Matching Items (143)
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Speciation, or the process by which one population diverges into multiple populations that can no longer interbreed with each other, has brought about the incredible diversity of life. Mechanisms underlying this process can be more visible in the early stages of the speciation process. The mechanisms that restrict gene flow

Speciation, or the process by which one population diverges into multiple populations that can no longer interbreed with each other, has brought about the incredible diversity of life. Mechanisms underlying this process can be more visible in the early stages of the speciation process. The mechanisms that restrict gene flow in highly mobile species with no absolute barriers to dispersal, especially marine species, are understudied. Similarly, human impacts are reshaping ecosystems globally, and we are only just beginning to understand the implications of these rapid changes on evolutionary processes. In this dissertation, I investigate patterns of speciation and evolution in two avian clades: a genus of widespread tropical seabirds (boobies, genus Sula), and two congeneric passerine species in an urban environment (cardinals, genus Cardinalis). First, I explore the prevalence of gene flow across land barriers within species and between sympatric species in boobies. I found widespread evidence of gene flow over all land barriers and between 3 species pairs. Next, I compared the effects of urbanization on the spatial distributions of two cardinal species, pyrrhuloxia (Cardinalis sinuatus) and northern cardinals (Cardinalis cardinalis), in Tucson, Arizona. I found that urbanization has different effects on the spatial distributions of two closely related species that share a similar environmental niche, and I identified environmental variables that might be driving this difference. Then I tested for effects of urbanization on color and size traits of these two cardinal species. In both of these species, urbanization has altered traits involved in signaling, heat tolerance, foraging, and maneuverability. Finally, I tested for evidence of selection on the urban populations of both cardinal species and found evidence of both parallel selection and introgression between the species, as well as selection on different genes in each species. The functions of the genes that experienced positive selection suggest that light at night, energetics, and air pollution may have acted as strong selective pressures on these species in the past. Overall, my dissertation emphasizes the role of introgression in the speciation process, identifies environmental stressors faced by wildlife in urban environments, and characterizes their evolutionary responses to those stressors.
ContributorsJackson, Daniel Nelson (Author) / McGraw, Kevin J (Thesis advisor) / Amdam, Gro (Committee member) / Sweazea, Karen (Committee member) / Taylor, Scott (Committee member) / Arizona State University (Publisher)
Created2023
Description
The objective of this meta-analysis is to holistically evaluate the existing body of literature on the anti-neoplastic potential of snake and bee venom. In recent years, venom-based therapeutics have emerged as a promising solution for combating cancer, generating a notable rise in publications on the topic. Consequently, this comprehensive study

The objective of this meta-analysis is to holistically evaluate the existing body of literature on the anti-neoplastic potential of snake and bee venom. In recent years, venom-based therapeutics have emerged as a promising solution for combating cancer, generating a notable rise in publications on the topic. Consequently, this comprehensive study aims to assess the current state of research and identify trends that may guide future investigations. Following the guidelines established by PRISMA, a total sample of 26 research papers were extracted from the electronic databases, PubMed and Scopus. These papers were categorized based on their publication dates, and research questions were formulated regarding three main topics: venom type, cancer-targeting mechanism, and cancer type. Statistical analysis of the research questions was performed using 2x2 contingency tables for a chi-square test. The results of the analysis reveal a statistically significant increase in publications focused on cell death mechanisms and breast cancer in recent years. This increase in publications reflects a growing interest in the potential for venom to induce apoptosis in cancer cells and target the unique biological properties of breast cancer. Overall, this meta-analysis offers insight into the evolving sphere of venom-based cancer research, providing a glimpse into the potential trajectory of this field.
ContributorsHolder, Marina (Author) / Amdam, Gro (Thesis director) / Mana, Miyeko (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor) / Economics Program in CLAS (Contributor)
Created2023-12
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Description
A remarkable phenomenon in contemporary physics is quantum scarring in classically chaoticsystems, where the wave functions tend to concentrate on classical periodic orbits. Quantum scarring has been studied for more than four decades, but the problem of efficiently detecting quantum scars has remained to be challenging, relying mostly on human visualization of wave

