Matching Items (69)
Filtering by
- Language: English
Description
The technology expansion seen in the last decade for genomics research has permitted the generation of large-scale data sources pertaining to molecular biological assays, genomics, proteomics, transcriptomics and other modern omics catalogs. New methods to analyze, integrate and visualize these data types are essential to unveil relevant disease mechanisms. Towards these objectives, this research focuses on data integration within two scenarios: (1) transcriptomic, proteomic and functional information and (2) real-time sensor-based measurements motivated by single-cell technology. To assess relationships between protein abundance, transcriptomic and functional data, a nonlinear model was explored at static and temporal levels. The successful integration of these heterogeneous data sources through the stochastic gradient boosted tree approach and its improved predictability are some highlights of this work. Through the development of an innovative validation subroutine based on a permutation approach and the use of external information (i.e., operons), lack of a priori knowledge for undetected proteins was overcome. The integrative methodologies allowed for the identification of undetected proteins for Desulfovibrio vulgaris and Shewanella oneidensis for further biological exploration in laboratories towards finding functional relationships. In an effort to better understand diseases such as cancer at different developmental stages, the Microscale Life Science Center headquartered at the Arizona State University is pursuing single-cell studies by developing novel technologies. This research arranged and applied a statistical framework that tackled the following challenges: random noise, heterogeneous dynamic systems with multiple states, and understanding cell behavior within and across different Barrett's esophageal epithelial cell lines using oxygen consumption curves. These curves were characterized with good empirical fit using nonlinear models with simple structures which allowed extraction of a large number of features. Application of a supervised classification model to these features and the integration of experimental factors allowed for identification of subtle patterns among different cell types visualized through multidimensional scaling. Motivated by the challenges of analyzing real-time measurements, we further explored a unique two-dimensional representation of multiple time series using a wavelet approach which showcased promising results towards less complex approximations. Also, the benefits of external information were explored to improve the image representation.
ContributorsTorres Garcia, Wandaliz (Author) / Meldrum, Deirdre R. (Thesis advisor) / Runger, George C. (Thesis advisor) / Gel, Esma S. (Committee member) / Li, Jing (Committee member) / Zhang, Weiwen (Committee member) / Arizona State University (Publisher)
Created2011
Description
Hepatocellular carcinoma (HCC) is a malignant tumor and seventh most common cancer in human. Every year there is a significant rise in the number of patients suffering from HCC. Most clinical research has focused on HCC early detection so that there are high chances of patient's survival. Emerging advancements in functional and structural imaging techniques have provided the ability to detect microscopic changes in tumor micro environment and micro structure. The prime focus of this thesis is to validate the applicability of advanced imaging modality, Magnetic Resonance Elastography (MRE), for HCC diagnosis. The research was carried out on three HCC patient's data and three sets of experiments were conducted. The main focus was on quantitative aspect of MRE in conjunction with Texture Analysis, an advanced imaging processing pipeline and multi-variate analysis machine learning method for accurate HCC diagnosis. We analyzed the techniques to handle unbalanced data and evaluate the efficacy of sampling techniques. Along with this we studied different machine learning algorithms and developed models using them. Performance metrics such as Prediction Accuracy, Sensitivity and Specificity have been used for evaluation for the final developed model. We were able to identify the significant features in the dataset and also the selected classifier was robust in predicting the response class variable with high accuracy.
ContributorsBansal, Gaurav (Author) / Wu, Teresa (Thesis advisor) / Mitchell, Ross (Thesis advisor) / Li, Jing (Committee member) / Arizona State University (Publisher)
Created2013
Description
A P-value based method is proposed for statistical monitoring of various types of profiles in phase II. The performance of the proposed method is evaluated by the average run length criterion under various shifts in the intercept, slope and error standard deviation of the model. In our proposed approach, P-values are computed at each level within a sample. If at least one of the P-values is less than a pre-specified significance level, the chart signals out-of-control. The primary advantage of our approach is that only one control chart is required to monitor several parameters simultaneously: the intercept, slope(s), and the error standard deviation. A comprehensive comparison of the proposed method and the existing KMW-Shewhart method for monitoring linear profiles is conducted. In addition, the effect that the number of observations within a sample has on the performance of the proposed method is investigated. The proposed method was also compared to the T^2 method discussed in Kang and Albin (2000) for multivariate, polynomial, and nonlinear profiles. A simulation study shows that overall the proposed P-value method performs satisfactorily for different profile types.
