Optimization of purification conditions for papain in a polyethylene glycol-phosphate aqueous two-phase system using quaternary ammonium ionic liquids as adjuvants by BBD-RSM.

This work attempts to study and optimize the conditions for separating and purifying papain in aqueous two-phase systems (ATPSs). Quaternary ammonium ionic liquids (ILs, 4 wt%) were added as adjuvants to a PEG-phosphate ATPS. On the basis of single-factor experiments, a Box-Behnken design with response surface methodology (BBD-RSM) was used to optimize the purification conditions of papain in the ATPS by setting the NaH2PO4·2H2O concentration, PEG concentration and pH as independent variables and the overall desirability (OD) of the recovery rate of papain, the protein recovery rate and the purification factor as dependent variables. The following optimum conditions were determined: PEG4000 16.4 wt%, NaH2PO4·2H2O 13.7 wt%, pH 6.22, temperature 60 °C and enzyme concentration 12.0 mg/ml. Under the optimized conditions, the purification factor for the ATPS supplemented with commercial enzyme increased from 1.331 (no ILs) to 3.380 (containing 4 wt% [N2222]BF4). The total evaluation OD was 0.9979, the maximum predicted OD was 0.9994, and the deviation rate was -0.15%. Therefore, the model established in this experiment could predict the experimental value well. To verify the practical effect of the model, papain obtained from fresh papaya latex (papain crude extract) was applied to the same ATPS. The results showed that the purification factor of the ATPS with papain crude extract increased from 3.517 (no ILs) to 12.04 (containing 4 wt% [N2222]BF4). In summary, the addition of 4 wt% ILs to partially replace PEG greatly improved the purification factor for crude papain extract enriched in the phosphate phase, providing a potential method for the large-scale industrial production of papain.

Load balancing prediction method of cloud storage based on analytic hierarchy process and hybrid hierarchical genetic algorithm.

With the continuous expansion of the cloud computing platform scale and rapid growth of users and applications, how to efficiently use system resources to improve the overall performance of cloud computing has become a crucial issue. To address this issue, this paper proposes a method that uses an analytic hierarchy process group decision (AHPGD) to evaluate the load state of server nodes. Training was carried out by using a hybrid hierarchical genetic algorithm (HHGA) for optimizing a radial basis function neural network (RBFNN). The AHPGD makes the aggregative indicator of virtual machines in cloud, and become input parameters of predicted RBFNN. Also, this paper proposes a new dynamic load balancing scheduling algorithm combined with a weighted round-robin algorithm, which uses the predictive periodical load value of nodes based on AHPPGD and RBFNN optimized by HHGA, then calculates the corresponding weight values of nodes and makes constant updates. Meanwhile, it keeps the advantages and avoids the shortcomings of static weighted round-robin algorithm.