A remarkable phenomenon in contemporary physics is quantum scarring in classically chaoticsystems, where the wave functions tend to concentrate on classical periodic orbits. Quantum scarring has been studied for more than four decades, but the problem of efficiently detecting quantum scars has remained to be challenging, relying mostly on human visualization of wave function patterns. This paper develops a machine learning approach to detecting quantum scars in an automated and highly efficient manner. In particular, this paper exploits Meta learning. The first step is to construct a few-shot classification algorithm, under the requirement that the one-shot classification accuracy be larger than 90%. Then propose a scheme based on a combination of neural networks to improve the accuracy. This paper shows that the machine learning scheme can find the correct quantum scars from thousands images of wave functions, without any human intervention, regardless of the symmetry of the underlying classical system. This will be the first application of Meta learning to quantum systems. Interacting spin networks are fundamental to quantum computing. Data-based tomography oftime-independent spin networks has been achieved, but an open challenge is to ascertain the structures of time-dependent spin networks using time series measurements taken locally from a small subset of the spins. Physically, the dynamical evolution of a spin network under time-dependent driving or perturbation is described by the Heisenberg equation of motion. Motivated by this basic fact, this paper articulates a physics-enhanced machine learning framework whose core is Heisenberg neural networks. This paper demonstrates that, from local measurements, not only the local Hamiltonian can be recovered but the Hamiltonian reflecting the interacting structure of the whole system can also be faithfully reconstructed. Using Heisenberg neural machine on spin networks of a variety of structures. In the extreme case where measurements are taken from only one spin, the achieved tomography fidelity values can reach about 90%. The developed machine learning framework is applicable to any time-dependent systems whose quantum dynamical evolution is governed by the Heisenberg equation of motion.
ContributorsHan, Chendi (Author) / Lai, Ying-Cheng (Thesis advisor) / Yu, Hongbin (Committee member) / Dasarathy, Gautam (Committee member) / Seo, Jae-Sun (Committee member) / Arizona State University (Publisher)
Created2022
Description

Insect pheromones are crucial for survival and reproduction because they influence insect behavior, communication, and interactions within and outside the colony. Honey bees (Apis mellifera) have one of the most complex pheromonal communication systems. One pheromone, known as Queen Mandibular Pheromone (QMP), is released by the queen bee to regulate

Insect pheromones are crucial for survival and reproduction because they influence insect behavior, communication, and interactions within and outside the colony. Honey bees (Apis mellifera) have one of the most complex pheromonal communication systems. One pheromone, known as Queen Mandibular Pheromone (QMP), is released by the queen bee to regulate physiology, behavior, and gene expression in the female worker caste. The pheromone acts as a signal of queen presence that suppresses worker reproduction. In the absence of reproduction, young workers focus on taking care of the queen and larvae, known as nurse tasks, while older workers forage. In nurse bees, QMP has fundamental physiological impacts, including increasing abdominal lipid stores and increasing the protein content of hypopharyngeal glands (HPG). The HPG are worker-specific glands that can synthesize royal jelly used in colony nourishment. In workers, larger HPG signifies the ability to secrete royal jelly, while shrunken glands are characteristic of foragers that do not make jelly. While it is known that QMP increases abdominal lipid stores, the underlying mechanism is unclear: Does the pheromone simply make workers consume more pollen which provides lipids and protein, or does QMP also increase lipogenesis? In this study, I measured abdominal lipogenesis as fatty acid synthase (FAS) activity and monitored abdominal protein content and HPG size in caged, nurse-aged worker bees. In cages, workers were exposed to QMP or not, and they were provided with a lipid less diet in a full factorial design experiment. I found that QMP did not influence abdominal FAS activity or protein, but significantly increased HPG size. The data also revealed a significant positive correlation between abdominal protein and HPG size. My results do not support the idea that QMP modulates lipogenesis in worker bees, but my data can be interpreted to reflect that QMP mobilizes abdominal protein for the production of jelly in the HPG. This finding is in line with a previous study revealing a role of honey bee Brood Pheromone in mobilization of a major protein used in jelly production. Overall, my results support a fundamental role of QMP in worker metabolic processes associated with colony nourishment.