ContributorsAdibi, Azadeh (Author) / Montgomery, Douglas C. (Thesis advisor) / Borror, Connie (Thesis advisor) / Li, Jing (Committee member) / Zhang, Muhong (Committee member) / Arizona State University (Publisher)
Created2013
Description
Rapid advance in sensor and information technology has resulted in both spatially and temporally data-rich environment, which creates a pressing need for us to develop novel statistical methods and the associated computational tools to extract intelligent knowledge and informative patterns from these massive datasets. The statistical challenges for addressing these massive datasets lay in their complex structures, such as high-dimensionality, hierarchy, multi-modality, heterogeneity and data uncertainty. Besides the statistical challenges, the associated computational approaches are also considered essential in achieving efficiency, effectiveness, as well as the numerical stability in practice. On the other hand, some recent developments in statistics and machine learning, such as sparse learning, transfer learning, and some traditional methodologies which still hold potential, such as multi-level models, all shed lights on addressing these complex datasets in a statistically powerful and computationally efficient way. In this dissertation, we identify four kinds of general complex datasets, including "high-dimensional datasets", "hierarchically-structured datasets", "multimodality datasets" and "data uncertainties", which are ubiquitous in many domains, such as biology, medicine, neuroscience, health care delivery, manufacturing, etc. We depict the development of novel statistical models to analyze complex datasets which fall under these four categories, and we show how these models can be applied to some real-world applications, such as Alzheimer's disease research, nursing care process, and manufacturing.
ContributorsHuang, Shuai (Author) / Li, Jing (Thesis advisor) / Askin, Ronald (Committee member) / Ye, Jieping (Committee member) / Runger, George C. (Committee member) / Arizona State University (Publisher)
Created2012
Description
The Department of Defense (DoD) acquisition system is a complex system riddled with cost and schedule overruns. These cost and schedule overruns are very serious issues as the acquisition system is responsible for aiding U.S. warfighters. Hence, if the acquisition process is failing that could be a potential threat to our nation's security. Furthermore, the DoD acquisition system is responsible for proper allocation of billions of taxpayer's dollars and employs many civilians and military personnel. Much research has been done in the past on the acquisition system with little impact or success. One reason for this lack of success in improving the system is the lack of accurate models to test theories. This research is a continuation of the effort on the Enterprise Requirements and Acquisition Model (ERAM), a discrete event simulation modeling research on DoD acquisition system. We propose to extend ERAM using agent-based simulation principles due to the many interactions among the subsystems of the acquisition system. We initially identify ten sub models needed to simulate the acquisition system. This research focuses on three sub models related to the budget of acquisition programs. In this thesis, we present the data collection, data analysis, initial implementation, and initial validation needed to facilitate these sub models and lay the groundwork for a full agent-based simulation of the DoD acquisition system.
ContributorsBucknell, Sophia Robin (Author) / Wu, Teresa (Thesis director) / Li, Jing (Committee member) / Colombi, John (Committee member) / Industrial, Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
In today's global market, companies are facing unprecedented levels of uncertainties in supply, demand and in the economic environment. A critical issue for companies to survive increasing competition is to monitor the changing business environment and manage disturbances and changes in real time. In this dissertation, an integrated framework is proposed using simulation and online calibration methods to enable the adaptive management of large-scale complex supply chain systems. The design, implementation and verification of the integrated approach are studied in this dissertation. The research contributions are two-fold. First, this work enriches symbiotic simulation methodology by proposing a framework of simulation and advanced data fusion methods to improve simulation accuracy. Data fusion techniques optimally calibrate the simulation state/parameters by considering errors in both the simulation models and in measurements of the real-world system. Data fusion methods - Kalman Filtering, Extended Kalman Filtering, and Ensemble Kalman Filtering - are examined and discussed under varied conditions of system chaotic levels, data quality and data availability. Second, the proposed framework is developed, validated and demonstrated in `proof-of-concept' case studies on representative supply chain problems. In the case study of a simplified supply chain system, Kalman Filtering is applied to fuse simulation data and emulation data to effectively improve the accuracy of the detection of abnormalities. In the case study of the `beer game' supply chain model, the system's chaotic level is identified as a key factor to influence simulation performance and the choice of data fusion method. Ensemble Kalman Filtering is found more robust than Extended Kalman Filtering in a highly chaotic system. With appropriate tuning, the improvement of simulation accuracy is up to 80% in a chaotic system, and 60% in a stable system. In the last study, the integrated framework is applied to adaptive inventory control of a multi-echelon supply chain with non-stationary demand. It is worth pointing out that the framework proposed in this dissertation is not only useful in supply chain management, but also suitable to model other complex dynamic systems, such as healthcare delivery systems and energy consumption networks.
