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Mechanisms Underlying Migraine Headache Pathophysiology: Novel Insights From Preclinical Models
No full textMigraine is a highly prevalent and complex disorder characterized by severe, unilateral, pulsating
headaches associated with photophobia, phonophobia, nausea, and, in some cases, auras.
Headaches are the most disabling component of the condition and, while treatments have improved
over the last few decades, the complexity of migraine pathophysiology has made it extremely
challenging to develop highly efficacious therapeutics. Patients are particularly susceptible to
attacks following exposure to normally innocuous stimuli and mounting clinical and preclinical
evidence suggests that this may be due to maladaptive sensitization of the trigeminal sensory
system. Although it is widely accepted that the trigeminovascular system is responsible for the
pain associated with migraine, the mechanisms by which dura-projecting trigeminal ganglia (TG)
nociceptors become activated and sensitized remain poorly understood. In other preclinical pain
models, reactive nitroxidative species such as nitric oxide (NO), but particularly peroxynitrite
(PN), have been implicated in establishing long-lasting hypersensitivity and targeting these
molecules has achieved antinociceptive efficacy. Despite NO donors being one of the most
consistent triggers of headache, little is known about the role of nitroxidative species in migraine
mechanisms. Similarly, other mechanisms that have been shown to contribute to nociceptor
activation and sensitization in preclinical pain models, such as translational dysregulation of
mRNA, have not been studied in the context of migraine. Thus, the goal of our research was to
utilize pharmacological techniques and transgenic animals in our novel preclinical migraine
models to further understand the mechanisms that contribute to the development and persistence
of migraine headache. The first part of our work highlights a novel, critical role for PN formation
in mediating long-lasting hypersensitivity in preclinical models of migraine while the second part
of our work defines MNK regulation of eIF4E phosphorylation as a key target for migraine
therapeutics
A Review of the Relationship Between Federal Entrepreneurship Programs and Regional Development: the Youth Enterprise With Innovation in Nigeria (YouWIN) Program
No full textThe economic development across regions in Nigeria have often been described as uneven and
polarized due to the historical patterns of placing a larger proportion of infrastructural amenities
in few administrative centers. The most prominent of these centers has been Lagos, the nation’s
commercial hub and Abuja, the political hub. Literature suggests that these centers would
continue to attract entrepreneurs and those in search of economic prosperity. Hence, this research
seeks to empirically examine if business survivorship varied with regional location of business,
controlling for demographics, award status, business sector and regional development
characteristics and if receiving grant funding would increase the likelihood of relocation to large
cities. Data from the largest business plan competition in history, the Youth Enterprise with
Innovation in Nigeria (YouWIN) program, was used to determine individual and business sector
characteristics of entrepreneurs, while data on regional characteristics was obtained from Nigeria
Data Portal. Results from the mixed effects logistic regression models found that locating a
business in a state with higher economic development in Nigeria does not increase the chance of
business survival. Also, entrepreneurs who received YouWIN funding were more likely to
remain in the same residence and business location compared to those without YouWIN funding.