ContributorsOreshkova, Angela (Author) / Amdam, Gro (Thesis director) / Scofield, Sebastian (Committee member) / Barrett, The Honors College (Contributor) / College of Health Solutions (Contributor) / School of Life Sciences (Contributor)
Created2023-05
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Description
Few-layer black phosphorous (FLBP) is one of the most important two-dimensional (2D) materials due to its strongly layer-dependent quantized bandstructure, which leads to wavelength-tunable optical and electrical properties. This thesis focuses on the preparation of stable, high-quality FLBP, the characterization of its optical properties, and device applications.Part I presents an

Few-layer black phosphorous (FLBP) is one of the most important two-dimensional (2D) materials due to its strongly layer-dependent quantized bandstructure, which leads to wavelength-tunable optical and electrical properties. This thesis focuses on the preparation of stable, high-quality FLBP, the characterization of its optical properties, and device applications.Part I presents an approach to preparing high-quality, stable FLBP samples by combining O2 plasma etching, boron nitride (BN) sandwiching, and subsequent rapid thermal annealing (RTA). Such a strategy has successfully produced FLBP samples with a record-long lifetime, with 80% of photoluminescence (PL) intensity remaining after 7 months. The improved material quality of FLBP allows the establishment of a more definitive relationship between the layer number and PL energies. Part II presents the study of oxygen incorporation in FLBP. The natural oxidation formed in the air environment is dominated by the formation of interstitial oxygen and dangling oxygen. By the real-time PL and Raman spectroscopy, it is found that continuous laser excitation breaks the bonds of interstitial oxygen, and free oxygen atoms can diffuse around or form dangling oxygen under low heat. RTA at 450 °C can turn the interstitial oxygen into dangling oxygen more thoroughly. Such oxygen-containing samples show similar optical properties to the pristine BP samples. The bandgap of such FLBP samples increases with the concentration of the incorporated oxygen. Part III deals with the investigation of emission natures of the prepared samples. The power- and temperature-dependent measurements demonstrate that PL emissions are dominated by excitons and trions, with a combined percentage larger than 80% at room temperature. Such measurements allow the determination of trion and exciton binding energies of 2-, 3-, and 4-layer BP, with values around 33, 23, 15 meV for trions and 297, 276, 179 meV for excitons at 77K, respectively. Part IV presents the initial exploration of device applications of such FLBP samples. The coupling between photonic crystal cavity (PCC) modes and FLBP's emission is realized by integrating the prepared sandwich structure onto 2D PCC. Electroluminescence has also been achieved by integrating such materials onto interdigital electrodes driven by alternating electric fields.
ContributorsLi, Dongying (Author) / Ning, Cun-Zheng (Thesis advisor) / Vasileska, Dragica (Committee member) / Lai, Ying-Cheng (Committee member) / Yu, Hongbin (Committee member) / Arizona State University (Publisher)
Created2022
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Description
Particulate Guanylyl Cyclase Receptor A (pGC-A) is an atrial natriuretic peptide receptor, which plays a vital role in controlling cardiovascular, renal, and endocrine functions. The extracellular domain of pGC-A interacts with natriuretic peptides and triggers the intracellular guanylyl cyclase domain to convert GTP to cGMP. To effectively develop a method

