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Description
Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis, is the 10th leading cause of death, worldwide. The prevalence of drug-resistant clinical isolates and the paucity of newly-approved antituberculosis drugs impedes the successful eradication of Mtb. Bacteria commonly use two-component systems (TCS) to sense their environment and genetically modulate adaptive responses. The prrAB TCS is essential in Mtb, thus representing an auspicious drug target; however, the inability to generate an Mtb ΔprrAB mutant complicates investigating how this TCS contributes to pathogenesis. Mycobacterium smegmatis, a commonly used M. tuberculosis genetic surrogate was used here. This work shows that prrAB is not essential in M. smegmatis. During ammonium stress, the ΔprrAB mutant excessively accumulates triacylglycerol lipids, a phenotype associated with M. tuberculosis dormancy and chronic infection. Additionally, triacylglycerol biosynthetic genes were induced in the ΔprrAB mutant relative to the wild-type and complementation strains during ammonium stress. Next, RNA-seq was used to define the M. smegmatis PrrAB regulon. PrrAB regulates genes participating in respiration, metabolism, redox balance, and oxidative phosphorylation. The M. smegmatis ΔprrAB mutant is compromised for growth under hypoxia, is hypersensitive to cyanide, and fails to induce high-affinity respiratory genes during hypoxia. Furthermore, PrrAB positively regulates the hypoxia-responsive dosR TCS response regulator, potentially explaining the hypoxia-mediated growth defects in the ΔprrAB mutant. Despite inducing genes encoding the F1F0 ATP synthase, the ΔprrAB mutant accumulates significantly less ATP during aerobic, exponential growth compared to the wild-type and complementation strains. Finally, the M. smegmatis ΔprrAB mutant exhibited growth impairment in media containing gluconeogenic carbon sources. M. tuberculosis mutants unable to utilize these substrates fail to establish chronic infection, suggesting that PrrAB may regulate Mtb central carbon metabolism in response to chronic infection. In conclusion, 1) prrAB is not universally essential in mycobacteria; 2) M. smegmatis PrrAB regulates genetic responsiveness to nutrient and oxygen stress; and 3) PrrAB may provide feed-forward control of the DosRS TCS and dormancy phenotypes. The data generated in these studies provide insight into the mycobacterial PrrAB TCS transcriptional regulon, PrrAB essentiality in Mtb, and how PrrAB may mediate stresses encountered by Mtb during the transition to chronic infection.
ContributorsMaarsingh, Jason (Author) / Haydel, Shelley E (Thesis advisor) / Roland, Kenneth (Committee member) / Sandrin, Todd (Committee member) / Bean, Heather (Committee member) / Arizona State University (Publisher)
Created2019
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
Understanding the complexity of temporal and spatial characteristics of gene expression over brain development is one of the crucial research topics in neuroscience. An accurate description of the locations and expression status of relative genes requires extensive experiment resources. The Allen Developing Mouse Brain Atlas provides a large number of in situ hybridization (ISH) images of gene expression over seven different mouse brain developmental stages. Studying mouse brain models helps us understand the gene expressions in human brains. This atlas collects about thousands of genes and now they are manually annotated by biologists. Due to the high labor cost of manual annotation, investigating an efficient approach to perform automated gene expression annotation on mouse brain images becomes necessary. In this thesis, a novel efficient approach based on machine learning framework is proposed. Features are extracted from raw brain images, and both binary classification and multi-class classification models are built with some supervised learning methods. To generate features, one of the most adopted methods in current research effort is to apply the bag-of-words (BoW) algorithm. However, both the efficiency and the accuracy of BoW are not outstanding when dealing with large-scale data. Thus, an augmented sparse coding method, which is called Stochastic Coordinate Coding, is adopted to generate high-level features in this thesis. In addition, a new multi-label classification model is proposed in this thesis. Label hierarchy is built based on the given brain ontology structure. Experiments have been conducted on the atlas and the results show that this approach is efficient and classifies the images with a relatively higher accuracy.
