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Biochemical Insights Into the Coordination Plasticity of the Nitrate-binding Protein NreA From Staphylococcus Carnosus
No full textNitrate is an oxyanion that is an integral part of the global nitrogen cycle. In bacteria it is an
essential nutrient, as it can be incorporated into macromolecules or used as the terminal electron
acceptor in anaerobic respiration. The reduction of nitrate to ammonia is regulated by its uptake at
the membrane and detection in the cell. The NreABC pathway is an example of such regulation in
Staphylococcus carnosus and related bacterial species. Key to this pathway is NreA, a soluble
nitrate sensor. To understand how NreA can carry out its role, previous studies have crystallized
NreA with both iodide and nitrate, the former as a surrogate. Interestingly, the protein bound both
iodide and nitrate through the amide backbone of three residues with the side chain of a tryptophan
residue. This discovery inspired in vivo testing, determining that anions activate the downstream
pathway, increasing nitrate reduction. The molecular principles governing this binding plasticity
were not studied, motivating us to further characterize NreA. We narrowed our focus to connect
the rates of binding to the affinity of NreA for nitrate, iodide, and nitrite, as monitored by the
intrinsic fluorescence of the binding site tryptophan.
Tryptophan fluorescence was used to determine the affinity of binding to the three anions,
providing the first quantitative measurements of the plasticity of this binding site. Nitrate was
bound with the highest affinity, followed by iodide, and then nitrite. To complement these
measurements, stopped-flow fluorimetry was used to determine the kinetic rates of binding,
showing that the differences in affinity are predominately determined by the rate of dissociation
of the anion from the protein.
These experiments were conducted with chloride as a stabilizing ion in solution. Because chloride
is an anion with similar properties to the tested anions, it is possible that chloride could interfere
with the binding of nitrate and iodide, motivating additional testing. Because we could not remove
chloride without affecting protein stability, we tested chloride’s effect by varying its concentration
in the protein solution. These data showed that the binding of NreA to nitrate, iodide, and nitrite
was impacted by the concentration of chloride, with higher levels of chloride resulting in a weaking
of binding of each anion. However, this process was determined to be due to an equal contribution
of both the rate of association and dissociation of binding for nitrate.
These results have offered the first quantitative measurements of the binding of non-natural
binding partners to NreA. The discovery that the relative affinities of different anions is correlated
with their relative dissociation rates helps explain how this protein can selectively bind nitrate over
similar anions. The interference of this binding by chloride also shows the importance of the
protein’s environment, inspiring future testing to determine if chloride interacts specifically or
through ionic strength effects. The discovered kinetic contributions can be complemented by
thermodynamic measurements from isothermal titration calorimetry or by in silico techniques
giving a better understanding of this anion-binding site, developing the general principles of
selectively binding anions in proteins
Robust and Adaptive Optimization
No full textOptimization is one of the most interesting and well-studied domains in Mathematics and
Computer Science. It has attracted the interest of researcher communities from diverse backgrounds for centuries. Some of the optimization problems are harder than others, because of
the uncertainties involved in them due to the structure of the problem, or due to the uncertainty in the input data. The uncertainties can be passive or they can be induced actively
by an adversary. Many of those problems are NP-Hard. Scheduling problems and spanning
tree problems have also attracted the researchers for a long time.
In this work, we have developed robust and adaptive algorithms for some problems from the
above-mentioned domains. We have provided polynomial-time algorithms for tractable problems. As we investigated many NP-Hard problems, either we have provided polynomial-time
algorithms for those problems with special structures, or we have designed approximation algorithms and heuristics for the general problems. We have reported experimental results and
the outcomes of comparative studies between different schemes to evaluate those heuristics
and approximation algorithms
Propagation of Very Low Frequency Transmitter Signals in the Inner Magnetosphere
No full textSignals from ground-based very low frequency (VLF) transmitters can leak through the
ionosphere and propagate in the inner magnetosphere as whistler-mode waves. They interact with energetic electrons from the Earth’s radiation belts, and precipitate them into
the ionosphere. The effect of wave-particle interactions is affected by signal propagations,
which depend on the spatial variation of the cold plasma population (as the propagation
medium). Therefore, a further understanding of transmitter signal propagation and cold
plasma medium is essential for investigating wave-particle interactions.
