Showing 31,741 - 31,760 results of 103,867 for search '(( 5 ((nn decrease) OR (a decrease)) ) OR ( e ((fold decrease) OR (point decrease)) ))', query time: 1.70s Refine Results
  1. 31741

    Drill image dataset for training part II. 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. …”
  2. 31742

    U<sup>2</sup>-Net network structure diagram [8]. 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. …”
  3. 31743

    RSU-7 structure diagram [8]. 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. …”
  4. 31744

    Drill image dataset for training part I. 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. …”
  5. 31745

    Image_1.PDF by Wioletta Lisicka (4988342)

    Published 2018
    “…However, little is known about oxygen-dependent gene expression in Dickeya spp. that might contribute to its success as a pathogen. Using a Tn5 transposon, harboring a promoterless gusA reporter gene, 146 mutants of D. solani IPO2222 were identified that exhibited oxygen-regulated expression of the gene into which the insertion had occurred. …”
  6. 31746

    Image_2.PDF by Wioletta Lisicka (4988342)

    Published 2018
    “…However, little is known about oxygen-dependent gene expression in Dickeya spp. that might contribute to its success as a pathogen. Using a Tn5 transposon, harboring a promoterless gusA reporter gene, 146 mutants of D. solani IPO2222 were identified that exhibited oxygen-regulated expression of the gene into which the insertion had occurred. …”
  7. 31747

    Sensitivity of the long-term reduction in microfilarial load in individuals under 20 years of age to the assumed rate of decay of vaccine efficacy. by Hugo C. Turner (407442)

    Published 2015
    “…<p>The mean duration of vaccine prophylactic (against incoming L3 larvae) and therapeutic (against microfilariae) activity is 1/the rate of decay (i.e. 5, 10, 20 and 50 years). We illustrate with a pre-control endemicity of 40% microfilarial prevalence. …”
  8. 31748
  9. 31749

    Image_2_m6A RNA Methylation Regulators Contribute to Eutopic Endometrium and Myometrium Dysfunction in Adenomyosis.TIF by Junyu Zhai (819072)

    Published 2020
    “…Functional, co-expression, and correlational analyses of endometrium from cases versus controls revealed decreased total m<sup>6</sup>A levels, induced by METTL3, and the downstream elevated insulin−like growth factor−1(IGF1) and D-Dopachrome Tautomerase (DDT), with the latter two having known functions in epithelial proliferation and cell migration, which are important processes in the pathogenesis of adenomyosis in endometrium. m<sup>6</sup>A RNA methylation regulators, including RBM15/15B, ALKBH5, FTO, YTHDF1/2, KIAA1429, HNRNPC, METTL3, ZC3H13, and YTHDC2, were also differentially expressed in the myometrium from cases versus controls. …”
  10. 31750

    Table_1_m6A RNA Methylation Regulators Contribute to Eutopic Endometrium and Myometrium Dysfunction in Adenomyosis.docx by Junyu Zhai (819072)

    Published 2020
    “…Functional, co-expression, and correlational analyses of endometrium from cases versus controls revealed decreased total m<sup>6</sup>A levels, induced by METTL3, and the downstream elevated insulin−like growth factor−1(IGF1) and D-Dopachrome Tautomerase (DDT), with the latter two having known functions in epithelial proliferation and cell migration, which are important processes in the pathogenesis of adenomyosis in endometrium. m<sup>6</sup>A RNA methylation regulators, including RBM15/15B, ALKBH5, FTO, YTHDF1/2, KIAA1429, HNRNPC, METTL3, ZC3H13, and YTHDC2, were also differentially expressed in the myometrium from cases versus controls. …”
  11. 31751

    Image_3_m6A RNA Methylation Regulators Contribute to Eutopic Endometrium and Myometrium Dysfunction in Adenomyosis.TIF by Junyu Zhai (819072)

    Published 2020
    “…Functional, co-expression, and correlational analyses of endometrium from cases versus controls revealed decreased total m<sup>6</sup>A levels, induced by METTL3, and the downstream elevated insulin−like growth factor−1(IGF1) and D-Dopachrome Tautomerase (DDT), with the latter two having known functions in epithelial proliferation and cell migration, which are important processes in the pathogenesis of adenomyosis in endometrium. m<sup>6</sup>A RNA methylation regulators, including RBM15/15B, ALKBH5, FTO, YTHDF1/2, KIAA1429, HNRNPC, METTL3, ZC3H13, and YTHDC2, were also differentially expressed in the myometrium from cases versus controls. …”
  12. 31752

    Image_1_m6A RNA Methylation Regulators Contribute to Eutopic Endometrium and Myometrium Dysfunction in Adenomyosis.TIF by Junyu Zhai (819072)

    Published 2020
    “…Functional, co-expression, and correlational analyses of endometrium from cases versus controls revealed decreased total m<sup>6</sup>A levels, induced by METTL3, and the downstream elevated insulin−like growth factor−1(IGF1) and D-Dopachrome Tautomerase (DDT), with the latter two having known functions in epithelial proliferation and cell migration, which are important processes in the pathogenesis of adenomyosis in endometrium. m<sup>6</sup>A RNA methylation regulators, including RBM15/15B, ALKBH5, FTO, YTHDF1/2, KIAA1429, HNRNPC, METTL3, ZC3H13, and YTHDC2, were also differentially expressed in the myometrium from cases versus controls. …”
  13. 31753

    Spatial patterns of species richness and nestedness in ant assemblages along an elevational gradient in a Mediterranean mountain range by Omar Flores (6102749)

    Published 2018
    “…Ant assemblages were nested; therefore species assemblages with a decreased number of species were a subset of the richer assemblages, although species turnover was more important than pure nestedness in all surveys. …”
  14. 31754
  15. 31755
  16. 31756
  17. 31757
  18. 31758

    X-ray Single-Crystal Structure and Magnetic Properties of Fe[CH<sub>3</sub>PO<sub>3</sub>)]·H<sub>2</sub>O:  A Layered Weak Ferromagnet by Carlo Bellitto (2069203)

    Published 2002
    “…Crystal data for Fe[CH<sub>3</sub>PO<sub>3</sub>]·H<sub>2</sub>O are the following:  orthorhombic space group <i>Pna</i>2<sub>1</sub>; <i>a =</i>17.538(2), <i>b</i> = 4.814(1), <i>c</i> = 5.719(1) Å. …”
  19. 31759

    DataSheet1_The Variants at APOA1 and APOA4 Contribute to the Susceptibility of Schizophrenia With Inhibiting mRNA Expression in Peripheral Blood Leukocytes.pdf by Yao Fan (1977274)

    Published 2021
    “…SZ patients during the episode presented lower levels of apoA1, HDL-C, mRNA of APOA1 common variants and transcript variant 4, and APOA4 mRNA than controls (p < 0.01) while SZ patients in remission showed a significantly decreased APOA1 transcript variant 3 expression level and increased APOA4 mRNA expression level (p < 0.01). mRNA expression levels of APOA1 transcript variant 4 significantly increased with the variations of rs5072 in SZ during the episode (p<sub>trend</sub> = 0.017). …”
  20. 31760

    DataSheet1_FFAR1/GPR40 Contributes to the Regulation of Striatal Monoamine Releases and Facilitation of Cocaine-Induced Locomotor Activity in Mice.doc by Yuko Sadamura (11304522)

    Published 2021
    “…Interestingly, local application of a FFAR1 agonist, GW9508, markedly augmented the striatal 5-HT release in FFAR1 wild-type (+/+) mice, whereas topical application of a FFAR1 antagonist, GW1100, significantly reduced the 5-HT release. …”