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Little is known about how cognitive and brain aging patterns differ in older adults with autism spectrum disorder (ASD). However, recent evidence suggests that individuals with ASD may be at greater risk of pathological aging conditions than their neurotypical (NT) counterparts. A growing body of research indicates that older adults with ASD may experience accelerated cognitive decline and neurodegeneration as they age, although studies are limited by their cross-sectional design in a population with strong age-cohort effects. Studying aging in ASD and identifying biomarkers to predict atypical aging is important because the population of older individuals with ASD is growing. Understanding the unique challenges faced as autistic adults age is necessary to develop treatments to improve quality of life and preserve independence. In this study, a longitudinal design was used to characterize cognitive and brain aging trajectories in ASD as a function of autistic trait severity. Principal components analysis (PCA) was used to derive a cognitive metric that best explains performance variability on tasks measuring memory ability and executive function. The slope of the integrated persistent feature (SIP) was used to quantify functional connectivity; the SIP is a novel, threshold-free graph theory metric which summarizes the speed of information diffusion in the brain. Longitudinal mixed models were using to predict cognitive and brain aging trajectories (measured via the SIP) as a function of autistic trait severity, sex, and their interaction. The sensitivity of the SIP was also compared with traditional graph theory metrics. It was hypothesized that older adults with ASD would experience accelerated cognitive and brain aging and furthermore, age-related changes in brain network topology would predict age-related changes in cognitive performance.
For both cognitive and brain aging, autistic traits and sex interacted to predict trajectories, such that older men with high autistic traits were most at risk for poorer outcomes. In men with autism, variability in SIP scores across time points trended toward predicting cognitive aging trajectories. Findings also suggested that autistic traits are more sensitive to differences in brain aging than diagnostic group and that the SIP is more sensitive to brain aging trajectories than other graph theory metrics. However, further research is required to determine how physiological biomarkers such as the SIP are associated with cognitive outcomes.
ContributorsSullivan, Georgia (Author) / Braden, Blair (Thesis advisor) / Kodibagkar, Vikram (Thesis advisor) / Schaefer, Sydney (Committee member) / Wang, Yalin (Committee member) / Arizona State University (Publisher)
Created2022
Description
As robots become increasingly integrated into the environments, they need to learn how to interact with the objects around them. Many of these objects are articulated with multiple degrees of freedom (DoF). Multi-DoF objects have complex joints that require specific manipulation orders, but existing methods only consider objects with a single joint. To capture the joint structure and manipulation sequence of any object, I introduce the "Object Kinematic State Machines" (OKSMs), a novel representation that models the kinematic constraints and manipulation sequences of multi-DoF objects. I also present Pokenet, a deep neural network architecture that estimates the OKSMs from the sequence of point cloud data of human demonstrations. I conduct experiments on both simulated and real-world datasets to validate my approach. First, I evaluate the modeling of multi-DoF objects on a simulated dataset, comparing against the current state-of-the-art method. I then assess Pokenet's real-world usability on a dataset collected in my lab, comprising 5,500 data points across 4 objects. Results showcase that my method can successfully estimate joint parameters of novel multi-DoF objects with over 25% more accuracy on average than prior methods.
ContributorsGUPTA, ANMOL (Author) / Gopalan, Nakul (Thesis advisor) / Zhang, Yu (Committee member) / Wang, Yalin (Committee member) / Arizona State University (Publisher)
Created2024
Description
In today's data-driven world, privacy is a significant concern. It is crucial to preserve the privacy of sensitive information while visualizing data. This thesis aims to develop new techniques and software tools that support Vega-Lite visualizations while maintaining privacy. Vega-Lite is a visualization grammar based on Wilkinson's grammar of graphics. The project extends Vega-Lite to incorporate privacy algorithms such as k-anonymity, l-diversity, t-closeness, and differential privacy. This is done by using a unique multi-input loop module logic that generates combinations of attributes as a new anonymization method. Differential privacy is implemented by adding controlled noise (Laplace or Exponential) to the sensitive columns in the dataset. The user defines custom rules in the JSON schema, mentioning the privacy methods and the sensitive column. The schema is validated using Another JSON Validation library, and these rules help identify the anonymization techniques to be performed on the dataset before sending it back to the Vega-Lite visualization server. Multiple datasets satisfying the privacy requirements are generated, and their utility scores are provided so that the user can trade-off between privacy and utility on the datasets based on their requirements. The interface developed is user-friendly and intuitive and guides users in using it. It provides appropriate feedback on the privacy-preserving visualizations generated through various utility metrics. This application is helpful for technical or domain experts across multiple domains where privacy is a big concern, such as medical institutions, traffic and urban planning, financial institutions, educational records, and employer-employee relations. This project is novel as it provides a one-stop solution for privacy-preserving visualization. It works on open-source software, Vega-Lite, which several organizations and users use for business and educational purposes.
