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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
Charge transport in molecular systems, including DNA (Deoxyribonucleic acid), is involved in many basic chemical and biological processes. Studying their charge transport properties can help developing DNA based electronic devices with many tunable functionalities. This thesis investigates the electric properties of double-stranded DNA, DNA G-quadruplex and dsDNA with modified base.
First, double-stranded DNA with alternating GC sequence and stacked GC sequence were measured with respect to length. The resistance of DNA sequences increases linearly with length, indicating a hopping transport mechanism. However, for DNA sequences with stacked GC, a periodic oscillation is superimposed on the linear length dependence, indicating a partial coherent transport. The result is supported by the finding of delocalization of the highest occupied molecular orbitals of Guanines from theoretical simulation and by fitting based on the Büttiker’s theory.
Then, a DNA G4-duplex structures with a G-quadruplex as the core and DNA duplexes as the arms were studied. Similar conductance values were observed by varying the linker positions, thus a charge splitter is developed. The conductance of the DNA G-tetrads structures was found to be sensitive to the π-stacking at the interface between the G-quadruplex and DNA duplexes by observing a higher conductance value when one duplex was removed and a polyethylene glycol (PEG) linker was added into the interface. This was further supported by molecular dynamic simulations.
Finally, a double-stranded DNA with one of the bases replaced by an anthraquinone group was studied via electrochemical STM break junction technique. Anthraquinone can be reversibly switched into the oxidized state or reduced state, to give a low conductance or high conductance respectively. Furthermore, the thermodynamics and kinetics properties of the switching were systematically studied. Theoretical simulation shows that the difference between the two states is due to a difference in the energy alignment with neighboring Guanine bases.
First, double-stranded DNA with alternating GC sequence and stacked GC sequence were measured with respect to length. The resistance of DNA sequences increases linearly with length, indicating a hopping transport mechanism. However, for DNA sequences with stacked GC, a periodic oscillation is superimposed on the linear length dependence, indicating a partial coherent transport. The result is supported by the finding of delocalization of the highest occupied molecular orbitals of Guanines from theoretical simulation and by fitting based on the Büttiker’s theory.
Then, a DNA G4-duplex structures with a G-quadruplex as the core and DNA duplexes as the arms were studied. Similar conductance values were observed by varying the linker positions, thus a charge splitter is developed. The conductance of the DNA G-tetrads structures was found to be sensitive to the π-stacking at the interface between the G-quadruplex and DNA duplexes by observing a higher conductance value when one duplex was removed and a polyethylene glycol (PEG) linker was added into the interface. This was further supported by molecular dynamic simulations.
Finally, a double-stranded DNA with one of the bases replaced by an anthraquinone group was studied via electrochemical STM break junction technique. Anthraquinone can be reversibly switched into the oxidized state or reduced state, to give a low conductance or high conductance respectively. Furthermore, the thermodynamics and kinetics properties of the switching were systematically studied. Theoretical simulation shows that the difference between the two states is due to a difference in the energy alignment with neighboring Guanine bases.
ContributorsXiang, Liming (Author) / Tao, Nongjian (Thesis advisor) / Lindsay, Stuart (Committee member) / Gould, Ian (Committee member) / Arizona State University (Publisher)
Created2016
Description
Detection of molecular interactions is critical for understanding many biological processes, for detecting disease biomarkers, and for screening drug candidates. Fluorescence-based approach can be problematic, especially when applied to the detection of small molecules. Various label-free techniques, such as surface plasmon resonance technique are sensitive to mass, making it extremely challenging to detect small molecules. In this thesis, novel detection methods for molecular interactions are described.
First, a simple detection paradigm based on reflectance interferometry is developed. This method is simple, low cost and can be easily applied for protein array detection.
Second, a label-free charge sensitive optical detection (CSOD) technique is developed for detecting of both large and small molecules. The technique is based on that most molecules relevant to biomedical research and applications are charged or partially charged. An optical fiber is dipped into the well of a microplate. It detects the surface charge of the fiber, which does not decrease with the size (mass) of the molecule, making it particularly attractive for studying small molecules.
Third, a method for mechanically amplification detection of molecular interactions (MADMI) is developed. It provides quantitative analysis of small molecules interaction with membrane proteins in intact cells. The interactions are monitored by detecting a mechanical deformation in the membrane induced by the molecular interactions. With this novel method small molecules and membrane proteins interaction in the intact cells can be detected. This new paradigm provides mechanical amplification of small interaction signals, allowing us to measure the binding kinetics of both large and small molecules with membrane proteins, and to analyze heterogeneous nature of the binding kinetics between different cells, and different regions of a single cell.
