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Unmanned aerial vehicle (UAV) remote sensing technology provides a new tool for precise agricultural management. High precision automatic stitching of UAV images is the basis for application of UAV remote sensing technology. At present, there exist researches to compare the performance of stitching software packages on their spatial precision, but few researches on the evaluation of their spectral precision, which is very important for quantitative remote sensing inversion. The objective of this study was to compare the influence of the two most popular UAVs orthomosaic-processing software packages, Pix4D and Photoscan, on the spectral information of original single image when stitching the image in agricultural application and to recommend the better one for users. For this purpose, multispectral sensor was carried on a UAV to acquire images over a winter wheat fertilization experiment. The acquired images were processed by Pix4D and Photoscan software packages to produce mosaicked image. In the meanwhile, single multispectral images were also produced using the method released by sensor manufacturer. Based on above data, randomly placed samples in the fertilization treatment, extracted spectral information of each point in the three types of images individually, and compared their reflectance value and image contrast. Although there were significant correlations in reflectance values between the mosaicked images proceed by Pix4D and Photoscan and the single image for each band, the reflectance value and its coefficients of variation from Photoscan mosaicked image were closer to the corresponding values of single image, compared with Pix4D mosaicked image. It means Photoscan retains more spectral information of the original image during stitching image. Considering the geometric accuracy results and software package prices from existing studies, Photoscan is suggested as the better package for users.

International audience; A crop growth model, integrating genotype, environment, and management factor, was developed to serve as an analytical tool to study the influence of these factors on crop growth, production, and agricultural planning. A major challenge of model application is the optimization and calibration of a considerable number of parameters. Sensitivity analysis (SA) has become an effective method to identify the importance of various parameters. In this study, the extended Fourier Amplitude Sensitivity Test (EFAST) approach was used to evaluate the sensitivity of the DSSAT-CERES model output responses of interest to 39 crop genotype parameters and six soil parameters. The outputs for the SA included grain yield and quality (take grain protein content (GPC) as an indicator) at maturity stage, as well as leaf area index, aboveground biomass, and aboveground nitrogen accumulation at the critical process variables. The key results showed that (1) the influence of parameter bounds on the sensitivity results was slight and less than the impacts from the significance of the parameters themselves; (2) the sensitivity parameters of grain yield and GPC were different, and the sensitivity of the interactions between parameters to GPC was greater than those between the parameters to grain yield; and (3) the sensitivity analyses of some process variables, including leaf area index, aboveground biomass, and aboveground nitrogen accumulation, should be performed differently. Finally, some parameters, which improve the model's structure and the accuracy of the process simulation, should not be ignored when maturity output as an objective variable is studied.

Porcine circovirus type 2 (PCV2) is the primary causative agent that causing porcine circovirus-associated disease (PCVAD). The open reading frame 5 (ORF5) protein is a newly discovered non-structural protein in PCV2, which the function in viral pathogenesis remains unknown. The aim of this study was to investigate the mechanism of PCV2 ORF5 protein on autophagy and viral replication. The pEGFP-tagged ORF5 gene was ectopic expressed in PK-15 cells and an ORF5-deficient PCV2 mutant strain (PCV2ΔORF5) were used to infected PK-15 cells. This study demonstrated that the ORF5 is essential for the of PCV2-induced autophagy. The ORF5 protein triggers the phosphorylation of PERK, eIF2α and the expression of downstream transcription factor ATF4. In addition, ORF5 protein activated the AMPK-ERK1/2-mTOR signaling pathways. These findings suggest that ORF5 play essential roles in the induction of autophagy by PCV2. We further revealed that PCV2 ORF5 promotes viral replication through PERK-eIF2α-ATF4 and AMPK-ERK1/2-mTOR pathways. In conclusion, we showed that PCV2 ORF5 induces autophagy to promote virus replication in PK-15 cells., Graphical Abstract PCV2 ORF5 inducing autophagy to promote PCV2 replication.

