Tumor-derived extracellular vesicles regulate macrophage polarization: role and therapeutic perspectives.

Extracellular vesicles (EVs) are important cell-to-cell communication mediators. This paper focuses on the regulatory role of tumor-derived EVs on macrophages. It aims to investigate the causes of tumor progression and therapeutic directions. Tumor-derived EVs can cause macrophages to shift to M1 or M2 phenotypes. This indicates they can alter the M1/M2 cell ratio and have pro-tumor and anti-inflammatory effects. This paper discusses several key points: first, the factors that stimulate macrophage polarization and the cytokines released as a result; second, an overview of EVs and the methods used to isolate them; third, how EVs from various cancer cell sources, such as hepatocellular carcinoma, colorectal carcinoma, lung carcinoma, breast carcinoma, and glioblastoma cell sources carcinoma, promote tumor development by inducing M2 polarization in macrophages; and fourth, how EVs from breast carcinoma, pancreatic carcinoma, lungs carcinoma, and glioblastoma cell sources carcinoma also contribute to tumor development by promoting M2 polarization in macrophages. Modified or sourced EVs from breast, pancreatic, and colorectal cancer can repolarize M2 to M1 macrophages. This exhibits anti-tumor activities and offers novel approaches for tumor treatment. Therefore, we discovered that macrophage polarization to either M1 or M2 phenotypes can regulate tumor development. This is based on the description of altering macrophage phenotypes by vesicle contents.

Enhancing local governments' environmental attention through open government data: evidence from China.

Environmental conservation has ascended to a prominent position on the global agenda, and China, recognizing the urgent need for environmental protection, has implemented nationwide measures. However, varying levels of environmental attentiveness among local governments have resulted in uneven implementation of these national directives across regions. Therefore, it is crucial to investigate the factors that drive local governments' environmental attention. Our study explores the impact of open government data (OGD) on local governments' environmental attention. Utilizing city-level data from 2010 to 2020, we employ a staggered difference-in-differences (DID) model for empirical analysis. The results reveal that OGD significantly and positively influences local governments' environmental attention. This influence is partly attributed to OGD's role in promoting government digitization, mitigating fiscal pressures, and increasing energy demand. Further analysis, including heterogeneity assessments, demonstrates that OGD has a more pronounced positive effect on environmental attention in cities with higher online political participation activity and a larger internet user base. Such empirical insights underscore the imperative for an integrative policy framework that accentuates the refinement of OGD platform in tandem with strategic enhancements in political participatory mechanisms and digital infrastructure investments, thereby fostering robust local environmental stewardship paradigms.

Agricultural diversification promotes sustainable and resilient global rice production.

Rice is a staple food for half of the human population, but the effects of diversification on yields, economy, biodiversity and ecosystem services have not been synthesized. Here we quantify diversification effects on environmental and socio-economic aspects of global rice production. We performed a second-order meta-analysis based on 25 first-order meta-analyses covering four decades of research, showing that diversification can maintain soil fertility, nutrient cycling, carbon sequestration and yield. We used three individual first-order meta-analyses based on 39 articles to close major research gaps on the effects of diversification on economy, biodiversity and pest control, showing that agricultural diversification can increase biodiversity by 40%, improve economy by 26% and reduce crop damage by 31%. Trade-off analysis showed that agricultural diversification in rice production promotes win-win scenarios between yield and other ecosystem services in 81% of all cases. Knowledge gaps remain in understanding the spatial and temporal effects of specific diversification practices and trade-offs.

EIF3C Promotes Lung Cancer Tumorigenesis by Regulating the APP/HSPA1A/LMNB1 Axis.

