Background estimation and player detection in badminton video clips using histogram of pixel values along temporal dimension

NASA Astrophysics Data System (ADS)

Peng, Yahui; Ma, Xiao; Gao, Xinyu; Zhou, Fangxu

2015-12-01

Computer vision is an important tool for sports video processing. However, its application in badminton match analysis is very limited. In this study, we proposed a straightforward but robust histogram-based background estimation and player detection methods for badminton video clips, and compared the results with the naive averaging method and the mixture of Gaussians methods, respectively. The proposed method yielded better background estimation results than the naive averaging method and more accurate player detection results than the mixture of Gaussians player detection method. The preliminary results indicated that the proposed histogram-based method could estimate the background and extract the players accurately. We conclude that the proposed method can be used for badminton player tracking and further studies are warranted for automated match analysis.

Foreign object detection and removal to improve automated analysis of chest radiographs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hogeweg, Laurens; Sanchez, Clara I.; Melendez, Jaime

2013-07-15

Purpose: Chest radiographs commonly contain projections of foreign objects, such as buttons, brassier clips, jewellery, or pacemakers and wires. The presence of these structures can substantially affect the output of computer analysis of these images. An automated method is presented to detect, segment, and remove foreign objects from chest radiographs.Methods: Detection is performed using supervised pixel classification with a kNN classifier, resulting in a probability estimate per pixel to belong to a projected foreign object. Segmentation is performed by grouping and post-processing pixels with a probability above a certain threshold. Next, the objects are replaced by texture inpainting.Results: The methodmore » is evaluated in experiments on 257 chest radiographs. The detection at pixel level is evaluated with receiver operating characteristic analysis on pixels within the unobscured lung fields and an A{sub z} value of 0.949 is achieved. Free response operator characteristic analysis is performed at the object level, and 95.6% of objects are detected with on average 0.25 false positive detections per image. To investigate the effect of removing the detected objects through inpainting, a texture analysis system for tuberculosis detection is applied to images with and without pathology and with and without foreign object removal. Unprocessed, the texture analysis abnormality score of normal images with foreign objects is comparable to those with pathology. After removing foreign objects, the texture score of normal images with and without foreign objects is similar, while abnormal images, whether they contain foreign objects or not, achieve on average higher scores.Conclusions: The authors conclude that removal of foreign objects from chest radiographs is feasible and beneficial for automated image analysis.« less

Using multiple PCR and CE with chemiluminescence detection for simultaneous qualitative and quantitative analysis of genetically modified organism.

PubMed

Guo, Longhua; Qiu, Bin; Chi, Yuwu; Chen, Guonan

2008-09-01

In this paper, an ultrasensitive CE-CL detection system coupled with a novel double-on-column coaxial flow detection interface was developed for the detection of PCR products. A reliable procedure based on this system had been demonstrated for qualitative and quantitative analysis of genetically modified organism-the detection of Roundup Ready Soy (RRS) samples was presented as an example. The promoter, terminator, function and two reference genes of RRS were amplified with multiplex PCR simultaneously. After that, the multiplex PCR products were labeled with acridinium ester at the 5'-terminal through an amino modification and then analyzed by the proposed CE-CL system. Reproducibility of analysis times and peak heights for the CE-CL analysis were determined to be better than 0.91 and 3.07% (RSD, n=15), respectively, for three consecutive days. It was shown that this method could accurately and qualitatively detect RRS standards and the simulative samples. The evaluation in terms of quantitative analysis of RRS provided by this new method was confirmed by comparing our assay results with those of the standard real-time quantitative PCR (RT-QPCR) using SYBR Green I dyes. The results showed a good coherence between the two methods. This approach demonstrated the possibility for accurate qualitative and quantitative detection of GM plants in a single run.

Detection of semi-volatile organic compounds in permeable ...

EPA Pesticide Factsheets

Abstract The Edison Environmental Center (EEC) has a research and demonstration permeable parking lot comprised of three different permeable systems: permeable asphalt, porous concrete and interlocking concrete permeable pavers. Water quality and quantity analysis has been ongoing since January, 2010. This paper describes a subset of the water quality analysis, analysis of semivolatile organic compounds (SVOCs) to determine if hydrocarbons were in water infiltrated through the permeable surfaces. SVOCs were analyzed in samples collected from 11 dates over a 3 year period, from 2/8/2010 to 4/1/2013.Results are broadly divided into three categories: 42 chemicals were never detected; 12 chemicals (11 chemical test) were detected at a rate of less than 10% or less; and 22 chemicals were detected at a frequency of 10% or greater (ranging from 10% to 66.5% detections). Fundamental and exploratory statistical analyses were performed on these latter analyses results by grouping results by surface type. The statistical analyses were limited due to low frequency of detections and dilutions of samples which impacted detection limits. The infiltrate data through three permeable surfaces were analyzed as non-parametric data by the Kaplan-Meier estimation method for fundamental statistics; there were some statistically observable difference in concentration between pavement types when using Tarone-Ware Comparison Hypothesis Test. Additionally Spearman Rank order non-parame

Applying spatial analysis tools in public health: an example using SaTScan to detect geographic targets for colorectal cancer screening interventions.

PubMed

Sherman, Recinda L; Henry, Kevin A; Tannenbaum, Stacey L; Feaster, Daniel J; Kobetz, Erin; Lee, David J

2014-03-20

Epidemiologists are gradually incorporating spatial analysis into health-related research as geocoded cases of disease become widely available and health-focused geospatial computer applications are developed. One health-focused application of spatial analysis is cluster detection. Using cluster detection to identify geographic areas with high-risk populations and then screening those populations for disease can improve cancer control. SaTScan is a free cluster-detection software application used by epidemiologists around the world to describe spatial clusters of infectious and chronic disease, as well as disease vectors and risk factors. The objectives of this article are to describe how spatial analysis can be used in cancer control to detect geographic areas in need of colorectal cancer screening intervention, identify issues commonly encountered by SaTScan users, detail how to select the appropriate methods for using SaTScan, and explain how method selection can affect results. As an example, we used various methods to detect areas in Florida where the population is at high risk for late-stage diagnosis of colorectal cancer. We found that much of our analysis was underpowered and that no single method detected all clusters of statistical or public health significance. However, all methods detected 1 area as high risk; this area is potentially a priority area for a screening intervention. Cluster detection can be incorporated into routine public health operations, but the challenge is to identify areas in which the burden of disease can be alleviated through public health intervention. Reliance on SaTScan's default settings does not always produce pertinent results.

Robust Mokken Scale Analysis by Means of the Forward Search Algorithm for Outlier Detection

ERIC Educational Resources Information Center

Zijlstra, Wobbe P.; van der Ark, L. Andries; Sijtsma, Klaas

2011-01-01

Exploratory Mokken scale analysis (MSA) is a popular method for identifying scales from larger sets of items. As with any statistical method, in MSA the presence of outliers in the data may result in biased results and wrong conclusions. The forward search algorithm is a robust diagnostic method for outlier detection, which we adapt here to…

Use of the high-resolution satellite images for detection of fractures related to the ore deposits

NASA Astrophysics Data System (ADS)

Cruz-Mondaca, M.; Soto-Pinto, C. A.; Arellano-Baeza, A. A.

2012-12-01

The Aster and GeoEye satellite high-resolution images were used to detect the structures related to the fracturing of the upper crust in the North of Chile. In particular, lineament analysis has been applied to detect the presence of epithermal fluids of low sulfurization associated with the Paleozoic ore deposits. These results have been compared with the location of the minerals altered by the presence of geothermal fluids detected using the spectral libraries. Later, the presence of fractures has been corroborated during recognition of fractures in situ and the geochemical analysis of samples of minerals altered by the presence of fluids. It was shown that the results obtained are relevant for the gold vein detection.

Top-attack modeling and automatic target detection using synthetic FLIR scenery

NASA Astrophysics Data System (ADS)

Weber, Bruce A.; Penn, Joseph A.

2004-09-01

A series of experiments have been performed to verify the utility of algorithmic tools for the modeling and analysis of cold-target signatures in synthetic, top-attack, FLIR video sequences. The tools include: MuSES/CREATION for the creation of synthetic imagery with targets, an ARL target detection algorithm to detect imbedded synthetic targets in scenes, and an ARL scoring algorithm, using Receiver-Operating-Characteristic (ROC) curve analysis, to evaluate detector performance. Cold-target detection variability was examined as a function of target emissivity, surrounding clutter type, and target placement in non-obscuring clutter locations. Detector metrics were also individually scored so as to characterize the effect of signature/clutter variations. Results show that using these tools, a detailed, physically meaningful, target detection analysis is possible and that scenario specific target detectors may be developed by selective choice and/or weighting of detector metrics. However, developing these tools into a reliable predictive capability will require the extension of these results to the modeling and analysis of a large number of data sets configured for a wide range of target and clutter conditions. Finally, these tools should also be useful for the comparison of competitive detection algorithms by providing well defined, and controllable target detection scenarios, as well as for the training and testing of expert human observers.