ContributorsSekar, Manimozhi (Author) / Bryan, Chris (Thesis advisor) / Wang, Yalin (Committee member) / Cao, Zhichao (Committee member) / Arizona State University (Publisher)
Created2024
Description
Image denoising, a fundamental task in computer vision, poses significant challenges due to its inherently inverse and ill-posed nature. Despite advancements in traditional methods and supervised learning approaches, particularly in medical imaging such as Medical Resonance Imaging (MRI) scans, the reliance on paired datasets and known noise distributions remains a practical hurdle. Recent progress in noise statistical independence theory and diffusion models has revitalized research interest, offering promising avenues for unsupervised denoising. However, existing methods often yield overly smoothed results or introduce hallucinated structures, limiting their clinical applicability. This thesis tackles the core challenge of progressing towards unsupervised denoising of MRI scans. It aims to retain intricate details without smoothing or introducing artificial structures, thus ensuring the production of high-quality MRI images. The thesis makes a three-fold contribution: Firstly, it presents a detailed analysis of traditional techniques, early machine learning algorithms for denoising, and new statistical-based models, with an extensive evaluation study on self-supervised denoising methods highlighting their limitations. Secondly, it conducts an evaluation study on an emerging class of diffusion-based denoising methods, accompanied by additional empirical findings and discussions on their effectiveness and limitations, proposing solutions to enhance their utility. Lastly, it introduces a novel approach, Unsupervised Multi-stage Ensemble Deep Learning with diffusion models for denoising MRI scans (MEDL). Leveraging diffusion models, this approach operates independently of signal or noise priors and incorporates weighted rescaling of multi-stage reconstructions to balance over-smoothing and hallucination tendencies. Evaluation using benchmark datasets demonstrates an average gain of 1dB and 2% in PSNR and SSIM metrics, respectively, over existing approaches.
ContributorsVora, Sahil (Author) / Li, Baoxin (Thesis advisor) / Wang, Yalin (Committee member) / Zhou, Yuxiang (Committee member) / Arizona State University (Publisher)
Created2024
Description
Most drugs work by binding to receptors on the cell surface. These receptors can then carry the message into the cell and have a wide array of results. However, studying how fast the binding is can be difficult. Current methods involve extracting the receptor and labeling them, but both these steps have issues. Previous works found that binding on the cell surface is accompanied with a small change in cell size, generally an increase. They have also developed an algorithm that can track these small changes without a label using a simple bright field microscope. Here, this relationship is further explored by comparing edge tracking results to a more widely used method, surface plasmon resonance. The kinetic constants found from the two methods are in agreement. No corrections or manipulations were needed to create agreement. The Bland-Altman plots shows that the error between the two methods is about 0.009 s-1. This is about the same error between cells, making it a non-dominant source of error.
ContributorsHunt, Ashley (Author) / Tao, Nongjian (Thesis advisor) / Ros, Alexandra (Committee member) / Borges, Chad (Committee member) / Arizona State University (Publisher)
Created2018
Description
Unsupervised learning of time series data, also known as temporal clustering, is a challenging problem in machine learning. This thesis presents a novel algorithm, Deep Temporal Clustering (DTC), to naturally integrate dimensionality reduction and temporal clustering into a single end-to-end learning framework, fully unsupervised. The algorithm utilizes an autoencoder for temporal dimensionality reduction and a novel temporal clustering layer for cluster assignment. Then it jointly optimizes the clustering objective and the dimensionality reduction objective. Based on requirement and application, the temporal clustering layer can be customized with any temporal similarity metric. Several similarity metrics and state-of-the-art algorithms are considered and compared. To gain insight into temporal features that the network has learned for its clustering, a visualization method is applied that generates a region of interest heatmap for the time series. The viability of the algorithm is demonstrated using time series data from diverse domains, ranging from earthquakes to spacecraft sensor data. In each case, the proposed algorithm outperforms traditional methods. The superior performance is attributed to the fully integrated temporal dimensionality reduction and clustering criterion.
