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Open Access Publications from the University of California

Scholarly Works (71 results)

  • Thesis
  • Peer Reviewed
UCLA Electronic Theses and Dissertations (2018)

The extensive utilization of fossil fuels since 2nd industry revolution bears a major responsibility for climate change. The raising awareness towards sustainable and renewable energy supply calls for game-changing research and progress in field of electrochemical energy storage, among which lithium-ion batteries (LIBs) is of particular interest. The developments of LIBs, in conjunction with the revolutions in the area of semiconductor and information technologies, have triggered the rapid growth of portable electronics and electric vehicles. Particularly, the transition of gasoline-powered automobiles to electrification ones requires better LIBs with higher energy density, faster charging rate, cheaper cost and longer-lasting lifetime.

To achieve the goals, it is essential to rethink and closely examine the fundamental electrochemistry beneath the conversion between electricity and chemical reactions. The operation of batteries relies on the separation of electrons and ions in electrodes, and their subsequent respective translocation through the electronic pathways and the electrolytes. The electronic conductivity of electrodes has been improved by rational architecture design and incorporation of conductive agents. While optimizing ionic transport is more challenging since the electrode-electrolyte interface is dynamic during cycling. Variation of electrolytes would not only impact the electrochemical reactions in electrodes, but also the ohmic and concentration polarizations throughout the devices. Therefore, advances in electrolyte are vital for driving innovations in battery technologies.

Commercial liquid electrolytes, which are based on ion diffusion in fluidic medium, have merit in ionic conductivity. However, its suitability for next-generation LIBs is under dispute. Firstly, the Li+ transference number, defined as the ratio of conductivity carried by Li+ versus by Li+ and counter anions, is typically as low as 0.3, indicating an inferior transport efficiency. Such scenario is responsible for severe polarization and deterioration of the cycling life, particularly, during fast charging/discharging process. Second, liquid electrolytes are not compatible with high energy electrodes (e.g. Li anode, high voltage cathode, etc.) viewed from the aspects of electrochemical voltage window and safety. To address these issues, solid electrolytes and polymer electrolytes have been extensively explored due to their high Li+ transference number and superior safety. Yet their implementation to commercial LIBs still encounters considerable challenges from the aspects of low ionic conductivity and manufactural difficulties.

In this dissertation, a novel class of ionic conductors with biomimetic ionic channels have been developed to overcome the aforementioned limitations in liquid electrolytes. By thermal activation, porous metal-organic frameworks (MOFs) yield unsaturated metal centers which could be complexed with liquid electrolytes. The anions in liquid electrolytes can spontaneously bind with the unsaturated metal centers, forming ionic channels mimicking those of in the biologic systems and allowing effective transport of Li+. The ionic conductors built upon MOFs outperform liquid electrolytes in terms of high ionic conductivity, high transference number, broad electrochemical window and improved safety.

The dissertation research could be outlined briefly with following two parts:

1. Development of MOFs-based electrolytes with high ionic conductivity and high Li+ transfer number. This part of work firstly demonstrated the concept of biomimetic ionic channels within MOFs. Second, optimization of MOF pore structures according to infiltrated liquid electrolyte affords the synthesis of suitable MOF-based electrolytes with high Li+ ionic conductivity and low cost.

2. Integration of MOFs-based electrolytes into batteries. Three strategies were explored in this part to integrate the MOFs-based ionic conductors as following components: 1) separator; 2) electrolyte additive; 3) electrode additive.

Overall, this dissertation research has developed a new class of fast lithium ion conductors based on MOFs and commercially available liquid electrolytes, a variety of architecture designs for incorporating these fast Li+ conductors into battery device could be implemented in a cost-effective manner. By taking advantage of unsaturated metal sites in MOFs, immobilized anions and fast Li+ mobility enable superior device performances with prolonged cycling performance, especially at fast charging rate. Based on these works, one can expect the advances in electrolytes will impact the markets of lithium rechargeable batteries in the near future.

