991 resultados para Outlier detection


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One important issue implied by the finite nature of real-world networks regards the identification of their more external (border) and internal nodes. The present work proposes a formal and objective definition of these properties, founded on the recently introduced concept of node diversity. It is shown that this feature does not exhibit any relevant correlation with several well-established complex networks measurements. A methodology for the identification of the borders of complex networks is described and illustrated with respect to theoretical (geographical and knitted networks) as well as real-world networks (urban and word association networks), yielding interesting results and insights in both cases.

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The efficacy of fluorescence spectroscopy to detect squamous cell carcinoma is evaluated in an animal model following laser excitation at 442 and 532 nm. Lesions are chemically induced with a topical DMBA application at the left lateral tongue of Golden Syrian hamsters. The animals are investigated every 2 weeks after the 4th week of induction until a total of 26 weeks. The right lateral tongue of each animal is considered as a control site (normal contralateral tissue) and the induced lesions are analyzed as a set of points covering the entire clinically detectable area. Based on fluorescence spectral differences, four indices are determined to discriminate normal and carcinoma tissues, based on intraspectral analysis. The spectral data are also analyzed using a multivariate data analysis and the results are compared with histology as the diagnostic gold standard. The best result achieved is for blue excitation using the KNN (K-nearest neighbor, a interspectral analysis) algorithm with a sensitivity of 95.7% and a specificity of 91.6%. These high indices indicate that fluorescence spectroscopy may constitute a fast noninvasive auxiliary tool for diagnostic of cancer within the oral cavity. (C) 2008 Society of Photo-Optical Instrumentation Engineers.

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We have investigated the detection of mechanical and disease stresses in citrus plants (Citrus limonia [L.] Osbeck) using laser-induced fluorescence spectroscopy. Due to its economic importance we have chosen to investigate the citrus canker disease, which is caused by the Xanthomonas axonopodis pv. citri bacteria. Mechanical stress was also studied because it plays an important role in the plant's infection by such bacteria. A laser-induced fluorescence spectroscopy system, composed of a spectrometer and a 532 nm 10 mW excitation laser was used to perform fluorescence spectroscopy. The ratio of two chlorophyll fluorescence bands allows us to detect and discriminate between mechanical and disease stresses. This ability to discriminate may have an important application in the field to detect citrus canker infected trees. (c) 2008 Optical Society of America.

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Serpentine receptors comprise a large family of membrane receptors distributed over diverse organisms, such as bacteria, fungi, plants and all metazoans. However, the presence of serpentine receptors in protozoan parasites is largely unknown so far. In the present study we performed a genome-wide search for proteins containing seven transmembrane domains (7TM) in the human malaria parasite Plasmodium falciparum and identified four serpentine receptor-like proteins. These proteins, denoted PfSR1, PfSR10, PfSR12 and PfSR25, show membrane topologies that resemble those exhibited by members belonging to different families of serpentine receptors. Expression of the pfsrs genes was detected by Real Time PCR in P. falciparum intraerythrocytic stages, indicating that they potentially code for functional proteins. We also found corresponding homologues for the PfSRs in five other Plasmodium species, two primate and three rodent parasites. PfSR10 and 25 are the most conserved receptors among the different species, while PfSR1 and 12 are more divergent. Interestingly, we found that PfSR10 and PfSR12 possess similarity to orphan serpentine receptors of other organisms. The identification of potential parasite membrane receptors raises a new perspective for essential aspects of malaria parasite host cell infection.

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Alternative splicing of gene transcripts greatly expands the functional capacity of the genome, and certain splice isoforms may indicate specific disease states such as cancer. Splice junction microarrays interrogate thousands of splice junctions, but data analysis is difficult and error prone because of the increased complexity compared to differential gene expression analysis. We present Rank Change Detection (RCD) as a method to identify differential splicing events based upon a straightforward probabilistic model comparing the over-or underrepresentation of two or more competing isoforms. RCD has advantages over commonly used methods because it is robust to false positive errors due to nonlinear trends in microarray measurements. Further, RCD does not depend on prior knowledge of splice isoforms, yet it takes advantage of the inherent structure of mutually exclusive junctions, and it is conceptually generalizable to other types of splicing arrays or RNA-Seq. RCD specifically identifies the biologically important cases when a splice junction becomes more or less prevalent compared to other mutually exclusive junctions. The example data is from different cell lines of glioblastoma tumors assayed with Agilent microarrays.

