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  1. Article ; Online: Corrigendum

    Qian Zhou / Di Zhang / Heng Zhang / Xingyang Wan / Bang Hu / Qi Zou / Dan Su / Hui Peng / Dandan Huang / Donglin Ren

    Frontiers in Pharmacology, Vol

    Effects of Xiao Chengqi formula on slow transit constipation by assessing gut microbiota and metabolomics analysis in vitro and in vivo

    2023  Volume 14

    Keywords Xiao Chengqi formula ; traditional Chinese medicine ; slow transit constipation ; butyl aminobenzene ; interstitial cells of cajal ; interleukin-21 receptor ; Therapeutics. Pharmacology ; RM1-950
    Language English
    Publishing date 2023-10-01T00:00:00Z
    Publisher Frontiers Media S.A.
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Generating Hypergraph-Based High-Order Representations of Whole-Slide Histopathological Images for Survival Prediction.

    Di, Donglin / Zou, Changqing / Feng, Yifan / Zhou, Haiyan / Ji, Rongrong / Dai, Qionghai / Gao, Yue

    IEEE transactions on pattern analysis and machine intelligence

    2023  Volume 45, Issue 5, Page(s) 5800–5815

    Abstract: Patient survival prediction based on gigapixel whole-slide histopathological images (WSIs) has become increasingly prevalent in recent years. A key challenge of this task is achieving an informative survival-specific global representation from those WSIs ...

    Abstract Patient survival prediction based on gigapixel whole-slide histopathological images (WSIs) has become increasingly prevalent in recent years. A key challenge of this task is achieving an informative survival-specific global representation from those WSIs with highly complicated data correlation. This article proposes a multi-hypergraph based learning framework, called "HGSurvNet," to tackle this challenge. HGSurvNet achieves an effective high-order global representation of WSIs via multilateral correlation modeling in multiple spaces and a general hypergraph convolution network. It has the ability to alleviate over-fitting issues caused by the lack of training data by using a new convolution structure called hypergraph max-mask convolution. Extensive validation experiments were conducted on three widely-used carcinoma datasets: Lung Squamous Cell Carcinoma (LUSC), Glioblastoma Multiforme (GBM), and National Lung Screening Trial (NLST). Quantitative analysis demonstrated that the proposed method consistently outperforms state-of-the-art methods, coupled with the Bayesian Concordance Readjust loss. We also demonstrate the individual effectiveness of each module of the proposed framework and its application potential for pathology diagnosis and reporting empowered by its interpretability potential.
    MeSH term(s) Humans ; Bayes Theorem ; Algorithms ; Learning
    Language English
    Publishing date 2023-04-03
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 1939-3539
    ISSN (online) 1939-3539
    DOI 10.1109/TPAMI.2022.3209652
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article: Corrigendum: Effects of Xiao Chengqi formula on slow transit constipation by assessing gut microbiota and metabolomics analysis

    Zhou, Qian / Zhang, Di / Zhang, Heng / Wan, Xingyang / Hu, Bang / Zou, Qi / Su, Dan / Peng, Hui / Huang, Dandan / Ren, Donglin

    Frontiers in pharmacology

    2023  Volume 14, Page(s) 1256600

    Abstract: This corrects the article DOI: 10.3389/fphar.2022.864598.]. ...

    Abstract [This corrects the article DOI: 10.3389/fphar.2022.864598.].
    Language English
    Publishing date 2023-10-31
    Publishing country Switzerland
    Document type Published Erratum
    ZDB-ID 2587355-6
    ISSN 1663-9812
    ISSN 1663-9812
    DOI 10.3389/fphar.2023.1256600
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Big-Hypergraph Factorization Neural Network for Survival Prediction From Whole Slide Image.

    Di, Donglin / Zhang, Jun / Lei, Fuqiang / Tian, Qi / Gao, Yue

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society

    2022  Volume 31, Page(s) 1149–1160

    Abstract: Survival prediction for patients based on histopa- thological whole-slide images (WSIs) has attracted increasing attention in recent years. Due to the massive pixel data in a single WSI, fully exploiting cell-level structural information (e.g., stromal/ ... ...