Objective: This study was designed to explore the role and mechanism of eukaryotic initiation factor 3C (EIF3C) in the proliferation and apoptosis of lung cancer cells. Methods: EIF3C expression in clinic lung cancer tissues was detected by immunohistochemistry assay. Cell transfection with lentivirus EIF3C short hairpin RNA (shRNA) was performed with Lipofectamine 2000. Cell proliferation was evaluated by Celigo and MTT assays. Caspase-3/7 activity was assessed using caspase-3/7 assay kit for cell apoptosis detection. The apoptosis rate of lung cancer cells was assessed by flow cytometry. A transplanted tumor nude-mouse model was established to clarify the role of EIF3C in lung cancer. The potential mechanism of EIF3C was explored by mRNA microarray analysis. Among the top 30 up- and downregulated mRNAs selected for RT-qPCR, 5 were chosen for western blot analysis. Results: EIF3C was abnormally overexpressed in lung cancer cell lines and tissues. Silencing EIF3C suppressed the proliferation and promoted the apoptosis of lung cancer cells. In vivo experiments using transplanted tumor nude-mouse model suggested that EIF3C promoted lung cancer tumorigenesis. Further, mRNA microarray analyses identified 189 upregulated and 83 downregulated differentially expressed mRNA between the KD and negative control groups. After validation by RT-qPCR and western blot, three downstream genes (APP, HSPA1A, and LMNB1) were confirmed. Conclusion: EIF3C overexpression may facilitate the proliferation and hamper the apoptosis of lung cancer cells by regulating the APP/HSPA1A/LMNB1 axis.

Overexpressed lncRNA FTX promotes the cell viability, proliferation, migration and invasion of renal cell carcinoma via FTX/miR­4429/UBE2C axis.

The present study aimed to explore the role of long non­coding (lnc)RNA FTX and ubiquitin­conjugating enzyme E2C (UBE2C) in promoting the progression of renal cell carcinoma (RCC) and the underlying regulatory mechanism. Relative levels of lncRNA FTX, UBE2C, AKT, CDK1 and CDK6 in RCC cell lines were detected by reverse transcription­quantitative (RT­q). Expression levels of UBE2C, phosphorylated (p)­AKT/AKT, p­CDK1/CDK1 and p­CDK6/CDK6 in RCC and paracancerous specimens and RCC cells were measured by western blot or immunohistochemistry assay. In addition, the proliferative rate, cell viability, cell cycle progression, migratory rate and invasive rate of RCC cells overexpressing lncRNA FTX by lentivirus transfection were determined by a series of functional experiments, including the colony formation assay, MTT assay, flow cytometry, Transwell assay and wound healing assay. The targeted binding relationship in the lncRNA FTX/miR­4429/UBE2C axis was validated by dual­luciferase reporter assay. By intervening microRNA (miR)­4492 and UBE2C by the transfection of miR­4429­mimics or short interfering UBE2C­2, the regulatory effect of lncRNA FTX/miR­4429/UBE2C axis on the progression of RCC was evaluated. Finally, a xenograft model of RCC in nude mice was established by subcutaneous implantation, thus evaluating the in vivo function of lncRNA FTX in the progression of RCC. The results showed that lncRNA FTX and UBE2C were upregulated in RCC specimens and cell lines. The overexpression of lncRNA FTX in RCC cells upregulated UBE2C. In addition, the overexpression of lncRNA FTX promoted the cell viability and proliferative, migratory and invasive capacities of RCC cells and accelerated the cell cycle progression. A dual­luciferase reporter assay validated that lncRNA FTX exerted the miRNA sponge effect on miR­4429, which was bound to UBE2C 3'UTR. Knockdown of UBE2C effectively reversed the regulatory effects of overexpressed lncRNA FTX on the abovementioned phenotypes of RCC cells. In the xenograft model of RCC, the mice implanted with RCC cells overexpressing lncRNA FTX showed a larger tumor size and higher tumor weight than those of controls, while the in vivo knockdown of UBE2C significantly reduced the size of RCC lesions, indicating the reversed cancer­promoting effect of lncRNA FTX. Overall, the present study showed that lncRNA FTX was upregulated in RCC and could significantly promote the proliferative, migratory and invasive capacities, enhancing the viability and accelerating the cell cycle progression of RCC cells by exerting the miRNA sponge effect on miR­4429 and thus upregulating UBE2C. lncRNA FTX and UBE2C are potential molecular biomarkers and therapeutic targets of RCC.

LncRNA SNHG15 modulates gastric cancer tumorigenesis by impairing miR-506-5p expression.