A habituation based approach for detection of visual changes in surveillance camera

NASA Astrophysics Data System (ADS)

Sha'abani, M. N. A. H.; Adan, N. F.; Sabani, M. S. M.; Abdullah, F.; Nadira, J. H. S.; Yasin, M. S. M.

2017-09-01

This paper investigates a habituation based approach in detecting visual changes using video surveillance systems in a passive environment. Various techniques have been introduced for dynamic environment such as motion detection, object classification and behaviour analysis. However, in a passive environment, most of the scenes recorded by the surveillance system are normal. Therefore, implementing a complex analysis all the time in the passive environment resulting on computationally expensive, especially when using a high video resolution. Thus, a mechanism of attention is required, where the system only responds to an abnormal event. This paper proposed a novelty detection mechanism in detecting visual changes and a habituation based approach to measure the level of novelty. The objective of the paper is to investigate the feasibility of the habituation based approach in detecting visual changes. Experiment results show that the approach are able to accurately detect the presence of novelty as deviations from the learned knowledge.

Using recurrence plot analysis for software execution interpretation and fault detection

NASA Astrophysics Data System (ADS)

Mosdorf, M.

2015-09-01

This paper shows a method targeted at software execution interpretation and fault detection using recurrence plot analysis. In in the proposed approach recurrence plot analysis is applied to software execution trace that contains executed assembly instructions. Results of this analysis are subject to further processing with PCA (Principal Component Analysis) method that simplifies number coefficients used for software execution classification. This method was used for the analysis of five algorithms: Bubble Sort, Quick Sort, Median Filter, FIR, SHA-1. Results show that some of the collected traces could be easily assigned to particular algorithms (logs from Bubble Sort and FIR algorithms) while others are more difficult to distinguish.

Indel analysis by droplet digital PCR: a sensitive method for DNA mixture detection and chimerism analysis.

PubMed

Santurtún, Ana; Riancho, José A; Arozamena, Jana; López-Duarte, Mónica; Zarrabeitia, María T

2017-01-01

Several methods have been developed to determinate genetic profiles from a mixed samples and chimerism analysis in transplanted patients. The aim of this study was to explore the effectiveness of using the droplet digital PCR (ddPCR) for mixed chimerism detection (a mixture of genetic profiles resulting after allogeneic hematopoietic stem cell transplantation (HSCT)). We analyzed 25 DNA samples from patients who had undergone HSCT and compared the performance of ddPCR and two established methods for chimerism detection, based upon the Indel and STRs analysis, respectively. Additionally, eight artificial mixture DNA samples were created to evaluate the sensibility of ddPCR. Our results show that the chimerism percentages estimated by the analysis of a single Indel using ddPCR were very similar to those calculated by the amplification of 15 STRs (r 2  = 0.970) and with the results obtained by the amplification of 38 Indels (r 2  = 0.975). Moreover, the amplification of a single Indel by ddPCR was sensitive enough to detect a minor DNA contributor comprising down to 0.5 % of the sample. We conclude that ddPCR can be a powerful tool for the determination of a genetic profile of forensic mixtures and clinical chimerism analysis when traditional techniques are not sensitive enough.

Shadow detection of moving objects based on multisource information in Internet of things

NASA Astrophysics Data System (ADS)

Ma, Zhen; Zhang, De-gan; Chen, Jie; Hou, Yue-xian

2017-05-01

Moving object detection is an important part in intelligent video surveillance under the banner of Internet of things. The detection of moving target's shadow is also an important step in moving object detection. On the accuracy of shadow detection will affect the detection results of the object directly. Based on the variety of shadow detection method, we find that only using one feature can't make the result of detection accurately. Then we present a new method for shadow detection which contains colour information, the invariance of optical and texture feature. Through the comprehensive analysis of the detecting results of three kinds of information, the shadow was effectively determined. It gets ideal effect in the experiment when combining advantages of various methods.

Incorporating Functional Imaging Information into rpFNA Analysis for Breast Cancer Detection in High Risk Women

DTIC Science & Technology

2010-03-01

TITLE: INCORPORATING FUNCTIONAL IMAGING INFORMATION TO rpFNA ANALYSIS FOR BREAST CANCER DETECTION IN HIGH-RISK WOMEN PRINCIPAL INVESTIGATOR...Imaging Information into rpFNA 5a. CONTRACT NUMBER Analysis for Breast Cancer Detection in High Risk Women 5b. GRANT NUMBER W81XWH-08-1-0192 5c...results of random periareolar fine needle aspiration (rpFNA) in women at high risk for breast cancer. In this second year of work, efforts have been

In situ mass analysis of particles by surface ionization mass spectrometry

NASA Technical Reports Server (NTRS)

Lassiter, W. S.; Moen, A. L.

1974-01-01

A qualitative study of the application of surface ionization and mass spectrometry to the in situ detection and constituent analysis of atmospheric particles was conducted. The technique consists of mass analysis of ions formed as a result of impingement of a stream of particles on a hot filament where, it is presumed, surface ionization takes place. Laboratory air particles containing K, Ca, and possibly hydrocarbons were detected. Other known particles such as Al2O3, Pb(NO3)2, and Cr2O3 were analyzed by detecting the respective metal atoms making up the particles. In some cases, mass numbers indicative of compounds making up the particles were detected showing surface ionization of particles sometimes leads to chemical analysis as well as to elemental analysis. Individual particles were detected, and it was shown that the technique is sensitive to Al2O3 particles with a mass of a few nanograms.

CUSUM-Logistic Regression analysis for the rapid detection of errors in clinical laboratory test results.

PubMed

Sampson, Maureen L; Gounden, Verena; van Deventer, Hendrik E; Remaley, Alan T

2016-02-01

The main drawback of the periodic analysis of quality control (QC) material is that test performance is not monitored in time periods between QC analyses, potentially leading to the reporting of faulty test results. The objective of this study was to develop a patient based QC procedure for the more timely detection of test errors. Results from a Chem-14 panel measured on the Beckman LX20 analyzer were used to develop the model. Each test result was predicted from the other 13 members of the panel by multiple regression, which resulted in correlation coefficients between the predicted and measured result of >0.7 for 8 of the 14 tests. A logistic regression model, which utilized the measured test result, the predicted test result, the day of the week and time of day, was then developed for predicting test errors. The output of the logistic regression was tallied by a daily CUSUM approach and used to predict test errors, with a fixed specificity of 90%. The mean average run length (ARL) before error detection by CUSUM-Logistic Regression (CSLR) was 20 with a mean sensitivity of 97%, which was considerably shorter than the mean ARL of 53 (sensitivity 87.5%) for a simple prediction model that only used the measured result for error detection. A CUSUM-Logistic Regression analysis of patient laboratory data can be an effective approach for the rapid and sensitive detection of clinical laboratory errors. Published by Elsevier Inc.

Optimizing detection and analysis of slow waves in sleep EEG.

PubMed

Mensen, Armand; Riedner, Brady; Tononi, Giulio

2016-12-01

Analysis of individual slow waves in EEG recording during sleep provides both greater sensitivity and specificity compared to spectral power measures. However, parameters for detection and analysis have not been widely explored and validated. We present a new, open-source, Matlab based, toolbox for the automatic detection and analysis of slow waves; with adjustable parameter settings, as well as manual correction and exploration of the results using a multi-faceted visualization tool. We explore a large search space of parameter settings for slow wave detection and measure their effects on a selection of outcome parameters. Every choice of parameter setting had some effect on at least one outcome parameter. In general, the largest effect sizes were found when choosing the EEG reference, type of canonical waveform, and amplitude thresholding. Previously published methods accurately detect large, global waves but are conservative and miss the detection of smaller amplitude, local slow waves. The toolbox has additional benefits in terms of speed, user-interface, and visualization options to compare and contrast slow waves. The exploration of parameter settings in the toolbox highlights the importance of careful selection of detection METHODS: The sensitivity and specificity of the automated detection can be improved by manually adding or deleting entire waves and or specific channels using the toolbox visualization functions. The toolbox standardizes the detection procedure, sets the stage for reliable results and comparisons and is easy to use without previous programming experience. Copyright © 2016 Elsevier B.V. All rights reserved.

Detection of Organophosphorus Pesticides with Colorimetry and Computer Image Analysis.

PubMed

Li, Yanjie; Hou, Changjun; Lei, Jincan; Deng, Bo; Huang, Jing; Yang, Mei

2016-01-01

Organophosphorus pesticides (OPs) represent a very important class of pesticides that are widely used in agriculture because of their relatively high-performance and moderate environmental persistence, hence the sensitive and specific detection of OPs is highly significant. Based on the inhibitory effect of acetylcholinesterase (AChE) induced by inhibitors, including OPs and carbamates, a colorimetric analysis was used for detection of OPs with computer image analysis of color density in CMYK (cyan, magenta, yellow and black) color space and non-linear modeling. The results showed that there was a gradually weakened trend of yellow intensity with the increase of the concentration of dichlorvos. The quantitative analysis of dichlorvos was achieved by Artificial Neural Network (ANN) modeling, and the results showed that the established model had a good predictive ability between training sets and predictive sets. Real cabbage samples containing dichlorvos were detected by colorimetry and gas chromatography (GC), respectively. The results showed that there was no significant difference between colorimetry and GC (P > 0.05). The experiments of accuracy, precision and repeatability revealed good performance for detection of OPs. AChE can also be inhibited by carbamates, and therefore this method has potential applications in real samples for OPs and carbamates because of high selectivity and sensitivity.