These findings are a departure from previous studies and anecdotical assumptions, that described
locations such as Lagos and Abuja to be catalysts of business longevity due to their advancement
in regional development
Synthesis and Characterization of Metal-organic Frameworks for Potential Uses in Cancer Therapy
No full textMetal-organic frameworks (MOFs) are crystalline materials, characterized for their high surface
areas and defined pore architectures. These materials, first synthesized and characterized in the
1990s, have seen an increase interest due to their inherent properties. As a result of their hybrid
nature, MOFs can be used in a wide range of applications, from catalysis and gas storage, to drug
delivery and cancer therapy. While commonly using transition metals as building blocks, using
lanthanide as their metal centers further increases the range of MOF applications. Using holmium
in these materials could potentially create an improved cancer therapy method by delivering both
a radiation source and a radiosensitizer to the cancer sites. This work in particular focuses on the
synthesis and characterization of several frameworks, with the focus of using these materials for
cancer therapy applications
Methods for on-board Condition Monitoring of SiC MOSFET Based Converters
No full textThe power electronics industry is continuously striving to improve the efficiency and density
of power converters. At the same time, with increasing electrification and automation across
application domains, the power electronic systems are expected to meet stringent reliability
requirements, especially in safety-critical applications such as aerospace, autonomous vehicles,
data centers, etc. Silicon Carbide (SiC) power semiconductor devices promise significantly
superior electro-thermal performance to traditional silicon IGBTs and MOSFETs. However,
given their relative nascence, the field reliability of SiC devices is unproven and certain
fundamental reliability challenges exist. This dissertation aims to study on-board condition
monitoring methods as a potential solution to addressing reliability challenges with SiC
MOSFET based converters. The dissertation first presents a detailed architecture for a
modular, highly-scalable accelerated testing platform for SiC MOSFETs. The proposed
testing setup enables rapid aging of large batches of SiC MOSFETs for the purpose of
generating large datasets to study long-term reliability, and identify electrical precursors
that can be used for on-board condition monitoring of SiC devices. Testing on a batch of
discrete SiC MOSFETs using the developed test platform revealed the frequent occurrences of
gate-open failure in discrete SiC MOSFETs. Therefore, in this dissertation, gate-open failures
are systematically studied in the context of SiC MOSFETs, and potential causes for SiC
MOSFETs’ increased susceptibility to gate-open failures is discussed. Importantly, a robust
cycle-by-cycle gate-open failure detection solution is presented and its superior performance
over traditional protection schemes is experimentally validated. Lastly, this dissertation
proposes an end-to-end practical online condition monitoring solution for SiC MOSFET-
based traction inverters using device on-state resistance (Rds−on) as an aging precursor.
The proposed solution includes accurate on-board on-state resistance (Rds−on) measurement
circuits along with code-efficient data acquisition and filtering algorithms. Importantly, the
presented solution uses a stochastic Bayesian state-of-health estimation algorithm. The
algorithm presents an elegant solution to the fundamental problem of separating aging-related
Rds−on change from operating conditions-related changes by exploiting the symmetrical
nature of the inverter’s operation. In particular, the presented solution is highly scalable as
it automatically accounts for device and system level variations and eliminates the need for
extensive system/device specific calibration
Comprop Computational Propaganda on Reddit.com (2013-2022)
No full textThis dissertation concerns the use of computational propaganda, or the use of bots, trolls,
algorithms, disinformation, misinformation, and astroturfing campaigns on the social media
website reddit.com. Simple heuristics and tools like Word2vec are used to identify ideological
groups of users. Three separate case studies include the annexation of Crimea by Russia in
2013, the 2016 presidential election in the United States, and the 2022 invasion of Ukraine
by Russia. They establish the historic context of advances in communication technology and
find evidence of ideological capture by pro-Russian and other users
Investigating the Cell-specific Mechanisms That Drive Sex Differences During Neuropathic Pain Development
No full textChronic pain patients often suffer from a decline in quality of life due to a lack of efficacious long-
term therapeutics. Moreover, the prevalence of chronic pain conditions is on the rise, with an
average increase of nearly 10 percent in patients per decade. This, coupled with the devastating
impact of the opioid crisis, highlights the need for novel pain therapeutics. An extensive literature
has placed microglia, the resident immune cells of the central nervous system, at the forefront of
male-specific mechanisms that mediate chronic pain plasticity. Recently, efforts have been made
to design studies aimed at dissecting female specific mechanisms in pain plasticity. Evidence
suggests that immune-related components of nociceptors are heavily dysregulated following insult
and are more directly responsible for changes in female-specific sensitization. Despite advances
in the field of pain neurobiology, there remains a clear disconnect between the cellular mechanisms
that underlie maladaptive chronic pain in males and females. Moreover, a lack of studies directed
towards interventions during early neuropathic pain development make it difficult to assess how
functional changes in cellular phenotypes following injury can be manipulated to prevent
maladaptive pain plasticity from taking place. The goal of our research was to use an innovative
approach to identify nociceptor and immune cell-specific mechanisms in both the peripheral and
central nervous systems that mediate sex differences during neuropathic pain development. Our
findings suggest that the initial phase of neuropathic development is sexually dimorphic,
characterized by nociceptor-specific signaling mechanisms in females and immune cell mediated
sensitization in males which may be modulated by genetic and pharmacological manipulation of
toll-like receptor 4 signaling
Dual Braid Presentations and Cluster Algebras
No full textPresentations for Coxeter groups and their braid groups are encoded by Dynkin diagrams.
