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Computational Investigations of a Series of Enzymes Using Coupled Quantum and Molecular Mechanics Methods
No full textThis dissertation employs computational methods such as molecular dynamics (MD) simulations
and coupled quantum mechanics/molecular mechanics (QM/MM) optimizations to explore
various biological systems. Initially, it examines the inhibition mechanisms of xanthine
oxidoreductase (XOR) by two commonly-used inhibitors, oxipurinol and topiroxostat, and
explores the impact of amino acids on the inhibition reaction to design more enhanced drugs. It
is then followed by an extensive investigation into the inhibition of lysyl hydroxylase-2 (LH2)
by 1,3-diketone analogs, where the crucial impact of enolate-based derivatives of the proposed
inhibitors was emphasized by various analyses. Afterward, the investigation delves into the
cleavage mechanism of CRISPR-Cas9, elucidating the impact of matched and mismatched target
DNA on catalysis and the significant role of the protein’s environment in the reaction, guiding
recommendations for mutagenesis studies. The impact of SARS-CoV-2 RNA-dependent RNA
polymerase (RdRp) structure by remdesivir and two analogs was assessed, explaining the role
played by remdesivir in the disruption of RNA synthesis by RdRp, and identifying the main
drivers of these disruptions. Additionally, it explores the dynamics and kinetics of wild-type
human polymerase kappa (Pol κ) and its cancer-associated variant, Y432S, revealing insights into
the third metal ion's role and non-bonded contributions from protein’s residues in stabilizing the
reaction's transition state. The final ongoing aim of this dissertation is to explore electrolytic DNA
chemistry on noisy quantum devices. This reaction occurs exclusively in a far-from-equilibrium
current-carrying states, suitable for simulation on noisy intermediate-scale quantum (NISQ)
devices. Beginning with small systems and employing the most basic quantum mechanical level
of theory feasible, we successfully mapped the H2 Hamiltonians into qubit space using our novel
variational technique, yielding promising outcomes
Improving Electrofacies Prediction by Combining Supervised and Unsupervised Learning Methods
No full textElectrofacies classification from well logs is an indispensable part of seismic interpretation
and is important in the determination of sequence stratigraphy, and ultimately reservoir
characterization. Although there have been improvements in the tools used to perform this
task, it remains laborious, subjective, and error-prone. Achieving a proper classification is
complicated by increasing dataset sizes as well as the need for correlated multidisciplinary
models. Recent developments in machine learning provide an opportunity to assist interpreters
in accomplishing this task while also improving the accuracy of classification results.
Applications of machine learning methods for automating facies classification from well logs
have previously been explored, however these have mostly focused on evaluations or comparisons
of individual algorithms or of ensembles of homogeneous agents. The proposed methods
combine heterogeneous agents to enhance prediction accuracy and expedite the assessment
of large datasets. This approach seamlessly integrates supervised and unsupervised learning
techniques, effectively capitalizing on their individual strengths and mitigating their inherent
limitations. The overarching objective is to offer valuable support to geoscientists by
not only improving prediction accuracy beyond the constraints of current methodologies, but
also by significantly accelerating the evaluation process for progressively expanding datasets.
