Showing 121 - 140 results of 73,468 for search '(((( via large decrease ) OR ( ((b large) OR (a large)) decrease ))) OR ( i rate increased ))', query time: 2.03s Refine Results
  1. 121

    Change in smooth muscle NO from a dynamic increase in flow. by William Davis Haselden (9175156)

    Published 2020
    “…C) Plot of the NO concentration in the smooth muscle at the midpoint of the vessel (200μm from the end) versus time. The increase in velocity resulted in a decrease in GC activity by ~0.03%. which would correspond to a 0.1% constriction for an arteriole with sensitivity of m = 3 .…”
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    In superadditive networks, more enhancers decrease noise and fidelity. by Alvaro Fletcher (15675430)

    Published 2023
    “…Meanwhile <i>d</i><sub>1</sub>, the rate of increase for <i>k</i><sub>on</sub>, was chosen to be 0.01 for both <i>T</i><sub>1</sub> and <i>T</i><sub>2</sub> (B) Unlike in the subadditive case, enhancer numbers decrease transcriptional fidelity and also decrease noise. …”
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    S1 Text - by Steven L. Yu (18150306)

    Published 2024
    “…</p><p>Results</p><p>With increasing DB size, recovery of our reliable prophage GI calls reached a plateau, while recovery of less reliable GI calls (FPs) increased rapidly as DB sizes exceeded ~500 genomes; i.e., overlarge DBs can increase FP rates. …”
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    Image_1_The gut microbiota correlate with the disease characteristics and immune status of patients with untreated diffuse large B-cell lymphoma.jpeg by Zhouning Lin (14648597)

    Published 2023
    “…Background<p>Gut microbiota characteristics in patients with diffuse large B-cell lymphoma (DLBCL) are reportedly different when compared with the healthy population and it remains unclear if the gut microbiota affects host immunity and clinical disease features. …”
  15. 135

    Table_1_Mutational Profile and Clonal Evolution of Relapsed/Refractory Diffuse Large B-Cell Lymphoma.xlsx by Boram Lee (549836)

    Published 2021
    “…The genes most frequently altered (> 20%) were (in decreasing order of frequency) CDKN2A, PIM1, CD79B, TP53, MYD88, MYC, BTG2, BTG1, CDKN2B, DTX1, CD58, ETV6, and IRF4. …”
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