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  1. Book ; Online: An Adaptable IoT Rule Engine Framework for Dataflow Monitoring and Control Strategies

    Chen, Ken

    2023  

    Abstract: The monitoring of data generated by a large number of devices in Internet of Things (IoT) systems is an important and complex issue. Several studies have explored the use of generic rule engine, primarily based on the RETE algorithm, for monitoring the ... ...

    Abstract The monitoring of data generated by a large number of devices in Internet of Things (IoT) systems is an important and complex issue. Several studies have explored the use of generic rule engine, primarily based on the RETE algorithm, for monitoring the flow of device data. In order to solve the performance problem of the RETE algorithm in IoT scenarios, some studies have also proposed improved RETE algorithms. However, implementing modifications to the general rule engine remains challenges in practical applications. The Thingsboard open-source platform introduces an IoT-specific rule engine that does not rely on the RETE algorithm. Its interactive mode attracted attention from developers and researchers. However, the close integration between its rule module and the platform, as well as the difficulty in formulating rules for multiple devices, limits its flexibility. This paper presents an adaptable and user-friendly rule engine framework for monitoring and control IoT device data flows. The framework is easily extensible and allows for the formulation of rules contain multiple devices. We designed a Domain-Specific Language (DSL) for rule description. A prototype system of this framework was implemented to verify the validity of theoretical method. The framework has potential to be adaptable to a wide range of IoT scenarios and is especially effective in where real-time control demands are not as strict.

    Comment: 15 pages,10 figures
    Keywords Computer Science - Software Engineering
    Subject code 629
    Publishing date 2023-10-09
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Book ; Online: Advanced Topics in Neurological Disorders

    Chen, Ken-Shiung

    2012  

    Keywords Neurology & clinical neurophysiology
    Size 1 electronic resource (256 pages)
    Publisher IntechOpen
    Document type Book ; Online
    Note English ; Open Access
    HBZ-ID HT021049115
    ISBN 9789535169024 ; 9535169025
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  3. Article ; Online: Negative findings but positive contributions in cardiovascular research.

    Chen, Ken / Zeng, Chunyu

    Life sciences

    2023  Volume 326, Page(s) 121494

    Abstract: Researchers have always concluded that results that do not support the hypothesis as unimportant, unworthy, or simply not good enough for publication. However, negative findings are essential for the progress of science and its self-correcting nature. We ...

    Abstract Researchers have always concluded that results that do not support the hypothesis as unimportant, unworthy, or simply not good enough for publication. However, negative findings are essential for the progress of science and its self-correcting nature. We also believe in the importance and indispensability of negative results. Therefore, in this review, we discussed the factors contributing to the publication bias of negative results and the problems to assess the factuality and validity of negative results. Moreover, we emphasized the importance of reporting negative results in cardiovascular research, including treatments, and suggest that the negative results could clarify previously controversial topics in the treatment of cardiovascular diseases and prompt the translation of research on precision cardiovascular disease prevention and treatment.
    MeSH term(s) Humans ; Publication Bias ; Cardiovascular Diseases/therapy
    Language English
    Publishing date 2023-03-16
    Publishing country Netherlands
    Document type Review ; Journal Article
    ZDB-ID 3378-9
    ISSN 1879-0631 ; 0024-3205
    ISSN (online) 1879-0631
    ISSN 0024-3205
    DOI 10.1016/j.lfs.2023.121494
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Label-aware distance mitigates temporal and spatial variability for clustering and visualization of single-cell gene expression data.

    Liang, Shaoheng / Dou, Jinzhuang / Iqbal, Ramiz / Chen, Ken

    Communications biology

    2024  Volume 7, Issue 1, Page(s) 326

    Abstract: Clustering and visualization are essential parts of single-cell gene expression data analysis. The Euclidean distance used in most distance-based methods is not optimal. The batch effect, i.e., the variability among samples gathered from different times, ...

