Identification of potential diagnostic biomarkers and immune infiltration features in diabetic foot ulcer by bioinformatics analysis and validation.

Diabetic foot ulcer (DFU) is the most serious and costly chronic complication that may lead to disability and even death in patients suffering from diabetes mellitus (DM). However, the clinical diagnosis and prognosis of DFU is inadequate. There is still a lack of effective biomarkers for its early diagnosis. We obtained the circRNA expression dataset GSE114248 and mRNA expression dataset GSE80178 from the GEO. R software was used to identify the differentially expressed circRNAs (DECs). The mRNAs associated with DFU were identified by a random forest algorithm and intersected with mRNAs predicted by circRNAs. Then, the circRNA-miRNA-mRNA network was established and the hub genes were screened using GO semantic similarity and were validated by the GSE199939 dataset. Meanwhile, the expression level of the biomarkers was verified by RT-PCR assays and immunohistochemistry. Finally, GSEA was conducted to determine differential immune cell infiltration and the immunological cells' relationships with hub genes. We identified three hub genes including KIAA1109, ENPP5, and NRP1 that might play an important role in DFU. ROC curve results also showed a good performance of these three genes in the validation dataset. Furthermore, RT-PCR assays and immunohistochemistry confirmed the results above. Immune infiltration analysis indicated that DFU had a significant increase in Neutrophils. Moreover, three hub genes were closely correlated with a variety of inflammatory cells. KIAA1109, ENPP5, and NRP1 are key hub genes of DFU. They might play an important role in the development of DFU and could be potential biomarkers in DFU.

Wavelength selection of dual-mechanism LiDAR with reflection and fluorescence spectra for plant detection.

With the continuous expansion and refinement in plant detection range, reflection, and fluorescence spectra present great research potentials and commercial values. Referring technical advantages with hyperspectral and fluorescence lidar for monitoring plants, the synchronous observation with reflection and fluorescence signals achieved by one lidar system has attracted wide attention. This paper plans to design and construct a dual-mechanism lidar system that can obtain spatial information, reflection, and fluorescence signals simultaneously. How to select the optimal detected bands to the dual-mechanism lidar system for monitoring plants is an essential step. Therefore, this paper proposes a two-step wavelength selection method to determine the optimal bands combination by considering the spectral characteristic of reflection and fluorescence signals themselves, and the hardware performance of lidar units comprehensively. The optimal bands combination of 4 reflection bands of 481 nm, 541 nm, 711.5 nm, 775.5 nm, and 2 fluorescence bands of 686.5 nm, 737 nm was determined. Besides, compared with the original reflection or fluorescence bands, the overall accuracy and average accuracy of the optimal band combination were respectively improved by 2.51%, 15.45%, and 7.8%, 29.06%. The study demonstrated the reliability and availability of the two-step wavelength selection method, and can provide references for dual-mechanism lidar system construction.

True color 3D imaging optimization with missing spectral bands based on hyperspectral LiDAR.

True color 3D imaging plays an essential role in expressing target characteristics and 3D scene reconstruction. It can express the colors, and spatial position of targets and is beneficial for classification and identification to investigate the target material. As a special case of target imaging, true color 3D imaging is important in understanding and reconstructing real scenes. The fusion of 3D point clouds with RGB images can achieve object reconstructions, yet varying illumination conditions and registration problems still exist. As a new active imaging technique, hyperspectral LiDAR (HSL) system, can avoid these problems through hardware configuration, and provide technical support for reconstructing 3D scenes. The spectral range of the HSL system is 431-751nm. However, spectral information obtained with HSL measurements may be influenced by various factors, that further impinge on the true color 3D imaging. This study aims to propose a new color reconstruction method to improve color reconstruction challenges with missing spectral bands. Two indoor experiments and five color reconstruction schemes were utilized to evaluate the feasibility and repeatability of the method. Compared with the traditional method of color reconstruction, color reconstruction effect and color similarity were considerably improved. The similarity of color components was improved from 0.324 to 0.762. Imaging results demonstrated the reliability of improving color reconstruction effect with missing spectral bands through the new method, thereby expanded the application scopes of HSL measurements.

Analyzing the effect of incident angle on echo intensity acquired by hyperspectral lidar based on the Lambert-Beckman model.

