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Heme Sequestration as an Effective Strategy for the Suppression of Tumor Growth
No full textHeme is an essential prosthetic group in proteins and enzymes involved in oxygen utilization and
metabolism. Heme also plays versatile and fascinating roles in regulating fundamental biological
processes ranging from aerobic respiration to drug metabolism. Increasing experimental and
epidemiological data have also shown that altered heme homeostasis accelerates the development
and progression of common diseases, including various cancers, diabetes, vascular diseases, and
Alzheimer's disease. The effects of heme on the pathogenesis of these diseases may be mediated
via its action on various cellular signaling and regulatory proteins, as well as its function in cellular
bioenergetics, specifically, oxidative phosphorylation (OXPHOS). Elevated heme levels in cancer
cells intensify OXPHOS, leading to higher ATP generation and fueling tumorigenic functions. In
contrast, lowered heme levels in neurons may reduce OXPHOS, leading to defects in bioenergetics
and causing neurological deficits. Additionally, heme has been shown to modulate the activities
of diverse cellular proteins influencing disease pathogenesis. These include tumor suppressor P53
protein, progesterone receptor membrane component 1 protein PGRMC1, cystathionine-βsynthase CBS, and the nuclear receptor subfamily member Rev-Erbα.
Here, we generated small heme-sequestering proteins (HeSPs) based on bacterial hemophores.
These HeSPs contain neutral mutations in the heme-binding pocket of hemophores and hybrid
sequences from hemophores of different bacteria. We showed that HeSPs bound to heme and
effectively extracted heme from hemoglobin. They strongly inhibited heme uptake and cell
proliferation and induced apoptosis in non-small lung cancer (NSCLC) cells, while their effects
on non-tumorigenic cell lines representing normal lung cells were not significant. HeSPs strongly
suppressed the growth of human NSCLC tumor xenografts in mice. HeSPs decreased oxygen
consumption rates and ATP levels in tumor cells isolated from treated mice, while they did not
affect liver and blood cell functions. Immunohistochemistry revealed that HeSPs reduced the
levels of key enzymes and transporters involved in heme synthesis and uptake, as well as the
uptake and metabolism of the main fuels for cancer cells, glucose and glutamine. Further, we found
that HeSPs reduced the levels of angiogenic and vascular markers, as well as vessel density in
tumor tissues. Together, these results demonstrate that HeSPs act via multiple mechanisms,
including the inhibition of oxidative phosphorylation, to suppress tumor growth and progression.
Evidently, heme sequestration can be a powerful strategy for suppressing lung tumors and likely
drug-resistant tumors that rely on oxidative phosphorylation for survival
Design and Applications of Nanoscale Light Sources
No full textFast and efficient nanoscale light sources are at the heart of on-chip optical communication and
computation systems. With the rapid development of advanced fabrication techniques and the use
of metal in cavity designs, light confinement, and manipulation at the nanoscale, far below the
diffraction limit of light, have become possible. Over the years, various nanoscale lasers and LEDs
have been analytically or experimentally demonstrated. From the modulation bandwidth
perspective, nanolasers are ultimately limited by gain compression at high injection currents. From
the energy efficiency perspective, nanolasers are inefficient due to the required high injection
current to reach the lasing threshold. In contrast, nanoLEDs can simultaneously support large
modulation bandwidth due to the Purcell effect, and high energy efficiency because they can be
operated at low injection currents without the need to reach the lasing threshold. This dissertation
is focused on the design and applications of nanoscale light sources towards the realization of
nanoLEDs that can support high speed modulation and efficient operation.
Firstly, we present an optically pumped version of a shifted-core coaxial nanoLED, with a footprint
of merely 1/3 of its emission wavelength in all three dimensions at telecommunication
wavelengths. By shifting the metallic core off the center of the coaxial cavity, the effective mode
volume can be reduced to 0.0078×(λ0/na)3, resulting in a Purcell factor over 390 and a modulation
bandwidth exceeding 60 GHz. Furthermore, this nano-emitter features improved emission
directivity, which increases its coupling efficiency to an on-chip waveguide. As this nano-emitter
supports only one TEM-like mode over the entire material gain spectrum, the spontaneous
emission factor becomes close to unity, which greatly improves its internal quantum efficiency. In
order to calculate the Purcell factor precisely, we exhaustively studied the effective modal volume,
Veff. We found that for cavities with poor confinement and low quality factors, the choice of a
correct field normalization method is crucial to adequately describe the diverging behavior of the
cavity’s effective modal volume.
