Effect of interesting respiratory rehabilitation training for the treatment of refractory Mycoplasma pneumoniae pneumonia in children.

BACKGROUNDS: Refractory Mycoplasma pneumoniae pneumonia (RMPP) cause damage of pulmonary function and physical therapy assisting medical treatment is needed. OBJECTIVE: The aim of this study was to investigate the effect of interesting respiratory rehabilitation training on pulmonary function in children with RMPP. METHODS: A total of 76 children with diagnoses of RMPP in our hospital from January 2020 to February 2021 were enrolled in this prospective study. According to the random number table method, they were divided into the control group and the study group, with 38 cases in each group. The control group were given conventional treatment, and the study group received interesting respiratory rehabilitation training in the basis of conventional treatment. The antipyretic time, disappearance time of pulmonary shadow and cough, length of hospital stay, pulmonary function (first second of expiratory volume (FEV1), forced vital capacity (FVC), FEV1/FVC) at 1 day before and after intervention, serum interleukin-6 (IL-6), C-reactive protein (CRP), tumor necrosis factor (TNF-α), and quality of life (Pediatric Quality of Life Inventory, PedsQL 4.0 scale) were observed in the two groups. RESULTS: The antipyretic time, disappearance time of pulmonary shadow and cough, length of hospital stay in the study group were shorter than those in the control group (P < 0.05). One day before intervention, there was no significant difference in FVC, FEV1, FEV1/FVC IL-6, CRP, and TNF-α between the two groups (P > 0.05). One day after intervention, FVC, FEV1 and FEV1/FVC in the study group were better than those in the control group (P < 0.05), and the levels of IL-6, CRP, and TNF-α in the study group were lower than those in the control group with significant difference (P < 0.05). One day before intervention, there were no significant differences in physiological function, emotional function, social function, and school function between the two groups (P > 0.05). After intervention, physiological function, emotional function, social function, and school function of the study group were better than those of the control group (P < 0.05). CONCLUSION: The interesting respiratory rehabilitation training can effectively improve the pulmonary function of children with RMPP, with strong flexibility, which is worthy of clinical application.

Application of UAV Multisensor Data and Ensemble Approach for High-Throughput Estimation of Maize Phenotyping Traits.

High-throughput estimation of phenotypic traits from UAV (unmanned aerial vehicle) images is helpful to improve the screening efficiency of breeding maize. Accurately estimating phenotyping traits of breeding maize at plot scale helps to promote gene mining for specific traits and provides a guarantee for accelerating the breeding of superior varieties. Constructing an efficient and accurate estimation model is the key to the application of UAV-based multiple sensors data. This study aims to apply the ensemble learning model to improve the feasibility and accuracy of estimating maize phenotypic traits using UAV-based red-green-blue (RGB) and multispectral sensors. The UAV images of four growth stages were obtained, respectively. The reflectance of visible light bands, canopy coverage, plant height (PH), and texture information were extracted from RGB images, and the vegetation indices were calculated from multispectral images. We compared and analyzed the estimation accuracy of single-type feature and multiple features for LAI (leaf area index), fresh weight (FW), and dry weight (DW) of maize. The basic models included ridge regression (RR), support vector machine (SVM), random forest (RF), Gaussian process (GP), and K-neighbor network (K-NN). The ensemble learning models included stacking and Bayesian model averaging (BMA). The results showed that the ensemble learning model improved the accuracy and stability of maize phenotypic traits estimation. Among the features extracted from UAV RGB images, the highest accuracy was obtained by the combination of spectrum, structure, and texture features. The model had the best accuracy constructed using all features of two sensors. The estimation accuracies of ensemble learning models, including stacking and BMA, were higher than those of the basic models. The coefficient of determination (R 2) of the optimal validation results were 0.852, 0.888, and 0.929 for LAI, FW, and DW, respectively. Therefore, the combination of UAV-based multisource data and ensemble learning model could accurately estimate phenotyping traits of breeding maize at plot scale.

