Ultrasensitive detection of pathological prion protein aggregates by dual-color scanning for intensely fluorescent targets

A definite diagnosis of prion diseases such as Creutzfeldt–Jakob disease (CJD) relies on the detection of pathological prion protein (PrPSc). However, no test for PrPSc in cerebrospinal fluid (CSF) has been available thus far. Based on a setup for confocal dual-color fluorescence correlation spectroscopy, a technique suitable for single molecule detection, we developed a highly sensitive detection method for PrPSc. Pathological prion protein aggregates were labeled by specific antibody probes tagged with fluorescent dyes, resulting in intensely fluorescent targets, which were measured by dual-color fluorescence intensity distribution analysis in a confocal scanning setup. In a diagnostic model system, PrPSc aggregates were detected down to a concentration of 2 pM PrPSc, corresponding to an aggregate concentration of approximately 2 fM, which was more than one order of magnitude more sensitive than Western blot analysis. A PrPSc-specific signal could also be detected in a number of CSF samples from patients with CJD but not in control samples, providing the basis for a rapid and specific test for CJD and other prion diseases. Furthermore, this method could be adapted to the sensitive detection of other disease-associated amyloid aggregates such as in Alzheimer's disease.

Immunoassays with rolling circle DNA amplification: A versatile platform for ultrasensitive antigen detection

We describe an adaptation of the rolling circle amplification (RCA) reporter system for the detection of protein Ags, termed “immunoRCA.” In immunoRCA, an oligonucleotide primer is covalently attached to an Ab; thus, in the presence of circular DNA, DNA polymerase, and nucleotides, amplification results in a long DNA molecule containing hundreds of copies of the circular DNA sequence that remain attached to the Ab and that can be detected in a variety of ways. Using immunoRCA, analytes were detected at sensitivities exceeding those of conventional enzyme immunoassays in ELISA and microparticle formats. The signal amplification afforded by immunoRCA also enabled immunoassays to be carried out in microspot and microarray formats with exquisite sensitivity. When Ags are present at concentrations down to fM levels, specifically bound Abs can be scored by counting discrete fluorescent signals arising from individual Ag–Ab complexes. Multiplex immunoRCA also was demonstrated by accurately quantifying Ags mixed in different ratios in a two-color, single-molecule-counting assay on a glass slide. ImmunoRCA thus combines high sensitivity and a very wide dynamic range with an unprecedented capability for single molecule detection. This Ag-detection method is of general applicability and is extendable to multiplexed immunoassays that employ a battery of different Abs, each labeled with a unique oligonucleotide primer, that can be discriminated by a color-coded visualization system. ImmunoRCA-profiling based on the simultaneous quantitation of multiple Ags should expand the power of immunoassays by exploiting the increased information content of ratio-based expression analysis.

Real-time detection of DNA hybridization and melting on oligonucleotide arrays by using optical wave guides.

The challenge of the Human Genome Project is to increase the rate of DNA sequence acquisition by two orders of magnitude to complete sequencing of the human genome by the year 2000. The present work describes a rapid detection method using a two-dimensional optical wave guide that allows measurement of real-time binding or melting of a light-scattering label on a DNA array. A particulate label on the target DNA acts as a light-scattering source when illuminated by the evanescent wave of the wave guide and only the label bound to the surface generates a signal. Imaging/visual examination of the scattered light permits interrogation of the entire array simultaneously. Hybridization specificity is equivalent to that obtained with a conventional system using autoradiography. Wave guide melting curves are consistent with those obtained in the liquid phase and single-base discrimination is facile. Dilution experiments showed an apparent lower limit of detection at 0.4 nM oligonucleotide. This performance is comparable to the best currently known fluorescence-based systems. In addition, wave guide detection allows manipulation of hybridization stringency during detection and thereby reduces DNA chip complexity. It is anticipated that this methodology will provide a powerful tool for diagnostic applications that require rapid cost-effective detection of variations from known sequences.

