ZIL: An Energy-Efficient Indoor Localization System Using ZigBee Radio to Detect WiFi Fingerprints

In existing WiFi-based localization methods, smart mobile devices consume quite a lot of power as WiFi interfaces need to be used for frequent AP scanning during the localization process. In this work, we design an energy-efficient indoor localization system called ZigBee assisted indoor localization (ZIL) based on WiFi fingerprints via ZigBee interference signatures. ZIL uses ZigBee interfaces to collect mixed WiFi signals, which include non-periodic WiFi data and periodic beacon signals. However, WiFi APs cannot be identified from these WiFi signals by ZigBee interfaces directly. To address this issue, we propose a method for detecting WiFi APs to form WiFi fingerprints from the signals collected by ZigBee interfaces. We propose a novel fingerprint matching algorithm to align a pair of fingerprints effectively. To improve the localization accuracy, we design the K-nearest neighbor (KNN) method with three different weighted distances and find that the KNN algorithm with the Manhattan distance performs best. Experiments show that ZIL can achieve the localization accuracy of 87%, which is competitive compared to state-of-the-art WiFi fingerprint-based approaches, and save energy by 68% on average compared to the approach based on WiFi interface.

Genetically monomorphic brown trout (Salmo trutta L.) populations as revealed by mitochondrial DNA, multi-locus and single-locus minisatellite (VNTR) analyses.

Normally, populations of brown trout are genetically highly variable. Two adjacent populations from NW Scotland, which had previously been found to be monomorphic for 46 protein-coding loci, were studied by higher resolution techniques. Analyses of mitochondrial DNA, multilocus DNA fingerprints and eight specific minisatellite loci revealed no genetic variation among individuals or genetic differences between the two populations. Continual low effective population sizes or severe repeated bottlenecks, as a result of low or variable recruitment, probably explain the atypical absence of genetic variation in these trout populations. Growth data do not provide any evidence of a reduction in fitness in trout from these populations.

FPGA-based minutia matching for biometric fingerprint image database retrieval

In this paper, a parallel-matching processor architecture with early jump-out (EJO) control is proposed to carry out high-speed biometric fingerprint database retrieval. The processor performs the fingerprint retrieval by using minutia point matching. An EJO method is applied to the proposed architecture to speed up the large database retrieval. The processor is implemented on a Xilinx Virtex-E, and occupies 6,825 slices and runs at up to 65 MHz. The software/hardware co-simulation benchmark with a database of 10,000 fingerprints verifies that the matching speed can achieve the rate of up to 1.22 million fingerprints per second. EJO results in about a 22% gain in computing efficiency.

Age-dependent accumulation of lipofuscin in perivascular and subretinal microglia in experimental mice

Fundus autofluorescence (AF) imaging by confocal scanning laser ophthalmoscopy has been widely used by ophthalmologists in the diagnosis/monitoring of various retinal disorders. It is believed that fundus AF is derived from lipofuscin in retinal pigment epithelial (RPE) cells; however, direct clinicopathological correlation has not been possible in humans. We examined fundus AF by confocal scanning laser ophthalmoscopy and confocal microscopy in normal C57BL/6 mice of different ages. Increasingly strong AF signals were observed with age in the neuroretina and subretinal/RPE layer by confocal scanning laser ophthalmoscopy. Unlike fundus AF detected in normal human subjects, mouse fundus AF appeared as discrete foci distributed throughout the retina. Most of the AF signals in the neuroretina were distributed around retinal vessels. Confocal microscopy of retinal and choroid/RPE flat mounts demonstrated that most of the AF signals were derived from Iba-1+ perivascular and subretinal microglia. An age-dependent accumulation of Iba-1+ microglia at the subretinal space was observed. Lipofuscin granules were detected in large numbers in subretinal microglia by electron microscopy. The number of AF+ microglia and the amount of AF granules/cell increased with age. AF granules/lipofuscin were also observed in RPE cells in mice older than 12 months, but the number of AF+ RPE cells was very low (1.48 mm-2 and 5.02 mm-2 for 12 and 24 months, respectively) compared to the number of AF+ microglial cells (20.63 mm-2 and 76.36 mm-2 for 6 and 24 months, respectively). The fluorescence emission fingerprints of AF granules in subretinal microglia were the same as those in RPE cells. Our observation suggests that perivascular and subretinal microglia are the main cells producing lipofuscin in normal aged mouse retina and are responsible for in vivo fundus AF. Microglia may play an important role in retinal aging and age-related retinal diseases.