Secondly, we present the design of an electrically pumped shifted-core coaxial nanoLED. We
design the multiple quantum well III-V gain material to achieve high internal quantum efficiency
and an impedance transformer to improve the injection efficiency into the nanoLED.
Lastly, we propose a biochemical sensor based on plasmonic nanofocusing phenomenon in a pair
of coupled shifted-core coaxial nano-cavities. By placing a fluidic channel between the two
cavities in close vicinity to the hotspots created by the coupled modes, the sensitivity of this
biochemical sensor can be greatly enhanced. In our simulation, this biochemical sensor shows an
ultra-high sensitivity up to 1.5179×104 nm/RIU
Automated extraction of data constraints from software documentation
No full textData constraints encompass crucial business rules that specify the values allowed or required
for the data utilized within a software system. These constraints are typically described
in textual software artifacts (e.g., requirements and design documents, or user manuals).
Previous research on data constraints in software focused on studying their implementation
in the code for identifying inconsistencies or to support their traceability.
This thesis contribute to the existing knowledge by studying 548 data constraints described
in the documentation of nine systems. We identified and documented 15 linguistic discourse
patterns employed by stakeholders to describe data constraints in natural language. In a
comprehensive extensive study, we explore the use of the discourse patterns we discovered,
along with linguistic elements, the operands of the data constraints and their types, as
features for automatically classifying sentence fragments as data constraint descriptions.
The best combination of features and learner achieves 70.87% precision and 59.73% recall
(64.76% F1).
The discoveries made in this thesis represent a significant advancement in the automated
identification and extraction of data constraints from natural language text, which in turn is
essential for enabling the automation of traceability to code and facilitating test generation
associated with these constraints
A Deterministic Model for Non-monotone Relationship Between Translation of Upstream and Downstream Open Reading Frames
No full textThe TASEP modeling was shown to offer a parsimonious explanation for the experimentally confirmed ability of a single upstream Open Reading Frames (uORFs) to upregulate a
downstream translation during the integrated stress response. As revealed by the numerical
simulations, the model predicts that reducing the density of scanning ribosomes upstream of
certain uORFs increases the flow of ribosomes downstream. To gain a better insight into the
mechanism which ensures the counter intuitive non-monotone relation between the upstream
and the downstream flows we propose a phenomenological deterministic model to approximate the modified TASEP model of the translation process. We establish the existence of
a stationary solution featuring the decreasing density along the uORF for the deterministic
model. Further, we find an explicit non-monotone relation between the upstream ribosome
density and the downstream flow for the stationary solution in the limit of increasing uORF
length and increasingly leaky initiation. The stationary distribution of the modified TASEP
model, the stationary solution of the deterministic model and the explicit limit are compared
numerically
Markov Random Fields, Homomorphism Counting, and Sidorenko’s Conjecture
No full textGraph covers and the Bethe free energy (BFE) have been useful theoretical tools for producing lower bounds on various counting problems in graphical models, including the permanent
and the ferromagnetic Ising model. Here, we investigate homomorphism counting problems
over bipartite graphs that are related to a conjecture of Sidorenko. We show that the BFE
does yield a lower bound in various natural settings. When it yields a lower bound, it necessarily improves upon the lower bound conjectured by Sidorenko. Conversely, we show that
there exist bipartite graphs for which the BFE does not yield a lower bound on the homo-
morphism number. Finally, we use the characterizations developed as part of this work to
provide a simple proof of Sidorenko’s conjecture in some particular cases
The Story Portrait™: a New Genre of Personal Representation
No full textPeople have been depicted in two-dimensional works of portraiture—drawings, paintings, and
frescos—since ancient times. Similarly, the use of written and spoken narratives to describe
individuals is as old as language itself. While images and language have been used concurrently
in countless ways, this dissertation explores how they can be more directly conjoined in a new
and powerful genre of personal representation.
The Story Portrait™ is an art form that melds the visual and the verbal in an effort to go beyond
the depiction of physical appearance in a single moment by also providing insight into the
subject’s character. At its core, a Story Portrait™ is a trompe-l’oeil image that appears to be a
monotone or duotone photographic portrait of an individual, but that, upon closer inspection,
reveals in its tints and shades not flat planes of color but, rather, the typeset words of that
person’s autobiographical “story.” Whether it is an abbreviated recapitulation of a life or the
recollection of a particular moment, the crafted story not only allows viewers to see the person’s
face, but also understand a portion of that individual’s history and—based on the vocabulary,
voice, and tone of the testimony—gain insight into his or her personality and moral fiber. The
additional integration of new media has the power to transform that representation into a more
engaging and memorable experience for viewers.