Improving subjectivity detection using unsupervised subjectivity word sense disambiguation

En este trabajo se presenta un método para la detección de subjetividad a nivel de oraciones basado en la desambiguación subjetiva del sentido de las palabras. Para ello se extiende un método de desambiguación semántica basado en agrupamiento de sentidos para determinar cuándo las palabras dentro de la oración están siendo utilizadas de forma subjetiva u objetiva. En nuestra propuesta se utilizan recursos semánticos anotados con valores de polaridad y emociones para determinar cuándo un sentido de una palabra puede ser considerado subjetivo u objetivo. Se presenta un estudio experimental sobre la detección de subjetividad en oraciones, en el cual se consideran las colecciones del corpus MPQA y Movie Review Dataset, así como los recursos semánticos SentiWordNet, Micro-WNOp y WordNet-Affect. Los resultados obtenidos muestran que nuestra propuesta contribuye de manera significativa en la detección de subjetividad.

Algorithm for the detection of outliers based on the theory of rough sets

Outliers are objects that show abnormal behavior with respect to their context or that have unexpected values in some of their parameters. In decision-making processes, information quality is of the utmost importance. In specific applications, an outlying data element may represent an important deviation in a production process or a damaged sensor. Therefore, the ability to detect these elements could make the difference between making a correct and an incorrect decision. This task is complicated by the large sizes of typical databases. Due to their importance in search processes in large volumes of data, researchers pay special attention to the development of efficient outlier detection techniques. This article presents a computationally efficient algorithm for the detection of outliers in large volumes of information. This proposal is based on an extension of the mathematical framework upon which the basic theory of detection of outliers, founded on Rough Set Theory, has been constructed. From this starting point, current problems are analyzed; a detection method is proposed, along with a computational algorithm that allows the performance of outlier detection tasks with an almost-linear complexity. To illustrate its viability, the results of the application of the outlier-detection algorithm to the concrete example of a large database are presented.

Modeling of optical detection of spin-polarized carrier injection into light-emitting devices

We investigate the emission of multimodal polarized light from light emitting devices due to spin-aligned carrier injection. The results are derived through operator Langevin equations, which include thermal and carrier-injection fluctuations, as well as nonradiative recombination and electronic g-factor temperature dependence. We study the dynamics of the optoelectronic processes and show how the temperature-dependent g factor and magnetic field affect the degree of polarization of the emitted light. In addition, at high temperatures, thermal fluctuation reduces the efficiency of the optoelectronic detection method for measuring the degree of spin polarization of carrier injection into nonmagnetic semicondutors.

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.

Comparison of ion pair and buffered mobile phase chromatography of haloacetic acids using direct and indirect UV and fluorescence detection

Reversed-phase high performance liquid chromatographic methods for the analysis of Haloacetic acids have been developed and compared to conventional direct detection methods. Haloacetic acids commonly found in drinking water, including monochloro-, dichloro-, bromo-, iodo- and trichloroacetic acids- have been studied. The ion pairing agent benzyltributylammonium ion was studied in detail using indirect UV and indirect fluorescence detection. Five different competing ions were evaluated to decrease analysis times and lower the detection limit by this new method. The direct detection method utilized an ammonium sulfate buffer and UV detection yielding a detection limit of 100 ppb. The indirect method developed has the advantage of being able to simultaneously analyze UV and non-UV absorbing ions and molecules but requires long equilibration times and demonstrated lower sensitivity than the direct method. ^

Method development for the analysis of smokeless powders and organic gunshot residue by ultra performance liquid chromatography with tandem mass spectrometry

The goal of this project was to develop a rapid separation and detection method for analyzing organic compounds in smokeless powders and then test its applicability on gunshot residue (GSR) samples. In this project, a total of 20 common smokeless powder additives and their decomposition products were separated by ultra performance liquid chromatography (UPLC) and confirmed by tandem mass spectrometry (MS/MS) using multiple reaction monitoring mode (MRM). Some of the targeted compounds included diphenylamines, centralites, nitrotoluenes, nitroglycerin, and various phthalates. The compounds were ionized in the MS source using simultaneous positive and negative electrospray ionization (ESI) with negative atmospheric pressure chemical ionization (APCI) in order to detect all compounds in a single analysis. The developed UPLC/MS/MS method was applied to commercially available smokeless powders and gunshot residue samples recovered from the hands of shooters, spent cartridges, and smokeless powder retrieved from unfired cartridges. Distinct compositions were identified for smokeless powders from different manufacturers and from separate manufacturing lots. The procedure also produced specific chemical profiles when tested on gunshot residues from different manufacturers. Overall, this thesis represents the development of a rapid and reproducible procedure capable of simultaneously detecting the widest possible range of components present in organic gunshot residue.^