To reduce the economic losses caused by diseases in aquaculture industry, more efficient and economic prophylactic measures should be urgently investigated. In this research, the effects of a novel functionalized single-walled carbon nanotubes (SWCNTs) applied as a delivery vehicle for recombinant Aeromonas hydrophila vaccine administration via bath or injection in juvenile grass carp were studied. The results showed that SWCNT as a vector for the recombinant protein aerA, augmented the production of specific antibodies, apparently stimulated the induction of immune-related genes, and induced higher level of survival rate compared with free aerA subunit vaccine. Furthermore, we compared the routes of bath and intramuscular injection immunization by SWCNTs-aerA vaccine, and found that similar antibody levels induced by SWCNTs-aerA were observed in both immunization routes. Meanwhile, a similar relative percentage survival (approximately 80%) was found in both a 40 mg/L bath immunization group, and a 20 μg injection group. The results indicate that functionalized SWCNTs could be a promising delivery vehicle to potentiate the immune response of recombinant vaccines, and might be used to vaccinate juvenile fish by bath administration method.

Winter wheat freeze injury is one of the main agro-meteorological disasters affecting wheat production. In order to evaluate the severity of freeze injury on winter wheat systematically, we proposed a grey-system model (GSM) to monitor the degree and the distribution of the winter wheat freeze injury. The model combines remote sensing (RS) and geographic information system (GIS) technology. It gave examples of wheat freeze injury monitoring applications in Gaocheng and Jinzhou of Hebei Province, China. We carried out a quantitative evaluation method study on the severity of winter wheat freeze injury. First, a grey relational analysis (GRA) was conducted. At the same time, the weights of the stressful factors were determined. Then a wheat freezing injury stress multiple factor spatial matrix was constructed using spatial interpolation technology. Finally, a winter wheat freeze damage evaluation model was established through grey clustering algorithm (GCA), and classifying the study area into three sub-areas, affected by severe, medium or light disasters. The evaluation model were verified by the Kappa model, the overall accuracy reached 78.82% and the Kappa coefficient was 0.6754. Therefore, through integration of GSM with RS images as well as GIS analysis, quantitative evaluation and study of winter wheat freeze disasters can be conducted objectively and accurately, making the evaluation model more scientific.

Considering spatial heterogeneity of LAI and nonlinearity of its inversion model, a new spatial scaling method based on Gaussian distribution theory was proposed, aiming to quantitatively analyze scale effects and reveal scaling rules. In this method, higher spatial resolution data, obeying Gaussian distribution when the volume is large enough, were taken as baseline. Statistical parameters and Gaussian distribution forms were integrated into the process of spatial scaling. Barley was selected as experimental object. Firstly, multi-resolution data at 10m, 15m, 20m and 30m were constructed based on 5m resolution data through up-scaling algorithms. Secondly, extracting statistics of these data and constructing scaling data based on Gaussian distribution theory. Finally, quantitatively analyzing scale effects of LAI by introducing sensitive bands ordering and multivariate linear regression. Number of effective bands, R2 of observations and estimations, and MRA could fully confirm the feasibility and validity of this proposed method.

A virus strain, showing cytopathic effect in BHK-21 cell, was isolated from swine brains in Shaanxi Province, China, in 2007. The isolate was confirmed as Japanese encephalitis virus (JEV) by immunofluorescence assay (IFA) and reverse transcription polymerase chain reaction (RT-PCR), and named SXBJ07. The complete nucleotide and deduced amino acid sequences of the JEV strain SXBJ07 were determined. Its single open reading frame has a total of 3 432 amino acid residues. An extensive E gene based phylogenetic analysis was performed, the result showed that SXBJ07 strain belongs to genotype I. Comparison of the SXBJ07 genomic sequence with those of the 24 fully sequenced JEV strains in published databases showed nucleotide homology ranging from 99.0 to 83.7%; amino acid homology ranged from 99.8 to 94.8%. Compared SXBJ07 with SA14-14-2 strain, the current live vaccine strain in China, the homology of amino acid in envelope gene was 97.0%; and there were amino acid substitutions in 13 sites of the active domains of E protein (E1-E411).

The heat capacities, Cp, of trimethyl phosphite, 2-dimethylphosphonomethylbenzonitrile, and 2-chloromethylbenzonitrile are measured with an adiabatic calorimeter, and the standard enthalpies of formation, ΔfHo(298.15 K) (l or cr), for the three substances are determined. For this purpose, Cp(T) values of trimethyl phosphite and 2-dimethylphosphonomethylbenzonitrile are measured in the temperature range from (298.15 to 350.32) K, and the Cp(T) curves of 2-chloromethylbenzonitrile are also obtained for the crystal (300.35 to 329.00 K) and the liquid (337.26 to 372.45 K), respectively. The deviation of experimental heat capacities from the corresponding smoothed values lies within ± 1.5 %. A fusion transition phenomenon is found from the Cp(T) curves of 2-chloromethylbenzonitrile, and the enthalpy of fusion is measured with a differential scanning calorimeter (DSC). The gas-phase standard enthalpies of formation, ΔfHo(g, 298.15 K), of the three compounds are evaluated by the Benson group additive method. The...

