Genome-wide assessment of population structure and genetic diversity of Chinese Lou onion using specific length amplified fragment (SLAF) sequencing.

Lou onion (Allium fistulosum L. var. viviparum) is an abundant source of flavonols which provides additional health benefits to diseases. Genome-wide specific length amplified fragment (SLAF) sequencing method is a rapidly developed deep sequencing technologies used for selection and identification of genetic loci or markers. This study aimed to elucidate the genetic diversity of 122 onion accessions in China using the SLAF-seq method. A set of 122 onion accessions including 107 A.fistulosum L. var. viviparum Makino, 3 A.fistulosum L. var. gigantum Makino, 3 A.mongolicum Regel and 9 A.cepa L. accessions (3 whites, 3 reds and 3 yellows) from different regions in China were enrolled. Genomic DNA was isolated from young leaves and prepared for the SLAF-seq, which generated a total of 1,387.55 M reads and 162,321 high quality SNPs (integrity >0.5 and MAF >0.05). These SNPs were used for the construction of neighbor-joining phylogenetic tree, in which 10 A.fistulosum L. var. viviparum Makino accessions from Yinchuan (Ningxia province) and Datong (Qinghai province) had close genetic relationship. The 3 A.cepa L. clusters (red, white and yellow) had close genetic relationship especially with the 97 A.fistulosum L. var. viviparum Makino accessions. Population structure analysis suggested entire population could be clustered into 3 groups, while principal component analysis (PCA) showed there were 4 genetic groups. We confirmed the SLAF-seq approach was effective in genetic diversity analysis in red onion accessions. The key findings would provide a reference to the Lou onion germplasm in China.

Sliding mode control and sampling rate strategy for Networked control systems with packet disordering via Markov chain prediction.

This paper investigates sliding mode control combined with sampling rate control for networked control systems subject to packet disordering via Markov chain prediction. The main objectives of the proposed method are to predict the probability of the occurrence of packet disordering when packet disordering is unknown in the networks, to control sampling rate to restrain heavy packet disordering, and to stabilize the Markovian jump system with variable parameters by sliding mode techniques. Firstly, an argument system with sampling rate and a plant state is established. Then, the Networked control system based on Markov chain probability prediction and statistical analysis of this probability is modeled as a Markovian jump system with two Markov chains. Next, sliding mode controller is designed to stabilize the dynamic Markovian jump system. Finally, experiments are conducted to illustrate the effectiveness and benefits of proposed method.