    Cover page: Metal-Organic Frameworks-Based Electrolytes for Lithium Rechargeable Batteries
    • Article
    • Peer Reviewed
    UC San Diego Previously Published Works (2010)
    Abstract Background An exciting application of genetic network is to predict phenotypic consequences for environmental cues or genetic perturbations. However, de novo prediction for quantitative phenotypes based on network topology is always a challenging task. Results Using yeast sporulation as a model system, we have assembled a genetic network from literature and exploited Boolean network to predict sporulation efficiency change upon deleting individual genes. We observe that predictions based on the curated network correlate well with the experimentally measured values. In addition, computational analysis reveals the robustness and hysteresis of the yeast sporulation network and uncovers several patterns of sporulation efficiency change caused by double gene deletion. These discoveries may guide future investigation of underlying mechanisms. We have also shown that a hybridized genetic network reconstructed from both temporal microarray data and literature is able to achieve a satisfactory prediction accuracy of the same quantitative phenotypes. Conclusions This case study illustrates the value of predicting quantitative phenotypes based on genetic network and provides a generic approach.
    • 7 supplemental files
    Cover page: Prediction of quantitative phenotypes based on genetic networks: a case study in yeast sporulation
    • Article
    • Peer Reviewed
    ICTS Publications (2012)
    Motivation: Recent advances in brain imaging and high-throughput genotyping techniques enable new approaches to study the influence of genetic and anatomical variations on brain functions and disorders. Traditional association studies typically perform independent and pairwise analysis among neuroimaging measures, cognitive scores and disease status, and ignore the important underlying interacting relationships between these units.Results: To overcome this limitation, in this article, we propose a new sparse multimodal multitask learning method to reveal complex relationships from gene to brain to symptom. Our main contributions are three-fold: (i) introducing combined structured sparsity regularizations into multimodal multitask learning to integrate multidimensional heterogeneous imaging genetics data and identify multimodal biomarkers; (ii) utilizing a joint classification and regression learning model to identify disease-sensitive and cognition-relevant biomarkers; (iii) deriving a new efficient optimization algorithm to solve our non-smooth objective function and providing rigorous theoretical analysis on the global optimum convergency. Using the imaging genetics data from the Alzheimer's Disease Neuroimaging Initiative database, the effectiveness of the proposed method is demonstrated by clearly improved performance on predicting both cognitive scores and disease status. The identified multimodal biomarkers could predict not only disease status but also cognitive function to help elucidate the biological pathway from gene to brain structure and function, and to cognition and disease.Availability: Software is publicly available at: http://ranger.uta.edu/%7eheng/multimodal/Contact:heng@uta.edu; shenli@iupui.edu
      Cover page: Identifying disease sensitive and quantitative trait-relevant biomarkers from multidimensional heterogeneous imaging genetics data via sparse multimodal multitask learning
      • Article
      • Peer Reviewed
        Cover page: Genome-wide analysis reveals TET-and TDG-mediated 5-methylcytosine oxidation dynamics
        • Article
        • Peer Reviewed
        UC San Diego Previously Published Works (2014)
        Predicting cognitive performance of subjects from their magnetic resonance imaging (MRI) measures and identifying relevant imaging biomarkers are important research topics in the study of Alzheimer's disease. Traditionally, this task is performed by formulating a linear regression problem. Recently, it is found that using a linear sparse regression model can achieve better prediction accuracy. However, most existing studies only focus on the exploitation of sparsity of regression coefficients, ignoring useful structure information in regression coefficients. Also, these linear sparse models may not capture more complicated and possibly nonlinear relationships between cognitive performance and MRI measures. Motivated by these observations, in this work we build a sparse multivariate regression model for this task and propose an empirical sparse Bayesian learning algorithm. Different from existing sparse algorithms, the proposed algorithm models the response as a nonlinear function of the predictors by extending the predictor matrix with block structures. Further, it exploits not only inter-vector correlation among regression coefficient vectors, but also intra-block correlation in each regression coefficient vector. Experiments on the Alzheimer's Disease Neuroimaging Initiative database showed that the proposed algorithm not only achieved better prediction performance than state-of-the-art competitive methods, but also effectively identified biologically meaningful patterns.
          Cover page: Identifying the Neuroanatomical Basis of Cognitive Impairment in Alzheimer's Disease by Correlation- and Nonlinearity-Aware Sparse Bayesian Learning
          • Article
          • Peer Reviewed
          UCLA Previously Published Works (2017)
          The Pap test diagnosed cervical dysplasia, which could recover to normal or progress to cervical cancer (CC), is an early stage of cell abnormality before CC. This case-control study analyzed the differences in the risk to develop CC between Chinese medicine (CM) users and nonusers among women who had ever been diagnosed as having cervical dysplasia. A total of 750 CC patients with a cervical dysplasia history were collected between 1998 and 2011 from National Health Insurance Research Database, and controls were women with cervical dysplasia history but did not develop CC. Adjusted odds ratio (aOR) for developing CC was assessed using multivariable logistic regression after adjusting for age, urbanization of residence, and occupation. The proportion of using CM among CC patients was lower than that among CC nonpatients, with an aOR of 0.8. By analyzing the relationship between CC development and the frequency of CM usage, the trend test revealed a significant decreasing trend for developing CC among high-frequency CM users. Moreover, the most frequently used single herb high-frequency was Rheum palmatum (Da-Huang). The usage of CM might be an effective complementary method to prevent uterine cervix from progressing to CC after cervical dysplasia has occurred.
