TSA treatment significantly decreases total cholesterol content in neuroblastoma cells. by Maria João Nunes (160499)

Schematic illustration of the possible steps of ‘stalk mechanism’ of the electrofusion of two ‘single GUVs’. by Md. Tariqul Islam Bhuiyan (18794375)

Brivanib decreases viability of PDGF-BB treated LX-2 cells. by Ikuo Nakamura (548298)

“…(B) Cells stimulated with 5 ng/ml PDGF-BB in serum-free medium showed a similar trend albeit more pronounced decrease in viability compared to cells cultured in 10% FBS. …”

Knockdown of chemerin decreased proteins associated with p-p38-MAPK. by Wei Xiong (58411)

Free Fatty Acid treatment decreases adipocyte PPARγ expression. by M. T. Audrey Nguyen (326349)

Blocking C5L2 decreased C5a-primed neutrophils for ANCA-induced degranulation. by Jian Hao (160736)

Graphic representation of freezing detection by video inspection and a computerized algorithm. by Hiram Cantú (6009626)

Impaired Functions of Macrophage from Cystic Fibrosis Patients: CD11b, TLR-5 Decrease and sCD14, Inflammatory Cytokines Increase by Karin Simonin-Le Jeune (464354)

“…Furthermore expression of CD11b and TLR-5 were sorely decreased on CF macrophages. Beside, no difference was observed for mCD14, CD16, CD64, TLR-4 and TLR1/TLR-2 expressions. …”

Increased miR200c decreases FAP-1 expression. by Sabarinathan Ramachandran (444500)

Values for vertical foot clearance, foot overhang, anteroposterior MoS and mediolateral MoS on steps 4, 5 and 6 across conditions. by Timmion K. Skervin (11432568)

Subjects:

Microscopic Movement of Slow-Diffusing Nanoparticles in Cylindrical Nanopores Studied with Three-Dimensional Tracking by Luyang Zhao (1903252)

“…In viscous solvents, the particle steps decrease proportionally to the increment of the viscosity, leading to macroscopically slow diffusion. …”

Microscopic Movement of Slow-Diffusing Nanoparticles in Cylindrical Nanopores Studied with Three-Dimensional Tracking by Luyang Zhao (1903252)

“…In viscous solvents, the particle steps decrease proportionally to the increment of the viscosity, leading to macroscopically slow diffusion. …”

Microscopic Movement of Slow-Diffusing Nanoparticles in Cylindrical Nanopores Studied with Three-Dimensional Tracking by Luyang Zhao (1903252)

“…In viscous solvents, the particle steps decrease proportionally to the increment of the viscosity, leading to macroscopically slow diffusion. …”

Microscopic Movement of Slow-Diffusing Nanoparticles in Cylindrical Nanopores Studied with Three-Dimensional Tracking by Luyang Zhao (1903252)

“…In viscous solvents, the particle steps decrease proportionally to the increment of the viscosity, leading to macroscopically slow diffusion. …”

Microscopic Movement of Slow-Diffusing Nanoparticles in Cylindrical Nanopores Studied with Three-Dimensional Tracking by Luyang Zhao (1903252)

“…In viscous solvents, the particle steps decrease proportionally to the increment of the viscosity, leading to macroscopically slow diffusion. …”

Microscopic Movement of Slow-Diffusing Nanoparticles in Cylindrical Nanopores Studied with Three-Dimensional Tracking by Luyang Zhao (1903252)

“…In viscous solvents, the particle steps decrease proportionally to the increment of the viscosity, leading to macroscopically slow diffusion. …”

Microscopic Movement of Slow-Diffusing Nanoparticles in Cylindrical Nanopores Studied with Three-Dimensional Tracking by Luyang Zhao (1903252)

“…In viscous solvents, the particle steps decrease proportionally to the increment of the viscosity, leading to macroscopically slow diffusion. …”

Microscopic Movement of Slow-Diffusing Nanoparticles in Cylindrical Nanopores Studied with Three-Dimensional Tracking by Luyang Zhao (1903252)

“…In viscous solvents, the particle steps decrease proportionally to the increment of the viscosity, leading to macroscopically slow diffusion. …”

Predictions of random forests (RF), gradient boosting machines (GBM), and neural networks (NN) trained on 12 covariates chosen using subjective feature selection. by Dennis Ochola (11626912)

Predictions of random forests (RF), gradient boosting machines (GBM), and neural networks (NN) trained on 17 covariates selected after recursive feature selection. by Dennis Ochola (11626912)