Matching Items (133)
Description
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 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
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 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
Description
Communicating with computers through thought has been a remarkable achievement in recent years. This was made possible by the use of Electroencephalography (EEG). Brain-computer interface (BCI) relies heavily on Electroencephalography (EEG) signals for communication between humans and computers. With the advent ofdeep learning, many studies recently applied these techniques to EEG data to perform various tasks like emotion recognition, motor imagery classification, sleep analysis, and many more.
Despite the rise of interest in EEG signal classification, very few studies have explored the MindBigData dataset, which collects EEG signals recorded at the stimulus of seeing a digit and thinking about it. This dataset takes us closer to realizing the idea of mind-reading or communication via thought. Thus classifying these signals into the respective digit that the user thinks about is a challenging task. This serves as a motivation to study this dataset and apply existing deep learning techniques to study it.
Given the recent success of transformer architecture in different domains like Computer Vision and Natural language processing, this thesis studies transformer architecture for EEG signal classification. Also, it explores other deep learning
techniques for the same. As a result, the proposed classification pipeline achieves comparable performance with the existing methods.
ContributorsMuglikar, Omkar Dushyant (Author) / Wang, Yalin (Thesis advisor) / Liang, Jianming (Committee member) / Venkateswara, Hemanth (Committee member) / Arizona State University (Publisher)
Created2021
Description
Statistical Shape Modeling is widely used to study the morphometrics of deformable objects in computer vision and biomedical studies. There are mainly two viewpoints to understand the shapes. On one hand, the outer surface of the shape can be taken as a two-dimensional embedding in space. On the other hand, the outer surface along with its enclosed internal volume can be taken as a three-dimensional embedding of interests. Most studies focus on the surface-based perspective by leveraging the intrinsic features on the tangent plane. But a two-dimensional model may fail to fully represent the realistic properties of shapes with both intrinsic and extrinsic properties. In this thesis, severalStochastic Partial Differential Equations (SPDEs) are thoroughly investigated and several methods are originated from these SPDEs to try to solve the problem of both two-dimensional and three-dimensional shape analyses. The unique physical meanings of these SPDEs inspired the findings of features, shape descriptors, metrics, and kernels in this series of works. Initially, the data generation of high-dimensional shapes, here, the tetrahedral meshes, is introduced. The cerebral cortex is taken as the study target and an automatic pipeline of generating the gray matter tetrahedral mesh is introduced. Then, a discretized Laplace-Beltrami operator (LBO) and a Hamiltonian operator (HO) in tetrahedral domain with Finite Element Method (FEM) are derived. Two high-dimensional shape descriptors are defined based on the solution of the heat equation and Schrödinger’s equation. Considering the fact that high-dimensional shape models usually contain massive redundancies, and the demands on effective landmarks in many applications, a Gaussian process landmarking on tetrahedral meshes is further studied. A SIWKS-based metric space is used to define a geometry-aware Gaussian process. The study of the periodic potential diffusion process further inspired the idea of a new kernel call the geometry-aware convolutional kernel. A series of Bayesian learning methods are then introduced to tackle the problem of shape retrieval and classification. Experiments of every single item are demonstrated. From the popular SPDE such as the heat equation and Schrödinger’s equation to the general potential diffusion equation and the specific periodic potential diffusion equation, it clearly shows that classical SPDEs play an important role in discovering new features, metrics, shape descriptors and kernels. I hope this thesis could be an example of using interdisciplinary knowledge to solve problems.
ContributorsFan, Yonghui (Author) / Wang, Yalin (Thesis advisor) / Lepore, Natasha (Committee member) / Turaga, Pavan (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2021
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 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
Description
Graph matching is a fundamental but notoriously difficult problem due to its NP-hard nature, and serves as a cornerstone for a series of applications in machine learning and computer vision, such as image matching, dynamic routing, drug design, to name a few. Although there has been massive previous investigation on high-performance graph matching solvers, it still remains a challenging task to tackle the matching problem under real-world scenarios with severe graph uncertainty (e.g., noise, outlier, misleading or ambiguous link).In this dissertation, a main focus is to investigate the essence and propose solutions to graph matching with higher reliability under such uncertainty. To this end, the proposed research was conducted taking into account three perspectives related to reliable graph matching: modeling, optimization and learning. For modeling, graph matching is extended from typical quadratic assignment problem to a more generic mathematical model by introducing a specific family of separable function, achieving higher capacity and reliability. In terms of optimization, a novel high gradient-efficient determinant-based regularization technique is proposed in this research, showing high robustness against outliers. Then learning paradigm for graph matching under intrinsic combinatorial characteristics is explored. First, a study is conducted on the way of filling the gap between discrete problem and its continuous approximation under a deep learning framework. Then this dissertation continues to investigate the necessity of more reliable latent topology of graphs for matching, and propose an effective and flexible framework to obtain it. Coherent findings in this dissertation include theoretical study and several novel algorithms, with rich experiments demonstrating the effectiveness.
