Uninterrupted Detection of Segment Nodes in Wireless Sensor Networks

Abstract Neighbor discovery is an important task in wireless networks, and especially in sensor networks. Neighbor information can be used to improve routing, clustering and scheduling algorithms. A sensor network may contain a huge number of simple sensor nodes that are deployed at some inspected t site. In large areas, such a network usually has a mesh structure. In this case, some of the sensor nodes act as routers, forwarding messages from one of their neighbors to another. The nodes are configured to turn their communication hardware on and off to minimize energy consumption. Therefore, in order for two neighboring sensors to communicate, both must be in active mode. In the sensor network model considered in this paper, the nodes are placed randomly over the area of interest and their first step is to detect their immediate neighbors -the nodes with which they have a direct wireless communication -and to establish routes to the gateway. In networks with incessantly heavy traffic, the sensors need not invoke any special neighbor detection protocol during normal operation. This is because any new node, or a node that has lost connectivity to its neighbors, can hear its neighbors simply by listening to the channel for a short time. However, for sensor networks with low and irregular traffic, a special neighbor detection scheme should be used

A Review on Data Clustering Algorithms for Mixed Data

Clustering is the unsupervised classification of patterns into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines; this reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. In general, clustering is a method of dividing the data into groups of similar objects. One of significant research areas in data mining is to develop methods to modernize knowledge by using the existing knowledge, since it can generally augment mining efficiency, especially for very bulky database. Data mining uncovers hidden, previously unknown, and potentially useful information from large amounts of data. This paper presents a general survey of various clustering algorithms. In addition, the paper also describes the efficiency of Self-Organized Map (SOM) algorithm in enhancing the mixed data clustering

Genetic divergence study in greengram [Vigna radiata (L.) Wilczek]

The present investigation was undertaken to obtain information on the nature and extent of genetic diversity among 60 greengram genotypes for yield related traits and quality traits by using Mahalanobis’s D2 statistics. The genotypes were grouped into eleven clusters. Cluster I was found to be the largest with 38 genotypes followed by cluster V with 13 genotypes and all the other clusters were found to be solitary, each containing a single genotype. Clusters VIII and XI had the maximum inter-cluster distance, followed by clusters IV and XI. Cluster XI had the highest mean values for yield and other yield attributing traits. Iron content contributed high towards total genetic diversity followed by protein content and test weight. Based on the mean performance and diversity studies, the genotypes COGG 18-17, LGG 460, Daftri vikas and IPM 1603-3 were found to be the best for further yield improvement in greengram. Utilizing the genotypes from the more divergent clusters as parents in breeding programmes will yield relatively good amount of heterosis in F1 and high frequency of transgressive segregants and genetic variability in subsequent generations can be acquired

A chickpea genetic variation map based on the sequencing of 3,366 genomes

Zero hunger and good health could be realized by 2030 through effective conservation, characterization and utilization of germplasm resources1 . So far, few chickpea (Cicerarietinum) germplasm accessions have been characterized at the genome sequence level2 . Here we present a detailed map of variation in 3,171 cultivated and 195 wild accessions to provide publicly available resources for chickpea genomics research and breeding. We constructed a chickpea pan-genome to describe genomic diversity across cultivated chickpea and its wild progenitor accessions. A divergence tree using genes present in around 80% of individuals in one species allowed us to estimate the divergence of Cicer over the last 21 million years. Our analysis found chromosomal segments and genes that show signatures of selection during domestication, migration and improvement. The chromosomal locations of deleterious mutations responsible for limited genetic diversity and decreased fitness were identified in elite germplasm. We identified superior haplotypes for improvement-related traits in landraces that can be introgressed into elite breeding lines through haplotype-based breeding, and found targets for purging deleterious alleles through genomics-assisted breeding and/or gene editing. Finally, we propose three crop breeding strategies based on genomic prediction to enhance crop productivity for 16 traits while avoiding the erosion of genetic diversity through optimal contribution selection (OCS)-based pre-breeding. The predicted performance for 100-seed weight, an important yield-related trait, increased by up to 23% and 12% with OCS- and haplotype-based genomic approaches, respectively. On the basis of WGS of 3,366 chickpea germplasm accessions, we report here a rich map of the genetic variation in chickpea. We provide a chickpea pan-genome and offer insights into species divergence, the migration of the cultigen (C. arietinum), rare allele burden and fitness loss in chickpea. We propose three genomic breeding approaches— haplotype-based breeding, genomic prediction and OCS—for developing tailor-made high-yielding and climate-resilient chickpea varieties. We sequenced 3,366 chickpea germplasm lines, including 3,171 cultivated and 195 wild accessions at an average coverage of around 12× (Methods, Extended Data Fig. 1, Supplementary Data 1 Tables 1, 2). Alignment of WGS data to the CDC Frontier reference genome11 identified 3.94 million and 19.57 million single-nucleotide polymorphisms (SNPs) in 3,171 cultivated and 195 wild accessions, respectively (Extended Data Table 1, Supplementary Data 1 Tables 3–7, Supplementary Notes). This SNP dataset was used to assess linkage disequilibrium (LD) decay (Supplementary Data 2 Tables 1, 2, Extended Data Fig. 2, Supplementary Notes) and identify private and population-enriched SNPs (Supplementary Data 3 Tables 1–4, Supplementary Notes). These private and population-enriched SNPs suggest rapid adaptation and can enhance the genetic foundation in the elite gene pool

