Biotransformation with a New <i>Acinetobacter</i> sp. Isolate for Highly Enantioselective Synthesis of a Chiral Intermediate of Miconazole

(R)-2-Chloro-1-(2,4-dichlorophenyl) ethanol is a chiral intermediate of the antifungal agent Miconazole. A bacterial strain, ZJPH1806, capable of the biocatalysis of 2-chloro-1-(2,4-dichlorophenyl) ethanone, to (R)-2-chloro-1-(2,4-dichlorophenyl) ethanol with highly stereoselectivity was isolated from a soil sample. It was identified as the Acinetobacter sp., according to its morphological observation, physiological-biochemical identification, and 16S rDNA sequence analysis. After optimizing the key reaction conditions, it was demonstrated that the bioreduction of 2-chloro-1-(2,4-dichlorophenyl) ethanone was effectively transformed at relatively high conversion temperatures, along with glycerol as cosubstrate in coenzyme regeneration. The asymmetric reduction of the substrate had reached 83.2% yield with an enantiomeric excess (ee) of greater than 99.9% at 2 g/L of 2-chloro-1-(2,4-dichlorophenyl) ethanone; the reaction was conducted at 40 &#176;C for 26 h using resting cells of the Acinetobacter sp. ZJPH1806 as the biocatalyst. The yield had increased by nearly 2.9-fold (from 28.6% to 83.2%). In the present study, a simple and novel whole-cell-mediated biocatalytic route was applied for the highly enantioselective synthesis of (R)-2-chloro-1-(2,4-dichlorophenyl) ethanol, which allowed the production of a valuable chiral intermediate method to be transformed into a versatile tool for drug synthesis

Regional Ground Surface Mass Variations Inversed by Radial Point-mass Model Method with Spatial Constraints

Radial point-mass model method is the disturbance gravity downward continuation in essence, which is an ill-posed problem. In general, the regularization method is an efficient way to get the reliable solution. To solve this problem, the radial point-mass model method is improved by using Helmert variance component estimation with adding spatial constraints from a practical point of view. Taking South America continent as study area, radial point-mass model method with spatial constraints is verified by experimental results. The experiments results show that the condition number of normal equations is decreasing obviously after adding spatial constraints. The inversion results of radial point-mass model method with spatial constraints are consistent with results of other methods. Furthermore, the radial point-mass model method with spatial constraints provides an alternative way to monitor regional surface mass variations by satellite gravimetry