In their foundational work on cluster algebras, Fomin and Zelevinsky defined an operation
on quivers (oriented Dynkin diagrams) called mutation. It is reasonable to ask if a quiver
mutation-equivalent to (an orientation of) a Dynkin diagram also encodes a presentation of
a Coxeter or braid group. By explicitly writing down a set of relations, Barot and Marsh
constructed such presentations for Coxeter groups, which Grant and Marsh generalized to
the corresponding braid groups. We explain and generalize these results for simply-laced
types using presentations encoded by reduced factorizations (into reflections) of a Coxeter
element—the results above are recovered by specializing to certain two-part factorizations
(in bijection with vertices of the cluster exchange graph) and certain compositions of Hurwitz
moves (paralleling quiver mutation)
Three Essays on Adoption and Consumption of Entertainment Products
No full textWe study users’ adoption and consumption of entertainment products in three chapters.
Using unique data collected from online video gaming platforms on users’ playing times, we
study how consumers make decisions to adopt and play video games.
In the first chapter, we investigate the factors that influence an individual’s adoption of
a video game. Specifically, we study how the adoption of a video game in a franchise is
influenced by the adoption of previous games in that franchise and users’ experiences with
those games. We also introduce some measures of dissimilarity between a video game and
its franchise. The results show that being franchised does not affect the time of adoption in
general. However, users who have adopted the last game in the franchise and have better
experience with the franchise adopt the new game faster. We also find that a game’s changes
in genres are more favored by general users. However, users who have adopted the last game
in the franchise adopt the new game later when there are some changes in genres.
In the second chapter, we investigate the effects of esports events and product update on
players’ decisions to play a video game using individual player’s gaming history data of
Dota 2. To study individual video game playing decisions in continuous time, we develop a
continuous-time discrete choice structural model of product usage. Counterfactual analysis
results show that decreasing the frequency of esports events and decreasing the frequency
of product updates can both increase the total game playing time of players. In addition,
joint scheduling of product updates and esports events can increase product usage further.
Considering these findings, product managers in the video game industry might want to
decrease product update frequency and allocate budgets to host less esports events. They
should also consider joint scheduling of product updates and esports events to take advantage
of the synergy between these marketing actions.
In the third chapter, we investigate the interdependencies in consumers’ video game consumption, specifically how esports events of two particular video games affect the consumption of
those video games as well as other video games in the same genres (i.e., product categories)
and other genres. Players may consume more than one video game in a specific time window.
Standard choice models are not appropriate to model contexts entailing this phenomenon
called multiple discreteness. Multiple discreteness is a characteristics of consumption be-
havior as the choice of multiple, but not necessarily all alternatives simultaneously (Bhat,
2008). To address these challenges, we extend the model developed by Bhat (2005, 2008)
called multiple discrete-continuous extreme value (MDCEV) model. The results show that
esports events of Dota 2 and CS:GO do not increase the baseline preference for these video
games during these events or after these events. We also find that the impact of esports events
spillover to other genres, depending on the interdependencies in consumers’ consumption.
Platform managers and video game developers can utilize the methodology in this paper to
predict the probable impacts of their marketing actions on consumers’ consumption
Providing Wavelength Resolved Irradiance Measurements by Using Machine Learning
No full textSunlight incident on the Earth’s atmosphere is essential for life and is the driving force
for atmospheric photo-chemistry. Atmospheric photo-chemistry is central to understanding
urban air quality and the host of associated human health impacts. In this dissertation, two
solutions were proposed to address the current lack of real-time wavelength-resolved solar
irradiance data across cities.
