Matching Items (468)
Filtering by
- All Subjects: Nutrition
- Creators: Arizona State University
Description
Alzheimer’s disease (AD), is a chronic neurodegenerative disease that usually starts slowly and gets worse over time. It is the cause of 60% to 70% of cases of dementia. There is growing interest in identifying brain image biomarkers that help evaluate AD risk pre-symptomatically. High-dimensional non-linear pattern classification methods have been applied to structural magnetic resonance images (MRI’s) and used to discriminate between clinical groups in Alzheimers progression. Using Fluorodeoxyglucose (FDG) positron emission tomography (PET) as the pre- ferred imaging modality, this thesis develops two independent machine learning based patch analysis methods and uses them to perform six binary classification experiments across different (AD) diagnostic categories. Specifically, features were extracted and learned using dimensionality reduction and dictionary learning & sparse coding by taking overlapping patches in and around the cerebral cortex and using them as fea- tures. Using AdaBoost as the preferred choice of classifier both methods try to utilize 18F-FDG PET as a biological marker in the early diagnosis of Alzheimer’s . Addi- tional we investigate the involvement of rich demographic features (ApoeE3, ApoeE4 and Functional Activities Questionnaires (FAQ)) in classification. The experimental results on Alzheimer’s Disease Neuroimaging initiative (ADNI) dataset demonstrate the effectiveness of both the proposed systems. The use of 18F-FDG PET may offer a new sensitive biomarker and enrich the brain imaging analysis toolset for studying the diagnosis and prognosis of AD.
ContributorsSrivastava, Anant (Author) / Wang, Yalin (Thesis advisor) / Bansal, Ajay (Thesis advisor) / Liang, Jianming (Committee member) / Arizona State University (Publisher)
Created2017
Description
Identifying chemical compounds that inhibit bacterial infection has recently gained a considerable amount of attention given the increased number of highly resistant bacteria and the serious health threat it poses around the world. With the development of automated microscopy and image analysis systems, the process of identifying novel therapeutic drugs can generate an immense amount of data - easily reaching terabytes worth of information. Despite increasing the vast amount of data that is currently generated, traditional analytical methods have not increased the overall success rate of identifying active chemical compounds that eventually become novel therapeutic drugs. Moreover, multispectral imaging has become ubiquitous in drug discovery due to its ability to provide valuable information on cellular and sub-cellular processes using florescent reagents. These reagents are often costly and toxic to cells over an extended period of time causing limitations in experimental design. Thus, there is a significant need to develop a more efficient process of identifying active chemical compounds.
This dissertation introduces novel machine learning methods based on parallelized cellomics to analyze interactions between cells, bacteria, and chemical compounds while reducing the use of fluorescent reagents. Machine learning analysis using image-based high-content screening (HCS) data is compartmentalized into three primary components: (1) \textit{Image Analytics}, (2) \textit{Phenotypic Analytics}, and (3) \textit{Compound Analytics}. A novel software analytics tool called the Insights project is also introduced. The Insights project fully incorporates distributed processing, high performance computing, and database management that can rapidly and effectively utilize and store massive amounts of data generated using HCS biological assessments (bioassays). It is ideally suited for parallelized cellomics in high dimensional space.
Results demonstrate that a parallelized cellomics approach increases the quality of a bioassay while vastly decreasing the need for control data. The reduction in control data leads to less fluorescent reagent consumption. Furthermore, a novel proposed method that uses single-cell data points is proven to identify known active chemical compounds with a high degree of accuracy, despite traditional quality control measurements indicating the bioassay to be of poor quality. This, ultimately, decreases the time and resources needed in optimizing bioassays while still accurately identifying active compounds.
