25 Works

Two new sesquiterpenoid lactone derivatives from Lindera aggregata

Song-Song Wen, Yan Wang, Jia-Ping Xu, Qi Liu, Lei Zhang, Jing Zheng, Lin Li, Na Zhang, Xin Liu, Yu-Wen Xu & Zhen-Liang Sun
Two new sesquiterpenoid lactone derivatives, linderin A (1) and linderin B (2) comprising a sesquiterpenoid lactone and a methyl geranylhomogentisate moiety together with six known compounds were isolated from the roots of Lindera aggregata. Their chemical structures were elucidated using extensive spectroscopic analysis including 1 D, 2 D NMR, and HR-ESI-MS data and compared with previously reported data. The absolute configurations of 1 and 2 were assigned based on the electronic circular dichroism calculation. Compound...

Aucubin slows the development of osteoporosis by inhibiting osteoclast differentiation via the nuclear factor erythroid 2-related factor 2-mediated antioxidation pathway

Yongfeng Zhang, Xin Liu, Yangyang Li, Minkai Song, Yutong Li, Anhui Yang, Yaqin Zhang, Di Wang & Min Hu
Osteoporosis (OP) is a metabolic disease. We have previously demonstrated that aucubin (AU) has anti-OP effects that are due to its promotion of the formation of osteoblasts. To investigate the mechanisms of anti-OP effects of AU. C57BL/6 mice were randomly divided into control group, 30 mg/kg Dex-induced OP group (OP model group, 15 μg/kg oestradiol-treated positive control group, 5 or 45 mg/kg AU-treated group), and 45 mg/kg AU-alone-treated group. The administration lasted for 7 weeks....

Block-Wise Variable Selection for Clustering Via Latent States of Mixture Models

Beomseok Seo, Lin Lin & Jia Li
Mixture modeling is a major paradigm for clustering in statistics. In this article, we develop a new block-wise variable selection method for clustering by exploiting the latent states of the hidden Markov model on variable blocks or the Gaussian mixture model. The variable blocks are formed by depth-first-search on a dendrogram created based on the mutual information between any pair of variables. It is demonstrated that the latent states of the variable blocks together with...

Temporal and spatial characteristics of flocculated suspended solids in a deep reservoir: an in situ observation in the Biliuhe Reservoir

Yuyu Liu, Yuqing Feng, Xin Jiang, Shiguo Xu, Lin Zhu & Guoqing Sang
The amount of total suspended solids (TSS) is the most visible indicator for evaluating water quality in reservoirs. Previous investigations paid more attention to TSS of the surface layer in reservoirs, while suspended particles are prone to settle, resuspend, and aggregate at the bottom of reservoir. There may be different patterns of the TSS in different depths. This study is to assess the TSS concentration by weight analysis, find the evidence of the existence of...

Block-Wise Variable Selection for Clustering Via Latent States of Mixture Models

Beomseok Seo, Lin Lin & Jia Li
Mixture modeling is a major paradigm for clustering in statistics. In this article, we develop a new block-wise variable selection method for clustering by exploiting the latent states of the hidden Markov model on variable blocks or the Gaussian mixture model. The variable blocks are formed by depth-first-search on a dendrogram created based on the mutual information between any pair of variables. It is demonstrated that the latent states of the variable blocks together with...

Structural insights into dpCoA-RNA decapping by NudC

Wei Zhou, Zeyuan Guan, Fen Zhao, Yage Ye, Fang Yang, Ping Yin & Delin Zhang
Various kinds of cap structures, such as m7G, triphosphate groups, NAD and dpCoA, protect the 5′ terminus of RNA. The cap structures bond covalently to RNA and affect its stability, translation, and transport. The removal of the caps is mainly executed by Nudix hydrolase family proteins, including Dcp2, RppH and NudC. Numerous efforts have been made to elucidate the mechanism underlying the removal of m7G, triphosphate group, and NAD caps. In contrast, few studies related...

Extraction and characterization of a functional protein from Millettia speciosa Champ. leaf

Si-Yuan Luo, Zhi Huang, Xi Chen, Min-Hua Zong & Wen-Yong Lou
Natural plant-derived protein with excellent bioactivities has attracted much attention so a functional protein with molecular weight of 15.2 kDa was extracted from Millettia speciosa Champ. leaf for the first time. Under the pH of 12.0, solid-liquid ratio of 1:40 (w/v), extraction time of 2.0 h, and extraction temperature of 50 °C, the highest extracting efficiency (79.25 ± 0.78%) of the Millettia speciosa Champ. leaf protein (MLP) was achieved. The main structure of MLP contained...