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A new approach for the integration of dual contactless conductivity and amperometric detection with an electrophoresis microchip system is presented. The PDMS layer with the embedded channels was reversibly sealed to a thin glass substrate (400 mu m), on top of which a palladium electrode had been previously fabricated enabling end-channel amperometric detection. The thin glass substrate served also as a physical wall between the separation channel and the sensing copper electrodes for contactless conductivity detection. The latter were not integrated in the microfluidic device, but fabricated on an independent plastic substrate allowing a simpler and more cost-effective fabrication of the chip. PDMS/glass chips with merely contactless conductivity detection were first characterized in terms of sensitivity, efficiency and reproducibility. The separation efficiency of this system was found to be similar or slightly superior to other systems reported in the literature. The simultaneous determination of ionic and electroactive species was illustrated by the separation of peroxynitrite degradation products, i.e. NO(3)(-) (non-electroactive) and NO(2)(-) (electroactive), using hybrid PDMS/glass chips with dual contactless conductivity and amperometric detection. While both ions were detected by contactless conductivity detection with good efficiency, NO(2)(-) was also simultaneously detected amperometrically with a significant enhancement in sensitivity compared to contactless conductivity detection.

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This study outlines the quantification of low levels of Alicyclobacillus acidoterrestris in pure cultures, since this bacterium is not inactivated by pasteurization and may remain in industrialized foods and beverages. Electroconductive polymer-modified fluorine tin oxide (FTO) electrodes and multiple nanoparticle labels were used for biosensing. The detection of A. acidoterrestris in pure cultures was performed by reverse transcription polymerase chain reaction (RT-PCR) and the sensitivity was further increased by asymmetric nested RT-PCR using electrochemical detection for quantification of the amplicon. The quantification of nested RT-PCR products by Ag/Au-based electrochemical detection was able to detect 2 colony forming units per mL (CFU mL(-1)) of spores in pure culture and low detection and quantification limits (7.07 and 23.6 nM, respectively) were obtained for the target A. acidoterrestris on the electrochemical detection bioassay.

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This article describes an effective microchip protocol based on electrophoretic-separation and electrochemical detection for highly sensitive and rapid measurements of nitrate ester explosives, including ethylene glycol dinitrate (EGDN), pentaerythritol tetranitrate (PETN), propylene glycol dinitrate (PGDN) and glyceryl trinitrate (nitroglycerin, NG). Factors influencing the separation and detection processes were examined and optimized. Under the optimal separation conditions obtained using a 15 mM borate buffer (pH 9.2) containing 20 mM SDS, and applying a separation voltage of 1500 V, the four nitrate ester explosives were separated within less than 3 min. The glassy-carbon amperometric detector (operated at -0.9 V vs. Ag/AgCl) offers convenient cathodic detection down to the picogram level, with detection limits of 0.5 ppm and 0.3 ppm for PGDN and for NG, respectively, along with good repeatability (RSD of 1.8-2.3%; n = 6) and linearity (over the 10-60 ppm range). Such effective microchip operation offers great promise for field screening of nitrate ester explosives and for supporting various counter-terrorism surveillance activities.

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A simple and easy approach to produce polymeric microchips with integrated copper electrodes for capacitively coupled contactless conductivity detection (CD) is described. Copper electrodes were fabricated using a printed circuit board (PCB) as an inexpensive thin-layer of metal. The electrode layout was first drawn and laser printed on a wax paper sheet. The toner layer deposited on the paper sheet was thermally transferred to the PCB surface working as a mask for wet chemical etching of the copper layer. After the etching step, the toner was removed with an acetonitrile-dampened cotton. A poly(ethylene terephthalate) (PET) film coated with a thin thermo-sensitive adhesive layer was used to laminate the PCB plate providing an insulator layer of the electrodes to perform CID measurements. Electrophoresis microchannels were fabricated in poly(dimethylsiloxane) (PDMS) by soft lithography and reversibly sealed against the PET film. These hybrid PDMS/PET chips exhibited a stable electroosmotic mobility of 4.25 +/- 0.04 x 10(-4) V cm(-2) s(-1), at pH 6.1, over fifty runs. Efficiencies ranging from 1127 to 1690 theoretical plates were obtained for inorganic cations.

