High sensitivity (1 ppm) hydrogen detection using an unconventional Pd/n-InP Schottky device

Hydrogen is detected using a Pd/n-InP Schottky diode in which the elongated, very thin Pd electrode is of greater resistance than the underlying semiconductor substrate. Four-probe measurements of the device resistance, as a function of hydrogen concentration, are made by contacting only the Pd electrode, with a sensitivity of 1 ppm being achieved. On hydrogen exposure the device resistance drops from an initial high value, characteristic of the Pd electrode alone, to a lower value due to a hydrogen-induced lowering of the Schottky barrier that opens up the InP substrate as a parallel current carrying channel.

A strategy for sensitivity and specificity enhancements in prostate specific antigen-[alpha] 1-antichymotrypsin detection based on surface plasmon resonance

A biochip based on surface plasmon resonance was fabricated to detect prostate specific antigen-a1-antichymotrypsin (PSA-ACT complex) in both HBS buffer and human serum. To reduce non-specific binding and steric hindrance effect, the chemical surface of the sensor chips was constructed by using various oligo(ethylene glycol) mixtures of different molar ratios of HS(CH2)11(OCH2CH2)6OCH2COOH and HS(CH2)11(OCH2CH2)3OH. The self-assembled monolayers were biotinylated to facilitate the immobilization of streptavidin. Using the chip surfaces, PSA-ACT complex in HBS buffer and human serum was detected at 20.7 and 47.5 ng/ml by primary immunoresponse, respectively. However, the limit of detection could be simply enhanced by a sandwich strategy to improve the sensitivity and specificity of the immunoassay. An intact PSA polyclonal antibody was used as an amplifying agent in the strategy. As a result, PSA-ACT complex concentrations as low as 10.2 and 18.1 ng/ml were found in the HBS buffer and human serum sample, respectively. The result indicates that this approach could satisfy our goal without modifying the secondary interactant.

Detection of avian influenza virus by fluorescent DNA barcode-based immunoassay with sensitivity comparable to PCR

In this paper, a coupling of fluorophore-DNA barcode and bead-based immunoassay for detecting avian influenza virus (AIV) with PCR-like sensitivity is reported. The assay is based on the use of sandwich immunoassay and fluorophore-tagged oligonucleotides as representative barcodes. The detection involves the sandwiching of the target AIV between magnetic immunoprobes and barcode-carrying immunoprobes. Because each barcode-carrying immunoprobe is functionalized with a multitude of fluorophore-DNA barcode strands, many DNA barcodes are released for each positive binding event resulting in amplification of the signal. Using an inactivated H16N3 AIV as a model, a linear response over five orders of magnitude was obtained, and the sensitivity of the detection was comparable to conventional RT-PCR. Moreover, the entire detection required less than 2 hr. The results indicate that the method has great potential as an alternative for surveillance of epidemic outbreaks caused by AIV, other viruses and microorganisms.

Detection of Avian Influenza Virus with PCR-Like Sensitivity by Fluorescent DNA Barcode-Based Immunoassay

In this paper, we report a coupling of fluorophore-DNA barcode and bead-based
immunoassay for the detection of Avian Influenza Virus (AIV), a potential pandemic threat for human health and enormous economic losses. The detection strategy is based on the use of sandwich immunoassay and fluorophore-tagged oligonucleotides as representatively fluorescent barcodes. Despite its simplicity the assay has sensitivity comparable to RT-PCR amplification, and possesses a great potential as a rapid and sensitive on-chip detection format.

High sensitivity fiber optic angular displacement sensor and its application for detection of ultrasound

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Sensitivity and specificity of serological tests, histopathology and immunohistochemistry for detection of Toxoplasma gondii infection in domestic chickens

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Increased sensitivity of ETEC detection in stool cultures by increasing the number of E. coli colonies tested