ContributorsZhao, Xinlin (Author) / Ye, Jieping (Thesis advisor) / Wang, Yalin (Thesis advisor) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2016
Description
The rapid growth of social media in recent years provides a large amount of user-generated visual objects, e.g., images and videos. Advanced semantic understanding approaches on such visual objects are desired to better serve applications such as human-machine interaction, image retrieval, etc. Semantic visual attributes have been proposed and utilized in multiple visual computing tasks to bridge the so-called "semantic gap" between extractable low-level feature representations and high-level semantic understanding of the visual objects.
Despite years of research, there are still some unsolved problems on semantic attribute learning. First, real-world applications usually involve hundreds of attributes which requires great effort to acquire sufficient amount of labeled data for model learning. Second, existing attribute learning work for visual objects focuses primarily on images, with semantic analysis on videos left largely unexplored.
In this dissertation I conduct innovative research and propose novel approaches to tackling the aforementioned problems. In particular, I propose robust and accurate learning frameworks on both attribute ranking and prediction by exploring the correlation among multiple attributes and utilizing various types of label information. Furthermore, I propose a video-based skill coaching framework by extending attribute learning to the video domain for robust motion skill analysis. Experiments on various types of applications and datasets and comparisons with multiple state-of-the-art baseline approaches confirm that my proposed approaches can achieve significant performance improvements for the general attribute learning problem.
Despite years of research, there are still some unsolved problems on semantic attribute learning. First, real-world applications usually involve hundreds of attributes which requires great effort to acquire sufficient amount of labeled data for model learning. Second, existing attribute learning work for visual objects focuses primarily on images, with semantic analysis on videos left largely unexplored.
In this dissertation I conduct innovative research and propose novel approaches to tackling the aforementioned problems. In particular, I propose robust and accurate learning frameworks on both attribute ranking and prediction by exploring the correlation among multiple attributes and utilizing various types of label information. Furthermore, I propose a video-based skill coaching framework by extending attribute learning to the video domain for robust motion skill analysis. Experiments on various types of applications and datasets and comparisons with multiple state-of-the-art baseline approaches confirm that my proposed approaches can achieve significant performance improvements for the general attribute learning problem.
ContributorsChen, Lin (Author) / Li, Baoxin (Thesis advisor) / Turaga, Pavan (Committee member) / Wang, Yalin (Committee member) / Liu, Huan (Committee member) / Arizona State University (Publisher)
Created2016
Description
The emergence of invasive non-Typhoidal Salmonella (iNTS) infections belonging to sequence type (ST) 313 are associated with severe bacteremia and high mortality in sub-Saharan Africa. Distinct features of ST313 strains include resistance to multiple antibiotics, extensive genomic degradation, and atypical clinical diagnosis including bloodstream infections, respiratory symptoms, and fever. Herein, I report the use of dynamic bioreactor technology to profile the impact of physiological fluid shear levels on the pathogenesis-related responses of ST313 pathovar, 5579. I show that culture of 5579 under these conditions induces profoundly different pathogenesis-related phenotypes than those normally observed when cultures are grown conventionally. Surprisingly, in response to physiological fluid shear, 5579 exhibited positive swimming motility, which was unexpected, since this strain was initially thought to be non-motile. Moreover, fluid shear altered the resistance of 5579 to acid, oxidative and bile stress, as well as its ability to colonize human colonic epithelial cells. This work leverages from and advances studies over the past 16 years in the Nickerson lab, which are at the forefront of bacterial mechanosensation and further demonstrates that bacterial pathogens are “hardwired” to respond to the force of fluid shear in ways that are not observed during conventional culture, and stresses the importance of mimicking the dynamic physical force microenvironment when studying host-pathogen interactions. The results from this study lay the foundation for future work to determine the underlying mechanisms operative in 5579 that are responsible for these phenotypic observations.
