Global Illumina Sequencing and the Development of EST-SSR Markers in Alfalfa

RNA-Seq, a massively parallel sequencing method for transcriptome analysis, only analyzes transcribed portions of the genome. Recently, RNA-Seq has provided an opportunity to expand the identification of alfalfa (Medicago sativa) genes. Using Illumina sequencing, 124,025 unique sequences from MSGI 1.0 have been identified from the elongating stem and post-elongation stem internodes of two alfalfa genotypes (Yang et al. 2011). Using 454 sequencing, 54,216 unique sequences were obtained from the roots and shoots of two alfalfa genotypes (Han et al., 2011). In addition, Illumina sequencing of old and young stems of 27 alfalfa genotypes led to the identification of 25,183 contigs (Li et al. 2012). While these experiments have identified numerous transcripts, the transcripts were derived only from stems, roots, and shoots. Therefore, further transcriptome sequencing of a broader array of tissues permit the global identification of transcripts that would be useful in modern alfalfa breeding programs

High-efficiency and positivity-preserving stabilized SAV methods for gradient flows

The scalar auxiliary variable (SAV)-type methods are very popular techniques for solving various nonlinear dissipative systems. Compared to the semi-implicit method, the baseline SAV method can keep a modified energy dissipation law but doubles the computational cost. The general SAV approach does not add additional computation but needs to solve a semi-implicit solution in advance, which may potentially compromise the accuracy and stability. In this paper, we construct a novel first- and second-order unconditional energy stable and positivity-preserving stabilized SAV (PS-SAV) schemes for $L^2$ and $H^{-1}$ gradient flows. The constructed schemes can reduce nearly half computational cost of the baseline SAV method and preserve its accuracy and stability simultaneously. Meanwhile, the introduced auxiliary variable is always positive while the baseline SAV cannot guarantee this positivity-preserving property. Unconditionally energy dissipation laws are derived for the proposed numerical schemes. We also establish a rigorous error analysis of the first-order scheme for the Allen-Cahn type equation in $l^{\infty}(0,T; H^1(\Omega) )$ norm. In addition we propose an energy optimization technique to optimize the modified energy close to the original energy. Several interesting numerical examples are presented to demonstrate the accuracy and effectiveness of the proposed methods

Experimental Implementation of An All-Optical Reservoir Computer Using Photonic Time Stretch and Spectral Mixing

Reservoir computing (RC) has been widely used in processing temporal information and classification tasks due to its high efficiency in training and testing. In this paper, we have experimentally demonstrated the performance of an all-optical reservoir computer based on time stretch and spectral mixing. Spectral comb lines of the stretched optical pulse are chosen as virtual nodes in the reservoir layer. Nonlinear spectral mixing is achieved through phase modulation and semiconductor optical amplification. A simple temporal waveform classification task was implemented using the demonstrated RC system to verify the approach