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Title: FUNCTIONAL CONNECTIVITY LABELS FOR THE MULTICHANNEL IIT AND RUSH UNIVERSITY AGING (MIITRA) ATLAS
Creator: Badhon, Rashadul Hasan
Date: 2022
Description: In the field of medical imaging, a brain atlas refers to a specific model of the brain of a population where different parts of the atlas...
Show moreIn the field of medical imaging, a brain atlas refers to a specific model of the brain of a population where different parts of the atlas correspond to different anatomical parts of the average brain of the population. A brain atlas is composed of MRI templates and semantic labels and is a crucial component of neuroscience for its critical role in facilitating spatial normalization, temporal characterization and automated segmentation for the purposes of voxel-wise, region of interest and network analyses. Building a brain atlas requires registering multi-dimensional brain datasets from a population into a reference space and, during the last decade, the advent of new technologies and computational modeling approaches has made it possible to build high-quality, detailed brain atlases. At the same time developments in data acquisition now allow the construction of comprehensive brain atlases containing a variety of information about the brain. The Multichannel Illinois Institute of Technology and Rush university Aging (MIITRA) atlas project is developing a high-quality comprehensive atlas of the older adult brain containing a multitude of templates and labels. These templates are constructed with state-of-the-art spatial normalization of high-quality data and as a result, they are characterized by higher image quality, are more representative of the brain of non-demented older adults and provide higher inter-subject spatial normalization accuracy of older adult data compared to other available templates. The methodology used in the development of the MIITRA templates facilitates the construction of accurate structural and connectivity labels. Functional connectivity MRI reveals sets of functionally connected brain regions, forming networks, by investigating synchronous fluctuations in MRI signal over time across these brain regions during rest. The purpose of this work was to generate functional connectivity labels for several brain networks in MIITRA space.
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Title: Development and evaluation of high resolution MRI templates and labels of the MIITRA atlas
Creator: Niaz, Mohammad Rakeen
Date: 2022
Description: A digital human brain atlas consisting of MRI-based multi-modal templates and semantic labels delineating brain regions are commonly used as...
Show moreA digital human brain atlas consisting of MRI-based multi-modal templates and semantic labels delineating brain regions are commonly used as references for spatial normalization in a wide range of neuroimaging studies. Magnetic resonance imaging (MRI) studies of the aging brain is of significant interest in recent times to explore the role of brain characteristics associated with cognitive functions. The introduction of advanced image reconstruction techniques, and the recent trend in MRI acquisitions at submillimeter in-plane resolution have resulted in an easier availability of MRI data on older adults at high spatial resolution. An atlas with a comprehensive set of high-resolution templates representative of the older adult brain and detailed labels accurately mapping brain regions can increase the sensitivity and specificity of such neuroimaging studies. Additionally, most neuroimaging studies can benefit from a high-resolution atlas with templates where fine brain structures are resolved and, where the transition between different tissue can be more accurately defined. However, such an atlas is not publicly available for older adults. Hence the goal of this thesis is to develop a comprehensive, high-resolution digital human brain atlas for older adults termed as Multi-channel Illinois Institute of Technology and Rush University Aging (MIITRA) atlas.This dissertation aims a) to develop a new technique based on the principles of super-resolution for the construction of high-resolution structural and diffusion tensor templates, and evaluate the templates for use in studies on older adults, b) to construct and evaluate high-resolution structural and diffusion tensor templates constructed using the method developed in (a) for the MIITRA atlas using MRI data collected on 400 nondemented older adults, c) to investigate and develop a technique for the construction of high-resolution labels and evaluate the performance of gray matter labels constructed using this technique in segmenting the gray matter of older adults, and d) to develop and evaluate a comprehensive set of high-resolution labels using the technique developed in (c) for the MIITRA atlas using data on 400 non-demented older adults. Based on the aforementioned points, the thesis is structured as follows: Firstly, this thesis presents a novel approach for the construction of a high-resolution T1-weighted structural template based on the principles of super resolution. This method introduced a forward mapping technique to minimize signal interpolation, and a weighted averaging method to account for residual misregistration. The new template was shown to resolve finer brain structures compared to a lower resolution template constructed using the same data. It was demonstrated through systematic comparison of this new template to several other standardized templates of different resolutions that a) it exhibited high image sharpness, b) was free of image artifacts, c) allowed for high spatial normalization accuracy and detection of smaller inter-group morphometric differences compared to other standardized templates, d) was highly representative of the older adult brain. This novel approach was further modified for the construction of a high spatial resolution diffusion tensor imaging template. The new DTI template is the first high spatial resolution population-based DTI template of the older adult brain and exhibits high image quality, high sharpness, is free of artifacts, resolves fine white matter structures, and provides higher spatial normalization accuracy of older adult DTI data compared to other available DTI templates. Secondly, the aforementioned techniques were utilized in the development of high resolution T1-weighted and DTI templates, and tissue probability maps for the MIITRA atlas using high quality MRI images on 400 diverse, community cohort of non-demented older adults. Thirdly, a novel approach for generating high resolution gray matter labels is presented that involves a) utilization of the super resolution technique to ensure sharp delineation of structures, and b) a multi atlas based correction technique to reduce errors due to misregistration. High-resolution gray matter labels were constructed using the super resolution technique. When used for regional segmentation of the gray matter of older adults, the new gray matter labels of the showed high overlap, high geometric correlation, and low dissimilarity with the manually edited reference labels, demonstrating that there is a high agreement between the new labels and the manually edited Freesurfer labels. Finally, this thesis presents the development of a comprehensive array of gyral-based, cytoarchitecture-based, and functional connectivity-based gray matter labels in MIITRA space utilizing the aforementioned techniques. These labels include gyral-based, cytoarchitecture-based, and functional connectivity-based labels which will enhance the functionality of the MIITRA atlas. The new labels will also enhance the interoperability of MIITRA with the source atlases.