Signal Detection Analysis of Factors Associated with Diabetes among Semirural Mexican American Adults

ERIC Educational Resources Information Center

Hanni, K. D.; Ahn, D. A.; Winkleby, M. A.

2013-01-01

Signal detection analysis was used to evaluate a combination of sociodemographic, acculturation, mental health, health care, and chronic disease risk factors potentially associated with diabetes in a sample of 4,505 semirural Mexican American adults. Overall, 8.9% of adults had been diagnosed with diabetes. The analysis resulted in 12 mutually…

Epstein-Barr virus is associated with periodontal diseases: A meta-analysis based on 21 case-control studies.

PubMed

Gao, Zilong; Lv, Juan; Wang, Min

2017-02-01

Some controversies still exist between the detection of Epstein-Barr virus (EBV)'s DNA and risks of periodontal diseases. Hence, a comprehensive meta-analysis on all available literatures was performed to clarify the relationship between EBV and preidontitis.A comprehensive search was conducted within the PUBMED, EMBASE, and WANFANG databases up to October 10th, 2016 according to inclusion and exclusion criteria and finally 21 case-control literatures were obtained. The outcomes including odds ratios (ORs) with 95% confidence intervals (CIs) were used to assess the strength of associations. Publication bias was determined by Begg or Egger test. Sensitivity analysis was used to investigate reliability and stability of the results.According to the data from included trials, the association between overall increased risks of periodontitis and the detection of EBV was significant (OR = 6.199, 95% CI = 3.119-12.319, P < 0.001). In the disease-type analysis, the pooled ORs for chronic periodontitis and aggressive periodontitis were 6.586 (95% CI = 3.042-14.262, P < 0.001) and 8.361 (95% CI = 2.109-33.143, P = 0.003), respectively. In the subgroup analysis of ethnicity, our results suggested that high EBV-detecting frequencies were correlated with increased risks of periodontitis in Asians, Europeans, and Americans (P < 0.001). Subgroup analysis by the sample type showed that subgingival plaque (SgP) samples and tissue samples were available for EBV detecting (P < 0.001). Detecting EBV of samples in ≥5 (6) mm sites of periodontal pockets were easier than in ≤3-mm sites (P = 0.023).This meta-analysis indicates that high frequent detection of EBV correlates with increased risk of periodontal diseases. SgP and tissue are available for detecting EBV in patients of periodontitis. At last, our results suggest that detecting EBV of samples in =5 (6) mm sites of periodontal pockets are more sensitive than in ≤3-mm sites.

Application of principal component regression and partial least squares regression in ultraviolet spectrum water quality detection

NASA Astrophysics Data System (ADS)

Li, Jiangtong; Luo, Yongdao; Dai, Honglin

2018-01-01

Water is the source of life and the essential foundation of all life. With the development of industrialization, the phenomenon of water pollution is becoming more and more frequent, which directly affects the survival and development of human. Water quality detection is one of the necessary measures to protect water resources. Ultraviolet (UV) spectral analysis is an important research method in the field of water quality detection, which partial least squares regression (PLSR) analysis method is becoming predominant technology, however, in some special cases, PLSR's analysis produce considerable errors. In order to solve this problem, the traditional principal component regression (PCR) analysis method was improved by using the principle of PLSR in this paper. The experimental results show that for some special experimental data set, improved PCR analysis method performance is better than PLSR. The PCR and PLSR is the focus of this paper. Firstly, the principal component analysis (PCA) is performed by MATLAB to reduce the dimensionality of the spectral data; on the basis of a large number of experiments, the optimized principal component is extracted by using the principle of PLSR, which carries most of the original data information. Secondly, the linear regression analysis of the principal component is carried out with statistic package for social science (SPSS), which the coefficients and relations of principal components can be obtained. Finally, calculating a same water spectral data set by PLSR and improved PCR, analyzing and comparing two results, improved PCR and PLSR is similar for most data, but improved PCR is better than PLSR for data near the detection limit. Both PLSR and improved PCR can be used in Ultraviolet spectral analysis of water, but for data near the detection limit, improved PCR's result better than PLSR.

A miniaturised image based fluorescence detection system for point-of-care-testing of cocaine abuse

NASA Astrophysics Data System (ADS)

Walczak, Rafał; Krüger, Jan; Moynihan, Shane

2015-08-01

In this paper, we describe a miniaturised image-based fluorescence detection system and demonstrate its viability as a highly sensitive tool for point-of-care-analysis of drugs of abuse in human sweat with a focus on monitor individuals for drugs of abuse. Investigations of miniaturised and low power optoelectronic configurations and methodologies for real-time image analysis were successfully carried out. The miniaturised fluorescence detection system was validated against a reference detection system under controlled laboratory conditions by analysing spiked sweat samples in dip stick and then strip with sample pad. As a result of the validation studies, a 1 ng mL-1 limit of detection of cocaine in sweat and full agreement of test results with the reference detection system can be reported. Results of the investigations open the way towards a detection system that integrates a hand-held fluorescence reader and a wearable skinpatch, and which can collect and in situ analyse sweat for the presence of cocaine at any point for up to tenths hours.

Performance analysis of a fault inferring nonlinear detection system algorithm with integrated avionics flight data

NASA Technical Reports Server (NTRS)

Caglayan, A. K.; Godiwala, P. M.; Morrell, F. R.

1985-01-01

This paper presents the performance analysis results of a fault inferring nonlinear detection system (FINDS) using integrated avionics sensor flight data for the NASA ATOPS B-737 aircraft in a Microwave Landing System (MLS) environment. First, an overview of the FINDS algorithm structure is given. Then, aircraft state estimate time histories and statistics for the flight data sensors are discussed. This is followed by an explanation of modifications made to the detection and decision functions in FINDS to improve false alarm and failure detection performance. Next, the failure detection and false alarm performance of the FINDS algorithm are analyzed by injecting bias failures into fourteen sensor outputs over six repetitive runs of the five minutes of flight data. Results indicate that the detection speed, failure level estimation, and false alarm performance show a marked improvement over the previously reported simulation runs. In agreement with earlier results, detection speed is faster for filter measurement sensors such as MLS than for filter input sensors such as flight control accelerometers. Finally, the progress in modifications of the FINDS algorithm design to accommodate flight computer constraints is discussed.

Examination of influential observations in penalized spline regression

NASA Astrophysics Data System (ADS)

Türkan, Semra

2013-10-01

In parametric or nonparametric regression models, the results of regression analysis are affected by some anomalous observations in the data set. Thus, detection of these observations is one of the major steps in regression analysis. These observations are precisely detected by well-known influence measures. Pena's statistic is one of them. In this study, Pena's approach is formulated for penalized spline regression in terms of ordinary residuals and leverages. The real data and artificial data are used to see illustrate the effectiveness of Pena's statistic as to Cook's distance on detecting influential observations. The results of the study clearly reveal that the proposed measure is superior to Cook's Distance to detect these observations in large data set.

Computer-Aided Diagnostic System For Mass Survey Chest Images

NASA Astrophysics Data System (ADS)

Yasuda, Yoshizumi; Kinoshita, Yasuhiro; Emori, Yasufumi; Yoshimura, Hitoshi

1988-06-01

In order to support screening of chest radiographs on mass survey, a computer-aided diagnostic system that automatically detects abnormality of candidate images using a digital image analysis technique has been developed. Extracting boundary lines of lung fields and examining their shapes allowed various kind of abnormalities to be detected. Correction and expansion were facilitated by describing the system control, image analysis control and judgement of abnormality in the rule type programing language. In the experiments using typical samples of student's radiograms, good results were obtained for the detection of abnormal shape of lung field, cardiac hypertrophy and scoliosis. As for the detection of diaphragmatic abnormality, relatively good results were obtained but further improvements will be necessary.

A robust detector for rolling element bearing condition monitoring based on the modulation signal bispectrum and its performance evaluation against the Kurtogram

NASA Astrophysics Data System (ADS)

Tian, Xiange; Xi Gu, James; Rehab, Ibrahim; Abdalla, Gaballa M.; Gu, Fengshou; Ball, A. D.

2018-02-01

Envelope analysis is a widely used method for rolling element bearing fault detection. To obtain high detection accuracy, it is critical to determine an optimal frequency narrowband for the envelope demodulation. However, many of the schemes which are used for the narrowband selection, such as the Kurtogram, can produce poor detection results because they are sensitive to random noise and aperiodic impulses which normally occur in practical applications. To achieve the purposes of denoising and frequency band optimisation, this paper proposes a novel modulation signal bispectrum (MSB) based robust detector for bearing fault detection. Because of its inherent noise suppression capability, the MSB allows effective suppression of both stationary random noise and discrete aperiodic noise. The high magnitude features that result from the use of the MSB also enhance the modulation effects of a bearing fault and can be used to provide optimal frequency bands for fault detection. The Kurtogram is generally accepted as a powerful means of selecting the most appropriate frequency band for envelope analysis, and as such it has been used as the benchmark comparator for performance evaluation in this paper. Both simulated and experimental data analysis results show that the proposed method produces more accurate and robust detection results than Kurtogram based approaches for common bearing faults under a range of representative scenarios.

