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Creators/Authors contains: "Li, Xiaodong"

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  1. Abstract

    Lithium dendrite‐induced short circuits and material loss are two major obstacles to the commercialization of lithium–sulfur (Li−S) batteries. Here, a nanocarbon composite consisting of cotton‐derived Fe3C‐encapsulated multiwalled carbon nanotubes (Fe3C‐MWCNTs) and graphene effectively traps polysulfides to suppress lithium dendrite growth is reported. Machine learning combined with molecular dynamics (MD) simulations unveils a new polysulfide‐induced lithium dendrite formation mechanism: the migration of polysulfides away from the anode drags out lithium protrusions through localized lattice distortion of the lithium anode and traps lithium ions in the surrounding electrolyte, leading to lithium dendrite formation. The Li−S battery, constructed using the composite of cotton‐derived Fe3C‐MWCNTs and graphene that serves as both the sulfur host and the anode interlayer, exhibits exceptional cycling stability, impressive capacity retention, and effective mitigation of lithium dendrite formation. The findings offer valuable strategies to prevent lithium dendrite formation and enhance understanding of lithium dendrite growth in Li−S batteries.

     
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  2. Techniques of matrix completion aim to impute a large portion of missing entries in a data matrix through a small portion of observed ones. In practice, prior information and special structures are usually employed in order to improve the accuracy of matrix completion. In this paper, we propose a unified nonconvex optimization framework for matrix completion with linearly parameterized factors. In particular, by introducing a condition referred to as Correlated Parametric Factorization, we conduct a unified geometric analysis for the nonconvex objective by establishing uniform upper bounds for low-rank estimation resulting from any local minimizer. Perhaps surprisingly, the condition of Correlated Parametric Factorization holds for important examples including subspace-constrained matrix completion and skew-symmetric matrix completion. The effectiveness of our unified nonconvex optimization method is also empirically illustrated by extensive numerical simulations. 
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  3. Anandkumar Animashree (Ed.)
    Techniques of matrix completion aim to impute a large portion of missing entries in a data matrix through a small portion of observed ones. In practice, prior information and special structures are usually employed in order to improve the accuracy of matrix completion. In this paper, we propose a unified nonconvex optimization framework for matrix completion with linearly parameterized factors. In particular, by introducing a condition referred to as Correlated Parametric Factorization, we conduct a unified geometric analysis for the nonconvex objective by establishing uniform upper bounds for low-rank estimation resulting from any local minimizer. Perhaps surprisingly, the condition of Correlated Parametric Factorization holds for important examples including subspace-constrained matrix completion and skew-symmetric matrix completion. The effectiveness of our unified nonconvex optimization method is also empirically illustrated by extensive numerical simulations. 
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  4. Path-based solutions have been shown to be useful for various graph analysis tasks, such as link prediction and graph clustering. However, they are no longer adequate for handling complex and gigantic graphs. Recently, motif-based analysis has attracted a lot of attention. A motif, or a small graph with a few nodes, is often considered as a fundamental unit of a graph. Motif-based analysis captures high-order structure between nodes, and performs better than traditional "edge-based" solutions. In this paper, we study motif-path , which is conceptually a concatenation of one or more motif instances. We examine how motif-paths can be used in three path-based mining tasks, namely link prediction, local graph clustering and node ranking. We further address the situation when two graph nodes are not connected through a motif-path, and develop a novel defragmentation method to enhance it. Experimental results on real graph datasets demonstrate the use of motif-paths and defragmentation techniques improves graph analysis effectiveness. 
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  5. Abstract

    Benzo[a]pyrene (BaP) is a common air pollutant that has been reported to cause oxidative stress and carcinogenesis. Wogonin, a flavonoid compound extracted from the roots ofScutellaria baicalensis, has been found to possess a variety of pharmacological activities, including anti‐inflammatory and anti‐cancer effects. The purpose of this study was to examine the ability of wogonin to alleviate the cytotoxicity induced by BaP in human airway epithelial cells and explore the corresponding mechanism. Our study found that wogonin treatment inhibited DNA damage and reactive oxygen species overproduction induced by BaP in human airway epithelial cells. In vitro enzyme assays showed that wogonin significantly inhibited the enzymatic activity of CYP1A1. In addition, wogonin decreased the basal level of CYP1A1 and inhibited the CYP1A1 overexpression induced by BaP, whereas overexpression of CYP1A1 partially reversed the effect of wogonin on BaP‐induced DNA damage. Meanwhile, a CYP1A1 inhibitor and CYP1A1 knockdown also showed these same effects. Further studies showed that wogonin regulates CYP1A1 expression by inhibiting CDK7 and CDK9 activity. The use of CDK7 or CDK9 inhibitors decreased BaP‐induced cytotoxicity and CYP1A1 expression. Finally, we found that the methoxy group of wogonin was crucial for its inhibitory activity. In conclusion, our data indicated that wogonin could effectively relieve BaP induced cytotoxicity, and its mechanism was related to the dual inhibition of CYP1A1 activity and expression.

     
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