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The regulation of neural precursor cells (NPCs), which encompass neural progenitor and neural stem cells (NSCs), is fundamental for proper brain development and function. These cells are regulated by orchestrated signalling within their local environment. Aberrant aspects of cell proliferation, differentiation, survival, or integration have been linked to various neurological diseases including Fragile X syndrome (FXS)—a disorder characterized by intellectual and social changes due to the silencing of the gene encoding FMRP. The biology of hippocampal NPCs in FXS during early postnatal development has not been studied, despite high FMRP expression levels in the hippocampus at the end of the first postnatal week. In this thesis, the Fmr1-knockout (KO) mouse model was used to study hippocampal cell biology during early postnatal development. A tissue culture assay, used to study the effect of astrocyte-secreted factors on the proliferation of NSCs, indicated that astrocyte secreted factors from Fmr1-KO brains enhanced the proliferation of wild type, but not Fmr1-KO NSCs (Chapter 3). Next, the proliferation and cell cycle profiles of NPCs in vitro and in vivo studied with immunocytochemistry, Western blotting, and flow cytometry revealed decreased proliferation of NPCs in the Fmr1-KO hippocampus (Chapter 4). Finally, cells isolated from the P7 dentate gyrus and characterized by flow cytometry, showed a reduced proportion of NSCs and an increased proportion of neuroblasts—neuronal committed progenitors—in Fmr1-KO mice. Together, these results indicate that hippocampal NPCs show aberrant proliferation and neurogenesis during early postnatal development. This could indicate stem-cell depletion, increased quiescence, or a developmental delay in relation to lack of FMRP and uncovers a new role for FMRP in the early postnatal hippocampus. In turn, elucidating the mechanisms that underlie FXS will aid in the development of targeted treatments. / Thesis / Doctor of Philosophy (PhD) / Fragile X syndrome is the leading inherited cause of intellectual impairment and autism spectrum disorder. The syndrome is caused by a defect in one gene. This gene has been suggested to play a role in regulating the birth of new brain cells termed neural precursor cells. The importance of neural precursor cells stems from their ability to generate neurons and glia, the main cells in the brain. In this thesis, I focus on studying neural precursor cells from the hippocampus, a brain region important for learning and memory. A mouse model was used to compare neural precursor cells from healthy and Fragile X mice during early postnatal development. I found that neural precursor cells do not divide as much as they should in the Fragile X mouse hippocampus. The results help to determine the causes for learning and memory deficits in Fragile X and potentially open avenues for intervention.

With the increased application of large eddy simulations and hybrid Reynolds-averaged Navier-Stokes techniques, the generation of realistic turbulence at inflow boundaries is crucial for the accuracy of numerical results. In this dissertation research, two novel turbulence inflow generation methods are derived and validated. The first method, the Triple Hill's Vortex Synthetic Eddy Method, is a new type of synthetic eddy method, where the fundamental eddy is constructed through a superposition of three orthogonal Hill's vortices. The amplitudes of the three vortices that form the fundamental eddy are calculated from known Reynolds stress profiles through a transformation from the physical reference frame to the principal-axis reference frame. In this way, divergenceree anisotropic turbulent velocity fields are obtained that can reproduce a given Reynolds stress tensor. The model was tested on isotropic turbulence decay, turbulent channel flow, and a spatially developing turbulent mixing layer. The Triple Hill's Vortex Synthetic Eddy Method exhibited a quicker recovery of the desired turbulent flow conditions when compared with other current synthetic turbulence methods. The second method is the Control Forced Concurrent Precursor Method which combines an existing concurrent precursor method and a mean flow forcing method with a new extension of the controlled forcing method. Turbulent inflow boundary conditions are imposed through a region of body forces added as source terms to the momentum equations of the main simulation which transfer flow variables from the precursor simulation. Controlled forcing planes imposed in the precursor simulation, allow for specific Reynolds stress tensors and mean velocities to be imposed. A unique feature of the approach is that the proposed fluctuating flow controlled forcing method can be applied to multiple fluctuating velocity components and couple their calculation to amplify the existing fluctuations present in the precursor flow field so that prescribed anisotropic Reynolds stress tensors can be reproduced. The new method was tested on high and low Reynolds number turbulent boundary layer flows, where the proposed fluctuating flow controlled forcing method greatly accelerated the development of the turbulent boundary layers when compared with cases without controlled forcing and with only the original controlled forcing.