Hyperspectral light detection and ranging (HSL) can acquire the spatial and spectral information simultaneously, which can provide more information than hyperspectral imaging and single band lidar. However, the echo intensity from targets is influenced by incident angle, and relative studies were still limited which result in the effect of incident angle on HSL not being completely understood. In this study, the incident angle effect in the whole band of HSL was analyzed and corrected. Then, five types of vegetation sample with different spectral characteristics were collected at the leaf level. Spectral range changing from 550 to 830 nm with a 1 nm spectral resolution was obtained. Lambert-Beckman model was applied to analyze the effect of the incident angle on the echo intensity. The experimental results demonstrated that the Lambert-Beckman model can efficiently apply in fitting the changing of echo intensity with incidence angle and efficiently eliminate the specular effect of target. In addition, the coefficient of variation ratio is significantly improved compared to the reference target-based model. The results illustrated that, compared to reference target-based model, the Lambert-Beckman model can efficiently explain and correct the incident angle effect with specular reflection in HSL. In addition, it was found that the specular fraction Ks, which is reduced with the increasing of reflectance, is dominating the incident angle effect in the whole band, while roughness m keeps stable at different wavelengths. Thus, this research will provide notably advanced insight into correcting the echo intensity of HSL.

Assessing different regression algorithms for paddy rice leaf nitrogen concentration estimations from the first-derivative fluorescence spectrum.

The non-destructive and rapid estimation of the crop's leaf nitrogen concentration (LNC) is significant for the quality evaluation and precise management of nitrogen (N) fertilizer. First derivative can be applied to reduce the noise in the spectral analysis, which is suited to estimate leaf N and chlorophyll concentration with different fertilization levels. In this study, the first-derivative fluorescence spectrum (FDFS) was calculated in terms of the laser-induced fluorescence (LIF) spectra and was combined with different regression algorithms, including principal component analysis (PCA), partial least-square regression (PLSR), random forest (RF), radial basic function neural network (RBF-NN), and back-propagation neural network (BPNN) for paddy rice LNC estimation. Then, the effect of diverse inner parameters on regression algorithm for LNC estimation based on the calculated FDFS served as input variables were discussed, and the optimal parameters of each model were acquired. Subsequently, the performance of different models (PLSR, RF, BPNN, RBF-NN, PCA-RF, PCA-BPNN, and PCA-RBFNN) with the optimal parameter for LNC estimation based on FDFS was discussed. Results demonstrated that PCA can efficiently extract major spectral information without obviously losing, which can improve the stability and robustness of model (PLSR, PCA-RF, PCA-BNN, and PCA-RBFNN) for LNC estimation. Then, PCA-RBFNN model exhibited better potential for LNC estimation with higher average R2 (R2=0.8743) and lower SD values (SD=0.0256) than that the other regression models in this study. And, PLSR also exhibited promising potential for LNC estimation in which the R2 values (average R2=0.8412) are higher than that the other models except for PCA-RBFNN.

Estimation of Multi-Species Leaf Area Index Based on Chinese GF-1 Satellite Data Using Look-Up Table and Gaussian Process Regression Methods.

Leaf area index (LAI) is an important biophysical parameter, which can be effectively applied in the estimation of vegetation growth status. At present, amounts of studies just focused on the LAI estimation of a single plant type, while plant types are usually mixed rather than single distribution. In this study, the suitability of GF-1 data for multi-species LAI estimation was evaluated by using Gaussian process regression (GPR), and a look-up table (LUT) combined with a PROSAIL radiative transfer model. Then, the performance of the LUT and GPR for multi-species LAI estimation was analyzed in term of 15 different band combinations and 10 published vegetation indices (VIs). Lastly, the effect of the different band combinations and published VIs on the accuracy of LAI estimation was discussed. The results indicated that GF-1 data exhibited a good potential for multi-species LAI retrieval. Then, GPR exhibited better performance than that of LUT for multi-species LAI estimation. What is more, modified soil adjusted vegetation index (MSAVI) was selected based on the GPR algorithm for multi-species LAI estimation with a lower root mean squared error (RMSE = 0.6448 m2/m2) compared to other band combinations and VIs. Then, this study can provide guidance for multi-species LAI estimation.

Active 3D Imaging of Vegetation based on Multi-Wavelength Fluorescence LiDAR.