Moisture content is an important indicator for crop water stress condition, timely and effective monitoring crop water content is of great significance for evaluate crop water deficit balance and guide agriculture irrigation. In order to improve the saturated problems of different forms of typical NDWI (Normalized Different Water Index), we tried to introduce EVI (Enhanced Vegetation Index) to build new vegetation water indices (NDWI#) to estimate crop water content. Firstly, PROSAIL model was used to study the saturation sensitivity of NDWI, and NDWI# to canopy water content and LAI (Leaf Area Index). Then, the estimated model and verified model were estimated using the spectral data and moisture data in the field. The result showed that the new indices have significant relationships with canopy water content. In particular, by implementing modified standardized for NDWI1450, NDWI1940, NDWI2500. The result indicated that newly developed indices with visible-infrared and shortwave infrared spectral feature may have greater advantage for estimation winter canopy water content.

Moisture content is an important index of crop water stress condition, timely and effective monitoring of crop water content is of great significance for evaluating crop water deficit balance and guiding agriculture irrigation. The present paper was trying to build a new crop water index for winter wheat vegetation water content based on NIR-Red spectral space. Firstly, canopy spectrums of winter wheat with narrow-band were resampled according to relative spectral response function of HJ-CCD and ZY-3. Then, a new index (PWI) was set up to estimate vegetation water content of winter wheat by improveing PDI (perpendicular drought index) and PVI (perpendicular vegetation index) based on NIR-Red spectral feature space. The results showed that the relationship between PWI and VWC (vegetation water content) was stable based on simulation of wide-band multispectral data HJ-CCD and ZY-3 with R2 being 0.684 and 0.683, respectively. And then VWC was estimated by using PWI with the R2 and RMSE being 0.764 and 0.764, 3.837% and 3.840%, respectively. The results indicated that PWI has certain feasibility to estimate crop water content. At the same time, it provides a new method for monitoring crop water content using remote sensing data HJ-CCD and ZY-3.

This study is to report the establishment of an UPLC-MS/MS method for the determination of plasma concentration of UA carried in self-microemulsifying drug delivery system (SMEDDS) and its pharmacokinetics in rats. It was used for determination and analysis when serum with internal standard was extracted from C18 solid-phase column. Acquity UPLC BEH C18 column (100 mm x 2.1 mm, 1.7 microm) was used for separation. The mobile phase was acetonitrile -0.1% ammonia with gradient elution at the flow rate of 0.2 mL x min(-1). The column temperature was 40 degrees C and the detection wave length was 210 nm. It was detected by negative ion using electrospray ionization source (ESI) and scanned by multiple reaction ion monitoring (MRM) mode. The liner relationship of UA was very good in the range of 1.19-3 815.00 ng x mL(-1) (r = 0.999 0). Recovery rate of different concentrations were 87.42%-89.95%. The precision of inter-day and intra-day were less than 11%. The method developed in our study was proved to be sensitive, rapid and simple. It is suitable for the pharmacokinetic study of UA-SMEDDS in rats.

Wheat protein content is an important indicator often employed in wheat sale price. Spectral indexes and concurrent winter wheat protein content (WWPC) samples were obtained across three years. Data from 2008/2009 and 2009/2010 were utilized to build the new ratio indexes and product indexes, and then selected grey relational method and partial least squares method were used to improve the estimation accuracy of WWPC, data from 2011/2012 was utilized to validate model. The results showed that the correlation coefficients between ratio indexes and WWPC were better than that between single indexesand WWPC. The r of single indexes and ratio indexes were 0.726 and 0.751, respectively, and the product indexes were used to improve the parts of single indexes. The estimation accuracy of WWPC was improved by using GRA-PLS, the determination coefficients (R2) of single indexes, ratio indexes and product indexes were 0.537, 0.631 and 0.521, respectively, and corresponding root mean square errors (RMSE) were 0.665%, 0.564% and 0.574%, respectively. The results indicated that it was feasible to estimate WWPC by building new ratio indexes and product indexes, and then applying the GRA-PLS.

