Thellungiella salsuginea (also known as T. halophila) is a close relative of Arabidopsis that is very tolerant of drought, freezing, and salinity and may be an appropriate model to identify the molecular mechanisms underlying abiotic stress tolerance in plants. We produced 6578 ESTs, which represented 3628 unique genes (unigenes), from cDNA libraries of cold-, drought-, and salinity-stressed plants from the Yukon ecotype of Thellungiella. Among the unigenes, 94.1% encoded products that were most similar in amino acid sequence to Arabidopsis and 1.5% had no match with a member of the family Brassicaceae. Unigenes from the cold library were more similar to Arabidopsis sequences than either drought- or salinity-induced sequences, indicating that latter responses may be more divergent between Thellungiella and Arabidopsis. Analysis of gene ontology using the best matched Arabidopsis locus showed that the Thellungiella unigenes represented all biological processes and all cellular components, with the highest number of sequences attributed to the chloroplast and mitochondria. Only 140 of the unigenes were found in all three abiotic stress cDNA libraries. Of these common unigenes, 70% have no known function, which demonstrates that Thellungiella can be a rich resource of genetic information about environmental responses. Some of the ESTs in this collection have low sequence similarity with those in Genbank suggesting that they may encode functions that may contribute to Thellungiella's high degree of stress tolerance when compared with Arabidopsis. Moreover, Thellungiella is a closer relative of agriculturally important Brassica spp. than Arabidopsis, which may prove valuable in transferring information to crop improvement programs.

A jump diffusion model coupled with a local volatility function has been suggested by Andersen and Andreasen (2000). This model is attractive in that it shows promise in terms of being able to capture observed market cross-sectional implied volatilities, without being unduly complex. By generating a discrete set of American option prices assuming a jump diffusion with known parameters (i.e. in a synthetic market), we investigate two crucial challenges intrinsic to this type of model: calibration of parameters and hedging of jump risk. Our investigation suggests that it can be difficult to estimate the model parameters that govern the jump size distribution. However, the local volatility function is easier to estimate when an appropriate regularization (e.g. splines) is used to avoid over-fitting. In general, even though the estimation problem is ill-posed, it appears that combining jump diffusion with a local volatility function produces a model which can be calibrated with sufficient accuracy to prices of liquid vanilla options. With regard to hedging jump risk, two different hedging strategies are explored: a semi-static approach which uses a portfolio of the underlying and traded short maturity options to hedge a long maturity option, and a dynamic technique which involves frequent trading of options and the underlying. Simulation experiments in the synthetic market suggest that both of these methods can be used to sharply reduce the standard deviation of the hedging portfolio relative profit and loss distribution.

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This paper presents an automated workflow for pixel-wise land cover (LC) classification from multispectral airborne laser scanning (ALS) data using deep learning methods. It mainly contains three procedures: data pre-processing, land cover classification, and accuracy assessment. First, a total of nine raster images with different information were generated from the pre-processed point clouds. These images were assembled into six input data combinations. Meanwhile, the labelled dataset was created using the orthophotos as the ground truth. Also, three deep learning networks were established. Then, each input data combination was used to train and validate each network, which developed eighteen LC classification models with different parameters to predict LC types for pixels. Finally, accuracy assessments and comparisons were done for the eighteen classification results to determine an optimal scheme. The proposed method was tested on six input datasets with three deep learning classification networks (i.e., 1D CNN, 2D CNN, and 3D CNN). The highest overall classification accuracy of 97.2% has been achieved using the proposed 3D CNN. The overall accuracy (OA) of the 2D and 3D CNNs was, on average, 8.4% higher than that of the 1D CNN. Although the OA of the 2D CNN was at most 0.3% lower than that of the 3D CNN, the runtime of the 3D CNN was five times longer than the 2D CNN. Thus, the 2D CNN was the best choice for the multispectral ALS LC classification when considering efficiency. The results demonstrated the proposed methods can successfully classify land covers from multispectral ALS data.

Triethylenetetramine (TETA) is introduced as a new and outstanding candidate for solvent engineering of the residual PbI2 content and MAPbI3 morphology.