Comprehensive and accurate vegetation monitoring is required in forestry and agricultural applications. The optical remote sensing method could be a solution. However, the traditional light detection and ranging (LiDAR) scans a surface to create point clouds and provide only 3D-state information. Active laser-induced fluorescence (LIF) only measures the photosynthesis and biochemical status of vegetation and lacks information about spatial structures. In this work, we present a new Multi-Wavelength Fluorescence LiDAR (MWFL) system. The system extended the multi-channel fluorescence detection of LIF on the basis of the LiDAR scanning and ranging mechanism. Based on the principle prototype of the MWFL system, we carried out vegetation-monitoring experiments in the laboratory. The results showed that MWFL simultaneously acquires the 3D spatial structure and physiological states for precision vegetation monitoring. Laboratory experiments on interior scenes verified the system's performance. Fluorescence point cloud classification results were evaluated at four wavelengths and by comparing them with normal vectors, to assess the MWFL system capabilities. The overall classification accuracy and Kappa coefficient increased from 70.7% and 0.17 at the single wavelength to 88.9% and 0.75 at four wavelengths. The overall classification accuracy and Kappa coefficient improved from 76.2% and 0.29 at the normal vectors to 92.5% and 0.84 at the normal vectors with four wavelengths. The study demonstrated that active 3D fluorescence imaging of vegetation based on the MWFL system has a great application potential in the field of remote sensing detection and vegetation monitoring.

Effect of different regression algorithms on the estimating leaf parameters based on selected characteristic wavelengths by using the PROSPECT model.

In this study, the characteristic wavelengths of leaf biochemical parameters (including carotenoid content, chlorophyll ${a} + { b}$a+b content, dry matter content, equivalent water thickness, and leaf structure parameter) were obtained through a sensitivity analysis based on a physical model. Then, performance of the selected characteristic wavelengths for monitoring leaf biochemical contents (LBC) was analyzed by using the following six popular regression algorithms: random forest, backpropagation neural network, support vector regression, radial basic function neural network, partial least-squares regression, and Gaussian process regression of different parameter values/kernel functions/training functions. In addition, the optimal parameters of each regression algorithm for estimating LBC were determined. Lastly, the effect of different regression algorithms on the accuracy of LBC estimation using four different data sets was also discussed. The results demonstrated that the selected 10 characteristic wavelengths combined with the Gaussian process regression model can be efficiently applied in estimating LBC.

Hyperspectral lidar point cloud segmentation based on geometric and spectral information.

Light detection and ranging (lidar) can record a 3D environment as point clouds, which are unstructured and difficult to process efficiently. Point cloud segmentation is an effective technology to solve this problem and plays a significant role in various applications, such as forestry management and 3D building reconstruction. The spectral information from images could improve the segmentation result, but suffers from the varying illumination conditions and the registration problem. New hyperspectral lidar sensor systems can solve these problems, with the capacity to obtain spectral and geometric information simultaneously. The former segmentation on hyperspectral lidar were mainly based on spectral information. The geometric segmentation method widely used by single wavelength lidar was not employed for hyperspectral lidar yet. This study aims to fill this gap by proposing a hyperspectral lidar segmentation method with three stages. First, Connected-Component Labeling (CCL) using the geometric information is employed for base segmentation. Second, the output components of the first stage are split by the spectral difference using Density-Based Spatial Clustering of Applications with Noise (DBSCAN). Third, the components of the second stage are merged based on the spectral similarity using Spectral Angle Match (SAM). Two indoor experimental scenes were setup for validation. We compared the performance of our mothed with that of the 3D and intensity feature based method. The quantitative analysis indicated that, our proposed method improved the point-weighted score by 19.35% and 18.65% in two experimental scenes, respectively. These results showed that the geometric segmentation method for single wavelength lidar could be combined with the spectral information, and contribute to the more effective hyperspectral lidar point cloud segmentation.

Selection of the optimal bands of first-derivative fluorescence characteristics for leaf nitrogen concentration estimation.

Laser-induced fluorescence technology provides a nondestructive and rapid method for monitoring leaf nitrogen concentration (LNC) based on its optical characteristics. Crop growth status can be efficiently diagnosed and quality evaluated by monitoring LNC. In this study, the first-derivative fluorescence spectrum (FDFS) was proposed and calculated based on the fluorescence spectra excited by 355, 460, and 556 nm excitation lights for rice LNC estimation. Then, the performance of each band FDFS characteristics and the FDFS ratio for LNC estimation were comprehensively discussed using principal component analysis and backpropagation neural network (BPNN). We analyzed the number of FDFS characteristics' influence on the accuracy of LNC monitoring. Results showed that R2 does not clearly improve for the LNC monitoring based on the BPNN model when the number of extracted FDFS features exceeds 4 or 5. Therefore, the FDFS optimal band combination of different excitation light wavelengths mentioned was selected for LNC monitoring. The selected band combinations contained the majority of FDFS characteristics and could effectively be applied in monitoring LNC (for 355, 460, and 556 nm excitation lights, with R2 of 0.764, 0.625, and 0.738, respectively) based on the BPNN model.

