Identification of immune-related genes in diagnosing retinopathy of prematurity with sepsis through bioinformatics analysis and machine learning

Background: There is increasing evidence indicating that immune system dysregulation plays a pivotal role in the pathogenesis of retinopathy of prematurity (ROP) and sepsis. This study aims to identify key diagnostic candidate genes in ROP with sepsis.Methods: We obtained publicly available data on ROP and sepsis from the gene expression omnibus database. Differential analysis and weighted gene correlation network analysis (WGCNA) were performed to identify differentially expressed genes (DEGs) and key module genes. Subsequently, we conducted functional enrichment analysis to gain insights into the biological functions and pathways. To identify immune-related pathogenic genes and potential mechanisms, we employed several machine learning algorithms, including Support Vector Machine Recursive Feature Elimination (SVM-RFE), Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest (RF). We evaluated the diagnostic performance using nomogram and Receiver Operating Characteristic (ROC) curves. Furthermore, we used CIBERSORT to investigate immune cell dysregulation in sepsis and performed cMAP analysis to identify potential therapeutic drugs.Results: The sepsis dataset comprised 352 DEGs, while the ROP dataset had 307 DEGs and 420 module genes. The intersection between DEGs for sepsis and module genes for ROP consisted of 34 genes, primarily enriched in immune-related pathways. After conducting PPI network analysis and employing machine learning algorithms, we pinpointed five candidate hub genes. Subsequent evaluation using nomograms and ROC curves underscored their robust diagnostic potential. Immune cell infiltration analysis revealed immune cell dysregulation. Finally, through cMAP analysis, we identified some small molecule compounds that have the potential for sepsis treatment.Conclusion: Five immune-associated candidate hub genes (CLEC5A, KLRB1, LCN2, MCEMP1, and MMP9) were recognized, and the nomogram for the diagnosis of ROP with sepsis was developed

Combining spectral and texture feature of UAV image with plant height to improve LAI estimation of winter wheat at jointing stage

IntroductionLeaf area index (LAI) is a critical physiological and biochemical parameter that profoundly affects vegetation growth. Accurately estimating the LAI for winter wheat during jointing stage is particularly important for monitoring wheat growth status and optimizing variable fertilization decisions. Recently, unmanned aerial vehicle (UAV) data and machine/depth learning methods are widely used in crop growth parameter estimation. In traditional methods, vegetation indices (VI) and texture are usually to estimate LAI. Plant Height (PH) unlike them, contains information about the vertical structure of plants, which should be consider.MethodsTaking Xixingdian Township, Cangzhou City, Hebei Province, China as the research area in this paper, and four machine learning algorithms, namely, support vector machine(SVM), back propagation neural network (BPNN), random forest (RF), extreme gradient boosting (XGBoost), and two deep learning algorithms, namely, convolutional neural network (CNN) and long short-term memory neural network (LSTM), were applied to estimate LAI of winter wheat at jointing stage by integrating the spectral and texture features as well as the plant height information from UAV multispectral images. Initially, Digital Surface Model (DSM) and Digital Orthophoto Map (DOM) were generated. Subsequently, the PH, VI and texture features were extracted, and the texture indices (TI) was further constructed. The measured LAI on the ground were collected for the same period and calculated its Pearson correlation coefficient with PH, VI and TI to pick the feature variables with high correlation. The VI, TI, PH and fusion were considered as the independent features, and the sample set partitioning based on joint x-y distance (SPXY) method was used to divide the calibration set and validation set of samples.ResultsThe ability of different inputs and algorithms to estimate winter wheat LAI were evaluated. The results showed that (1) The addition of PH as a feature variable significantly improved the accuracy of the LAI estimation, indicating that wheat plant height played a vital role as a supplementary parameter for LAI inversion modeling based on traditional indices; (2) The combination of texture features, including normalized difference texture indices (NDTI), difference texture indices (DTI), and ratio texture indices (RTI), substantially improved the correlation between texture features and LAI; Furthermore, multi-feature combinations of VI, TI, and PH exhibited superior capability in estimating LAI for winter wheat; (3) Six regression algorithms have achieved high accuracy in estimating LAI, among which the XGBoost algorithm estimated winter wheat LAI with the highest overall accuracy and best results, achieving the highest R2 (R2 = 0.88), the lowest RMSE (RMSE=0.69), and an RPD greater than 2 (RPD=2.54).DiscussionThis study provided compelling evidence that utilizing XGBoost and integrating spectral, texture, and plant height information extracted from UAV data can accurately monitor LAI during the jointing stage of winter wheat. The research results will provide a new perspective for accurate monitoring of crop parameters through remote sensing

Analysis of Dynamic Characteristics and Power Losses of High Speed on/off Valve with Pre-Existing Control Algorithm

A high-speed on/off valve (HSV) is generally the core component of a digital hydraulic transmission system. Therefore, its dynamic characteristics often restrict the overall performance of the digital hydraulic system. Most of the current studies focus on the optimization on the dynamic characteristics or the energy characteristics, few studies have comprehensively considered the two characteristics of the valve together. In this paper, a pre-existing control algorithm (PECA) is proposed to improve the dynamic characteristics of the HSV, and simultaneously optimize the power losses of the HSV to improve its energy conversion efficiency. The results show that, compared with the traditional single-voltage driven strategy, the opening time of the PECA decreases by 29.4%, the closing time decreases by 59.6%, and the energy conversion rate increases by 7.9%

An Insight into the Temperature Field and Particle Flow Patterns in a Fluidized Bed Reactor for Nonpelletizing Polyethylene Process Using a 3D CFD-PBM Model

This work aims at exploring the temperature and polyethylene (PE) particle flow patterns in a pilot-plant fluidized bed reactor via 3D CFD modeling approach. A Eulerian–Eulerian model involving ethylene polymerization kinetics is integrated with population balance model to investigate the issues for both traditional pelletizing PE process (TPPP) and nonpelletizing PE process (NPPP). The results show that the regions with large temperature gradients have been observed in top area. The revealed particle flow patterns in two cases are analyzed using four typical flow patterns (patterns <b>a</b>–<b>d</b>) reported in literature. PE particles are vigorously contacted and mixed along the lateral and vertical planes of the reactor in TPPP (patterns <b>b</b>–<b>d</b>). Solid dispersion is intensely enhanced in the bed vertical direction (pattern <b>d</b>), and the flow patterns show larger circular flow (pattern <b>a</b>) in the radial direction under NPPP due to the larger particle size and gas velocity