Wavelength modulation spectroscopy--digital detection of gas absorption harmonics based on Fourier analysis.

PubMed

Mei, Liang; Svanberg, Sune

2015-03-20

This work presents a detailed study of the theoretical aspects of the Fourier analysis method, which has been utilized for gas absorption harmonic detection in wavelength modulation spectroscopy (WMS). The lock-in detection of the harmonic signal is accomplished by studying the phase term of the inverse Fourier transform of the Fourier spectrum that corresponds to the harmonic signal. The mathematics and the corresponding simulation results are given for each procedure when applying the Fourier analysis method. The present work provides a detailed view of the WMS technique when applying the Fourier analysis method.

Evolution and Advances in Satellite Analysis of Volcanoes

NASA Astrophysics Data System (ADS)

Dean, K. G.; Dehn, J.; Webley, P.; Bailey, J.

2008-12-01

Over the past 20 years satellite data used for monitoring and analysis of volcanic eruptions has evolved in terms of timeliness, access, distribution, resolution and understanding of volcanic processes. Initially satellite data was used for retrospective analysis but has evolved to proactive monitoring systems. Timely acquisition of data and the capability to distribute large data files paralleled advances in computer technology and was a critical component for near real-time monitoring. The sharing of these data and resulting discussions has improved our understanding of eruption processes and, even more importantly, their impact on society. To illustrate this evolution, critical scientific discoveries will be highlighted, including detection of airborne ash and sulfur dioxide, cloud-height estimates, prediction of ash cloud movement, and detection of thermal anomalies as precursor-signals to eruptions. AVO has been a leader in implementing many of these advances into an operational setting such as, automated eruption detection, database analysis systems, and remotely accessible web-based analysis systems. Finally, limitations resulting from trade-offs between resolution and how they impact some weakness in detection techniques and hazard assessments will be presented.

Electro-optical system for gunshot detection: analysis, concept, and performance

NASA Astrophysics Data System (ADS)

Kastek, M.; Dulski, R.; Madura, H.; Trzaskawka, P.; Bieszczad, G.; Sosnowski, T.

2011-08-01

The paper discusses technical possibilities to build an effective electro-optical sensor unit for sniper detection using infrared cameras. This unit, comprising of thermal and daylight cameras, can operate as a standalone device but its primary application is a multi-sensor sniper and shot detection system. At first, the analysis was presented of three distinguished phases of sniper activity: before, during and after the shot. On the basis of experimental data the parameters defining the relevant sniper signatures were determined which are essential in assessing the capability of infrared camera to detect sniper activity. A sniper body and muzzle flash were analyzed as targets and the descriptions of phenomena which make it possible to detect sniper activities in infrared spectra as well as analysis of physical limitations were performed. The analyzed infrared systems were simulated using NVTherm software. The calculations for several cameras, equipped with different lenses and detector types were performed. The simulation of detection ranges was performed for the selected scenarios of sniper detection tasks. After the analysis of simulation results, the technical specifications of infrared sniper detection system were discussed, required to provide assumed detection range. Finally the infrared camera setup was proposed which can detected sniper from 1000 meters range.

Real time automatic detection of bearing fault in induction machine using kurtogram analysis.

PubMed

Tafinine, Farid; Mokrani, Karim

2012-11-01

A proposed signal processing technique for incipient real time bearing fault detection based on kurtogram analysis is presented in this paper. The kurtogram is a fourth-order spectral analysis tool introduced for detecting and characterizing non-stationarities in a signal. This technique starts from investigating the resonance signatures over selected frequency bands to extract the representative features. The traditional spectral analysis is not appropriate for non-stationary vibration signal and for real time diagnosis. The performance of the proposed technique is examined by a series of experimental tests corresponding to different bearing conditions. Test results show that this signal processing technique is an effective bearing fault automatic detection method and gives a good basis for an integrated induction machine condition monitor.

Strategies for rare-event detection: an approach for automated fetal cell detection in maternal blood.

PubMed Central

Oosterwijk, J C; Knepflé, C F; Mesker, W E; Vrolijk, H; Sloos, W C; Pattenier, H; Ravkin, I; van Ommen, G J; Kanhai, H H; Tanke, H J

1998-01-01

This article explores the feasibility of the use of automated microscopy and image analysis to detect the presence of rare fetal nucleated red blood cells (NRBCs) circulating in maternal blood. The rationales for enrichment and for automated image analysis for "rare-event" detection are reviewed. We also describe the application of automated image analysis to 42 maternal blood samples, using a protocol consisting of one-step enrichment followed by immunocytochemical staining for fetal hemoglobin (HbF) and FISH for X- and Y-chromosomal sequences. Automated image analysis consisted of multimode microscopy and subsequent visual evaluation of image memories containing the selected objects. The FISH results were compared with the results of conventional karyotyping of the chorionic villi. By use of manual screening, 43% of the slides were found to be positive (>=1 NRBC), with a mean number of 11 NRBCs (range 1-40). By automated microscopy, 52% were positive, with on average 17 NRBCs (range 1-111). There was a good correlation between both manual and automated screening, but the NRBC yield from automated image analysis was found to be superior to that from manual screening (P=.0443), particularly when the NRBC count was >15. Seven (64%) of 11 XY fetuses were correctly diagnosed by FISH analysis of automatically detected cells, and all discrepancies were restricted to the lower cell-count range. We believe that automated microscopy and image analysis reduce the screening workload, are more sensitive than manual evaluation, and can be used to detect rare HbF-containing NRBCs in maternal blood. PMID:9837832

Change Detection of High-Resolution Remote Sensing Images Based on Adaptive Fusion of Multiple Features

NASA Astrophysics Data System (ADS)

Wang, G. H.; Wang, H. B.; Fan, W. F.; Liu, Y.; Chen, C.

2018-04-01

In view of the traditional change detection algorithm mainly depends on the spectral information image spot, failed to effectively mining and fusion of multi-image feature detection advantage, the article borrows the ideas of object oriented analysis proposed a multi feature fusion of remote sensing image change detection algorithm. First by the multi-scale segmentation of image objects based; then calculate the various objects of color histogram and linear gradient histogram; utilizes the color distance and edge line feature distance between EMD statistical operator in different periods of the object, using the adaptive weighted method, the color feature distance and edge in a straight line distance of combination is constructed object heterogeneity. Finally, the curvature histogram analysis image spot change detection results. The experimental results show that the method can fully fuse the color and edge line features, thus improving the accuracy of the change detection.

Psychophysical Models for Signal Detection with Time Varying Uncertainty. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Gai, E.

1975-01-01

Psychophysical models for the behavior of the human operator in detection tasks which include change in detectability, correlation between observations and deferred decisions are developed. Classical Signal Detection Theory (SDT) is discussed and its emphasis on the sensory processes is contrasted to decision strategies. The analysis of decision strategies utilizes detection tasks with time varying signal strength. The classical theory is modified to include such tasks and several optimal decision strategies are explored. Two methods of classifying strategies are suggested. The first method is similar to the analysis of ROC curves, while the second is based on the relation between the criterion level (CL) and the detectability. Experiments to verify the analysis of tasks with changes of signal strength are designed. The results show that subjects are aware of changes in detectability and tend to use strategies that involve changes in the CL's.

Portable point-of-care blood analysis system for global health (Conference Presentation)

NASA Astrophysics Data System (ADS)

Dou, James J.; Aitchison, James Stewart; Chen, Lu; Nayyar, Rakesh

2016-03-01

In this paper we present a portable blood analysis system based on a disposable cartridge and hand-held reader. The platform can perform all the sample preparation, detection and waste collection required to complete a clinical test. In order to demonstrate the utility of this approach a CD4 T cell enumeration was carried out. A handheld, point-of-care CD4 T cell system was developed based on this system. In particular we will describe a pneumatic, active pumping method to control the on-chip fluidic actuation. Reagents for the CD4 T cell counting assay were dried on a reagent plug to eliminate the need for cold chain storage when used in the field. A micromixer based on the active fluidic actuation was designed to complete sample staining with fluorescent dyes that was dried on the reagent plugs. A novel image detection and analysis algorithm was developed to detect and track the flight of target particles and cells during each analysis. The handheld, point-of-care CD4 testing system was benchmarked against clinical cytometer. The experimental results demonstrated experimental results were closely matched with the flow cytometry. The same platform can be further expanded into a bead-array detection system where other types of biomolecules such as proteins can be detected using the same detection system.