This dissertation introduces novel machine learning methods based on parallelized cellomics to analyze interactions between cells, bacteria, and chemical compounds while reducing the use of fluorescent reagents. Machine learning analysis using image-based high-content screening (HCS) data is compartmentalized into three primary components: (1) \textit{Image Analytics}, (2) \textit{Phenotypic Analytics}, and (3) \textit{Compound Analytics}. A novel software analytics tool called the Insights project is also introduced. The Insights project fully incorporates distributed processing, high performance computing, and database management that can rapidly and effectively utilize and store massive amounts of data generated using HCS biological assessments (bioassays). It is ideally suited for parallelized cellomics in high dimensional space.
Results demonstrate that a parallelized cellomics approach increases the quality of a bioassay while vastly decreasing the need for control data. The reduction in control data leads to less fluorescent reagent consumption. Furthermore, a novel proposed method that uses single-cell data points is proven to identify known active chemical compounds with a high degree of accuracy, despite traditional quality control measurements indicating the bioassay to be of poor quality. This, ultimately, decreases the time and resources needed in optimizing bioassays while still accurately identifying active compounds.
ContributorsTrevino, Robert (Author) / Liu, Huan (Thesis advisor) / Lamkin, Thomas J (Committee member) / He, Jingrui (Committee member) / Lee, Joohyung (Committee member) / Arizona State University (Publisher)
Created2016
Description
Background: College freshmen are exposed to a variety of environmental and social factors that can alter changes to health habits and encourage weight gain. Weight-related conversations had with friends may be related to self-perception of weight and alterations to health behaviors, but this association has yet to be assessed in the college population.
Objective: This study aims to examine the relationship between friend advice about weight management, self-perception of weight, and alterations to weight change intentions, physical activity, and eating habits in college freshmen over time.
Methods: College freshmen from ASU with complete data for three time points (n=321) were found to be predominantly female (72.2%) and non-white (53.2%) with a mean age of 17.5±41. Complete data included responses for items included in analysis which were related to friend encouragement about weigh management, self-perception of weight, physical activity, eating behaviors, and weight change intentions. A longitudinal multivariate mediation analysis using negative binomial regression adjusted for sociodemographics and clustering by dorm was used to assess the relationship between 1) friend encouragement about weight management at time 1 and behavioral outcomes at time 3, 2) friend encouragement about weight management at time 1 and self-perception of weight at time 2, and 3) self-perception of weight at time 2 and behavioral outcomes at time 3.
Results: A small proportion of population perceived friend encouragement about weight loss (18.3%) and weight gain (14.4%) at time 1. Half the population (50.9%) had the self-perception of overweight at time 2. At time 3, more than half (54.3%) of individuals performed at least 60 minutes of MVPA and consumed at least ½ a serving of sugar-sweetened beverages each day, while nearly half (48.6%) consumed at least 2 servings of fruits and vegetables each day. Males perceived more friend encouragement to gain weight (27.4%; p<0.01), but more females had the self-perception of overweight (54%; p=0.04) and were attempting to lose weight (59.3%; p<0.01). Individuals who perceived friend encouragement to lose weight at time 1 had a 14.8% greater prevalence (p<0.001) of overweight perception of time two, and a 9.6% and 6.9%; decreased prevalence (p<0.001) of weight change and weight loss intentions (p=0.023) at time three respectively. Individuals who perceived friend encouragement to gain weight had a 34.9% decreased prevalence of (p<0.001) of self-perception of overweight at time 1. In individuals with the self-perception of overweight at time 2, there was a 18.1% increased prevalence (p<0.001) of consuming at least ½ a serving of sugar-sweetened beverages/day and an increased prevalence of 22.8% and 24.0% for weight change intentions and weight loss intentions at time 3 (p<0.001).
Conclusion: These findings suggest that there was not a mediation effect of self-perception of overweight in the relationship between friend encouragement about weight management and behavioral outcomes in the current sample. However, the increased prevalence of overweight perception in individuals who perceived friend encouragement about weight management may inform future interventions to focus on how weight-related conversations with friends is related to overweight perception. More research about the relationship between weight-related conversations had with friends, self-perception of weight, and health behaviors is needed to confirm these findings.
