A survey of localization in wireless sensor network

Localization is one of the key techniques in wireless sensor network. The location estimation methods can be classified into target/source localization and node self-localization. In target localization, we mainly introduce the energy-based method. Then we investigate the node self-localization methods. Since the widespread adoption of the wireless sensor network, the localization methods are different in various applications. And there are several challenges in some special scenarios. In this paper, we present a comprehensive survey of these challenges: localization in non-line-of-sight, node selection criteria for localization in energy-constrained network, scheduling the sensor node to optimize the tradeoff between localization performance and energy consumption, cooperative node localization, and localization algorithm in heterogeneous network. Finally, we introduce the evaluation criteria for localization in wireless sensor network

First Report of a Novel Hepatozoon sp. in Giant Pandas (Ailuropoda melanoleuca)

The first report of giant pandas (Ailuropoda melanoleuca) infected with a novel Hepatozoon species is presented. An intraleukocytic parasite was detected via routine blood smear from a zoo-housed giant panda at the National Zoological Park. Ribosomal DNA sequences indicated a previously undescribed Hepatozoon species. Phylogenetic and distance analyses of the sequences placed it within its own branch, clustered with Old World species with carnivore (primarily ursid and mustelid) hosts. Retrospective and opportunistic testing of other individuals produced additional positive detections (17/23, 73.9%), demonstrating 100% prevalence (14/14) across five institutions. All animals were asymptomatic at time of sampling, and health implications for giant pandas remain unknown

PATRIC, the bacterial bioinformatics database and analysis resource

The Pathosystems Resource Integration Center (PATRIC) is the all-bacterial Bioinformatics Resource Center (BRC) (http://www.patricbrc.org). A joint effort by two of the original National Institute of Allergy and Infectious Diseases-funded BRCs, PATRIC provides researchers with an online resource that stores and integrates a variety of data types [e.g. genomics, transcriptomics, protein-protein interactions (PPIs), three-dimensional protein structures and sequence typing data] and associated metadata. Datatypes are summarized for individual genomes and across taxonomic levels. All genomes in PATRIC, currently more than 10 000, are consistently annotated using RAST, the Rapid Annotations using Subsystems Technology. Summaries of different data types are also provided for individual genes, where comparisons of different annotations are available, and also include available transcriptomic data. PATRIC provides a variety of ways for researchers to find data of interest and a private workspace where they can store both genomic and gene associations, and their own private data. Both private and public data can be analyzed together using a suite of tools to perform comparative genomic or transcriptomic analysis. PATRIC also includes integrated information related to disease and PPIs. All the data and integrated analysis and visualization tools are freely available. This manuscript describes updates to the PATRIC since its initial report in the 2007 NAR Database Issu

The Mitochondrial Genome of Baylisascaris procyonis

BACKGROUND: Baylisascaris procyonis (Nematoda: Ascaridida), an intestinal nematode of raccoons, is emerging as an important helminthic zoonosis due to serious or fatal larval migrans in animals and humans. Despite its significant veterinary and public health impact, the epidemiology, molecular ecology and population genetics of this parasite remain largely unexplored. Mitochondrial (mt) genomes can provide a foundation for investigations in these areas and assist in the diagnosis and control of B. procyonis. In this study, the first complete mt genome sequence of B. procyonis was determined using a polymerase chain reaction (PCR)-based primer-walking strategy. METHODOLOGY/PRINCIPAL FINDINGS: The circular mt genome (14781 bp) of B. procyonis contained 12 protein-coding, 22 transfer RNA and 2 ribosomal RNA genes congruent with other chromadorean nematodes. Interestingly, the B. procyonis mtDNA featured an extremely long AT-rich region (1375 bp) and a high number of intergenic spacers (17), making it unique compared with other secernentean nematodes characterized to date. Additionally, the entire genome displayed notable levels of AT skew and GC skew. Based on pairwise comparisons and sliding window analysis of mt genes among the available 11 Ascaridida mtDNAs, new primer pairs were designed to amplify specific short fragments of the genes cytb (548 bp fragment) and rrnL (200 bp fragment) in the B. procyonis mtDNA, and tested as possible alternatives to existing mt molecular beacons for Ascaridida. Finally, phylogenetic analysis of mtDNAs provided novel estimates of the interrelationships of Baylisasaris and Ascaridida. CONCLUSIONS/SIGNIFICANCE: The complete mt genome sequence of B. procyonis sequenced here should contribute to molecular diagnostic methods, epidemiological investigations and ecological studies of B. procyonis and other related ascaridoids. The information will be important in refining the phylogenetic relationships within the order Ascaridida and enriching the resource of markers for systematic, population genetic and evolutionary biological studies of parasitic nematodes of socio-economic importance

