VictorJuiz/thesis huggingface dataset:
Myeloid cells in cancer progression and cancer treatment

The importance of the immune system in cancer and cancer therapy has become firmly established over the last decade. Current treatments have great potential, but remain ineffective for many patients and tumor types. Much of the work in cancer immunotherapy has focused on components of the adaptive immune system. Thus, the impact of innate immune components, including myeloid cells, on cancer immunotherapy is poorly understood and not well-utilized with current cancer therapies. Myeloid cells are a major subset of innate immune cells that are known to modulate immune behavior to generate pro- or anti-tumor environments in mechanisms that are both dependent upon and independent of the adaptive immune system. The work described here is a collection of projects investigating several aspects of the myeloid cell compartment in various tumor models. First, we found that retinoic acid deficiency in intestinal tissue was critical in the process of intestinal tumor growth and development. Pharmacologic correction of this retinoic acid deficit leads to reduction of tumor number and size, while diet-induced exacerbation of the deficit leads to increased tumor number and size. These findings correlate with the capacity of intestinal dendritic cells from the lamina propria to induce CD4 T cells of the inflammatory Th17 phenotype or regulatory T cell. Additional lines of experiments focused on the unmet clinical need for intestinal tumor detection tools. We report development of novel fluorescent probe for cathepsins, a frequent tumor-associated protein product of myeloid cells, which facilitates robust detection of both mouse and human intestinal tumors. In another line of experiments, we investigated the signaling pathways important in licensing bone marrow dendritic cells to internalize tumor cell-IgG antibody immune complexes and to stimulate potent anti-tumor T cell responses. Namely, alterations in SHP-1 activity and the PI3K/Akt pathway were identified as major checkpoints of dendritic cell immunostimulatory function. Finally, we investigate the essential innate immune components of tumor-binding antibody immunotherapy in established B16 melanoma tumors. Neutrophils are necessary for clearance of established B16 tumors via a complement cascade- and reactive oxygen species-dependent mechanism. The immunotherapeutic approach studied here demonstrates the complex interactions of the innate immune system underlying tumor clearance via a mechanism requiring neutrophils, complement and iron-dependent reactive oxygen species.

Advisor: Engleman Edgar G. 
Alizadeh Ash
Shizuru Judith Anne
Nanoengineering and knockoffs for an electronic molecular fingerprinting platform

To diagnose complex diseases and perform advanced drug screening and chemical monitoring, we need sensors capable of broadband, multi-analyte detection. This is achieved either by multiplexing affinity probes to capture different target analytes, or through mass or vibrational spectroscopy. While affinity-based techniques currently dominate and are capable of high specificity and sensitivity, probe multiplexing can be time consuming, expensive, and difficult to execute reliably outside of a laboratory environment. On the other hand, spectroscopic chemical detection is label- and affinity-free, but often requires costly and bulky instrumentation. In this thesis, we describe an affinity-free sensor for measuring vibrational signatures of analytes in liquid samples, which can be implemented on a scalable and fully-electronic platform. This relies on resonant electrochemical charge transfer at room temperature, which is made possible by nanoscale interface engineering and low-noise potentiostatic feedback. We describe advances in the knowledge and technology required to realize key aspects of this platform, including the nanoscale sensor site, electrochemical measurement system, and signal analysis framework. Together, these pave the way for continued development of a practical electronic platform for molecular fingerprinting platform in complex samples.

Keywords: Electrical Engineering.
Advisor: Howe Roger Thomas 
Jeffrey Stefanie
Soh H. Tom
Nanophotonics for polarization control and thermal management

Polarization is one of the most fundamental degree of freedoms of photons. Techniques to control polarization are therefore of crucial importance from a fundamental perspective and find applications in many areas such as optical communications, sensing, and imaging. An important task in the control of polarization is to achieve polarization conversion, where one seeks to generate a target output polarization for a given input polarization. In the first part of this dissertation, we show that a photonic crystal slab with a mirror underneath can provide complete conversion between linear polarizations in a reflection process. Moreover, such an effect is topological since the complex reflection coefficients have a nonzero winding number in the wavevector space. We next show that such a structure can also achieve arbitrary polarization conversion. For a given incident light with any fixed polarization, one can generate arbitrary polarization in the reflected light, by varying the direction of the incident light. Photons are also important heat carriers. The ability to control thermal radiation and radiative heat transfer is of fundamental importance for a wide range of energy applications. In the second part of this dissertation, we use nanophotonic structures and principles to control thermal radiation and heat flow, which can provide thermal capabilities that are unavailable from conventional structures. In the first exmaple, we demonstrate narrowband thermal radiation with unity emissivity peak in the near-infrared range by critically coupling a flat tungsten surface with guided resonances of a dielectric photonic crystal slab. The frequency and linewidth of the emissivity peak are highly tunable. In the second example, we propose a scheme for achieving thermal switch in near-field radiative heat transfer. We present a practical implementation consisting of three nanospheres that are aligned in a straight line. Exploiting the thermal dependent resonances of the nanospheres, we show that the heat transfer between the two side spheres can be turned `on' and `off' by varing the temperature of the center sphere.