    Abstract Clustering and visualization are essential parts of single-cell gene expression data analysis. The Euclidean distance used in most distance-based methods is not optimal. The batch effect, i.e., the variability among samples gathered from different times, tissues, and patients, introduces large between-group distance and obscures the true identities of cells. To solve this problem, we introduce Label-Aware Distance (LAD), a metric using temporal/spatial locality of the batch effect to control for such factors. We validate LAD on simulated data as well as apply it to a mouse retina development dataset and a lung dataset. We also found the utility of our approach in understanding the progression of the Coronavirus Disease 2019 (COVID-19). LAD provides better cell embedding than state-of-the-art batch correction methods on longitudinal datasets. It can be used in distance-based clustering and visualization methods to combine the power of multiple samples to help make biological findings.
    MeSH term(s) Animals ; Mice ; Cluster Analysis ; Gene Expression
    Language English
    Publishing date 2024-03-14
    Publishing country England
    Document type Journal Article ; Validation Study
    ISSN 2399-3642
    ISSN (online) 2399-3642
    DOI 10.1038/s42003-024-05988-y
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Prioritizing genomic variants pathogenicity via DNA, RNA, and protein-level features based on extreme gradient boosting.

    Ding, Maolin / Chen, Ken / Yang, Yuedong / Zhao, Huiying

    Human genetics

    2024  

    Abstract: Genetic diseases are mostly implicated with genetic variants, including missense, synonymous, non-sense, and copy number variants. These different kinds of variants are indicated to affect phenotypes in various ways from previous studies. It remains ... ...

    Abstract Genetic diseases are mostly implicated with genetic variants, including missense, synonymous, non-sense, and copy number variants. These different kinds of variants are indicated to affect phenotypes in various ways from previous studies. It remains essential but challenging to understand the functional consequences of these genetic variants, especially the noncoding ones, due to the lack of corresponding annotations. While many computational methods have been proposed to identify the risk variants. Most of them have only curated DNA-level and protein-level annotations to predict the pathogenicity of the variants, and others have been restricted to missense variants exclusively. In this study, we have curated DNA-, RNA-, and protein-level features to discriminate disease-causing variants in both coding and noncoding regions, where the features of protein sequences and protein structures have been shown essential for analyzing missense variants in coding regions while the features related to RNA-splicing and RBP binding are significant for variants in noncoding regions and synonymous variants in coding regions. Through the integration of these features, we have formulated the Multi-level feature Genomic Variants Predictor (ML-GVP) using the gradient boosting tree. The method has been trained on more than 400,000 variants in the Sherloc-training set from the 6th critical assessment of genome interpretation with superior performance. The method is one of the two best-performing predictors on the blind test in the Sherloc assessment, and is further confirmed by another independent test dataset of de novo variants.
    Language English
    Publishing date 2024-04-04
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 223009-4
    ISSN 1432-1203 ; 0340-6717
    ISSN (online) 1432-1203
    ISSN 0340-6717
    DOI 10.1007/s00439-024-02667-0
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 256: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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  6. Article ; Online: Negative findings but positive contributions in cardiovascular research

    Chen, Ken / Zeng, Chunyu

    Life Sciences. 2023 Aug., v. 326 p.121494-

    2023  

    Abstract: Researchers have always concluded that results that do not support the hypothesis as unimportant, unworthy, or simply not good enough for publication. However, negative findings are essential for the progress of science and its self-correcting nature. We ...

    Abstract Researchers have always concluded that results that do not support the hypothesis as unimportant, unworthy, or simply not good enough for publication. However, negative findings are essential for the progress of science and its self-correcting nature. We also believe in the importance and indispensability of negative results. Therefore, in this review, we discussed the factors contributing to the publication bias of negative results and the problems to assess the factuality and validity of negative results. Moreover, we emphasized the importance of reporting negative results in cardiovascular research, including treatments, and suggest that the negative results could clarify previously controversial topics in the treatment of cardiovascular diseases and prompt the translation of research on precision cardiovascular disease prevention and treatment.
    Keywords cardiovascular diseases ; disease prevention ; publications ; therapeutics ; Negative results ; Cardiovascular ; Clinical researches ; Translational researches
    Language English
    Dates of publication 2023-08
    Publishing place Elsevier Inc.
    Document type Article ; Online
    ZDB-ID 3378-9
    ISSN 1879-0631 ; 0024-3205
    ISSN (online) 1879-0631
    ISSN 0024-3205
    DOI 10.1016/j.lfs.2023.121494
    Database NAL-Catalogue (AGRICOLA)

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    In stock of ZB MED Bonn / Germany

    Z 73/732: Show issues

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  7. Article ; Online: Two-stage orthodontic extraction for impacted third molar deep to the mandible inferior border caused by giant odontoma.

    Huang, Wei-Chen / Wang, Sheng-Hong / Hsiao, Kai-Yuan / Chen, Ken-Chung

    Journal of dental sciences

    2024  Volume 19, Issue 2, Page(s) 1219–1221

    Language English
    Publishing date 2024-02-15
    Publishing country Netherlands
    Document type Journal Article
    ISSN 2213-8862
    ISSN (online) 2213-8862
    DOI 10.1016/j.jds.2024.02.006
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article: Interpretable Spatial Gradient Analysis for Spatial Transcriptomics Data.