Comparative analysis of synovial fluid and plasma proteomes in juvenile arthritis - Proteomic patterns of joint inflammation in early stage disease

Synovial fluid is a potential source of novel biomarkers for many arthritic disorders involving joint inflammation, including juvenile idiopathic arthritis. We first compared the distinctive protein ‘fingerprints’ of local inflammation in synovial fluid with systemic profiles within matched plasma samples. The synovial fluid proteome at the time of joint inflammation was then evaluated across clinical subgroups to identify early disease associated proteins. We measured the synovial fluid and plasma proteomes using the two-dimensional fluorescence difference gel electrophoresis approach. Image analysis software was used to highlight the expression levels of joint and subgroup associated proteins across the study cohort (n = 32). A defined subset of 30 proteins had statistically significant differences (p < 0.05) between sample types such that synovial fluid could be differentiated from plasma. Furthermore distinctive synovial proteome expression patterns segregate patient subgroups. Protein expression patterns localized in the chronically inflamed joint therefore have the potential to identify patients more likely to suffer disease which will spread from a single joint to multiple joints. The proteins identified could act as criteria to prevent disease extension by more aggressive therapeutic intervention directed at an earlier stage than is currently possible.

The human synovial fluid proteome: A key factor in the pathology of joint disease

This review aims to summarise our knowledge to date on the protein complement of the synovial fluid (S F). The tissues, structure and pathophysiology of the synovial joint are briefly described. The salient features of the S F proteome, how it is composed and the influence of arthritic disease are highlighted and discussed. The concentrations of proteins that have been detected and quantified in SF are drawn together from the literature on osteoarthritis, rheumatoid arthritis and juvenile idiopathic arthritis. The measurements are plotted to give a perspective on the dynamic range of protein levels within the SF. Approaches to proteomic analysis of SF to date are discussed along with their findings. From the recent literature reviewed within, it is becoming increasingly clear that analysis of the SF proteome as a whole, could deliver the most valuable differential diagnostic fingerprints of a number of arthritic disorders. Further development of proteomic platforms could characterise prognostic profiles to improve the cliniciads ability to resolve unremitting disease by existing and novel therapeutics.

Raman spectroscopy for the detection of AGEs/ALEs

Raman spectroscopy is a noninvasive, nondestructive tool for capturing multiplexed biochemical information across diverse molecular species including proteins, lipids, DNA, and mineralizations. Based on light scattering from molecules, cells, and tissues, it is possible to detect molecular fingerprints and discriminate between subtly different members of each biochemical class. Raman spectroscopy is ideal for detecting perturbations from the expected molecular structure such as those occurring during senescence and the modification of long-lived proteins by metabolic intermediates as we age. Here, we describe the sample preparation, data acquisition, signal processing, data analysis and interpretation involved in using Raman spectroscopy for detecting age-related protein modifications in complex biological tissues.

Tailored Microarray Platform for the Detection of Marine Toxins

Currently, there are no fast in vitro broad spectrum screening bioassays for the detection of marine toxins. The aim of this study was to develop such an assay. In gene expression profiling experiments 17 marker genes were provisionally selected that were differentially regulated in human intestinal Caco-2 cells upon exposure to the lipophilic shellfish poisons azaspiracid-1 (AZA1) or dinophysis toxin-1 (DTX1). These 17 genes together with two control genes were the basis for the design of a tailored microarray platform for the detection of these marine toxins and potentially others. Five out of the 17 selected marker genes on this dedicated DNA microarray gave dear signals, whereby the resulting fingerprints could be used to detect these toxins. CEACAM1, DDIT4, and TUBB3 were up-regulated by both AZA1 and DTX1, TRIB3 was up-regulated by AZA1 only, and OSR2 by DTX1 only. Analysis by singleplex qRT-PCR revealed the up- and down-regulation of the selected RGS16 and NPPB marker genes by DTX1, that were not envisioned by the new developed dedicated array. The qRT-PCR targeting the DDIT4, RSG16 and NPPB genes thus already resulted in a specific pattern for AZA1 and DTX1 indicating that for this specific case qRT-PCR might a be more suitable approach than a dedicated array.

A novel toxicity fingerprinting method for pollutant identification with lux-marked biosensors

A novel technique is described for the identification and quantification of environmental pollutants based on toxicity fingerprinting with a metabolic lux-marked bacterial biosensor. This method involved characterizing the toxicity-based responses of the biosensor to seven calibration pollutants as acute temporal-dose response fingerprints. An algorithm is described to allow comparisons of responses of an unknown pollutant to be made against the calibration data. This is based on predicting pollutant concentration at each of six different time points over the course of a 5-min assay. If the prediction is consistent between the unknown pollutant and a calibration pollutant at the 95% test level, this is considered to be a positive identification. All seven calibration pollutants could be successfully distinguished from each other with this technique. Environmental samples, individually spiked with single concentrations of pollutants, were compared in this way against the calibration pollutants. An 83% identification success was achieved, with no false positives at the 95% test level. This is a simple and rapid technique that potentially can be applied to monitoring of industrial wastewater or as a screening tool for regulators.