To accomplish this, supervised learning, which establishes a direct mapping from the data
domain to the solution domain while introducing some bias to generalize the mapping, is
coupled with unsupervised learning, which operates without reliance on similar generalization
bias or predefined training data but does not offer a direct mapping between the data
and solution domains. This fusion is achieved through the utilization of a joint probability
density function (PDF) derived from the supervised classification. The PDF serves
to guide the identification of clusters outlined by unsupervised learning. This multi-agent
approach can effectively detect bias introduced during training for as many as one in five
samples present in well log data, and forms the basis for generating a probability distribution
for individual samples, rather than simply assigning a discrete classification for each
sample. Consequently, this distribution proves valuable in more accurately representing the
continuous nature of well log signals, and captures the intrinsic continuity present within
lithological regimes. A modified version of this approach can be applied to rapidly identify
regions of interest within complex depositional environments which may involve hundreds or
even thousands of boreholes equipped with extensive suites of well log data. This innovative
adaptation streamlines the evaluation process, reducing an interpretation task that could
have consumed months to just hours. As a result, geoscientists can dramatically scale up
their interpretation efforts, increasing their output substantially. The research concludes by
establishing a foundation for comparing the predictive accuracy of the proposed approach
with that of traditional petrophysical analysis and of core interpretation; where these proven
methods serve as benchmark references for the evaluation
Periodic Solutions to Reversible Second Order Autonomous Systems With Commensurable Delays
No full textIn this dissertation, we develop a method based on the equivariant Brouwer degree to study
the existence and spatio-temporal symmetries of periodic solutions to second order reversible
equivariant autonomous systems with commensurate delays. The considered delayed system
of differential equations with Γ-symmetries is reformulated as a nonlinear operator equation
in appropriate functional space. Then the Brouwer O(2)ΓZ2-equivariant degree theory
is applied to prove the existence of 2π-periodic solutions. The group O(2) is related to the
reversing symmetry, Γ reflects the spatial symmetries of the system (for instance related to
symmetries of a network of coupled identical oscillators), and Z2 is related to the oddness
of the right-hand-side. Abstract results are supported by a concrete example with Γ = D6 –
the dihedral group of order 12
Yawing effects in wind power plants: stochastic wake modeling and control
No full textThe success of model-based closed-loop yaw control strategies relies on the accuracy of models that are used to estimate the spatio-temporal attributes of wakes behind wind turbines. We utilize a stochastic dynamical modeling framework to develop reduced-order models of wind farm turbulence that capture the effects of yaw misalignment due to control or atmospheric variability on turbine wakes and their interactions. In this approach, stochastically forced linear models of the turbulent velocity field are used to augment analytical descriptions of the wake velocity provided by low-fidelity engineering wake models. The power-spectral densities of the stochastic models are identified via convex optimization to ensure statistical consistency with high-fidelity large-eddy simulations while preserving model parsimony. We first demonstrate the utility of our approach in capturing turbulence intensity variations at the hub-height of turbines that are yawed against the inflow velocity field impinging on the wind farm. We then extend our two-dimensional (2D) models of hub-height velocity to three-dimensional (3D) wind field models that account for the dynamics of the normal velocity
and can thus capture complex attributes of yawed wind turbine wakes such as their rotation and curl. Our results in training 2D and 3D stochastic linear models provide insight into the significance of sparse field measurements in reproducing the statistical signature of wind farm turbulence and demonstrate the robustness of our modeling approach to atmospheric uncertainties and yaw misalignment effects. In the final part of this thesis, we use actuator disc concepts to demonstrate the utility of infinite-horizon stopping in optimizing the yaw angles of wind turbines for constrained maximum power production
Developing a Flexible, Affinity-based, Electrochemical Sensing Platform Towards Multiplexed Detection of Apocrine and Eccrine Sweat Based Biomarkers; SLOCK: Sensor for Circadian Clock
No full textChronobiology is defined as the temporal fluctuations occurring in the human physiology due to
the circadian cycle. These fluctuations are good indicators of the functioning of the HypothalamicPituitary-Adrenal axis (HPA axis) and can be tracked by using biomarkers: Cortisol and
Dehydroepiandrosteron (DHEA). Low volume tracking systems are beneficial for patients
exposed to chronic stress, patients suffering from endocrine conditions manifested by circadian
disruption and act as a lifestyle monitoring tool. An estimate of 50-70 million Americans suffer
from chronic sleep disorders (NHLBI, 2003) which hinders routine functioning and has deleterious
health implications including elevated cortisol levels, increased risk of hypertension, diabetes,
depression, cardiac conditions, and stroke. Currently available technologies for monitoring the
circadian rhythm (e.g., Polysomnography (Ambulatory monitoring of nocturnal sleep) or
Actigraphy (Multiday ambulatory assessment)) do not provide an accurate estimation of the extent
of circadian disruption, as they are superficial. Biomarker assays are the diagnostic gold standard
techniques used for the diagnosis of circadian dysregulation caused by adrenal disorders. These assays usually rely on the use of blood or serum and take about 3-4 hours for the test results which
proves to be an ineffective solution, from the point of view of developing a circadian profile for
the user. Adrenal steroids like cortisol and DHEA are expressed in sweat in the nanogram range
and can be used as biomarkers to facilitate self-monitoring. SLOCK is a sweat based
chronobiology tracking system that works on the principles of electrochemically transduced
affinity-based systems. The sensor can detect cortisol and DHEA in the physiologically relevant
ranges i.e., 8-200 ng/ml and 2-131 ng/ml respectively. The sensor was also tested through human
subject-based studies and is able to capture rise and fall in biomarker levels on-body. In addition
to this, the use of serpentine interdigitated electrodes provides mechanical stability when exposed
to oscillations caused due to a wearable form factor. Based on the cross-reactivity studies, the
response for target biomarkers is highly specific to the biomarker of interest. Towards the last part
of this work, the SLOCK platform was tuned for detection of a protein biomarker, interleukin-31
(IL-31) for the purpose of creating a chronic disease diagnosis and management platform. The
benchmarking for this was carried out by modelling it for detection of atopic dermatitis related
flares. The platform is able to sensitively detect IL-31 over the dynamic range of 50-1000 pg/mL.