            Cover page: Use of Chinese Medicine Reduces the Development of Cervical Cancer from Pap Smear-Diagnosed Cervical Dysplasia: A Case-Control Study
            • Article
            • Peer Reviewed
            UC Davis Previously Published Works (2015)
            Adipose tissue is the body largest free cholesterol reservoir and abundantly expresses ATP binding cassette transporter A1 (ABCA1), which maintains plasma high-density lipoprotein (HDL) levels. HDLs have a protective role in atherosclerosis by mediating reverse cholesterol transport (RCT). Soluble epoxide hydrolase (sEH) is a cytosolic enzyme whose inhibition has various beneficial effects on cardiovascular disease. The sEH is highly expressed in adipocytes, and it converts epoxyeicosatrienoic acids (EETs) into less bioactive dihydroxyeicosatrienoic acids. We previously showed that increasing EETs levels with a sEH inhibitor (sEHI) (t-AUCB) resulted in elevated ABCA1 expression and promoted ABCA1-mediated cholesterol efflux from 3T3-L1 adipocytes. The present study investigates the impacts of t-AUCB in mice deficient for the low density lipoprotein (LDL) receptor (Ldlr(-/-) mice) with established atherosclerotic plaques. The sEH inhibitor delivered in vivo for 4 weeks decreased the activity of sEH in adipose tissue, enhanced ABCA1 expression and cholesterol efflux from adipose depots, and consequently increased HDL levels. Furthermore, t-AUCB enhanced RCT to the plasma, liver, bile and feces. It also showed the reduction of plasma LDL-C levels. Consistently, t-AUCB-treated mice showed reductions in the size of atherosclerotic plaques. These studies establish that raising adipose ABCA1 expression, cholesterol efflux, and plasma HDL levels with t-AUCB treatment promotes RCT, decreasing LDL-C and atherosclerosis regression, suggesting that sEH inhibition may be a promising strategy to treat atherosclerotic vascular disease.
              Cover page: Inhibition of soluble epoxide hydrolase in mice promotes reverse cholesterol transport and regression of atherosclerosis
              • Article
              • Peer Reviewed
              UC San Francisco Previously Published Works (2017)
              Neuroimaging genetics is an emerging field that aims to identify the associations between genetic variants (e.g., single nucleotide polymorphisms (SNPs)) and quantitative traits (QTs) such as brain imaging phenotypes. In recent studies, in order to detect complex multi-SNP-multi-QT associations, bi-multivariate techniques such as various structured sparse canonical correlation analysis (SCCA) algorithms have been proposed and used in imaging genetics studies. However, associations between genetic markers and imaging QTs identified by existing bi-multivariate methods may not be all disease specific. To bridge this gap, we propose an analytical framework, based on three-way sparse canonical correlation analysis (T-SCCA), to explore the intrinsic associations among genetic markers, imaging QTs, and clinical scores of interest. We perform an empirical study using the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort to discover the relationships among SNPs from AD risk gene APOE, imaging QTs extracted from structural magnetic resonance imaging scans, and cognitive and diagnostic outcomes. The proposed T-SCCA model not only outperforms the traditional SCCA method in terms of identifying strong associations, but also discovers robust outcome-relevant imaging genetic patterns, demonstrating its promise for improving disease-related mechanistic understanding.
                Cover page: Mining Outcome-relevant Brain Imaging Genetic Associations via Three-way Sparse Canonical Correlation Analysis in Alzheimer’s Disease
                • Article
                • Peer Reviewed
                UC San Francisco Previously Published Works (2018)
                Brain imaging genetics studies the genetic basis of brain structures and functions via integrating both genotypic data such as single nucleotide polymorphism (SNP) and imaging quantitative traits (QTs). In this area, both multi-task learning (MTL) and sparse canonical correlation analysis (SCCA) methods are widely used since they are superior to those independent and pairwise univariate analyses. MTL methods generally incorporate a few of QTs and are not designed for feature selection from a large number of QTs; while existing SCCA methods typically employ only one modality of QTs to study its association with SNPs. Both MTL and SCCA encounter computational challenges as the number of SNPs increases. In this paper, combining the merits of MTL and SCCA, we propose a novel multi-task SCCA (MTSCCA) learning framework to identify bi-multivariate associations between SNPs and multi-modal imaging QTs. MTSCCA could make use of the complementary information carried by different imaging modalities. Using the G 2,1-norm regularization, MTSCCA treats all SNPs in the same group together to enforce sparsity at the group level. The l 2 , 1 -norm penalty is used to jointly select features across multiple tasks for SNPs, and across multiple modalities for QTs. A fast optimization algorithm is proposed using the grouping information of SNPs. Compared with conventional SCCA methods, MTSCCA obtains improved performance regarding both correlation coefficients and canonical weights patterns. In addition, our method runs very fast and is easy-to-implement, and thus could provide a powerful tool for genome-wide brain-wide imaging genetic studies.
                  Cover page: Fast Multi-Task SCCA Learning with Feature Selection for Multi-Modal Brain Imaging Genetics
                  • Article
                  • Peer Reviewed
                  UC Irvine Previously Published Works (2012)