Analyzing the effect of the incidence angle on chlorophyll fluorescence intensity based on laser-induced fluorescence lidar.

Laser-induced fluorescence (LIF) technology has been widely applied to monitor vegetation growth status and biochemical concentrations. Thus, it is important to accurately acquire the fluorescence information for the quantitative monitoring of vegetation growth status. In this study, firstly, the incidence angle's effect on chlorophyll fluorescence intensity was analyzed by using the FluorMODleaf model. Then, comprehensive experimental data on the angle dependence of the fluorescence intensity to vegetation leaf surface were collected. Numerical and experimental results showed that proposed corrected cosine expression could be used to describe the relationship between the incidence angle and the fluorescence intensity in the LIF-Lidar. Lastly, fluorescence signals at 685 and 740 nm extracted at different incident angles of excitation lights were fitted with the corrected cosine expression. The coefficient of determination (R2) of the fitting results reached a maximum value of 0.93 for Salix babylonica.

Analyzing the performance of the first-derivative fluorescence spectrum for estimating leaf nitrogen concentration.

Nitrogen (N) is an essential nutrient for crop growth. The rapid and non-destructive monitoring of N nutrition in crops through remote sensing is important for the accurate diagnosis and quality evaluation of crop growth status. Leaf nitrogen concentration (LNC), which has been widely utilized in remote sensing, serves as a crucial indicator for the monitoring of crops growth status. In this study, the first-derivative fluorescence spectrum (FDFS) based on laser-induced fluorescence (LIF) was proposed for LNC estimation in paddy rice. First, the correlation between the LNC and FDFS at each wavelength was analyzed in detail using different excitation light wavelengths (ELWs; 355, 420, and 556 nm). Then, FDFS was used as an input parameter to train a back-propagation neural networks (BPNN) model for LNC estimation. The coefficients of determination (R2) of the linear regression analysis between the measured and predicted LNC were 0.823, 0.743, and 0.837, corresponding to 355, 420, and 556 nm ELWs, respectively. Second, the principal components analysis was performed for the extraction of the main characteristics of FDFS, and the calculated variables were used for LNC inversion. The R2 values were 0.891, 0.815, and 0.907 for 355, 420, and 556 nm ELWs, respectively. In addition, the correlation between the ratio of FDFS and LNC was also analyzed, which can provide a reference for the selection of optimal wavelengths for LNC monitoring. The experimental results exhibited the promising potential of FDFS combined with multivariate analysis for LNC monitoring, which can allow additional fluorescence characteristics to improve the accuracy of LNC monitoring.

Potential of vegetation indices combined with laser-induced fluorescence parameters for monitoring leaf nitrogen content in paddy rice.

Nitrogen (N) is important for the growth of crops. Leaf nitrogen content (LNC) serves as a crucial indicator of the growth status of crops and can help determine the dose of N fertilizer. Laser-induced fluorescence (LIF) technology and the reflectance spectra of crops are widely used to detect the biochemical content of leaves. Many vegetation indices (VIs) and fluorescence parameters have been developed to estimate LNC. However, the comparison among VIs and between fluorescence parameters and VIs has been rarely studied in the estimation of LNC. In this study, the performances of several published empirical VIs and fluorescence parameters for the estimation of paddy rice LNC were analyzed using the support vector machine (SVM) algorithm. Then, the optimal VIs (TVI, MTVI1, MTVI2, and MSAVI) and fluorescence parameters (F735/F460 and F685/F460), which were suitable for LNC monitoring in this study, were chosen. In addition, the combination of the VIs and fluorescence parameters was proposed as the input variables in the SVM model and used to estimate the LNC. Experimental results exhibited the promising potential of the LIF technology combined with reflectance for the accurate estimation of LNC, which provided guidance for monitoring the LNC.

Effect of fluorescence characteristics and different algorithms on the estimation of leaf nitrogen content based on laser-induced fluorescence lidar in paddy rice.