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This paper proposes a three-stage offline approach to detect, identify, and correct series and shunt branch parameter errors. In Stage 1 the branches suspected of having parameter errors are identified through an Identification Index (II). The II of a branch is the ratio between the number of measurements adjacent to that branch, whose normalized residuals are higher than a specified threshold value, and the total number of measurements adjacent to that branch. Using several measurement snapshots, in Stage 2 the suspicious parameters are estimated, in a simultaneous multiple-state-and-parameter estimation, via an augmented state and parameter estimator which increases the V - theta state vector for the inclusion of suspicious parameters. Stage 3 enables the validation of the estimation obtained in Stage 2, and is performed via a conventional weighted least squares estimator. Several simulation results (with IEEE bus systems) have demonstrated the reliability of the proposed approach to deal with single and multiple parameter errors in adjacent and non-adjacent branches, as well as in parallel transmission lines with series compensation. Finally the proposed approach is confirmed on tests performed on the Hydro-Quebec TransEnergie network.

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The main purpose of this paper is to present architecture of automated system that allows monitoring and tracking in real time (online) the possible occurrence of faults and electromagnetic transients observed in primary power distribution networks. Through the interconnection of this automated system to the utility operation center, it will be possible to provide an efficient tool that will assist in decisionmaking by the Operation Center. In short, the desired purpose aims to have all tools necessary to identify, almost instantaneously, the occurrence of faults and transient disturbances in the primary power distribution system, as well as to determine its respective origin and probable location. The compilations of results from the application of this automated system show that the developed techniques provide accurate results, identifying and locating several occurrences of faults observed in the distribution system.

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In this study, the innovation approach is used to estimate the measurement total error associated with power system state estimation. This is required because the power system equations are very much correlated with each other and as a consequence part of the measurements errors is masked. For that purpose an index, innovation index (II), which provides the quantity of new information a measurement contains is proposed. A critical measurement is the limit case of a measurement with low II, it has a zero II index and its error is totally masked. In other words, that measurement does not bring any innovation for the gross error test. Using the II of a measurement, the masked gross error by the state estimation is recovered; then the total gross error of that measurement is composed. Instead of the classical normalised measurement residual amplitude, the corresponding normalised composed measurement residual amplitude is used in the gross error detection and identification test, but with m degrees of freedom. The gross error processing turns out to be very simple to implement, requiring only few adaptations to the existing state estimation software. The IEEE-14 bus system is used to validate the proposed gross error detection and identification test.

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On-line leak detection is a main concern for the safe operation of pipelines. Acoustic and mass balance are the most important and extensively applied technologies in field problems. The objective of this work is to compare these leak detection methods with respect to a given reference situation, i.e., the same pipeline and monitoring signals acquired at the inlet and outlet ends. Experimental tests were conducted in a 749 m long laboratory pipeline transporting water as the working fluid. The instrumentation included pressure transducers and electromagnetic flowmeters. Leaks were simulated by opening solenoid valves placed at known positions and previously calibrated to produce known average leak flow rates. Results have clearly shown the limitations and advantages of each method. It is also quite clear that acoustics and mass balance technologies are, in fact, complementary. In general, an acoustic leak detection system sends out an alarm more rapidly and locates the leak more precisely, provided that the rupture of the pipeline occurs abruptly enough. On the other hand, a mass balance leak detection method is capable of quantifying the leak flow rate very accurately and of detecting progressive leaks.

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Leakage reduction in water supply systems and distribution networks has been an increasingly important issue in the water industry since leaks and ruptures result in major physical and economic losses. Hydraulic transient solvers can be used in the system operational diagnosis, namely for leak detection purposes, due to their capability to describe the dynamic behaviour of the systems and to provide substantial amounts of data. In this research work, the association of hydraulic transient analysis with an optimisation model, through inverse transient analysis (ITA), has been used for leak detection and its location in an experimental facility containing PVC pipes. Observed transient pressure data have been used for testing ITA. A key factor for the success of the leak detection technique used is the accurate calibration of the transient solver, namely adequate boundary conditions and the description of energy dissipation effects since PVC pipes are characterised by a viscoelastic mechanical response. Results have shown that leaks were located with an accuracy between 4-15% of the total length of the pipeline, depending on the discretisation of the system model.

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In this paper, a framework for detection of human skin in digital images is proposed. This framework is composed of a training phase and a detection phase. A skin class model is learned during the training phase by processing several training images in a hybrid and incremental fuzzy learning scheme. This scheme combines unsupervised-and supervised-learning: unsupervised, by fuzzy clustering, to obtain clusters of color groups from training images; and supervised to select groups that represent skin color. At the end of the training phase, aggregation operators are used to provide combinations of selected groups into a skin model. In the detection phase, the learned skin model is used to detect human skin in an efficient way. Experimental results show robust and accurate human skin detection performed by the proposed framework.