Characteristics of ecosystem service values in response to strong human activities in water sources of the South-to-North Water Diversion Middle Route in China.

A systematic grasp of the evolution of the spatial and temporal patterns of ecosystem service value (ESV) in the Central Line Project for South-to-North Water Diversion (CLPSNWD) water source area is conducive to deepening the ecological protection and promoting high-quality development of the water source area. In this paper, the dynamically adjusted equivalent factor method is used to reveal the spatial and temporal evolution of ESV in the water source area under strong human activities from 1991 to 2020. The results show that (1) during the 30-year period, urban point expansion increased the construction land area by 63.66 km2, and the degree of fragmentation increased. The water area increased the most, reaching 209.43 km2. (2) The total increase in ESV over the 30-year period was $1434 million, with forests and water accounting for the largest increase, i.e., 98% of the total increase in value. Among the individual service functions, hydrologic regulation generated the most significant service value.

Enhancement of recombinant human interleukin-22 production by fusing with human serum albumin and supplementing N-acetylcysteine in Pichia Pastoris.

Interleukin-22 (IL-22) plays an important role in the treatment of organ failure, which can induce anti-apoptotic and proliferative signaling pathways; Nevertheless, the practical utilization of IL-22 is hindered by the restricted efficacy of its production. Pichia pastoris presents a viable platform for both industrial and pharmaceutical applications. In this study, we successfully generated a fusion protein consisting of truncated human serum albumin and human IL-22 (HSA-hIL-22) using P. pastoris, and examined the impact of antioxidants on HSA-hIL-22 production. We have achieved the production of HSA-hIL-22 in the culture medium at a yield of approximately 2.25 mg/ml. Moreover, 0-40 mM ascorbic acid supplementation did not significantly affect HSA-hIL-22 production or the growth rate of the recombinant strain. However, 80 mM ascorbic acid treatment had a detrimental effect on the expression of HSA-hIL-22. In addition, 5-10 mM N-acetyl-l-cysteine (NAC) resulted in an increase of HSA-hIL-22 production, accompanied by a reduction in the growth rate of the recombinant strain. Conversely, 20-80 mM NAC supplementation inhibited the growth of the recombinant strains and reduced intact HSA-hIL-22 production. However, neither NAC nor ascorbic acid exhibited any effect on superoxide dismutase (SOD) and malondialdehyde (MDA) levels, except that NAC increased GSH content. Furthermore, our findings indicate that recombinant HSA-hIL-22, which demonstrated the ability to stimulate the proliferation of HepG2 cells, possesses bioactivity. In addition, NAC did not affect HSA-hIL-22 bioactivity. In conclusion, our study demonstrates that NAC supplementation can enhance the secretion of functional HSA-hIL-22 proteins produced in P. pastoris without compromising their activity.

Quantitative Analysis of the Influence of the Xiaolangdi Reservoir on Water and Sediment in the Middle and Lower Reaches of the Yellow River.

The Xiaolangdi Reservoir is the second largest water conservancy project in China and the last comprehensive water conservancy hub on the mainstream of the Yellow River, playing a vital role in the middle and lower reaches of the Yellow River. To study the effects of the construction of the Xiaolangdi Reservoir (1997-2001) on the runoff and sediment transport in the middle and lower reaches of the Yellow River, runoff and sediment transport data from 1963 to 2021 were based on the hydrological stations of Huayuankou, Gaocun, and Lijin. The unevenness coefficient, cumulative distance level method, Mann-Kendall test method, and wavelet transform method were used to analyze the runoff and sediment transport in the middle and lower reaches of the Yellow River at different time scales. The results of the study reveal that the completion of the Xiaolangdi Reservoir in the interannual range has little impact on the runoff in the middle and lower reaches of the Yellow River and a significant impact on sediment transport. The interannual runoff volumes of Huayuankou station, Gaocun station, and Lijin station were reduced by 20.1%, 20.39%, and 32.87%, respectively. In addition, the sediment transport volumes decreased by 90.03%, 85.34%, and 83.88%, respectively. It has a great influence on the monthly distribution of annual runoff. The annual runoff distribution is more uniform, increasing the runoff in the dry season, reducing the runoff in the wet season, and bringing forward the peak flow. The runoff and Sediment transport have obvious periodicity. After the operation of the Xiaolangdi Reservoir, the main cycle of runoff increases and the second main cycle disappears. The main cycle of Sediment transport did not change obviously, but the closer it was to the estuary, the less obvious the cycle was. The research results can provide a reference for ecological protection and high-quality development in the middle and lower reaches of the Yellow River.