Paddy rice is one of the most significant food sources and an important part of the ecosystem. Thus, accurate monitoring of paddy rice growth is highly necessary. Leaf nitrogen content (LNC) serves as a crucial indicator of growth status of paddy rice and determines the dose of nitrogen (N) fertilizer to be used. This study aims to compare the predictive ability of the fluorescence spectra excited by different excitation wavelengths (EWs) combined with traditional multivariate analysis algorithms, such as principal component analysis (PCA), back-propagation neural network (BPNN), and support vector machine (SVM), for estimating paddy rice LNC from the leaf level with three different fluorescence characteristics as input variables. Then, six estimation models were proposed. Compared with the five other models, PCA-BPNN was the most suitable model for the estimation of LNC by improving R2 and reducing RMSE and RE. For 355, 460 and 556 nm EWs, R2 was 0.89, 0.80 and 0.88, respectively. Experimental results demonstrated that the fluorescence spectra excited by 355 and 556 nm EWs were superior to those excited by 460 nm for the estimation of LNC with different models. BPNN algorithm combined with PCA may provide a helpful exploratory and predictive tool for fluorescence spectra excited by appropriate EW based on practical application requirements for monitoring the N status of crops.

Evaluation of hyperspectral LiDAR for monitoring rice leaf nitrogen by comparison with multispectral LiDAR and passive spectrometer.

Fast and nondestructive assessment of leaf nitrogen concentration (LNC) is critical for crop growth diagnosis and nitrogen management guidance. In the last decade, multispectral LiDAR (MSL) systems have promoted developments in the earth and ecological sciences with the additional spectral information. With more wavelengths than MSL, the hyperspectral LiDAR (HSL) system provides greater possibilities for remote sensing crop physiological conditions. This study compared the performance of ASD FieldSpec Pro FR, MSL, and HSL for estimating rice (Oryza sativa) LNC. Spectral reflectance and biochemical composition were determined in rice leaves of different cultivars (Yongyou 4949 and Yangliangyou 6) throughout two growing seasons (2014-2015). Results demonstrated that HSL provided the best indicator for predicting rice LNC, yielding a coefficient of determination (R2) of 0.74 and a root mean square error of 2.80 mg/g with a support vector machine, similar to the performance of ASD (R2 = 0.73). Estimation of rice LNC could be significantly improved with the finer spectral resolution of HSL compared with MSL (R2 = 0.56).

Estimating the leaf nitrogen content of paddy rice by using the combined reflectance and laser-induced fluorescence spectra.

Paddy rice is one of the most important crops in China, and leaf nitrogen content (LNC) serves as a significant indictor for monitoring crop status. A reliable method is needed for precise and fast quantification of LNC. Laser-induced fluorescence (LIF) technology and reflectance spectra of crops are widely used to monitor leaf biochemical content. However, comparison between the fluorescence and reflectance spectra has been rarely investigated in the monitoring of LNC. In this study, the performance of the fluorescence and reflectance spectra for LNC estimation was discussed based on principal component analysis (PCA) and back-propagation neural network (BPNN). The combination of fluorescence and reflectance spectra was also proposed to monitor paddy rice LNC. The fluorescence and reflectance spectra exhibited a high degree of multi-collinearity. About 95.38%, and 97.76% of the total variance included in the spectra were efficiently extracted by using the first three PCs in PCA. The BPNN was implemented for LNC prediction based on new variables calculated using PCA. The experimental results demonstrated that the fluorescence spectra (R2 = 0.810, 0.804 for 2014 and 2015, respectively) are superior to the reflectance spectra (R2 = 0.721, 0.671 for 2014 and 2015, respectively) for estimating LNC based on the PCA-BPNN model. The proposed combination of fluorescence and reflectance spectra can greatly improve the accuracy of LNC estimation (R2 = 0.912, 0.890 for 2014 and 2015, respectively).

Analyzing the performance of fluorescence parameters in the monitoring of leaf nitrogen content of paddy rice.

Leaf nitrogen content (LNC) is a significant factor which can be utilized to monitor the status of paddy rice and it requires a reliable approach for fast and precise quantification. This investigation aims to quantitatively analyze the correlation between fluorescence parameters and LNC based on laser-induced fluorescence (LIF) technology. The fluorescence parameters exhibited a consistent positive linear correlation with LNC in different growing years (2014 and 2015) and different rice cultivars. The R(2) of the models varied from 0.6978 to 0.9045. Support vector machine (SVM) was then utilized to verify the feasibility of the fluorescence parameters for monitoring LNC. Comparison of the fluorescence parameters indicated that F740 is the most sensitive (the R(2) of linear regression analysis of the between predicted and measured values changed from 0.8475 to 0.9226, and REs ranged from 3.52% to 4.83%) to the changes in LNC among all fluorescence parameters. Experimental results demonstrated that fluorescence parameters based on LIF technology combined with SVM is a potential method for realizing real-time, non-destructive monitoring of paddy rice LNC, which can provide guidance for the decision-making of farmers in their N fertilization strategies.