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  • 1
    Online Resource
    Online Resource
    CX3CL1/CX3CR1-Mediated Microglia Activation Plays a Detrimental Role in Ischemic Mice Brain via p38MAPK/PKC Pathway
    SAGE Publications ; 2015
    In:  Journal of Cerebral Blood Flow & Metabolism Vol. 35, No. 10 ( 2015-10), p. 1623-1631
    Details
    In: Journal of Cerebral Blood Flow & Metabolism, SAGE Publications, Vol. 35, No. 10 ( 2015-10), p. 1623-1631
    Abstract: The exact roles of activated microglia and fractalkine (CX3CL1)/fractalkine receptor (CX3CR1) signaling are not fully understood in brain ischemic injury and the findings reported are controversial. Here, we investigated the effects of CX3CR1 siRNA on the expression of CX3CR1, p38 mitogen-activated protein kinase (p38MAPK), Protein Kinase C (PKC) and inflammatory cytokines, microglia activation, white matter lesions, and cognitive function in mice treated with bilateral common carotid artery stenosis (BCAS) in vivo as well as effects of exogenous CX3CL1, CX3CR1 siRNA, and SB2035080 on expression of inflammatory cytokines in BV2 microglia treated with oxygen–glucose deprivation (OGD) in vitro. We showed that CX3CR1 siRNA significantly inhibited the increased expression of CX3CR1, p38MAPK, PKC as well as tumor necrosis factor-α (TNF-α), interleukin (IL)-1β, and IL-6, and also attenuated microglia activation, white matter lesions, and cognitive deficits induced by BCAS in mice brain. We also showed that exogenous CX3CL1 could induce a further enhancement in TNF-α and IL-1β expression, which could be suppressed by CX3CR1 siRNA or by the p38MAPK inhibitor in OGD-treated BV2 microglial cells in vitro. Our findings indicated that CX3CL1/CX3CR1-mediated microglial activation plays a detrimental role in ischemic brain via p38MAPK/PKC signaling and also suggested that CX3CL1/CX3CR1 axis might be a putative therapeutic target to disrupt the cascade of deleterious events that lead to brain ischemic injury.
    Type of Medium: Online Resource
    ISSN: 0271-678X , 1559-7016
    URL: Article
    DOI: 10.1038/jcbfm.2015.97
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2015
    detail.hit.zdb_id: 2039456-1
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  • 2
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    Online Resource
    Prediction of yarn unevenness based on BMNN
    SAGE Publications ; 2021
    In:  Journal of Engineered Fibers and Fabrics Vol. 16 ( 2021-01), p. 155892502110379-
    Details
    In: Journal of Engineered Fibers and Fabrics, SAGE Publications, Vol. 16 ( 2021-01), p. 155892502110379-
    Abstract: With the continuous development of deep learning, due to the complexity of the deep neural network structure and the limitation of training time, some scholars have proposed broad learning, the Broad Learning System (BLS). However, BLS currently only verifies that it has excellent effects on some of the network training data sets, and it does not necessarily have excellent effects on some actual data sets. In response to this, this paper uses the effect of BLS in predicting the unevenness of yarn quality in the yarn data set, and proposes a BLS-based multi-layer neural network (MNN) for the problems, which is called Broad Multilayer Neural Network (BMNN).
    Type of Medium: Online Resource
    ISSN: 1558-9250 , 1558-9250
    URL: Article
    DOI: 10.1177/15589250211037978
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2021
    detail.hit.zdb_id: 2393988-6
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  • 3
    Online Resource
    Online Resource
    Beyond Field Effect: Analysis of Shrunken Centroids in Normal Esophageal Epithelia Detects Concomitant Esophageal Adenocarcinoma
    SAGE Publications ; 2007
    In:  Bioinformatics and Biology Insights Vol. 1 ( 2007-01), p. BBI.S311-
    Details
    In: Bioinformatics and Biology Insights, SAGE Publications, Vol. 1 ( 2007-01), p. BBI.S311-
    Abstract: Because of the extremely low neoplastic progression rate in Barrett's esophagus, it is difficult to diagnose patients with concomitant adenocarcinoma early in their disease course. If biomarkers existed in normal squamous esophageal epithelium to identify patients with concomitant esophageal adenocarcinoma, potential applications would be far-reaching. The aim of the current study was to identify global gene expression patterns in normal esophageal epithelium capable of revealing simultaneous esophageal adenocarcinoma, even located remotely in the esophagus. Methods Tissues comprised normal esophageal epithelia from 9 patients with esophageal adenocarcinoma, 8 patients lacking esophageal adenocarcinoma or Barrett's, and 6 patients with Barrett's esophagus alone. cDNA microarrays were performed, and pattern recognition in each of these subgroups was achieved using shrunken nearest centroid predictors. Results Our method accurately discriminated normal esophageal epithelia of 8/8 patients without esophageal adenocarcinoma or Barrett's esophagus and of 6/6 patients with Barrett's esophagus alone from normal esophageal epithelia of 9/9 patients with Barrett's esophagus and concomitant esophageal adenocarcinoma. Moreover, we identified genes differentially expressed between the above subgroups. Thus, based on their corresponding normal esophageal epithelia alone, our method accurately diagnosed patients who had concomitant esophageal adenocarcinoma. Conclusions These global gene expression patterns, along with individual genes culled from them, represent potential biomarkers for the early diagnosis of esophageal adenocarcinoma from normal esophageal epithelia. Genes discovered in normal esophagus that are differentially expressed in patients with vs. without esophageal adenocarcinoma merit further pursuit in molecular genetic, functional, and therapeutic interventional studies.
    Type of Medium: Online Resource
    ISSN: 1177-9322 , 1177-9322
    URL: Article
    DOI: 10.4137/BBI.S311
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2007
    detail.hit.zdb_id: 2423808-9
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  • 4
    Online Resource
    Online Resource
    Prediction of yarn strength based on an expert weighted neural network optimized by particle swarm optimization
    SAGE Publications ; 2021
    In:  Textile Research Journal Vol. 91, No. 23-24 ( 2021-12), p. 2911-2924
    Details
    In: Textile Research Journal, SAGE Publications, Vol. 91, No. 23-24 ( 2021-12), p. 2911-2924
    Abstract: Aiming at solving the problem that existing artificial neural networks (ANNs) still have low accuracy in predicting yarn strength, this study combines traditional expert experience and an ANN to propose a hybrid network, named the expert weighted neural network. Many studies have shown that it is reliable to predict yarn strength based on ANN technology. However, most ANN training models face with problems of low accuracy and easy trapping into their local minima. The strength prediction of traditional yarns relies on expert experience. Obvious expert experience can help the model perform preliminary learning and help the algorithm model achieve higher accuracy. Therefore, this study proposes a neural network model that combines expert weights and particle swarm optimization (PSO). The model uses PSO to optimize the weights of experts and investigates its effectiveness in yarn strength prediction.
    Type of Medium: Online Resource
    ISSN: 0040-5175 , 1746-7748
    URL: Article
    DOI: 10.1177/00405175211022619
    RVK:
    ZS 0001
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2021
    detail.hit.zdb_id: 2209596-2
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