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Title: Image Synthesis with Generative Adversarial Networks
Creator: Ouyang, Xu
Date: 2023
Description: Image synthesis refers to the process of generating new images from an existing dataset, with the objective of creating images that closely...
Show moreImage synthesis refers to the process of generating new images from an existing dataset, with the objective of creating images that closely resemble the target images, learned from the source data distribution. This technique has a wide range of applications, including transforming captions into images, deblurring blurred images, and enhancing low-resolution images. In recent years, deep learning techniques, particularly Generative Adversarial Network (GAN), has achieved significant success in this field. GAN consists of a generator (G) and a discriminator (D) and employ adversarial learning to synthesize images. Researchers have developed various strategies to improve GAN performance, such as controlling learning rates for different models and modifying the loss functions. This thesis focuses on image synthesis from captions using GANs and aims to improve the quality of generated images. The study is divided into four main parts:In the first part, we investigate the LSTM conditional GAN which is to generate images from captions. We use the word2vec as the caption features and combine these features’ information by LSTM and generate images via conditional GAN. In the second part, to improve the quality of generated images, we address the issue of convergence speed and enhance GAN performance using an adaptive WGAN update strategy. We demonstrate that this update strategy is applicable to Wasserstein GAN(WGAN) and other GANs that utilize WGAN-related loss functions. The proposed update strategy is based on a loss change ratio comparison between G and D. In the third part, to further enhance the quality of synthesized images, we investigate a transformer-based Uformer GAN for image restoration and propose a two-step refinement strategy. Initially, we train a Uformer model until convergence, followed by training a Uformer GAN using the restoration results obtained from the first step.In the fourth part, to generate fine-grained image from captions, we delve into the Recurrent Affine Transformation (RAT) GAN for fine-grained text-to-image synthesis. By incorporating an auxiliary classifier in the discriminator and employing a contrastive learning method, we improve the accuracy and fine-grained details of the synthesized images.Throughout this thesis, we strive to enhance the capabilities of GANs in various image synthesis applications and contribute valuable insights to the field of deep learning and image processing.
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Title: MEN, WOMEN, AND LEADERS: THE EFFECT OF GENDER-LEADER CATEGORY CONGRUENCE ON SUPERVISOR EVALUATIONS
Creator: Lauritsen, Matthew William
Date: 2020
Description: Researchers employing Schein’s (1973, 1975) paradigm, ubiquitously conclude that the greater conceptual distance between leaders and women...
Show moreResearchers employing Schein’s (1973, 1975) paradigm, ubiquitously conclude that the greater conceptual distance between leaders and women compared to leaders and men is problematic for women in leadership roles. Six hundred eighty participants were recruited from MTurk to rate men, women, and leaders on agency and communion. Using polynomial regression analysis, the category congruence hypothesis was tested using two theories as interpretive frameworks: implicit leadership theory (ILT) and role congruity theory (RCT). A strict congruence effect was not found for any of the models. The results generally supported ILT, supervisor evaluations were highest when perceived supervisor characteristics exceeded the respondents’ leader category expectations. The results did not support RCT’s hypothesis about the negative effects of incongruence of women and leader category. Supervisor evaluations were highest when respondents held traditional gender stereotypes, not when they were congruent with the leader prototype. However, a general incongruence effect was found between male communion stereotypes and leader communion stereotypes leading to lower evaluations for male supervisors. That is, for men supervisors, the highest ratings were associated with high communion ratings of both men and leader categories. The results of this study are further discussed in relation to gender-leader category congruence and leadership.
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Title: Design and Evaluation of Engineering Systems to Remove VOCs from Groundwater (Spring 2003) IPRO 304B
Date: 2003, 2003-05
Description: The objective of this project is the design and cost estimation for various pollution control devices that can remove volatile organic...