Considering the great relationships between shortwave infrared (SWIR) and leaf area index (LAI), innovative indices based on water vegetation indices and visible-infrared vegetation indices were presented. In the present work, PROSAIL model was used to study the saturation sensitivity of new vegetation indices to LAI. The estimate models about LAI of winter wheat were built on the basis of the experiment data in 2009 acting as train sample and their precisions were evaluated and tested on the basis of the experiment data in 2008. Ten visible-infrared vegetation indices and five water vegetation indices were used to construct new indices. The result showed that newly developed indices have significant relationships with LAI by numerical simulations and in-situ measurements. In particular, by implementing modified standardized LAI Determining Index (sLAIDI *), all new indices were neither sensitive to water variations nor affected by saturation at high LAI levels. The evaluation models could improve prediction accuracy and have well reliability for LAI retrieval. The result indicated that visible-infrared vegetation indices combined with water index have greater advantage for LAI estimation.

The major limitation of using existing vegetation indices for crop biomass estimation is that it approaches a saturation level asymptotically for a certain range of biomass. In order to resolve this problem, band depth analysis and partial least square regression (PLSR) were combined to establish winter wheat biomass estimation model in the present study. The models based on the combination of band depth analysis and PLSR were compared with the models based on common vegetation indexes from the point of view of estimation accuracy, subsequently. Band depth analysis was conducted in the visible spectral domain (550-750 nm). Band depth, band depth ratio (BDR), normalized band depth index, and band depth normalized to area were utilized to represent band depth information. Among the calibrated estimation models, the models based on the combination of band depth analysis and PLSR reached higher accuracy than those based on the vegetation indices. Among them, the combination of BDR and PLSR got the highest accuracy (R2 = 0.792, RMSE = 0.164 kg x m(-2)). The results indicated that the combination of band depth analysis and PLSR could well overcome the saturation problem and improve the biomass estimation accuracy when winter wheat biomass is large.

The objective of the present study was to compare two methods for the precision of estimating leaf water content (LWC) in winter wheat by combining stepwise regression method and partial least squares (SRM-PLS) or PLS based on the relational degree of grey relational analysis (GRA) between water vegetation indexes (WVIs) and LWC. Firstly, data utilized to analyze the grey relationships between LWC and the selected typical WVIs were used to determine the sensitivity of different WVIs to LWC. Secondly, the two methods of estimating LWC in winter wheat were compared, one was to directly use PLS and the other was to combine SRM and PLS, and then the method with the highest determination coefficient (R2) and lowest root mean square error (RMSE) was selected to estimate LWC in winter wheat. The results showed that the relationships between the first five WVI and LWC were stable by using GRA, and then LWC was estimated by using PLS and SRM-PLS at the whole stages with the R2 and RMSEs being 0.605 and 0.575, 4.75% and 7.35%, respectively. The results indicated that the estimation accuracy of LWC could be improved by using GRA firstly and then by using PLS and SRM-PLS.

As an image-spectrum merging technology, the field-hperspectral imaging technology is a need for dynamic monitoring and real-time management of crop growth information acquiring at field scale in modern digital agriculture, and it is also an effective approach to promoting the development of quantitative remote sensing on agriculture. In the present study, the hyperspectral images of maize in potted trial and in field were acquired by a self-development push broom imaging spectrometer (PIS). The reflectance spectra of maize leaves in different layers were accurately extracted and then used to calculate the spectral vegetation indices, such as TCARI, OSAVI, CARI and NDVI. The spectral vegetation indices were used to construct the prediction model for measuring chlorophyll content. The results showed that the prediction model constructed by spectral index of MCARI/OSAVI had high accuracy. The coefficient of determination for the validation samples was R2 = 0.887, and RMSE was 1.8. The study indicated that PIS had extensive application potentiality on detecting spectral information of crop components in the micro-scale.

It is of significance to monitor chlorophyll content with hyperspectral data for crop growth diagnosis in field. In the study, with the point of view that spectral curve shapes display "tall, low, fat and thin" morphological changes, we proposed some new characteristic parameters from spectral curve such as the ascensive or degressive velocities of segments composing peak or valley shapes in spectral curve, and angles formed by the lines fitting the segments of two sides of peak or valley curves, and used the normalized spectra to analyze correlation between these parameters and rice chlorophyll content. The result shows that (1) there is a good negative correlation between rice chlorophyll content and normalized reflectance spectra from 520-740 nm; (2) characteristic parameters from green peak region of spectral curve display better correlation with rice chlorophyll content, which makes it possible to utilize the parameters to monitor crop chlorophyll content, and will provide new ideas and methods for carrying out crop growth diagnosis with hyperspectral data.