VictorJuiz/thesis huggingface dataset:
An integrated framework to identify and isolate novel, functionally distinct tissue subpopulations using high-resolution single cell analysis [electronic resource]

Complex cell populations are characterized by a level of transcriptional heterogeneity that likely facilitates their biological role. While this heterogeneity is believed to be associated with functional differences, until recently the granularity afforded by measurement tools has been insufficient to efficiently interrogate the transcriptomes of individual cells. In the last several years, high-throughput single cell gene expression analysis has become possible through microfluidic-based devices; however, the potential utility of such platforms has yet to be fully explored. Two issues limit the usefulness of such systems. First, these techniques are generally limited in the number of gene targets that may be simultaneously interrogated (typically < 100), making selection of an appropriate gene panel critical. Second, since these measurement tools require cell lysis, there is no obvious mechanism for functional evaluation of characterized cells. In this thesis proposal, I first describe a novel technique for the detection of transcriptionally-defined subpopulations from complex tissue using single cell gene expression data, which I developed and have successfully applied to characterize a wide variety of target populations. I then present my work in extending these methods to construct a fully integrated pipeline for the systematic identification, isolation, and validation of novel subpopulations. This includes (1) using public microarray data to predict gene targets likely to be either (i) heterogeneous within a single target population or (ii) differentially expressed among multiple target populations; (2) performing single cell transcriptional interrogation against this gene set to identify (i) transcriptionally-defined subpopulations and (ii) surface marker genes whose expression profiles differentiate among these subpopulations; and (3) prospective isolation of select subpopulations using FACS for validation of functional properties in vitro and in vivo. Together, these methods permit the efficient evaluation of previously unknown subpopulations. This approach is highly generalizable and can be applied to characterize essentially any complex cell population, as evidenced by the wide range of tissues and phenotypes examined in the 150 pages that follow.

Advisor: Butte Atul J. primary advisor. Thesis advisor
Gurtner Geoffrey
Khatri Purvesh advisor. Thesis advisor
Shah Nigam advisor. Thesis advisor
Interface engineering in inorganic-absorber nanostructured solar cells [electronic resource]

The focus of this work is variants on the dye-sensitized solar cell (DSSC) that employ inorganic materials as the light absorber, replacing the organic dye molecules used in DSSCs. Such DSSC-inspired devices are emerging technologies in the broader class of thin film solar cells, and include quantum-dot sensitized solar cells (QDSSCs) and perovskites solar cells (PSCs). Quantum-dot sensitized solar cells employ semiconductor nanocrystals, or quantum dots, as the light absorber. The band gap of quantum dots varies with size, allowing for a tunable absorption onset in these devices, among other benefits. PSCs, in which the absorber is CH3NH3PbI3, or variants thereof, with the perovskites crystal structure, first attracted attention in 2012 and have shown an unprecedented rise in efficiency to current record values of 20.1%. QDSSCs and PSCs can be fabricated completely from solution processed materials that can be low-purity, contrasting favorably with the industrial standard, silicon solar cells, which require expensively-processed, high-purity silicon. This tolerance to defects is partially due to the nanostructured design of some PSCs and all QDSSCs, in which a nanostructured bulk heterojunction is formed between the electron-transport material, the absorber, and the hole-transport material. However, the high interfacial areas involved in such designs leads to high rates of interfacial recombination, causing losses in photocurrent, and limiting device efficiency. In this work, I will present methods to reduce interfacial recombination in these inorganic-absorber nanostructured solar cells though surface modifications. In QDSSCs, these include growing ultra-thin insulating metal oxide films by atomic layer deposition (ALD) at the interface and controlling of the nucleation and growth of the inorganic absorber. These studies provide insight into the working mechanisms of QDSSCs, through a combination of the highly-controlled nature of ALD, where films can be grown a single atomic layer at a time and an interface can be atomically engineered, X-ray absorption measurements of interfacial geometric and electronic structure, and detailed studies of the resulting solar cell performance. I will also detail the use of ALD to grow entire material layers in perovskites solar cells, both ALD TiO2 as the electron-transport material, and ALD NiOx as the hole-transport material. Despite their high efficiencies, PSCs are unstable and rapidly degrade when exposed to moisture or excessive heat. The use of ultra-conformal inorganic layers grown by ALD to cap the perovskites absorber, instead of the currently-employed organic layers, has the potential to improve the stability, and thus efficiency, of perovskites solar cells.

