Improvement of the recombinant phytase expression by intermittent feeding of glucose during the induction phase of methylotrophic yeast Pichia pastoris.

PURPOSE: For growth of methylotrophic yeast, glycerol is usually used as a carbon source. Glucose is used in some cases, but not widely consumed due to strong repressive effect on AOX1 promoter. However, glucose is still considered as a carbon source of choice since it has low production cost and guarantees growth rate comparable to glycerol. RESULTS: In flask cultivation of the recombinant yeast, Pichia pastoris GS115(pPIC9K-appA38M), while methanol induction point(OD600) and methanol concentration significantly affected the phytase expression, glucose addition in induction phase could enhance phytase expression. The optimal flask cultivation conditions illustrated by Response Surface Methodology were 10.37 OD600 induction point, 2.02 h before methanol feeding, 1.16% methanol concentration and 40.36µL glucose feeding amount(for 20 mL culture volume) in which the expressed phytase activity was 613.4 ± 10.2U/mL, the highest activity in flask cultivation. In bioreactor fermentation, the intermittent glucose feeding showed several advantageous results such as 68 h longer activity increment, 149.2% higher cell density and 200.1% higher activity compared to the sole methanol feeding method. These results implied that remaining glucose at induction point might exhibit a positive effect on the phytase expression. CONCLUSION: Glucose intermittent feeding could be exploited for economic phytase production and the other recombinant protein expression by P. pastoris GS115.

Spatiotemporal informer: A new approach based on spatiotemporal embedding and attention for air quality forecasting.

Accurate prediction of air pollution is essential for public health protection. Air quality, however, is difficult to predict due to the complex dynamics, and its accurate forecast still remains a challenge. This study suggests a spatiotemporal Informer model, which uses a new spatiotemporal embedding and spatiotemporal attention, to improve AQI forecast accuracy. In the first phase of the proposed forecast mechanism, the input data is transformed by the spatiotemporal embedding. Next, the spatiotemporal attention is applied to extract spatiotemporal features from the embedded data. The final forecast is obtained based on the attention tensors. In the proposed forecast model, the input is a 3-dimensional data that consists of air quality data (AQI, PM2.5, O3, SO2, NO2, CO) and geographic information, and the output is a multi-positional, multi-temporal data that shows the AQI forecast result of all the monitoring stations in the study area. The proposed forecast model was evaluated by air quality data of 34 monitoring stations in Beijing, China. Experiments showed that the proposed forecast model could provide highly accurate AQI forecast: the average of MAPE values for from 1 h to 20 h ahead forecast was 11.61%, and it was much smaller than other models. Moreover, the proposed model provided a highly accurate and stable forecast even at the extreme points. These results demonstrated that the proposed spatiotemporal embedding and attention techniques could sufficiently capture the spatiotemporal correlation characteristics of air quality data, and that the proposed spatiotemporal Informer could be successfully applied for air quality forecasting.

Water quality forecasting based on data decomposition, fuzzy clustering and deep learning neural network.

Water quality forecasting can provide useful information for public health protection and support water resources management. In order to forecast water quality more accurately, this paper proposes a novel hybrid model by combining data decomposition, fuzzy C-means clustering and bidirectional gated recurrent unit. Firstly, the original water quality data is decomposed into several subseries by empirical wavelet transform, and then, the decomposed subseries are recombined by fuzzy C-means clustering. Next, for each clustered series, bidirectional gated recurrent unit is applied to develop prediction model. Finally, the forecast result is obtained by the summation of the predictions for the subseries. The proposed forecast model is evaluated by the water quality data of Poyang Lake, China. Results show that the proposed forecast model provides highly accurate forecast result for all of the six water quality data: the average of MAPE of the forecast results for the six water quality datasets is 4.59% for 7 day ahead prediction. Furthermore, our model shows better forecast performance than the other models. Particularly, compared with the single BiGRU model, MAPE decreased by 32.86% in average. Results demonstrate that the proposed forecast model can be used effectively for water quality forecasting.