Keywords: Electrical Engineering.
Advisor: Fan Shanhui
Brongersma Mark L. 
Miller D. A. B.
Natural language interfaces for semi-structured web pages

Question answering (QA) systems take natural language questions and then compute answers based on a knowledge source. This dissertation focuses on improving QA systems along two axes. First, instead of operating on knowledge sources with a fixed schema such as a database, we propose to use web pages, which contain a large amount of up-to-date open-domain information (high BREADTH), as the knowledge source. Second, we want the QA system to understand more complex questions and perform different types of multistep reasoning to compute the answer (high DEPTH). Unlike most previous works on retrieval-based QA (which operate on open-domain unstructured text but target only factoid questions) and knowledge-based QA (which can handle compositional questions but on knowledge sources with fixed schemata), we aim to address the two axes simultaneously. One important aspect of web pages is that they are semi-structured: they contain structural constructs such as tables and template-generated product listings, but the schemata of such structures are not known in advance by the QA system. To explore the semi-structured nature of web pages, we first investigate the task of extracting a list of entities from the web page based on the natural language specification (e.g., from "(What are) hiking trails near Baltimore", extract the trail names from a table column). Then, to increase the complexity of the questions, we next study the task of answering complex questions on open-domain semistructured web tables using question-answer pairs as supervision (e.g., answering "Where did the last 1st place finish occur?" in an athlete's statistics table). To handle compositional questions with different types of operations, we frame the task as learning a semantic parser, which maps questions into compositional logical forms that can be executed to get the answer. Our semantic parser can answer complex questions on unseen web tables and achieves an accuracy of 43.7% on the dataset. Overall, we show that while the unknown schema of the tables (increased BREADTH) and complexity in the questions (increased DEPTH) lead to an exploding search space of logical forms, our proposed methods control the search space to a manageable size, enabling us to train a QA system that can operate on open-domain web pages. The resulting QA system can potentially enable virtual assistants, search engines, and other similar products to handle a much wider range of user's utterances.

Advisor: Liang Percy 
Jurafsky Dan
Manning Christopher D.
Nearshore processes of a coastal island : physical dynamics and ecological implications

Mixing and transport in coastal upwelling systems have dramatic effects on larval dispersal, contaminant dilution, hypoxia development, and exposure to ocean acidification -- all targets for marine policy and conservation efforts. In the coastal zone, winds, waves, tides, and density gradients interact extensively, making understanding the dominant processes and their effects on coastal ecosystems difficult. The goal of this work is to improve our understanding of how mixing and transport are affected by topography, density gradients, and the presence of headlands and kelp beds in coastal upwelling regions of the California Current. I examine the hydrodynamics of shelf circulation patterns and nearshore (within two kilometers offshore) physical processes using a combination of long and short-term observational studies at Isla San Jeronímo, Baja California Norte, Isla Natividad and Isla Magdalena, Baja California Sur. Our results show that exposure to larger scale dynamics and regional water masses can have a direct impact on local water quality and residence time, depending on nearshore hydrodynamics, exposure, and geographical restrictions. The effects from interannual-to-interdecadal events on local water characteristics can also influence variation in kelp density, with further effects on nearshore water flow. Furthermore, flow drivers around the Isla Natividad vary significantly due to coastal geometry and regional forcing.

Advisor: Monismith Stephen Gene 
Micheli Fiorenza 
Woodson Brock
Network-based methods for the elucidation of disease and drug mechanisms

Complex, chronic diseases represent some of the most common diseases known to man, yet often little is understood regarding the underlying biological mechanisms, leading to challenges in their prevention and treatment. Characterizing complex disease can be daunting, as combinations of perturbations in a delicate web of genetic and environmental interactions may lead to a similar disease phenotype. These intricacies provide an opportunity for computational approaches, in particular network-based approaches, which have the ability to capture and model large, complex biological systems. I have leveraged the power and versatility of these approaches and developed novel network-based methods for phenotype classification and drug discovery. I applied my methods to inflammatory bowel disease (IBD), a chronic, heterogeneous disease which has significant unmet need in subtyping patients, predicting treatment response, and optimizing treatment regimens. I first discovered new candidate genes implicated in IBD by traversing a network consisting of IBD related genes and Mendelian phenotypes. Next, I developed a new supervised, individualized pathway-based classification method, and demonstrated its superiority over existing gene-based and pathway-based methods for distinguishing the two common subtypes of IBD. I showed that pathway-based methods can extract robust features for disease differentiation as well as provide mechanistic biological insight into disease subtypes. Finally, I developed a network-based method for drug repurposing which predicts personalized drug treatments for individual patients. Through these works, I demonstrate the potential of network-based bioinformatics tools in furthering patient care by advancing our understanding of disease and drug mechanisms