Objective: This study aims to examine the relationship between friend advice about weight management, self-perception of weight, and alterations to weight change intentions, physical activity, and eating habits in college freshmen over time.
Methods: College freshmen from ASU with complete data for three time points (n=321) were found to be predominantly female (72.2%) and non-white (53.2%) with a mean age of 17.5±41. Complete data included responses for items included in analysis which were related to friend encouragement about weigh management, self-perception of weight, physical activity, eating behaviors, and weight change intentions. A longitudinal multivariate mediation analysis using negative binomial regression adjusted for sociodemographics and clustering by dorm was used to assess the relationship between 1) friend encouragement about weight management at time 1 and behavioral outcomes at time 3, 2) friend encouragement about weight management at time 1 and self-perception of weight at time 2, and 3) self-perception of weight at time 2 and behavioral outcomes at time 3.
Results: A small proportion of population perceived friend encouragement about weight loss (18.3%) and weight gain (14.4%) at time 1. Half the population (50.9%) had the self-perception of overweight at time 2. At time 3, more than half (54.3%) of individuals performed at least 60 minutes of MVPA and consumed at least ½ a serving of sugar-sweetened beverages each day, while nearly half (48.6%) consumed at least 2 servings of fruits and vegetables each day. Males perceived more friend encouragement to gain weight (27.4%; p<0.01), but more females had the self-perception of overweight (54%; p=0.04) and were attempting to lose weight (59.3%; p<0.01). Individuals who perceived friend encouragement to lose weight at time 1 had a 14.8% greater prevalence (p<0.001) of overweight perception of time two, and a 9.6% and 6.9%; decreased prevalence (p<0.001) of weight change and weight loss intentions (p=0.023) at time three respectively. Individuals who perceived friend encouragement to gain weight had a 34.9% decreased prevalence of (p<0.001) of self-perception of overweight at time 1. In individuals with the self-perception of overweight at time 2, there was a 18.1% increased prevalence (p<0.001) of consuming at least ½ a serving of sugar-sweetened beverages/day and an increased prevalence of 22.8% and 24.0% for weight change intentions and weight loss intentions at time 3 (p<0.001).
Conclusion: These findings suggest that there was not a mediation effect of self-perception of overweight in the relationship between friend encouragement about weight management and behavioral outcomes in the current sample. However, the increased prevalence of overweight perception in individuals who perceived friend encouragement about weight management may inform future interventions to focus on how weight-related conversations with friends is related to overweight perception. More research about the relationship between weight-related conversations had with friends, self-perception of weight, and health behaviors is needed to confirm these findings.
ContributorsThibodeau, Tristan (Author) / Bruening, Meg (Thesis advisor) / Ohri-Vachaspati, Punam (Committee member) / Huberty, Jennifer (Committee member) / Arizona State University (Publisher)
Created2016
Description
Buildings consume nearly 50% of the total energy in the United States, which drives the need to develop high-fidelity models for building energy systems. Extensive methods and techniques have been developed, studied, and applied to building energy simulation and forecasting, while most of work have focused on developing dedicated modeling approach for generic buildings. In this study, an integrated computationally efficient and high-fidelity building energy modeling framework is proposed, with the concentration on developing a generalized modeling approach for various types of buildings. First, a number of data-driven simulation models are reviewed and assessed on various types of computationally expensive simulation problems. Motivated by the conclusion that no model outperforms others if amortized over diverse problems, a meta-learning based recommendation system for data-driven simulation modeling is proposed. To test the feasibility of the proposed framework on the building energy system, an extended application of the recommendation system for short-term building energy forecasting is deployed on various buildings. Finally, Kalman filter-based data fusion technique is incorporated into the building recommendation system for on-line energy forecasting. Data fusion enables model calibration to update the state estimation in real-time, which filters out the noise and renders more accurate energy forecast. The framework is composed of two modules: off-line model recommendation module and on-line model calibration module. Specifically, the off-line model recommendation module includes 6 widely used data-driven simulation models, which are ranked by meta-learning recommendation system for off-line energy modeling on a given building scenario. Only a selective set of building physical and operational characteristic features is needed to complete the recommendation task. The on-line calibration module effectively addresses system uncertainties, where data fusion on off-line model is applied based on system identification and Kalman filtering methods. The developed data-driven modeling framework is validated on various genres of buildings, and the experimental results demonstrate desired performance on building energy forecasting in terms of accuracy and computational efficiency. The framework could be easily implemented into building energy model predictive control (MPC), demand response (DR) analysis and real-time operation decision support systems.