ContributorsMadiraju, NaveenSai (Author) / Liang, Jianming (Thesis advisor) / Wang, Yalin (Thesis advisor) / He, Jingrui (Committee member) / Arizona State University (Publisher)
Created2018
Description
Measuring molecular interaction with membrane proteins is critical for understanding cellular functions, validating biomarkers and screening drugs. Despite the importance, developing such a capability has been a difficult challenge, especially for small molecules binding to membrane proteins in their native cellular environment. The current mainstream practice is to isolate membrane proteins from the cell membranes, which is difficult and often lead to the loss of their native structures and functions. In this thesis, novel detection methods for in situ quantification of molecular interactions with membrane proteins are described.
First, a label-free surface plasmon resonance imaging (SPRi) platform is developed for the in situ detection of the molecular interactions between membrane protein drug target and its specific antibody drug molecule on cell surface. With this method, the binding kinetics of the drug-target interaction is quantified for drug evaluation and the receptor density on the cell surface is also determined.
Second, a label-free mechanically amplification detection method coupled with a microfluidic device is developed for the detection of both large and small molecules on single cells. Using this method, four major types of transmembrane proteins, including glycoproteins, ion channels, G-protein coupled receptors (GPCRs) and tyrosine kinase receptors on single whole cells are studied with their specific drug molecules. The basic principle of this method is established by developing a thermodynamic model to express the binding-induced nanometer-scale cellular deformation in terms of membrane protein density and cellular mechanical properties. Experiments are carried out to validate the model.
Last, by tracking the cell membrane edge deformation, molecular binding induced downstream event – granule exocytosis is measured with a dual-optical imaging system. Using this method, the single granule exocytosis events in single cells are monitored and the temporal-spatial distribution of the granule fusion-induced cell membrane deformation are mapped. Different patterns of granule release are resolved, including multiple release events occurring close in time and position. The label-free cell membrane deformation tracking method was validated with the simultaneous fluorescence recording. And the simultaneous cell membrane deformation detection and fluorescence recording allow the study of the propagation of the granule release-induced membrane deformation along cell surfaces.
First, a label-free surface plasmon resonance imaging (SPRi) platform is developed for the in situ detection of the molecular interactions between membrane protein drug target and its specific antibody drug molecule on cell surface. With this method, the binding kinetics of the drug-target interaction is quantified for drug evaluation and the receptor density on the cell surface is also determined.
Second, a label-free mechanically amplification detection method coupled with a microfluidic device is developed for the detection of both large and small molecules on single cells. Using this method, four major types of transmembrane proteins, including glycoproteins, ion channels, G-protein coupled receptors (GPCRs) and tyrosine kinase receptors on single whole cells are studied with their specific drug molecules. The basic principle of this method is established by developing a thermodynamic model to express the binding-induced nanometer-scale cellular deformation in terms of membrane protein density and cellular mechanical properties. Experiments are carried out to validate the model.
Last, by tracking the cell membrane edge deformation, molecular binding induced downstream event – granule exocytosis is measured with a dual-optical imaging system. Using this method, the single granule exocytosis events in single cells are monitored and the temporal-spatial distribution of the granule fusion-induced cell membrane deformation are mapped. Different patterns of granule release are resolved, including multiple release events occurring close in time and position. The label-free cell membrane deformation tracking method was validated with the simultaneous fluorescence recording. And the simultaneous cell membrane deformation detection and fluorescence recording allow the study of the propagation of the granule release-induced membrane deformation along cell surfaces.
ContributorsZhang, Fenni (Author) / Tao, Nongjian (Thesis advisor) / Chae, Junseok (Committee member) / Borges, Chad (Committee member) / Jing, Tianwei (Committee member) / Wang, Shaopeng (Committee member) / Arizona State University (Publisher)
Created2018
Description
Proteins play a central role to human body and biological activities. As powerful tools for protein detections, many surface plasmon resonance based techniques have been developed to enhance the sensitivity. However, sensitivity is not the only final goal. As a biosensor, four things really matter: sensitivity, specificity, resolution (temporal/spatial) and throughput.