The cause of infection of about a third of all travelers' diarrhea patients studied is not identified. Stools of these diarrhea patients tested for known enteric pathogens are shown to be negative, and identified as pathogen negative stools. We proposed that the third of these diarrhea patients might not only include at present unknown pathogens, but also known pathogens that go undetected. Conventionally, a probability sample of five E. coli colonies are used detect enterotoxigenic E. coli (ETEC) and other diarrhea-producing E. coli from stool cultures. We compared this conventional method of using five E. coli colonies, to the use of up to twenty E. coli colonies. Testing for up to fifteen E. coli colonies detected about twice as many ETEC when compared to the detection of ETEC, testing for five E. coli colonies. When the number of E. coli colonies tested was increased from 5 to 15, the detection of ETEC increased from 19.0% to 38.8%. The sensitivity of the assay with 5 E. coli colonies was statistically significantly different to the sensitivity of the assay with 10 E. coli colonies, suggesting that for the detection of ETEC at least 10 colonies of E. coli should be tested.^

High-sensitivity array analysis of gene expression for the early detection of disseminated breast tumor cells in peripheral blood

Early detection is an effective means of reducing cancer mortality. Here, we describe a highly sensitive high-throughput screen that can identify panels of markers for the early detection of solid tumor cells disseminated in peripheral blood. The method is a two-step combination of differential display and high-sensitivity cDNA arrays. In a primary screen, differential display identified 170 candidate marker genes differentially expressed between breast tumor cells and normal breast epithelial cells. In a secondary screen, high-sensitivity arrays assessed expression levels of these genes in 48 blood samples, 22 from healthy volunteers and 26 from breast cancer patients. Cluster analysis identified a group of 12 genes that were elevated in the blood of cancer patients. Permutation analysis of individual genes defined five core genes (P ≤ 0.05, permax test). As a group, the 12 genes generally distinguished accurately between healthy volunteers and patients with breast cancer. Mean expression levels of the 12 genes were elevated in 77% (10 of 13) untreated invasive cancer patients, whereas cluster analysis correctly classified volunteers and patients (P = 0.0022, Fisher's exact test). Quantitative real-time PCR confirmed array results and indicated that the sensitivity of the assay (1:2 × 108 transcripts) was sufficient to detect disseminated solid tumor cells in blood. Expression-based blood assays developed with the screening approach described here have the potential to detect and classify solid tumor cells originating from virtually any primary site in the body.

Detection of organic aromatic compounds in paraffin by a long-period fiber grating optical sensor with optimized sensitivity

A long-period grating (LPG) sensor is used to detect small variations in the concentration of an organic aromatic compound (xylene) in a paraffin (heptane) solution. A new design procedure is adopted and demonstrated to maximize the sensitivity of LPG (wavelength shift for a change in the surrounding refractive index, (dλ/dn3)) for a given application. The detection method adopted is comparable to the standard technique used in industry (high performance liquid chromatograph and UV spectroscopy) which has a relative accuracy between ∼±0.5% and 5%. The minimum detectable change in volumetric concentration is 0.04% in a binary fluid with the detection system presented. This change of concentration relates to a change in refractive index of Δn ∼ 6 × 10-5. © 2001 Elsevier Science B.V.

Sensitivity and Bias within the Binary Signal Detection Theory, BSDT

Similar to classic Signal Detection Theory (SDT), recent optimal Binary Signal Detection Theory (BSDT) and based on it Neural Network Assembly Memory Model (NNAMM) can successfully reproduce Receiver Operating Characteristic (ROC) curves although BSDT/NNAMM parameters (intensity of cue and neuron threshold) and classic SDT parameters (perception distance and response bias) are essentially different. In present work BSDT/NNAMM optimal likelihood and posterior probabilities are analytically analyzed and used to generate ROCs and modified (posterior) mROCs, optimal overall likelihood and posterior. It is shown that for the description of basic discrimination experiments in psychophysics within the BSDT a ‘neural space’ can be introduced where sensory stimuli as neural codes are represented and decision processes are defined, the BSDT’s isobias curves can simultaneously be interpreted as universal psychometric functions satisfying the Neyman-Pearson objective, the just noticeable difference (jnd) can be defined and interpreted as an atom of experience, and near-neutral values of biases are observers’ natural choice. The uniformity or no-priming hypotheses, concerning the ‘in-mind’ distribution of false-alarm probabilities during ROC or overall probability estimations, is introduced. The BSDT’s and classic SDT’s sensitivity, bias, their ROC and decision spaces are compared.