Detection of pitting corrosion in steel using image processing

This paper presents an image processing based detection method for detecting pitting corrosion in steel structures. High Dynamic Range (HDR) imaging has been carried out in this regard to demonstrate the effectiveness of such relatively inexpensive techniques that are of immense benefit to Non – Destructive – Tesing (NDT) community. The pitting corrosion of a steel sample in marine environment is successfully detected in this paper using the proposed methodology. It is observed, that the proposed method has a definite potential to be applied to a wider range of applications.

Development of a novel probe integrated with a micro-structured impedance sensor for the detection of breast cancer

The work described in this thesis focuses on the development of an innovative bioimpedance device for the detection of breast cancer using electrical impedance as the detection method. The ability for clinicians to detect and treat cancerous lesions as early as possible results in improved patient outcomes and can reduce the severity of the treatment the patient has to undergo. Therefore, new technology and devices are continually required to improve the specificity and sensitivity of the accepted detection methods. The gold standard for breast cancer detection is digital x-ray mammography but it has some significant downsides associated with it. The development of an adjunct technology to aid in the detection of breast cancers could represent a significant patient and economic benefit. In this project silicon substrates were pattern with two gold microelectrodes that allowed electrical impedance measurements to be recorded from intact tissue structures. These probes were tested and characterised using a range of in vitro and ex vivo experiments. The end application of this novel sensor device was in a first-in-human clinical trial. The initial results of this study showed that the silicon impedance device was capable of differentiating between normal and abnormal (benign and cancerous) breast tissue. The mean separation between the two tissue types 4,340 Ω with p < 0.001. The cancer type and grade at the site of the probe recordings was confirmed histologically and correlated with the electrical impedance measurements to determine if the different subtypes of cancer could each be differentiated. The results presented in this thesis showed that the novel impedance device demonstrated excellent electrochemical recording potential; was biocompatible with the growth of cultured cell lines and was capable of differentiating between intact biological tissues. The results outlined in this thesis demonstrate the potential feasibility of using electrical impedance for the differentiation of biological tissue samples. The novelty of this thesis is in the development of a new method of tissue determination with an application in breast cancer detection.

ADVANCED ISLANDING DETECTION METHODS FOR SINGLE PHASE GRID CONNECTED PV INVERTERS

Due to the growing concerns associated with fossil fuels, emphasis has been placed on clean and sustainable energy generation. This has resulted in the increase in Photovoltaics (PV) units being integrated into the utility system. The integration of PV units has raised some concerns for utility power systems, including the consequences of failing to detect islanding. Numerous methods for islanding detection have been introduced in literature. They can be categorized into local methods and remote methods. The local methods are categorically divided into passive and active methods. Active methods generally have smaller Non-Detection Zone (NDZ) but the injecting disturbances will slightly degrade the power quality and reliability of the power system. Slip Mode Frequency Shift Islanding Detection Method (SMS IDM) is an active method that uses positive feedback for islanding detection. In this method, the phase angle of the converter is controlled to have a sinusoidal function of the deviation of the Point of Common Coupling (PCC) voltage frequency from the nominal grid frequency. This method has a non-detection zone which means it fails to detect islanding for specific local load conditions. If the SMS IDM employs a different function other than the sinusoidal function for drifting the phase angle of the inverter, its non-detection zone could be smaller. In addition, Advanced Slip Mode Frequency Shift Islanding Detection Method (Advanced SMS IDM), which has been introduced in this thesis, eliminates the non-detection zone of the SMS IDM. In this method the parameters of SMS IDM change based on the local load impedance value. Moreover, the stability of the system is investigated by developing the dynamical equations of the system for two operation modes; grid connected and islanded mode. It is mathematically proven that for some loading conditions the nominal frequency is an unstable point and the operation frequency slides to another stable point, while for other loading conditions the nominal frequency is the only stable point of the system upon islanding occurring. Simulation and experimental results show the accuracy of the proposed methods in detection of islanding and verify the validity of the mathematical analysis.