Show moreThe objective of this project is the design and cost estimation for various pollution control devices that can remove volatile organic chemicals (VOCs) from ground water. Effective and efficient treatment methods are needed to meet this clean up challenge. Full-scale performance and cost data that minimizes energy requirements and documents the costs associated with design, construction, and operation will be required. Integration of various technologies and the application of unit operations involving adsorption, absorption, and biological reactor systems will be addressed. The need to address present and future environmental emission standards will be integrated into the design procedures. The design process will be part of a general evaluation of emission standards required for the control and removal of hazardous waste.
Sponsorship: IIT Collaboratory for Interprofessional Studies
Project Plan for IPRO 304B: Design and Evaluation of Engineering Systems to Remove VOCs from Groundwater for the Spring 2003 semester
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Title: NUMERICAL STUDY OF MICRO, MESO, AND MACRO-MECHANICAL BEHAVIOR OF COHESIONLESS GRANULAR MATERIALS USING 3D DEM ROLLING/TWISTING RESISTANCE MODELS
Creator: Goudarzi, Nima
Date: 2018
Description: It has been frequently demonstrated that the mechanical behavior of cohesionlessgranular materials including sand and gravel is significantly...
Show moreIt has been frequently demonstrated that the mechanical behavior of cohesionlessgranular materials including sand and gravel is significantly influenced by theirmorphological features including the shape and surface texture. Therefore, the primaryobjective of this thesis is to take a more critical look at micro-, meso- and macromechanicalbehaviors of cohesionless granular materials in response to effective modelingof the grains morphology and to establish a practical yet straightforward causal relationshipbetween micro-scale modeling and macro-scale soil behavior.To precisely investigate the effects of morphology on the macroscopic behavior, aparticle-based microscopic approach using the Discrete Element Method (DEM) wasemployed. In this regard, a novel 3D micro-mechanical contact model, based on the MTL(moment transfer law) theory, incorporating both rolling and twisting resistances, waspicked to describe the inter granular behavior between cohesionless particles. Severaltriaxial and direct shear tests were run to characterize link (s), if any, between the microscalefeatures and the macroscopic soil responses. Results from these tests were analyzedat both the peak and critical state. Through the development of a comprehensive calibrationmethodology and finding a reasonable match between numerical and experimental results,it was found that even in the ideal case of perfectly spherical grains, it is still possible toeffectively model the presence and effects of influential micro-scale morphologicalfeatures without the need for direct modeling of geometrical complexities followed bychallenging issues such as limitations in computational resources and almost unresolvabledifficulties in tracing the evolution of the modeled morphology during the loading.
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Title: ENGINEERING HUMAN ADIPOSE TISSUE WITHIN A MICROFLUIDIC DEVICE
Creator: Yang, Feipeng
Date: 2019
Description: Adipose tissue models can be used for in vitro drug screening of therapeutics designed for the treatment of obesity or adipose tissue-related...
Show moreAdipose tissue models can be used for in vitro drug screening of therapeutics designed for the treatment of obesity or adipose tissue-related diseases. This work aimed to engineer functional three-dimensional (3D) adipose microtissue models that could be incorporated within a microfluidic system. To support the on-chip 3D culture, a microfluidic device consisted of cell culture chambers flanked by two side channels was designed. The mold for the microfluidic device was manufactured using computer numeric control (CNC) micro-milling. Soft lithography with polydimethylsiloxane (PDMS) was used to construct the microchannels and chambers in the microfluidic device. A model was developed by the monoculture of adipocytes within the microfluidic device. Human adipose-derived stem cells (ADSCs) were differentiated toward adipocyte in the cell culture chambers and formed a 3D adipose microtissue. The effect of interstitial flow on the adipogenic differentiation of ADSCs was explored. Adipocytes showed decreased adiponectin secretion and increased lipolysis in response to increased interstitial shear stress. Meanwhile, multiple adipogenic genes were downregulated with the increase in shear stress.To engineer vascularized adipose tissue, a co-culture system with ADSCs, human umbilical vein endothelial cells (HUVECs) and normal human lung fibroblasts (NHLFs) was applied. Culture conditions (media, cell ratios, temporal conditions, etc.) for optimal differentiation of ADSCs and induction of network formation were identified. ADSCs were induced toward adipogenesis before mixed with HUVECs and NHLFs. The cell mixture was loaded into the microfluidic device and formed an adipose microtissue with a vessel network in a mixed culture media. An interconnected vascular network was established within 2 weeks and formed anastomoses with the side channels. Perfusion of fluorescent dextran confirmed the interconnections and lumen formation of the vascular network. Perfusion of fluorescently labeled fatty acid analog through vessels resulted in the accumulation of the fatty acid in adipocytes, confirming the functionality of the adipose microtissue. In conclusion, this work presented adipose tissue models within a microfluidic device that can potentially be utilized for on-chip drug screening, as well as provide insights into the engineering of complex tissues.
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Title: Socially Responsible Investing and Style Investing
Creator: He, Di
Date: 2020
Description: This study focuses on two popular investment strategies. The first one is a combination of socially responsible investing and factor investing...