First, a case study is performed on Russian Alpha transmitter signals observed by the Van
Allen Probes. The signals are in ducted propagation, experience multiple reflections, and
excite triggered emissions. The ducted propagation is justified by a ray-tracing technique,
and the nonlinear cyclotron resonance theory is tested by the observed triggered emissions.
Second, we perform a statistical study on the distribution of the two propagation modes,
ducted and nonducted, by the use of observed Russian Alpha transmitter signals. The
statistics show the dominance of nonducted signals in the plasmasphere in terms of both
occurrence and power. The proportion of ducted signals is enhanced at higher L-shells and
during active geomagnetic conditions.
Finally, we statistically analyze the spatial and temporal distributions of inner-magnetospheric
cold plasma density irregularities, which are responsible for ducted propagation. The density
irregularities deep inside the plasmasphere are dominant in the night and dusk sectors and
show no significant variation with geomagnetic conditions. In contrast, the density irregularities in and near the plasmasphere boundary layer occur at post-midnight during quiet
times and expand throughout the night sector during active times
Stabilization of Nonholonomic Euler–poincaré Mechanical Systems With Broken Symmetry by Controlled Lagrangians
No full textWe extend the method of Controlled Lagrangians to nonholonomic Euler–Poincaré mechanical
systems with broken symmetry by considering the problem of stabilizing what we call a pendulum
skate, a simple model of a figure skater developed by Gzenda and Putkaradze. By exploiting the
symmetry of the system as well as taking care of the part of the symmetry broken by the gravity, the
equations of motion are given as nonholonomic Euler–Poincaré equation with advected parameters.
After that, we discovered the general form of the equilibrium points and presented the classification
of two special ones, designated as sliding and spinning. Of our main interest is the stability of the
sliding and spinning equilibria of the system. We show that the former is unstable and the latter is
stable only under certain conditions. We use the method of Controlled Lagrangians to find a control
to stabilize the sliding equilibrium and also show how to achieve the stabilization for the general
equilibrium point
Enhancing Energy Efficiency and Ranging Accuracy in IoT Networks
No full textWe consider an IoT network and try to address some of the issues and limitations of this
new wireless system paradigm. Some of the issues come from the design requirements of
IoT devices such as low cost, low energy consumption, and extended battery lifetime. These
requirements limit the ability of such devices to operate at large bandwidth or use complex
receiver circuitry that consume high energy. We focus on these two problems and investigate
possible solutions to overcome such issues. We address the problem of receiver power consumption by introducing the usage of low-resolution Analog to Digital Converters (ADCs)
under Differential-Phase Shift Keying (D-PSK) modulation. Outage-constrained receiver
energy efficiency is then used as our metric to ensure low power consumption while operating at reasonable rates which are reflected in achieving low transmission latency values.
Additionally, the effects of bandwidth limitations on ranging accuracy are taken into consideration. The idea of Channel Frequency Response (CFR) stitching is applied to expand
the bandwidth, where two-way CFR is introduced to ensure CFR coherency upon stitching.
Two-way CFR is further studied in details highlighting its advantages and disadvantages.
Moreover, two alternative approaches are proposed to overcome two-way CFR’s accuracy
degradation drawback by working with one-way response instead. The first techniques is
a two-way to one-way CFR conversion where we apply signal processing techniques to detect and correct any phase errors in the CFR after conversion. While the second approach
utilizes the novel idea of frequency overlap-based CFR alignment to mitigate the system’s
impairments we experience while operating with the one-way approach. Significant ranging
accuracy gains were achieved by the proposed techniques and verified by means of accurate
system simulation as well as hardware prototyping
Intellectual Property Protection Using a Transistor-level Programmable Fabric
No full textOver the years, the semiconductor industry has followed the overarching economic trend of
globalization and offshore manufacturing. While the widespread utilization of third-party
foundries has helped design houses lower manufacturing costs, it has also exposed their products to security threats such as intellectual property (IP) theft and integrated circuit (IC)
counterfeiting. Therefore, the ability to hide sensitive designs from a potentially untrusted
foundry is becoming paramount for IP protection. In response, the research community has
proposed various design obfuscation solutions for thwarting reverse-engineering and unauthorized reproduction/usage of ICs. Unfortunately, while the state-of-the-art design obfuscation
schemes can offer protection against brute-force attacks, they remain vulnerable to intelligent
attacks, such as ones that leverage a Boolean Satisfiability (SAT) solver.