Aucubin slows the development of osteoporosis by inhibiting osteoclast differentiation via the nuclear factor erythroid 2-related factor 2-mediated antioxidation pathway

Yongfeng Zhang, Xin Liu, Yangyang Li, Minkai Song, Yutong Li, Anhui Yang, Yaqin Zhang, Di Wang & Min Hu
Osteoporosis (OP) is a metabolic disease. We have previously demonstrated that aucubin (AU) has anti-OP effects that are due to its promotion of the formation of osteoblasts. To investigate the mechanisms of anti-OP effects of AU. C57BL/6 mice were randomly divided into control group, 30 mg/kg Dex-induced OP group (OP model group, 15 μg/kg oestradiol-treated positive control group, 5 or 45 mg/kg AU-treated group), and 45 mg/kg AU-alone-treated group. The administration lasted for 7 weeks....

Transcriptome profiling revealed heat stress-responsive genes in Arabidopsis through integrated bioinformatics analysis

Meili Guo, Xin Liu, Jiahui Wang, Yusu Jiang, Jinhuan Yu & Jing Gao
Heat stress is an environmental challenge that reduces plant productivity and growth. Plants have developed corresponding mechanisms to survive this adverse environmental stress. To demonstrate the mechanisms of how plants adapt to the environmental challenge, the heat response experiments involving Arabidopsis thaliana were retrieved from the GEO database. After quantile normalization of the GEO raw data, the differentially expressed genes (DEGs) in response to heat stress were identified by robust rank aggregation (RRA) algorithm, including...

Development of cisplatin-loaded hydrogels for trans-portal vein chemoembolization in an orthotopic liver cancer mouse model

Xinxiang Yang, Wai-Ho Oscar Yeung, Kel Vin Tan, Tak-Pan Kevin Ng, Li Pang, Jie Zhou, Jinyang Li, Changxian Li, Xiangcheng Li, Chung Mau Lo, Weiyuan John Kao & Kwan Man
Transarterial chemoembolization is a standard treatment for intermediate-stage hepatocellular carcinoma (HCC). This study evaluated the anti-tumor effect of the semi-interpenetrating network (IPN) hydrogel as a novel embolic material for trans-portal vein chemoembolization (TPVE) in vivo. A nude mice orthotopic HCC model was established, followed by TPVE using IPN hydrogel loaded with or without cisplatin. Portal vein blockade was visualized by MRI and the development of tumor was monitored by IVIS Spectrum Imaging. Tumor proliferation and...

Development of cisplatin-loaded hydrogels for trans-portal vein chemoembolization in an orthotopic liver cancer mouse model

Xinxiang Yang, Wai-Ho Oscar Yeung, Kel Vin Tan, Tak-Pan Kevin Ng, Li Pang, Jie Zhou, Jinyang Li, Changxian Li, Xiangcheng Li, Chung Mau Lo, Weiyuan John Kao & Kwan Man
Transarterial chemoembolization is a standard treatment for intermediate-stage hepatocellular carcinoma (HCC). This study evaluated the anti-tumor effect of the semi-interpenetrating network (IPN) hydrogel as a novel embolic material for trans-portal vein chemoembolization (TPVE) in vivo. A nude mice orthotopic HCC model was established, followed by TPVE using IPN hydrogel loaded with or without cisplatin. Portal vein blockade was visualized by MRI and the development of tumor was monitored by IVIS Spectrum Imaging. Tumor proliferation and...

Elevated Ras related GTP binding B (RRAGB) expression predicts poor overall survival and constructs a prognostic nomogram for colon adenocarcinoma

Jianjia Xiao, Qingqing Liu, Weijie Wu, Ying Yuan, Jie Zhou, Jieyu Shi & Shaorong Zhou
Currently, no articles have explored the roles of RRAGB gene in the occurrence and development of cancer. By means of The Cancer Genome Atlas (TCGA) data mining, we found that this gene might be a novel prognostic predictor for colon adenocarcinoma (COAD). Hence, this article was carried out to explore its roles in COAD and associations with immunity. RRAGB single-gene expression matrix and corresponding clinical information were extracted from TCGA database. Univariate/multivariate cox regression analyses...

