VictorJuiz/thesis huggingface dataset:
The microfluidic electrokinetic sample holder for serial femtosecond X-ray crystallography [electronic resource]
Keywords: Mechanical Engineering.

Serial femtosecond X-ray crystallography (SFX) is sitting at the frontier of a paradigm shift in structural biology. The intense X-rays with short femtosecond pulses available from free electron lasers (FELs) has allowed us to view biological structures with similar, or sometimes better, resolution than current techniques. Refinement of these methods will lead us to structures of macromolecules once not attainable at current generation lightsources or with other imaging techniques. The SFX technique can yield high resolution room temperature data of biological structures. It relies on gathering statistics on tens of thousands of protein crystals, typically on the order of a dozen microns or smaller as opposed to larger millimeter-sized crystals common at synchrotrons. The intense power of the X-rays destroys the sample and thus does not allow for multiple collections on the same crystal. This has required careful consideration on sample delivery techniques. The current sample environments for data collection are in mid-vacuum (10e-6 Torr) and need a constant supply of sample, while not consuming too much sample, remaining stable and minimizing background while keeping the proteins crystalline in solution. This multifaceted optimization has made sample delivery one of the cruxes of the SFX field. This dissertation will outline one of many techniques that have been developed in the past years to address many of the issues of sample injection: the microfluidic electrokinetic sample holder (MESH). This technique leverages the moderate viscosities of the mother liquor and common additives found in crystallization conditions in order to reduce the sample consumption and use an applied electric field in order to focus and stabilize the meniscus to better interact with the incident X-rays probing the sample. This technique, similar to the electrospin process, will be described along with some of the different apparatus designed to interface it at the Coherent X-ray Imaging (CXI) endstation at the Linac Coherent Light Source (LCLS) at the SLAC National Accelerator Lab (SLAC). Finally, a new concentric technique will be described, the coMESH, which attempts to address some shortcomings of the first technique, while delivering delicate crystals at low sample consumption. From solution scattering, to protein crystal standards, nanocrystals, large complexes, time-resolved experiments, and experimental phasing, the electrokinetic technique proves quite capable to move between experimental, geometric, and biological constraints in order to leverage the advantages of SFX to the larger structural biology community with it's minimal sample consumption (micro- to milligrams) and simplistic and modular design.
Microfluidic manipulation of droplets using synchronous universal logic operations [electronic resource]
Keywords: Mechanical Engineering.

From Zuse's thesis, which pioneered digital physics, to Landauer and Wheeler's famous `it from bit', we know that information is fundamentally linked to its physical representations; manipulation of information, for example, represented by the flow of electrons in a microcircuit or biological molecules, intrinsically leads to algorithmic manipulation of matter. Although such algorithmic assembly is commonplace nowadays at the nanoscale, including DNA- based tile nanostructures, its counterpart at the mesoscale is surprisingly missing. Droplets in microfluidic chambers are a growing platform for both physics and biology experiments. Droplets are indeed versatile digital materials; they can be produced in large quantities at high throughput, perform chemical reactions as miniature beakers and carry biological entities. Several manipulation techniques, including electric, optical, acoustic and magnetic forces, have been used for serially manipulating droplets. Because these systems depend on external feedback by the user and lack any inherent computational capability, large scale-up and parallel information-based manipulation is limited. A simple assignment of presence or absence of a droplet as a bit of information can allow us to look at these multi-phase systems as information processing units. Recent work has demonstrated the universal computational capabilities of low-Reynolds-number multi-phase hydrodynamics using pressure-based flow controllers . However, all of these prior systems suffer from a fundamental drawback; they are asynchronous and thus lack the capacity to scale to complex cascaded digital logic. To address this problem, I introduce a new platform that uses a single global clock, that is, a rotating magnetic field to synchronize the motion of arbitrary numbers of droplets and direct or store them in permalloy tracks using digital logic operations. The manipulation of the droplets is magnetophoretic; the rotating magnetic field activates the permalloy tracks to create a dynamic energy landscape that sets the droplets in motion. I demonstrate that the droplets in my platform can either be paramagnetic, that is ferrofluidic, or diamagnetic, for water that is essentially diamagnetic - in practice non-magnetic. Through magnetic and hydrodynamic interaction forces between droplets, we developed OR/AND logic gates, universal XOR/AND and XOR/NAND gates, fanouts, memory loops, flip-flops, a finite-state machine and a full adder that adds droplet `bits'. Our platform demonstrates both combinational and sequential logic and does not rely on pressure-based flow controllers, therefore enabling a unique `non-volatile' conservative physical logic and memory. Our platform enables large-scale integration of droplet logic, analogous to the scaling seen in digital electronics, and opens new avenues in mesoscale material processing.
Mind-reading in the dark [electronic resource] : social cognition in nineteenth-century American fiction
Keywords: English.