New halo-carbon precursor, CH3Cl, is used in this work to replace the traditional C3H8 gas as a carbon precursor for the homoepitaxial growth of 4H-SiC. The traditional SiH4-C3H8-H2 systems require high growth temperatures to enable the desirable steplow growth for high-quality epilayers. A well known problem of the regular-temperature growth is the homogeneous gas-phase nucleation caused by SiH4 decomposition. However, the degree of Si cluster formation in the gas phase and its influence on our low-temperature epitaxial growth was unknown prior to this work. Growth at temperatures below 1400°C was demonstrated previously only for a limited range of substrate surface orientations and with poor quality. Mirror-like epilayer surface without foreign polytype inclusions and with rare surface defects was demonstrated at temperatures down to 1280-1300°C for our halo-carbon growth. Quantitatively different growth-rate dependences on the carbon-precursor flow rate suggested different precursor decomposition kinetics and different surface reactions in CH3Cl and C3H8 systems. Photoluminescence measurement indicated the high quality of the epilayers grown at 1300°C. A mirror-like surface morphology with rare surface defects was demonstrated for the growth on low off-axis substrates at 1380°C. The most critical growth-rate limiting mechanism during the low-temperature epitaxial growth is the formation of Si clusters, which depleted the Si supply to the growth surface, in the gas phase. Presence of chlorine in the CH3Cl precursor significantly reduces but does not completely eliminate this problem. The addition of HCl during growths improved the growth rate and surface morphology drastically but also brought up some complex results, suggesting more complex mechanisms of HCl interaction with the gas-phase clusters. These complicated results were explained partly by an additional mechanism of precursor depletion enhanced in presence of HCl. Complex changes in the effective silicon to carbon ratio in the growth zone indicated that the supply of carbon species may also be enhanced at least at low HCl flow rates. This fact allowed us to suggest that the gas-phase clusters may contain a significant amount of carbon. The new model assuming coexistence of the silicon and carbon in the gas-phase clusters enabled the explanation of the complex experimental trends reported in this work.

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This dissertation is focused on the design and evaluation of machine learning algorithms for modeling information precursors for use in event modeling and forecasting. Given an online stream of information (e.g., news articles, social media postings), how can we model and understand how events unfold, how they influence each other, and how they can act as determinants of future events? First, we study information reciprocity in joint news and social media streams to capture how events evolve. We present an online story chaining algorithm which links related news articles together in a low complexity manner and a mechanism to classify the interaction between a news article and social media (Twitter) activity into four categories. This is followed by identification of major information sources for a given story chain based on the interaction states of news and Twitter. We demonstrate through this study that Twitter as a social network platform serves as a fast way to draw attention from the public to many social events such as sports, whereas news media is quicker to report events regarding political, economical, and business issues. In the second problem we focus on forecasting and understanding large-scale societal events from open source datasets. Our goal here is to develop algorithms that can automatically reconstruct precursors to societal events. We develop a nested framework involving multi-instance learning for mining precursors by harnessing temporal constraints. We evaluate the proposed model for various event categories in multiple geo-locations with comprehensive experiments. Next, to reinforce the fact that events are typically inter-connected and influenced by events in other locations, we develop an approach that creates personalized models for exploring spatio-temporal event correlations; this approach also helps tackle data/label sparsity problems across geolocations. Finally, this dissertation demonstrates how our algorithms can be used to study key characteristics of mass events such as protests. Some mass gatherings run the risk of turning violent, causing damage to both property and people. We propose a tailored solution for uncovering triggers from both news media and social media for violent event analysis. This work was partially supported by the Intelligence Advanced Research Projects Activity (IARPA) via Department of Interior National Business Center (DoI/NBC) contract number D12PC000337, the Office of Naval Research under contract N00014-16-C-1054, and the U.S. Department of Homeland Security under Grant Award Number 2017-ST-061-CINA01. The US Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. The views and conclusions contained herein are those of the author and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of NSF, IARPA, DoI/NBC, or the US Government. / Ph. D. / Today, massive open source information is widely available through news and social media, but analyzing this information is a complex task. It is imperative to develop algorithms that can automatically reconstruct the clues to societal events that are reported in news or social media. The focus of this dissertation is on simultaneously uncovering precursors to societal events and using such precursors to forecast upcoming events. We develop various machine learning algorithms that can model event-related data and determine the key happenings prior to an event that have the greatest predictability to such events in the future. We use our algorithms to understand the nature of precursors to civil unrest events (protests, strikes, and ‘occupy’ events) and why some of these events turn violent.