ContributorsYu, Tianshu (Author) / Li, Baoxin (Thesis advisor) / Wang, Yalin (Committee member) / Yang, Yezhou (Committee member) / Yang, Yingzhen (Committee member) / Arizona State University (Publisher)
Created2021
Description
Graph matching is a fundamental but notoriously difficult problem due to its NP-hard nature, and serves as a cornerstone for a series of applications in machine learning and computer vision, such as image matching, dynamic routing, drug design, to name a few. Although there has been massive previous investigation on high-performance graph matching solvers, it still remains a challenging task to tackle the matching problem under real-world scenarios with severe graph uncertainty (e.g., noise, outlier, misleading or ambiguous link).In this dissertation, a main focus is to investigate the essence and propose solutions to graph matching with higher reliability under such uncertainty. To this end, the proposed research was conducted taking into account three perspectives related to reliable graph matching: modeling, optimization and learning. For modeling, graph matching is extended from typical quadratic assignment problem to a more generic mathematical model by introducing a specific family of separable function, achieving higher capacity and reliability. In terms of optimization, a novel high gradient-efficient determinant-based regularization technique is proposed in this research, showing high robustness against outliers. Then learning paradigm for graph matching under intrinsic combinatorial characteristics is explored. First, a study is conducted on the way of filling the gap between discrete problem and its continuous approximation under a deep learning framework. Then this dissertation continues to investigate the necessity of more reliable latent topology of graphs for matching, and propose an effective and flexible framework to obtain it. Coherent findings in this dissertation include theoretical study and several novel algorithms, with rich experiments demonstrating the effectiveness.
ContributorsYu, Tianshu (Author) / Li, Baoxin (Thesis advisor) / Wang, Yalin (Committee member) / Yang, Yezhou (Committee member) / Yang, Yingzhen (Committee member) / Arizona State University (Publisher)
Created2021
Description
Alzheimer's disease (AD) is a neurodegenerative disease that damages the cognitive abilities of a patient. It is critical to diagnose AD early to begin treatment as soon as possible which can be done through biomarkers. One such biomarker is the beta-amyloid (Aβ) peptide which can be quantified using the centiloid (CL) scale. For identifying the Aβ biomarker, A deep learning model that can model AD progression by predicting the CL value for brain magnetic resonance images (MRIs) is proposed. Brain MRI images can be obtained through the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Open Access Series of Imaging Studies (OASIS) datasets, however a single model cannot perform well on both datasets at once. Thus, A regularization-based continuous learning framework to perform domain adaptation on the previous model is also proposed which captures the latent information about the relationship between Aβ and AD progression within both datasets.
ContributorsTrinh, Matthew Brian (Author) / Wang, Yalin (Thesis advisor) / Liang, Jianming (Committee member) / Su, Yi (Committee member) / Arizona State University (Publisher)
Created2022
Description
The Population Receptive Field (pRF) model is widely used to predict the location (retinotopy) and size of receptive fields on the visual space. Doing so allows for the creation of a mapping from locations in the visual field to the associated groups of neurons in the cortical region (within the visual cortex of the brain). However, using the pRF model is very time consuming. Past research has focused on the creation of Convolutional Neural Networks (CNN) to mimic the pRF model in a fraction of the time, and they have worked well under highly controlled conditions. However, these models have not been thoroughly tested on real human data. This thesis focused on adapting one of these CNNs to accurately predict the retinotopy of a real human subject using a dataset from the Human Connectome Project. The results show promise towards creating a fully functioning CNN, but they also expose new challenges that must be overcome before the model could be used to predict the retinotopy of new human subjects.
ContributorsBurgard, Braeden (Author) / Wang, Yalin (Thesis director) / Ta, Duyan (Committee member) / Barrett, The Honors College (Contributor) / School of International Letters and Cultures (Contributor) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2022-05