ContributorsCastro, Christian (Author) / Nickerson, Cheryl A. (Thesis advisor) / Ott, C. Mark (Committee member) / Roland, Kenneth (Committee member) / Barrila, Jennifer (Committee member) / Arizona State University (Publisher)
Created2016
Description
In many fields one needs to build predictive models for a set of related machine learning tasks, such as information retrieval, computer vision and biomedical informatics. Traditionally these tasks are treated independently and the inference is done separately for each task, which ignores important connections among the tasks. Multi-task learning aims at simultaneously building models for all tasks in order to improve the generalization performance, leveraging inherent relatedness of these tasks. In this thesis, I firstly propose a clustered multi-task learning (CMTL) formulation, which simultaneously learns task models and performs task clustering. I provide theoretical analysis to establish the equivalence between the CMTL formulation and the alternating structure optimization, which learns a shared low-dimensional hypothesis space for different tasks. Then I present two real-world biomedical informatics applications which can benefit from multi-task learning. In the first application, I study the disease progression problem and present multi-task learning formulations for disease progression. In the formulations, the prediction at each point is a regression task and multiple tasks at different time points are learned simultaneously, leveraging the temporal smoothness among the tasks. The proposed formulations have been tested extensively on predicting the progression of the Alzheimer's disease, and experimental results demonstrate the effectiveness of the proposed models. In the second application, I present a novel data-driven framework for densifying the electronic medical records (EMR) to overcome the sparsity problem in predictive modeling using EMR. The densification of each patient is a learning task, and the proposed algorithm simultaneously densify all patients. As such, the densification of one patient leverages useful information from other patients.
ContributorsZhou, Jiayu (Author) / Ye, Jieping (Thesis advisor) / Mittelmann, Hans (Committee member) / Li, Baoxin (Committee member) / Wang, Yalin (Committee member) / Arizona State University (Publisher)
Created2014
Description
Many learning models have been proposed for various tasks in visual computing. Popular examples include hidden Markov models and support vector machines. Recently, sparse-representation-based learning methods have attracted a lot of attention in the computer vision field, largely because of their impressive performance in many applications. In the literature, many of such sparse learning methods focus on designing or application of some learning techniques for certain feature space without much explicit consideration on possible interaction between the underlying semantics of the visual data and the employed learning technique. Rich semantic information in most visual data, if properly incorporated into algorithm design, should help achieving improved performance while delivering intuitive interpretation of the algorithmic outcomes. My study addresses the problem of how to explicitly consider the semantic information of the visual data in the sparse learning algorithms. In this work, we identify four problems which are of great importance and broad interest to the community. Specifically, a novel approach is proposed to incorporate label information to learn a dictionary which is not only reconstructive but also discriminative; considering the formation process of face images, a novel image decomposition approach for an ensemble of correlated images is proposed, where a subspace is built from the decomposition and applied to face recognition; based on the observation that, the foreground (or salient) objects are sparse in input domain and the background is sparse in frequency domain, a novel and efficient spatio-temporal saliency detection algorithm is proposed to identify the salient regions in video; and a novel hidden Markov model learning approach is proposed by utilizing a sparse set of pairwise comparisons among the data, which is easier to obtain and more meaningful, consistent than tradition labels, in many scenarios, e.g., evaluating motion skills in surgical simulations. In those four problems, different types of semantic information are modeled and incorporated in designing sparse learning algorithms for the corresponding visual computing tasks. Several real world applications are selected to demonstrate the effectiveness of the proposed methods, including, face recognition, spatio-temporal saliency detection, abnormality detection, spatio-temporal interest point detection, motion analysis and emotion recognition. In those applications, data of different modalities are involved, ranging from audio signal, image to video. Experiments on large scale real world data with comparisons to state-of-art methods confirm the proposed approaches deliver salient advantages, showing adding those semantic information dramatically improve the performances of the general sparse learning methods.
ContributorsZhang, Qiang (Author) / Li, Baoxin (Thesis advisor) / Turaga, Pavan (Committee member) / Wang, Yalin (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2014
Description
In this thesis, the application of pixel-based vertical axes used within parallel coordinate plots is explored in an attempt to improve how existing tools can explain complex multivariate interactions across temporal data. Several promising visualization techniques are combined, such as: visual boosting to allow for quicker consumption of large data sets, the bond energy algorithm to find finer patterns and anomalies through contrast, multi-dimensional scaling, flow lines, user guided clustering, and row-column ordering. User input is applied on precomputed data sets to provide for real time interaction. General applicability of the techniques are tested against industrial trade, social networking, financial, and sparse data sets of varying dimensionality.
