Browsing by Author "Liu, Xiang"

Now showing 1 - 9 of 9
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    Boosting Superior Lithium Storage Performance of Alloy‐Based Anode Materials via Ultraconformal Sb Coating–Derived Favorable Solid‐Electrolyte Interphase
    (Wiley, 2020-01) Xiong, Bing-Qing; Zhou, Xinwei; Xu, Gui-Liang; Liu, Yuzi; Zhu, Likun; Hu, Youcheng; Shen, Shou-Yu; Hong, Yu-Hao; Wan, Si-Cheng; Liu, Xiao-Chen; Liu, Xiang; Chen, Shengli; Huang, Ling; Sun, Shi-Gang; Amine, Khalil; Ke, Fu-Sheng; Mechanical and Energy Engineering, School of Engineering and Technology
    Alloy materials such as Si and Ge are attractive as high‐capacity anodes for rechargeable batteries, but such anodes undergo severe capacity degradation during discharge–charge processes. Compared to the over‐emphasized efforts on the electrode structure design to mitigate the volume changes, understanding and engineering of the solid‐electrolyte interphase (SEI) are significantly lacking. This work demonstrates that modifying the surface of alloy‐based anode materials by building an ultraconformal layer of Sb can significantly enhance their structural and interfacial stability during cycling. Combined experimental and theoretical studies consistently reveal that the ultraconformal Sb layer is dynamically converted to Li3Sb during cycling, which can selectively adsorb and catalytically decompose electrolyte additives to form a robust, thin, and dense LiF‐dominated SEI, and simultaneously restrain the decomposition of electrolyte solvents. Hence, the Sb‐coated porous Ge electrode delivers much higher initial Coulombic efficiency of 85% and higher reversible capacity of 1046 mAh g−1 after 200 cycles at 500 mA g−1, compared to only 72% and 170 mAh g−1 for bare porous Ge. The present finding has indicated that tailoring surface structures of electrode materials is an appealing approach to construct a robust SEI and achieve long‐term cycling stability for alloy‐based anode materials.
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    Deep Learning-Enabled Integration of Histology and Transcriptomics for Tissue Spatial Profile Analysis
    (American Association for the Advancement of Science, 2025-01-17) Ge, Yongxin; Leng, Jiake; Tang, Ziyang; Wang, Kanran; U, Kaicheng; Zhang, Sophia Meixuan; Han, Sen; Zhang, Yiyan; Xiang, Jinxi; Yang, Sen; Liu, Xiang; Song, Yi; Wang, Xiyue; Li, Yuchen; Zhao, Junhan; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    Spatially resolved transcriptomics enable comprehensive measurement of gene expression at subcellular resolution while preserving the spatial context of the tissue microenvironment. While deep learning has shown promise in analyzing SCST datasets, most efforts have focused on sequence data and spatial localization, with limited emphasis on leveraging rich histopathological insights from staining images. We introduce GIST, a deep learning-enabled gene expression and histology integration for spatial cellular profiling. GIST employs histopathology foundation models pretrained on millions of histology images to enhance feature extraction and a hybrid graph transformer model to integrate them with transcriptome features. Validated with datasets from human lung, breast, and colorectal cancers, GIST effectively reveals spatial domains and substantially improves the accuracy of segmenting the microenvironment after denoising transcriptomics data. This enhancement enables more accurate gene expression analysis and aids in identifying prognostic marker genes, outperforming state-of-the-art deep learning methods with a total improvement of up to 49.72%. GIST provides a generalizable framework for integrating histology with spatial transcriptome analysis, revealing novel insights into spatial organization and functional dynamics.