                  Motivation

                  Recent advances in brain imaging and high-throughput genotyping techniques enable new approaches to study the influence of genetic and anatomical variations on brain functions and disorders. Traditional association studies typically perform independent and pairwise analysis among neuroimaging measures, cognitive scores and disease status, and ignore the important underlying interacting relationships between these units.

                  Results

                  To overcome this limitation, in this article, we propose a new sparse multimodal multitask learning method to reveal complex relationships from gene to brain to symptom. Our main contributions are three-fold: (i) introducing combined structured sparsity regularizations into multimodal multitask learning to integrate multidimensional heterogeneous imaging genetics data and identify multimodal biomarkers; (ii) utilizing a joint classification and regression learning model to identify disease-sensitive and cognition-relevant biomarkers; (iii) deriving a new efficient optimization algorithm to solve our non-smooth objective function and providing rigorous theoretical analysis on the global optimum convergency. Using the imaging genetics data from the Alzheimer's Disease Neuroimaging Initiative database, the effectiveness of the proposed method is demonstrated by clearly improved performance on predicting both cognitive scores and disease status. The identified multimodal biomarkers could predict not only disease status but also cognitive function to help elucidate the biological pathway from gene to brain structure and function, and to cognition and disease.

                  Availability

                  Software is publicly available at: http://ranger.uta.edu/%7eheng/multimodal/.
                    Cover page: Identifying disease sensitive and quantitative trait-relevant biomarkers from multidimensional heterogeneous imaging genetics data via sparse multimodal multitask learningCreative Commons 'BY' version 4.0 license
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