[Prediction Spatial Distribution of Soil Organic Matter Based on Improved BP Neural Network with Optimized Sparrow Search Algorithm].

Soil organic matter is an important indicator of soil fertility, and it is necessary to improve the accuracy of regional organic matter spatial distribution prediction. In this study, we analyzed the organic matter content of 1 690 soil surface layers (0-20 cm) and collected data on the natural environment and human activities in the Weining Plain of the Yellow River Basin. The SOM spatial distribution prediction model was established with 1 348 points using classical statistics, deterministic interpolation, geostatistical interpolation, and machine learning, respectively, and 342 sample points data were used as the test set to test and analyze the prediction accuracy of different models. The results showed that the average SOM content of the surface soil of the Weining Plain was 14.34 g·kg-1, and the average soil organic matter variation across 1 690 sampling points was 34.81%, indicating a medium degree of variability. The results also revealed a spatial distribution trend, with low soil organic matter content in the northeast and southwest, high soil organic matter on the left and right banks of the Yellow River in the middle, and relatively high soil organic matter in the sloping terrain of the Weining Plain. The four types of methods in order of high to low prediction accuracy were the machine learning method, geostatistical interpolation method, deterministic interpolation method, and classical statistical method. Through comparison, the BP neural network that was improved based on the optimized sparrow search algorithm had the best prediction accuracy, and the optimized sparrow search algorithm had better convergence accuracy, avoided falling into local optimization, prevented data overfitting, and had better prediction ability. This optimization algorithm can improve the accuracy of SOM prediction and has good application prospects in soil attribute prediction.

[Spatiotemporal Pattern and Driving Force Analysis of Ecological Environmental Quality in Typical Ecological Areas of the Yellow River Basin from 1990 to 2020].

Scientific evaluation of ecological environmental quality is the premise of realizing regional ecological sustainable development. Taking Landsat series satellite images from 1990 to 2020 as the data source, on the basis of the entropy remote sensing ecological index (E-RSEI), combining the Mann-Kendall significance test, Theil-Sen Median analysis, Hurst exponent, and stability analysis, the spatial-temporal variation characteristics of ecological environmental quality in typical ecological areas of the Yellow River Basin were analyzed in the context of multi-spatiotemporal scales. In addition, the effects of eight environmental and human factors on the change in E-RSEI were quantified using a geodetector. The results showed that:① in the past 31 years, the average value of E-RSEI was 67.5%, which showed an increasing trend on the time scale, with an average increase of 0.066·(10 a)-1. On the spatial scale, E-RSEI was higher in the west and the south lower in the east and the north. ② The ecological environmental quality will continue to improve in the future, but 9.33% of the areas have potential risks of degradation. ③ Precipitation was the dominant environmental factor that affected the spatial distribution of E-RSEI in this area, and the influence of human factors was low. Compared with that of single factors, the interaction of factors had a stronger impact on ecological environmental quality, and the interaction between precipitation and other factors played a leading role. The results of this study can provide a scientific reference for the sustainable development of ecological environmental quality in the ecological zone of the Yellow River Basin.

[Spatial-temporal differentiation and driving factors of vegetation coverage in Ningxia Helan Mountain based on geodetector]. / åŸºäºŽåœ°ç†æŽ¢æµ‹å™¨çš„å®å¤è´ºå °å±±æ¤è¢«è¦†ç›–åº¦æ—¶ç©ºåˆ†å¼‚åŠé©±åŠ¨å› å­.

Monitoring the regional changes in vegetation coverage and analyzing its driving factors are beneficial to realizing the sustainable development of ecological environment. Based on Landsat 5/8 remote sensing images from 1989 to 2021, vegetation coverage of Helan Mountain in Ningxia was estimated by pixel dichotomy model. In addition, the influence of 10 factors, including environmental factors and human factors, on the spatial-temporal variations of vegetation coverage was quantified by geodetector. The results showed that average vegetation coverage was 35.8% in the study area from 1989 to 2021. On the temporal scale, it showed an increasing trend, with an average increasing rate of 0.043·(10 a)-1. On the spatial scale, vegetation coverage presented a distribution characteristic of decreasing from southwest to northeast. 58.1% of vegetation coverage in the study area would continue to improve in the future, but 30.7% of vegetation would have the potential risk of degradation. Precipitation was the dominant environmental factor driving the distribution of vegetation. Compared with single factor, the interaction between environmental factors and human factors had a stronger impact on vegetation coverage, while the interaction between precipitation and other factors played a leading role.

Surface-decorated S. cerevisiae for flow cytometric array immunoassay.

Our laboratory had developed a cell-based bio-bead for protein quantification. However, the selection of antibody in the above immunoassay is limited. This study describes a surface-decorated Saccharomyces cerevisiae for flow cytometric array immunoassay. S. cerevisiae was labeled with fluorescein isothiocyanate (FITC) and oxidized by sodium periodate, in which the saccharide group on the cytoderm outer layer was converted to an aldehyde group. In succession, adipic dihydrazide was bio-conjugated to the aldehyde group and glutaraldehyde bound to the hydrazide group. Phycoerythrin (PE)-labeled goat anti-mouse polyclonal antibody was used to assess the conjugation of mouse anti-human monoclonal antibody to surface-decorated S. cerevisiae. Cytokeratin 19 fragment (Cyfra21-1) and neuron-specific enolase (NSE) antigens were also employed to evaluate the flow cytometric array immunoassay based on surface-decorated S. cerevisiae. Flow cytometry demonstrated that FITC-barcoded S. cerevisiae as two legible populations. PE-labeled polyclonal antibody validated the coating of surface-decorated S. cerevisiae with the monoclonal antibody. The flow cytometric array immunoassays for Cyfra21-1 and NSE documented that the limit of detection (LOD) was at least 0.4 ng/mL. Precision and accuracy assessments appeared that the relative standard deviation (R.S.D.) was <15%, and the relative error (R.E.) ranged from 0.9 to 1.1. The correlation coefficient between this immunoassay and electrochemiluminescence immunoassay was 0.9622 for serum Cyfra21-1 and 0.9918 for serum NSE. In conclusion, the surface-decorated S. cerevisiae may be of use in flow cytometric array immunoassay.