Showing 14,901 - 14,920 results of 104,446 for search '(( a step decrease ) OR ( 5 ((point decrease) OR (((mean decrease) OR (a decrease)))) ))', query time: 1.74s Refine Results
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  2. 14902

    Data_Sheet_5_P16INK4a Deletion Ameliorates Damage of Intestinal Epithelial Barrier and Microbial Dysbiosis in a Stress-Induced Premature Senescence Model of Bmi-1 Deficiency.docx by Jiawen Zhou (2323381)

    Published 2021
    “…P16<sup>INK4a</sup> deletion could maintain barrier function and microbiota balance in Bmi-1<sup>–/–</sup> mice through strengthening formation of TJ and decreasing macrophages-secreted TNF-α induced by Desulfovibrio entering the intestinal epithelium. …”
  3. 14903

    Data_Sheet_5_P16INK4a Deletion Ameliorates Damage of Intestinal Epithelial Barrier and Microbial Dysbiosis in a Stress-Induced Premature Senescence Model of Bmi-1 Deficiency.docx by Jiawen Zhou (2323381)

    Published 2021
    “…P16<sup>INK4a</sup> deletion could maintain barrier function and microbiota balance in Bmi-1<sup>–/–</sup> mice through strengthening formation of TJ and decreasing macrophages-secreted TNF-α induced by Desulfovibrio entering the intestinal epithelium. …”
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  6. 14906

    Data cleaning and preparation algorithm. by Ayana Ablayeva (22103708)

    Published 2025
    “…</p><p>Results</p><p>Over the decade, age-standardized incidence rates decreased from 5.55 to 5.40 per 100,000, while mortality rates rose from 3.75 to 4.75 per 100,000. …”
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  13. 14913

    Training set data expansion. by Qingjun Yu (1649473)

    Published 2024
    “…Based on the PyTorch deep learning framework, the initial U<sup>2</sup>-Net network weights were set, the learning rate was set to 0.001, the training batch was 4, and the Adam optimizer adaptively adjusted the learning rate during the training process. A dedicated network model for segmenting structural planes was obtained, and the model achieved a maximum F-measure value of 0.749 when the confidence threshold was set to 0.7, with an accuracy rate of up to 0.85 within the range of recall rate greater than 0.5. …”
  14. 14914

    Structural plane recognition effect. by Qingjun Yu (1649473)

    Published 2024
    “…Based on the PyTorch deep learning framework, the initial U<sup>2</sup>-Net network weights were set, the learning rate was set to 0.001, the training batch was 4, and the Adam optimizer adaptively adjusted the learning rate during the training process. A dedicated network model for segmenting structural planes was obtained, and the model achieved a maximum F-measure value of 0.749 when the confidence threshold was set to 0.7, with an accuracy rate of up to 0.85 within the range of recall rate greater than 0.5. …”
  15. 14915

    Structural plane classification. by Qingjun Yu (1649473)

    Published 2024
    “…Based on the PyTorch deep learning framework, the initial U<sup>2</sup>-Net network weights were set, the learning rate was set to 0.001, the training batch was 4, and the Adam optimizer adaptively adjusted the learning rate during the training process. A dedicated network model for segmenting structural planes was obtained, and the model achieved a maximum F-measure value of 0.749 when the confidence threshold was set to 0.7, with an accuracy rate of up to 0.85 within the range of recall rate greater than 0.5. …”
  16. 14916

    Mixup data expansion. by Qingjun Yu (1649473)

    Published 2024
    “…Based on the PyTorch deep learning framework, the initial U<sup>2</sup>-Net network weights were set, the learning rate was set to 0.001, the training batch was 4, and the Adam optimizer adaptively adjusted the learning rate during the training process. A dedicated network model for segmenting structural planes was obtained, and the model achieved a maximum F-measure value of 0.749 when the confidence threshold was set to 0.7, with an accuracy rate of up to 0.85 within the range of recall rate greater than 0.5. …”
  17. 14917

    Marking example. by Qingjun Yu (1649473)

    Published 2024
    “…Based on the PyTorch deep learning framework, the initial U<sup>2</sup>-Net network weights were set, the learning rate was set to 0.001, the training batch was 4, and the Adam optimizer adaptively adjusted the learning rate during the training process. A dedicated network model for segmenting structural planes was obtained, and the model achieved a maximum F-measure value of 0.749 when the confidence threshold was set to 0.7, with an accuracy rate of up to 0.85 within the range of recall rate greater than 0.5. …”
  18. 14918

    Neuroreceptor kinetics in rats repeatedly exposed to quinpirole as a model for OCD by Stijn Servaes (6433580)

    Published 2019
    “…Subsequently, sagittal slides were made of the CP in the right hemisphere and a staining was done with the D2R, mGluR5 and GLT-1 antibody to visualize the corresponding receptor.…”
  19. 14919

    Data_Sheet_1_Determining 5HT7R’s Involvement in Modifying the Antihyperalgesic Effects of Electroacupuncture on Rats With Recurrent Migraine.pdf by Lu Liu (171341)

    Published 2021
    “…<p>Electroacupuncture (EA) is widely used in clinical practice to relieve migraine pain. 5-HT<sub>7</sub> receptor (5-HT<sub>7</sub>R) has been reported to play an excitatory role in neuronal systems and regulate hyperalgesic pain and neurogenic inflammation. 5-HT<sub>7</sub>R could influence phosphorylation of protein kinase A (PKA)- or extracellular signal-regulated kinase<sub>1</sub><sub>/</sub><sub>2</sub> (ERK<sub>1</sub><sub>/</sub><sub>2</sub>)-mediated signaling pathways, which mediate sensitization of nociceptive neurons via interacting with cyclic adenosine monophosphate (cAMP). …”
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