In this work, we present a novel IP protection methodology for structurally obfuscating
sensitive parts of a design through pre-fabrication omission and post-fabrication programming. We introduce a transistor-level programmable (TRAP) fabric tailored to replace
portions of an ASIC design for our obfuscation purposes. Unfortunately, the state-of-theart computer-aided design (CAD) tools and testing solutions are designed for conventional
application-specific ICs (ASICs) and field-programmable gate arrays (FPGAs) and cannot
support the new architecture. To this end, we develop the ancillary ancillary infrastructure required for practical adoption of the TRAP fabric. Specifically, we present a full-stack CAD
solution that takes an RTL description as input, performs synthesis, placement, routing, and
finally generates the bitstream to program the design on a TRAP fabric. We also propose
a novel application-agnostic test methodology for TRAP, which consists of a multi-phase,
cascadable scheme to efficiently test the programmable transistors, the built-in gates, and
the interconnect network in the fabric. We then theoretically analyze the complexity of
attacking TRAP-obfuscated designs through both brute-force and intelligent SAT-based attacks. Finally, we present a hardware testbed for experimenting with TRAP and evaluate
the efficacy of the proposed method through selective obfuscation of various benchmark circuits and two modern microprocessor designs. Our results corroborate that, as compared to
an FPGA implementation, TRAP-based obfuscation offers superior resistance against both
brute-force and oracle-guided SAT attacks while incurring an order of magnitude less area,
power, and delay overhead
Biological Experiments in Art and Architecture: Biocentrism in El Lissitzky’s Prouns
No full textRussian avant-garde artist El Lissitzky produced a series of nonobjective works he called Proun
(1919-1927). His neologism, the word “Proun”, stands for “project for the affirmation of the
new.” While the Prouns are most often analyzed through an architectural or even mathematical
lens, recent scholarship shows evidence of influences from the natural sciences, biology in
particular. Evidenced by Lissitzky’s writings, Austro-Hungarian biologist Raoul Heinrich
Francé’s works of popular science were a potent source, specifically the soil botanist’s concept
of biotechnik. The purpose of this study is to investigate the extent to which Lissitzky’s Prouns
were compositionally and conceptionally affected by biocentrism after he began reading the
works of Raoul Francé’s biological philosophy. This study analyzes three sequential Prouns
produced between 1923 to 1924 primarily through the lens of biocentrism. Developed originally
as an architectural and social experiment, the Prouns became, for Lissitzky, an organic idea-and-
form machine, providing him a means to approximate immersive experiences for the full body.
This is evident through the evolution of the Prouns from two-dimensional works to three-
dimensional spatialized experiences
Enhancing Gravitational Wave Detection Using Machine Learning and Ambient Noise Suppression
No full textIn this thesis, we present two separate deep learning pipelines for the detection and parameter
estimation of astrophysical gravitational waves. In part one, we present a convolutional
neural network, designed in the auto-encoder configuration that can detect and denoise
gravitational waves from merging black hole binaries, orders of magnitude faster than the
conventional matched-filtering based detection that is currently employed at advanced-LIGO
(aLIGO) and the LVK (LIGO-VIRGO-KAGRA) network in general. The Neural-Network
architecture is such that it learns from the sparse representation of data in the time-frequency
domain and constructs a non-linear mapping function that maps this representation into
two separate masks for signal and noise, facilitating the separation of the two, from raw
data. This approach is the first of its kind to apply machine learning based gravitational
wave detection/denoising from binary mergers in the 2D representation of gravitational wave
data. We applied our formalism to the first gravitational wave event detected, GW150914,
successfully recovering the signal at all three phases of coalescence at both aLIGO detectors.
This method is further tested on the gravitational wave data from the second observing run
(O2) of aLIGO, reproducing all binary black hole mergers detected in O2 at both detectors.
The Neural-Net seems to have uncovered a pattern of ‘ringing’ after the ringdown phase
of the coalescence, which is not a feature that is present in the conventional binary merger
templates. This method can also interpolate and extrapolate between modeled templates and
explore gravitational waves that are unmodeled and hence not present in the template bank
of signals used in the matched-filtering detection pipelines. Faster and efficient detection
schemes, such as this method, will be instrumental as ground based detectors reach their
design sensitivity, likely to result in several hundreds of potential detections in a few months
of observing runs.