The gastric cancer (GC) patients commonly have a poor prognosis due to its invasiveness and distant metastasis. Growing evidence proved that aberrant long non-coding RNAs (lncRNAs) expression contributes to tumor development and progression. LncRNA SNHG15 has been reported to be involved in many different kinds of cancer, while its role in GC remains unclear. In the present study, we found that SNHG15 was up-regulated in GC tissues and cell lines. Silencing SNHG15 suppressed proliferation migration, invasion and promoted apoptosis of AGS cells. More importantly, microRNA-506-5p (miR-506-5p) was predicted as a direct target of SNHG15 by binding its 3'-UTR and further verified using luciferase reporter assay. Meanwhile, the results of rescue experiments revealed that knockdown of miR-506-5p expression reversed the functional effects of SNHG15 silenced cell proliferation, migration, invasion and apoptosis. In conclusion, our findings revealed that SNHG15 executed oncogenic properties in GC progression through targeting miR-506-5p, which might provide a novel target for the GC treatment.

The 26S Proteasome Regulatory Subunit GmPSMD Promotes Resistance to Phytophthora sojae in Soybean.

Phytophthora root rot, caused by Phytophthora sojae is a destructive disease of soybean (Glycine max) worldwide. We previously confirmed that the bHLH transcription factor GmPIB1 (P. sojae-inducible bHLH transcription factor) reduces accumulation of reactive oxygen species (ROS) in cells by inhibiting expression of the peroxidase-related gene GmSPOD thus improving the resistance of hairy roots to P. sojae. To identify proteins interacting with GmPIB1 and assess their participation in the defense response to P. sojae, we obtained transgenic soybean hairy roots overexpressing GmPIB1 by Agrobacterium rhizogenes mediated transformation and examined GmPIB1 protein-protein interactions using immunoprecipitation combined with mass spectrometry. We identified 392 proteins likely interacting with GmPIB1 and selected 20 candidate genes, and only 26S proteasome regulatory subunit GmPSMD (Genbank accession no. XP_014631720) interacted with GmPIB1 in luciferase complementation and pull-down experiments and yeast two-hybrid assays. Overexpression of GmPSMD (GmPSMD-OE) in soybean hairy roots remarkably improved resistance to P. sojae and RNA interference of GmPSMD (GmPSMD -RNAi) increased susceptibility. In addition, accumulation of total ROS and hydrogen peroxide (H2O2) in GmPSMD-OE transgenic soybean hairy roots were remarkably lower than those of the control after P. sojae infection. Moreover, in GmPSMD-RNAi transgenic soybean hairy roots, H2O2 and the accumulation of total ROS exceeded those of the control. There was no obvious difference in superoxide anion (O2 -) content between control and transgenic hairy roots. Antioxidant enzymes include peroxidase (POD), glutathione peroxidase (GPX), superoxide dismutase (SOD), catalase (CAT) are responsible for ROS scavenging in soybean. The activities of these antioxidant enzymes were remarkably higher in GmPSMD-OE transgenic soybean hairy roots than those in control, but were reduced in GmPSMD-RNAi transgenic soybean hairy roots. Moreover, the activity of 26S proteasome in GmPSMD-OE and GmPIB1-OE transgenic soybean hairy roots was significantly higher than that in control and was significantly lower in PSMD-RNAi soybean hairy roots after P. sojae infection. These data suggest that GmPSMD might reduce the production of ROS by improving the activity of antioxidant enzymes such as POD, SOD, GPX, CAT, and GmPSMD plays a significant role in the response of soybean to P. sojae. Our study reveals a valuable mechanism for regulation of the pathogen response by the 26S proteasome in soybean.

The anti-hepatitis drug use effect and inventory management optimization from the perspective of hospital drug supply chain.