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    DEPOT: graph learning delineates the roles of cancers in the progression trajectories of chronic kidney disease using electronic medical records
    (medRxiv, 2023-08-16) Song, Qianqian; Liu, Xiang; Li, Zuotian; Zhang, Pengyue; Eadon, Michael; Su, Jing; Biostatistics and Health Data Science, School of Medicine
    Chronic kidney disease (CKD) is a common, complex, and heterogeneous disease impacting aging populations. Determining the landscape of disease progression trajectories from midlife to senior age in a real-world context allows us to better understand the progression of CKD, the heterogeneity of progression patterns among the risk population, and the interactions with other clinical conditions like cancers. In this study, we use electronic health records (EHRs) to outline the CKD progression trajectory roadmap for the Wake Forest Baptist Medical Center (WFBMC) patient population. We establish an EHR cohort (n = 79,434) with patients' health status identified by 18 Essential Clinical Indices across 508,732 clinical encounters. We develop the DisEase PrOgression Trajectory (DEPOT) approach to model CKD progression trajectories and individualize clinical decision support. The DEPOT is an evidence-driven, graph-based clinical informatics approach that addresses the unique challenges in longitudinal EHR data by systematically using the graph artificial intelligence (graph-AI) model for representation learning and reverse graph embedding for trajectory reconstruction. Moreover, DEPOT includes a prediction model to assign new patients along the progression trajectory. We successfully establish the EHR-based CKD progression trajectories with DEPOT in the WFUBMC cohort. We annotate the trajectories with clinical features, including kidney function, age, and other indices, including cancer. This CKD progression trajectory roadmap reveals diverse kidney failure pathways associated with different clinical conditions. Specifically, we have identified one high-risk trajectory and two low-risk trajectories. Switching pathways from low-risk trajectories to the high-risk one is associated with accelerated decline in kidney function. On this roadmap, high-risk patients are enriched in the skin and GU cancers, which differs from low-risk patients, suggesting fundamentally different disease progression mechanisms. Overall, the CKD progression trajectory roadmap reveals novel diverse renal failure pathways in type 2 diabetes mellitus and highlights disease progression patterns associated with cancer phenotypes.
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    In Situ Construction of an Ultrarobust and Lithiophilic Li-Enriched Li–N Nanoshield for High-Performance Ge-Based Anode Materials
    (ACS, 2020-11) Xiong, Bing-Qing; Zhou, Xinwei; Xu, Gui-Liang; Liu, Xiang; Hu, Youcheng; Liu, Yuzi; Zhu, Likun; Shi, Chen-Guang; Hong, Yu-Hao; Wan, Si-Cheng; Sun, Cheng-Jun; Chen, Shengli; Huang, Ling; Sun, Shi-Gang; Amine, Khalil; Ke, Fu-Sheng; Mechanical and Energy Engineering, School of Engineering and Technology
    Alloy-based materials are promising anodes for rechargeable batteries because of their higher theoretical capacities in comparison to graphite. Unfortunately, the huge volume changes during cycling cause serious structural degradation and undesired parasitic reactions with electrolytes, resulting in fragile solid-electrolyte interphase formation and serious capacity decay. This work proposes to mitigate the volume changes and suppress the interfacial reactivity of Ge anodes without sacrificing the interfacial Li+ transport, through in situ construction of an ultrarobust and lithiophilic Li-enriched Li–N nanoshield, which demonstrated improved chemical, electrochemical, mechanical, and environmental stability. Therefore, it can serve as a versatile interlayer to facilitate Li+ transport and effectively block the attack of electrolyte solvents, thus boosting the long-term cycle stability and fast charging capability of Ge anodes. This work offers an alternative methodology to tune the interfaces of other electrode materials as well by screening for more N-containing compounds that can react with Li+ during battery operation.