Keywords: Materials Science and Engineering.
Advisor: Bent Stacey primary advisor. Thesis advisor
McGehee Michael
Prinz F. B. advisor. Thesis advisor
Intrinsic retroviral reactivation in human preimplantation embryos and pluripotent cells [electronic resource]

Endogenous retroviruses (ERVs) are remnants of ancient retroviral infections, which comprise nearly 8% of the human genome. The most recently acquired human ERV is HERV-K (HML-2), which repeatedly infected the primate lineage both before and after the divergence of humans and chimpanzees. Unlike most other human ERVs, HERV-K retained multiple copies of intact open reading frames (ORFs) encoding retroviral proteins. However, HERV-K is transcriptionally silenced by the host with exception of certain pathological contexts, such as germ cell tumors, melanoma, or HIV infection. Here we demonstrate that DNA hypomethylation at LTR elements representing the most recent genomic integrations, together with transactivation by OCT4, synergistically facilitate HERV-K expression. Consequently, HERV-K is transcribed during normal human embryogenesis beginning with embryonic genome activation (EGA) at the 8-cell stage, continuing through the emergence of epiblast cells in pre-implantation blastocysts, and ceasing during hESC derivation from blastocyst outgrowths. Remarkably, HERV-K viral-like particles and Gag proteins are detected in human blastocysts, indicating that early human development proceeds in the presence of retroviral products. We further show that overexpression of one such product, HERV-K accessory protein Rec, in a pluripotent cell line is sufficient to increase IFITM1 levels on the cell surface and inhibit viral infection, suggesting at least one mechanism through which HERV-K can induce viral restriction pathways in early embryonic cells. Moreover, Rec directly binds a subset of cellular RNAs and modulates their ribosome occupancy, arguing that complex interactions between retroviral proteins and host factors can fine-tune regulatory properties of early human development.

Keywords: Genetics.
Advisor: Wysocka Joanna
Baker Julie (Professor of genetics) advisor. Thesis advisor
Fire Andrew Zachary advisor. Thesis advisor
Sidow Arend advisor. Thesis advisor
Invariance for perceptual recognition through deep learning [electronic resource]

The brain implements recognition systems with incredible competence. Our perceptual systems recognize an object from various perspectives as it transforms through space and time. A key property of effective recognition is invariance to changes in the input. In fact, invariant representations focus on high-level information and neglect irrelevant changes, facilitating effective recognition. It is desirable for computational simulations to capture invariant properties. However, quantifying and designing invariance is difficult, because the input signals to a perceptual system are high dimensional, and the number of input variations, conceived in terms of separate dimensions of variation, such as position, rotation, scale can be exponentially large. Natural invariance resides in a subspace of this exponential space, one that, I argue, can be more effectively captured through learning than through design. To capture perceptual invariance, I take the approach of modeling through deep neural networks. These models are classic AI algorithms. The deep neural network is characteristic of composing simple features from lower layers into more complex representations in higher layers. Going up in the hierarchy, the network forms high-level representations which capture various forms of invariance found in natural images. Within this framework, I present three applications. First, I investigate position-preserving invariance properties of a classical architecture, the convolutional neural network. Indeed, with convolutional networks, I show results surpassing the previous state-of-the-art performance in detecting the location of objects in images. In such models, however, translational invariance is designed, limiting their ability to capture the full invariance structure of real inputs. To learn invariance without design, I exploit unsupervised learning from videos using the 'slowness' principle. Concretely, the unsupervised learning algorithm discovers invariance arising from transformations such as rotation, out-of-plane changes, or warping from motions in video. When quantitatively measured, the learned invariant features are more robust than ones that are hand-crafted. Using such invariant features, recognition in still images is consistently improved. Finally, I explore the development of invariant representations of number through learning from unlabeled examples in a generic neural network. By learning from examples of 'visual numbers', this network forms number representations invariant to object size. With these representations, I illustrate novel simulations for cognitive processes of the 'Approximate Number Sense'. Concretely, I correlate simulations with deep networks with the sensitivity of discrimination across a range of numbers. These simulations capture properties of human number representation, focusing on approximate invariance to other stimulus factors.

Keywords: Electrical Engineering.
Advisor: McClelland James L. primary advisor. Thesis advisor
Guibas Leonidas J. advisor. Thesis advisor
Widrow Bernard
Investigating strain hardening by simulations of dislocation dynamics [electronic resource]

