Matching Items (123)
Description
Antibodies are the immunoglobulins which are secreted by the B cells after a microbial invasion. They are stable and stays in the serum for a long time which makes them an excellent biomarker for disease diagnosis. Inflammatory bowel disease is a type of autoimmune disease where the immune system mistakenly attacks the commensal bacteria and leads to inflammation. We studied antibody response of 100 Crohn’s disease (CD), 100 ulcerative colitis (UC) and 100 healthy controls against 1,173 bacterial and 397 viral proteins. We found some anti-bacterial antibodies higher in CD compared to controls while some antibodies lower in UC compared to controls. We were able to build biomarker panels with AUCs of 0.81, 0.87, and 0.82 distinguishing CD vs. control, UC vs. control, and CD vs. UC, respectively. Subgroup analysis based on the Montreal classification revealed that penetrating CD behavior (B3), colonic CD location (L2), and extensive UC (E3) exhibited highest antibody reactivity among all patients. We also wanted to study the reason for the presence of autoantibodies in the sera of healthy individuals. A meta-analysis of 9 independent biomarker study was performed to find 77 common autoantibodies shared by healthy individuals. There was no gender bias; however, the number of autoantibodies increased with age, plateauing around adolescence. Molecular mimicry likely contributed to the elicitation of a subset of these common autoantibodies as 21 common autoantigens had 7 or more ungapped amino acid matches with viral proteins. Intrinsic properties of protein like hydrophilicity, basicity, aromaticity, and flexibility were enriched for common autoantigens. Subcellular localization and tissue expression analysis indicated the sequestration of some autoantigens from circulating autoantibodies can explain the absence of autoimmunity in these healthy individuals.
ContributorsShome, Mahasish (Author) / LaBaer, Joshua (Thesis advisor) / Borges, Chad (Committee member) / Stephanopoulos, Nicholas (Committee member) / Arizona State University (Publisher)
Created2021
Description
Exposure of liquid biospecimens like plasma and serum (P/S) to improper handling and storage can impact the integrity of biomolecules, potentially leading to apparent quantitative changes of important clinical proteins. An accurate and quick estimate of the quality of biospecimens employed in biomarker discovery and validation studies is essential to facilitating accurate conclusions. ΔS-Cys-Albumin is a marker of blood P/S exposure to thawed conditions that can quantitatively track the exposure of P/S to temperatures greater than their freezing point of -30 C. Reported here are studies carried out to evaluate the potential of ΔS-Cys-Albumin to track the stability of clinically important analytes present in P/S upon their exposure to thawed conditions. P/S samples obtained from both cancer-free donors and cancer patients were exposed to 23 C (room temperature), 4 C and -20 C degrees, and the degree to which the apparent concentrations of clinically relevant biomolecules present in P/S were impacted during the time it took ΔS-Cys-Albumin to reach zero was measured. Analyte concentrations measured by molecular interaction-based assays were significantly impacted when samples were exposed to the point where average ΔS-Cys-Albumin fell below 12% at each temperature. Furthermore, the percentage of proteins that became unstable with time under thawed conditions exhibited a strong inverse linear relationship to ΔS-Cys-Albumin, indicating that ΔS-Cys-Albumin can serve as an effective surrogate marker to track the stability of other clinically relevant proteins in plasma as well as to estimate the fraction of proteins that have been destabilized by exposure to thawed conditions, regardless of what the exposure temperature(s) may have been. These results indicated that P/S exposure to thawed conditions disrupts epitopes required for clinical protein quantification via molecular interaction-based assays. In continuation of this theme, a spurious binding event between two clinically important proteins, Apolipoprotein E (ApoE) and Interferon- (IFN) present in human plasma under in vitro experimental conditions is also reported. The interaction was confirmed to be evident only when ApoE was expressed in vitro with a Glutathione-S-Transferase (GST) fusion tag. Future steps required to find the exact manner in which the GST fusion tag facilitated the association between ApoE and IFNγ are discussed with emphasis on the possible pitfalls associated with using fusion proteins for studying novel protein-protein interactions.