A new rainfall prediction model based on ICEEMDAN-WSD-BiLSTM and ESN.

Precipitation, as an important indicator describing the evolution of the regional climate system, plays an important role in understanding the spatial and temporal distribution characteristics of regional precipitation. Scientific and accurate prediction of regional precipitation is helpful to provide theoretical basis for relevant departments to guide flood and drought control. To address the uncertainty and nonlinear characteristics of precipitation series, this paper uses the established improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN)-wavelet signal denoising (WSD)-bi-directional long short-term memory (BiLSTM), and echo state network (ESN) models to predict precipitation of four cities in southern Anhui Province. The BiLSTM is used to predict the high-frequency components and the ESN to predict the low-frequency components, thus avoiding the influence between the two neural network predictions. The results show that the ICEEMDAN-WSD-BiLSTM and ESN models are more accurate. The average relative error reached 2.64% and the NSE (Nash-Sutcliffe efficiency coefficient) was 0.91, which was significantly better than the other four models. The model reveals the temporal change pattern and evolution characteristics of future precipitation, guides flood prevention and mitigation, and has certain theoretical significance and application value for promoting regional sustainable development.

A coupled CEEMD-BiLSTM model for regional monthly temperature prediction.

Temperature is an important indicator of climate change. With the gradual increase of global warming, a well-chosen model can improve the accuracy of temperature prediction. It is of great significance and value for future disaster prevention and mitigation and economic development. Monthly temperature is influenced by solar activity, monsoon, and other factors, with significant uncertainty, ambiguity, and randomness. A coupled CEEMD-BiLSTM temperature model is constructed based on the good decomposition-reconstruction characteristics of CEEMD for uncertain time series and the advantages of BiLSTM for solving stochastic prediction, and it is applied to the prediction of monthly temperature in Zhengzhou City. The results show that the minimum relative error of the coupled CEEMD-BiLSTM model is 0.01%, the maximum relative error is 0.99%, and the average relative error is 0.22%, and the prediction accuracy of this coupled model for monthly temperature in Zhengzhou is higher than that of the CEEMD-LSTM model, EEMD-BiLSTM model, and BP neural network model, with better stability and adaptability.

Application of improved seasonal GM(1,1) model based on HP filter for runoff prediction in Xiangjiang River.

Runoff forecasting is essential for the reasonable use of regional water resources, flood prevention, and mitigation, as well as the development of ecological civilization. Runoff is influenced by the intersection of many factors, and the change process is extremely complex, showing significant stochasticity, nonlinearity, and heterogeneity, making traditional prediction models less adaptable. The Hodrick-Prescott filter (HP filter) is a well-established signal separation method. The traditional GM(1,1) model is capable of portraying the growth trend of the time series but cannot capture the periodic characteristics of the time series. Therefore, a novel coupled prediction model-HPF-GM(1,1) model is proposed in this study and applied to the runoff prediction of the Zhuzhou section of Xiangjiang River in Hunan Province. This model enables to separate seasonal factors from non-seasonal factors in the runoff time series, and introduce seasonal factors based on the traditional GM(1,1) model, which solves the challenge that the traditional GM(1,1) model is unable to predict seasonal time series. The results show that the HPF-GM(1,1) model has a mean relative error of 4.82%, a root mean square error of 7.44, and a Nash efficiency coefficient of 0.93, which is better than the traditional GM(1,1) model, the DGGM(1,1) model and the SGM(1,1) model of prediction accuracy. Obviously, the HP filter provides a new approach to data pre-processing of runoff series and the proposed HPF-GM(1,1)-coupled model extends new ideas for nonlinear runoff prediction.