Show moreThis study focuses on two popular investment strategies. The first one is a combination of socially responsible investing and factor investing (SRIF), it is therefore a comparison between factor investing portfolios and their corresponding ESG screened factor investing portfolios, aiming at indicating whether there is an opportunity costs or benefits of being responsible in factor investing. Opportunity cost is regarded if the ESG screened factor investing portfolios have lower raw return, Sharpe ratio, and risk-adjusted return than their respective factor investing portfolios. In addition to simply comparison, I also build an empirical SRI strategy, achieving real outperformance of SRI. For the second strategy, investing in R&D intensity (high technology) stocks results in significant positive alpha over 40 years. However, the alphas decrease significantly after the “Tech Bubble”, because investors nowadays prefer those technology firms who can produce true profits. I provide empirical evidence to investor sentiment, proving both risk bearing and investor sentiment play important roles in the positive association between R&D-intensive and excess return.In the first SRIF strategy, five widely-accepted factors in academic: value, size, profit, investment, and momentum are used to construct original single factor investing portfolio as benchmarks, which can naturally solve the benchmark bias, factor bias in previous literature at some extent. In addition to fulfill empirical industry’s generalities and constraints, this study also covers multi-factor framework and constructs different long-short positions for investment processing. Following considerations of ESG measurement (ESG_net and ESG_Industry, the latter one for calibration of industry bias), sample period (whole period and sub period), portfolio weighting methods (equally weighted and capitalization weighted), and after excluding undiversified portfolio, there are total 192 comparisons between factor investing portfolios and ESG screened factor investing portfolios for each measures of performance. Results suggest that most investors (80% - 90%) have to bear non-statistically significant opportunity costs if they want to be socially responsible in factor investing. In addition, the opportunity costs in sub period (2004-2017) is remarkably less in scale than those in whole period (1992-2017), indicating an obvious “time effect” that investors will have less opportunity costs recently with more and more ESG information is disclosed. For empirical consideration of industry, I build a double sorting factor portfolio on profit and value, and its ESG screened portfolio outperform the single factor portfolio.For the second research, R&D expense is a key component of investment. There is long history literature claim that there is a positive relationship between R&D and stock returns. There are two main explanations of the positive association, which are mispricing and risk bearing. This study separates whole sample into two periods: before “Tech Bubble” and after “Tech Bubble”, indicating that the mispricing is weaker after “Tech Bubble” than that in before “Tech Bubble”, while risk bearing is persistent. In addition, this study finds that the excess returns are relatively high for those highly subjective and difficult to arbitrage technology securities, which are small stocks, high volatility stocks, unprofitable stocks, non-dividend-paying stocks before the “Tech Bubble”, but almost vanish after the “Tech Bubble”. Therefore, investor sentiment does exist. While for those true earning technology securities, their excess returns are persistent, indicating compensation of risk bearing.
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Title: THE MODERATING AND MEDIATING ROLE OF SELF-REPORTED FAMILY ACCOMMODATION ON THE RELATION BETWEEN OBSESSIVE-COMPULSIVE SYMPTOMS AND RELATIONSHIP SATISFACTION IN AN ADULT, CLINICAL SAMPLE OF INDIVIDUALS IN ROMANTIC RELATIONSHIPS
Creator: De Leonardis, Andrew J
Date: 2020
Description: Severity of obsessive-compulsive symptoms (OCS) is associated with treatment resistance, and in an interpersonal context, is associated with...
Show moreSeverity of obsessive-compulsive symptoms (OCS) is associated with treatment resistance, and in an interpersonal context, is associated with increased relationship distress and decreased relationship satisfaction. In addition, caregivers for those with clinical levels of OCS often engage in family accommodation (FA) behaviors that serve as an extension of the OCD patient’s compulsive behavior. However, the literature on the interchange of OCS, FA, and relationship satisfaction is limited in scope because it focuses mainly on the perspective of the caregiver or partner of the individual with OCD. The current study aims to address this limitation by examining OCS, FA, and relationship satisfaction variables from the perspective of the individual with OCD. Participants included 78 adults with self-reported OCD who were recruited in the US through clinics and clinicians specializing in OCD treatment, as well as from OCD non-profit organizations to target non-treatment-seeking participants. After controlling for demographic variables, results indicated the following: (1) a significant positive association between OCS and FA, (2) a significant negative association between OCS and relationship satisfaction, and (3) a lack of an interaction between FA and OCS when predicting relationship satisfaction. However, the third result was trending towards significance and may be statistically underpowered. Exploratory analyses found FA to be a partial mediator of the association of OCS and relationship satisfaction. The findings support current trends in the research literature as well as contradict extant research on the associations between OCS, FA, and relationship satisfaction. Additionally, findings continue to show the importance of addressing family accommodation in treatment of individuals with OCD.
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Title: Impact of powder heterogeneity on particle collection behaviors in a cylindrical electrostatic precipitator
Creator: Lee, Eric Monsu
Date: 2019
Description: Injection of powdered activated carbon (PAC) upstream of electrostatic precipitators (ESPs) has been the most commonly used strategy for post...
