Epilactose is a promising functional disaccharide, but its biomanufacturing is limited by insufficient enzyme activity, poor thermostability, and costly catalyst preparation. We first used the REME platform to computationally evaluate candidate enzymes. Among them, cellobiose 2-epimerase from Caldicellulosiruptor saccharolyticus (CsCE) showed the highest epilactose synthesis activity. We therefore developed an integrated strategy combining computational design, SpyTag/SpyCatcher-mediated cyclization, and ethanol-permeabilized whole-cell catalysis. By combining enzyme ligand binding energy analysis, protein stability prediction, and catalytic constant prediction, the V52N variant was obtained. Its epilactose synthesis activity was 3.45 times that of the wild type, while lactulose formation was reduced to 14.8% of the wild-type level. Cyclized CCT increased the optimum temperature to 80 °C and extended the half-life at 85 °C by 5.52-fold. The optimized whole-cell process produced 65.81 g/L epilactose from 200 g/L lactose within 20 min, corresponding to 32.90% conversion. This strategy provides a practical route for efficient epilactose biomanufacturing.
Large-scale high-performance computing workloads in petroleum geophysical exploration commonly use the Message Passing Interface (MPI) as their parallel programming model. However, MPI does not provide native mechanisms for resource management, which complicates the efficient execution of multiple MPI jobs in shared-resource environments. As MPI workloads migrate to cloud-native platforms, their high degrees of parallelism and communication-intensive behavior may lead to scheduling delays and contention for shared resources, resulting in prolonged execution time and inefficient resource utilization. This paper identifies two major limitations of existing cloud-native MPI deployments: cluster-unaware process-count selection and insufficient exploitation of Non-Uniform Memory Access (NUMA) locality. To address these limitations, we propose a resource-aware optimization framework that dynamically selects the MPI process count and performs node- and NUMA-aware process placement. Experimental results show that the proposed parallelism-selection method reduces task-sequence execution time by at least 18% and improves the evaluated resource-utilization metrics by more than 30%. The topology-aware placement method further reduces execution time by at least 10% compared with the evaluated affinity baselines under the tested shared-resource cluster configurations.
Wenxiao Wang, Zibo Gao, Guoding Ji et al.· Journal of Intelligent Compu...· 0 citations