Does the presence of exotic species decrease the initial development of native species in Brazilian seasonally dry tropical forests? by Carlos Leandro Costa Silva (12741450)

Colistin-phage combinations decrease antibiotic resistance in <i>Acinetobacter baumannii</i> via changes in envelope architecture by Xiaoqing Wang (514179)

SDG decreases iron-induced oxidative stress in H9c2 cells. by Stephanie Puukila (568109)

NbVO₅ 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)

“…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: NbVO₅ was chosen because it combines a challenging synthesis with reported severe structural distortions during the first lithium insertion in the bulk material. …”

Interpretation of bidirectional MR. by Laurence J. Howe (5579186)

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)

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

Inhibition of PKC or Rho protein kinase decreases Ad5 wild type replication <i>in vitro</i>. by Alice Liu (4814415)

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

“…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. …”

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

“…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. …”

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

“…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. …”

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

“…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. …”

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

“…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. …”

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

“…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. …”

Primary cilia frequency is decreased on both CK5+ and CK5- cancer cells. by Nadia B. Hassounah (429199)

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

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

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

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

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

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

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

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

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

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

Liability threshold model for nsCL/P. by Laurence J. Howe (5579186)