The exhibition that accompanies this dissertation includes the Story Portrait™ in a variety of
static, animated, and multimedia formats that depict historical figures, fictional characters, and
more. Those examples not only trace my development of this art form from its initial concept
and presentation, but also demonstrate the variety of ways in which a Story Portrait™ can be
leveraged to engage, educate, and entertain
Symmetry Index Analysis for Inter-turn Short Circuit Fault Detection in Electrical Machines
No full textElectric motors are a pivotal part of the ongoing shift in the transportation industry towards
electrification. Nonetheless, electric motors are subject to faults. Among all faults, interturn short-circuit can be the most damaging and drastically shorten the motor’s life. It is
admissible that if a motor is healthy, the distribution of the magnetic field will be symmetric around the motor. Therefore, it will be demonstrated that by capturing the magnetic
signature from the end winding of the motor and processing this data, the symmetry index
can be used as proof of the health of an electric machine.
This thesis uses the concept of symmetry index analysis to present a fault diagnosis method
to study the effects of faults specifically inter-turn short-circuits on two different types of
electrical machines with two different winding arrangements. First, the study was conducted
on an induction machine (IM) with a distributed winding, where the winding is customdesigned in such a way that inter-turn short circuits of 1%, 5%, and 10% can be manually
applied to phase A winding. The second motor under this study is a switched reluctance
motor (SRM) with concentrated winding, where 6% (1 turn out of 16 turns in phase A) and
43% (7-turn inter-turn short circuit of 16 turn in phase A) can be applied to the motor’s
phase A winding.
In order to collect the data, two integrated sensor boards are designed and installed at the
proximity of the end winding, where the magnetic data can be captured and processed. It
will be visually demonstrated that when the motor is healthy, the magnetic data will be
distributed in a linearly symmetric manner. Finally, A series of machine learning (ML)
methods will be applied and compared to this data to classify them. These machine learning
methods are Decision trees (DT), Support Vector Machine (SVM), Gradient Boosting (GB),
Random Forest (RF), and Logistic Regression (LR). Two key factors to compare the methods
which are the accuracy and the execution time that the data will fit to the machine learning
model are compared between these methods and then are reported using bar diagrams
Implementation of Machine Learning for Analysis of an on Demand Passive Sweat Cortisol Sensor
No full textCortisol is a steroid hormone produced by the adrenal glands for the purpose of regulating the
body’s response to stress. Stress, as a physiological condition, can be caused by a wide variety of
factors, such as mental exertion, diet, sleep, exercise, etc. For this reason, cortisol has the
potential to serve as a biomarker for general health, as it relates to the everyday habits of
patients. With the development of wearable technologies such as the smartwatch, increased
attention has been focused on the development of noninvasive sensors for on demand testing that
can integrated with wearable technologies. Current biosensing technologies for monitoring of
chemical biomarkers such as cortisol depend on blood or salivary testing, which is invasive,
costly, and time consuming. For this reason, the focus of this research is on the detection of
cortisol through passive sweat, which contains many of the biomarkers present in blood at
concentrations sufficient for detection. We have developed a noninvasive sensor on a flexible,
nano porous substrate that has the capability to detect cortisol passively through sweat. The
sensor data was then processed and input into a machine learning algorithm to analyze the rising
and falling trend of cortisol concentration with time. The use of machine learning to analyze
cortisol trends can be used to inform the wearer of rising or falling cortisol levels, which can
enable them to make informed decisions about their health and lifestyle. Sensor response was
measured by conducting Electrochemical Impedance Spectroscopy (EIS) assays of synthetic
sweat dosed with concentrations of cortisol within the physiological range, from which the
responses for low, medium, and high concentrations of cortisol were found to be significant.
Similar assays were performed within the frequency region of maximum capacitance with dosing
regimens ranging from high to low and low to high concentrations of cortisol, to simulate the rise
and fall of cortisol levels of a human patient over a short period of time. The assay data was
analyzed to find the rate of the change of the sensor response to a shift in cortisol concentration,
which was then used to train a weighted KNN supervised machine learning algorithm to detect
and classify increasing and decreasing cortisol concentrations in sweat. Algorithm accuracy was
validated to be 100% by k means cross validation, showing that a passive, wearable sweat sensor
can successfully be used with machine learning to detect rising and falling trends in cortisol
concentration for on demand, noninvasive cortisol sensing
Reversibly Modulating the Blood-brain Barrier by Laser Stimulation of Endothelial-targeted Nanoparticles
No full textThe blood-brain barrier (BBB) excludes or limits over 98% of approved and investigational drugs
and, as such, represents a major challenge in developing effective treatment strategies for the
myriad of acute and chronic brain diseases. There is increasing recognition that BBB dysfunction
is an integral component of many brain diseases, including neurodegenerative diseases and
primary malignancies, which contribute to neurocognitive dysfunction. Thus, it is critically
important that the strategies used to increase BBB permeability minimize the risks of additional
brain injury. Various methods have been developed to modulate the BBB permeability. Currently,
there are no molecularly targeted approaches for the non-invasive modulation of BBB
permeability.