    Abstract Survival prediction for patients based on histopa- thological whole-slide images (WSIs) has attracted increasing attention in recent years. Due to the massive pixel data in a single WSI, fully exploiting cell-level structural information (e.g., stromal/tumor microenvironment) from the gigapixel WSI is challenging. Most of the current studies resolve the problem by sampling limited image patches to construct a graph-based model (e.g., hypergraph). However, the sampling scale is a critical bottleneck since it is a fundamental obstacle of broadening samples for transductive learning. To overcome the limitation of the sampling scale for constructing a big hypergraph model, we propose a factorization neural network that embeds the correlation among large-scale vertices and hyperedges into two low-dimensional latent semantic spaces separately, empowering the dense sampling. Thanks to the compressed low-dimensional correlation embedding, the hypergraph convolutional layers generate the high-order global representation for each WSI. To minimize the effect of the uncertainty data as well as to achieve the metric-driven learning, we also propose a multi-level ranking supervision to enable the network learning by a queue of patients on the global horizon. Extensive experiments are conducted on three public carcinoma datasets (i.e., LUSC, GBM, and NLST), and the quantitative results demonstrate the proposed method outperforms state-of-the-art methods across-the-board.
    MeSH term(s) Humans ; Neural Networks, Computer
    Language English
    Publishing date 2022-01-19
    Publishing country United States
    Document type Journal Article
    ISSN 1941-0042
    ISSN (online) 1941-0042
    DOI 10.1109/TIP.2021.3139229
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Book ; Online: Multi-Scale U-Shape MLP for Hyperspectral Image Classification

    Lin, Moule / Jing, Weipeng / Di, Donglin / Chen, Guangsheng / Song, Houbing

    2023  

    Abstract: Hyperspectral images have significant applications in various domains, since they register numerous semantic and spatial information in the spectral band with spatial variability of spectral signatures. Two critical challenges in identifying pixels of ... ...

    Abstract Hyperspectral images have significant applications in various domains, since they register numerous semantic and spatial information in the spectral band with spatial variability of spectral signatures. Two critical challenges in identifying pixels of the hyperspectral image are respectively representing the correlated information among the local and global, as well as the abundant parameters of the model. To tackle this challenge, we propose a Multi-Scale U-shape Multi-Layer Perceptron (MUMLP) a model consisting of the designed MSC (Multi-Scale Channel) block and the UMLP (U-shape Multi-Layer Perceptron) structure. MSC transforms the channel dimension and mixes spectral band feature to embed the deep-level representation adequately. UMLP is designed by the encoder-decoder structure with multi-layer perceptron layers, which is capable of compressing the large-scale parameters. Extensive experiments are conducted to demonstrate our model can outperform state-of-the-art methods across-the-board on three wide-adopted public datasets, namely Pavia University, Houston 2013 and Houston 2018

    Comment: 5 pages
    Keywords Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Machine Learning
    Subject code 006
    Publishing date 2023-07-05
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article: Cost-Sensitive Uncertainty Hypergraph Learning for Identification of Lymph Node Involvement With CT Imaging.

    Ma, Qianli / Yan, Jielong / Zhang, Jun / Yu, Qiduo / Zhao, Yue / Liang, Chaoyang / Di, Donglin

    Frontiers in medicine

    2022  Volume 9, Page(s) 840319

    Abstract: Lung adenocarcinoma (LUAD) is the most common type of lung cancer. Accurate identification of lymph node (LN) involvement in patients with LUAD is crucial for prognosis and making decisions of the treatment strategy. CT imaging has been used as a tool to ...

