Comparison between Sampling Techniques for Virological Molecular Analyses: Dolphin Morbillivirus and Herpesvirus Detection from FTA® Card and Frozen Tissue.

Stranded animals offer valuable information on marine mammal physiology and pathology; however, the decomposition state of the carcasses and lack of a rigorous cold chain for sample preservation can sometimes discourage diagnostic analyses based on nucleic acid detection. The present paper aims at evaluating the reliability of FTA® card tissue imprints as an alternative matrix to frozen tissues for virological analyses based on biomolecular methods. Given the contribution of Cetacean morbillivirus (CeMV) to strandings and the increase of herpesvirus detection in cetaceans, these two pathogens were selected as representative of RNA and DNA viruses. Dolphin morbillivirus (DMV) and herpesvirus presence was investigated in parallel on tissue imprints on FTA® cards and frozen tissues collected during necropsy of dolphins stranded in Italy. Samples were analysed by nested RT-PCR for DMV and nested-PCR for herpesvirus. Only one animal was positive for herpesvirus, hampering further considerations on this virus. DMV was detected in all animals, both in FTA® card imprints and tissue samples, with differences possibly related to the decomposition condition category of the carcasses. Tissue sampling on FTA® cards seems a promising alternative to frozen tissues for biomolecular analyses, especially when ensuring adequate storage and shipment conditions for frozen tissues is difficult.

Hyperspectral Estimation of Canopy Leaf Biomass Phenotype per Ground Area Using a Continuous Wavelet Analysis in Wheat.

To extend agricultural productivity by knowledge-based breeding and tailoring varieties to adapt to specific environmental conditions, it is imperative to improve our ability to acquire the dynamic changes of the crop's phenotype under field conditions. Canopy leaf biomass (CLB) per ground area is one of the key crop phenotypic parameters in plant breeding. The most promising technique for effectively monitoring CLB is the hyperspectral vegetation index (VI). However, VI-based empirical models are limited by their poor stability and extrapolation difficulties when used to assess complex dynamic environments with different varieties, growth stages, and sites. It has been proven difficult to calibrate and validate some VI-based models. To address this problem, eight field experiments using eight wheat varieties were conducted during the period of 2003-2011 at four sites, and continuous wavelet transform (CWT) was applied to estimate CLB from large number of field experimental data. The analysis of 108 wavelet functions from all 15 wavelet families revealed that the best wavelet features for CLB in terms of wavelength (W) and scale (S) were observed in the near-infrared region and at high scales (7 and 8). The best wavelet-based model was derived from the Daubechies family (db), and was named db7 (W1197 nm, S8). The new model was more accurate ( R v 2 = 0.67 and RRMSE = 27.26%) than a model obtained using the best existing VI ( R v 2 = 0.54 and RRMSE = 34.71%). Furthermore, the stable performance of the optimal db7 wavelet feature was confirmed by its limited variation among the different varieties, growth stages, and sites, which confirmed the high stability of the CWT to estimate CLB with hyperspectral data. This study highlighted the potential of precision phenotyping to assess the dynamic genetics of complex traits, especially those not amenable to traditional phenotyping.

Exploring novel bands and key index for evaluating leaf equivalent water thickness in wheat using hyperspectra influenced by nitrogen.

Leaf equivalent water thickness (LEWT) is an important indicator of crop water status. Effectively monitoring the water status of wheat under different nitrogen treatments is important for effective water management in precision agriculture. Trends in the variation of LEWT in wheat plants during plant growth were analyzed based on field experiments in which wheat plants under various water and nitrogen treatments in two consecutive growing seasons. Two-band spectral indices [normalized difference spectral indices (NDSI), ratio spectral indices (RSI), different spectral indices (DSI)], and then three-band spectral indices were established based on the best two-band spectral index within the range of 350-2500 nm to reduce the noise caused by nitrogen and saturation. Then, optimal spectral indices were selected to construct models of LEWT monitoring in wheat. The results showed that the two-band spectral index NDSI(R1204, R1318) could be used for LEWT monitoring throughout the wheat growth season, but the model performed differently before and after anthesis. Therefore, further two-band spectral indices NDSIb(R1445, R487), NDSIa(R1714, R1395), and NDSI(R1429, R416), were constructed for the two developmental phases, with NDSI(R1429, R416) considered to be the best index. Finally, a three-band index (R1429-R416-R1865)/(R1429+R416+R1865), which was superior for monitoring LEWT and reducing the noise caused by nitrogen, was formed on the best two-band spectral index NDSI(R1429, R416) by adding the 1,865 nm wavelenght as the third band. This produced more uniformity and stable performance compared with the two-band spectral indices in the LEWT model. The results are of technical significance for monitoring the water status of wheat under different nitrogen treatments in precision agriculture.