Expression, Purification and Characterization of Arginase from Helicobacter pylori in Its Apo Form

Arginase, a manganese-dependent enzyme that widely distributed in almost all creatures, is a urea cycle enzyme that catalyzes the hydrolysis of L-arginine to generate L-ornithine and urea. Compared with the well-studied arginases from animals and yeast, only a few eubacterial arginases have been characterized, such as those from H. pylori and B. anthracis. However, these enzymes used for arginase activity assay were all expressed with LB medium, as low concentration of Mn2+ was detectable in the medium, protein obtained were partially Mn2+ bonded, which may affect the results of arginase activity assay. In the present study, H. pylori arginase (RocF) was expressed in a Mn2+ and Co2+ free minimal medium, the resulting protein was purified through affinity and gel filtration chromatography and the apo-form of RocF was confirmed by flame photometry analysis. Gel filtration indicates that the enzyme exists as monomer in solution, which was unique as compared with homologous enzymes. Arginase activity assay revealed that apo-RocF had an acidic pH optimum of 6.4 and exhibited metal preference of Co2+>Ni2+>Mn2+. We also confirmed that heat-activation and reducing regents have significant impact on arginase activity of RocF, and inhibits S-(2-boronoethyl)-L-Cysteine (BEC) and Nω-hydroxy-nor-Arginine (nor-NOHA) inhibit the activity of RocF in a dose-dependent manner

A Non-Iterative Method Combined with Neural Network Embedded in Physical Model to Solve the Imaging of Electromagnetic Inverse Scattering Problem

The main purpose of this paper is to solve the electromagnetic inverse scattering problem (ISP). Compared with conventional tomography technology, it considers the interaction between the internal structure of the scene and the electromagnetic wave in a more realistic manner. However, due to the nonlinearity of ISP, the conventional calculation scheme usually has some problems, such as the unsatisfactory imaging effect and high computational cost. To solve these problems and improve the imaging quality, this paper presents a simple method named the diagonal matrix inversion method (DMI) to estimate the distribution of scatterer contrast (DSC) and a Generative Adversarial Network (GAN) which could optimize the DSC obtained by DMI and make it closer to the real distribution of scatterer contrast. In order to make the distribution of scatterer contrast generated by GAN more accurate, the forward model is embedded in the GAN. Moreover, because of the existence of the forward model, not only is the DSC generated by the generator similar to the original distribution of the scatterer contrast in the numerical distribution, but the numerical of each point is also approximate to the original

A Non-Iterative Method Combined with Neural Network Embedded in Physical Model to Solve the Imaging of Electromagnetic Inverse Scattering Problem

The main purpose of this paper is to solve the electromagnetic inverse scattering problem (ISP). Compared with conventional tomography technology, it considers the interaction between the internal structure of the scene and the electromagnetic wave in a more realistic manner. However, due to the nonlinearity of ISP, the conventional calculation scheme usually has some problems, such as the unsatisfactory imaging effect and high computational cost. To solve these problems and improve the imaging quality, this paper presents a simple method named the diagonal matrix inversion method (DMI) to estimate the distribution of scatterer contrast (DSC) and a Generative Adversarial Network (GAN) which could optimize the DSC obtained by DMI and make it closer to the real distribution of scatterer contrast. In order to make the distribution of scatterer contrast generated by GAN more accurate, the forward model is embedded in the GAN. Moreover, because of the existence of the forward model, not only is the DSC generated by the generator similar to the original distribution of the scatterer contrast in the numerical distribution, but the numerical of each point is also approximate to the original