Surface Treatments Effect on Rainfall Canopy Interception and Runoff-Rainfall Ratio for in-Field Rainwater Harvesting

The purpose of this study was to ascertain the effectiveness of surface treatments to quantify the partitioning of rainwater falling on the runoff strips and basins as well as to determine the fraction of rainwater available to intercept by maize canopy and infiltrate into the root zone. The rainfall canopy interception （RCI） was estimated as a function of basin leaf area ratio per rain event. The runoffrainfall （RR） ratio was determined for both a single rainfall event and the whole growing season. Infiltration ratio of basin to runoff area was analysed for every unit millimeter of water that infiltrate in the runoff section, some additional of water will infiltrate in the basin area. The plateau value of RCI-rainfall relationships rendered about double in the wider （1.0-1.1 mm） compared to the narrow runoff strips （0.5-0.6 mm）. Statistically, the combined surface treatments （RSL x ML） affected the RR ratio with higher efficiency in bare 1 m runoff （27%） and the lower efficiency group （〈 10%） is associated with the widest runoff length covered with mulch. Variations in fractions of rainwater that can infiltrate into basins and runoffareas can lead one to select alternative strategies for water harvesting techniques.

Signature Based on Six Autophagy-related Genes to Predict Prognosis of Head and Neck Squamous Cell Carcinoma

Objective The present study aimed to develop an autophagy-related gene prognostic prediction model to provide survival risk prediction for head and neck squamous cell carcinoma(HNSCC)patients.Methods The K-mean cluster analysis was performed on HNSCC samples based on the expression values of 210 autophagy-related genes for candidate signature gene selection.LASSO Cox regression analysis was generated using the potential genes and the risk score was calculated from the prognosis model.The risk score was processed as an independent prognostic indicator to construct the nomogram model.The immune status including immune cell infiltration ratio and checkpoints of patients with HNSCC in high-and low-risk groups was also explored.Results LASSO Cox regression analysis was performed on the selected autophagy-related genes.According to the lambda value corresponding to the number of different genes in the LASSO Cox analysis,six genes(GABARAPL2,SAR1A,ST13,GAPDH,FADD and LAMP1)were finally chosen.The risk score based on the genes was generated,which was an independent prognostic marker for HNSCC.The prognostic prediction model(nomogram)was further optimized by the independent prognostic factors(risk score),which can better predict the prognosis and survival of patients.With the risk score and prognosis model,eight types of immune cells and six key immune checkpoints(CTLA4,PD1,IDO1,TDO2,LAG3,TIGIT)displayed expression specificity.Conclusion This study identified several potential prognostic biomarkers and established an autophagy-related prognostic prediction model for HNSCC,which provides a valuable reference for future clinical research.