ContributorsWang, Shanshan (Author) / Wu, Teresa (Thesis advisor) / Fowler, John (Thesis advisor) / Pfund, Michele (Committee member) / Li, Jing (Committee member) / Pavlicek, William (Committee member) / Arizona State University (Publisher)
Created2010
Description
Serial femtosecond crystallography requires reliable and efficient delivery of fresh crystals across the beam of an X-ray free-electron laser over the course of an experiment. We introduce a double-flow focusing nozzle to meet this challenge, with significantly reduced sample consumption, while improving jet stability over previous generations of nozzles. We demonstrate its use to determine the first room-temperature structure of RNA polymerase II at high resolution, revealing new structural details. Moreover, the double flow-focusing nozzles were successfully tested with three other protein samples and the first room temperature structure of an extradiol ring-cleaving dioxygenase was solved by utilizing the improved operation and characteristics of these devices.
ContributorsOberthuer, Dominik (Author) / Knoska, Juraj (Author) / Wiedorn, Max O. (Author) / Beyerlein, Kenneth R. (Author) / Bushnell, David A. (Author) / Kovaleva, Elena G. (Author) / Heymann, Michael (Author) / Gumprecht, Lars (Author) / Kirian, Richard (Author) / Barty, Anton (Author) / Mariani, Valerio (Author) / Tolstikova, Aleksandra (Author) / Adriano, Luigi (Author) / Awel, Salah (Author) / Barthelmess, Miriam (Author) / Dorner, Katerina (Author) / Xavier, P. Lourdu (Author) / Yefanov, Oleksandr (Author) / James, Daniel (Author) / Nelson, Garrett (Author) / Wang, Dingjie (Author) / Calvey, George (Author) / Chen, Yujie (Author) / Schmidt, Andrea (Author) / Szczepek, Michael (Author) / Frielingsdorf, Stefan (Author) / Lenz, Oliver (Author) / Snell, Edward (Author) / Robinson, Philip J. (Author) / Sarler, Bozidar (Author) / Belsak, Grega (Author) / Macek, Marjan (Author) / Wilde, Fabian (Author) / Aquila, Andrew (Author) / Boutet, Sebastien (Author) / Liang, Mengning (Author) / Hunter, Mark S. (Author) / Scheerer, Patrick (Author) / Lipscomb, John D. (Author) / Weierstall, Uwe (Author) / Kornberg, Roger D. (Author) / Spence, John (Author) / Pollack, Lois (Author) / Chapman, Henry N. (Author) / Bajt, Sasa (Author) / College of Liberal Arts and Sciences (Contributor) / Department of Physics (Contributor)
Created2017-03-16
Description
X-ray free-electron lasers provide novel opportunities to conduct single particle analysis on nanoscale particles. Coherent diffractive imaging experiments were performed at the Linac Coherent Light Source (LCLS), SLAC National Laboratory, exposing single inorganic core-shell nanoparticles to femtosecond hard-X-ray pulses. Each facetted nanoparticle consisted of a crystalline gold core and a differently shaped palladium shell. Scattered intensities were observed up to about 7 nm resolution. Analysis of the scattering patterns revealed the size distribution of the samples, which is consistent with that obtained from direct real-space imaging by electron microscopy. Scattering patterns resulting from single particles were selected and compiled into a dataset which can be valuable for algorithm developments in single particle scattering research.
ContributorsLi, Xuanxuan (Author) / Chiu, Chun-Ya (Author) / Wang, Hsiang-Ju (Author) / Kassemeyer, Stephan (Author) / Botha, Sabine (Author) / Shoeman, Robert L. (Author) / Lawrence, Robert (Author) / Kupitz, Christopher (Author) / Kirian, Richard (Author) / James, Daniel (Author) / Wang, Dingjie (Author) / Nelson, Garrett (Author) / Messerschmidt, Marc (Author) / Boutet, Sebastien (Author) / Williams, Garth J. (Author) / Hartman, Elisabeth (Author) / Jafarpour, Aliakbar (Author) / Foucar, Lutz M. (Author) / Barty, Anton (Author) / Chapman, Henry (Author) / Liang, Mengning (Author) / Menzel, Andreas (Author) / Wang, Fenglin (Author) / Basu, Shibom (Author) / Fromme, Raimund (Author) / Doak, R. Bruce (Author) / Fromme, Petra (Author) / Weierstall, Uwe (Author) / Huang, Michael H. (Author) / Spence, John (Author) / Schlichting, Ilme (Author) / Hogue, Brenda (Author) / Liu, Haiguang (Author) / ASU Biodesign Center Immunotherapy, Vaccines and Virotherapy (Contributor) / Biodesign Institute (Contributor) / Applied Structural Discovery (Contributor) / College of Liberal Arts and Sciences (Contributor) / School of Molecular Sciences (Contributor) / Department of Physics (Contributor) / School of Life Sciences (Contributor)
Created2017-04-11
Description
Single particle diffractive imaging data from Rice Dwarf Virus (RDV) were recorded using the Coherent X-ray Imaging (CXI) instrument at the Linac Coherent Light Source (LCLS). RDV was chosen as it is a well-characterized model system, useful for proof-of-principle experiments, system optimization and algorithm development. RDV, an icosahedral virus of about 70 nm in diameter, was aerosolized and injected into the approximately 0.1 μm diameter focused hard X-ray beam at the CXI instrument of LCLS. Diffraction patterns from RDV with signal to 5.9 Ångström were recorded. The diffraction data are available through the Coherent X-ray Imaging Data Bank (CXIDB) as a resource for algorithm development, the contents of which are described here.