ContributorsCui, Can (Author) / Wu, Teresa (Thesis advisor) / Weir, Jeffery D. (Thesis advisor) / Li, Jing (Committee member) / Fowler, John (Committee member) / Hu, Mengqi (Committee member) / Arizona State University (Publisher)
Created2016
Description
Feature learning and the discovery of nonlinear variation patterns in high-dimensional data is an important task in many problem domains, such as imaging, streaming data from sensors, and manufacturing. This dissertation presents several methods for learning and visualizing nonlinear variation in high-dimensional data. First, an automated method for discovering nonlinear variation patterns using deep learning autoencoders is proposed. The approach provides a functional mapping from a low-dimensional representation to the original spatially-dense data that is both interpretable and efficient with respect to preserving information. Experimental results indicate that deep learning autoencoders outperform manifold learning and principal component analysis in reproducing the original data from the learned variation sources.
A key issue in using autoencoders for nonlinear variation pattern discovery is to encourage the learning of solutions where each feature represents a unique variation source, which we define as distinct features. This problem of learning distinct features is also referred to as disentangling factors of variation in the representation learning literature. The remainder of this dissertation highlights and provides solutions for this important problem.
An alternating autoencoder training method is presented and a new measure motivated by orthogonal loadings in linear models is proposed to quantify feature distinctness in the nonlinear models. Simulated point cloud data and handwritten digit images illustrate that standard training methods for autoencoders consistently mix the true variation sources in the learned low-dimensional representation, whereas the alternating method produces solutions with more distinct patterns.
Finally, a new regularization method for learning distinct nonlinear features using autoencoders is proposed. Motivated in-part by the properties of linear solutions, a series of learning constraints are implemented via regularization penalties during stochastic gradient descent training. These include the orthogonality of tangent vectors to the manifold, the correlation between learned features, and the distributions of the learned features. This regularized learning approach yields low-dimensional representations which can be better interpreted and used to identify the true sources of variation impacting a high-dimensional feature space. Experimental results demonstrate the effectiveness of this method for nonlinear variation pattern discovery on both simulated and real data sets.
A key issue in using autoencoders for nonlinear variation pattern discovery is to encourage the learning of solutions where each feature represents a unique variation source, which we define as distinct features. This problem of learning distinct features is also referred to as disentangling factors of variation in the representation learning literature. The remainder of this dissertation highlights and provides solutions for this important problem.
An alternating autoencoder training method is presented and a new measure motivated by orthogonal loadings in linear models is proposed to quantify feature distinctness in the nonlinear models. Simulated point cloud data and handwritten digit images illustrate that standard training methods for autoencoders consistently mix the true variation sources in the learned low-dimensional representation, whereas the alternating method produces solutions with more distinct patterns.
Finally, a new regularization method for learning distinct nonlinear features using autoencoders is proposed. Motivated in-part by the properties of linear solutions, a series of learning constraints are implemented via regularization penalties during stochastic gradient descent training. These include the orthogonality of tangent vectors to the manifold, the correlation between learned features, and the distributions of the learned features. This regularized learning approach yields low-dimensional representations which can be better interpreted and used to identify the true sources of variation impacting a high-dimensional feature space. Experimental results demonstrate the effectiveness of this method for nonlinear variation pattern discovery on both simulated and real data sets.
