Search alternatives:
largest decrease » largest decreases (Expand Search), larger decrease (Expand Search), marked decrease (Expand Search)
linear decrease » linear increase (Expand Search)
latent decrease » latency decreased (Expand Search), content decreased (Expand Search), greatest decrease (Expand Search)
mean decrease » a decrease (Expand Search)
largest decrease » largest decreases (Expand Search), larger decrease (Expand Search), marked decrease (Expand Search)
linear decrease » linear increase (Expand Search)
latent decrease » latency decreased (Expand Search), content decreased (Expand Search), greatest decrease (Expand Search)
mean decrease » a decrease (Expand Search)
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9301
OPJ: Origin data for Fig 4a.
Published 2024“…<div><p>In hot dry regions, photovoltaic modules are exposed to excessive temperatures, which leads to a drop in performance and the risk of overheating. …”
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Figure S1 - Decrease in Formalin-Inactivated Respiratory Syncytial Virus (FI-RSV) Enhanced Disease with RSV G Glycoprotein Peptide Immunization in BALB/c Mice
Published 2013“…<p><b>Decreased pulmonary cell inflammatory response in FI-A2 or FI-B1 and RSV G-CH17 or G-B1 peptide vaccinated mice after RSV challenge.…”
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9309
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9310
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9311
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9314
Knockdown of MUC16 results in decreased tight junction function and ZO-1/occludin expression, whereas knockdown of MUC1 has no effect on tight junctions.
Published 2014“…<p>(A) Immunofluorescence analysis of occludin localization demonstrated normal linear distribution of occludin in the MUC16 scrambled control (scr16) cells (A) as compared to the disrupted localization seen in the shMUC16 cells (B). …”
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9315
Drying Contraction Assessment of Ceramic Products Produced by Extrusion or Pressing Formulated with Sheep Wool Waste
Published 2018“…<div><p>The main aim of this paper was to evaluate ceramic products containing a percentage of ash from sheep wool waste through drying linear shrinkage from a small brickyard in Bagé - RS. …”
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9316
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9317
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9319
Assessment values of machine learning models.
Published 2025“…The prediction results indicate that the StackBoost model excels in predicting aqueous solubility, achieving a coefficient of determination () of 0.90, a root mean square error (RMSE) of 0.29, and a mean absolute error (MAE) of 0.22, significantly outperforming the other comparative models. …”
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9320
List of datasets in AqSolDB.
Published 2025“…The prediction results indicate that the StackBoost model excels in predicting aqueous solubility, achieving a coefficient of determination () of 0.90, a root mean square error (RMSE) of 0.29, and a mean absolute error (MAE) of 0.22, significantly outperforming the other comparative models. …”