By analyzing the current hospital anti hepatitis drug use, dosage, indications and drug resistance, this article studied the drug inventory management and cost optimization. The author used drug utilization evaluation method, analyzed the amount and kind distribution of anti hepatitis drugs and made dynamic monitoring of inventory. At the same time, the author puts forward an effective scheme of drug classification management, uses the ABC classification method to classify the drugs according to the average daily dose of drugs, and implements the automatic replenishment plan. The design of pharmaceutical services supply chain includes drug procurement platform, warehouse management system and connect to the hospital system through data exchange. Through the statistical analysis of drug inventory, we put forward the countermeasures of drug logistics optimization. The results showed that drug replenishment plan can effectively improve drugs inventory efficiency.

Detection of nitrogen-overfertilized rice plants with leaf positional difference in hyperspectral vegetation index.

The main objective of this work was to compare the applicability of the single leaf (the uppermost leaf L1 and the third uppermost leaf L3) modified simple ratio (mSR(705) index) and the leaf positional difference in the vegetation index between L1 and L3 (mSR(705L1)-mSR(705L3)) in detecting nitrogen (N)-overfertilized rice plants. A field experiment consisting of three rice genotypes and five N fertilization levels (0, 75, 180, 285, and 390 kg N/ha) was conducted at Xiaoshan, Hangzhou, Zhejiang Province, China in 2008. The hyperspectral reflectance (350-2500 nm) and the chlorophyll concentration (ChlC) of L1 and L3 were measured at different stages. The mSR(705L1) and mSR(705L3) indices appeared not to be highly sensitive to the N rates, especially when the N rate was high (above 180 kg N/ha). The mean mSR(705L1)-mSR(705L3) across the genotypes increased significantly (P<0.05) or considerably from 180 to 285 kg N/ha treatment and from 285 to 390 kg N/ha treatment at all the stages. Also, use of the difference (mSR(705L1)-mSR(705L3)) greatly reduced the influence of the stages and genotypes in assessing the N status with reflectance data. The results of this study show that the N-overfertilized rice plants can be effectively detected with the leaf positional difference in the mSR(705) index.

[Discrimination and spectral response characteristic of stress leaves infected by rice Aphelenchoides besseyi Christie].

An ASD Field Spec Pro Full Range spectrometer was used to acquire the spectral reflectance of healthy and diseased leaves infected by rice Aphelenchoides besseyi Christie, which were cut from rice individuals in the paddy field. Firstly, foliar pigment content was investigated. As compared with healthy leaves, the total chlorophyll and carotene contents (mg x g(-1)) of diseased leaves decreased 18% and 22%, respectively. The diseased foliar content ratio of total chlorophyll to carotene was nearly 82% of the healthy ones. Secondly, the response characteristics of hyperspectral reflectance of diseased leaves were analyzed. The spectral reflectance in the blue (450-520 nm), green (520-590 nm) and red (630-690 nm) regions were 2.5, 2 and 3.3 times the healthy ones respectively due to the decrease in foliar pigment content, whereas in the near infrared (NIR, 770-890 nm) region was 71.7 of the healthy ones because of leaf twist, and 73.7% for shortwave infrared (SWIR, 1 500-2 400 nm) region, owing to water loss. Moreover, the hyperspectral feature parameters derived from the raw spectra and the first derivative spectra were analyzed. The red edge position (REP) and blue edge position (BEP) shifted about 8 and 10 nm toward the short wavelengths respectively. The green peak position (GPP) and red trough position (RTP) shifted about 8.5 and 6 nm respectively toward the longer wavelengths. Finally, the area of the red edge peak (the sum of derivative spectra from 680 to 740 nm) and red edge position (REP) as the input vectors entered into C-SVC, which was an soft nonlinear margin classification method of support vector machine, to recognize the healthy and diseased leaves. The kernel function was radial basis function (RBF) and the value of punishment coefficient (C) was obtained from the classification model of training data sets (n = 138). The performance of C-SVC was examined with the testing sample (n = 126), and healthy and diseased leaves could be successfully differentiated without errors. This research demonstrated that the response feature of spectral reflectance was obvious to disease stress in rice leaves, and it was feasible to discriminate diseased leaves from healthy ones based on C-SVC model and hyperspectral reflectance.

Spatial and seasonal characterization of net primary productivity and climate variables in southeastern China using MODIS data.