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    A practical phosphorus-based anode material for high-energy lithium-ion batteries
    (Elsevier, 2020-08) Amine, Rachid; Daali, Amine; Zhou, Xinwei; Liu, Xiang; Liu, Yuzi; Ren, Yang; Zhang, Xiaoyi; Zhu, Likun; Al-Hallaj, Said; Chen, Zonghai; Xu, Gui-Liang; Amine, Khalil; Mechanical and Energy Engineering, School of Engineering and Technology
    State-of-the-art lithium-ion batteries cannot satisfy the increasing energy demand worldwide because of the low specific capacity of the graphite anode. Silicon and phosphorus both show much higher specific capacity; however, their practical use is significantly hindered by their large volume changes during charge/discharge. Although significant efforts have been made to improve their cycle life, the initial coulombic efficiencies of the reported Si-based and P-based anodes are still unsatisfactory (<90%). Here, by using a scalable high-energy ball milling approach, we report a practical hierarchical micro/nanostructured P-based anode material for high-energy lithium-ion batteries, which possesses a high initial coulombic efficiency of 91% and high specific capacity of ~2500 mAh g−1 together with long cycle life and fast charging capability. In situ high-energy X-ray diffraction and in situ single-particle charging/discharging were used to understand its superior lithium storage performance. Moreover, proof-of-concept full-cell lithium-ion batteries using such an anode and a LiNi0.6Co0.2Mn0.2O2 cathode were assembled to show their practical use. The findings presented here can serve as a good guideline for the future design of high-performance anode materials for lithium-ion batteries.
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    SpaIM: Single-cell Spatial Transcriptomics Imputation via Style Transfer
    (bioRxiv, 2025-01-27) Li, Bo; Tang, Ziyang; Budhkar, Aishwarya; Liu, Xiang; Zhang, Tonglin; Yang, Baijian; Su, Jing; Song, Qianqian; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    Spatial transcriptomics (ST) technologies have revolutionized our understanding of cellular ecosystems. However, these technologies face challenges such as sparse gene signals and limited gene detection capacities, which hinder their ability to fully capture comprehensive spatial gene expression profiles. To address these limitations, we propose leveraging single-cell RNA sequencing (scRNA-seq), which provides comprehensive gene expression data but lacks spatial context, to enrich ST profiles. Herein, we introduce SpaIM, an innovative style transfer learning model that utilizes scRNA-seq information to predict unmeasured gene expressions in ST data, thereby improving gene coverage and expressions. SpaIM segregates scRNA-seq and ST data into data-agnostic contents and data-specific styles, with the contents capture the commonalities between the two data types, while the styles highlight their unique differences. By integrating the strengths of scRNA-seq and ST, SpaIM overcomes data sparsity and limited gene coverage issues, making significant advancements over 12 existing methods. This improvement is demonstrated across 53 diverse ST datasets, spanning sequencing- and imaging-based spatial technologies in various tissue types. Additionally, SpaIM enhances downstream analyses, including the detection of ligand-receptor interactions, spatial domain characterization, and identification of differentially expressed genes. Released as open-source software, SpaIM increases accessibility for spatial transcriptomics analysis. In summary, SpaIM represents a pioneering approach to enrich spatial transcriptomics using scRNA-seq data, enabling precise gene expression imputation and advancing the field of spatial transcriptomics research.
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    SpaRx: elucidate single-cell spatial heterogeneity of drug responses for personalized treatment
    (Oxford University Press, 2023) Tang, Ziyang; Liu, Xiang; Li, Zuotian; Zhang, Tonglin; Yang, Baijian; Su, Jing; Song, Qianqian; Biostatistics and Health Data Science, School of Medicine
    Spatial cellular authors heterogeneity contributes to differential drug responses in a tumor lesion and potential therapeutic resistance. Recent emerging spatial technologies such as CosMx, MERSCOPE and Xenium delineate the spatial gene expression patterns at the single cell resolution. This provides unprecedented opportunities to identify spatially localized cellular resistance and to optimize the treatment for individual patients. In this work, we present a graph-based domain adaptation model, SpaRx, to reveal the heterogeneity of spatial cellular response to drugs. SpaRx transfers the knowledge from pharmacogenomics profiles to single-cell spatial transcriptomics data, through hybrid learning with dynamic adversarial adaption. Comprehensive benchmarking demonstrates the superior and robust performance of SpaRx at different dropout rates, noise levels and transcriptomics coverage. Further application of SpaRx to the state-of-the-art single-cell spatial transcriptomics data reveals that tumor cells in different locations of a tumor lesion present heterogenous sensitivity or resistance to drugs. Moreover, resistant tumor cells interact with themselves or the surrounding constituents to form an ecosystem for drug resistance. Collectively, SpaRx characterizes the spatial therapeutic variability, unveils the molecular mechanisms underpinning drug resistance and identifies personalized drug targets and effective drug combinations.