Structural insights into dpCoA-RNA decapping by NudC

Wei Zhou, Zeyuan Guan, Fen Zhao, Yage Ye, Fang Yang, Ping Yin & Delin Zhang
Various kinds of cap structures, such as m7G, triphosphate groups, NAD and dpCoA, protect the 5′ terminus of RNA. The cap structures bond covalently to RNA and affect its stability, translation, and transport. The removal of the caps is mainly executed by Nudix hydrolase family proteins, including Dcp2, RppH and NudC. Numerous efforts have been made to elucidate the mechanism underlying the removal of m7G, triphosphate group, and NAD caps. In contrast, few studies related...

Elevated Ras related GTP binding B (RRAGB) expression predicts poor overall survival and constructs a prognostic nomogram for colon adenocarcinoma

Jianjia Xiao, Qingqing Liu, Weijie Wu, Ying Yuan, Jie Zhou, Jieyu Shi & Shaorong Zhou
Currently, no articles have explored the roles of RRAGB gene in the occurrence and development of cancer. By means of The Cancer Genome Atlas (TCGA) data mining, we found that this gene might be a novel prognostic predictor for colon adenocarcinoma (COAD). Hence, this article was carried out to explore its roles in COAD and associations with immunity. RRAGB single-gene expression matrix and corresponding clinical information were extracted from TCGA database. Univariate/multivariate cox regression analyses...

Transcriptome profiling revealed heat stress-responsive genes in Arabidopsis through integrated bioinformatics analysis

Meili Guo, Xin Liu, Jiahui Wang, Yusu Jiang, Jinhuan Yu & Jing Gao
Heat stress is an environmental challenge that reduces plant productivity and growth. Plants have developed corresponding mechanisms to survive this adverse environmental stress. To demonstrate the mechanisms of how plants adapt to the environmental challenge, the heat response experiments involving Arabidopsis thaliana were retrieved from the GEO database. After quantile normalization of the GEO raw data, the differentially expressed genes (DEGs) in response to heat stress were identified by robust rank aggregation (RRA) algorithm, including...

First-Order Newton-Type Estimator for Distributed Estimation and Inference

Xi Chen, Weidong Liu & Yichen Zhang
This article studies distributed estimation and inference for a general statistical problem with a convex loss that could be nondifferentiable. For the purpose of efficient computation, we restrict ourselves to stochastic first-order optimization, which enjoys low per-iteration complexity. To motivate the proposed method, we first investigate the theoretical properties of a straightforward divide-and-conquer stochastic gradient descent approach. Our theory shows that there is a restriction on the number of machines and this restriction becomes more...

First-order Newton-type Estimator for Distributed Estimation and Inference

Xi Chen, Weidong Liu & Yichen Zhang
This paper studies distributed estimation and inference for a general statistical problem with a convex loss that could be non-differentiable. For the purpose of efficient computation, we restrict ourselves to stochastic first-order optimization, which enjoys low per-iteration complexity. To motivate the proposed method, we first investigate the theoretical properties of a straightforward Divide-and-Conquer Stochastic Gradient Descent (DC-SGD) approach. Our theory shows that there is a restriction on the number of machines and this restriction becomes...

First-Order Newton-Type Estimator for Distributed Estimation and Inference

Xi Chen, Weidong Liu & Yichen Zhang
This article studies distributed estimation and inference for a general statistical problem with a convex loss that could be nondifferentiable. For the purpose of efficient computation, we restrict ourselves to stochastic first-order optimization, which enjoys low per-iteration complexity. To motivate the proposed method, we first investigate the theoretical properties of a straightforward divide-and-conquer stochastic gradient descent approach. Our theory shows that there is a restriction on the number of machines and this restriction becomes more...

Two new sesquiterpenoid lactone derivatives from Lindera aggregata

Song-Song Wen, Yan Wang, Jia-Ping Xu, Qi Liu, Lei Zhang, Jing Zheng, Lin Li, Na Zhang, Xin Liu, Yu-Wen Xu & Zhen-Liang Sun
Two new sesquiterpenoid lactone derivatives, linderin A (1) and linderin B (2) comprising a sesquiterpenoid lactone and a methyl geranylhomogentisate moiety together with six known compounds were isolated from the roots of Lindera aggregata. Their chemical structures were elucidated using extensive spectroscopic analysis including 1 D, 2 D NMR, and HR-ESI-MS data and compared with previously reported data. The absolute configurations of 1 and 2 were assigned based on the electronic circular dichroism calculation. Compound...