Keywords: Biomedical Informatics.
Advisor: Altman Russ 
Hastie Trevor 
Khatri Purvesh
Neural and behavioral measures of communication across accents

Communication in a global language like English provides historically unparalleled opportunities for connection between speakers of different language backgrounds, but differences in accent can drive phonologically based misunderstandings and cue social distance and stereotypes. Past work suggests that in conjunction with this behavioral friction, differences exist in neural responses to speech in a listener's own accent and a different accent, though there has been variation in findings across studies. This dissertation seeks to replicate previous findings of differences in neural responses between accents and identify sources of those differences, focusing on phonetic and social factors as candidates. To that end, we conducted EEG recordings while 49 monolingual native speakers of American English listened to semantically congruent and incongruent sentences produced by speakers of American and Indian accents. Each chapter of this dissertation examines a subset of the data collected in this combined paradigm. The first experimental chapter identifies robust differences between accents in neural responses to semantic incongruence, in particular a greater distributed N400 and posterior P600 in the American accent than the Indian accent, and it also identifies that both analytical parameters and study design variables are likely responsible for variation across previous studies. The second experimental chapter finds that differences in neural responses to speech across accents can be driven by differences between accents in realization of particular speech sounds. The third experimental chapter provides evidence that differences in neural responses to accents correlate with social distance to an out-group speaker and that these differences can be attenuated by a perspective-taking intervention intended to reduce intergroup bias. Taken together, these findings suggest that both phonetic and social factors drive differences in neural responses to accent, but that these differences are relatively malleable.

Advisor: McCandliss Bruce 
Cohen Geoffrey L. (Geoffrey Lawrence) 
Fujioka Takako
Thille Candace
Neural dynamics underlying initiation and control of pathological thalamocortical oscillations

Neural oscillations -- or, "brain waves" -- are a ubiquitous feature in a wide variety of cognitive processes. Underlying many of these oscillations are strong recurrent connections between the cortex and the thalamus, known as the cortico-thalamo-cortical (CTC) network. This network plays an important role in synchronizing sensory information, transitioning the brain into sleep, facilitating memory consolidation, modulating attention, and many other processes. However, under certain conditions the CTC network becomes hyper-synchronized and produces pathological oscillations termed absence seizures, which can hinder normal brain function and negatively impact learning and development. While the genetic mutations and single-cell physiology of absence seizures have been well characterized, there are conflicting hypotheses regarding the roles of the thalamus and cortex in the initiation and maintenance of absence seizures, and little understanding of network-wide dynamics during transitions into and out of seizures. This is partially due to the difficulty in selectively targeting the cortex or thalamus (given their recurrent connections), as well as the challenge of simultaneously recording neural activity from these regions. In this dissertation, I examine thalamic and cortical neural dynamics involved in seizure control, with the aim to improve our ability to treat patients with absence epilepsy and provide a more complete picture of how CTC oscillations can go awry. In chapter 1 I provide an overview of the current knowledge and working hypotheses in the field, and summarize the work contained in subsequent chapters. In chapter 2 I investigate the ability of the thalamus to control seizures in real-time by manipulating the firing mode of a large population of thalamic neurons. Phasic thalamic output to the cortex can induce seizures, while tonic thalamic output can interrupt them. In chapter 3 I describe efforts to understand the relationship between pre-seizure neural activity and subsequent seizure strength. Individual absence seizures can vary in their duration, hinting that the recruitment and/or synchronization of the CTC network may be heterogeneous and possibly dependent on pre-seizure brain activity. In chapter 4 I expand on this work by simultaneously recording individual neurons from both the cortex and thalamus to gain a more detailed description of pre-seizure neural dynamics. Lastly in chapter 5 I evaluate the ability to track individual neurons during seizures and demonstrate how inferences of neural activity become corrupted due to poor single-cell resolution during synchronous events.