We developed a sophisticated method to depict the spatial and seasonal characterization of net primary productivity (NPP) and climate variables. The role of climate variability in the seasonal variation of NPP exerts delayed and continuous effects. This study expands on this by mapping the seasonal characterization of NPP and climate variables from space using geographic information system (GIS) technology at the pixel level. Our approach was developed in southeastern China using moderate-resolution imaging spectroradiometer (MODIS) data. The results showed that air temperature, precipitation and sunshine percentage contributed significantly to seasonal variation of NPP. In the northern portion of the study area, a significant positive 32-d lagged correlation was observed between seasonal variation of NPP and climate (P<0.01), and the influences of changing climate on NPP lasted for 48 d or 64 d. In central southeastern China, NPP showed 16-d, 48-d, and 96-d lagged correlation with air temperature, precipitation, and sunshine percentage, respectively (P<0.01); the influences of air temperature and precipitation on NPP lasted for 48 d or 64 d, while sunshine influence on NPP only persisted for 16 d. Due to complex topography and vegetation distribution in the southern part of the study region, the spatial patterns of vegetation-climate relationship became complicated and diversiform, especially for precipitation influences on NPP. In the northern part of the study area, all vegetation NPP had an almost similar response to seasonal variation of air temperature except for broad crops. The impacts of seasonal variation of precipitation and sunshine on broad and cereal crop NPP were slightly different from other vegetation NPP.

Discrimination of rice panicles by hyperspectral reflectance data based on principal component analysis and support vector classification.

Detection of crop health conditions plays an important role in making control strategies of crop disease and insect damage and gaining high-quality production at late growth stages. In this study, hyperspectral reflectance of rice panicles was measured at the visible and near-infrared regions. The panicles were divided into three groups according to health conditions: healthy panicles, empty panicles caused by Nilaparvata lugens Stål, and panicles infected with Ustilaginoidea virens. Low order derivative spectra, namely, the first and second orders, were obtained using different techniques. Principal component analysis (PCA) was performed to obtain the principal component spectra (PCS) of the foregoing derivative and raw spectra to reduce the reflectance spectral dimension. Support vector classification (SVC) was employed to discriminate the healthy, empty, and infected panicles, with the front three PCS as the independent variables. The overall accuracy and kappa coefficient were used to assess the classification accuracy of SVC. The overall accuracies of SVC with PCS derived from the raw, first, and second reflectance spectra for the testing dataset were 96.55%, 99.14%, and 96.55%, and the kappa coefficients were 94.81%, 98.71%, and 94.82%, respectively. Our results demonstrated that it is feasible to use visible and near-infrared spectroscopy to discriminate health conditions of rice panicles.

[Estimating the severity of rice brown spot disease based on principal component analysis and radial basis function neural network].

An ASD Field Spec Pro Full Range spectrometer was used here to acquire the spectral reflectance of healthy and disease leaves cut from rice plants in the field. The leaf disease severity of rice brown spot was determined by estimating the percentage of infected surface area of rice leaves in the laboratory through phytopathologist's observation. Three steps were taken to estimate leaf disease severity of rice brown spot. The first step was that different spectra transforming methods, namely, resampling spectrum (10 nm interval), the first- and second-order derivative spectrum based on raw hyperspectral reflectance, were conducted. The second step was that the principal component analysis (PCA) was examined to obtain the principal components (PCs) from the above transformed spectra to reduce the spectra dimensions of hyperspectral reflectance and simplify the data structure of hyperspectra. The last step was that the resampling and PCs spectra entered the Radial Basis Function neural network (RBFN) as the input vectors, and the disease severity of rice brown spot entered RBFN as the target vectors. RBFN is an effective feed forward propagation neural network, which is based on the linear combinations of corresponding radial basis functions. In general RBFN can be used to solve the problems such as regression or classification with high operation rate and efficient extrapolation capability, and quickly designed with zero error to approximate functions. The total dataset (n = 262) was divided into two subsets, in which three quarters (n = 210) was the training subset to train the neural network, and the remaining quarter (n = 52) was the testing dataset to conduct the performance analysis of neural network. The spread constants of RBFN and various data processing methods were investigated in detail. The best prediction result was obtained by PCs spectra based on the first-order derivative using RBFN model, the root mean square of prediction error (RMSE) was small (7.73%) in the testing dataset, and the next was the resampling spectra with RMSE of 8.75%. This research demonstrated that it was feasible and reliable to estimate the disease severity of rice brown spot based on PCA-RBFN and hyperspectral reflectance at the leaf level.