The platform was successfully coupled with portable electronics and was able to record biomarker
fluctuations on-body through human subject-based testing. The SLOCK platforms offers highly
sensitive, non-invasive monitoring of circadian related or chronic disorders in a point-of-need
setting
Low Noise Integrated Circuits and Systems Using Nano-Scale MOSFETs and Intelligent Post-Fabrication Selection
No full textRecent advances in integrated radio design have enabled many applications such as wearable
healthcare, 5G communication, and beyond 5G or 6G applications for ultra-high data rate
communications, high-resolution imaging, sensing, and spectroscopy. All these applications
require low noise radio transceivers for achieving high performance. For example, applications
requiring high data rate and higher order modulation schemes need to achieve high signal to noise
ratio (SNR) and therefore a low noise figure to maintain a low bit-error rate (BER). In addition,
noise phenomena like jitter and phase noise can impact the critical parameters like maximum
achievable data rate and energy efficiency. This research aims to improve the noise performance
of integrated circuits and systems through intelligent post-fabrication selection of an array of nanoscale transistors sized near the minimum in CMOS processes.
A phase noise reduction technique in LC Voltage Controlled Oscillators (VCO’s) is demonstrated
by post-fabrication selection of a subset of an array of near minimum-size cross-coupled transistor
pairs with reduced low frequency noise and thermal noise. The technique reduces the phase noise
by taking advantage of the fact that when transistor dimensions are reduced, the low frequency noise and thermal noise vary significantly. Applying an intelligent post-fabrication selection
process using a genetic algorithm, the lowest phase noise of -122 dBc/Hz, -127 dBc/Hz,
-137.5 dBc/Hz at 600-kHz, 1-MHz, and 3-MHz offsets, respectively from a 3.8-GHz carrier has
been measured. The VCO prototype was fabricated in a 65-nm CMOS process and dissipates
7 mW of DC power. The maximum figure of merit (FoM) including phase noise, carrier frequency
and power consumption is 191 dBc/Hz and the figure of merit including the VCO core area, FoMA
is 207 dBc/Hz.
A technique is demonstrated to reduce both the in-band and out-of-band phase noise of a 4-GHz
Integer-N PLL by employing an array of individually selectable cross-coupled pairs formed using
near minimum-size transistors in an LC VCO and intelligent post-fabrication selection. By
reducing both the in-band and out-of-band phase noise, the overall integrated phase jitter in a
frequency synthesizer can be minimized. Applying an intelligent post-fabrication selection
process, the lowest phase noise of -72 dBc/Hz at 30-kHz offset, -106 dBc/Hz at 300-kHz offset,
-121.8 dBc/Hz at 1-MHz offset, and -132.5 dBc/Hz at 3-MHz offset, respectively from a
4.01-GHz locked carrier has been measured. The integrated rms jitter from 100-kHz to 100-MHz
offsets is 440 fs.
A mixer-first downconverter employing an array of passive mixers formed using near minimumsize transistors and intelligent post-fabrication selection achieves a double sideband noise figure
of 4.2 dB at RF of 6 GHz, which is the lowest at 6 GHz for CMOS mixer-first downconverters.