Show moreInjection of powdered activated carbon (PAC) upstream of electrostatic precipitators (ESPs) has been the most commonly used strategy for post-combustion mercury emissions control at coal-fired power plants. However, as PAC injection rate increases, the darkening filters with particulate matter (PM) samples collected downstream of ESPs indicates an unidentified performance anomaly. It has been hypothesized that injection of PAC can introduce unexpected heterogeneity to the PM collection process in ESPs as PAC differs greatly from fly ash in both physical and electrical properties and can potentially pose challenges to ESPs that were initially operated for coal fly ash removal. The present study is carried out by an experimental study and a numerical study. The experimental study centers on the differential collection of PAC-fly ash admixtures and shows increasing trends of unaccounted-for particles as PAC concentration increases in the admixtures. In addition, measurement of powder resistivity of the ESP-collected powder samples infers that the unaccounted-for particles become more PAC-concentrated as PAC concentration becomes higher in the admixtures. The numerical study aims to extract additional variable(s) leading to higher percentage of unaccounted-for particles by using COMSOL Multiphysics. The Euler-Lagrange numerical scheme enables the modeling of the cylindrical ESP used during the experimental study and allows for solving the interrelated physics, including electric field coupled with charge conservation, electro-hydro-dynamics (EHD) fluid velocity field, and particle tracing. The model shows that the induced EHD vortex flow field due to the inhomogeneous current density along the collection electrode can result in continue entrainment of sub-micrometer particles of both fly ash and PAC. The experimental and numerical results provide new understanding for explaining the increasingly darkening PM filters as PAC injection rate increases.
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Title: Fast mesh based reconstruction for cardiac-gated SPECT and methodology for medical image quality assessment
Creator: Massanes Basi, Francesc
Date: 2018
Description: In this work, we are studying two different subjects that are intricately connected. For the first subject we are considering tools to...
Show moreIn this work, we are studying two different subjects that are intricately connected. For the first subject we are considering tools to improve the quality of single photon emission computed tomography (SPECT) imaging. Currently, SPECT images assist physicians to evaluate perfusion levels within the myocardium, aide in the diagnosis of various types of carcinomas, and measure pulmonary function. The SPECT technique relies on injecting a radioactive material into the patient's body and then detecting the emitted radiation by means of a gamma camera. However, the amount of radioactive material that can be given to a patient is limited by the negative effects that the radiation will have on the patient's health. This causes SPECT images to be highly corrupted by noise. We will focus our work on cardiac SPECT, which adds the challenge of the heart's continuous motion during the acquisition process. First, we describe the methodology used in SPECT imaging and reconstruction. Our methodology uses a content adaptive model, which uses more samples on the regions of the body that we want to be reconstructed more accurately and less in other areas. Then we describe our algorithm and our novel implementation that lets us use the content adaptive model to perform the reconstruction. In this work, we show that our implementation outperforms the reconstruction method used for clinical applications. In the second subject we are evaluating tools to measure image quality in the context of medical diagnosis. In signal processing, accuracy is typically measured as the amount of similarity between an original signal and its reconstruction. This similarity is traditionally a numeric metric that does not take into account the intended purpose of the reconstructed images. In the field of medical imaging, a reconstructed image is meant to aid a physician to perform a diagnostic task. Therefore, the quality of the reconstruction should be measured by how much it helps to perform the diagnostic task. A model observer is a computer tool that aims to mimic the performance of human observer, usually a radiologist, at a relevant diagnosis task. In this work we present our linear model observer designed to automatically select the features needed to model a human observer response. This is a novelty from the model observers currently being used in the medical imaging field, which instead usually have ad-hoc chosen features. Our model observer dependents only on the resolution of the image, not the type of imaging technique used to acquire the image.
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Title: Inflammation induced changes in adipocytes
Creator: Kim, Kihwan
Date: 2019
Description: The significant features of Crohn’s Disease include creeping fat that covers more than 50% of both small and large intestine surfaces and high...
Show moreThe significant features of Crohn’s Disease include creeping fat that covers more than 50% of both small and large intestine surfaces and high level of inflammatory cytokines such as TNF-alpha and IL-6. However, the relationship between these two factors of Crohn’s Disease is still unknown. Therefore, verifying the relationship could contribute to understanding the cause of Crohn’s Disease. In this study, preadipocytes were used because they have a potential to grow as adipocytes which are developed as creeping fat. The objective of this study was to observe proliferation, differentiation, and chemotaxis of preadipocytes in inflammatory microenvironment. It was found that only TNF-alpha stimulates preadipocyte proliferation whereas IL-6 does not. However, both TNF-alpha and IL-6 inhibit differentiation of preadipocytes. Furthermore, preadipocytes did not have chemotactic responses towards both cytokines. Therefore this study concludes that these inflammatory microenvironments induce the preadipocytes proliferation in Crohn’s. However, they inhibit adipogenesis and recruitment of the preadipocytes in Crohn’s.