Here, we first demonstrate that short pulse laser stimulation of gold-nanoparticles (AuNPs),
functionalized to target an integral protein of the BBB tight-junction complex, JAM-A, causes a
graded and reversible increase in BBB permeability in vivo, referred to as OptoBBB. A short pulse
laser excitation of JAM-A targeted AuNPs can lead to sufficient loosening of the tight-junction
complex to allow passage of blood-circulating molecules (600 Da-70 kDa) through the opposing
faces of the tight-junction complex but without permanently compromising its integrity. This
approach allows delivery of immunoglobulins, viral gene therapy vectors, and liposomes to
specific locations in the brain. It provides high regional specificity and does not lead to significant
disruption in the spontaneous vasomotion or the structure of the neurovascular unit.
To better understand this technology, we further explored the targeting efficiency and cellular
mechanisms involved in OptoBBB using a human cerebral microvascular endothelial/D3 cell line
to establish an in vitro transwell BBB model. We demonstrate that targeting glycoprotein on the
BBB leads to >20-fold higher targeting efficiency compared with tight junction targeting. Using
live calcium (Ca2+), we uncover that OptoBBB is associated with a transient elevation of Ca2+ that
propagates among the endothelial cells after laser excitation and extends the region of BBB
opening. The Ca2+ response involves both internal Ca2+ depletion and Ca2+ influx. Furthermore,
we demonstrate the involvement of actin polymerization and phosphorylation of ERK1/2 (one of
the downstream messengers of Ca2+ signaling pathway) after laser treatment, which can contribute
to cytoskeletal contraction and BBB opening.
In summary, the OptoBBB is a promising strategy to screen and deliver therapeutic agents into the
central nervous system in preclinical models noninvasively and for clinical translation using
fiberoptics. The findings from the targeting efficiency and cellular mechanism study provide a
mechanistic insight into the BBB opening by laser excitation of AuNP and help guide future
development of this technology for brain diseases treatment
Heroes, Hedonists, Hell-raisers, and Heretics: Reading American Masculinity in Crisis, 1940-1995
No full textThis dissertation examines literary representations of masculinity by male authors during four
distinct historical periods of crisis. Chapter 1 introduces a brief history of masculinity and men’s
studies and traces how the change in understandings about gender and gender roles have played a
part in transforming perceptions of how masculinity is portrayed individually and as a broader
concept. Chapter 2 investigates the crisis of conformity versus individuality during the early
Cold War years with close readings of Ralph Ellison’s Invisible Man and John Updike’s Rabbit,
Run. The protagonists in these texts convey two unique representations of masculinity, each of
which is complicated by problems that manifest because of conformity to expectations in the
midst of quests for individuality. Chapter 3 explores the systemic annihilation of black
masculinity prior to and during the Civil Rights movement through a close reading of Richard
Wright’s Native Son. The novel exposes the realities of life for black men where legitimate entry
into the public sphere is denied through conscious or subconscious efforts to relegate issues
surrounding race and racism to second-tier, apolitical concerns. Chapter 4 investigates the quest
for recognition and visibility of gay men from the pre-Stonewall period through the AIDS crisis.
Close readings of John Rechy’s City of Night, Edmund White’s The Beautiful Room is Empty,
and various works by members of the Violet Quill highlight varying iterations of gay
masculinities during a period of monumental change and crisis and question perceptions of those
masculinities both within and from outside the gay community. Chapter 5 analyzes attempts to
redefine and reclaim “godly” masculinity among evangelicals in the waning years of the
twentieth century. Close readings of the first two novels from the Left Behind series by Tim
LaHaye and Jerry Jenkins reveal the authors’ attempt to portray evangelical masculinity in a way
that straddles both essential and expressive understandings of biblical manhood; these novels
address the anxieties about the perceived leftward shift in American culture and politics,
particularly where gender and gender roles are concerned. This project moves beyond the scope
of previous scholarship by examining how masculinities meet and respond to crises in
historically significant periods through their representations in contemporary literature