Sensitivity and specificity of parallel or serial serological testing for detection of canine Leishmania infection

In Brazil, human and canine visceral leishmaniasis (CVL) caused by Leishmania infantum has undergone urbanisation since 1980, constituting a public health problem, and serological tests are tools of choice for identifying infected dogs. Until recently, the Brazilian zoonoses control program recommended enzyme-linked immunosorbent assays (ELISA) and indirect immunofluorescence assays (IFA) as the screening and confirmatory methods, respectively, for the detection of canine infection. The purpose of this study was to estimate the accuracy of ELISA and IFA in parallel or serial combinations. The reference standard comprised the results of direct visualisation of parasites in histological sections, immunohistochemical test, or isolation of the parasite in culture. Samples from 98 cases and 1,327 noncases were included. Individually, both tests presented sensitivity of 91.8% and 90.8%, and specificity of 83.4 and 53.4%, for the ELISA and IFA, respectively. When tests were used in parallel combination, sensitivity attained 99.2%, while specificity dropped to 44.8%. When used in serial combination (ELISA followed by IFA), decreased sensitivity (83.3%) and increased specificity (92.5%) were observed. Serial testing approach improved specificity with moderate loss in sensitivity. This strategy could partially fulfill the needs of public health and dog owners for a more accurate diagnosis of CVL.

Improved correlation-based modal strain energy method for global damage detection of truss bridge structures

The previous investigations have shown that the modal strain energy correlation method, MSEC, could successfully identify the damage of truss bridge structures. However, it has to incorporate the sensitivity matrix to estimate damage and is not reliable in certain damage detection cases. This paper presents an improved MSEC method where the prediction of modal strain energy change vector is differently obtained by running the eigensolutions on-line in optimisation iterations. The particular trail damage treatment group maximising the fitness function close to unity is identified as the detected damage location. This improvement is then compared with the original MSEC method along with other typical correlation-based methods on the finite element model of a simple truss bridge. The contributions to damage detection accuracy of each considered mode is also weighed and discussed. The iterative searching process is operated by using genetic algorithm. The results demonstrate that the improved MSEC method suffices the demand in detecting the damage of truss bridge structures, even when noised measurement is considered.

Damage detection for truss bridge structures using correlation-based structural modal strain energy

This paper presents the feasibility of using structural modal strain energy as a parameter employed in correlation- based damage detection method for truss bridge structures. It is an extension of the damage detection method adopting multiple damage location assurance criterion. In this paper, the sensitivity of modal strain energy to damage obtained from the analytical model is incorporated into the correlation objective function. Firstly, the sensitivity matrix of modal strain energy to damage is conducted offline, and for an arbitrary damage case, the correlation coefficient (objective function) is calculated by multiplying the sensitivity matrix and damage vector. Then, a genetic algorithm is used to iteratively search the damage vector maximising the correlation between the corresponding modal strain energy change (hypothesised) and its counterpart in measurement. The proposed method is simulated and compared with the conventional methods, e.g. frequency-error method, coordinate modal assurance criterion and multiple damage location assurance criterion using mode shapes on a numerical truss bridge structure. The result demonstrates the modal strain energy correlation method is able to yield acceptable damage detection outcomes with less computing efforts, even in a noise contaminated condition.

Analytical modeling and sensitivity analysis for travel time estimation on signalized urban networks

This paper presents a model for estimation of average travel time and its variability on signalized urban networks using cumulative plots. The plots are generated based on the availability of data: a) case-D, for detector data only; b) case-DS, for detector data and signal timings; and c) case-DSS, for detector data, signal timings and saturation flow rate. The performance of the model for different degrees of saturation and different detector detection intervals is consistent for case-DSS and case-DS whereas, for case-D the performance is inconsistent. The sensitivity analysis of the model for case-D indicates that it is sensitive to detection interval and signal timings within the interval. When detection interval is integral multiple of signal cycle then it has low accuracy and low reliability. Whereas, for detection interval around 1.5 times signal cycle both accuracy and reliability are high.