A hybrid symplectic principal component analysis and central tendency measure method for detection of determinism in noisy time series with application to mechanomyography

NASA Astrophysics Data System (ADS)

Xie, Hong-Bo; Dokos, Socrates

2013-06-01

We present a hybrid symplectic geometry and central tendency measure (CTM) method for detection of determinism in noisy time series. CTM is effective for detecting determinism in short time series and has been applied in many areas of nonlinear analysis. However, its performance significantly degrades in the presence of strong noise. In order to circumvent this difficulty, we propose to use symplectic principal component analysis (SPCA), a new chaotic signal de-noising method, as the first step to recover the system dynamics. CTM is then applied to determine whether the time series arises from a stochastic process or has a deterministic component. Results from numerical experiments, ranging from six benchmark deterministic models to 1/f noise, suggest that the hybrid method can significantly improve detection of determinism in noisy time series by about 20 dB when the data are contaminated by Gaussian noise. Furthermore, we apply our algorithm to study the mechanomyographic (MMG) signals arising from contraction of human skeletal muscle. Results obtained from the hybrid symplectic principal component analysis and central tendency measure demonstrate that the skeletal muscle motor unit dynamics can indeed be deterministic, in agreement with previous studies. However, the conventional CTM method was not able to definitely detect the underlying deterministic dynamics. This result on MMG signal analysis is helpful in understanding neuromuscular control mechanisms and developing MMG-based engineering control applications.

A hybrid symplectic principal component analysis and central tendency measure method for detection of determinism in noisy time series with application to mechanomyography.

PubMed

Xie, Hong-Bo; Dokos, Socrates

2013-06-01

We present a hybrid symplectic geometry and central tendency measure (CTM) method for detection of determinism in noisy time series. CTM is effective for detecting determinism in short time series and has been applied in many areas of nonlinear analysis. However, its performance significantly degrades in the presence of strong noise. In order to circumvent this difficulty, we propose to use symplectic principal component analysis (SPCA), a new chaotic signal de-noising method, as the first step to recover the system dynamics. CTM is then applied to determine whether the time series arises from a stochastic process or has a deterministic component. Results from numerical experiments, ranging from six benchmark deterministic models to 1/f noise, suggest that the hybrid method can significantly improve detection of determinism in noisy time series by about 20 dB when the data are contaminated by Gaussian noise. Furthermore, we apply our algorithm to study the mechanomyographic (MMG) signals arising from contraction of human skeletal muscle. Results obtained from the hybrid symplectic principal component analysis and central tendency measure demonstrate that the skeletal muscle motor unit dynamics can indeed be deterministic, in agreement with previous studies. However, the conventional CTM method was not able to definitely detect the underlying deterministic dynamics. This result on MMG signal analysis is helpful in understanding neuromuscular control mechanisms and developing MMG-based engineering control applications.

Change detection for synthetic aperture radar images based on pattern and intensity distinctiveness analysis

NASA Astrophysics Data System (ADS)

Wang, Xiao; Gao, Feng; Dong, Junyu; Qi, Qiang

2018-04-01

Synthetic aperture radar (SAR) image is independent on atmospheric conditions, and it is the ideal image source for change detection. Existing methods directly analysis all the regions in the speckle noise contaminated difference image. The performance of these methods is easily affected by small noisy regions. In this paper, we proposed a novel change detection framework for saliency-guided change detection based on pattern and intensity distinctiveness analysis. The saliency analysis step can remove small noisy regions, and therefore makes the proposed method more robust to the speckle noise. In the proposed method, the log-ratio operator is first utilized to obtain a difference image (DI). Then, the saliency detection method based on pattern and intensity distinctiveness analysis is utilized to obtain the changed region candidates. Finally, principal component analysis and k-means clustering are employed to analysis pixels in the changed region candidates. Thus, the final change map can be obtained by classifying these pixels into changed or unchanged class. The experiment results on two real SAR images datasets have demonstrated the effectiveness of the proposed method.

Detection of Impaired Cerebral Autoregulation Using Selected Correlation Analysis: A Validation Study

PubMed Central

Brawanski, Alexander

2017-01-01

Multimodal brain monitoring has been utilized to optimize treatment of patients with critical neurological diseases. However, the amount of data requires an integrative tool set to unmask pathological events in a timely fashion. Recently we have introduced a mathematical model allowing the simulation of pathophysiological conditions such as reduced intracranial compliance and impaired autoregulation. Utilizing a mathematical tool set called selected correlation analysis (sca), correlation patterns, which indicate impaired autoregulation, can be detected in patient data sets (scp). In this study we compared the results of the sca with the pressure reactivity index (PRx), an established marker for impaired autoregulation. Mean PRx values were significantly higher in time segments identified as scp compared to segments showing no selected correlations (nsc). The sca based approach predicted cerebral autoregulation failure with a sensitivity of 78.8% and a specificity of 62.6%. Autoregulation failure, as detected by the results of both analysis methods, was significantly correlated with poor outcome. Sca of brain monitoring data detects impaired autoregulation with high sensitivity and sufficient specificity. Since the sca approach allows the simultaneous detection of both major pathological conditions, disturbed autoregulation and reduced compliance, it may become a useful analysis tool for brain multimodal monitoring data. PMID:28255331

Detection of Impaired Cerebral Autoregulation Using Selected Correlation Analysis: A Validation Study.

PubMed

Proescholdt, Martin A; Faltermeier, Rupert; Bele, Sylvia; Brawanski, Alexander

2017-01-01

Multimodal brain monitoring has been utilized to optimize treatment of patients with critical neurological diseases. However, the amount of data requires an integrative tool set to unmask pathological events in a timely fashion. Recently we have introduced a mathematical model allowing the simulation of pathophysiological conditions such as reduced intracranial compliance and impaired autoregulation. Utilizing a mathematical tool set called selected correlation analysis (sca), correlation patterns, which indicate impaired autoregulation, can be detected in patient data sets (scp). In this study we compared the results of the sca with the pressure reactivity index (PRx), an established marker for impaired autoregulation. Mean PRx values were significantly higher in time segments identified as scp compared to segments showing no selected correlations (nsc). The sca based approach predicted cerebral autoregulation failure with a sensitivity of 78.8% and a specificity of 62.6%. Autoregulation failure, as detected by the results of both analysis methods, was significantly correlated with poor outcome. Sca of brain monitoring data detects impaired autoregulation with high sensitivity and sufficient specificity. Since the sca approach allows the simultaneous detection of both major pathological conditions, disturbed autoregulation and reduced compliance, it may become a useful analysis tool for brain multimodal monitoring data.

Integrating Oil Debris and Vibration Measurements for Intelligent Machine Health Monitoring. Degree awarded by Toledo Univ., May 2002

NASA Technical Reports Server (NTRS)

Dempsey, Paula J.

2003-01-01

A diagnostic tool for detecting damage to gears was developed. Two different measurement technologies, oil debris analysis and vibration were integrated into a health monitoring system for detecting surface fatigue pitting damage on gears. This integrated system showed improved detection and decision-making capabilities as compared to using individual measurement technologies. This diagnostic tool was developed and evaluated experimentally by collecting vibration and oil debris data from fatigue tests performed in the NASA Glenn Spur Gear Fatigue Rig. An oil debris sensor and the two vibration algorithms were adapted as the diagnostic tools. An inductance type oil debris sensor was selected for the oil analysis measurement technology. Gear damage data for this type of sensor was limited to data collected in the NASA Glenn test rigs. For this reason, this analysis included development of a parameter for detecting gear pitting damage using this type of sensor. The vibration data was used to calculate two previously available gear vibration diagnostic algorithms. The two vibration algorithms were selected based on their maturity and published success in detecting damage to gears. Oil debris and vibration features were then developed using fuzzy logic analysis techniques, then input into a multi sensor data fusion process. Results show combining the vibration and oil debris measurement technologies improves the detection of pitting damage on spur gears. As a result of this research, this new diagnostic tool has significantly improved detection of gear damage in the NASA Glenn Spur Gear Fatigue Rigs. This research also resulted in several other findings that will improve the development of future health monitoring systems. Oil debris analysis was found to be more reliable than vibration analysis for detecting pitting fatigue failure of gears and is capable of indicating damage progression. Also, some vibration algorithms are as sensitive to operational effects as they are to damage. Another finding was that clear threshold limits must be established for diagnostic tools. Based on additional experimental data obtained from the NASA Glenn Spiral Bevel Gear Fatigue Rig, the methodology developed in this study can be successfully implemented on other geared systems.

Real-time Supervised Detection of Pink Areas in Dermoscopic Images of Melanoma: Importance of Color Shades, Texture and Location

PubMed Central

Kaur, Ravneet; Albano, Peter P.; Cole, Justin G.; Hagerty, Jason; LeAnder, Robert W.; Moss, Randy H.; Stoecker, William V.