ContributorsHayden, Thomas (Author) / Maciejewski, Ross (Thesis advisor) / Wang, Yalin (Committee member) / Runger, George C. (Committee member) / Mack, Elizabeth (Committee member) / Arizona State University (Publisher)
Created2014
Description
Invasive salmonellosis caused by Salmonella enterica serovar Typhimurium ST313 is a major health crisis in sub-Saharan Africa, with multidrug resistance and atypical clinical presentation challenging current treatment regimens and resulting in high mortality. Moreover, the increased risk of spreading ST313 pathovars worldwide is of major concern, given global public transportation networks and increased populations of immunocompromised individuals (as a result of HIV infection, drug use, cancer therapy, aging, etc). While it is unclear as to how Salmonella ST313 strains cause invasive disease in humans, it is intriguing that the genomic profile of some of these pathovars indicates key differences between classic Typhimurium (broad host range), but similarities to human-specific typhoidal Salmonella Typhi and Paratyphi. In an effort to advance fundamental understanding of the pathogenesis mechanisms of ST313 in humans, I report characterization of the molecular genetic, phenotypic and virulence profiles of D23580 (a representative ST313 strain). Preliminary studies to characterize D23580 virulence, baseline stress responses, and biochemical profiles, and in vitro infection profiles in human surrogate 3-D tissue culture models were done using conventional bacterial culture conditions; while subsequent studies integrated a range of incrementally increasing fluid shear levels relevant to those naturally encountered by D23580 in the infected host to understand the impact of biomechanical forces in altering these characteristics. In response to culture of D23580 under these conditions, distinct differences in transcriptional biosignatures, pathogenesis-related stress responses, in vitro infection profiles and in vivo virulence in mice were observed as compared to those of classic Salmonella pathovars tested.
Collectively, this work represents the first characterization of in vivo virulence and in vitro pathogenesis properties of D23580, the latter using advanced human surrogate models that mimic key aspects of the parental tissue. Results from these studies highlight the importance of studying infectious diseases using an integrated approach that combines actions of biological and physical networks that mimic the host-pathogen microenvironment and regulate pathogen responses.
Collectively, this work represents the first characterization of in vivo virulence and in vitro pathogenesis properties of D23580, the latter using advanced human surrogate models that mimic key aspects of the parental tissue. Results from these studies highlight the importance of studying infectious diseases using an integrated approach that combines actions of biological and physical networks that mimic the host-pathogen microenvironment and regulate pathogen responses.
ContributorsYang, Jiseon (Author) / Nickerson, Cheryl A. (Thesis advisor) / Chang, Yung (Committee member) / Stout, Valerie (Committee member) / Ott, C Mark (Committee member) / Roland, Kenneth (Committee member) / Barrila, Jennifer (Committee member) / Arizona State University (Publisher)
Created2015
Description
Sparse learning is a powerful tool to generate models of high-dimensional data with high interpretability, and it has many important applications in areas such as bioinformatics, medical image processing, and computer vision. Recently, the a priori structural information has been shown to be powerful for improving the performance of sparse learning models. A graph is a fundamental way to represent structural information of features. This dissertation focuses on graph-based sparse learning. The first part of this dissertation aims to integrate a graph into sparse learning to improve the performance. Specifically, the problem of feature grouping and selection over a given undirected graph is considered. Three models are proposed along with efficient solvers to achieve simultaneous feature grouping and selection, enhancing estimation accuracy. One major challenge is that it is still computationally challenging to solve large scale graph-based sparse learning problems. An efficient, scalable, and parallel algorithm for one widely used graph-based sparse learning approach, called anisotropic total variation regularization is therefore proposed, by explicitly exploring the structure of a graph. The second part of this dissertation focuses on uncovering the graph structure from the data. Two issues in graphical modeling are considered. One is the joint estimation of multiple graphical models using a fused lasso penalty and the other is the estimation of hierarchical graphical models. The key technical contribution is to establish the necessary and sufficient condition for the graphs to be decomposable. Based on this key property, a simple screening rule is presented, which reduces the size of the optimization problem, dramatically reducing the computational cost.
ContributorsYang, Sen (Author) / Ye, Jieping (Thesis advisor) / Wonka, Peter (Thesis advisor) / Wang, Yalin (Committee member) / Li, Jing (Committee member) / Arizona State University (Publisher)
Created2014