, I demonstrate the clinical utility of a known cell differentiation trajectory. Specifically, by aligning single leukemic cells onto a scaffold of human B-cell development, in collaboration with Profs. Kara L. Davis and Robert Tibshirani, we identify a signaling state at a developmental transition from late pro-B to early pre-B cells that predicts relapse based on a diagnostic bone marrow biopsy. Notably, such signaling behavior pre-exists at diagnosis and persists at relapse. In Chapters 4 and 5, in collaboration with Dr. Nikolay Samusik, we perform a comparative study and develop novel software for single-cell embedding, clustering, and visualization. Chapter 6 contains concluding remarks and future direction. Further, in collaboration with Prof. Purvesh Khatri, Prof. Mark M. Davis, and other Computational and Systems Immunology PhD students, we share advice on equipping a modern immunology graduate student with the computational skills they may need on their journey towards a career as a scientist.

Advisor: Bendall Sean
Nolan Garry P. primary advisor. Thesis advisor
Cleary Michael L. advisor. Thesis advisor
Plevritis Sylvia advisor. Thesis advisor
Wernig Marius advisor. Thesis advisor
Machine Learning Applied to Multiphase Production Problems

In reservoir engineering, it is important to understand the behavior of the reservoir, often by seeing the dynamics of bottom hole pressure (pwf) and flow rate (q) in each well. Bottom hole pressure and flow rate are commonly modeled using physical model, for example, reservoir numerical simulator. This approach requires physical parameters, including rock properties and reservoir geometry, which we do not always know. In some cases, collecting those physical parameters and building the physical model might not be practical. Applying this approach in a field that has thousands of wells with a very dynamic environment requires a lot of effort.
Another approach uses historical bottom hole pressure and flow rate, which are commonly easier to obtain. Using machine learning algorithms, we can build a reservoir model by learning the historical pattern of bottom hole pressure and flow rate (or training set) without the physics of the flow being programmed explicitly. Using this model, we can predict flow rate given bottom hole pressure (or vice versa). We can also utilize this model as a diagnostic tool. For example, if liquid loading or condensate banking or wax/asphaltene deposition starts to happen, the actual pressure and flow rate response will be different than that suggested to the model, hence flagging that the reservoir performance has changed in character.
Previous efforts by Liu (2013) and Tian (2014), have been successful in applying machine learning method in single-phase cases. Important features were identified and gave satisfactory results in several different cases. The study found that although pressure and flow rate relationship can be nonlinear, the problem can be formulated as a linear problem and the nonlinearity is expressed in the features.
This research focused on solving multiwell and multiphase problems using machine/deep learning approaches. The study has identified important features in such problems. This study explored ten different machine/deep learning algorithms and their performance evaluation. Python is very helpful for this purpose as it has a lot of machine/deep learning libraries to choose from and is handier in solving machine learning related problems. The study also examined the impact of multiphase flow, reservoir heterogeneity, and data noise.

Keywords: School of Earth Energy &amp; Environmental Sciences
production
multiphase flow
machine learning
Thesis
Advisor: Ristanto Tita
Machine learning approaches for permanent downhole gauge data interpretation

The continuous record of pressure, temperature, and sometimes flow rate by Permanent Downhole Gauges (PDGs) contains rich information about the reservoir. Due to the different conditions where data are collected, PDG data have unique characteristics that require different interpretation approaches than conventional well testing. One of the machine learning approaches developed earlier was the convolution-kernel-based data mining (Liu and Horne, 2013a, 2013b). The method was shown to be promising for pressure-rate deconvolution on noisy data. However, the bottlenecks of computation efficiency and incomplete recovery of reservoir behaviors limit the application of the convolution kernel method to interpret real PDG data. In this work, two classes of methods were applied for PDG data interpretation. One was the machine learning approach with feature handcrafting based on physics, the other was the deep learning approach by using recurrent neural networks to learn on raw PDG measurements. Both approaches were shown effective to extract the patterns containing the reservoir information from PDG data. The extracted patterns were demonstrated to be useful in three ways: one was deconvolution by predicting the pressure corresponding to a constant rate history; the other two were to predict pressure in the future with given rate control, and vice versa. Three feature-based machine learning techniques were explored to interpret PDG data, namely, the linear approach, the kernel method, and kernel ridge regression. First the linear approach was proved to learn with the same features as the previous convolution kernel method, while it required much lower computational cost. The linear approach was used further for pressure-rate deconvolution and it outperformed two conventional deconvolution methods when noise or outliers were contained in the data. Next the kernel method and kernel ridge regression were utilized to adjust the machine learning model capability and to model the pressure behaviors covering early-transient until late-transient periods. Additional machine learning models were developed for the problems of rate reconstruction and multiwell testing, by handcrafting new set of features. Recurrent neural networks were investigated for PDG data interpretation with a focus on nonlinear autoregressive exogenous models (NARX). It was shown that NARX learned the patterns behind PDG time series data with its uniquer recurrent architecture, by feeding the memory of previous computations back into the model. NARX was also applied for pressure-rate deconvolution and it identified different reservoir models successfully. One key advantage of NARX is that it learns on raw data, although thought and effort are needed to tune the neural network. A case study on temperature transient analysis demonstrated NARX's advantage over feature-based machine learning when feature handcrafting was difficult because of limited knowledge of the physics.

