Coefficient of variation method combined with XGboost ensemble model for wheat growth monitoring

IntroductionObtaining wheat growth information accurately and efficiently is the key to estimating yields and guiding agricultural development.MethodsThis paper takes the precision agriculture demonstration area of Jiaozuo Academy of Agriculture and Forestry in Henan Province as the research area to obtain data on wheat biomass, nitrogen content, chlorophyll content, and leaf area index. By using the coefficient of variation method, a Comprehensive Growth Monitoring Indicator (CGMI) was constructed to perform fractional derivative processing on drone spectral data, and correlation analysis was performed on the fractional derivative spectra with a single indicator and CGMI, respectively. Then, grey correlation analysis was carried out on differential spectral bands with high correlation, the grey correlation coefficients between differential spectral bands were calculated, and spectral bands with high correlation were screened and taken as input variables for the model. Next, ridge regression, random forest, and XGboost models were used to establish a wheat CGMI inversion model, and the coefficient of determination (R2) and root mean squared error (RMSE) were adopted for accuracy evaluation to optimize the wheat optimal growth inversion model.Results and discussionThe results of the study show that: using the data of wheat biomass, nitrogen content, chlorophyll content and leaf area index to construct the comprehensive growth monitoring indicators, the correlation between the wheat growth monitoring indicators and the spectra was calculated, and the results showed that the correlation between the comprehensive growth monitoring indicators and the single indicator correlation had different degrees of increase, and the growth rate could reach 82.22%. The correlation coefficient between the comprehensive growth monitoring indexes and the differential spectra reached 0.92 at the flowering stage, and compared with the correlation coefficient with the original spectra at the same period, the correlation coefficients increased to different degrees, which indicated that the differential processing of spectral data could effectively enhance the spectral correlation. The three models of Random Forest, Ridge Regression and XGBoost were used to construct the wheat growth inversion model with the best effect at the flowering stage, and the XGBoost model had the highest inversion accuracy when comparing in the same period, with the training and test sets reaching 0.904 and 0.870, and the RMSEs were 0.050 and 0.079, so that the XGBoost model can be used as an effective method of monitoring the growth of wheat. To sum up, this study demonstrates that the combination of constructing comprehensive growth monitoring indicators and differential processing spectra can effectively improve the accuracy of wheat growth monitoring, bringing new methods for precision agriculture management

Human neuroepithelial stem cell regional specificity enables spinal cord repair through a relay circuit

Traumatic spinal cord injury results in persistent disability due to disconnection of surviving neural elements. Neural stem cell transplantation has been proposed as a therapeutic option, but optimal cell type and mechanistic aspects remain poorly defined. Here, we describe robust engraftment into lesioned immunodeficient mice of human neuroepithelial stem cells derived from the developing spinal cord and maintained in self-renewing adherent conditions for long periods. Extensive elongation of both graft and host axons occurs. Improved functional recovery after transplantation depends on neural relay function through the grafted neurons, requires the matching of neural identity to the anatomical site of injury, and is accompanied by expression of specific marker proteins. Thus, human neuroepithelial stem cells may provide an anatomically specific relay function for spinal cord injury recovery

Carbon nitride nanotubes with in situ grafted hydroxyl groups for highly efficient spontaneous H2O2 production

An active and inexpensive photocatalyst for H2O2 production is desirable for industrial applications. However, obtaining high photocatalytic activity from metal-free catalysts without the use of sacrificial electron donors is difficult. Herein, g-C3N4 (CN) nanotubes functionalized with surface > OH groups that are grafted in situ were successfully synthesized via a novel alkalinization process. The nanotube structures provide a large surface area and improved mass transfer properties. In situ grafted > OH groups can capture photogenerated holes to promote separation of photogenerated charge, enabling the ready availability of electrons and hydrogen ions for H2O2 production. Further, the surface > OH groups help to suppress H2O2 self-decomposition. Consequently, a high rate of 240.36 μmol h−1 g−1 of H2O2 production can be achieved without sacrificial agents, which is the highest H2O2 production in a spontaneous system for metal-free photocatalysts. This work provides a new strategy for an efficient and spontaneous H2O2 production method using a metal-free CN photocatalyst. © 2021 Elsevier B.V.1

Zika Virus Disrupts Phospho-TBK1 Localization and Mitosis in Human Neuroepithelial Stem Cells and Radial Glia

Graphical Abstract Highlights d Derivation of human neocortical and spinal cord neuroepithelial stem (NES) cells d Zika virus (ZIKV) infects NES cells and radial glia, impairing mitosis and survival d ZIKV induces mitochondrial sequestration of centrosomal phospho-TBK1 d Nucleoside analogs inhibit ZIKV replication, protecting NES cells from cell death In Brief Onorati et al. establish neuroepithelial stem (NES) cells as a model for studying human neurodevelopment and ZIKV-induced microcephaly. Together with analyses in human brain slices and microcephalic human fetal tissue, they find that ZIKV predominantly infects NES and radial glial cells, reveal a pivotal role for pTBK1, and find that nucleoside analogs inhibit ZIKV replication, protecting NES cells from cell death