Estimation of amino acid contents in maize leaves based on hyperspectral imaging.

Estimation of the amino acid content in maize leaves is helpful for improving maize yield estimation and nitrogen use efficiency. Hyperspectral imaging can be used to obtain the physiological and biochemical parameters of maize leaves with the advantages of being rapid, non-destructive, and high throughput. This study aims to estimate the multiple amino acid contents in maize leaves using hyperspectral imaging data. Two nitrogen (N) fertilizer experiments were carried out to obtain the hyperspectral images of fresh maize leaves. The partial least squares regression (PLSR) method was used to build the estimation models of various amino acid contents by using the reflectance of all bands, sensitive band range, and sensitive bands. The models were then validated with the independent dataset. The results showed that (1) the spectral reflectance of most amino acids was more sensitive in the range of 400-717.08 nm than other bands. The estimation accuracy was better by using the reflectance of the sensitive band range than that of all bands; (2) the sensitive bands of most amino acids were in the ranges of 505.39-605 nm and 651-714 nm; and (3) among the 24 amino acids, the estimation models of the ß-aminobutyric acid, ornithine, citrulline, methionine, and histidine achieved higher accuracy than those of other amino acids, with the R 2, relative root mean square error (RE), and relative percent deviation (RPD) of the measured and estimated value of testing samples in the range of 0.84-0.96, 8.79%-19.77%, and 2.58-5.18, respectively. This study can provide a non-destructive and rapid diagnostic method for genetic sensitive analysis and variety improvement of maize.

The Application of UAV-Based Hyperspectral Imaging to Estimate Crop Traits in Maize Inbred Lines.

Crop traits such as aboveground biomass (AGB), total leaf area (TLA), leaf chlorophyll content (LCC), and thousand kernel weight (TWK) are important indices in maize breeding. How to extract multiple crop traits at the same time is helpful to improve the efficiency of breeding. Compared with digital and multispectral images, the advantages of high spatial and spectral resolution of hyperspectral images derived from unmanned aerial vehicle (UAV) are expected to accurately estimate the similar traits among breeding materials. This study is aimed at exploring the feasibility of estimating AGB, TLA, SPAD value, and TWK using UAV hyperspectral images and at determining the optimal models for facilitating the process of selecting advanced varieties. The successive projection algorithm (SPA) and competitive adaptive reweighted sampling (CARS) were used to screen sensitive bands for the maize traits. Partial least squares (PLS) and random forest (RF) algorithms were used to estimate the maize traits. The results can be summarized as follows: The sensitive bands for various traits were mainly concentrated in the near-red and red-edge regions. The sensitive bands screened by CARS were more abundant than those screened by SPA. For AGB, TLA, and SPAD value, the optimal combination was the CARS-PLS method. Regarding the TWK, the optimal combination was the CARS-RF method. Compared with the model built by RF, the model built by PLS was more stable. This study provides guiding significance and practical value for main trait estimation of maize inbred lines by UAV hyperspectral images at the plot level.

Establishing NDRE dynamic models of winter wheat under multi-nitrogen rates based on a field spectral sensor.

Field spectral sensors provide real-time, reliable, quantitative monitoring of crop growth. Fitting the continuous growth in the entire growing period from the measurements of limited frequency is helpful to the comparative analysis of interannual growth and fertilizer management in the field. To exploit this capacity, our work presents a model that uses the normalized difference red edge (NDRE) index derived from the field spectral sensor for real-time monitoring of the canopy growth of winter wheat in the whole growing period. We developed this model from experiments in three counties in Hebei province, China, where we obtained the near-infrared and red edge reflectance, grain yield, and canopy parameters for eight growth stages and for various nitrogen (N) rates. Given the correlation between effective accumulated temperature and crop growth, we used the growing degree-days as an adjustment parameter to develop models for dynamic monitoring of the NDRE of the winter wheat canopy during the entire growing period. The results show that high determination coefficients (R2=0.89 to 0.96) are obtained from all models based on relative NDRE and effective accumulative temperature (independent of N fertilization rates). The model based on the rational function is the best of all models tested, with the accuracy for normal and high N fertilization rates being slightly greater than that for low N fertilization rates. Therefore, a relative-NDRE model with the accumulative growing degree-days since sowing could allow monitoring canopy NDRE of winter wheat at any time, which could be helpful for overcoming the shortage of incomparable growth derived from the differences of sensing date, sowing date, and fertilizer, etc.