Fast and Highly Chemoselective Alkynylation of Thiols with Hypervalent Iodine Reagents Enabled through a Low Energy Barrier Concerted Mechanism

Among all functional groups, alkynes occupy a privileged position in synthetic and medicinal chemistry, chemical biology, and materials science. Thioalkynes, in particular, are highly useful, as they combine the enhanced reactivity of the triple bond with a sulfur atom frequently encountered in bioactive compounds and materials. Nevertheless, general methods to access these compounds are lacking. In this article, we describe the mechanism and full scope of the alkynylation of thiols using ethynyl benziodoxolone (EBX) hypervalent iodine reagents. Computations led to the discovery of a new, three-atom concerted transition state with a very low energy barrier, which rationalizes the high reaction rate. On the basis of this result, the scope of the reaction was extended to the synthesis of aryl- and alkyl-substituted alkynes containing a broad range of functional groups. New sulfur nucleophiles such as thioglycosides, thioacids, and sodium hydrogen sulfide were also alkynylated successfully to lead to the most general and practical method yet reported for the synthesis of thioalkynes

A critical analysis of extraction techniques used for botanicals: Trends, priorities, industrial uses and optimization strategies

Plant extracts have been long used by the traditional healers for providing health benefits and are nowadays suitable ingredient for the production of formulated health products and nutraceuticals. Traditional methods of extraction such as maceration, percolation, digestion, and preparation of decoctions and infusions are now been replaced by advanced extraction methods for increased extraction efficiency and selectivity of bioactive compounds to meet up the increasing market demand. Advanced techniques use different ways for extraction such as microwaves, ultrasound waves, supercritical fluids, enzymes, pressurized liquids, electric field, etc. These innovative extraction techniques, afford final extracts selectively rich in compounds of interest without formation of artifacts, and are often simple, fast, environmentally friendly and fully automated compared to existing extraction method. The present review is focused on the recent trends on the extraction of different bioactive chemical constituents depending on the nature of sample matrices and their chemical classes including anthocyanins, flavonoids, polyphenols, alkaloids, oils, etc. In addition, we review the strategies for designing extraction, selection of most suitable extraction methods, and trends of extraction methods for botanicals. Recent progress on the research based on these advanced methods of extractions and their industrial importance are also discussed in detail

S100A7-Downregulation Inhibits Epidermal Growth Factor-Induced Signaling in Breast Cancer Cells and Blocks Osteoclast Formation

S100A7 is a small calcium binding protein, which has been shown to be differentially expressed in psoriatic skin lesions, as well as in squamous cell tumors of the skin, lung and breast. Although its expression has been correlated to HER+ high-grade tumors and to a high risk of progression, the molecular mechanisms of these S100A7-mediated tumorigenic effects are not well known. Here, we showed for the first time that epidermal growth factor (EGF) induces S100A7 expression in both MCF-7 and MDA-MB-468 cell lines. We also observed a decrease in EGF-directed migration in shRNA-downregulated MDA-MB-468 cell lines. Furthermore, our signaling studies revealed that EGF induced simultaneous EGF receptor phosphorylation at Tyr1173 and HER2 phosphorylation at Tyr1248 in S100A7-downregulated cell lines as compared to the vector-transfected controls. In addition, reduced phosphorylation of Src at tyrosine 416 and p-SHP2 at tyrosine 542 was observed in these downregulated cell lines. Further studies revealed that S100A7-downregulated cells had reduced angiogenesis in vivo based on matrigel plug assays. Our results also showed decreased tumor-induced osteoclastic resorption in an intra-tibial bone injection model involving SCID mice. S100A7-downregulated cells had decreased osteoclast number and size as compared to the vector controls, and this decrease was associated with variations in IL-8 expression in in vitro cell cultures. This is a novel report on the role of S100A7 in EGF-induced signaling in breast cancer cells and in osteoclast formation