Particulate Guanylyl Cyclase Receptor A (pGC-A) is an atrial natriuretic peptide receptor, which plays a vital role in controlling cardiovascular, renal, and endocrine functions. The extracellular domain of pGC-A interacts with natriuretic peptides and triggers the intracellular guanylyl cyclase domain to convert GTP to cGMP. To effectively develop a method that can regulate pGC-A, structural information regarding its intact form is necessary. Currently, only the extracellular domain structure of rat pGC-A has been determined. However, structural data regarding the transmembrane domain, as well as functional intracellular domain regions, need to be elucidated.This dissertation presents detailed information regarding pGC-A expression and optimization in the baculovirus expression vector system, along with the first purification method for purifying functional intact human pGC-A. The first in vitro evidence of a purified intact human pGC-A tetramer was detected in detergent micellar solution. Intact pGC-A is currently proposed to function as a homodimer. Upon analyzing my findings and acknowledging that dimer formation is required for pGC-A functionality, I proposed the first tetramer complex model composed of two functional subunits (homodimer). Forming tetramer complexes on the cell membrane increases pGC-A binding efficiency and ligand sensitivity. Currently, a two-step mechanism has been proposed for ATP-dependent pGC-A signal transduction. Based on cGMP functional assay results, it can be suggested that the binding ligand also moderately activates pGC-A, and that ATP is not crucial for the activation of guanylyl cyclase. Instead, three modulators can regulate different activation levels in intact pGC-A. Crystallization of purified intact pGC-A was performed to determine its structure. During the crystallization condition screening process, I successfully selected seven promising initial crystallization conditions for intact human pGC-A crystallization. One selected condition led to the formation of excellent needle-shaped crystals. During the serial crystallography diffraction experiment, five diffraction patterns were detected. The highest diffraction resolution spot reached 3 Å. This work will allow the determination of the intact human pGC-A structure while also providing structural information on the protein signal transduction mechanism. Further structural knowledge may potentially lead to improved drug design. More precise mutation experiments could help verify the current pGC-A signal transduction and activation mechanism.
ContributorsZhang, Shangji (Author) / Fromme, Petra (Thesis advisor) / Johnston, Stephen (Committee member) / Mazor, Yuval (Committee member) / Arizona State University (Publisher)
Created2021
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Description

In this research, I surveyed existing methods of characterizing Epilepsy from Electroencephalogram (EEG) data, including the Random Forest algorithm, which was claimed by many researchers to be the most effective at detecting epileptic seizures [7]. I observed that although many papers claimed a detection of >99% using Random Forest, it

In this research, I surveyed existing methods of characterizing Epilepsy from Electroencephalogram (EEG) data, including the Random Forest algorithm, which was claimed by many researchers to be the most effective at detecting epileptic seizures [7]. I observed that although many papers claimed a detection of >99% using Random Forest, it was not specified “when” the detection was declared within the 23.6 second interval of the seizure event. In this research, I created a time-series procedure to detect the seizure as early as possible within the 23.6 second epileptic seizure window and found that the detection is effective (> 92%) as early as the first few seconds of the epileptic episode. I intend to use this research as a stepping stone towards my upcoming Masters thesis research where I plan to expand the time-series detection mechanism to the pre-ictal stage, which will require a different dataset.

ContributorsBou-Ghazale, Carine (Author) / Lai, Ying-Cheng (Thesis director) / Berisha, Visar (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2022-05
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Description
Predicting nonlinear dynamical systems has been a long-standing challenge in science. This field is currently witnessing a revolution with the advent of machine learning methods. Concurrently, the analysis of dynamics in various nonlinear complex systems continues to be crucial. Guided by these directions, I conduct the following studies. Predicting critical

Predicting nonlinear dynamical systems has been a long-standing challenge in science. This field is currently witnessing a revolution with the advent of machine learning methods. Concurrently, the analysis of dynamics in various nonlinear complex systems continues to be crucial. Guided by these directions, I conduct the following studies. Predicting critical transitions and transient states in nonlinear dynamics is a complex problem. I developed a solution called parameter-aware reservoir computing, which uses machine learning to track how system dynamics change with a driving parameter. I show that the transition point can be accurately predicted while trained in a sustained functioning regime before the transition. Notably, it can also predict if the system will enter a transient state, the distribution of transient lifetimes, and their average before a final collapse, which are crucial for management. I introduce a machine-learning-based digital twin for monitoring and predicting the evolution of externally driven nonlinear dynamical systems, where reservoir computing is exploited. Extensive tests on various models, encompassing optics, ecology, and climate, verify the approach’s effectiveness. The digital twins can extrapolate unknown system dynamics, continually forecast and monitor under non-stationary external driving, infer hidden variables, adapt to different driving waveforms, and extrapolate bifurcation behaviors across varying system sizes. Integrating engineered gene circuits into host cells poses a significant challenge in synthetic biology due to circuit-host interactions, such as growth feedback. I conducted systematic studies on hundreds of circuit structures exhibiting various functionalities, and identified a comprehensive categorization of growth-induced failures. I discerned three dynamical mechanisms behind these circuit failures. Moreover, my comprehensive computations reveal a scaling law between the circuit robustness and the intensity of growth feedback. A class of circuits with optimal robustness is also identified. Chimera states, a phenomenon of symmetry-breaking in oscillator networks, traditionally have transient lifetimes that grow exponentially with system size. However, my research on high-dimensional oscillators leads to the discovery of ’short-lived’ chimera states. Their lifetime increases logarithmically with system size and decreases logarithmically with random perturbations, indicating a unique fragility. To understand these states, I use a transverse stability analysis supported by simulations.
ContributorsKong, Lingwei (Author) / Lai, Ying-Cheng (Thesis advisor) / Tian, Xiaojun (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Alkhateeb, Ahmed (Committee member) / Arizona State University (Publisher)
Created2023
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Description
A notable challenge when assembling synthetic gene circuits is that modularity often fails to function as intended. A crucial underlying reason for this modularity failure is the existence of competition for shared and limited gene expression resources. By designing a synthetic cascading bistable switches (Syn-CBS) circuit in a single strain