Entropy Weight Ensemble Framework for Yield Prediction of Winter Wheat Under Different Water Stress Treatments Using Unmanned Aerial Vehicle-Based Multispectral and Thermal Data.

Crop breeding programs generally perform early field assessments of candidate selection based on primary traits such as grain yield (GY). The traditional methods of yield assessment are costly, inefficient, and considered a bottleneck in modern precision agriculture. Recent advances in an unmanned aerial vehicle (UAV) and development of sensors have opened a new avenue for data acquisition cost-effectively and rapidly. We evaluated UAV-based multispectral and thermal images for in-season GY prediction using 30 winter wheat genotypes under 3 water treatments. For this, multispectral vegetation indices (VIs) and normalized relative canopy temperature (NRCT) were calculated and selected by the gray relational analysis (GRA) at each growth stage, i.e., jointing, booting, heading, flowering, grain filling, and maturity to reduce the data dimension. The elastic net regression (ENR) was developed by using selected features as input variables for yield prediction, whereas the entropy weight fusion (EWF) method was used to combine the predicted GY values from multiple growth stages. In our results, the fusion of dual-sensor data showed high yield prediction accuracy [coefficient of determination (R 2) = 0.527-0.667] compared to using a single multispectral sensor (R 2 = 0.130-0.461). Results showed that the grain filling stage was the optimal stage to predict GY with R 2 = 0.667, root mean square error (RMSE) = 0.881 t ha-1, relative root-mean-square error (RRMSE) = 15.2%, and mean absolute error (MAE) = 0.721 t ha-1. The EWF model outperformed at all the individual growth stages with R 2 varying from 0.677 to 0.729. The best prediction result (R 2 = 0.729, RMSE = 0.831 t ha-1, RRMSE = 14.3%, and MAE = 0.684 t ha-1) was achieved through combining the predicted values of all growth stages. This study suggests that the fusion of UAV-based multispectral and thermal IR data within an ENR-EWF framework can provide a precise and robust prediction of wheat yield.

Monitoring Maize Lodging Grades via Unmanned Aerial Vehicle Multispectral Image.

Lodging is one of the main factors affecting the quality and yield of crops. Timely and accurate determination of crop lodging grade is of great significance for the quantitative and objective evaluation of yield losses. The purpose of this study was to analyze the monitoring ability of a multispectral image obtained by an unmanned aerial vehicle (UAV) for determination of the maize lodging grade. A multispectral Parrot Sequoia camera is specially designed for agricultural applications and provides new information that is useful in agricultural decision-making. Indeed, a near-infrared image which cannot be seen with the naked eye can be used to make a highly precise diagnosis of the vegetation condition. The images obtained constitute a highly effective tool for analyzing plant health. Maize samples with different lodging grades were obtained by visual interpretation, and the spectral reflectance, texture feature parameters, and vegetation indices of the training samples were extracted. Different feature transformations were performed, texture features and vegetation indices were combined, and various feature images were classified by maximum likelihood classification (MLC) to extract four lodging grades. Classification accuracy was evaluated using a confusion matrix based on the verification samples, and the features suitable for monitoring the maize lodging grade were screened. The results showed that compared with a multispectral image, the principal components, texture features, and combination of texture features and vegetation indices were improved by varying degrees. The overall accuracy of the combination of texture features and vegetation indices is 86.61%, and the Kappa coefficient is 0.8327, which is higher than that of other features. Therefore, the classification result based on the feature combinations of the UAV multispectral image is useful for monitoring of maize lodging grades.