Showing 981 - 1,000 results of 100,755 for search '(( 5 ((ppm decrease) OR (a decrease)) ) OR ( 5 ((we decrease) OR (nn decrease)) ))', query time: 1.46s Refine Results
  1. 981

    NbVO<sub>5</sub> Mesoporous Thin Films by Evaporation Induced Micelles Packing: Pore Size Dependence of the Mechanical Stability upon Thermal Treatment and Li Insertion/Extraction by Natacha Krins (2066203)

    Published 2011
    “…In order to investigate the potentialities and limits of the soft-templating approach in the case of complex transition metal oxide networks, we deliberately selected a “difficult” compound: NbVO<sub>5</sub> was chosen because it combines a challenging synthesis with reported severe structural distortions during the first lithium insertion in the bulk material. …”
  2. 982
  3. 983

    Loss of epithelial markers is an early event in oral dysplasia and is observed within the safety margin of dysplastic and T1 OSCC biopsies by Zahra Abdalla (4669927)

    Published 2017
    “…<div><p>Oral squamous cell carcinoma (OSCC) is a highly aggressive cancer that is associated with poor 5-year patient survival. …”
  4. 984
  5. 985

    Image_1_TRAPS mutations in Tnfrsf1a decrease the responsiveness to TNFα via reduced cell surface expression of TNFR1.tif by Takahiko Akagi (13138338)

    Published 2022
    “…T79M is a known mutation responsible for TRAPS, whereas G87V is a TRAPS mutation that we have reported, and T90I is a variant of unknown significance. …”
  6. 986

    Image_4_TRAPS mutations in Tnfrsf1a decrease the responsiveness to TNFα via reduced cell surface expression of TNFR1.tif by Takahiko Akagi (13138338)

    Published 2022
    “…T79M is a known mutation responsible for TRAPS, whereas G87V is a TRAPS mutation that we have reported, and T90I is a variant of unknown significance. …”
  7. 987

    Image_7_TRAPS mutations in Tnfrsf1a decrease the responsiveness to TNFα via reduced cell surface expression of TNFR1.tif by Takahiko Akagi (13138338)

    Published 2022
    “…T79M is a known mutation responsible for TRAPS, whereas G87V is a TRAPS mutation that we have reported, and T90I is a variant of unknown significance. …”
  8. 988

    Image_3_TRAPS mutations in Tnfrsf1a decrease the responsiveness to TNFα via reduced cell surface expression of TNFR1.tif by Takahiko Akagi (13138338)

    Published 2022
    “…T79M is a known mutation responsible for TRAPS, whereas G87V is a TRAPS mutation that we have reported, and T90I is a variant of unknown significance. …”
  9. 989

    Image_6_TRAPS mutations in Tnfrsf1a decrease the responsiveness to TNFα via reduced cell surface expression of TNFR1.tif by Takahiko Akagi (13138338)

    Published 2022
    “…T79M is a known mutation responsible for TRAPS, whereas G87V is a TRAPS mutation that we have reported, and T90I is a variant of unknown significance. …”
  10. 990

    Image_2_TRAPS mutations in Tnfrsf1a decrease the responsiveness to TNFα via reduced cell surface expression of TNFR1.tif by Takahiko Akagi (13138338)

    Published 2022
    “…T79M is a known mutation responsible for TRAPS, whereas G87V is a TRAPS mutation that we have reported, and T90I is a variant of unknown significance. …”
  11. 991
  12. 992

    Deciphering Nonbioavailable Substructures Improves the Bioavailability of Antidepressants by Serotonin Transporter by Zhi-Zheng Wang (6056033)

    Published 2023
    “…A machine learning model was developed to identify nonbioavailable substructures based on their molecular properties and shows the accuracy of 83.5%. …”
  13. 993

    Deciphering Nonbioavailable Substructures Improves the Bioavailability of Antidepressants by Serotonin Transporter by Zhi-Zheng Wang (6056033)

    Published 2023
    “…A machine learning model was developed to identify nonbioavailable substructures based on their molecular properties and shows the accuracy of 83.5%. …”
  14. 994

    Deciphering Nonbioavailable Substructures Improves the Bioavailability of Antidepressants by Serotonin Transporter by Zhi-Zheng Wang (6056033)

    Published 2023
    “…A machine learning model was developed to identify nonbioavailable substructures based on their molecular properties and shows the accuracy of 83.5%. …”
  15. 995

    Deciphering Nonbioavailable Substructures Improves the Bioavailability of Antidepressants by Serotonin Transporter by Zhi-Zheng Wang (6056033)

    Published 2023
    “…A machine learning model was developed to identify nonbioavailable substructures based on their molecular properties and shows the accuracy of 83.5%. …”
  16. 996

    Deciphering Nonbioavailable Substructures Improves the Bioavailability of Antidepressants by Serotonin Transporter by Zhi-Zheng Wang (6056033)

    Published 2023
    “…A machine learning model was developed to identify nonbioavailable substructures based on their molecular properties and shows the accuracy of 83.5%. …”
  17. 997
  18. 998

    Decreased MCM2-6 in Drosophila S2 Cells Does Not Generate Significant DNA Damage or Cause a Marked Increase in Sensitivity to Replication Interference by Isabelle Crevel (119003)

    Published 2011
    “…<div><p>A reduction in the level of some MCM proteins in human cancer cells (MCM5 in U20S cells or MCM3 in Hela cells) causes a rapid increase in the level of DNA damage under normal conditions of cell proliferation and a loss of viability when the cells are subjected to replication interference. …”
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