Characterization and structural analysis of a thermophilic GH11 xylanase from compost metatranscriptome.

Xylanase is efficient for xylan degradation and widely applied in industries. We found a GH11 family xylanase (Xyn11A) with high thermostability and catalytic activity from compost metatranscriptome. This xylanase has the optimal reaction temperature at 80 °C with the activity of 2907.3 U/mg. The X-ray crystallographic structure shows a typical "right hand" architecture, which is the characteristics of the GH11 family enzymes. Comparing it with the mesophilic XYN II, a well-studied GH11 xylanase from Trichoderma reesei, Xyn11A is more compact with more H-bonds. Our mutagenic results show that the electrostatic interactions in the thumb and palm region of Xyn11A could result in its high thermostability and activity. Introducing a disulfide bond at the N-terminus further increased its optimal reaction temperature to 90 °C with augmented activity. KEY POINTS: • A hyperthermophilic xylanase with high activity was discovered using the metatranscriptomic method. • The mechanisms of thermophilicity and high activity were revealed using X-ray crystallography, mutagenesis, and molecular dynamics simulations. • The thermostability and activity were further improved by introducing a disulfide bond.

Real-Time Massive Vector Field Data Processing in Edge Computing.

The spread of the sensors and industrial systems has fostered widespread real-time data processing applications. Massive vector field data (MVFD) are generated by vast distributed sensors and are characterized by high distribution, high velocity, and high volume. As a result, computing such kind of data on centralized cloud faces unprecedented challenges, especially on the processing delay due to the distance between the data source and the cloud. Taking advantages of data source proximity and vast distribution, edge computing is ideal for timely computing on MVFD. Therefore, we are motivated to propose an edge computing based MVFD processing framework. In particular, we notice that the high volume feature of MVFD results in high data transmission delay. To solve this problem, we invent Data Fluidization Schedule (DFS) in our framework to reduce the data block volume and the latency on Input/Output (I/O). We evaluated the efficiency of our framework in a practical application on massive wind field data processing for cyclone recognition. The high efficiency our framework was verified by the fact that it significantly outperformed classical big data processing frameworks Spark and MapReduce.