Off-center explosions of Chandrasekhar-mass white dwarfs:An explanation of super-bright type la supernovae?

Context. The recent discovery of a very bright type la supernova, SNLS-03D3bb (=SN 2003fg), in the Supernova Legacy Survey (SNLS) has raised the question of whether super-Chandrasekhar-mass white-dwarf stars are needed to explain such bright explosions. Progenitors of this sort could form by mergers of pairs of rather massive white dwarfs. Binary systems of two white dwarfs in close orbit, where their total mass significantly exceeds the Chandrasekhar mass, have not yet been found. Therefore SNLS-03D3bb could establish the first clear case of a double-degenerate progenitor of a (peculiar) type la supernovae. Moreover, if this interpretation is correct, it casts some doubt on the universality of the calibration relations used to make SNe la distance indicators for cosmology. Aims. We aim to evaluate the case for a super-Chandrasekhar-mass progenitor for SNLS-03D3bb in light of previous theoretical work on super-Chandrasekhar-mass explosions. Furthermore, we propose an alternative scenario involving only a Chandrasekhar-mass progenitor. Methods. We present a theoretically motivated critical discussion of the expected observational fingerprints of super-Chandrasekharmass explosions. As an alternative, we describe a simple class of aspherical Chandrasekhar-mass models in which the products of nuclear burning are displaced from the center. We then perform simple radiative transfer calculations to predict synthetic lightcurves for one such off-center explosion model. Results. In important respects, the expected observational consequences of super-Chandrasekhar-mass explosions are not consistent with the observations of SNLS-03D3bb. We demonstrate that the lopsided explosion of a Chandrasekhar-mass white dwarf could provide a better explanation. © ESO 2007.

Metabolomic Profiling of In Vivo Plasma Responses to DioxinAssociated Dietary Contaminant Exposure in Rats: Implications for Identification of Sources of Animal and Human Exposure

Dioxin contamination of the food chain typically occurs when cocktails of combustion residues or polychlorinated biphenyl (PCB) containing oils become incorporated into animal feed. These highly toxic compounds are bioaccumulative with small amounts posing a major health risk. The ability to identify animal exposure to these compounds prior to their entry into the food chain may be an invaluable tool to safeguard public health. Dioxin-like compounds act by a common mode of action and this suggests that markers or patterns of response may facilitate identification of exposed animals. However, secondary co-contaminating compounds present in typical dioxin sources may affect responses to compounds. This study has investigated for the first time the potential of a metabolomics platform to distinguish between animals exposed to different sources of dioxin contamination through their diet. Sprague-Dawley rats were given feed containing dioxin-like toxins from hospital incinerator soot, a common PCB oil standard and pure 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) (normalized at 0.1 µg/kg TEQ) and acquired plasma was subsequently biochemically profiled using ultra high performance liquid chromatography (UPLC) quadropole time-of-flight-mass spectrometry (QTof-MS). An OPLS-DA model was generated from acquired metabolite fingerprints and validated which allowed classification of plasma from individual animals into the four dietary exposure study groups with a level of accuracy of 97-100%. A set of 24 ions of importance to the prediction model, and which had levels significantly altered between feeding groups, were positively identified as deriving from eight identifiable metabolites including lysophosphatidylcholine (16:0) and tyrosine. This study demonstrates the enormous potential of metabolomic-based profiling to provide a powerful and reliable tool for the detection of dioxin exposure in food-producing animals.

Development of a novel continuous statistical modelling technique for detecting the adulteration of extra virgin olive oil with hazelnut oil by using spectroscopic data

The adulteration of extra virgin olive oil with other vegetable oils is a certain problem with economic and health consequences. Current official methods have been proved insufficient to detect such adulterations. One of the most concerning and undetectable adulterations with other vegetable oils is the addition of hazelnut oil. The main objective of this work was to develop a novel dimensionality reduction technique able to model oil mixtures as a part of an integrated pattern recognition solution. This final solution attempts to identify hazelnut oil adulterants in extra virgin olive oil at low percentages based on spectroscopic chemical fingerprints. The proposed Continuous Locality Preserving Projections (CLPP) technique allows the modelling of the continuous nature of the produced in house admixtures as data series instead of discrete points. This methodology has potential to be extended to other mixtures and adulterations of food products. The maintenance of the continuous structure of the data manifold lets the better visualization of this examined classification problem and facilitates a more accurate utilisation of the manifold for detecting the adulterants.