Last, by tracking the cell membrane edge deformation, binding caused downstream event – granule secretory has been measured. This method focuses on the plasma membrane change when granules fuse with the cell. The fusion of granules increases the plasma membrane area and thus the cell edge expands. The expansion is localized at the vesicle release location. Granule size was calculated based on measured edge expansion. The membrane deformation due to the granule release is real-time monitored by this method.
First, a simple detection paradigm based on reflectance interferometry is developed. This method is simple, low cost and can be easily applied for protein array detection.
Second, a label-free charge sensitive optical detection (CSOD) technique is developed for detecting of both large and small molecules. The technique is based on that most molecules relevant to biomedical research and applications are charged or partially charged. An optical fiber is dipped into the well of a microplate. It detects the surface charge of the fiber, which does not decrease with the size (mass) of the molecule, making it particularly attractive for studying small molecules.
Third, a method for mechanically amplification detection of molecular interactions (MADMI) is developed. It provides quantitative analysis of small molecules interaction with membrane proteins in intact cells. The interactions are monitored by detecting a mechanical deformation in the membrane induced by the molecular interactions. With this novel method small molecules and membrane proteins interaction in the intact cells can be detected. This new paradigm provides mechanical amplification of small interaction signals, allowing us to measure the binding kinetics of both large and small molecules with membrane proteins, and to analyze heterogeneous nature of the binding kinetics between different cells, and different regions of a single cell.
Last, by tracking the cell membrane edge deformation, binding caused downstream event – granule secretory has been measured. This method focuses on the plasma membrane change when granules fuse with the cell. The fusion of granules increases the plasma membrane area and thus the cell edge expands. The expansion is localized at the vesicle release location. Granule size was calculated based on measured edge expansion. The membrane deformation due to the granule release is real-time monitored by this method.
ContributorsGuan, Yan (Author) / Tao, Nongjian (Thesis advisor) / LaBaer, Joshua (Committee member) / Goryll, Michael (Committee member) / Wang, Shaopeng (Committee member) / Arizona State University (Publisher)
Created2015
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
Monitoring of air pollutants is critical for many applications and studies. In
order to access air pollutants with high spatial and temporal resolutions, it is
necessary to develop an affordable, small size and weight, low power, high
sensitivity and selectivity, and wireless enable device that can provide real time
monitoring of air pollutants. Three different kind of such devices are presented, they
are targeting environmental pollutants such as volatile organic components (VOCs),
nitrogen dioxide (NO2) and ozone. These devices employ innovative detection
methods, such as quartz crystal tuning fork coated with molecularly imprinted
polymer and chemical reaction induced color change colorimetric sensing. These
portable devices are validated using the gold standards in the laboratory, and their
functionality and capability are proved during the field tests, make them great tools
for various air quality monitoring applications.
order to access air pollutants with high spatial and temporal resolutions, it is
necessary to develop an affordable, small size and weight, low power, high
sensitivity and selectivity, and wireless enable device that can provide real time
monitoring of air pollutants. Three different kind of such devices are presented, they
are targeting environmental pollutants such as volatile organic components (VOCs),
nitrogen dioxide (NO2) and ozone. These devices employ innovative detection
methods, such as quartz crystal tuning fork coated with molecularly imprinted
polymer and chemical reaction induced color change colorimetric sensing. These
portable devices are validated using the gold standards in the laboratory, and their
functionality and capability are proved during the field tests, make them great tools
for various air quality monitoring applications.
ContributorsChen, Cheng, Ph.D (Author) / Tao, Nongjian (Thesis advisor) / Kiaei, Sayfe (Committee member) / Zhang, Yanchao (Committee member) / Tsow, Tsing (Committee member) / Arizona State University (Publisher)
Created2014
Description
Wide spread adoption of photovoltaic technology is limited by cost. Developing photovoltaics based on low-cost materials and processing techniques is one strategy for reducing the cost of electricity generated by photovoltaics. With this in mind, novel porphyrin and porphyrin-fullerene electropolymers have been developed here at Arizona State University. Porphyrins are attractive for inclusion in the light absorbing layer of photovoltaics due to their high absorption coefficients (on the order of 105 cm-1) and porphyrin-fullerene dyads are attractive for use in photovoltaics due to their ability to produce ultrafast photoinduced charge separation (on the order of 10-15 s). The focus of this thesis is the characterization of the photovoltaic properties of these electropolymer films. Films formed on transparent conductive oxide (TCO) substrates were contacted using a mercury drop electrode in order to measure photocurrent spectra and current-voltage curves. Surface treatment of both the TCO substrate and the mercury drop is shown to have a dramatic effect on the photovoltaic performance of the electropolymer films. Treating the TCO substrates with chlorotrimethylsilane and the mercury drop with hexanethiol was found to produce an optimal tradeoff between photocurrent and photovoltage. Incident photon to current efficiency spectra of the films show that the dominant photocurrent generation mechanism in this system is located at the polymer-mercury interface. The optical field intensity at this interface approaches zero due to interference from the light reflected by the mercury surface. Reliance upon photocurrent generation at this interface limits the performance of this system and suggests that these polymers may be useful in solar cells which have structures optimized to take advantage of their internal optical field distributions.