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Title: EXPRESSION OF PPAR-γ AND PGC-1α TO INFLAMMATION IN HEPATOCYTES
Creator: HE, QIFAN
Date: 2019
Description: In this study, we examined that Proliferator-Activated Receptor γ (PPAR-γ) and Peroxisome Proliferator-Activated Receptor γ Coactivator 1-α ...
Show moreIn this study, we examined that Proliferator-Activated Receptor γ (PPAR-γ) and Peroxisome Proliferator-Activated Receptor γ Coactivator 1-α (PGC1α) protein expression in hepatocytes have different degrees of expression to Tumor Necrosis Factor α (TNFα) and rosiglitazone. To verify this objective, we employed lentivirus, instead of traditional plasmids, to transfect human hepatocytes (HepG2). Fluorescence-related protein of PPAR-γ and PGC-1α were delivered to hepatocytes, and inflammation was induced by adding TNFα and rosiglitazone to the medium. We successfully designed and created the lentivirus with high delivery efficiency, and determined that the test was true, indicating that PPAR-γ and PGC-1α proteins have different expression to inflammation in human hepatocytes.
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Title: Ultrasensitive protein quantification using Rolling Circle Amplification
Creator: Hetzel, Laura Ann
Date: 2019
Description: There are several protein biomarkers that can aid in diagnosing and evaluating the progression of Alzheimer’s Disease (AD), including Amyloid...
Show moreThere are several protein biomarkers that can aid in diagnosing and evaluating the progression of Alzheimer’s Disease (AD), including Amyloid Beta-42 (Aβ42), Amyloid Beta-40 (Aβ40), and Tau proteins. The proteins are most prevalent in the brain and cerebral spinal fluid, becoming more diluted in the bloodstream. Since diagnosis and progression would require evaluating and comparing protein levels over time and identifying miniscule changes, an assay with high sensitivity is paramount. Similarly, evaluating how a drug treatment affects the levels of protein requires a highly sensitive assay. Currently, enzyme-linked immunosorbent assay (ELISA) is accepted as the most sensitive assay for protein detection and quantification. However, in the case of Aβ40 and Aβ42 proteins, oftentimes the levels of the proteins in patients are very close to the sensitivity of the commercial ELISA. The uncertainty in these measurements is very high, which results in reporting of conflicting outcomes. One of the challenges of quantifying proteins is that proteins, unlike nucleic acids, cannot be amplified. To overcome this limitation, we have cleverly pseudo-amplified proteins using rolling circle amplification (RCA). By doing so, we have demonstrated a ten to forty times improvement in sensitivity over ELISA and radioimmunoassays. In previous experiments, C-peptide has been used as the protein of interest, and ELISA reports the smallest detectable quantity is 0.01 ng in buffer. Using RCA, we have found that as little as 0.00075 ng C-peptide in buffer could be quantified, and 0.004 ng in 10% serum could be quantified. The same process can be applied to other proteins such as Aβ40 and Aβ42, and the results are expected to be similar. In fact, we have measured Type I Diabetes autoantibodies with approximately forty times improvement over the gold standard radioimmunoassay. With excellent results in buffer and 10% serum, expansion to human samples holds great potential. If the human experiments are as successful as anticipated, RCA could be used to precisely evaluate the effect of a drug on protein levels, contributing to the overall evaluation of the success of the drug.
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Title: ACTIVE INFERENCE FOR PREDICTIVE MODELS OF SPATIO-TEMPORAL DOMAINS
Creator: Komurlu, Caner
Date: 2019
Description: Active inference is the method of selective information gathering during prediction in order to increase a predictive machine learning model's...
Show moreActive inference is the method of selective information gathering during prediction in order to increase a predictive machine learning model's prediction performance. Unlike active learning, active inference does not update the model, but rather provides the model with useful information during prediction to boost the prediction performance. To be able to work with active inference, a predictive model needs to exploit correlations among variables that need to be predicted. Then the model, while being provided with true values for some of the variables, can make more accurate predictions for the remaining variables.In this dissertation, I propose active inference methods for predictive models of spatio-temporal domains. I formulate and investigate active inference in two different domains: tissue engineering and wireless sensor networks. I develop active inference for dynamic Bayesian networks (DBNs) and feed-forward neural networks (FFNNs).First, I explore the effect of active inference in the tissue engineering domain. I design a dynamic Bayesian network (DBN) model for vascularization of a tissue development site. The DBN model predicts probabilities of blood vessel invasion in regional scale through time. Then utilizing spatio-temporal correlations between regions represented as variables in the DBN model, I develop an active inference technique to detect the optimal time to stop a wet lab experiment. The empirical study shows that the active inference is able to detect the optimal time and the results are coherent with domain simulations and lab experiments.In the second phase of my research, I develop variance-based active inference techniques for dynamic Bayesian networks for the purpose of battery saving for wireless sensor networks (WSN). I propose the expected variance reduction active inference method to detect variables that reduce the overall variance the most. I first propose a DBN model of a WSN. I then compare the prediction performance of the DBN with Gaussian processes and linear chain graphical models on three different WSN data using several baseline active inference methods. After showing that DBNs perform better than the baseline predictive models, I compare the performance of expected variance reduction active inference method with the performances of baseline methods on the DBN, and show the superiority of the expected variance reduction on the three WSN data sets.Finally, to address the inference complexity and the limitation of representing linear correlations due to Gaussian assumption, I replace the DBN representation with a feed-forward neural network (FFNN) model. I first explore techniques to integrate observed values into predictions on neural networks. I adopt the input optimization technique. Finally, I discover two problems: model error and optimization overfitting. I show that the input optimization can mitigate the model error. Lastly, I propose a validation-based regularization approach to solve the overfitting problem.
