An Ontology-Driven Approach for Integrating Intelligence to Manage Human and Ecological Health Risks in the Geospatial Sensor Web.

Due to the rapid installation of a massive number of fixed and mobile sensors, monitoring machines are intentionally or unintentionally involved in the production of a large amount of geospatial data. Environmental sensors and related software applications are rapidly altering human lifestyles and even impacting ecological and human health. However, there are rarely specific geospatial sensor web (GSW) applications for certain ecological public health questions. In this paper, we propose an ontology-driven approach for integrating intelligence to manage human and ecological health risks in the GSW. We design a Human and Ecological health Risks Ontology (HERO) based on a semantic sensor network ontology template. We also illustrate a web-based prototype, the Human and Ecological Health Risk Management System (HaEHMS), which helps health experts and decision makers to estimate human and ecological health risks. We demonstrate this intelligent system through a case study of automatic prediction of air quality and related health risk.

Skin transcriptome profiles associated with coat color in sheep.

BACKGROUND: Previous molecular genetic studies of physiology and pigmentation of sheep skin have focused primarily on a limited number of genes and proteins. To identify additional genes that may play important roles in coat color regulation, Illumina sequencing technology was used to catalog global gene expression profiles in skin of sheep with white versus black coat color. RESULTS: There were 90,006 and 74,533 unigenes assembled from the reads obtained from white and black sheep skin, respectively. Genes encoding for the ribosomal proteins and keratin associated proteins were most highly expressed. A total of 2,235 known genes were differentially expressed in black versus white sheep skin, with 479 genes up-regulated and 1,756 genes down-regulated. A total of 845 novel genes were differentially expressed in black versus white sheep skin, consisting of 107 genes which were up-regulated (including 2 highly expressed genes exclusively expressed in black sheep skin) and 738 genes that were down-regulated. There was also a total of 49 known coat color genes expressed in sheep skin, from which 13 genes showed higher expression in black sheep skin. Many of these up-regulated genes, such as DCT, MATP, TYR and TYRP1, are members of the components of melanosomes and their precursor ontology category. CONCLUSION: The white and black sheep skin transcriptome profiles obtained provide a valuable resource for future research to understand the network of gene expression controlling skin physiology and melanogenesis in sheep.