[Study on hyperspectral estimation model of crop vegetation cover percentage].

In order to boost the study and application of hyperspectral remote sensing for the estimation of crop vegetation coverage percentage, an ASD FieldSpec Pro FRTM spectroradiometer was used for canopy spectral measurements of rape, corn and rice at different vegetation cover levels and photos of individual plants were taken simultaneously in order to calculate the vegetation cover percentage in computer. Firstly, data of three crops respectively and the mixed data of them were used to make correlation analysis between vegetation coverage percentage and reflectance spectra There was a high correlation between them and no obvious difference in correlation coefficient among different types of crop in the region of blue, red and near-infrared band. This indicated that it was feasible to make correlation analysis and build estimation model using mixed data Secondly, mixed data were used as unique analytical data to calculate red edge variables and pair combination of bands in the region of blue, red and near-infrared band was used to calculate normal difference vegetation index (NDVI). Hyperspectral estimation models with NDVI and red edge variable as independent variable were built individually. The correlation coefficient of the former was larger than the latter, which indicated that NDVI was most effective for the estimation of vegetation coverage percentage. Effective wavelength combinations of NDVI for vegetation cover percentage estimation were determined based on the principle of higher correlation coefficient. NDVI combined with bands in the regions from 350 to 590 nm and from 710 to 1150 nm or bands in the regions from 590 to 710 nm and from 710 to 1300 nm are most effective for vegetation coverage percentage estimation. The best estimation model is simple quadratic equation using NDVI(696-921) as independent variable. The correlation coefficient matrix shows that most of the correlation coefficients of vegetation coverage percentage and NDVI combined with bands in the regions from 630 to 690 nm and from 760 to 900 nm are larger than 0.8. These two band regions correspond to TM3 and TM4 of landsat 4,5,7. It proves that NDVI(TM3-TM4) can be used to and has been used to simulate vegetation coverage percentage. In order to further the study, TM3 and TM4 of Landsat5 was modeled according to spectral response function to calculate NDVI. Correlation analysis was made with NDVI and corresponding vegetation coverage percentage. The correlation coefficient of them was 0.80 and the regression equation was verified by experimental data. This is exploratory research for the calculation of vegetation coverage percentage using TM data in large area.

[Predicting nitrogen concentrations from hyperspectral reflectance at hyperspectral reflectance at leaf and canopy for rape].

An experiment was designed to determine whether nitrogen concentrations could be predicted from reflectance (R) spectra of rape leaves in laboratory, and, if so, whether the predictive spectral features could be correlated with nitrogen concentration of simple canopies of rape. The best predictors for nitrogen in leaves appeared with first-difference transformations of R, and the bands selected were similar to those found in other studies. Shortwave infrared bands were best predictors for nitrogen. In the shortwave infrared region, however, the absolute differences in reflectance at critical bands were extremely small, and the bands of high correlation were narrow. High spectral and radiance resolution are required to resolve these differences accurately. Variability in canopy reflectance in shortwave infrared region was at least an order of magnitude beyond that necessary to detect signals from chemicals. The variability in first-difference R and log 1/R on canopy scales were related to the arrangement of trees with respect to direct solar radiation, instrument noise, leaf fluttering, and small change in atmospheric moisture. The first-difference of reflectance R based regressions prediction of nitrogen concentration at canopy level gets a good fitness.

Monitoring rice nitrogen status using hyperspectral reflectance and artificial neural network.

