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greater decrease » greater increase (Expand Search), greater increases (Expand Search), rate decreased (Expand Search)
step decrease » sizes decrease (Expand Search), teer decrease (Expand Search), we decrease (Expand Search)
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a decrease » _ decrease (Expand Search), _ decreased (Expand Search), _ decreases (Expand Search)
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Showing 81 - 100 results of 100,526 for search '(( 5 ((((greater decrease) OR (a decrease))) OR (nn decrease)) ) OR ( 12 step decrease ))', query time: 1.33s Refine Results
  1. 81
    Effect of noradrenaline (NA) alone (A) and NA with α1A- (B), α1B- (C), α1D- (D) and α1A,B,D (E)–adrenoreceptors (ARs) antagonists (a dose of 10<sup>−4</sup> M) on the frequency of contractions in pig myometrium strips obtained from the CON (grey bars), SAL (hatched bars), and <i>E</i>. <i>coli</i> (black bars) groups.

    Effect of noradrenaline (NA) alone (A) and NA with α1A- (B), α1B- (C), α1D- (D) and α1A,B,D (E)–adrenoreceptors (ARs) antagonists (a dose of 10<sup>−4</sup> M) on the frequency of... by Barbara Jana (9110179)

    Published 2023
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  2. 82
    Effect of noradrenaline (NA) alone (A) and NA with α1A- (B), α1B- (C), α1D- (D) and α1A,B,D (E)–adrenoreceptors (ARs) antagonists (a dose of 10<sup>−4</sup> M) on the amplitude of contractions in pig endometrium/myometrium strips obtained from the CON (grey bars), SAL (hatched bars), and <i>E</i>. <i>coli</i> (black bars) groups.

    Effect of noradrenaline (NA) alone (A) and NA with α1A- (B), α1B- (C), α1D- (D) and α1A,B,D (E)–adrenoreceptors (ARs) antagonists (a dose of 10<sup>−4</sup> M) on the amplitude of... by Barbara Jana (9110179)

    Published 2023
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  3. 83
    Adjusted mobility change following the second restriction by neighborhood-level socioeconomic measures in the Greater Toronto Area.

    Adjusted mobility change following the second restriction by neighborhood-level socioeconomic measures in the Greater Toronto Area. by Siyi Wang (1412581)

    Published 2024
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  4. 84
    Fig 5 -

    Fig 5 - by Tasmin L. Rymer (12135918)

    Published 2023
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  5. 85
    The GmNN1/FT2a-GmNFYA-C-GmENOD40 module regulates soybean nodulation.

    The GmNN1/FT2a-GmNFYA-C-GmENOD40 module regulates soybean nodulation. by Xinxin Li (281237)

    Published 2022
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  6. 86
    Blocking C5L2 decreased MPO concentration in the supernatant of C5a-primed neutrophils.

    Blocking C5L2 decreased MPO concentration in the supernatant of C5a-primed neutrophils. by Jian Hao (160736)

    Published 2013
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  7. 87
    IFN-λ decreases miR-122 transcription in HCV cells.

    IFN-λ decreases miR-122 transcription in HCV cells. by Fatma Aboulnasr (736868)

    Published 2015
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  8. 88
    Microscopic Movement of Slow-Diffusing Nanoparticles in Cylindrical Nanopores Studied with Three-Dimensional Tracking

    Microscopic Movement of Slow-Diffusing Nanoparticles in Cylindrical Nanopores Studied with Three-Dimensional Tracking by Luyang Zhao (1903252)

    Published 2016
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  9. 89
    Microscopic Movement of Slow-Diffusing Nanoparticles in Cylindrical Nanopores Studied with Three-Dimensional Tracking

    Microscopic Movement of Slow-Diffusing Nanoparticles in Cylindrical Nanopores Studied with Three-Dimensional Tracking by Luyang Zhao (1903252)

    Published 2016
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  10. 90
    Microscopic Movement of Slow-Diffusing Nanoparticles in Cylindrical Nanopores Studied with Three-Dimensional Tracking

    Microscopic Movement of Slow-Diffusing Nanoparticles in Cylindrical Nanopores Studied with Three-Dimensional Tracking by Luyang Zhao (1903252)

    Published 2016
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  11. 91
    Microscopic Movement of Slow-Diffusing Nanoparticles in Cylindrical Nanopores Studied with Three-Dimensional Tracking

    Microscopic Movement of Slow-Diffusing Nanoparticles in Cylindrical Nanopores Studied with Three-Dimensional Tracking by Luyang Zhao (1903252)

    Published 2016
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  12. 92
    Microscopic Movement of Slow-Diffusing Nanoparticles in Cylindrical Nanopores Studied with Three-Dimensional Tracking

    Microscopic Movement of Slow-Diffusing Nanoparticles in Cylindrical Nanopores Studied with Three-Dimensional Tracking by Luyang Zhao (1903252)

    Published 2016
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  13. 93
    Microscopic Movement of Slow-Diffusing Nanoparticles in Cylindrical Nanopores Studied with Three-Dimensional Tracking

    Microscopic Movement of Slow-Diffusing Nanoparticles in Cylindrical Nanopores Studied with Three-Dimensional Tracking by Luyang Zhao (1903252)

    Published 2016
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  14. 94
    Microscopic Movement of Slow-Diffusing Nanoparticles in Cylindrical Nanopores Studied with Three-Dimensional Tracking

    Microscopic Movement of Slow-Diffusing Nanoparticles in Cylindrical Nanopores Studied with Three-Dimensional Tracking by Luyang Zhao (1903252)

    Published 2016
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  15. 95
    Microscopic Movement of Slow-Diffusing Nanoparticles in Cylindrical Nanopores Studied with Three-Dimensional Tracking

    Microscopic Movement of Slow-Diffusing Nanoparticles in Cylindrical Nanopores Studied with Three-Dimensional Tracking by Luyang Zhao (1903252)

    Published 2016
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  16. 96
    rhMG53 decreases membrane damage <i>in vitro</i>.

    rhMG53 decreases membrane damage <i>in vitro</i>. by Brian J. Paleo (8694246)

    Published 2020
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  17. 97
    Blocking C5L2 decreased C5a-primed neutrophils for ANCA-induced degranulation.

    Blocking C5L2 decreased C5a-primed neutrophils for ANCA-induced degranulation. by Jian Hao (160736)

    Published 2013
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  18. 98
    Nrf2 activation decreases PMN ROS production in response to zymosan.

    Nrf2 activation decreases PMN ROS production in response to zymosan. by Doumet Georges Helou (7255823)

    Published 2019
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  19. 99
    Predictions of random forests (RF), gradient boosting machines (GBM), and neural networks (NN) trained on 12 covariates chosen using subjective feature selection.

    Predictions of random forests (RF), gradient boosting machines (GBM), and neural networks (NN) trained on 12 covariates chosen using subjective feature selection. by Dennis Ochola (11626912)

    Published 2022
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  20. 100
    Predictions of random forests (RF), gradient boosting machines (GBM), and neural networks (NN) trained on 17 covariates selected after recursive feature selection.

    Predictions of random forests (RF), gradient boosting machines (GBM), and neural networks (NN) trained on 17 covariates selected after recursive feature selection. by Dennis Ochola (11626912)

    Published 2022
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