    Liang, Qingnan / Soto, Luisa Solis / Haymaker, Cara / Chen, Ken

    bioRxiv : the preprint server for biology

    2024  

    Abstract: Cellular anatomy and signaling vary across niches, which can induce gradated gene expressions in subpopulations of cells. Such spatial transcriptomic gradient (STG) makes a significant source of intratumor heterogeneity and can influence tumor invasion, ... ...

    Abstract Cellular anatomy and signaling vary across niches, which can induce gradated gene expressions in subpopulations of cells. Such spatial transcriptomic gradient (STG) makes a significant source of intratumor heterogeneity and can influence tumor invasion, progression, and response to treatment. Here we report
    Language English
    Publishing date 2024-03-21
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2024.03.19.585725
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: Pathway centric analysis for single-cell RNA-seq and spatial transcriptomics data with GSDensity.

    Liang, Qingnan / Huang, Yuefan / He, Shan / Chen, Ken

    Nature communications

    2023  Volume 14, Issue 1, Page(s) 8416

    Abstract: Advances in single-cell technology have enabled molecular dissection of heterogeneous biospecimens at unprecedented scales and resolutions. Cluster-centric approaches are widely applied in analyzing single-cell data, however they have limited power in ... ...

    Abstract Advances in single-cell technology have enabled molecular dissection of heterogeneous biospecimens at unprecedented scales and resolutions. Cluster-centric approaches are widely applied in analyzing single-cell data, however they have limited power in dissecting and interpreting highly heterogenous, dynamically evolving data. Here, we present GSDensity, a graph-modeling approach that allows users to obtain pathway-centric interpretation and dissection of single-cell and spatial transcriptomics (ST) data without performing clustering. Using pathway gene sets, we show that GSDensity can accurately detect biologically distinct cells and reveal novel cell-pathway associations ignored by existing methods. Moreover, GSDensity, combined with trajectory analysis can identify curated pathways that are active at various stages of mouse brain development. Finally, GSDensity can identify spatially relevant pathways in mouse brains and human tumors including those following high-order organizational patterns in the ST data. Particularly, we create a pan-cancer ST map revealing spatially relevant and recurrently active pathways across six different tumor types.
    MeSH term(s) Humans ; Animals ; Mice ; Single-Cell Gene Expression Analysis ; Gene Expression Profiling ; Cluster Analysis ; Technology ; Single-Cell Analysis ; Transcriptome
    Language English
    Publishing date 2023-12-18
    Publishing country England
    Document type Journal Article
    ZDB-ID 2553671-0
    ISSN 2041-1723 ; 2041-1723
    ISSN (online) 2041-1723
    ISSN 2041-1723
    DOI 10.1038/s41467-023-44206-x
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article: Batch-Corrected Distance Mitigates Temporal and Spatial Variability for Clustering and Visualization of Single-Cell Gene Expression Data.

    Liang, Shaoheng / Dou, Jinzhuang / Iqbal, Ramiz / Chen, Ken

    Research square

    2023  

    Abstract: Clustering and visualization are essential parts of single-cell gene expression data analysis. The Euclidean distance used in most distance-based methods is not optimal. The batch effect, i.e., the variability among samples gathered from different times, ...

    Abstract Clustering and visualization are essential parts of single-cell gene expression data analysis. The Euclidean distance used in most distance-based methods is not optimal. The batch effect, i.e., the variability among samples gathered from different times, tissues, and patients, introduces large between-group distance and obscures the true identities of cells. To solve this problem, we introduce Batch-Corrected Distance (BCD), a metric using temporal/spatial locality of the batch effect to control for such factors. We validate BCD on simulated data as well as applied it to a mouse retina development dataset and a lung dataset. We also found the utility of our approach in understanding the progression of the Coronavirus Disease 2019 (COVID-19). BCD achieves more accurate clusters and better visualizations than state-of-the-art batch correction methods on longitudinal datasets. BCD can be directly integrated with most clustering and visualization methods to enable more scientific findings.
    Language English
    Publishing date 2023-07-26
    Publishing country United States
    Document type Preprint
    DOI 10.21203/rs.3.rs-3134332/v1
    Database MEDical Literature Analysis and Retrieval System OnLINE

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