Materials deform, though different materials do not deform in the same way. They should not be expected to, as they can have very different underlying structures. In ductile metals, deformation occurs in three stages: elastic deformation, plastic deformation, and fracture. Elastic deformation is defined as recoverable deformation. Ductile metals are elastic for small deformations. This is in contrast to plastic deformation, which is unrecoverable deformation. Dislocations are thought to be the carriers of plasticity in metals, and as such play a key role in material deformation. This thesis presents simulation results that explore particular aspects of plasticity, specifically work hardening and cross-slip, at the mesoscale and microscale, respectively. In order to obtain these results, a number of mathematical and numerical advancements were made to improve the efficiency of the simulations and the resulting physics. With the code enhancements, a variety of novel results were obtained. The implementation of an expanded and more powerful 2D Dislocation Dynamics (DD) code, named DD2D, has resulted in efficiency gains of an order of magnitude or more. The advances included mathematical subtleties (implementing nonsingular stresses and a correction for conditional convergence), physical models (adding sources, obstacles, and sessile junctions), and numerical improvements (implementing different integrators and utilizing subcycling). Because of these, a large number of different simulations in DD2D were run and the stress-strain curve results are presented. These results are used to investigate the hardening behavior and propose a tensile yield surface for various configurations of glide planes and compare them to what Schmid's law predicts. Improving the algorithms of an atomistics code, MD++, resulted in the ability to explore stress regimes previously unable to produce results. A new "Reparametrization with Trimming" algorithm allowed the energy barrier for cross-slip to be calculated under high applied stress configurations at temperatures of 0 K. An equilibration algorithm allowed decreased equilibration times at finite temperature by an order of magnitude, greatly increasing the number of temperature and stress configurations that could be investigated. The 0 K simulations of cross-slip explored the effect of different stress components on the energy barrier for cross-slip. Two different theoretical mechanisms were explored: the Friedel-Escaig (FE) mechanism and the Fleischer mechanism. Over three hundred cross-slip simulations were run at different stress configurations at 0 K. The data were fit to 1D curves (one for each mechanism) using an "effective stress, " τ*, comprised of a combination of the applied stresses. Finite temperature simulations produced results used to find the cross-slip rate coefficient that, together with the energy barrier fit, was implemented into a 3D DD code.

Keywords: Mechanical Engineering.
Advisor: Cai Wei
Darve Eric advisor. Thesis advisor
Nix William D. advisor. Thesis advisor
Investigating the Leukemogenic Properties of DNMT3A R882H in Human Cells

Acute myeloid leukemia (AML) is a clonal malignancy of bone marrow progenitors that are arrested in their ability to mature, and for which clinical outcomes have not improved in over 30 years. Mutation in DNA methyltransferase 3A (DNMT3A), an enzyme responsible for catalyzing de novo methylation at CpG sites throughout the genome, is present in approximately 20-25% of normal karyotype AML cases, with more than half of all mutations altering R882 within the catalytic domain. We have recently shown that DNMT3A R882H occurs frequently in mutated but otherwise normal preleukemic hematopoietic stem cells, but little is known regarding its mechanism of action in contributing to leukemogenesis in human cells. Our studies thus focus on characterizing the biological phenotypes associated with DNMT3A R882H in AML cell lines and cord blood-derived hematopoietic stem and progenitor cells (HSPCs) with regard to proliferation, cell survival, immunophenotype, and differentiation. Interestingly, we found that the R882H mutation causes a time-dependent reduction in mature myeloid antigen expression and decreased apoptosis in THP-1 cells, with minimal effects on cell proliferation, mitochondrial mass, and ATRA-induced myeloid differentiation. Lentiviral transduction of DNMT3A R882H into FACS-purified human CD34+ stem and progenitor cells also resulted in increases in both the Lin-CD34+ percentage and fraction of CD34+CD38- HSPCs, consistent with a role for the mutant in retaining stem cell character. In addition, we found that mutant DNMT3A conferred time-dependent and reversible growth factor independence in TF-1 cells. Transcriptional profiling of these cells revealed upregulation of genes including ANGPT1 and CD36, which are also expressed in primary DNMT3A-mutated AML samples from The Cancer Genome Atlas Network (TCGA). These findings provide evidence for the role of mutant DNMT3A in maintaining a primitive cell state, enhancing cell survival, and promoting growth factor independence, and together offer insight into the mechanisms by which the mutation contributes to leukemogenesis.

Keywords:  Department of Biology
Biology
Thesis
Advisor: Majeti Ravindra
Morrison Ashby
Investigating the mechanisms for phototaxis in the cyanobacterium Synechocystis sp. PCC 6803 [electronic resource]

