No abstract

The mutant acid phytase (phyA ( m )) gene was modified by random mutagenesis to improve enzymatic activity by using an error-prone PCR (ep-PCR) strategy. The mutated gene was linearized and inserted into plasmid vector pPIC9K and transformed by electroporation into Pichia pastoris GS115. A single transformant, PP-NP(ep)-6A, showing the strongest phytase activity from among the 5,500 transformants, was selected for detailed analyses. Southern blot analysis of the mutant yeast transformant showed that phyA ( ep ) gene was integrated into the chromosome genome through single crossover with one copy of phyA. The kinetic parameters indicated that the mutant one showed 61% higher specific activity and 53% lower k (m) value than that of PP-NP(m)-8 (P < 0.05). In addition, the overall catalytic efficiency (k (cat)/k (m)) of the mutant one was 84% higher (P< 0.05) than that of PP-NP(m)-8. Nine bases were altered in the mutant sequences, which resulted in three amino acid changes, namely, Glu156Gly, Thr236Ala, and Gln396Arg. The structural predictions indicated that the mutations generated by ep-PCR somehow reorganized or remodeled the active site, which could lead to increasing catalytic efficiency.

No abstract

The ultra-fast and selective elimination of 137Cs from complex aqueous solutions is achieved through the ion exchange method by employing layered K2In2Sb2S7·2.2H2O obtained from cation activation of [(CH3)2NH2]2In2Sb2S7.

The accurate prediction of urban growth is pivotal for managing urbanization, especially in fast-urbanizing countries. For this purpose, cellular automata-based (CA) simulation tools have been widely developed and applied. Previous studies have extensively discussed various model building and calibration techniques to improve simulation performance. However, it has been a common practice that the simulation is conducted at and only at the spatial extent where the results are needed, while as we know, urban development in one place can also be influenced by the situations in the broader contexts. To tackle this gap, in this paper, the impact of the simulation of spatial extent on simulation performance is tested and discussed. We used five villages at the rural–urban fringe in Chengdu, China as the case study. Urban growth CA models are built and trained at the spatial extent of the village and the whole city. Comparisons between the simulation results and the actual urban growth in the study area from 2005 to 2015 show that the accuracy of the city model was 7.33% higher than the village model and the latter had more errors in simulating the growth of small clusters. Our experiment suggests that, at least in some cases, urban growth modeling at a larger spatial extent can yield better results than merely modeling the area of interest, and the impacts of the spatial extent of simulation should be considered by modelers.