Semantic feature-based visual attention model for pedestrian detection

Objective
Pedestrian detection under video surveillance systems has always been a hot topic in computer vision research. These systems are widely used in train stations, airports, large commercial plazas, and other public places. However, pedestrian detection remains difficult because of complex backgrounds. Given its development in recent years, the visual attention mechanism has attracted increasing attention in object detection and tracking research, and previous studies have achieved substantial progress and breakthroughs. We propose a novel pedestrian detection method based on the semantic features under the visual attention mechanism.
Method
The proposed semantic feature-based visual attention model is a spatial-temporal model that consists of two parts: the static visual attention model and the motion visual attention model. The static visual attention model in the spatial domain is constructed by combining bottom-up with top-down attention guidance. Based on the characteristics of pedestrians, the bottom-up visual attention model of Itti is improved by intensifying the orientation vectors of elementary visual features to make the visual saliency map suitable for pedestrian detection. In terms of pedestrian attributes, skin color is selected as a semantic feature for pedestrian detection. The regional and Gaussian models are adopted to construct the skin color model. Skin feature-based visual attention guidance is then proposed to complete the top-down process. The bottom-up and top-down visual attentions are linearly combined using the proper weights obtained from experiments to construct the static visual attention model in the spatial domain. The spatial-temporal visual attention model is then constructed via the motion features in the temporal domain. Based on the static visual attention model in the spatial domain, the frame difference method is combined with optical flowing to detect motion vectors. Filtering is applied to process the field of motion vectors. The saliency of motion vectors can be evaluated via motion entropy to make the selected motion feature more suitable for the spatial-temporal visual attention model.
Result
Standard datasets and practical videos are selected for the experiments. The experiments are performed on a MATLAB R2012a platform. The experimental results show that our spatial-temporal visual attention model demonstrates favorable robustness under various scenes, including indoor train station surveillance videos and outdoor scenes with swaying leaves. Our proposed model outperforms the visual attention model of Itti, the graph-based visual saliency model, the phase spectrum of quaternion Fourier transform model, and the motion channel model of Liu in terms of pedestrian detection. The proposed model achieves a 93% accuracy rate on the test video.
Conclusion
This paper proposes a novel pedestrian method based on the visual attention mechanism. A spatial-temporal visual attention model that uses low-level and semantic features is proposed to calculate the saliency map. Based on this model, the pedestrian targets can be detected through focus of attention shifts. The experimental results verify the effectiveness of the proposed attention model for detecting pedestrians.

Automatic Glaucoma Detection Based on Optic Disc Segmentation and Texture Feature Extraction

The use of digital image processing techniques is prominent in medical settings for the automatic diagnosis of diseases. Glaucoma is the second leading cause of blindness in the world and it has no cure. Currently, there are treatments to prevent vision loss, but the disease must be detected in the early stages. Thus, the objective of this work is to develop an automatic detection method of Glaucoma in retinal images. The methodology used in the study were: acquisition of image database, Optic Disc segmentation, texture feature extraction in different color models and classification of images in glaucomatous or not. We obtained results of 93% accuracy

Analysis and detection of partial discharges in an electric motor of a hybrid supercar.

The objective of this thesis was the development of a new detection method of partial discharge (PD) activity in the stator of an electrical hybrid supercar fed by a silicon carbide converter, for which detection with common methods make it very difficult to separate PD pulses from switching noise. This work focused on the analysis and detection of partial discharges making use of an antenna, a peak detector, and an oscilloscope capable of capturing the electromagnetic pulses emitted during PD activity. Validation of the proposed method was done by comparing the partial discharge inception voltage (PDIV) detected by this system with the one obtained from an optical method of proven accuracy, with different rise times and samples. Further development of this method, if proved successful on a full stator, can help increasing the overall reliability of the car, potentially allowing for real time detection of PD activity and predictive maintenance before failure of the insulation system in a hybrid vehicle.