Extraction and characterization of a functional protein from Millettia speciosa Champ. leaf

Si-Yuan Luo, Zhi Huang, Xi Chen, Min-Hua Zong & Wen-Yong Lou
Natural plant-derived protein with excellent bioactivities has attracted much attention so a functional protein with molecular weight of 15.2 kDa was extracted from Millettia speciosa Champ. leaf for the first time. Under the pH of 12.0, solid-liquid ratio of 1:40 (w/v), extraction time of 2.0 h, and extraction temperature of 50 °C, the highest extracting efficiency (79.25 ± 0.78%) of the Millettia speciosa Champ. leaf protein (MLP) was achieved. The main structure of MLP contained...

An enhanced methodology for predicting protein-protein interactions between human and hepatitis C virus via ensemble learning algorithms

Xin Liu, Liang Wang, Cheng-Hao Liang, Ya-Ping Lu, Ting Yang & Xiao Zhang
Hepatitis C virus (HCV) is responsible for a variety of human life-threatening diseases, which include liver cirrhosis, chronic hepatitis, fibrosis and hepatocellular carcinoma (HCC) . Computational study of protein-protein interactions between human and HCV could boost the findings of antiviral drugs in HCV therapy and might optimize the treatment procedures for HCV infections. In this analysis, we constructed a prediction model for protein-protein interactions between HCV and human by incorporating the features generated by pseudo...

An enhanced methodology for predicting protein-protein interactions between human and hepatitis C virus via ensemble learning algorithms

Xin Liu, Liang Wang, Cheng-Hao Liang, Ya-Ping Lu, Ting Yang & Xiao Zhang
Hepatitis C virus (HCV) is responsible for a variety of human life-threatening diseases, which include liver cirrhosis, chronic hepatitis, fibrosis and hepatocellular carcinoma (HCC) . Computational study of protein-protein interactions between human and HCV could boost the findings of antiviral drugs in HCV therapy and might optimize the treatment procedures for HCV infections. In this analysis, we constructed a prediction model for protein-protein interactions between HCV and human by incorporating the features generated by pseudo...

Block-wise Variable Selection for Clustering via Latent States of Mixture Models

Beomseok Seo, Lin Lin & Jia Li
Mixture modeling is a major paradigm for clustering in statistics. In this paper, we develop a new block-wise variable selection method for clustering by exploiting the latent states of the hidden Markov model on variable blocks or the Gaussian mixture model. The variable blocks are formed by depth-first-search on a dendrogram created based on the mutual information between any pair of variables. It is demonstrated that the latent states of the variable blocks together with...

Temporal and Spatial Characteristics of Flocculated Suspended Solids in a Deep Reservoir: an in situ Observation in the Biliuhe Reservoir

Yuyu Liu, Yuqing Feng, Xin Jiang, Shiguo Xu, Lin Zhu & Guoqing Sang
The amount of total suspended solids (TSS) is the most visible indicator for evaluating water quality in reservoirs. Previous investigations paid more attention to TSS of the surface layer in reservoirs, while suspended particles are prone to settle, resuspend, and aggregate at the bottom of reservoir. There may be different patterns of the TSS in different depths. Hence, it is significant to explore the temporal and spatial characteristics of suspended solids in different depths of...

Temporal and spatial characteristics of flocculated suspended solids in a deep reservoir: an in situ observation in the Biliuhe Reservoir

Yuyu Liu, Yuqing Feng, Xin Jiang, Shiguo Xu, Lin Zhu & Guoqing Sang
The amount of total suspended solids (TSS) is the most visible indicator for evaluating water quality in reservoirs. Previous investigations paid more attention to TSS of the surface layer in reservoirs, while suspended particles are prone to settle, resuspend, and aggregate at the bottom of reservoir. There may be different patterns of the TSS in different depths. This study is to assess the TSS concentration by weight analysis, find the evidence of the existence of...

Registration Year

  • 2021
    25

Resource Types

  • Dataset
    13
  • Text
    6
  • Image
    4
  • Other
    2

Affiliations

  • Third Affiliated Hospital of Guangzhou Medical University
    25
  • Chinese Academy of Sciences
    20
  • Southern Medical University
    18
  • Wuhan University
    17
  • Zhejiang University
    16
  • University of Chinese Academy of Sciences
    15
  • Sun Yat-sen University
    13
  • Chinese Academy of Medical Sciences & Peking Union Medical College
    13
  • Huazhong University of Science and Technology
    12
  • Nanjing Agricultural University
    12