In part two, we present another deep learning based architecture using convolutional neural
network to estimate the intrinsic parameter of the black holes from the observed raw gravi-
tational wave data. This framework has the capability to estimate parameters of coalescing
binaries, orders of magnitude faster than the conventional Bayesian analysis based param-
eter inference employed at the LVK network. We also attempt to estimate the individual
spin components of both black holes, which is often not possible using Bayesian inference.
This machine learning based parameter inference scheme, to the best of our knowledge is
the first of its kind that can make direct estimation of individual spin components of both
black holes from raw detector data.
In part three, we present an overview of Ambient Seismic Noise (ASN) and the importance
of its mitigation in enhancing ground based gravitational wave detection. ASN is one of the
biggest noise contributors below 20Hz in ground based gravitational wave detectors. Seismic
Newtonian noise arising from gravity gradients created by seismic waves will become the
limiting noise source at low frequencies for second generation gravitational wave detectors.
Low frequency noise suppression will especially enhance gravitational wave detection from
heavier coalescing astrophysical systems. In this part, we also present results from a series
of seismic weight-drop experiments performed at the gravitational wave test detector site at
Western Australia. This analysis would be one of many seismic experiments that can help
characterize and study the detector location for better seismic isolation and noise suppression
Magnetic and Catalytic Properties of Lanthanide Complexes
No full textLanthanides are an intriguing family of elements possessing unique properties useful in many
diverse applications. The first chapter of this work describes the origins of some of these
properties and their catalytic and magnetic applications. The second chapter will highlight a
highly unusual neodymium catalyst for diene polymerization. This coordination polymer
catalyst contains no halides and makes use of no halide donor, yet produces desirable 96% 1,4-
cis stereospecific material.
The third chapter is concerned with the surprising formation and superparamagnetism of a
neodymium-peroxide diimine cluster and the associated crystals. The cluster is formed by a rare
example of anion-templated assembly in which the anion is derived from dissolved atmospheric
oxygen. The resulting structural motif featured an array of tight three-metal clusters separated
by a distance long enough to prevent long-range magnetic order, which resulted in
superparamagnetic behavior in the solid state. This is believed to be the first report of
superparamagnetism in a bulk crystal state.
The fourth and final chapter is concerned with MRI contrast agents and presents an example of a
new variety of potential next-generation agents composed of coordination polymers. The gadolinium diethylphosphate polymer features a far longer rotational coordination time than
conventional small gadolinium complexes and thus offers dramatically improved T1 relaxation
performance at low-fields common in clinical imaging applications.
All of these lanthanide complexes are synthesized using an azeotropic distillation method. This
method avoids the need for strict water-free techniques and also occasionally allows for novel
structures to be obtained, as demonstrated in Chapter 3 in particular
The Role of Noradrenergic Signaling in Vagus Nerve Stimulation Dependent Motor Cortical Plasticity
No full textThe combination of vagus nerve stimulation (VNS) and motor rehabilitation is being reported as a promising therapy for enhancing motor function recovery after neural injuries. Recent preclinical studies in rats have shown that VNS in conjunction with skilled forelimb training leads to substantial reorganization of the somatotopic cortical motor map, which has been shown to be important for VNS efficacy. However, most of the preclinical research have been conducted in female rats, even though the risk of neural injuries of all kinds is considerably greater in males than in females. Furthermore, the neural mechanisms underlying VNS-induced neuroplasticity remain unclear. Here, we aim to deepen our understanding of VNS through three different, but linked, projects. First, we test whether VNS generates plasticity differently between male and females. Results from our experiments indicate that VNS is equally effective in inducing plasticity in both sexes. Second, we assess the necessity of activation of alpha2-adrenergic receptors (alpha2-ARs), the key regulator in governance of noradrenaline (NA) release and synaptic plasticity in the central nervous system (CNS), in VNS effect. Our results show that infusion of alpha2-ARs antagonist blocks VNS-driven neuroplasticity. Finally, we examine the effect of phasic activation of the locus coeruleus (LC), the noradrenergic center in the CNS, on motor cortical plasticity. We found that 10 Hz, but not 3 or 30 Hz, LC stimulation paired with learned motor task promote motor cortical map reorganization. Taken together, these findings inform the generalization of VNS therapy to the general population and broaden the understanding of LC-NA mechanisms underlying VNS-driven plasticity