Identification of EML4 as a key hub gene for endometriosis and its molecular mechanism and potential drug prediction based on the GEO database

Objective:Key genes were screened to analyze molecular mechanisms and their drug targets of endometriosis by applying a bioinformatics approach.Methods:Gene expression profiles of endometriosis and healthy controls were obtained from the Gene Expression Omnibus database.Significant differentially expressed genes were screened using the limma package.Correlation pathways were screened by Spearman correlation analysis on the echinoderm microtubule-associated protein-like 4(EML4)and enrichment in endometriosis pathways and estimated by the GSVA package.Immune characteristics were assessed by the“ESTIMATE”R package.Potential regulatory pathways were determined by enrichment analysis.The SWISS-MODE website was used in homology modeling with EML4 and EML4 protein activity was predicted.VarElect was employed in molecular docking for screening potential compound inhibitors targeting endometriosis.Results:Ten endometriosis and 10 normal samples were included.EML4 was significantly upregulated in endometriosis(p<0.05).Thirty significantly correlated pathways involving 18 positive and 12 negative correlations,including GLYCOSAMINOGLYCAN_BIOSYNTHESIS_HEPARAN_SULFATE and GLYCOSPHINGOLIPID_BIOSYNTHESIS_GANGLIO_SERIES were screened between EML4 and endometriosis.Immunocorrelation analysis showed a significant difference in immune-related pathways in endometriosis and normal samples(p<0.05).In endometriosis,EML4 was associated with T-cell CD4 resting memory,activated mast cells,plasma cells,activated NK cells,M2 macrophages,and follicular helper T cells(p<0.05).Molecular docking identified five potential inhibitors of EML4,and compound DB05104(asimadoline)bound well to EML4 protein to exert its physiological effects.Conclusion:Differential gene expression and immune correlation analyses revealed that EML4 may affect endometriosis through multiple targets and pathways,the mechanism of which involved immune cell activation and infiltration.Molecular docking and dynamics simulation verified DB05104 as a potential inhibitor of EML4 and a powerful target for endometriosis treatment.

Forest height mapping using inventory and multi-source satellite data over Hunan Province in southern China

Background:Accurate mapping of forest canopy heights at a fine spatial resolution over large geographical areas is challenging.It is essential for the estimation of forest aboveground biomass and the evaluation of forest ecosystems.Yet current regional to national scale forest height maps were mainly produced at coarse-scale.Such maps lack spatial details for decision-making at local scales.Recent advances in remote sensing provide great opportunities to fill this gap.Method:In this study,we evaluated the utility of multi-source satellite data for mapping forest heights over Hunan Province in China.A total of 523 plot data collected from 2017 to 2018 were utilized for calibration and validation of forest height models.Specifically,the relationships between three types of in-situ measured tree heights(maximum-,averaged-,and basal area-weighted-tree heights)and plot-level remote sensing metrics(multispectral,radar,and topo variables from Landsat,Sentinel-1/PALSAR-2,and SRTM)were analyzed.Three types of models(multilinear regression,random forest,and support vector regression)were evaluated.Feature variables were selected by two types of variable selection approaches(stepwise regression and random forest).Model parameters and model performances for different models were tuned and evaluated via a 10-fold cross-validation approach.Then,tuned models were applied to generate wall-to-wall forest height maps for Hunan Province.Results:The best estimation of plot-level tree heights(R2 ranged from 0.47 to 0.52,RMSE ranged from 3.8 to 5.3 m,and rRMSE ranged from 28%to 31%)was achieved using the random forest model.A comparison with existing forest height maps showed similar estimates of mean height,however,the ranges varied under different definitions of forest and types of tree height.Conclusions:Primary results indicate that there are small biases in estimated heights at the province scale.This study provides a framework toward establishing regional to national scale maps of vertical forest structure.

Machine learning for molecular thermodynamics

Thermodynamic properties of complex systems play an essential role in developing chemical engineering processes.It remains a challenge to predict the thermodynamic properties of complex systems in a wide range and describe the behavior of ions and molecules in complex systems.Machine learning emerges as a powerful tool to resolve this issue because it can describe complex relationships beyond the capacity of traditional mathematical functions.This minireview will summarize some fundamental concepts of machine learning methods and their applications in three aspects of the molecular thermodynamics using several examples.The first aspect is to apply machine learning methods to predict the thermodynamic properties of a broad spectrum of systems based on known data.The second aspect is to integer machine learning and molecular simulations to accelerate the discovery of materials.The third aspect is to develop machine learning force field that can eliminate the barrier between quantum mechanics and all-atom molecular dynamics simulations.The applications in these three aspects illustrate the potential of machine learning in molecular thermodynamics of chemical engineering.We will also discuss the perspective of the broad applications of machine learning in chemical engineering.

Application of heat-activated peroxydisulfate process for the chemical cleaning of fouled ultrafiltration membranes

Na Cl O has been widely used to restore membrane flux in practical membrane cleaning processes,which would induce the formation of toxic halogenated byproducts.In this study,we proposed a novel heatactivated peroxydisulfate(heat/PDS)process to clean the membrane fouling derived from humic acid(HA).The results show that the combination of heat and PDS can achieve almost 100%recovery of permeate flux after soaking the HA-fouled membrane in 1 mmol/L PDS solution at 50℃ for 2 h,which is attributed to the changes of HA structure and enhanced detachment of foulants from membranes.The properties of different treated membranes are characterized by scanning electron microscopy(SEM),atomic force microscope(AFM),attenuated total reflection Fourier transform infrared spectroscopy(ATRFTIR),and X-ray photoelectron spectroscopy(XPS),demonstrating that the reversible and irreversible foulants could be effectively removed by heat/PDS cleaning.The filtration process and fouling mechanism of the cleaned membrane were close to that of the virgin membrane,illustrating the good reusability of the cleaned membrane.Additionally,heat/PDS which can avoid the generation of halogenated byproducts shows comparable performance to Na Cl O on membrane cleaning and high performance for the removal of fouling caused by sodium alginate(SA),HA-bovine serum albumin(BSA)-SA mixture and algae,further suggesting that heat/PDS would be a potential alternative for membrane cleaning in practical application.