Search alternatives:
largest decrease » largest decreases (Expand Search), marked decrease (Expand Search)
larger decrease » marked decrease (Expand Search)
large decrease » marked decrease (Expand Search), large degree (Expand Search)
largest decrease » largest decreases (Expand Search), marked decrease (Expand Search)
larger decrease » marked decrease (Expand Search)
large decrease » marked decrease (Expand Search), large degree (Expand Search)
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Supplementary Material for: Effects of White Matter Hyperintensities on 90-Day Functional Outcome after Large Vessel and Non-Large Vessel Stroke
Published 2020“…<b><i>Material and Methods:</i></b> We reviewed acute ischemic stroke patients admitted between 2009 and 2017 at a large healthcare system in the USA. …”
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Data_Sheet_1_Phenotypic Clumping Decreases With Flock Richness in Mixed-Species Bird Flocks.csv
Published 2021“…We examined the relationship between phenotypic clumping and flock richness using four variables—body size, foraging behavior, foraging height and taxonomic relatedness. Using a null model approach, we found that small flocks were more phenotypically clumped for body size than expected by chance; however, phenotypic clumping decreased as flocks increased in size and approached expected phenotypic variation in large flocks. …”
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Data_Sheet_1_Decreased modular segregation of the frontal–parietal network in major depressive disorder.docx
Published 2022“…Fifty-one MDD patients and forty-three matched healthy controls (HC) were recruited in the present study. …”
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Decreased CNPase levels in the SG/RC in <i>Large</i><sup><i>myd/myd</i></sup> <i>mice</i>.
Published 2020“…<p>Lysates were obtained from the spiral ganglion (SG)/Rosenthal’s canal (RC) of the P5-7 control and <i>Large</i><sup><i>myd/myd</i></sup> mice. CNPase immunoblotting showed decreased levels of CNPase in the <i>Large</i><sup><i>myd/myd</i></sup> mice. …”
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S1 Dataset -
Published 2024“…While WBC classification was originally rooted in conventional ML, there has been a notable shift toward the use of DL, and particularly convolutional neural networks (CNN), with 54.4% of identified studies (n = 74) including the use of CNNs, and particularly in concurrence with larger datasets and bespoke features e.g., parallel data pre-processing, feature selection, and extraction. …”
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