    Abstract Lung adenocarcinoma (LUAD) is the most common type of lung cancer. Accurate identification of lymph node (LN) involvement in patients with LUAD is crucial for prognosis and making decisions of the treatment strategy. CT imaging has been used as a tool to identify lymph node involvement. To tackle the shortage of high-quality data and improve the sensitivity of diagnosis, we propose a Cost-Sensitive Uncertainty Hypergraph Learning (CSUHL) model to identify the lymph node based on the CT images. We design a step named "Multi-Uncertainty Measurement" to measure the epistemic and the aleatoric uncertainty, respectively. Given the two types of attentional uncertainty weights, we further propose a cost-sensitive hypergraph learning to boost the sensitivity of diagnosing, targeting task-driven optimization of the clinical scenarios. Extensive qualitative and quantitative experiments on the real clinical dataset demonstrate our method is capable of accurately identifying the lymph node and outperforming state-of-the-art methods across the board.
    Language English
    Publishing date 2022-02-10
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2775999-4
    ISSN 2296-858X
    ISSN 2296-858X
    DOI 10.3389/fmed.2022.840319
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Cost-Sensitive Uncertainty Hypergraph Learning for Identification of Lymph Node Involvement With CT Imaging

    Qianli Ma / Jielong Yan / Jun Zhang / Qiduo Yu / Yue Zhao / Chaoyang Liang / Donglin Di

    Frontiers in Medicine, Vol

    2022  Volume 9

    Abstract: Lung adenocarcinoma (LUAD) is the most common type of lung cancer. Accurate identification of lymph node (LN) involvement in patients with LUAD is crucial for prognosis and making decisions of the treatment strategy. CT imaging has been used as a tool to ...

    Abstract Lung adenocarcinoma (LUAD) is the most common type of lung cancer. Accurate identification of lymph node (LN) involvement in patients with LUAD is crucial for prognosis and making decisions of the treatment strategy. CT imaging has been used as a tool to identify lymph node involvement. To tackle the shortage of high-quality data and improve the sensitivity of diagnosis, we propose a Cost-Sensitive Uncertainty Hypergraph Learning (CSUHL) model to identify the lymph node based on the CT images. We design a step named “Multi-Uncertainty Measurement” to measure the epistemic and the aleatoric uncertainty, respectively. Given the two types of attentional uncertainty weights, we further propose a cost-sensitive hypergraph learning to boost the sensitivity of diagnosing, targeting task-driven optimization of the clinical scenarios. Extensive qualitative and quantitative experiments on the real clinical dataset demonstrate our method is capable of accurately identifying the lymph node and outperforming state-of-the-art methods across the board.
    Keywords lymph node involvement ; CT imaging ; hypergraph learning ; cost-sensitive ; lung cancer ; Medicine (General) ; R5-920
    Subject code 006
    Language English
    Publishing date 2022-02-01T00:00:00Z
    Publisher Frontiers Media S.A.
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article ; Online: PITX2 in pancreatic stellate cells promotes EMT in pancreatic cancer cells via the Wnt/β-catenin pathway.

    Wu, Di / Chen, Weibo / Yang, Yang / Qin, Yi / Zu, Guangchen / Zhang, Yue / An, Yong / Sun, Donglin / Xu, Xiaowu / Chen, Xuemin

    Acta biochimica et biophysica Sinica

    2023  Volume 55, Issue 9, Page(s) 1393–1403

    Abstract: Since the prognosis of patients with pancreatic cancer is very poor and there is a lack of treatment methods, this study is performed to investigate the function of PITX2 in pancreatic stellate cells (PSCs) in the progression of pancreatic cancer. ... ...

