Showing 11,901 - 11,920 results of 101,694 for search '(( 5 ((mean decrease) OR (a decrease)) ) OR ( 50 ((nn decrease) OR (greater decrease)) ))', query time: 1.38s Refine Results
  1. 11901
  2. 11902

    Tris(dithiolene) Complexes of Neodymium and Cerium:  Mononuclear Species, Chains, and Honeycomb Networks by Mathieu Roger (2459434)

    Published 2005
    “…Crystals of [Na(18c6)(py)<sub>2</sub>]<sub>2</sub>[Na(18c6)(py)][Nd(dddt)<sub>3</sub>(py)]·3py (<b>1</b>·3py) are built up from discrete mononuclear cationic and anionic species whereas crystals of {[Na(18c6)(py)<sub>2</sub>]<sub>0.5</sub>[Na(18c6)(py)<sub>1.5</sub>][Na<sub>1.5</sub>Nd(dddt)<sub>3</sub>]}<sub>∞</sub> (<b>2</b>) are composed of discrete [Na(18c6)(py)<i><sub>x</sub></i>]<sup>+</sup> cations and polymeric anionic two-dimensional layers in which the Nd(dddt)<sub>3</sub> units are linked to three neighbors by sodium atoms to form a honeycomb network. …”
  3. 11903

    Mean scores for veterinarian responses describing the impact of VFD. by Mary Ellen Dillon (11250468)

    Published 2021
    “…<p>Likert scale responses (n = 8) in which 1 = Decrease a lot, 2 = Decrease a little, 3 = No Change, 4 = Increase a little, 5 = Increase a lot. …”
  4. 11904
  5. 11905
  6. 11906
  7. 11907
  8. 11908

    Adenosine decreases the number of releasable vesicles and release probability without changing the rate of recovery from vesicle depletion. by Shouping Wang (402959)

    Published 2013
    “…The amplitude of EPSC evoked by each stimulus was measured by resetting the base line each time at a point within 0.5 ms before the beginning of each stimulation artifact. …”
  9. 11909
  10. 11910
  11. 11911
  12. 11912
  13. 11913

    VEGF and sFlt-1 mutants exhibit changes in stem cell marker gene expression by RT-qPCR. by Christopher R. Schlieve (3179439)

    Published 2016
    “…<p>(A) VEGF mutants demonstrated 0.55-fold reduction in Lgr5 expression compared to littermates (*p = 0.04). …”
  14. 11914

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

    Published 2023
    “…Here, we proposed the concept of “nonbioavailable substructures”, referring to substructures that are unfavorable to bioavailability. A machine learning model was developed to identify nonbioavailable substructures based on their molecular properties and shows the accuracy of 83.5%. …”
  15. 11915

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

    Published 2023
    “…Here, we proposed the concept of “nonbioavailable substructures”, referring to substructures that are unfavorable to bioavailability. A machine learning model was developed to identify nonbioavailable substructures based on their molecular properties and shows the accuracy of 83.5%. …”
  16. 11916

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

    Published 2023
    “…Here, we proposed the concept of “nonbioavailable substructures”, referring to substructures that are unfavorable to bioavailability. A machine learning model was developed to identify nonbioavailable substructures based on their molecular properties and shows the accuracy of 83.5%. …”
  17. 11917

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

    Published 2023
    “…Here, we proposed the concept of “nonbioavailable substructures”, referring to substructures that are unfavorable to bioavailability. A machine learning model was developed to identify nonbioavailable substructures based on their molecular properties and shows the accuracy of 83.5%. …”
  18. 11918

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

    Published 2023
    “…Here, we proposed the concept of “nonbioavailable substructures”, referring to substructures that are unfavorable to bioavailability. A machine learning model was developed to identify nonbioavailable substructures based on their molecular properties and shows the accuracy of 83.5%. …”
  19. 11919

    Video_2_Deficiency of TRIM32 Impairs Motor Function and Purkinje Cells in Mid-Aged Mice.AVI by Jian-Wei Zhu (285977)

    Published 2021
    “…In addition, deficiency of TRIM32 decreased Type I inositol 1,4,5-trisphosphate 5-phosphatase (INPP5A) levels in cerebellum. …”
  20. 11920

    Video_3_Deficiency of TRIM32 Impairs Motor Function and Purkinje Cells in Mid-Aged Mice.AVI by Jian-Wei Zhu (285977)

    Published 2021
    “…In addition, deficiency of TRIM32 decreased Type I inositol 1,4,5-trisphosphate 5-phosphatase (INPP5A) levels in cerebellum. …”