This dissertation presents several works on developing novel plasmonic based techniques for protein detections on the last two aspects to extend the application field. A fast electrochemically controlled plasmonic detection technique is first developed with the capability of monitoring electrochemical signal with nanosecond response time. The study reveals that the conformational gating of electron transfer in a redox protein (cytochrome c) takes place over a broad range of time scales (sub-µs to ms). The second platform integrates ultra-low volume piezoelectric liquid dispensing and plasmonic imaging detection to monitor different protein binding processes simultaneously with low sample cost. Experiment demonstrates the system can observe binding kinetics in 10×10 microarray of 6 nL droplet, with variations of kinetic rate constants among spots less than ±5%. A focused plasmonic imaging system with bi-cell algorithm is also proposed for spatial resolution enhancement. The two operation modes, scanning mode and focus mode, can be applied for different purposes. Measurement of bacterial aggregation demonstrates the higher spatial resolution. Detections of polystyrene beads binding and 50 nm gold nanoparticles oscillation show a high signal to noise ratio of the system.
The real properties of protein rely on its dynamic personalities. The above works shed light upon fast and high throughput detection of protein kinetics, and enable more applications for plasmonic imaging techniques. It is anticipated that such methods will help to invoke a new surge to unveil the mysteries of biological activities and chemical process.
This dissertation presents several works on developing novel plasmonic based techniques for protein detections on the last two aspects to extend the application field. A fast electrochemically controlled plasmonic detection technique is first developed with the capability of monitoring electrochemical signal with nanosecond response time. The study reveals that the conformational gating of electron transfer in a redox protein (cytochrome c) takes place over a broad range of time scales (sub-µs to ms). The second platform integrates ultra-low volume piezoelectric liquid dispensing and plasmonic imaging detection to monitor different protein binding processes simultaneously with low sample cost. Experiment demonstrates the system can observe binding kinetics in 10×10 microarray of 6 nL droplet, with variations of kinetic rate constants among spots less than ±5%. A focused plasmonic imaging system with bi-cell algorithm is also proposed for spatial resolution enhancement. The two operation modes, scanning mode and focus mode, can be applied for different purposes. Measurement of bacterial aggregation demonstrates the higher spatial resolution. Detections of polystyrene beads binding and 50 nm gold nanoparticles oscillation show a high signal to noise ratio of the system.
The real properties of protein rely on its dynamic personalities. The above works shed light upon fast and high throughput detection of protein kinetics, and enable more applications for plasmonic imaging techniques. It is anticipated that such methods will help to invoke a new surge to unveil the mysteries of biological activities and chemical process.
ContributorsWang, Yan (Author) / Tao, Nongjian (Thesis advisor) / Chae, Junseok (Committee member) / Goryll, Michael (Committee member) / Wang, Shaopeng (Committee member) / Arizona State University (Publisher)
Created2018
Description
Environmental pollution has been one of the most challenging problems in modern society and more and more health issues are now linked to environmental pollution and especially, air pollution. Certain sensitive group like patients with asthma are highly influenced by the environmental air quality and knowledge of the daily air pollution exposure is of great importance for the management and prevention of asthma attack. Hence small form factor, real time, accurate, sensitive and easy to use portable devices for environmental monitoring are of great value.
Three novel image-based methods for quantitative real time environmental monitoring were introduced and the sensing principle, sensor performances were evaluated through simulation and field tests. The first sensing principle uses surface plasmon resonance (SPR) image and home-made molecular sieve (MS) column to realize real time chemical separation and detection. SPR is sensitive and non-specific, which makes it a desirable optical method for sensitive biological and chemical sensing, the miniaturized MS column provides small area footprint and makes it possible for SPR to record images of the whole column area. The innovative and system level integration approach provide a new way for simultaneous chemical separation and detection. The second sensor uses scattered laser light, Complementary metal-oxide-semiconductor (CMOS) imager and image processing to realize real-time particulate matter (PM) sensing. Complex but low latency algorithm was developed to obtain real time information for PM including PM number, size and size distribution. The third sensor uses gradient based colorimetric sensor, absorbance light signal and image processing to realize real-time Ozone sensing and achieved high sensitivity and substantially longer lifetime compared to conventional colorimetric sensors. The platform provides potential for multi-analyte integration and large-scale consumer use as wearable device.
The three projects provide novel, state-of-the-art and sensitive solutions for environmental and personal exposure monitoring. Moreover, the sensing platforms also provide tools for clinicians and epidemiologists to conduct large scale clinical studies on the adverse health effects of pollutants on various kinds of diseases.
