996 resultados para Leakage detection


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The Tie-2 receptor has been shown to play a role in angiogenesis in atherosclerosis. The conventional method assaying the level of soluble Tie-2 (sTie-2) was ELISA. However, this method has some disadvantages. The aims of this research are to establish a more simple detection method, the optical protein-chip based on imaging ellipsomtry (OPC-IE) applying to Tie-2 assay. The sTie-2 biosensor surface on silicon wafer was prepared first, and then serum levels of sTie-2 in 38 patients with AMI were measured on admission (day 1), day 2, day 3 and day 7 after onset of chest pain and 41 healthy controls by ELISA and OPC-IE in parallel. Median level of sTie-2 increased significantly in the AMI patients when compared with the controls. Statistics showed there was a significant correlation in sTie-2 results between the two methods (r=0.923, P0.01). The result of this study showed that the level of sTie-2 increased in AMI, and OPC-IE assay was a fast, reliable, and convenient technique to measure sTie-2 in serum.

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A rapid detection and identification of pathogens is important for minimizing transfer and spread of disease. A label-free and multiplex biosensor based on imaging ellipsometry (BIE) had been developed for the detection of phage M13KO7. The surface of silicon wafer is modified with aldehyde, and proteins can be patterned homogeneously and simultaneously on the surface of silicon wafer in an array format by a microfluidic system. Avidin is immobilized on the surface for biotin-anti-M13 immobilization by means of interaction between avidin and biotin, which will serve as ligand against phage M13KO7. Phages M13KO7 are specifically captured by the ligand when phage M13KO7 solution passes over the surface, resulting in a significant increase of mass surface concentration of the anti-M13 binding phage M13KO7 layer, which could be detected by imaging ellipsometry with a sensitivity of 10(9) pfu/ml. Moreover, atomic force microscopy is also used to confirm the fact that phage M13KO7 has been directly captured by ligands on the surface. It indicates that BIE is competent for direct detection of phage M13KO7 and has potential in the field of virus detection. (C) 2008 Elsevier B.V. All rights reserved.

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A biosensor based on imaging ellipsometry (BIE) has been developed and validated in 169 patients for detecting five markers of hepatitis B virus (HBV) infection. The methodology has been established to pave the way for clinical diagnosis, including ligand screening, determination of the sensitivity, set-up of cut-off values (CoVs) and comparison with other clinical methods. A matrix assay method was established for ligand screening. The CoVs of HBV markers were derived with the help of receiver operating characteristic curves. Enzyme-linked immunosorbent assay (ELISA) was the reference method. Ligands with high bioactivity were selected and sensitivities of 1 ng/mL and 1 IU/mL for hepatitis B surface antigen (HBsAg) and surface antibody (anti-HBs) were obtained respectively. The CoVs of HBsAg, anti-HBs, hepatitis B e antigen, hepatitis B e antibody and core antibody were as follows: 15%, 18%, 15%, 20% and 15%, respectively, which were the percentages over the values of corresponding ligand controls. BIE can simultaneously detect up to five markers within 1 h with results in acceptable agreement with ELISA, and thus shows a potential for diagnosing hepatitis B with high throughput.

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This thesis presents theories, analyses, and algorithms for detecting and estimating parameters of geospatial events with today's large, noisy sensor networks. A geospatial event is initiated by a significant change in the state of points in a region in a 3-D space over an interval of time. After the event is initiated it may change the state of points over larger regions and longer periods of time. Networked sensing is a typical approach for geospatial event detection. In contrast to traditional sensor networks comprised of a small number of high quality (and expensive) sensors, trends in personal computing devices and consumer electronics have made it possible to build large, dense networks at a low cost. The changes in sensor capability, network composition, and system constraints call for new models and algorithms suited to the opportunities and challenges of the new generation of sensor networks. This thesis offers a single unifying model and a Bayesian framework for analyzing different types of geospatial events in such noisy sensor networks. It presents algorithms and theories for estimating the speed and accuracy of detecting geospatial events as a function of parameters from both the underlying geospatial system and the sensor network. Furthermore, the thesis addresses network scalability issues by presenting rigorous scalable algorithms for data aggregation for detection. These studies provide insights to the design of networked sensing systems for detecting geospatial events. In addition to providing an overarching framework, this thesis presents theories and experimental results for two very different geospatial problems: detecting earthquakes and hazardous radiation. The general framework is applied to these specific problems, and predictions based on the theories are validated against measurements of systems in the laboratory and in the field.

