Showing 221 - 240 results of 9,253 for search '(( 50 ((we decrease) OR (nn decrease)) ) OR ( 50 ((teer decrease) OR (mean decrease)) ))', query time: 0.53s Refine Results
  1. 221

    List of symbols used in this study. by Yonghui Zhang (279832)

    Published 2024
    “…The CSPM, driven by the optimized CSPs, is then evaluated against two independent phenological datasets from Exp. 2 and Exp. 4 described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0302098#pone.0302098.t002" target="_blank">Table 2</a>. Root means square error (RMSE) (mean absolute error (MAE), coefficient of determination (R<sup>2</sup>), and Nash Sutcliffe model efficiency (NSE)) are 15.50 (14.63, 0.96, 0.42), 4.76 (3.92, 0.97, 0.95), 4.69 (3.72, 0.98, 0.95), 3.91 (3.40, 0.99, 0.96) and 12.54 (11.67, 0.95, 0.60), 5.07 (4.61, 0.98, 0.93), 4.97 (4.28, 0.97, 0.94), 4.58 (4.02, 0.98, 0.95) for using one, two, three, and four observed phenological stages in the CSPs estimation. …”
  2. 222

    Data sources for calibration and evaluation. by Yonghui Zhang (279832)

    Published 2024
    “…The CSPM, driven by the optimized CSPs, is then evaluated against two independent phenological datasets from Exp. 2 and Exp. 4 described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0302098#pone.0302098.t002" target="_blank">Table 2</a>. Root means square error (RMSE) (mean absolute error (MAE), coefficient of determination (R<sup>2</sup>), and Nash Sutcliffe model efficiency (NSE)) are 15.50 (14.63, 0.96, 0.42), 4.76 (3.92, 0.97, 0.95), 4.69 (3.72, 0.98, 0.95), 3.91 (3.40, 0.99, 0.96) and 12.54 (11.67, 0.95, 0.60), 5.07 (4.61, 0.98, 0.93), 4.97 (4.28, 0.97, 0.94), 4.58 (4.02, 0.98, 0.95) for using one, two, three, and four observed phenological stages in the CSPs estimation. …”
  3. 223

    Relationship between pattern electroretinogram and optic disc morphology in glaucoma by Soo Ji Jeon (3364214)

    Published 2019
    “…However, in glaucoma patients, mean RNFL thickness was associated with PERG amplitude (<i>P =</i> 0.011 for P50 and 0.002 for N95).…”
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    Location of the study site. by Flora Erkin (16839339)

    Published 2023
    “…With the growth of <i>Tamarix</i> sp. seedlings, the taproot deepened, and the values ranged from 4.5 cm to 108.0 cm; the SRL, SRA, and SLA decreased, and the ranges of the values were 28.92–478.79 cm·g<sup>-1</sup>, 1.07–458.50 cm<sup>2</sup>·g<sup>-1</sup>, and 24.48–50.7 cm<sup>2</sup>·g<sup>-1</sup>; the RDW, RA, and LA increased, the ranges of the values were 0.16–21.34 g, 3.42–328.04 cm<sup>2</sup>, and 2.41–694.45 cm<sup>2</sup>; the more biomass was allocated to the aboveground parts, and the mean R: S ratio was 0.76. …”
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    The decrease or inhibition of Hsp90 induced REST degradation. by Raúl Orozco-Díaz (7067624)

    Published 2019
    “…(D) The level of REST dramatically reduced in differentiated SH-SY5Y cells treated with GA (1 μM) or PU-H71 (50 nM) at 24 h. (E) The REST level decreased by GA more than 50% and (F) PU-H71 more than 80%, respectively. …”
  13. 233

    S1 File - by Chantelle M. de Vet (16535257)

    Published 2024
    “…The student’s RV-GLS consistency was poor during the first 25 measurements, moderate-to good during the second 25 measurements and again poor-to-moderate during the final 50 measurements. Repeated measurements analysis showed a significant decrease in variability of the LV- and RV-GLS score differences between the expert and trainees over time (<i>p</i><sub>adj</sub><0.001), which was not significantly different between trainees. …”
  14. 234

    GLS analysis with 2D-STE software. by Chantelle M. de Vet (16535257)

    Published 2024
    “…The student’s RV-GLS consistency was poor during the first 25 measurements, moderate-to good during the second 25 measurements and again poor-to-moderate during the final 50 measurements. Repeated measurements analysis showed a significant decrease in variability of the LV- and RV-GLS score differences between the expert and trainees over time (<i>p</i><sub>adj</sub><0.001), which was not significantly different between trainees. …”
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    Tendency chart of monthly FVC mean. by Yichuan Zhang (2888345)

    Published 2024
    “…The dynamic changes of monthly FVC at regional scale were described through the dynamic changes in the monthly FVC mean and in the FVC areas at various scales, and the dynamic changes in the monthly FVC were analyzed using the coefficient of variation and curve change trends. …”
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