Transcriptome analysis of differentially expressed genes during anther development stages on male sterility and fertility in Cucumis melo L. line.

The genic male sterility (MS) plays a major role in melon hybrids production, it could reduce the cost of pollination and increase the yield and quality. However, the molecular mechanism underlying genetic male sterility is yet poorly understood. The morphological differences of flower buds of melon were observed showed that the flower buds were tetrad when they were 1 mm stage and monocyte microspore when they were 2 mm stage. Electron microscopy showed that there was significant difference between MS lines and MF (male fertility) lines. In order to detect the global expression of the genes during the melon anther development and association with MS, 12 DEGs (differentially expressed genes) libraries were constructed from the anther of MS and MF in the bud stage with 1 and 2 mm diameter, respectively. A total of 765 DEGs expressed in anther during different developmental stage (MS 1 mm vs. MS 2 mm), 148 and 309 DEGs were found to be related to MS as compared to MF (MS 1 mm vs. MF 1 mm, and MS 2 mm vs. MF 2 mm) at a false discovery rate FDR <0.01. Among these, 10 DEGs were expressed in all the three comparisons, including transcription factor bHLH genes. Among the DEGs in RNA-seq analysis, 28 were validated by qRT-PCR. Of these, a number of genes were involved in ABC transfactor B family, cytochrome-related genes, hormone-related genes (auxin transporter, gibberellin-regulated protein), MADS-box protein genes, F-box protein genes, peroxidase-related, and Zinc finger protein genes. These genes are involved in many biological pathways, including starch and sucrose metabolism, signal transduction mechanisms and transcription factors, etc. Compared to the same developmental stage of MS and MF, the different developmental stages of MS indicated diverse gene regulation pathways involved in the anther development in MS. These results would provide novel insight into the global network to male sterility in melon.

A Node Localization Algorithm Based on Multi-Granularity Regional Division and the Lagrange Multiplier Method in Wireless Sensor Networks.

With the integrated development of the Internet, wireless sensor technology, cloud computing, and mobile Internet, there has been a lot of attention given to research about and applications of the Internet of Things. A Wireless Sensor Network (WSN) is one of the important information technologies in the Internet of Things; it integrates multi-technology to detect and gather information in a network environment by mutual cooperation, using a variety of methods to process and analyze data, implement awareness, and perform tests. This paper mainly researches the localization algorithm of sensor nodes in a wireless sensor network. Firstly, a multi-granularity region partition is proposed to divide the location region. In the range-based method, the RSSI (Received Signal Strength indicator, RSSI) is used to estimate distance. The optimal RSSI value is computed by the Gaussian fitting method. Furthermore, a Voronoi diagram is characterized by the use of dividing region. Rach anchor node is regarded as the center of each region; the whole position region is divided into several regions and the sub-region of neighboring nodes is combined into triangles while the unknown node is locked in the ultimate area. Secondly, the multi-granularity regional division and Lagrange multiplier method are used to calculate the final coordinates. Because nodes are influenced by many factors in the practical application, two kinds of positioning methods are designed. When the unknown node is inside positioning unit, we use the method of vector similarity. Moreover, we use the centroid algorithm to calculate the ultimate coordinates of unknown node. When the unknown node is outside positioning unit, we establish a Lagrange equation containing the constraint condition to calculate the first coordinates. Furthermore, we use the Taylor expansion formula to correct the coordinates of the unknown node. In addition, this localization method has been validated by establishing the real environment.