ContributorsMunke, Anna (Author) / Andreasson, Jakob (Author) / Aquila, Andrew (Author) / Awel, Salah (Author) / Ayyer, Kartik (Author) / Barty, Anton (Author) / Bean, Richard J. (Author) / Berntsen, Peter (Author) / Bielecki, Johan (Author) / Boutet, Sebastien (Author) / Bucher, Maximilian (Author) / Chapman, Henry N. (Author) / Daurer, Benedikt J. (Author) / DeMirci, Hasan (Author) / Elser, Veit (Author) / Fromme, Petra (Author) / Hajdu, Janos (Author) / Hantke, Max F. (Author) / Higashiura, Akifumi (Author) / Hogue, Brenda (Author) / Hosseinizadeh, Ahmad (Author) / Kim, Yoonhee (Author) / Kirian, Richard (Author) / Reddy, Hemanth K. N. (Author) / Lan, Ti-Yen (Author) / Larsson, Daniel S. D. (Author) / Liu, Haiguang (Author) / Loh, N. Duane (Author) / Maia, Filipe R. N. C. (Author) / Mancuso, Adrian P. (Author) / Muhlig, Kerstin (Author) / Nakagawa, Atsushi (Author) / Nam, Daewoong (Author) / Nelson, Garrett (Author) / Nettelblad, Carl (Author) / Okamoto, Kenta (Author) / Ourmazd, Abbas (Author) / Rose, Max (Author) / van der Schot, Gijs (Author) / Schwander, Peter (Author) / Seibert, M. Marvin (Author) / Sellberg, Jonas A. (Author) / Sierra, Raymond G. (Author) / Song, Changyong (Author) / Svenda, Martin (Author) / Timneanu, Nicusor (Author) / Vartanyants, Ivan A. (Author) / Westphal, Daniel (Author) / Wiedom, Max O. (Author) / Williams, Garth J. (Author) / Xavier, Paulraj Lourdu (Author) / Soon, Chun Hong (Author) / Zook, James (Author) / College of Liberal Arts and Sciences (Contributor, Contributor) / School of Molecular Sciences (Contributor) / Biodesign Institute (Contributor) / Applied Structural Discovery (Contributor) / School of Life Sciences (Contributor) / Infectious Diseases and Vaccinology (Contributor) / Department of Physics (Contributor)
Created2016-08-01
Description
Serial femtosecond X-ray crystallography (SFX) using an X-ray free electron laser (XFEL) is a recent advancement in structural biology for solving crystal structures of challenging membrane proteins, including G-protein coupled receptors (GPCRs), which often only produce microcrystals. An XFEL delivers highly intense X-ray pulses of femtosecond duration short enough to enable the collection of single diffraction images before significant radiation damage to crystals sets in. Here we report the deposition of the XFEL data and provide further details on crystallization, XFEL data collection and analysis, structure determination, and the validation of the structural model. The rhodopsin-arrestin crystal structure solved with SFX represents the first near-atomic resolution structure of a GPCR-arrestin complex, provides structural insights into understanding of arrestin-mediated GPCR signaling, and demonstrates the great potential of this SFX-XFEL technology for accelerating crystal structure determination of challenging proteins and protein complexes.
ContributorsZhou, X. Edward (Author) / Gao, Xiang (Author) / Barty, Anton (Author) / Kang, Yanyong (Author) / He, Yuanzheng (Author) / Liu, Wei (Author) / Ishchenko, Andrii (Author) / White, Thomas A. (Author) / Yefanov, Oleksandr (Author) / Han, Gye Won (Author) / Xu, Qingping (Author) / de Waal, Parker W. (Author) / Suino-Powell, Kelly M. (Author) / Boutet, Sebastien (Author) / Williams, Garth J. (Author) / Wang, Meitian (Author) / Li, Dianfan (Author) / Caffrey, Martin (Author) / Chapman, Henry N. (Author) / Spence, John (Author) / Fromme, Petra (Author) / Weierstall, Uwe (Author) / Stevens, Raymond C. (Author) / Cherezov, Vadim (Author) / Melcher, Karsten (Author) / Xu, H. Eric (Author) / College of Liberal Arts and Sciences (Contributor) / School of Molecular Sciences (Contributor) / Biodesign Institute (Contributor) / Applied Structural Discovery (Contributor) / Department of Physics (Contributor)
Created2016-04-12