Our first solution is based on the machine learning calibration of low-cost light sensors. These
calibrated sensors have a strong performance and can be readily deployed at scale across
dense urban environments to measure the wavelength resolved irradiance on a neighborhood
scale. This work has been published in MDPI (Zhang et al., 2021).
Our second solution is based on the comprehensive dataset from public environmental sensors.
We developed another machine learning model to estimate the wavelength resolved solar
irradiance from solar zenith angle, earth distance, and multiple environmental dataset, such
as relative humidity, total column ozone, earth surface reflectance, and radar reflectivities
in the sky. All these factors can be accessed from the public datasets of weather stations
and remote sensing systems. Using this solution, wavelength resolved solar irradiance can be
estimated in a neighborhood scale, without implementing any additional sensors
Modeling of Driver Attention in Real World Scenarios Using Probabilistic Salient Maps
No full textMonitoring driver behavior can play a vital role in combating various road hazards. The
majority of accidents can be avoided if the driver gets an adequate warning few seconds
prior to the event. Monitoring driver actions can provide insights about the driver’s intent,
attention and vigilance. This information can be helpful in designing smart interfaces in the
vehicle that provides necessary warning to the driver or take control when necessary.
Visual attention is one of the most important factors in driver monitoring, since most driving
maneuvers strongly rely on vision. An inattentive driver may lack awareness about the factors
in the environment such as pedestrians, other vehicles and trac changes.
Visual attention of a driver can be monitored by either tracking the driver’s head pose or by
tracking their eye movement. While advancement in computer vision have inspired various
studies that can eciently track head and eye movement from the face, these models face
challenges in a naturalistic driving environment because of the changes in illumination, high
head rotation and occlusions.
This dissertation discusses various methods to predict the driver’s visual attention using
probabilistic visual maps. We collect a large scale multimodal dataset where 59 drivers
are recording when performing various secondary activities while driving, to capture the
vi
diversity of data in a naturalistic driving environment. The subjects fixate their gaze at
predetermined location which help us establish a correspondence between the driver’s face
and their gaze target. Using this dataset, we have performed various analysis that guided our
proposed models to predict the driver’s visual attention. We establish that while the head
pose of the driver has a strong correlation with the driver’s visual attention the relationship
is not one to one. Hence, it is not feasible to design models that can predict a single value
of driver’s gaze from the head pose. Therefore, we take a probabilistic approach where
the driver’s visual attention is predicted as a probabilistic visual map whose value at each
point depend on the probability that the driver is looking at a certain direction. First,
we design parametric regression models that provide a Gaussian distribution of the driver’s
gaze from the driver’s head pose. The model is heteroscedastic based on Gaussian Process
Regression (GPR) which learns the distribution of gaze as a gaussian random process which
is function of the head pose in 6 degrees of freedom. Next, we propose deep networks with
convolutional and upsampling layers that performs classification on a 2D grid to obtain
visual map. The model is non-parametric and learns the distribution from the data. We
propose two di↵erent models. The first model takes the head pose of the driver as the input
and passes it through a fully connected layer followed by convolution and upsampling to
predict the visual attention at di↵erent resolutions. The second model takes an image of the
eye patch as an input and passes it through multiple layers of convolution and maxpooling
to obtain a low dimensional representation of the visual attention. Consecutively, this low
dimensional representation is passed through upsampling and convolution layers to obtain
a high dimension representation of visual attention. In our final approach, We design a
fusion model that integrates the information from the driver’s head pose as well as their eye
appearance to predict a visual attention map at multiple resolution. This model follows an
encoder-decoder architecture with two encoders, one each for the head pose and the gaze
and a decoder that concatenates the information from both the head pose and gaze to obtain the final visual map. We project the model prediction onto the road and evaluate it on data
when the subject looks at the landmarks on the road