Signal transduction in bacteria is complex, ranging across scales from molecular sig- nal detectors and effectors to cellular and community responses to a stimulus. The unicellular, photosynthetic cyanobacterium Synechocystis sp. PCC6803 transduces a light stimulus into directional movement known as phototaxis. This single-cell response occurs via a biased random walk toward or away from a directional light source, which is sensed by intracellular photoreceptors and mediated by Type IV pili. In this dissertation, I will discuss how I have used the combined manipulations of the light conditions, genetic perturbations, quantitative, single-cell measurements, and a reaction diffusion model to derive simple guiding principles across length scales for cell motility. I first demonstrate that cells indirectly interact with each other via a secreted substance that enhances their mobility, These intercellular interactions re- sult in the dynamic spatial organization of the multicellular community, which do not appear to require changes in individual behavior. I then show how movement directionality of the single cell is determined mainly by the light signal, and is the result of a polarization of pilus pulling forces along the light axis. Finally, I will reveal the newly identified role of the photoreceptor TaxD1 in enhancing movement direction- ality, in addition to its previously shown role in promoting positive phototaxis. Our broadly applicable framework can also be used to dissect similar complex regulatory networks that effects robust responses from input signals.

Keywords: Bioengineering.
Advisor: Bhaya Devaki
Huang Kerwyn Casey
Riedel-Kruse Hans
Sonnenburg Justin
Investigating the molecular and genetic basis of human kidney aging and age-associated renal diseases [electronic resource]

Human kidney function declines with age, accompanied by stereotyped changes in gene expression and histopathology, but the mechanisms underlying these changes are largely unknown. To identify potential regulators of kidney age-associated gene expression changes, we compared age-associated transcriptional changes in the human kidney with genome-wide maps of transcription factor occupancy from ChIP-seq datasets in human cells. The strongest candidate regulators were the inflammation-associated transcription factors NFκB, STAT1 and STAT3. The activities of these transcription factors increase with age in epithelial compartments of the renal cortex, and stimulation of renal tubular epithelial cells with the inflammatory cytokines IL-6 (a STAT3 activator), IFN-γ (a STAT1 activator), or TNFα (an NFκB activator) was sufficient to recapitulate age-associated gene expression changes. We found that macrophage abundance increases with age and is tightly correlated with the activities of NFκB, STAT1 and STAT3 in individual kidneys, independent of chronological age. Thus, we hypothesized that a coordinated mode of regulation links macrophage infiltration to the activation of inflammatory transcription factors in the renal epithelium. We found that the general transcriptional response of human cells to macrophage-conditioned media recapitulates kidney age-associated gene expression changes and mirrors the transcriptional response to inflammatory cytokines, suggesting that macrophages may contribute locally to activation of NFκB, STAT1 and STAT3 via paracrine production of inflammatory cytokines. We next sought to determine whether common genetic variation in the genes encoding NFκB, STAT1 and STAT3 are associated with variation in renal function or chronic kidney disease risk in humans. We performed a candidate gene association study using public genotype data from gene association studies to ask whether polymorphisms in STAT1, STAT3 or the NFκB genes (RELA and NFKB1) show an association with renal function or chronic kidney disease. We found that common DNA variants in RELA and NFKB1, the two genes encoding subunits of the NFκB transcription factor, associate with kidney function and chronic kidney disease, providing the first genetic evidence that NFκB contributes to renal aging-related phenotypes in the human population. Our results suggest that NFκB, STAT1 and STAT3 are major regulators of transcriptional changes, chronic inflammation and functional decline in the aging human kidney.

Advisor: Kim Stuart
Brunet Anne
Rando Thomas A.
West Robert
Investigation of the switching mechanism in hafnium-oxide-based non-volatile resistance change memory [electronic resource]

In recent years, an increasingly complex memory hierarchy has emerged to enable the scale, availability, security, and reliability demanded by new modes of content creation and consumption. However, the overall efficiency and performance of many memory architectures is limited by the latency-power-endurance gap that presently exists between DRAM and NAND FLASH technologies. Resistive Random Access Memory (RRAM) is a high density, low power, non-volatile memory technology that can fill this gap. Despite great interest and numerous modeling attempts, however, fundamental understanding of the RRAM operation mechanism through direct observation is lacking. In this work, first the merits of RRAM in fulfilling evolving memory technology requirements are explored and the challenges to its adoption are reviewed. Next, a novel characterization methodology, based on a highly scaled, membrane-supported lateral device structure is presented. This test device is optimized for in-situ electron microscopy and spectroscopy, while also capturing the fundamental physics governing a conventional, technologically relevant RRAM cell. The new processing techniques developed for the fabrication of sub-10 nm TiN-HfOx-TiN lateral devices on 50nm-thick membranes are described in detail. In the second part of this work, the test devices are thoroughly studied using a comprehensive set of ex-situ and in-situ characterization techniques, in order to distinguish the fundamental physical mechanisms based on direct observation of the electrical, structural, and spectral data.

Keywords: Electrical Engineering.
Advisor: Nishi Yoshio
Saraswat Krishna advisor. Thesis advisor
Wong Hon-Sum Philip