This dissertation uses a sustained examination of implicit models of cognition in nineteenth-century American fiction to construct a new narrative of the formation of the nineteenth-century literary canon and, by extension, to reevaluate the tradition of American "exceptionalism" in literary studies. Examining major texts of the nineteenth-century American canon from Charles Brockden Brown to Henry James, I find that these fictions evince a radical skepticism regarding explanatory constructs like beliefs, intentions, and motivations, often encouraging readers to understand characters' actions not in terms of internal mental states but as conditioned reflexes or, even more radically, as meaningless epiphenomena. The works examined herein not only undermine interiorizing psychological models through their revision and perversion of metaphors and genres that construct consciousness as private and propositional; many of them, like Poe's Dupin trilogy and Melville's "Bartleby, " also offer alternative methods of behavioral interpretation that emphasize material causation over agential action. Methodologically, this dissertation points toward a new understanding of the relation between literary studies and the social sciences, revealing that fictional representations of character can actively challenge the methods by which readers explain and predict other agents' behavior ― and that textual models of the mind constitute a rich and diverse archive of theories from which literary scholars can productively talk back to cognitive and social psychologists. The text's four chapters are thus organized by four prerequisites for any successful theory of mind: a metaphorical vocabulary with which to label minds and mental states; a narrative framework for subjective experience throughout the lifespan; a capacity to ask and answer "why" questions about human behavior; and a set of tools for manipulating other individuals, whether for prosocial or self-interested ends. While Mind-Reading in the Dark makes a targeted intervention in cognitive literary studies, the dissertation also has significant implications for the place of American studies in the larger field of world literature. My analysis suggests that morphological differences between American and British fiction registered by "exceptionalist" critics were indeed real -- but, rather than resulting from these traditions' supposed insulation from each other, they instead constitute a strategic response to their remarkable proximity and mutual imbrication. We can best understand the American canon's radical narrowing and revision in the early twentieth century as an adaptive attempt to recenter American fiction on these cognitively unconventional texts, transforming the United States' historically peripheral position in the Europe-centered literary system into an advantage. This dissertation thus asks us to view American literature as one peripheral tradition among many -- albeit one that attained global significance through a kind of strategic marginality.
Miniaturized diaphragm-based fiber-tip acoustic sensors for the identification of stem-cell-derived cardiomyocytes [electronic resource]
Keywords: Electrical Engineering.

Historically, acoustic sensors have been used in a variety of applications spanning land, water, and air. This dissertation expands their potential application space into the human body, both figuratively and literally. A diaphragm-based, fiber-tip hydrophone is presented, with the required sensitivity at low frequencies to detect the action potential of a single cardiomyocyte (heart muscle cell).
Model-based artifact correction in MRI [electronic resource]
Keywords: Electrical Engineering.

Magnetic Resonance Imaging (MRI) is an imaging modality known for its safety and excellent soft tissue contrast. It is estimated that two out of every hundred Americans have an artificial hip or knee. MRI can serve as a useful tool for postsurgical examination of joint replacement surgeries. However, current techniques that remove image distortion near metallic implants struggle near the metal and also blur the images, potentially making diagnosis harder for radiologists. In this work we present an approach to deblur MRI images near metallic implants. This method consists of improved field-map estimation and spectral bin combination techniques. We also introduce a method to find a good balance between blurring and distortion in regions where the field-map estimation is unreliable. This method is compared in phantoms and in vivo and shown to perform substantially better than current approaches. Additionally, due to its superb soft-tissue contrast, MRI also excels at diagnosis of lesions and cysts. These tissues are often easier to visualize and diagnose when signal from fat, which is often bright in MRI images, is suppressed or separated from that of other "water" tissues. Current techniques for suppressing or separating fat are often unable to do so throughout the entire image and/or lead to long acquisition times. In this work we first present an approach that enables the separation of fat and water while enabling high-resolution imaging and T2-like contrast in short scan times. This approach uses improved signal modeling to enable improved fat/water separation iv using an SNR-efficient balanced Steady-State Free-Precession (bSSFP) two-point Dixon method, while simultaneously reducing the banding artifacts associated with bSSFP sequences. Furthermore, an analysis to assess the viability of a method that combines two-point Dixon fat/water separation with the time-saving benefits of Homodyne reconstructions is also performed. This analysis finds that while the described approach could save time, it's clinical usability may be limited due to artifacts created at fat/water tissue interfaces.
Modeling and validation of RNA secondary structures and ensembles through multidimensional chemical mapping [electronic resource]
Keywords: Biochemistry.

