A Dual Inhibitor of DYRK1A and GSK3β for β-Cell Proliferation: Aminopyrazine Derivative GNF4877

Loss of β-cell mass and function can lead to insufficient insulin levels and ultimately to hyperglycemia and diabetes mellitus. The mainstream treatment approach involves regulation of insulin levels; however, approaches intended to increase β-cell mass are less developed. Promoting β-cell proliferation with low-molecular-weight inhibitors of dual-specificity tyrosine-regulated kinase 1A (DYRK1A) offers the potential to treat diabetes with oral therapies by restoring β-cell mass, insulin content and glycemic control. GNF4877, a potent dual inhibitor of DYRK1A and glycogen synthase kinase 3β (GSK3β) was previously reported to induce primary human β-cell proliferation in vitro and in vivo. Herein, we describe the lead optimization that lead to the identification of GNF4877 from an aminopyrazine hit identified in a phenotypic high-throughput screening campaign measuring β-cell proliferation.     

Study on Wettability and Corrosion Behavior of Al2O3 Doped Polyurea Coatings

Protection of Metals and Physical Chemistry of Surfaces - The Al2O3 powder was added as a filler to polyurea and the effect of doping amount on Shore hardness and wetting property were...     

Super-oleophilicity Co-doping Co2+/F-/TiO2 one-dimensional nanotubes for photocatalytic denitrification of light oil

In this study, a simple hydrothermal synthesis method was adapted for the preparation of Co-doping Co²⁺/F^-/TiO₂ nanotubes photocatalyst, and the micro-nano structure of catalysts prepared by biomimetic technology which makes the catalyst have super-oleophilicity property. Co²⁺/F^-/TiO₂ revealed improved photocatalytic performance for denitrification of light oil compared to single TiO₂ photocatalysts. The enhance of photocatalytic activity can be attributed to narrowing the band gap, increasing the light response wavelength and exposing more highly active crystal surfaces due to synergistic effects of Co²⁺ and F^? in the photocatalyst.     

Distributed learning consensus control based on neural networks for heterogeneous nonlinear multiagent systems

This paper considers a novel distributed iterative learning consensus control algorithm based on neural networks for the control of heterogeneous nonlinear multiagent systems. The system's unknown nonlinear function is approximated by suitable neural networks; the approximation error is countered by a robust term in the control. Two types of control algorithms, both of which utilize distributed learning laws, are provided to achieve consensus. In the provided control algorithms, the desired reference is considered to be an unknown factor and then estimated using the associated learning laws. The consensus convergence is proven by the composite energy function method. A numerical simulation is ultimately presented to demonstrate the efficacy of the proposed control schemes.     

Study on wax precipitation characteristics of wax deposit in pipes

Wax deposit properties are a significant concern in pipeline pigging during waxy crude oil transportation. In the present work, the impacts of flow conditions and oil properties on the wax precipitation characteristics of wax deposits are investigated. A flow loop apparatus was developed to conduct wax deposition experiments using four crude oils collected from different field pipes. The differential scanning calorimetry (DSC) technique was employed to observe the wax precipitation characteristics of crude oil and wax deposit. The results show that the wax content and the wax appearance temperature (WAT) of the deposits increase with shear stress and radial temperature gradient, and decrease with radial wax molecule concentration gradient near the pipe wall. The DSC tests on the wax deposits revealed that the deposit wax content is strongly correlated to the oil wax content. Furthermore, an empirical correlation was developed to predict the wax content and the WAT of the wax deposit. Verification of the empirical correlation using the different oils indicated that the average relative error of the wax content prediction and average absolute error of WAT prediction were 13.2% and 3.6°C, respectively.     

Influence of Si-Doping on the Performance of InGaN/GaN Multiple Quantum Well Solar Cells

Semiconductors - The performance of InGaN/GaN multiple quantum well (MQW) solar cells with five different Si-doping concentrations, namely 0, 4 × 1017 cm–3, 1 × 1018 cm–3, 3...