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Title: SHARPEN FACTOR INVESTING WITH A CLOSER LOOK AT PROFITABILITY
Creator: Li, Shengsi
Date: 2019
Description: Stock market anomalies have been long researched by academia and used by practitioners. Factor-based allocation has been shown to provide...
Show moreStock market anomalies have been long researched by academia and used by practitioners. Factor-based allocation has been shown to provide better diversification and risk-adjusted returns than the more traditional portfolio approaches. Numerous studies have shown traditional factors such as value, size, and profitability are effective in a cross-sectional fashion, meaning they are effective to all sections. It is found that the factor-return link is not robust across different sectors. Based on this observation, some stylized factor-based investing strategies are refined to improve the return performance measured by risk-adjusted metrics. Further analysis of the firm age moderation effect on the prediction power of profitability over stock return is explored. It is shown that firm age could have a significant moderation effect on the academically proven profitability factor.
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Title: DETERMINING CELL STIFFNESS USING MICROFLUIDICS
Creator: Penumarthy, Vineet Shyam
Date: 2019
Description: Sorting healthy cells from diseased cells is critical to detecting diseases and treating them before damage is done to a patient. These...
Show moreSorting healthy cells from diseased cells is critical to detecting diseases and treating them before damage is done to a patient. These diseases can be characterized based upon proteins, cytokines, DNA, pathology, blood tests, etc. However, another way of detecting them is using the mechanical properties of a cell, specifically the cell stiffness. In this study, a long microfluidic channel was designed, fabricated, and tested for flow using 6.7 µm polystyrene beads. Following this, Caco-2 cells and preadipocytes were flown through the channel and the travel time each cell took to flow through the channel was recorded, along with its cell diameter. The cells were then treated with blebbistatin, a myosin-II inhibitor, in order to soften the cell actin cytoskeleton and reduce the cell stiffness and were then flown through the channel again and the times taken to flow through were again recorded. We hypothesized that the stiffer a cell, the longer it would take to flow through the channel. From the results obtained using Caco-2 cells, we found that the blebbistatin treated cell times were much lower than the untreated cells, thus indicating that our hypothesis is true.
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Title: Cultivating Narrative Change in Collective Sensemaking
Creator: Sungu, Azra Tugce
Date: 2024
Description: The complexity of social and ecological crises and the need for collective action to tackle them brought forth collective inquiry as an...
Show moreThe complexity of social and ecological crises and the need for collective action to tackle them brought forth collective inquiry as an essential capacity for societies to combine their knowledge and creativity, navigate complexity, and respond with adaptive solutions. However, current approaches to sensemaking often remain anchored in the same mindsets and worldviews that underpin these crises, constraining their ability to account for the fundamental shifts needed. This research addresses this gap by exploring how collective inquiry, when framed as a narrative practice, can open space for alternative perspectives and pathways in systems transformation. This study positions narratives as dynamic meaning systems that shape how groups interpret issues, determine relevance, and envision new possibilities. The dissertation is structured as three studies, situated in the context of food systems, each exploring ways to engage and mobilize these meaning systems across different contexts and scales of collective inquiry. A central contribution of this research is the framework of narrative infrastructures—the social and material contexts through which narratives of change are constructed, circulated, and sustained within systems change efforts. This framework supports designers in navigating and shaping the spaces where narratives are articulated, contested, and maintained, to foster more critical, pluralistic, and transformative approaches to collective inquiry. Ultimately, this work enhances design’s capacity to foster the shifts in mindsets through which societies envision their collective futures, using its narrative agency to disrupt harmful paradigms and open space for radical possibilities in the making.
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Title: Automated techniques for enhancing developer productivity on disaggregated software stacks
Creator: Tauro, Brian Richard
Date: 2024
Description: The rapid advances in specialized hardware, capabilities including FPGAs, TPUs,microsecond network interconnects, RDMA, GPUs, and other...