Advisor: Huguenard John 
Ding Jun (Jun B.) 
Ganguli Surya
Giocomo Lisa
Neural mechanisms for integrating vision and internal state

Understanding how the brain integrates sensory information about the outside world with internal processes is a central goal in neuroscience. Vision is the sense humans rely on most to navigate the world and survive. When confronted with a threat, vision provides a fast and reliable source of information essential to avoiding danger. The field of visual neuroscience as a whole has made tremendous progress in dissecting how visual percepts are processed at many levels, from photoreception at the level of the retina all the way up to the neural-networks that govern object and face perception. However, where and how visual information is merged with information about internal state remains unclear. In this dissertation, I address this question using viral-based techniques, in vivo electrophysiology, calcium imaging, and behavioral experiments. This work is divided into four sections. (1) I discuss how recent advances due to emerging technology have demonstrated that vision, even in early visual processing centers, is highly influenced by state-dependent processes. In addition, I explore proposed routes by which affective information is merged with visual information. (2) I demonstrate that a group of thalamic nuclei within the ventral midline thalamus combines information about vision and state in order to bias behavioral decisions under threat scenarios. (3) I examine the role of a ventral visual thalamic nucleus that encodes environmental light and modulates state-dependent behavioral responses. (4) I reveal thalamo-thalamo connections between this ventral visual thalamic nucleus and the midline thalamic nuclei and discuss outstanding questions about the role of this pathway in guiding adaptive behaviors. Together these results advance our understanding of how vision and state is integrated in the brain which may have implications for how these processes may be disrupted in disorders such as PTSD and anxiety.

Advisor: Huberman Andrew 
Clandinin Thomas R. (Thomas Robert)
Luo Liqun
Shah Nirao
Neural networks and the satisfiability problem

Neural networks and satisfiability (SAT) solvers are two of the crowning achievements of computer science, and have each brought vital improvements to diverse real-world problems. In the past few years, researchers have begun to apply increasingly sophisticated neural network architectures to increasingly challenging problems, with many encouraging results. In the SAT field, on the other hand, after two decades of consistent, staggering improvements in the performance of SAT solvers, the rate of improvement has declined significantly. Together these observations raise two critical scientific questions. First, what are the fundamental capabilities of neural networks? Second, can neural networks be leveraged to improve high-performance SAT solvers? We consider these two questions and make the following two contributions. First, we demonstrate a surprising capability of neural networks. We show that a simple neural network architecture trained in a certain way can learn to solve SAT problems on its own without the help of hard-coded search procedures, even after only end-to-end training with minimal supervision. Thus we establish that neural networks are capable of learning to perform discrete search. Second, we show that neural networks can indeed be leveraged to improve high-performance SAT solvers. We use the same neural network architecture to provide heuristic guidance to several state-of-the-art SAT solvers, and find that each enhanced solver solves substantially more problems than the original on a benchmark of challenging and diverse real-world SAT problems.

Advisor: Dill David L. 
Liang Percy 
Ré Christopher
A new approach to adaptive particle filters for joint state and parameter estimation in hidden Markov models

The landmark paper by Gordon, Salmond and Smith \cite{GSS93} launched the development of sequential Monte Carlo (SMC), also called particle filters, for the estimation of latent states in hidden Markov models (HMM). Liu \cite{Liu01} contains a collection of the techniques that have been developed since then, with examples of applications in computational biology and engineering, and Chan and Lai \cite{CL12} provide a general theory of particle filters, assuming the model parameters to be pre-specified. This assumption is too restrictive in practice, since the model parameters are usually unknown and also need to be estimated sequentially from the observed data. The obvious approach that treats the parameters as latent states and thereby incorporates them in a larger state vector suffers from severe degeneracy difficulties of the particle filter because the subvector corresponding to the parameters does not undergo Markovian dynamics. Beginning with Liu and West \cite{LW01} and Storvik \cite{Sto02}, there have been many proposals to address this difficulty; see \cite{ADH10}. In particular, Andrieu, Doucet and Holenstein \cite{ADH10} developed the particle MCMC (PMCMC) approach and Chopin, Jacob, and Papaspiliopoulos\cite{CJP12} subsequently introduced the SMC$^2$ method. These two approaches have achieved the state-of-the-art performance. In this thesis, we introduce a new approach to adaptive particle filters for joint parameter and state estimation in HMMs and develop a complete asymptotic theory showing its computational and statistical advantages over previous methods. This approach also provides consistent estimates of (a) the standard errors for the Monte Carlo estimate and (b) mean squared errors of the adaptive particle filter. We accomplish this by combining the theory of particle filters for state estimation in Chan and Lai \cite{CL12} when the parameters are known with that of a novel MCMC scheme using sequential state substitutions for parameter estimation (MCMC-SS) in Lai, Zhu and Chan\cite{LZC19}. Chapter 2 describes our new adaptive particle filter, its computational advantages and how it seamlessly combines the aforementioned two components (a) and (b). Applications to nonlinear state space models in automatic navigation and to HMMs in quantitative finance are given in Chapter 3. Concluding remarks are given in Chapter 4, in which we also provide further discussion of our approach and additional related references in the literature.

Advisor: Lai T. L. 
Hong Han 
Lu Ying