2015-01-01

Background/Purpose Early detection of malignant melanoma is an important public health challenge. In the USA, dermatologists are seeing more melanomas at an early stage, before classic melanoma features have become apparent. Pink color is a feature of these early melanomas. If rapid and accurate automatic detection of pink color in these melanomas could be accomplished, there could be significant public health benefits. Methods Detection of three shades of pink (light pink, dark pink, and orange pink) was accomplished using color analysis techniques in five color planes (red, green, blue, hue and saturation). Color shade analysis was performed using a logistic regression model trained with an image set of 60 dermoscopic images of melanoma that contained pink areas. Detected pink shade areas were further analyzed with regard to the location within the lesion, average color parameters over the detected areas, and histogram texture features. Results Logistic regression analysis of a separate set of 128 melanomas and 128 benign images resulted in up to 87.9% accuracy in discriminating melanoma from benign lesions measured using area under the receiver operating characteristic curve. The accuracy in this model decreased when parameters for individual shades, texture, or shade location within the lesion were omitted. Conclusion Texture, color, and lesion location analysis applied to multiple shades of pink can assist in melanoma detection. When any of these three details: color location, shade analysis, or texture analysis were omitted from the model, accuracy in separating melanoma from benign lesions was lowered. Separation of colors into shades and further details that enhance the characterization of these color shades are needed for optimal discrimination of melanoma from benign lesions. PMID:25809473

Efficiency of Airborne Sample Analysis Platform (ASAP) bioaerosol sampler for pathogen detection

PubMed Central

Sharma, Anurag; Clark, Elizabeth; McGlothlin, James D.; Mittal, Suresh K.

2015-01-01

The threat of bioterrorism and pandemics has highlighted the urgency for rapid and reliable bioaerosol detection in different environments. Safeguarding against such threats requires continuous sampling of the ambient air for pathogen detection. In this study we investigated the efficacy of the Airborne Sample Analysis Platform (ASAP) 2800 bioaerosol sampler to collect representative samples of air and identify specific viruses suspended as bioaerosols. To test this concept, we aerosolized an innocuous replication-defective bovine adenovirus serotype 3 (BAdV3) in a controlled laboratory environment. The ASAP efficiently trapped the surrogate virus at 5 × 103 plaque-forming units (p.f.u.) [2 × 105 genome copy equivalent] concentrations or more resulting in the successful detection of the virus using quantitative PCR. These results support the further development of ASAP for bioaerosol pathogen detection. PMID:26074900

Application of artificial neural network to fMRI regression analysis.

PubMed

Misaki, Masaya; Miyauchi, Satoru

2006-01-15

We used an artificial neural network (ANN) to detect correlations between event sequences and fMRI (functional magnetic resonance imaging) signals. The layered feed-forward neural network, given a series of events as inputs and the fMRI signal as a supervised signal, performed a non-linear regression analysis. This type of ANN is capable of approximating any continuous function, and thus this analysis method can detect any fMRI signals that correlated with corresponding events. Because of the flexible nature of ANNs, fitting to autocorrelation noise is a problem in fMRI analyses. We avoided this problem by using cross-validation and an early stopping procedure. The results showed that the ANN could detect various responses with different time courses. The simulation analysis also indicated an additional advantage of ANN over non-parametric methods in detecting parametrically modulated responses, i.e., it can detect various types of parametric modulations without a priori assumptions. The ANN regression analysis is therefore beneficial for exploratory fMRI analyses in detecting continuous changes in responses modulated by changes in input values.

Detection of neovascularization based on fractal and texture analysis with interaction effects in diabetic retinopathy.

PubMed

Lee, Jack; Zee, Benny Chung Ying; Li, Qing

2013-01-01

Diabetic retinopathy is a major cause of blindness. Proliferative diabetic retinopathy is a result of severe vascular complication and is visible as neovascularization of the retina. Automatic detection of such new vessels would be useful for the severity grading of diabetic retinopathy, and it is an important part of screening process to identify those who may require immediate treatment for their diabetic retinopathy. We proposed a novel new vessels detection method including statistical texture analysis (STA), high order spectrum analysis (HOS), fractal analysis (FA), and most importantly we have shown that by incorporating their associated interactions the accuracy of new vessels detection can be greatly improved. To assess its performance, the sensitivity, specificity and accuracy (AUC) are obtained. They are 96.3%, 99.1% and 98.5% (99.3%), respectively. It is found that the proposed method can improve the accuracy of new vessels detection significantly over previous methods. The algorithm can be automated and is valuable to detect relatively severe cases of diabetic retinopathy among diabetes patients.

Fast and objective detection and analysis of structures in downhole images

NASA Astrophysics Data System (ADS)

Wedge, Daniel; Holden, Eun-Jung; Dentith, Mike; Spadaccini, Nick

2017-09-01

Downhole acoustic and optical televiewer images, and formation microimager (FMI) logs are important datasets for structural and geotechnical analyses for the mineral and petroleum industries. Within these data, dipping planar structures appear as sinusoids, often in incomplete form and in abundance. Their detection is a labour intensive and hence expensive task and as such is a significant bottleneck in data processing as companies may have hundreds of kilometres of logs to process each year. We present an image analysis system that harnesses the power of automated image analysis and provides an interactive user interface to support the analysis of televiewer images by users with different objectives. Our algorithm rapidly produces repeatable, objective results. We have embedded it in an interactive workflow to complement geologists' intuition and experience in interpreting data to improve efficiency and assist, rather than replace the geologist. The main contributions include a new image quality assessment technique for highlighting image areas most suited to automated structure detection and for detecting boundaries of geological zones, and a novel sinusoid detection algorithm for detecting and selecting sinusoids with given confidence levels. Further tools are provided to perform rapid analysis of and further detection of structures e.g. as limited to specific orientations.

Comparison of public peak detection algorithms for MALDI mass spectrometry data analysis.

PubMed

Yang, Chao; He, Zengyou; Yu, Weichuan

2009-01-06

In mass spectrometry (MS) based proteomic data analysis, peak detection is an essential step for subsequent analysis. Recently, there has been significant progress in the development of various peak detection algorithms. However, neither a comprehensive survey nor an experimental comparison of these algorithms is yet available. The main objective of this paper is to provide such a survey and to compare the performance of single spectrum based peak detection methods. In general, we can decompose a peak detection procedure into three consequent parts: smoothing, baseline correction and peak finding. We first categorize existing peak detection algorithms according to the techniques used in different phases. Such a categorization reveals the differences and similarities among existing peak detection algorithms. Then, we choose five typical peak detection algorithms to conduct a comprehensive experimental study using both simulation data and real MALDI MS data. The results of comparison show that the continuous wavelet-based algorithm provides the best average performance.

Determination of glucose and uric acid with bienzyme colorimetry on microfluidic paper-based analysis devices.

PubMed

Chen, Xi; Chen, Jin; Wang, Fubin; Xiang, Xia; Luo, Ming; Ji, Xinghu; He, Zhike

2012-05-15

In this work, we first employ a drying method combining with the bienzyme colorimetric detection of glucose and uric acid on microfluidic paper-based analysis devices (μPADs). The channels of 3D μPADs are also designed by us to get better results. The color results are recorded by both Gel Documentation systems and a common camera. By using Gel Documentation systems, the limits of detection (LOD) of glucose and uric acid are 3.81 × 10(-5)M and 4.31 × 10(-5)M, respectively one order of magnitude lower than that of the reported methods on μPADs. By using a common camera, the limits of detection (LOD) of glucose and uric acid are 2.13 × 10(-4)M and 2.87 × 10(-4)M, respectively. Furthermore, the effects of detection conditions have been investigated and discussed comprehensively. Human serum samples are detected with satisfactory results, which are comparable with the clinical testing results. A low-cost, simple and rapid colorimetric method for the simultaneous detection of glucose and uric acid on the μPADs has been developed with enhanced sensitivity. Copyright © 2012 Elsevier B.V. All rights reserved.

Cell edge detection in JPEG2000 wavelet domain - analysis on sigmoid function edge model.

PubMed

Punys, Vytenis; Maknickas, Ramunas

2011-01-01

Big virtual microscopy images (80K x 60K pixels and larger) are usually stored using the JPEG2000 image compression scheme. Diagnostic quantification, based on image analysis, might be faster if performed on compressed data (approx. 20 times less the original amount), representing the coefficients of the wavelet transform. The analysis of possible edge detection without reverse wavelet transform is presented in the paper. Two edge detection methods, suitable for JPEG2000 bi-orthogonal wavelets, are proposed. The methods are adjusted according calculated parameters of sigmoid edge model. The results of model analysis indicate more suitable method for given bi-orthogonal wavelet.

Statistical evaluation of vibration analysis techniques

NASA Technical Reports Server (NTRS)

Milner, G. Martin; Miller, Patrice S.

1987-01-01

An evaluation methodology is presented for a selection of candidate vibration analysis techniques applicable to machinery representative of the environmental control and life support system of advanced spacecraft; illustrative results are given. Attention is given to the statistical analysis of small sample experiments, the quantification of detection performance for diverse techniques through the computation of probability of detection versus probability of false alarm, and the quantification of diagnostic performance.

Accurate feature detection and estimation using nonlinear and multiresolution analysis

NASA Astrophysics Data System (ADS)

Rudin, Leonid; Osher, Stanley

1994-11-01

A program for feature detection and estimation using nonlinear and multiscale analysis was completed. The state-of-the-art edge detection was combined with multiscale restoration (as suggested by the first author) and robust results in the presence of noise were obtained. Successful applications to numerous images of interest to DOD were made. Also, a new market in the criminal justice field was developed, based in part, on this work.