ContributorsKapuruge, Erandi Prasadini (Author) / Borges, Chad R (Thesis advisor) / LaBaer, Joshua (Committee member) / Van Horn, Wade (Committee member) / Arizona State University (Publisher)
Created2021
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
Osteocalcin (Oc) is the most abundant non-collagen protein found in the bone, but its precise function is still not completely understood. Three glutamic acid (Glu) residues within its sequence are sites for vitamin K-dependent post-translational modification, replacing a hydrogen with a carboxylate located at the γ-carbon position, converting these to γ-carboxyglutamic acid (Gla) residues. This modification confers increased binding of Oc to Ca2+ and hydroxyapatite matrix. Presented here, novel metal binding partners Mn2+, Fe3+, and Cr3+ of human Oc were determined, while the previously identified binders to (generally) non-human Oc, Ca2+, Mg2+, Pb2+ and Al3+ were validated as binders to human Oc by direct infusion mass spectrometry with all metals binding with higher affinity to the post-translationally modified form (Gla-Oc) compared to the unmodified form (Glu-Oc). Oc was also found to form pentamer (Gla-Oc) and pentamer and tetramer (Glu-Oc) homomeric self-assemblies in the absence of NaCl, which disassembled to monomers in the presence of near physiological Na+ concentrations. Additionally, Oc was found to form filamentous structures in vitro by negative stain TEM in the presence of increased Ca2+ titrations in a Gla- and pH-dependent manner. Finally, by combining circular dichroism spectroscopy to determine the fraction of Gla-Oc bound, and inductively-coupled plasma mass spectrometry to quantify total Al concentrations, the data were fit to a single-site binding model and the equilibrium dissociation constant for Al3+ binding to human Gla-Oc was determined (Kd = 1.0 ± 0.12 nM). Including citrate, a known competitive binder of Al3+, maintained Al in solution and enabled calculation of free Al3+ concentrations using a Matlab script to solve the complex set of linear equations. To further improve Al solubility limits, the pH of the system was lowered to 4.5, the pH during bone resorption. Complementary binding experiments with Glu-Oc were not possible due to the observed precipitation of Glu-Oc at pH 4.5, although qualitatively if Glu-Oc binds Al3+, it is with much lower affinity compared to Gla-Oc. Taken together, the results presented here further support the importance of post-translational modification, and thus adequate nutritional intake of vitamin K, on the binding and self-assembly properties of human Oc.
ContributorsThibert, Stephanie (Author) / Borges, Chad R (Thesis advisor) / LaBaer, Joshua (Committee member) / Chiu, Po-Lin (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
The 5-year survival rate for late-stage metastatic melanoma is only ~30%. A major reason for this low survival rate is that one of the most commonly mutated genes in melanoma, NRAS, has no FDA-approved targeted therapies. Because the RAS protein does not have any targeted therapies, patients with RAS mutant tumors have an ongoing need for treatments that indirectly target RAS. This thesis project aims to identify expression and phosphorylation levels of proteins downstream of RAS in melanoma cell lines with the most common driver mutations. By analyzing the protein-level differences between these genetic mutants, we hope to identify additional indirect RAS protein targets for the treatment of NRAS mutant melanoma. RAS has several downstream effector proteins involved in oncogenic signaling pathways including FAK, Paxillin, AKT, and ERK. 5 melanoma cell lines (2 BRAF mutant, 2 NRAS mutant, and 1 designated wildtype) were analyzed using western bloting for FAK, Paxillin, AKT, and ERK phosphorylation and total expression levels. The results of western blot analysis showed that NRAS mutant cell lines had increased expression of phosphorylated Paxillin. Increased Paxillin phosphorylation corresponds to increased Paxillin binding at the FAT domain of FAK. Therefore, cell lines with increased FAK FAT – Paxillin interaction would be more sensitive to FAK FAT domain inhibition. The data presented provide an an explanation for the reduction in cell viability in NRAS mutant cell lines infected with Ad-FRNK. This information also has significant clinical relevance as researchers work to develop synthetic FAK FAT domain inhibitors, such as cyclic peptides. Additionally, cell lines with high levels of phosphorylated AKT showed a significant reduction in the amount of phosphorylated ERK. The identification of this inverse relationship may help to explain why BRAF and NRAS mutations are mutually exclusive. To conclude, NRAS mutant cell lines have increased expression of phosphorylated Paxillin and AKT which may explain why NRAS mutant cell lines are more sensitive to FAK FAT domain inhibition.
ContributorsSherwood, Nicole (Author) / Gould, Ian (Thesis director) / LaBaer, Joshua (Committee member) / Marlowe, Timothy (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor)
Created2022-05
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