Over use of nitrogen fertilization can result in groundwater pollution. Tools that can rapidly quantify the nitrogen status are needed for efficient fertilizer management and would be very helpful in reducing the environmental pollution caused by excessive nitrogen application. Remote sensing has a proven ability to provide spatial and temporal measurements of surface properties. In this study, the MLR (multiple linear regression) and ANN (artificial neural network) modeling methods were applied to the monitoring of rice N (nitrogen concentration, mg nitrogen g(-1) leaf dry weight) status using leaf level hyperspectral reflectance with two different input variables, and as a result four estimation models were proposed. RMSE (root-mean-square error), REP (relative error of prediction), R2 (coefficient of determination), as well as the intercept and slope between the observed and predicted N were used to test the performance of models. Very good agreements between the observed and the predicted N were obtained with all proposed models, which was especially true for the R-ANN (artificial neural network based on reflectance selected using MLR) model. Compared to the other three models, the R-ANN model improved the results by lowering the RMSE by 14.2%, 32.1%, and 31.5% for the R-LR (linear regression based on reflectance) model, PC-LR (linear regression based on principal components scores) model, and PC-ANN (artificial neural network based on principal components scores) model, respectively. It was concluded that the ANN algorithm may provide a useful exploratory and predictive tool when applied on hyperspectral reflectance data for nitrogen status monitoring. Besides, although the performance of MLR was superior to PCA used for ANN inputs selection, the encouraging results of PC-based models indicated the promising potential of ANN combined with PCA application on hyperspectral reflectance analysis.

Characterizing and estimating rice brown spot disease severity using stepwise regression, principal component regression and partial least-square regression.

Detecting plant health conditions plays a key role in farm pest management and crop protection. In this study, measurement of hyperspectral leaf reflectance in rice crop (Oryzasativa L.) was conducted on groups of healthy and infected leaves by the fungus Bipolaris oryzae (Helminthosporium oryzae Breda. de Hann) through the wavelength range from 350 to 2,500 nm. The percentage of leaf surface lesions was estimated and defined as the disease severity. Statistical methods like multiple stepwise regression, principal component analysis and partial least-square regression were utilized to calculate and estimate the disease severity of rice brown spot at the leaf level. Our results revealed that multiple stepwise linear regressions could efficiently estimate disease severity with three wavebands in seven steps. The root mean square errors (RMSEs) for training (n=210) and testing (n=53) dataset were 6.5% and 5.8%, respectively. Principal component analysis showed that the first principal component could explain approximately 80% of the variance of the original hyperspectral reflectance. The regression model with the first two principal components predicted a disease severity with RMSEs of 16.3% and 13.9% for the training and testing dataset, respectively. Partial least-square regression with seven extracted factors could most effectively predict disease severity compared with other statistical methods with RMSEs of 4.1% and 2.0% for the training and testing dataset, respectively. Our research demonstrates that it is feasible to estimate the disease severity of rice brown spot using hyperspectral reflectance data at the leaf level.

[Hyperspectral remote sensing estimation models on vegetation coverage of natural grassland].

By using ASD FieldSpec Pro FR spectroradiometer, the spectral measurement of natural grassland in Xilingole Leaguer of Inner Mongolia was performed, with the vegetation coverage of natural grassland calculated, and the correlation of 25 hyperspectral feature variables with the vegetation coverage of natural grassland was analyzed. The results showed that there were 17 variables correlated significantly with the vegetation coverage of natural grassland, among which, the correlation coefficient between vegetation coverage and the area of red edge peak calculated as the sum of the amplitudes between 680 nm and 780 nm (sigma dr 680 - 780 nm) was the highest, with the value of 0.781. The basic experimental data including the vegetation coverage and canopy reflectance of natural grassland were classified into two groups. One group was used as the training sample to build the regression models with one-sample linear method, nonlinear method, and stepwise analysis method, while the other was used as the testing sample to test the precision of regression models. It was suggested that the variable of the area of red edge peak calculated as the sum of amplitudes between 680 nm and 780 nm (sigma dr 680 - 780 nm) was the best one to univariate general linear model, with a standard deviation of 10.4% and an estimation precision of 83.99%, while the stepwise regression technique was not effective to estimate the grassland coverage with raw hyperspectral canopy reflectance.