Detection of traffic incidents using nonlinear time series analysis

NASA Astrophysics Data System (ADS)

Fragkou, A. D.; Karakasidis, T. E.; Nathanail, E.

2018-06-01

In this study, we present results of the application of nonlinear time series analysis on traffic data for incident detection. More specifically, we analyze daily volume records of Attica Tollway (Greece) collected from sensors located at various locations. The analysis was performed using the Recurrence Plot (RP) and Recurrence Quantification Analysis (RQA) method of the volume data of the lane closest to the median. The results show that it is possible to identify, through the abrupt change of the dynamics of the system revealed by RPs and RQA, the occurrence of incidents on the freeway and differentiate from recurrent traffic congestion. The proposed methodology could be of interest for big data traffic analysis.

Brightness-preserving fuzzy contrast enhancement scheme for the detection and classification of diabetic retinopathy disease.

PubMed

Datta, Niladri Sekhar; Dutta, Himadri Sekhar; Majumder, Koushik

2016-01-01

The contrast enhancement of retinal image plays a vital role for the detection of microaneurysms (MAs), which are an early sign of diabetic retinopathy disease. A retinal image contrast enhancement method has been presented to improve the MA detection technique. The success rate on low-contrast noisy retinal image analysis shows the importance of the proposed method. Overall, 587 retinal input images are tested for performance analysis. The average sensitivity and specificity are obtained as 95.94% and 99.21%, respectively. The area under curve is found as 0.932 for the receiver operating characteristics analysis. The classifications of diabetic retinopathy disease are also performed here. The experimental results show that the overall MA detection method performs better than the current state-of-the-art MA detection algorithms.

Hyperspectral remote sensing for advanced detection of early blight (Alternaria solani) disease in potato (Solanum tuberosum) plants

NASA Astrophysics Data System (ADS)

Atherton, Daniel

Early detection of disease and insect infestation within crops and precise application of pesticides can help reduce potential production losses, reduce environmental risk, and reduce the cost of farming. The goal of this study was the advanced detection of early blight (Alternaria solani) in potato (Solanum tuberosum) plants using hyperspectral remote sensing data captured with a handheld spectroradiometer. Hyperspectral reflectance spectra were captured 10 times over five weeks from plants grown to the vegetative and tuber bulking growth stages. The spectra were analyzed using principal component analysis (PCA), spectral change (ratio) analysis, partial least squares (PLS), cluster analysis, and vegetative indices. PCA successfully distinguished more heavily diseased plants from healthy and minimally diseased plants using two principal components. Spectral change (ratio) analysis provided wavelengths (490-510, 640, 665-670, 690, 740-750, and 935 nm) most sensitive to early blight infection followed by ANOVA results indicating a highly significant difference (p < 0.0001) between disease rating group means. In the majority of the experiments, comparisons of diseased plants with healthy plants using Fisher's LSD revealed more heavily diseased plants were significantly different from healthy plants. PLS analysis demonstrated the feasibility of detecting early blight infected plants, finding four optimal factors for raw spectra with the predictor variation explained ranging from 93.4% to 94.6% and the response variation explained ranging from 42.7% to 64.7%. Cluster analysis successfully distinguished healthy plants from all diseased plants except for the most mildly diseased plants, showing clustering analysis was an effective method for detection of early blight. Analysis of the reflectance spectra using the simple ratio (SR) and the normalized difference vegetative index (NDVI) was effective at differentiating all diseased plants from healthy plants, except for the most mildly diseased plants. Of the analysis methods attempted, cluster analysis and vegetative indices were the most promising. The results show the potential of hyperspectral remote sensing for the detection of early blight in potato plants.

Non-contact FBG sensing based steam turbine rotor dynamic balance vibration detection system

NASA Astrophysics Data System (ADS)

Li, Tianliang; Tan, Yuegang; Cai, Lin

2015-10-01

This paper has proposed a non-contact vibration sensor based on fiber Bragg grating sensing, and applied to detect vibration of steam turbine rotor dynamic balance experimental platform. The principle of the sensor has been introduced, as well as the experimental analysis; performance of non-contact FBG vibration sensor has been analyzed in the experiment; in addition, turbine rotor dynamic vibration detection system based on eddy current displacement sensor and non-contact FBG vibration sensor have built; finally, compared with results of signals under analysis of the time domain and frequency domain. The analysis of experimental data contrast shows that: the vibration signal analysis of non-contact FBG vibration sensor is basically the same as the result of eddy current displacement sensor; it verified that the sensor can be used for non-contact measurement of steam turbine rotor dynamic balance vibration.

Automatic Detection of Optic Disc in Retinal Image by Using Keypoint Detection, Texture Analysis, and Visual Dictionary Techniques

PubMed Central

Bayır, Şafak

2016-01-01

With the advances in the computer field, methods and techniques in automatic image processing and analysis provide the opportunity to detect automatically the change and degeneration in retinal images. Localization of the optic disc is extremely important for determining the hard exudate lesions or neovascularization, which is the later phase of diabetic retinopathy, in computer aided eye disease diagnosis systems. Whereas optic disc detection is fairly an easy process in normal retinal images, detecting this region in the retinal image which is diabetic retinopathy disease may be difficult. Sometimes information related to optic disc and hard exudate information may be the same in terms of machine learning. We presented a novel approach for efficient and accurate localization of optic disc in retinal images having noise and other lesions. This approach is comprised of five main steps which are image processing, keypoint extraction, texture analysis, visual dictionary, and classifier techniques. We tested our proposed technique on 3 public datasets and obtained quantitative results. Experimental results show that an average optic disc detection accuracy of 94.38%, 95.00%, and 90.00% is achieved, respectively, on the following public datasets: DIARETDB1, DRIVE, and ROC. PMID:27110272

Characterization of emission microscopy and liquid crystal thermography in IC fault localization

NASA Astrophysics Data System (ADS)

Lau, C. K.; Sim, K. S.

2013-05-01

This paper characterizes two fault localization techniques - Emission Microscopy (EMMI) and Liquid Crystal Thermography (LCT) by using integrated circuit (IC) leakage failures. The majority of today's semiconductor failures do not reveal a clear visual defect on the die surface and therefore require fault localization tools to identify the fault location. Among the various fault localization tools, liquid crystal thermography and frontside emission microscopy are commonly used in most semiconductor failure analysis laboratories. Many people misunderstand that both techniques are the same and both are detecting hot spot in chip failing with short or leakage. As a result, analysts tend to use only LCT since this technique involves very simple test setup compared to EMMI. The omission of EMMI as the alternative technique in fault localization always leads to incomplete analysis when LCT fails to localize any hot spot on a failing chip. Therefore, this research was established to characterize and compare both the techniques in terms of their sensitivity in detecting the fault location in common semiconductor failures. A new method was also proposed as an alternative technique i.e. the backside LCT technique. The research observed that both techniques have successfully detected the defect locations resulted from the leakage failures. LCT wass observed more sensitive than EMMI in the frontside analysis approach. On the other hand, EMMI performed better in the backside analysis approach. LCT was more sensitive in localizing ESD defect location and EMMI was more sensitive in detecting non ESD defect location. Backside LCT was proven to work as effectively as the frontside LCT and was ready to serve as an alternative technique to the backside EMMI. The research confirmed that LCT detects heat generation and EMMI detects photon emission (recombination radiation). The analysis results also suggested that both techniques complementing each other in the IC fault localization. It is necessary for a failure analyst to use both techniques when one of the techniques produces no result.

Data fusion for QRS complex detection in multi-lead electrocardiogram recordings

NASA Astrophysics Data System (ADS)

Ledezma, Carlos A.; Perpiñan, Gilberto; Severeyn, Erika; Altuve, Miguel

2015-12-01

Heart diseases are the main cause of death worldwide. The first step in the diagnose of these diseases is the analysis of the electrocardiographic (ECG) signal. In turn, the ECG analysis begins with the detection of the QRS complex, which is the one with the most energy in the cardiac cycle. Numerous methods have been proposed in the bibliography for QRS complex detection, but few authors have analyzed the possibility of taking advantage of the information redundancy present in multiple ECG leads (simultaneously acquired) to produce accurate QRS detection. In our previous work we presented such an approach, proposing various data fusion techniques to combine the detections made by an algorithm on multiple ECG leads. In this paper we present further studies that show the advantages of this multi-lead detection approach, analyzing how many leads are necessary in order to observe an improvement in the detection performance. A well known QRS detection algorithm was used to test the fusion techniques on the St. Petersburg Institute of Cardiological Technics database. Results show improvement in the detection performance with as little as three leads, but the reliability of these results becomes interesting only after using seven or more leads. Results were evaluated using the detection error rate (DER). The multi-lead detection approach allows an improvement from DER = 3:04% to DER = 1:88%. Further works are to be made in order to improve the detection performance by implementing further fusion steps.