The downconverter is fabricated in 65-nm CMOS and demonstrates out-of-band IIP3 and IIP2 of
25 dBm and 65 dBm, respectively at 80-MHz IF, while dissipating 11.5 mW. Post-fabrication selection is performed by a genetic algorithm which takes ~17 generations to converge to the
combinations exhibiting the lowest noise
Knowledge-rich Bridging Anaphora Resolution
No full textBridging anaphora resolution, the task of identifying anaphoric noun phrases (i.e., bridging anaphors) and linking them to their antecedents in a document, is crucial for machine
comprehension of the relations between discourse entities for various downstream applications, such as question answering and dialogue systems. Bridging resolution is arguably
less studied but more challenging than entity coreference resolution, the task of determining
which entity mentions refer to the same entity in the real world. Specifically, while linguistic
constraints on coreference exist at the grammatical (e.g., gender and number agreement),
syntactic (e.g., c-command), and semantic (e.g., semantic type agreement) levels that can
be used to filter candidate antecedents, such constraints are largely absent for bridging resolution. For instance, a singular bridging anaphor (e.g., ”the book”) can refer to a plural
antecedent (e.g., ”books”), and bridging relations can be formed from mentions with different
entity types (e.g., ”the house” and ”the window”). In fact, while many coreference relations
can be identified via string matching facilities, it is not uncommon for bridging relations to
be identified using background knowledge and/or sophisticated inference mechanisms. The
complexity of bridging resolution is further complicated by the lack of a large corpus annotated with bridging relations: while the most extensively-used coreference-annotated corpus,
OntoNotes, contain more than 2000 documents, two of the most commonly-used corpora
for bridging resolution, ISNotes and BASHI, each contains only 50 documents taken from
OntoNotes. In this dissertation, we investigate knowledge-rich approaches in which we derive potentially useful knowledge for bridging anaphora resolution from a variety of sources,
including tasks that we believe are closely related to bridging, manually defined rules based
on various syntactic and semantic properties that are directly relevant to the prediction
of bridging links, constraints that that encode commonsense knowledge of when two event
mentions should or should not have a bridging link, as well as a pre-training objective that
are relevant to bridging
Solid-state Devices for Ionizing Radiation Detection
No full textDetection of ionizing radiation is an increasingly important topic. As technology advances in
renewable fission/fusion energy, medical imaging, and the ever-increasing threat to national
security from foreign threats, radiation detection must be at the forefront to provide real-time
responses to safety concerns. Improvements in materials, synthesis, fabrication, and device
engineering with intrinsic benefits are required to outperform current state-of-the-art detection
mechanisms. In this regard, solid-state detectors can provide highly efficient detection modes
and enable a portable and low-power solution. Examples include efficient detection of ionizing
radiation from neutrons, and high energy photons (X-rays) are highly desired as they pose health
effects if not considered. Biological interactions with neutrons may cause mutations in DNA due
to ionization, while long X-ray exposure can induce burns and cancer at high dosages.
This research focuses on the fabrication and scalability of highly efficient microstructured
thermal neutron detectors (MSNDs) based on PIN-structured Si wafers and highly sensitive
inorganic perovskite X-ray detectors. Presented with novel deposition techniques that include a
solvent-free material synthesis and device fabrication process, thin and thick-film inorganic lead-
halide perovskite X-ray detectors which exceed performances of the commonly used materials,
such as amorphous Selenium and cadmium-zinc-telluride (CZT), are achieved. Lastly, a path to
technology integration with commonly used signal processing units is discussed
Novel Monomer Designs for 2D Polymeric Materials With Directed Supramolecular Interactions
No full textCovalent organic frameworks (COFs) are a class of polymers that have garnered
significant attention in recent years for their unique physical properties. Consisting
exclusively of lightweight elements (C, N, O, B, H, etc.), COFs possess highly
crystalline, low-density, and permanently porous architectures. Their synthesis is
carried out under thermodynamic control, where through solvothermal approaches
directionally oriented dynamic covalent bonds form between monomeric units.