Ribonucleic acid (RNA) molecules play important roles in many of the core processes of life. The versatility of RNA functions relies on its ability to adopt and interconvert between distinct folding states. However, RNA conformations have largely eluded experimental dissection and validation due to the lack of generally applicable quantitative methods. In this dissertation, I present the recent development of the lock-mutate-map-rescue (LM2R) workflow, which leverages systematic mutagenesis and high-throughput RNA chemical mapping to unambiguously validate or falsify base-pairing models. First, the mutate-map-rescue (M2R) method was established on a small bacterial 16S ribosomal RNA domain, provided incisive evidence falsifying a prior SHAPE-derived model, and instead revealed a structural equilibrium between a crystallographic secondary structure and one with a single-nucleotide register shift. After the initial success, I applied M2R to two novel real-world RNA regulatory elements that possess crucial biological functions. The relevance of M2R-validated secondary structure models was further strengthened by in vivo assays, fueling discoveries of new paradigms in biology. Furthermore, to dissect the solution structures of a paradoxical adenine riboswitch, I advanced the technique into LM2R with an additional layer of mutagenesis to obtain 'locking' mutants that each isolates a single state. With such stabilizers enabling rigorous tests for co-existence of helices within a particular state, LM2R gave rise to a unified ensemble model that resolves discrepancies between previous alternative models. Finally, I made available an algorithm that optimizes PCR assembly design for RNA preparation, and created the Primerize-2D server that greatly simplifies LM2R library synthesis. In sum, as the central component of the multidimensional chemical mapping (MCM) pipeline, LM2R provides a general and practical route to robust validation of RNA secondary structures and ensembles.
Modeling catalytic properties of metal nanoparticles [electronic resource]
Keywords: Chemistry.

Catalysts are materials that can accelerate chemical reactions, and they are key to creating sustainable processes and a greener environment. Catalysts in the form of metal nanoparticles are ubiquitous in current industrial processes, and they are critical to creating a sustainable energy future. Theory has provided vital insights into the fundamental limitations of various types of processes, and density functional theory (DFT) calculations have inspired the discovery of new active materials. Advancements in supercomputing resources and scalable quantum chemistry codes have enabled us to explore the catalytic behavior of metal nanoparticles from the fundamental atomic level. We present calculated adsorption energies of oxygen on gold and platinum clusters with up to 923 atoms (3 nm diameter) using Density Functional Theory. We find that surface tension of the clusters induces a compression of which weakens the bonding of adsorbates compared to the bonding on extended surfaces. The effect is largest for close packed surfaces and almost non-existent on the more reactive steps and edges. The effect is largest at low coverage and decreases, even changing sign, at higher coverages where the strain changes from compressive to tensile. Quantum-size-effects also influence adsorption energies but only below a critical size of 1.5 nm for platinum and 2.5 nm for gold. We develop a model to describe the strain-induced size effects on adsorption energies, which is able to describe the influence of surface structure, adsorbate, metal, and coverage. Stability of metal nanoparticle is also a great concern in the field of heterogeneous, and a dominant process that degrades the activity of these catalysts is the agglomeration of individual nanoparticles. In order to better understand the sintering mechanism, we propose a kinetic Monte-Carlo (kMC) model for simulating the movement of platinum particles on supports, based on atom-by-atom diffusion on the surface the particle. The proposed model was able to reproduce equilibrium cluster shapes predicted using Wulff-construction. The diffusivity of platinum particles was simulated both purely based on random motion and assisted using a drift velocity. The overall particle diffusivity increases with temperature, however the extracted activation barrier appears to be temperature independent. In addition, this barrier was found to increase with particle size, as well as, with the adhesion between the particle and the support.
Modeling creative and social uses of language [electronic resource]
Keywords: Psychology.