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We present a universal analyzer for the three-particle Greenberger-Horne-Zeilinger (GHZ) states with quantum nondemolition parity detectors and linear-optics elements. In our scheme, all of the three-photon GHZ states can be discriminated with nearly unity probability in the regime of weak nonlinearity feasible at the present state of the art experimentally. We also show that our scheme can be easily extended to the analysis of the multi-particle GHZ states.

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The dynamic properties of a structure are a function of its physical properties, and changes in the physical properties of the structure, including the introduction of structural damage, can cause changes in its dynamic behavior. Structural health monitoring (SHM) and damage detection methods provide a means to assess the structural integrity and safety of a civil structure using measurements of its dynamic properties. In particular, these techniques enable a quick damage assessment following a seismic event. In this thesis, the application of high-frequency seismograms to damage detection in civil structures is investigated.

Two novel methods for SHM are developed and validated using small-scale experimental testing, existing structures in situ, and numerical testing. The first method is developed for pre-Northridge steel-moment-resisting frame buildings that are susceptible to weld fracture at beam-column connections. The method is based on using the response of a structure to a nondestructive force (i.e., a hammer blow) to approximate the response of the structure to a damage event (i.e., weld fracture). The method is applied to a small-scale experimental frame, where the impulse response functions of the frame are generated during an impact hammer test. The method is also applied to a numerical model of a steel frame, in which weld fracture is modeled as the tensile opening of a Mode I crack. Impulse response functions are experimentally obtained for a steel moment-resisting frame building in situ. Results indicate that while acceleration and velocity records generated by a damage event are best approximated by the acceleration and velocity records generated by a colocated hammer blow, the method may not be robust to noise. The method seems to be better suited for damage localization, where information such as arrival times and peak accelerations can also provide indication of the damage location. This is of significance for sparsely-instrumented civil structures.

The second SHM method is designed to extract features from high-frequency acceleration records that may indicate the presence of damage. As short-duration high-frequency signals (i.e., pulses) can be indicative of damage, this method relies on the identification and classification of pulses in the acceleration records. It is recommended that, in practice, the method be combined with a vibration-based method that can be used to estimate the loss of stiffness. Briefly, pulses observed in the acceleration time series when the structure is known to be in an undamaged state are compared with pulses observed when the structure is in a potentially damaged state. By comparing the pulse signatures from these two situations, changes in the high-frequency dynamic behavior of the structure can be identified, and damage signals can be extracted and subjected to further analysis. The method is successfully applied to a small-scale experimental shear beam that is dynamically excited at its base using a shake table and damaged by loosening a screw to create a moving part. Although the damage is aperiodic and nonlinear in nature, the damage signals are accurately identified, and the location of damage is determined using the amplitudes and arrival times of the damage signal. The method is also successfully applied to detect the occurrence of damage in a test bed data set provided by the Los Alamos National Laboratory, in which nonlinear damage is introduced into a small-scale steel frame by installing a bumper mechanism that inhibits the amount of motion between two floors. The method is successfully applied and is robust despite a low sampling rate, though false negatives (undetected damage signals) begin to occur at high levels of damage when the frequency of damage events increases. The method is also applied to acceleration data recorded on a damaged cable-stayed bridge in China, provided by the Center of Structural Monitoring and Control at the Harbin Institute of Technology. Acceleration records recorded after the date of damage show a clear increase in high-frequency short-duration pulses compared to those previously recorded. One undamage pulse and two damage pulses are identified from the data. The occurrence of the detected damage pulses is consistent with a progression of damage and matches the known chronology of damage.