Systems Integration of Biodefense Omics Data for Analysis of Pathogen-Host Interactions and Identification of Potential Targets

The NIAID (National Institute for Allergy and Infectious Diseases) Biodefense Proteomics program aims to identify targets for potential vaccines, therapeutics, and diagnostics for agents of concern in bioterrorism, including bacterial, parasitic, and viral pathogens. The program includes seven Proteomics Research Centers, generating diverse types of pathogen-host data, including mass spectrometry, microarray transcriptional profiles, protein interactions, protein structures and biological reagents. The Biodefense Resource Center (www.proteomicsresource.org) has developed a bioinformatics framework, employing a protein-centric approach to integrate and support mining and analysis of the large and heterogeneous data. Underlying this approach is a data warehouse with comprehensive protein + gene identifier and name mappings and annotations extracted from over 100 molecular databases. Value-added annotations are provided for key proteins from experimental findings using controlled vocabulary. The availability of pathogen and host omics data in an integrated framework allows global analysis of the data and comparisons across different experiments and organisms, as illustrated in several case studies presented here. (1) The identification of a hypothetical protein with differential gene and protein expressions in two host systems (mouse macrophage and human HeLa cells) infected by different bacterial (Bacillus anthracis and Salmonella typhimurium) and viral (orthopox) pathogens suggesting that this protein can be prioritized for additional analysis and functional characterization. (2) The analysis of a vaccinia-human protein interaction network supplemented with protein accumulation levels led to the identification of human Keratin, type II cytoskeletal 4 protein as a potential therapeutic target. (3) Comparison of complete genomes from pathogenic variants coupled with experimental information on complete proteomes allowed the identification and prioritization of ten potential diagnostic targets from Bacillus anthracis. The integrative analysis across data sets from multiple centers can reveal potential functional significance and hidden relationships between pathogen and host proteins, thereby providing a systems approach to basic understanding of pathogenicity and target identification

A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings

BackgroundA composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data.MethodsWe assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low-glucose and low-glucose hypoglycemia; very high-glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation.ResultsThe analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals.ConclusionThe GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments

Characterization of Bioactive Recombinant Human Lysozyme Expressed in Milk of Cloned Transgenic Cattle

BACKGROUND: There is great potential for using transgenic technology to improve the quality of cow milk and to produce biopharmaceuticals within the mammary gland. Lysozyme, a bactericidal protein that protects human infants from microbial infections, is highly expressed in human milk but is found in only trace amounts in cow milk. METHODOLOGY/PRINCIPAL FINDINGS: We have produced 17 healthy cloned cattle expressing recombinant human lysozyme using somatic cell nuclear transfer. In this study, we just focus on four transgenic cattle which were natural lactation. The expression level of the recombinant lysozyme was up to 25.96 mg/L, as measured by radioimmunoassay. Purified recombinant human lysozyme showed the same physicochemical properties, such as molecular mass and bacterial lysis, as its natural counterpart. Moreover, both recombinant and natural lysozyme had similar conditions for reactivity as well as for pH and temperature stability during in vitro simulations. The gross composition of transgenic and non-transgenic milk, including levels of lactose, total protein, total fat, and total solids were not found significant differences. CONCLUSIONS/SIGNIFICANCE: Thus, our study not only describes transgenic cattle whose milk offers the similar nutritional benefits as human milk but also reports techniques that could be further refined for production of active human lysozyme on a large scale