Diversity in the successful incorporation of boron ester, imine, azine, and hydrazone
linkages in tandem with stabilization forces such as dipolar and π-π stacking
interactions has demonstrated the crystallization problem can be overcome to confer
COFs with highly crystalline structures. Reticular methodologies afford COFs with
atomically precise molecular assembly, providing the ability to predefine pore size,
geometry, and dimensionality. This fascinating feature of COF design suggests great
advancements in long-range order, surface area, pore size, and sorption capabilities
are possible. Thus, the goal of this research details the efforts made to leverage the
‘customizable’ utility of COF design, both in regards to the monomers employed in
their fabrication and with respect to the stabilization of supramolecular complexes,
for the purpose of improving materials properties of 2D-COFs and broadening the
practical scope of COF materials in the future.
In the first chapter, COF design is discussed through the lens of scientific literature.
Progress in the underlying mechanisms of formation, monomer design, and COF
application as materials in gas storage are discussed.
In the second chapter, two novel monomers with intended application in 2D COFs are
synthesized and discussed. The first monomer,
2,4,6-trihydroxy-1,3,5-tris(p-formylphenyl)benzene, possesses out-of-plane hydroxyl
groups that may participate in interlayer hydrogen bonding to improve the stability
between 2D COF sheets. The second monomer,
N,N',N''-(benzene-1,3,5-triyl)tris(1,1-diphenylmethanimine), is a stable
benzophenone imine protected analog of 1,3,5-triaminobenzene and is suitable for
use in the synthesise of a 2D imine-linked COF.
In the third chapter, preliminary reactions using the novel monomer discussed in
Chapter 2 are detailed and the resultant polymers are characterized
Plasmonic Nanoparticles Enabled Rapid and Ultrasensitive Infectious Disease Diagnostics
No full textIn vitro diagnosis of respiratory infectious diseases is of paramount importance as evidenced by
the current COVID-19 pandemic. Standard diagnostic methods, such as polymerase chain reaction
(PCR) and enzyme-linked immunoassay (ELISA), provide specific and sensitive detection yet
cause delayed sample-to-answer time. In contrast, rapid methods such as lateral flow assays
(LFAs), have compromised sensitivity and specificity. To address these issues, herein, we have
developed new plasmonic nanoparticle-based techniques for rapid and ultrasensitive diagnostics
of infectious diseases, including respiratory syncytial virus (RSV) and severe acute respiratory
syndrome (SARS)-associated coronavirus 2 (SARS-CoV-2).
First, we studied gold nanourchins for colorimetric detection of RSV with a one-step sample-to-
answer homogeneous immunoassay. We found that nanourchins have improved plasmonic
coupling and virus targeting properties. We further integrated this rapid detection method onto a
smartphone-based spectrometer and realized a sensitive diagnosis of the intact virus at room
temperature within 30 minutes.
Second, we developed plasmonic sensing of loop-mediated isothermal amplification (termed as
Plasmonic LAMP). We engineered gold and silver (Au-Ag) alloy nanoshells with strong extinction
in the visible wavelengths for SARS-CoV-2 detection. It also provides an additional sequence
identification enabled by the plasmonic recognition of the LAMP products, thus improving
detection specificity and sensitivity over the conventional LAMP.
Third, we utilize the unique photothermal effects of plasmonic gold nanoparticles to substantially
lower the detection limit of conventional plasmonic coupling assay by innovative DIgitAl
plasMONic nanobubble Detection (DIAMOND). Taking RSV as a model target, we endeavor to
build a pump-probe two laser system to generate and detect plasmonic nanobubbles (PNB)
synchronically. Upon digital counting of PNB signals, we achieved rapid and ultrasensitive
diagnostics of intact viruses at the single molecular level, representing viral infections in the early
phase.
Last, we built a simplified digital photoacoustic (dPA) detection module based on the DIAMOND
platform. The plasmonic nanoparticles generate acoustic waves at much lower pulse energy with
a single pulse laser stimulation than the vapor nanobubble phenomenon. Also, the dPA detection
approach only requires a low-cost ultrasound transducer in the setup, which significantly reduces
the complexity of the device and is practical for both laboratory and POC testing. We integrated
dPA detection technique in a benchtop device and realize RSV detection at high performance (i.e.,
a single copy equivalent detection) and high specificity.
Collectively, our work provides new capabilities for rapid, sensitive, and specific detection of
infectious and other diseases