Three novel image-based methods for quantitative real time environmental monitoring were introduced and the sensing principle, sensor performances were evaluated through simulation and field tests. The first sensing principle uses surface plasmon resonance (SPR) image and home-made molecular sieve (MS) column to realize real time chemical separation and detection. SPR is sensitive and non-specific, which makes it a desirable optical method for sensitive biological and chemical sensing, the miniaturized MS column provides small area footprint and makes it possible for SPR to record images of the whole column area. The innovative and system level integration approach provide a new way for simultaneous chemical separation and detection. The second sensor uses scattered laser light, Complementary metal-oxide-semiconductor (CMOS) imager and image processing to realize real-time particulate matter (PM) sensing. Complex but low latency algorithm was developed to obtain real time information for PM including PM number, size and size distribution. The third sensor uses gradient based colorimetric sensor, absorbance light signal and image processing to realize real-time Ozone sensing and achieved high sensitivity and substantially longer lifetime compared to conventional colorimetric sensors. The platform provides potential for multi-analyte integration and large-scale consumer use as wearable device.
The three projects provide novel, state-of-the-art and sensitive solutions for environmental and personal exposure monitoring. Moreover, the sensing platforms also provide tools for clinicians and epidemiologists to conduct large scale clinical studies on the adverse health effects of pollutants on various kinds of diseases.
ContributorsDu, Zijian (Author) / Tao, Nongjian (Thesis advisor) / Goryll, Michael (Committee member) / Herckes, Pierre (Committee member) / Tsow, Tsing (Committee member) / Arizona State University (Publisher)
Created2019
Description
Mechanical properties of cells are important in maintaining physiological functions of biological systems. Quantitative measurement and analysis of mechanical properties can help understand cellular mechanics and its functional relevance and discover physical biomarkers for diseases monitoring and therapeutics.
This dissertation presents a work to develop optical methods for studying cell mechanics which encompasses four applications. Surface plasmon resonance microscopy based optical method has been applied to image intracellular motions and cell mechanical motion. This label-free technique enables ultrafast imaging with extremely high sensitivity in detecting cell deformation. The technique was first applied to study intracellular transportation. Organelle transportation process and displacement steps of motor protein can be tracked using this method. The second application is to study heterogeneous subcellular membrane displacement induced by membrane potential (de)polarization. The application can map the amplitude and direction of cell deformation. The electromechanical coupling of mammalian cells was also observed. The third application is for imaging electrical activity in single cells with sub-millisecond resolution. This technique can fast record actions potentials and also resolve the fast initiation and propagation of electromechanical signals within single neurons. Bright-field optical imaging approach has been applied to the mechanical wave visualization that associated with action potential in the fourth application. Neuron-to-neuron viability of membrane displacement was revealed and heterogeneous subcellular response was observed.
All these works shed light on the possibility of using optical approaches to study millisecond-scale and sub-nanometer-scale mechanical motions. These studies revealed ultrafast and ultra-small mechanical motions at the cellular level, including motor protein-driven motions and electromechanical coupled motions. The observations will help understand cell mechanics and its biological functions. These optical approaches will also become powerful tools for elucidating the interplay between biological and physical functions.
This dissertation presents a work to develop optical methods for studying cell mechanics which encompasses four applications. Surface plasmon resonance microscopy based optical method has been applied to image intracellular motions and cell mechanical motion. This label-free technique enables ultrafast imaging with extremely high sensitivity in detecting cell deformation. The technique was first applied to study intracellular transportation. Organelle transportation process and displacement steps of motor protein can be tracked using this method. The second application is to study heterogeneous subcellular membrane displacement induced by membrane potential (de)polarization. The application can map the amplitude and direction of cell deformation. The electromechanical coupling of mammalian cells was also observed. The third application is for imaging electrical activity in single cells with sub-millisecond resolution. This technique can fast record actions potentials and also resolve the fast initiation and propagation of electromechanical signals within single neurons. Bright-field optical imaging approach has been applied to the mechanical wave visualization that associated with action potential in the fourth application. Neuron-to-neuron viability of membrane displacement was revealed and heterogeneous subcellular response was observed.
All these works shed light on the possibility of using optical approaches to study millisecond-scale and sub-nanometer-scale mechanical motions. These studies revealed ultrafast and ultra-small mechanical motions at the cellular level, including motor protein-driven motions and electromechanical coupled motions. The observations will help understand cell mechanics and its biological functions. These optical approaches will also become powerful tools for elucidating the interplay between biological and physical functions.
ContributorsYang, Yunze (Author) / Tao, Nongjian (Thesis advisor) / Wang, Shaopeng (Committee member) / Goryll, Michael (Committee member) / Si, Jennie (Committee member) / Arizona State University (Publisher)
Created2016