Population genomic analysis of North American eastern wolves (Canic lycaon) support their conservation priority status

The threatened eastern wolf is found predominantly in protected areas of central Ontario and has an evolutionary history obscured by interbreeding with coyotes and gray wolves, which challenges its conservation status and subsequent management. Here, we used a population genomics approach to uncover spatial patterns of variation in 281 canids in central Ontario and the Great Lakes region. This represents the first genome-wide single nucleotide polymorphism (SNP) dataset with substantial sample sizes of representative populations. Although they comprise their own genetic cluster, we found evidence of eastern wolf dispersal outside of the boundaries of protected areas, in that the frequency of eastern wolf genetic variation decreases with increasing distance from provincial parks. We detected eastern wolf alleles in admixed coyotes along the northeastern regions of Lake Huron and Lake Ontario. Our analyses confirm the unique genomic composition of eastern wolves, which are mostly restricted to small fragmented patches of protected habitat in central Ontario. We hope this work will encourage an innovative discussion regarding a plan for managed introgression, which could conserve eastern wolf genetic material in any genome regardless of their potential mosaic ancestry composition and the habitats that promote them

High genomic diversity and candidate genes under selection associated with range expansion in eastern coyote (Canis latrans) populations

Range expansion is a widespread biological process, with well‐described theoretical expectations associated with the colonization of novel ranges. However, comparatively few empirical studies address the genomic outcomes accompanying the genome‐wide consequences associated with the range expansion process, particularly in recent or ongoing expansions. Here, we assess two recent and distinct eastward expansion fronts of a highly mobile carnivore, the coyote (Canis latrans), to investigate patterns of genomic diversity and identify variants that may have been under selection during range expansion. Using a restriction‐associated DNA sequencing (RADseq), we genotyped 394 coyotes at 22,935 SNPs and found that overall population structure corresponded to their 19th century historical range and two distinct populations that expanded during the 20th century. Counter to theoretical expectations for populations to bottleneck during range expansions, we observed minimal evidence for decreased genomic diversity across coyotes sampled along either expansion front, which is likely due to hybridization with other Canis species. Furthermore, we identified 12 SNPs, located either within genes or putative regulatory regions, that were consistently associated with range expansion. Of these 12 genes, three (CACNA1C, ALK, and EPHA6) have putative functions related to dispersal, including habituation to novel environments and spatial learning, consistent with the expectations for traits under selection during range expansion. Although coyote colonization of eastern North America is well‐publicized, this study provides novel insights by identifying genes associated with dispersal capabilities in coyotes on the two eastern expansion fronts

Energy-efficient big data analytics in datacenters

The volume of generated data increases by the rapid growth of Internet of Things, leading to the big data proliferation and more opportunities for datacenters. Highly virtualized cloud-based datacenters are currently considered for big data analytics. However, big data requires datacenters with promoted infrastructure capable of undertaking more responsibilities for handling and analyzing data. Also, as the scale of the datacenter is increasingly expanding, minimizing energy consumption and operational cost is a vital concern. Future datacenters infrastructure including interconnection network, storage, and servers should be able to handle big data applications in an energy-efficient way. In this chapter, we aim to explore different aspects of could-based datacenters for big data analytics. First, the datacenter architecture including computing and networking technologies as well as datacenters for cloud-based services will be illustrated. Then the concept of big data, cloud computing, and some of the existing cloud-based datacenter platforms including tools for big data analytics will be introduced. We later discuss the techniques for improving energy efficiency in the cloud-based datacenters for big data analytics. Finally, the current and future trends for datacenters in particular with respect to energy consumption to support big data analytics will be discussed