This thesis examines the computational basis of how people understand and appreciate creative uses of language. In the first half of the thesis, I describe a computational model that interprets diverse types of creative and figurative language use, such as hyperbole, irony, and metaphor. In the second half, I focus on modeling the social consequences of using language in creative ways, such as strengthening social bonds and evoking humor. Across the chapters, I describe computational models that formalize theories of language comprehension and show that these formalizations predict people's interpretations of a wide range of creative language use. Part of the goal of this thesis is to show that the interpretation of creative and social uses of language arises through many of the same rational principles that govern standard language understanding. In Chapter 2, I formalize basic principles of communication by extending the Rational Speech-Acts (RSA) framework (Frank & Goodman, 2012) with an explicit representation of speakers' communicative goals. I show that by incorporating inferences regarding which dimensions of the world the speaker wishes to address, the extended model produces appropriate interpretations for three types of figurative uses: hyperbole, verbal irony, and metaphor, suggesting that diverse figurative meanings may arise from the same principles of communication. In Chapter 3, I focus specifically on nonliteral interpretations of number words such as hyperbole and pragmatic halo. I provide a detailed account of how utterance cost, informativity, background knowledge, and communicative goals interact to produce hyperbolic and loose interpretations as well as associated affective subtexts. In Chapter 4, I explore the social consequences of figurative language use by formalizing the idea that listeners reason about common ground during communication. Through a series of simulations, I show that the extended RSA model infers a higher probability of common ground given figurative utterances than given literal ones, suggesting that figurative language may uniquely license inferences that strengthen social bonds. Finally, in Chapter 5, I use a simple model of sentence processing to derive quantitative measures of humor that are motivated by both humor theory and general theories of sentence comprehension. Taken together, this work sheds light on creative and social uses of language and advances formal approaches to language understanding, such that computational models can explain a broader range of phenomena that enrich our linguistic and social lives.
Modeling natural language semantics in learned representations [electronic resource]
Keywords: Linguistics.

The last few years have seen many striking successes from artificial neural network models on hard natural language processing tasks. These models replace complex hand-engineered systems for extracting and representing the meanings of sentences with learned functions that construct and use their own internal vector-based representations. Though these learned representations are effective in many domains, they aren't interpretable in familiar terms and their ability to capture the full range of meanings expressible in language is not yet well understood. In this dissertation, I argue that neural network models are capable of learning to represent and reason with the meanings of sentences to a substantial extent. First, I use entailment experiments over artificial languages to show that existing models can learn to reason logically over clean language-like data. I then present a large new corpus of entailments in English and use experiments on that corpus to show that these abilities extend to natural language as well. Finally, I introduce a new model that uses the semantic principle of compositionality to more efficiently and more effectively learn language from large volumes of data.
Modeling, optimization, and decision support for integrated urban and infrastructure planning [electronic resource]
Keywords: Civil and Environmental Engineering.

Reducing the energy consumption and associated greenhouse gas emissions of urban areas is paramount in research and practice. As the urban population grows to 6.2 billion people in 2050, advancing the practice of urban and energy infrastructure planning to enable livable, low-carbon growth is essential. Despite this imperative, prior approaches to urban and infrastructure planning have failed to connect analytical models of energy performance with planning and infrastructure decisions on building use, form, and energy supply. This dissertation introduces a new approach to integrated urban and energy infrastructure planning that is based on desired municipal outcomes, and uses a novel optimization and analysis model for early-stage urban and infrastructure planning decisions. The model developed to facilitate this planning approach is capable of simultaneously evaluating and optimizing energy supply technologies, individual building mix and form, and site layout. Case studies for neighborhoods in Oakland, CA, and San Francisco, CA, are used to show how this model-driven, outcome-based planning approach can be used by policymakers to evaluate policy options. It is shown that the model has value as a decision support tool by enabling identification of statistically significant differences between potential outcomes of municipal planning and investment decisions. Furthermore, the model is shown to have value in identifying patterns of development that lead to lower carbon emissions and improved municipal economic outcomes.
Modeling organism-wide immunity in health and cancer [electronic resource]
Keywords: Immunology.

Immune cells function in an interacting hierarchy that coordinates activities of various cell types according to genetic and environmental contexts. We developed graphical approaches to construct an extensible immune reference map from mass cytometry data of cells from different organs, incorporating landmark cell populations as flags on the map to compare cells from distinct samples. The maps recapitulated canonical cellular phenotypes and revealed reproducible, tissue-specific deviations. The approach revealed influences of genetic variation and circadian rhythms on immune system structure, enabled direct comparisons of murine and human blood cell phenotypes, and even enabled archival fluorescence-based flow cytometry data to be mapped onto the reference framework. This foundational reference map provides a working definition of systemic immune organization to which new data can be integrated to reveal deviations driven by genetics, environment, or pathology. We have further extended this concept to initiate a framework for modeling human immunity, using whole peripheral blood samples as an inaugural dataset. Moreover, we demonstrate that this method is capable of modeling the effects of immunotherapeutic interventions on immune responses across the system. We have recently developed novel strategies for inducing anti-tumor immune responses that leverage the stimulatory capacity of tumor-binding antibodies. When combined with adjuvants to activated dendritic cells, this combination therapy is capable of inducing sterilizing immunity in a number of translatable and autochthonous animal models as well as in primary cells from cancer patients in culture. Using these therapies as a model of an effective anti-tumor immune response, our system-wide modeling approach revealed the systemic events resulting in the eradication of spontaneous breast tumors.