Disease management at the wildlife-livestock interface: using whole-genome sequencing to study the role of elk in Mycobacterium bovis transmission in Michigan, USA

The role of wildlife in the persistence and spread of livestock diseases is difficult to quantify and control. These difficulties are exacerbated when several wildlife species are potentially involved. Bovine tuberculosis (bTB), caused by Mycobacterium bovis, has experienced an ecological shift in Michigan, with spillover from cattle leading to an endemically infected white‐tailed deer (deer) population. It has potentially substantial implications for the health and well‐being of both wildlife and livestock and incurs a significant economic cost to industry and government. Deer are known to act as a reservoir of infection, with evidence of M. bovis transmission to sympatric elk and cattle populations. However, the role of elk in the circulation of M. bovis is uncertain; they are few in number, but range further than deer, so may enable long distance spread. Combining Whole Genome Sequences (WGS) for M. bovis isolates from exceptionally well‐observed populations of elk, deer and cattle with spatiotemporal locations, we use spatial and Bayesian phylogenetic analyses to show strong spatiotemporal admixture of M. bovis isolates. Clustering of bTB in elk and cattle suggests either intraspecies transmission within the two populations, or exposure to a common source. However, there is no support for significant pathogen transfer amongst elk and cattle, and our data are in accordance with existing evidence that interspecies transmission in Michigan is likely only maintained by deer. This study demonstrates the value of whole genome population studies of M. bovis transmission at the wildlife‐livestock interface, providing insights into bTB management in an endemic system

Photoluminescence Properties of the Zn1-x Y (x) O Tubes Prepared by Polycarbonate Templates

We have prepared Zn1-x Y (x) O (x=0 and 0.01) tubes to study its structural and photoluminescent properties. A pore wetting process of porous polycarbonate templates with the liquid precursor and following thermal treatment were utilized for preparing the Zn1-x Y (x) O tube structure. Using the polycarbonate template with pore size of about 2 mu m diameter, the Zn1-x Y (x) O tubes were obtained. Photoluminescence (PL) spectroscopy was used to measure optical emissions from 350 to 650 nm with a He-Cd laser. The results of the PL spectra show that the Zn1-x Y (x) O tubes have evident emission peaks at the UV (about 380 nm) and visible (around 500 to 650 nm) region. The emission peak at the UV region was slightly shifted to higher wavelengths with increasing Y content. Meanwhile, the green and yellow emission peaks intensity increases as Y content increases. These results are explained by the structure tuning and oxygen deficiency with the introduction of Y

Clustering Algorithms: Their Application to Gene Expression Data

Gene expression data hide vital information required to understand the biological process that takes place in a particular organism in relation to its environment. Deciphering the hidden patterns in gene expression data proffers a prodigious preference to strengthen the understanding of functional genomics. The complexity of biological networks and the volume of genes present increase the challenges of comprehending and interpretation of the resulting mass of data, which consists of millions of measurements; these data also inhibit vagueness, imprecision, and noise. Therefore, the use of clustering techniques is a first step toward addressing these challenges, which is essential in the data mining process to reveal natural structures and iden-tify interesting patterns in the underlying data. The clustering of gene expression data has been proven to be useful in making known the natural structure inherent in gene expression data, understanding gene functions, cellular processes, and subtypes of cells, mining useful information from noisy data, and understanding gene regulation. The other benefit of clustering gene expression data is the identification of homology, which is very important in vaccine design. This review examines the various clustering algorithms applicable to the gene expression data in order to discover and provide useful knowledge of the appropriate clustering technique that will guarantee stability and high degree of accuracy in its analysis procedure

Search for massive resonances decaying in to WW,WZ or ZZ bosons in proton-proton collisions at root s=13 TeV

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Search for leptophobic Z ' bosons decaying into four-lepton final states in proton-proton collisions at root s=8 TeV

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Search for black holes and other new phenomena in high-multiplicity final states in proton-proton collisions at root s=13 TeV

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Search for heavy resonances decaying into a vector boson and a Higgs boson in final states with charged leptons, neutrinos, and b quarks

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Search for high-mass diphoton resonances in proton-proton collisions at 13 TeV and combination with 8 TeV search

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Measurements of differential production cross sections for a Z boson in association with jets in pp collisions at root s=8 TeV

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Measurement of the jet mass in highly boosted t(t)over-bar events from pp collisions at root s=8TeV

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