Molecular signature of soil organic matter under different land uses in the Lake Chaohu Basin

The concentration and molecular composition of soil organic matter(SOM)are important factors in mitigation against climate change as well as providing other ecosystem services.Our quantitative understanding of how land use influences SOM molecular composition and associated turnover dynamics is limited,which underscores the need for high-throughput analytical approaches and molecular marker signatures to clarify this etiology.Combining a high-throughput untargeted mass spectrometry screening and molecular markers,we show that forest,farmland and urban land uses result in distinct molecular signatures of SOM in the Lake Chaohu Basin.Molecular markers indicate that forest SOM has abundant carbon contents from vegetation and condensed organic carbon,leading to high soil organic carbon(SOC)concentration.Farmland SOM has moderate carbon contents from vegetation,and limited content of condensed organic carbon,with SOC significantly lower than that of forest soils.Urban SOM has high abundance of condensed organic carbon markers due to anthropogenic activities but relatively low in markers from vegetation.Consistently,urban soils have the highest black carbon/SOC ratio among these land uses.Overall,our results suggested that the molecular signature of SOM varies significantly with land use in the Lake Chaohu Basin,influencing carbon dynamics.Our strategy of molecular fingerprinting and marker discovery is expected to enlighten further research on SOM molecular signatures and cycling dynamics.

Predicting Trend of High Frequency CSI 300 Index Using Adaptive Input Selection and Machine Learning Techniques

High-frequency stock trend prediction using machine learners has raised substantial interest in literature. Nevertheless, there is no gold standard to select the inputs for the learners. This paper investigates the approach of adaptive input selection(AIS) for the trend prediction of high-frequency stock index price and compares it with the commonly used deterministic input setting(DIS) approach.The DIS approach is implemented through computation of technical indicator values on deterministic period parameters. The AIS approach selects the most suitable indicators and their parameters for the time-varying dataset using feature selection methods. Two state-of-the-art machine learners, support vector machine(SVM) and artificial neural network(ANN), are adopted as learning models. Accuracy and F-measure of SVM and ANN models with both the approaches are computed based on the high-frequency data of CSI 300 index. The results suggest that the AIS approach using t-statistics,information gain and ROC methods can achieve better prediction performance than the DIS approach.Also, the investment performance evaluation shows that the AIS approach with the same three feature selection methods provides significantly higher returns than the DIS approach.