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    TrajVis: a visual clinical decision support system to translate artificial intelligence trajectory models in the precision management of chronic kidney disease
    (Oxford University Press, 2024) Li, Zuotian; Liu, Xiang; Tang, Ziyang; Jin, Nanxin; Zhang, Pengyue; Eadon, Michael T.; Song, Qianqian; Chen, Yingjie V.; Su, Jing; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    Objective: Our objective is to develop and validate TrajVis, an interactive tool that assists clinicians in using artificial intelligence (AI) models to leverage patients' longitudinal electronic medical records (EMRs) for personalized precision management of chronic disease progression. Materials and methods: We first perform requirement analysis with clinicians and data scientists to determine the visual analytics tasks of the TrajVis system as well as its design and functionalities. A graph AI model for chronic kidney disease (CKD) trajectory inference named DisEase PrOgression Trajectory (DEPOT) is used for system development and demonstration. TrajVis is implemented as a full-stack web application with synthetic EMR data derived from the Atrium Health Wake Forest Baptist Translational Data Warehouse and the Indiana Network for Patient Care research database. A case study with a nephrologist and a user experience survey of clinicians and data scientists are conducted to evaluate the TrajVis system. Results: The TrajVis clinical information system is composed of 4 panels: the Patient View for demographic and clinical information, the Trajectory View to visualize the DEPOT-derived CKD trajectories in latent space, the Clinical Indicator View to elucidate longitudinal patterns of clinical features and interpret DEPOT predictions, and the Analysis View to demonstrate personal CKD progression trajectories. System evaluations suggest that TrajVis supports clinicians in summarizing clinical data, identifying individualized risk predictors, and visualizing patient disease progression trajectories, overcoming the barriers of AI implementation in healthcare. Discussion: The TrajVis system provides a novel visualization solution which is complimentary to other risk estimators such as the Kidney Failure Risk Equations. Conclusion: TrajVis bridges the gap between the fast-growing AI/ML modeling and the clinical use of such models for personalized and precision management of chronic diseases.
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    xSiGra: explainable model for single-cell spatial data elucidation
    (Oxford University Press, 2024) Budhkar, Aishwarya; Tang, Ziyang; Liu, Xiang; Zhang, Xuhong; Su, Jing; Song, Qianqian; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    Recent advancements in spatial imaging technologies have revolutionized the acquisition of high-resolution multichannel images, gene expressions, and spatial locations at the single-cell level. Our study introduces xSiGra, an interpretable graph-based AI model, designed to elucidate interpretable features of identified spatial cell types, by harnessing multimodal features from spatial imaging technologies. By constructing a spatial cellular graph with immunohistology images and gene expression as node attributes, xSiGra employs hybrid graph transformer models to delineate spatial cell types. Additionally, xSiGra integrates a novel variant of gradient-weighted class activation mapping component to uncover interpretable features, including pivotal genes and cells for various cell types, thereby facilitating deeper biological insights from spatial data. Through rigorous benchmarking against existing methods, xSiGra demonstrates superior performance across diverse spatial imaging datasets. Application of xSiGra on a lung tumor slice unveils the importance score of cells, illustrating that cellular activity is not solely determined by itself but also impacted by neighboring cells. Moreover, leveraging the identified interpretable genes, xSiGra reveals endothelial cell subset interacting with tumor cells, indicating its heterogeneous underlying mechanisms within complex cellular interactions.