Crosslinked Aggregates of Fusion Enzymes in Microaqueous Organic Media

Baeyer-Villiger monooxygenases (BVMOs) are attractive for selectively oxidizing various ketones using oxygen into valuable esters and lactones. However, the application of BVMOs is restrained by cofactor dependency and enzyme instability combined with water-related downsides such as low substrate loading, low oxygen capacity, and water-induced side reactions. Herein, we described a redox-neutral linear cascade with in-situ cofactor regeneration catalyzed by fused alcohol dehydrogenase and cyclohexanone monooxygenase in aqueous and microaqueous organic media. The cascade conditions have been optimized regarding substrate concentrations as well as the amounts of enzymes and cofactors with the Design of Experiments (DoE). The carrier-free immobilization technique, crosslinked enzyme aggregates (CLEAs), was applied to fusion enzymes. The resultant fusion CLEAs were proven to function in microaqueous organic systems, in which the enzyme ratios, water contents (0.5–5 vol. %), and stability have been systematically studied. The fusion CLEAs showed promising operational (up to 5 cycles) and storage stability.     

An improved adaptive FastSLAM algorithm with time-varying noise estimator

In this study, an adaptive FastSLAM (AFastSLAM) algorithm, which is obtained by estimating the time-varying noise statistics and improving FastSLAM algorithm, is proposed. This improvement was accomplished by using maximum likelihood estimation and expectation maximization criterion and a one-step smoothing algorithm in importance sampling. In addition, innovation covariance estimation (ICE) method was used to prevent loss of positive definiteness of the process and measurement noise covariance matrices. The proposed method was compared with FastSLAM by calculating the root mean square error (RMSE) using different particle numbers at varying initial process and measurement noise values. Simulation studies have shown that AFastSLAM provides much more accurate, consistent, and successful estimates than FastSLAM for both robot and landmark positions.