Keywords: Energy Resources Engineering.
Advisor: Horne Roland N. 
Caers Jef 
Mukerji Tapan
Making Americans : schooling, diversity, and inclusion in the twenty-first century

My research explores how schools teach what it means to be American in the context of immigration-driven diversity and rising intergroup conflict. Through a comparative ethnographic study of two middle schools in dramatically different political and demographic contexts, I show how students are socialized into two different Americas: one that represents pluralism, equity, and progressive politics, and another that represents colorblindness, conservatism, and a white status quo. Additionally, I highlight the limitations of multicultural education and liberal inclusion to lead to equality and incorporation for all students due to the contradictions in how these models are currently practiced in schools. I therefore offer a new paradigm for teaching nationhood called critical nationalism. Critical nationalism captures the dynamic and socially-constructed nature of the nation while supporting students in developing a language of critique and hope for national transformation.

Keywords: Education.
Advisor: Jiménez Tomás R. (Tomás Roberto)
Valdés Guadalupe 
Carter Prudence L.
Male sex is a risk factor for placental vulnerability and cascading neurodevelopmental disruption in utero [electronic resource]

There is a widely recognized male bias in the incidence of neurodevelopmental disorders, and males in utero are also at higher risk for complications of pregnancy. This has led some to propose male sex as a risk factor for disrupted prenatal neurodevelopment. The central regulator of fetal development is the placenta, which is derived from the embryo and shares the chromosomal sex of the fetus. Clinical findings suggest that male and female placentas function differently in humans in response to maternal stress and inflammation. Using a mouse model of maternal immune activation during pregnancy, we tested the hypothesis that one factor underlying male-selective vulnerability to neurodevelopmental disruption is exacerbated placental dysfunction following a maternal inflammatory event. A mild challenge with lipopolysaccharide (LPS) on day 12.5 of the mouse pregnancy produces growth reduction and necrosis that is more severe in the male placenta. Male fetuses in LPS-challenged pregnancies also show elevated hypoxia and decreased proliferation in the developing cortex relative to females. This fetal sex-based divergence in neurodevelopmental trajectory persists into adulthood, producing lamina-specific reductions in Satb2+ and Parvalbumin+ cell densities in neocortex that are markedly more severe in males. Prenatal LPS exposure also produces significantly diminished social interaction, impaired spatial learning, and elevated behavioral stereotypies in males only. In contrast, females show an acute elevation in fetal brain cytokine abundance and a delay in postnatal body growth. Transcriptomic analyses of midgestation mouse and human placenta reveal sex-specific gene expression signatures that center on hubs of innate immune signaling, revealing an evolutionarily conserved divergence that can help explain differential vulnerability. Given these results, it may be useful to view male-selective neurodevelopmental impairment on a continuum of male vulnerability to in utero adversity. These findings also highlight the value of sex inclusion in animal research, since the customary exclusion of females would have led to spurious extrapolations from male to female biology. We also emphasize the existence of sex-dependent differences in many baseline measures. If improperly balanced in experimental design, these differences could lead to results that reflect the sex imbalance rather than treatment effects.

Advisor: Palmer Theo primary advisor. Thesis advisor
Huguenard John advisor. Thesis advisor
Stefanick Marcia Lynn
Winn Virginia advisor. Thesis advisor
Managing the risk of satellite collisions : a probabilistic risk analysis of improving space surveillance systems

Over the last several decades, the world has become increasingly reliant on the space domain. With the insertion of new spacecraft in various orbits, the risk of collisions with spacecraft or debris has increased. Currently, several space surveillance systems provide signals of such impending collisions and opportunities for owners and operators to respond to these alerts. The monitoring systems, however, are imperfect, and the current number of sensors of different types, costs, and levels of accuracy may need to be increased to allow better space traffic management. The objective of this dissertation is to estimate and optimize the benefits of such improvements. We present first, a general model to evaluate sensor systems based on a Bayesian updating of the prior probability of collision given two types of independent signals: some from more accurate but more expensive sensors, and some from less accurate but cheaper sensors. We use this model to evaluate the risks of collision of space assets given the current monitoring systems. We then assess the risk of losing a satellite constellation, which is where the satellites' values reside. Next, we assess the risk reduction value of adding one or more sensors of either type to the current monitoring systems, based on the classic notion of value of information. For simplicity, we assume a constant risk aversion in rational decisions, both for the sensor system managers and for the satellite operators. We illustrate the model by a fictionalized version of the United States Space Surveillance Network operated by the United States Air Force, and other sensor systems of lesser accuracy. Together, these networks collect optical and radar data, which allow synthesis of observations into a coherent picture of space. The results of this analysis can be used to support the selection of an optimal number of additional space surveillance sensors, either "large" or "small, " under resource constraints. This choice is sensitive to the risk aversion of both the users of the signals and the managers of the monitoring systems. The method can be extended to other types of monitoring system with similar structures.

Keywords: Management Science and Engineering.
Advisor: Paté-Cornell M. Elisabeth (Marie Elisabeth) 
Close Sigrid
Shachter Ross D.