ContributorsBridgewater, James W (Author) / Gust, Devens (Thesis advisor) / Tao, Nongjian (Thesis advisor) / Gould, Ian (Committee member) / Diaz, Rodolfo (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
Understanding the interplay between the electrical and mechanical properties of single molecules is of fundamental importance for molecular electronics. The sensitivity of charge transport to mechanical fluctuations is a key problem in developing long lasting molecular devices. Furthermore, harnessing this response to mechanical perturbation, molecular devices which can be mechanically gated can be developed. This thesis demonstrates three examples of the unique electromechanical properties of single molecules.
First, the electromechanical properties of 1,4-benzenedithiol molecular junctions are investigate. Counterintuitively, the conductance of this molecule is found to increase by more than an order of magnitude when stretched. This conductance increase is found to be reversible when the molecular junction is compressed. The current-voltage, conductance-voltage and inelastic electron tunneling spectroscopy characteristics are used to attribute the conductance increase to a strain-induced shift in the frontier molecular orbital relative to the electrode Fermi level, leading to resonant enhancement in the conductance.
Next, the effect of stretching-induced structural changes on charge transport in DNA molecules is studied. The conductance of single DNA molecules with lengths varying from 6 to 26 base pairs is measured and found to follow a hopping transport mechanism. The conductance of DNA molecules is highly sensitive to mechanical stretching, showing an abrupt decrease in conductance at surprisingly short stretching distances, with weak dependence on DNA length. This abrupt conductance decrease is attributed to force-induced breaking of hydrogen bonds in the base pairs at the end of the DNA sequence.
Finally, the effect of small mechanical modulation of the base separation on DNA conductance is investigated. The sensitivity of conductance to mechanical modulation is studied for molecules of different sequence and length. Sequences with purine-purine stacking are found to be more responsive to modulation than purine-pyrimidine sequences. This sensitivity is attributed to the perturbation of &pi-&pi stacking interactions and resulting effects on the activation energy and electronic coupling for the end base pairs.
First, the electromechanical properties of 1,4-benzenedithiol molecular junctions are investigate. Counterintuitively, the conductance of this molecule is found to increase by more than an order of magnitude when stretched. This conductance increase is found to be reversible when the molecular junction is compressed. The current-voltage, conductance-voltage and inelastic electron tunneling spectroscopy characteristics are used to attribute the conductance increase to a strain-induced shift in the frontier molecular orbital relative to the electrode Fermi level, leading to resonant enhancement in the conductance.
Next, the effect of stretching-induced structural changes on charge transport in DNA molecules is studied. The conductance of single DNA molecules with lengths varying from 6 to 26 base pairs is measured and found to follow a hopping transport mechanism. The conductance of DNA molecules is highly sensitive to mechanical stretching, showing an abrupt decrease in conductance at surprisingly short stretching distances, with weak dependence on DNA length. This abrupt conductance decrease is attributed to force-induced breaking of hydrogen bonds in the base pairs at the end of the DNA sequence.
Finally, the effect of small mechanical modulation of the base separation on DNA conductance is investigated. The sensitivity of conductance to mechanical modulation is studied for molecules of different sequence and length. Sequences with purine-purine stacking are found to be more responsive to modulation than purine-pyrimidine sequences. This sensitivity is attributed to the perturbation of &pi-&pi stacking interactions and resulting effects on the activation energy and electronic coupling for the end base pairs.
ContributorsBruot, Christopher, 1986- (Author) / Tao, Nongjian (Thesis advisor) / Lindsay, Stuart (Committee member) / Mujica, Vladimiro (Committee member) / Ferry, David (Committee member) / Arizona State University (Publisher)
Created2014