A notable challenge when assembling synthetic gene circuits is that modularity often fails to function as intended. A crucial underlying reason for this modularity failure is the existence of competition for shared and limited gene expression resources. By designing a synthetic cascading bistable switches (Syn-CBS) circuit in a single strain with two coupled self-activation modules to achieve successive cell fate transitions, nonlinear resource competition within synthetic gene circuits is unveiled. However, in vivo it can be seen that the transition path was redirected with the activation of one switch always prevailing over that of the other, contradictory to coactivation theoretically expected. This behavior is a result of resource competition between genes and follows a ‘winner-takes-all’ rule, where the winner is determined by the relative connection strength between the two modules. Despite investigation demonstrating that resource competition between gene modules can significantly alter circuit deterministic behaviors, how resource competition contributes to gene expression noise and how this noise can be controlled is still an open issue of fundamental importance in systems biology and biological physics. By utilizing a two-gene circuit, the effects of resource competition on protein expression noise levels can be closely studied. A surprising double-edged role is discovered: the competition for these resources decreases noise while the constraint on resource availability adds its own term of noise into the system, denoted “resource competitive” noise. Noise reduction effects are then studied using orthogonal resources. Results indicate that orthogonal resources are a good strategy for eliminating the contribution of resource competition to gene expression noise. Noise propagation through a cascading circuit has been considered without resource competition. It has been noted that the noise from upstream genes can be transmitted downstream. However, resource competition’s effects on this cascading noise have yet to be studied. When studied, it is found that resource competition can induce stochastic state switching and perturb noise propagation. Orthogonal resources can remove some of the resource competitive behavior and allow for a system with less noise.
ContributorsGoetz, Hanah Elizabeth (Author) / Tian, Xiaojun (Thesis advisor) / Wang, Xiao (Committee member) / Lai, Ying-Cheng (Committee member) / Arizona State University (Publisher)
Created2022
Description

Maternal morbidity and mortality rates in the United States continues to rise, with a wide range of contributing factors such as mental illness, cardiovascular disease and systemic inequality. This metastudy provides a holistic view of the research that has been published on the issue of U.S. maternal healthcare from 2000-2022.

Maternal morbidity and mortality rates in the United States continues to rise, with a wide range of contributing factors such as mental illness, cardiovascular disease and systemic inequality. This metastudy provides a holistic view of the research that has been published on the issue of U.S. maternal healthcare from 2000-2022. The patterns of publications on specific topics over time can tell us what is perceived as a current major cause by physicians, public leaders, researchers, and the public. A deeper dive into systemic inequality as a cause of maternal morbidity and mortality highlights it as a major contributor to these high rates, but that progress is slowly being made through the implementation of detection and prevention tactics, as well as accessible prenatal programs and care.

ContributorsRettig, Lelia (Author) / Amdam, Gro (Thesis director) / Bang, Christofer (Committee member) / Barrett, The Honors College (Contributor) / School of Human Evolution & Social Change (Contributor) / School of Life Sciences (Contributor)
Created2023-05