ContributorsHoward, Phillip (Author) / Runger, George C. (Thesis advisor) / Montgomery, Douglas C. (Committee member) / Mirchandani, Pitu (Committee member) / Apley, Daniel (Committee member) / Arizona State University (Publisher)
Created2016
Description
Information divergence functions, such as the Kullback-Leibler divergence or the Hellinger distance, play a critical role in statistical signal processing and information theory; however estimating them can be challenge. Most often, parametric assumptions are made about the two distributions to estimate the divergence of interest. In cases where no parametric model fits the data, non-parametric density estimation is used. In statistical signal processing applications, Gaussianity is usually assumed since closed-form expressions for common divergence measures have been derived for this family of distributions. Parametric assumptions are preferred when it is known that the data follows the model, however this is rarely the case in real-word scenarios. Non-parametric density estimators are characterized by a very large number of parameters that have to be tuned with costly cross-validation. In this dissertation we focus on a specific family of non-parametric estimators, called direct estimators, that bypass density estimation completely and directly estimate the quantity of interest from the data. We introduce a new divergence measure, the $D_p$-divergence, that can be estimated directly from samples without parametric assumptions on the distribution. We show that the $D_p$-divergence bounds the binary, cross-domain, and multi-class Bayes error rates and, in certain cases, provides provably tighter bounds than the Hellinger divergence. In addition, we also propose a new methodology that allows the experimenter to construct direct estimators for existing divergence measures or to construct new divergence measures with custom properties that are tailored to the application. To examine the practical efficacy of these new methods, we evaluate them in a statistical learning framework on a series of real-world data science problems involving speech-based monitoring of neuro-motor disorders.
ContributorsWisler, Alan (Author) / Berisha, Visar (Thesis advisor) / Spanias, Andreas (Thesis advisor) / Liss, Julie (Committee member) / Bliss, Daniel (Committee member) / Arizona State University (Publisher)
Created2017
Description
A medical control system, a real-time controller, uses a predictive model of human physiology for estimation and controlling of drug concentration in the human body. Artificial Pancreas (AP) is an example of the control system which regulates blood glucose in T1D patients. The predictive model in the control system such as Bergman Minimal Model (BMM) is based on physiological modeling technique which separates the body into the number of anatomical compartments and each compartment's effect on body system is determined by their physiological parameters. These models are less accurate due to unaccounted physiological factors effecting target values. Estimation of a large number of physiological parameters through optimization algorithm is computationally expensive and stuck in local minima. This work evaluates a machine learning(ML) framework which has an ML model guided through physiological models. A support vector regression model guided through modified BMM is implemented for estimation of blood glucose levels. Physical activity and Endogenous glucose production are key factors that contribute in the increased hypoglycemia events thus, this work modifies Bergman Minimal Model ( Bergman et al. 1981) for more accurate estimation of blood glucose levels. Results show that the SVR outperformed BMM by 0.164 average RMSE for 7 different patients in the free-living scenario. This computationally inexpensive data driven model can potentially learn parameters more accurately with time. In conclusion, advised prediction model is promising in modeling the physiology elements in living systems.
ContributorsAgrawal, Anurag (Author) / Gupta, Sandeep K. S. (Thesis advisor) / Banerjee, Ayan (Committee member) / Kudva, Yogish (Committee member) / Arizona State University (Publisher)
Created2017
Description
A major challenge in automated text analysis is that different words are used for related concepts. Analyzing text at the surface level would treat related concepts (i.e. actors, actions, targets, and victims) as different objects, potentially missing common narrative patterns. Generalized concepts are used to overcome this problem. Generalization may result into word sense disambiguation failing to find similarity. This is addressed by taking into account contextual synonyms. Concept discovery based on contextual synonyms reveal information about the semantic roles of the words leading to concepts. Merger engine generalize the concepts so that it can be used as features in learning algorithms.
ContributorsKedia, Nitesh (Author) / Davulcu, Hasan (Thesis advisor) / Corman, Steve R (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2015