Show moreThe rapid advances in specialized hardware, capabilities including FPGAs, TPUs,microsecond network interconnects, RDMA, GPUs, and other technologies have posed significant challenges for the traditional monolithic client/server model in efficiently man- aging and scaling resources, both within industry and HPC systems. This challenge has in- stigated the emergence of a new paradigm known as resource disaggregation, which tackles the limitations of the monolithic client-server paradigm by enabling independent scaling of components such as compute, memory, and storage. The disaggregation of resources can be managed entirely through hardware or software solutions. Resource disaggregation en- hances resource utilization by fine grained scheduling of hardware resources to support dynamic workloads efficiently and empowers data centers to effectively address varying computational demands, optimize performance, and curtail operational costs, marking a pivotal evolution in modern computational infrastructure. However, software based disaggregation, whether over a single server or across multiple servers, has placed an increased burden on developers. They are compelled to continuously adapt to new software stacks and migrate applications accordingly. In some instances, despite the considerable porting efforts, the outcomes may not justify the in- vestment. Unfortunately, much of the existing research fails to adequately address the engineering investment challenge, instead prioritizing new software stacks primarily for performance gains, albeit often at the expense of developer productivity. This thesis focuses on improving developer productivity in software disaggregated environments and advocates that unless a developer has evidence they should not have to switch to a new software system or OS environment. Even when there is benefit for doing so, software tools should be able to prioritize compatibility by leveraging advancements in low-level system software stacks like the operating system and compilers. We found initial evidence on ways to improve developer productivity in software disaggregated systems by exploring analytical models backed with emulators to place bounds on application performance. Our speedup models equip developers with a tool to decide whether an application would benefit from resource disaggregation before actually trying to use such a system. While analytical models help developers gain insight before adapting to a new environment, developers may still have to port their applications to achieve high per- formance. We found out through TrackFM that compilers can enable automatic porting of applications with high performance, thereby improving developer productivity on memory disaggregated systems. One of the limitations of TrackFM was that the runtime memory policies had to be determined at static time for applications, which can lead to performance overheads for certain applications. We overcome this problem by building CARDS, a sys- tem that determines far memory policies proactively on software disaggregated systems by combining compiler and runtime information for each data structure within an applica- tion automatically. CARDS provides developers with a new alternative that determines far memory polices dynamically instead of using a complex profiling based system to improve policies. CARDS is built on top of TrackFM and overcomes the limitations of static com- pilers by codesigning compiler analysis with the runtime which enables informed policy decisions at data structure granularity. The co-design of modern compiler analysis with runtime systems opens a unique opportunity to create tools that enhance developer productivity within resource-disaggregated architectures. We also envision that such codesign can be extremely helpful in emulation of experimental hardware architectures to provide insights quickly without any application porting effort. Leveraging my expertise in low-level system software, my thesis aims to ad- vocate for the integration of automated tools in software disaggregated systems to prioritize developer productivity in datacenter environments.
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Title: Rapid Ex Vivo Detection of Cancer in Excised Lymph Nodes: Development of a Tissue Model and Initial Results
Creator: Sharma, Anjalika
Date: 2024
Description: There are millions of new head and neck cancers diagnosed each year, and it is one of the most aggressive cancers. The typical first line of...
Show moreThere are millions of new head and neck cancers diagnosed each year, and it is one of the most aggressive cancers. The typical first line of therapy for head and neck cancers is surgery; however, if the cancer has spread (metastasized) from the primary tumor, more advanced surgery and/or adjuvant therapy (chemotherapy and or radiation therapy) can be indicated. Clinically, metastasis is diagnosed by surgically removing one or more lymph nodes draining the primary tumor during the primary tumor resection. Each lymph node located and removed adds to the morbidity of the procedure, so many clinics are moving toward a “sentinel” lymph node biopsy strategy, where only the first lymph node draining the tumor is removed and sent to pathology. Assessment of the node for cancer can take up to a week. If this lymph node is found to have cancer, the patient is then asked to return for a secondary surgery where a complete neck dissection is carried out (removal of all the lymph nodes in the side of the neck ipsilateral to the tumor). This delay in diagnosis is stressful on patients, adds health care costs, and considering the invasiveness of some primary tumor resections, some patients opt not to return for callback surgeries even though it would improve their chances of survival. This thesis presents efforts to test the ability for a fluorescence molecular imaging system called “agent-dependent enhanced photon tomography” (ADEPT) to be able to detect cancer in an excised sentinel lymph node while the patient is still on the operating table. This would allow a significant reduction in the number of patients requiring callback surgeries. Specifically, this thesis explores (in chapter 1) the development of a porcine lymph node fresh tissue model using implanted human cancer spheroids to act as realistic models of a freshly excised sentinel lymph node; (in chapter 2) the advancement of this tissue model to include a range of cancer burden levels and cancer cells strains; (and in chapter 3) a first demonstration of the ADEPT system applied to these realistic tissue models to detect clinically relevant levels of cancer. The ADEPT is a prototype designed specifically for the purpose of being faster in terms of processing and eliminates the need for patient to come back surgeries. We were able to validate ADEPT by incorporating a metastatic model mimicking a human lymph node and verifying the presence of cancer tumor that was manually injected into the lymph node followed by infusion of imaging agents.
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