    Abstract Since the prognosis of patients with pancreatic cancer is very poor and there is a lack of treatment methods, this study is performed to investigate the function of PITX2 in pancreatic stellate cells (PSCs) in the progression of pancreatic cancer. Scientific hypotheses are proposed according to bioinformatics analysis and tissue microarray analysis. Stable knockdown of
    MeSH term(s) Humans ; beta Catenin/genetics ; beta Catenin/metabolism ; Vimentin/genetics ; Vimentin/metabolism ; Pancreatic Stellate Cells/metabolism ; Cell Movement/genetics ; Pancreatic Neoplasms/genetics ; Pancreatic Neoplasms/metabolism ; Wnt Signaling Pathway/genetics ; Cell Line, Tumor ; Cell Proliferation/genetics
    Chemical Substances beta Catenin ; Vimentin
    Language English
    Publishing date 2023-06-19
    Publishing country China
    Document type Journal Article
    ZDB-ID 2175256-4
    ISSN 1745-7270 ; 0582-9879 ; 1672-9145
    ISSN (online) 1745-7270
    ISSN 0582-9879 ; 1672-9145
    DOI 10.3724/abbs.2023118
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: AGNet

    Weipeng Jing / Wenjun Zhang / Linhui Li / Donglin Di / Guangsheng Chen / Jian Wang

    Remote Sensing, Vol 14, Iss 1036, p

    An Attention-Based Graph Network for Point Cloud Classification and Segmentation

    2022  Volume 1036

    Abstract: Classification and segmentation of point clouds have attracted increasing attention in recent years. On the one hand, it is difficult to extract local features with geometric information. On the other hand, how to select more important features correctly ...

    Abstract Classification and segmentation of point clouds have attracted increasing attention in recent years. On the one hand, it is difficult to extract local features with geometric information. On the other hand, how to select more important features correctly also brings challenges to the research. Therefore, the main challenge in classifying and segmenting the point clouds is how to locate the attentional region. To tackle this challenge, we propose a graph-based neural network with an attention pooling strategy (AGNet). In particular, local feature information can be extracted by constructing a topological structure. Compared to existing methods, AGNet can better extract the spatial information with different distances, and the attentional pooling strategy is capable of selecting the most important features of the topological structure. Therefore, our model can aggregate more information to better represent different point cloud features. We conducted extensive experiments on challenging benchmark datasets including ModelNet40 for object classification, as well as ShapeNet Part and S3DIS for segmentation. Both the quantitative and qualitative experiments demonstrated a consistent advantage for the tasks of point set classification and segmentation.
    Keywords geometric features ; 3D point clouds ; shape analysis ; neural network ; graph attention mechanism ; Science ; Q
    Subject code 004
    Language English
    Publishing date 2022-02-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Article: AGNet: An Attention-Based Graph Network for Point Cloud Classification and Segmentation

    Jing, Weipeng / Zhang, Wenjun / Li, Linhui / Di, Donglin / Chen, Guangsheng / Wang, Jian

    Remote Sensing. 2022 Feb. 21, v. 14, no. 4

    2022  

    Abstract: Classification and segmentation of point clouds have attracted increasing attention in recent years. On the one hand, it is difficult to extract local features with geometric information. On the other hand, how to select more important features correctly ...

    Abstract Classification and segmentation of point clouds have attracted increasing attention in recent years. On the one hand, it is difficult to extract local features with geometric information. On the other hand, how to select more important features correctly also brings challenges to the research. Therefore, the main challenge in classifying and segmenting the point clouds is how to locate the attentional region. To tackle this challenge, we propose a graph-based neural network with an attention pooling strategy (AGNet). In particular, local feature information can be extracted by constructing a topological structure. Compared to existing methods, AGNet can better extract the spatial information with different distances, and the attentional pooling strategy is capable of selecting the most important features of the topological structure. Therefore, our model can aggregate more information to better represent different point cloud features. We conducted extensive experiments on challenging benchmark datasets including ModelNet40 for object classification, as well as ShapeNet Part and S3DIS for segmentation. Both the quantitative and qualitative experiments demonstrated a consistent advantage for the tasks of point set classification and segmentation.
    Keywords data collection ; geometry ; models ; spatial data ; topology
    Language English
    Dates of publication 2022-0221
    Publishing place Multidisciplinary Digital Publishing Institute
    Document type Article
    ZDB-ID 2513863-7
    ISSN 2072-4292
    ISSN 2072-4292
    DOI 10.3390/rs14041036
    Database NAL-Catalogue (AGRICOLA)

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