Combined Dielectrophoresis and Impedance Systems for Bacteria Analysis in Microfluidic On-Chip Platforms

PubMed Central

Páez-Avilés, Cristina; Juanola-Feliu, Esteve; Punter-Villagrasa, Jaime; del Moral Zamora, Beatriz; Homs-Corbera, Antoni; Colomer-Farrarons, Jordi; Miribel-Català, Pere Lluís; Samitier, Josep

2016-01-01

Bacteria concentration and detection is time-consuming in regular microbiology procedures aimed to facilitate the detection and analysis of these cells at very low concentrations. Traditional methods are effective but often require several days to complete. This scenario results in low bioanalytical and diagnostic methodologies with associated increased costs and complexity. In recent years, the exploitation of the intrinsic electrical properties of cells has emerged as an appealing alternative approach for concentrating and detecting bacteria. The combination of dielectrophoresis (DEP) and impedance analysis (IA) in microfluidic on-chip platforms could be key to develop rapid, accurate, portable, simple-to-use and cost-effective microfluidic devices with a promising impact in medicine, public health, agricultural, food control and environmental areas. The present document reviews recent DEP and IA combined approaches and the latest relevant improvements focusing on bacteria concentration and detection, including selectivity, sensitivity, detection time, and conductivity variation enhancements. Furthermore, this review analyses future trends and challenges which need to be addressed in order to successfully commercialize these platforms resulting in an adequate social return of public-funded investments. PMID:27649201

Time-Gated Orthogonal Scanning Automated Microscopy (OSAM) for High-speed Cell Detection and Analysis

NASA Astrophysics Data System (ADS)

Lu, Yiqing; Xi, Peng; Piper, James A.; Huo, Yujing; Jin, Dayong

2012-11-01

We report a new development of orthogonal scanning automated microscopy (OSAM) incorporating time-gated detection to locate rare-event organisms regardless of autofluorescent background. The necessity of using long-lifetime (hundreds of microseconds) luminescent biolabels for time-gated detection implies long integration (dwell) time, resulting in slow scan speed. However, here we achieve high scan speed using a new 2-step orthogonal scanning strategy to realise on-the-fly time-gated detection and precise location of 1-μm lanthanide-doped microspheres with signal-to-background ratio of 8.9. This enables analysis of a 15 mm × 15 mm slide area in only 3.3 minutes. We demonstrate that detection of only a few hundred photoelectrons within 100 μs is sufficient to distinguish a target event in a prototype system using ultraviolet LED excitation. Cytometric analysis of lanthanide labelled Giardia cysts achieved a signal-to-background ratio of two orders of magnitude. Results suggest that time-gated OSAM represents a new opportunity for high-throughput background-free biosensing applications.

Research on vehicle detection based on background feature analysis in SAR images

NASA Astrophysics Data System (ADS)

Zhang, Bochuan; Tang, Bo; Zhang, Cong; Hu, Ruiguang; Yun, Hongquan; Xiao, Liping

2017-10-01

Aiming at vehicle detection on the ground through low resolution SAR images, a method is proposed for determining the region of the vehicles first and then detecting the target in the specific region. The experimental results show that this method not only reduces the target detection area, but also reduces the influence of terrain clutter on the detection, which greatly improves the reliability of the target detection.

Automated detection scheme of architectural distortion in mammograms using adaptive Gabor filter

NASA Astrophysics Data System (ADS)

Yoshikawa, Ruriha; Teramoto, Atsushi; Matsubara, Tomoko; Fujita, Hiroshi

2013-03-01

Breast cancer is a serious health concern for all women. Computer-aided detection for mammography has been used for detecting mass and micro-calcification. However, there are challenges regarding the automated detection of the architectural distortion about the sensitivity. In this study, we propose a novel automated method for detecting architectural distortion. Our method consists of the analysis of the mammary gland structure, detection of the distorted region, and reduction of false positive results. We developed the adaptive Gabor filter for analyzing the mammary gland structure that decides filter parameters depending on the thickness of the gland structure. As for post-processing, healthy mammary glands that run from the nipple to the chest wall are eliminated by angle analysis. Moreover, background mammary glands are removed based on the intensity output image obtained from adaptive Gabor filter. The distorted region of the mammary gland is then detected as an initial candidate using a concentration index followed by binarization and labeling. False positives in the initial candidate are eliminated using 23 types of characteristic features and a support vector machine. In the experiments, we compared the automated detection results with interpretations by a radiologist using 50 cases (200 images) from the Digital Database of Screening Mammography (DDSM). As a result, true positive rate was 82.72%, and the number of false positive per image was 1.39. There results indicate that the proposed method may be useful for detecting architectural distortion in mammograms.

Epidermal growth factor receptor mutations in adenocarcinoma in situ and minimally invasive adenocarcinoma detected using mutation-specific monoclonal antibodies.

PubMed

Nakamura, Haruhiko; Koizumi, Hirotaka; Kimura, Hiroyuki; Marushima, Hideki; Saji, Hisashi; Takagi, Masayuki

2016-09-01

Epidermal growth factor receptor (EGFR) mutation rates in adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) were studied using both DNA analysis and mutation-specific immunohistochemistry. The peptide nucleic acid-locked nucleic acid polymerase chain reaction clamp method was used to detect mutations in exons 18, 19, 20, and 21 of the EGFR gene in DNA samples extracted from paraffin-embedded tissue sections. Simultaneously, immunohistochemical analysis with two EGFR mutation-specific monoclonal antibodies was used to identify proteins resulting from an in-frame deletion in exon 19 (E746_A750del) and a point mutation replacing leucine with arginine at codon 858 of exon 21 (L858R). Forty-three tumors (22 AIS and 21 MIA) were examined. The EGFR mutation rate in AIS detected by DNA analysis was 27.3% (L858R, 5/22; exon 19 deletion,1/22), whereas that detected in MIA was 42.9% (L858R,4/21; exon 19 deletion,5/21). Mutations detected by immunohistochemical analysis included 22.7% (L858R, 4/22; exon 19 deletion, 1/22) in AIS and 42.9% (L858R, 4/21; exon 19 deletion, 5/21) in MIA. Although some results were contradictory, concordant results were obtained using both assays in 38 of 43 cases (88.4%). DNA and immunohistochemical analyses revealed similar EGFR mutation rates in both MIA and AIS, suggesting that mutation-specific monoclonal antibodies are useful to confirm DNA assay results. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Evaluation of a fault tolerant system for an integrated avionics sensor configuration with TSRV flight data

NASA Technical Reports Server (NTRS)

Caglayan, A. K.; Godiwala, P. M.

1985-01-01

The performance analysis results of a fault inferring nonlinear detection system (FINDS) using sensor flight data for the NASA ATOPS B-737 aircraft in a Microwave Landing System (MLS) environment is presented. First, a statistical analysis of the flight recorded sensor data was made in order to determine the characteristics of sensor inaccuracies. Next, modifications were made to the detection and decision functions in the FINDS algorithm in order to improve false alarm and failure detection performance under real modelling errors present in the flight data. Finally, the failure detection and false alarm performance of the FINDS algorithm were analyzed by injecting bias failures into fourteen sensor outputs over six repetitive runs of the five minute flight data. In general, the detection speed, failure level estimation, and false alarm performance showed a marked improvement over the previously reported simulation runs. In agreement with earlier results, detection speed was faster for filter measurement sensors soon as MLS than for filter input sensors such as flight control accelerometers.

Unique volatolomic signatures of TP53 and KRAS in lung cells

PubMed Central

Davies, M P A; Barash, O; Jeries, R; Peled, N; Ilouze, M; Hyde, R; Marcus, M W; Field, J K; Haick, H

2014-01-01

Background: Volatile organic compounds (VOCs) are potential biomarkers for cancer detection in breath, but it is unclear if they reflect specific mutations. To test this, we have compared human bronchial epithelial cell (HBEC) cell lines carrying the KRASV12 mutation, knockdown of TP53 or both with parental HBEC cells. Methods: VOC from headspace above cultured cells were collected by passive sampling and analysed by thermal desorption gas chromatography mass spectrometry (TD-GC–MS) or sensor array with discriminant factor analysis (DFA). Results: In TD-GC–MS analysis, individual compounds had limited ability to discriminate between cell lines, but by applying DFA analysis combinations of 20 VOCs successfully discriminated between all cell types (accuracies 80–100%, with leave-one-out cross validation). Sensor array detection DFA demonstrated the ability to discriminate samples based on their cell type for all comparisons with accuracies varying between 77% and 93%. Conclusions: Our results demonstrate that minimal genetic changes in bronchial airway cells lead to detectable differences in levels of specific VOCs identified by TD-GC–MS or of patterns of VOCs identified by sensor array output. From the clinical aspect, these results suggest the possibility of breath analysis for detection of minimal genetic changes for earlier diagnosis or for genetic typing of lung cancers. PMID:25051409

On-line/on-site analysis of heavy metals in water and soils by laser induced breakdown spectroscopy

NASA Astrophysics Data System (ADS)

Meng, Deshuo; Zhao, Nanjing; Wang, Yuanyuan; Ma, Mingjun; Fang, Li; Gu, Yanhong; Jia, Yao; Liu, Jianguo

2017-11-01

The enrichment method of heavy metal in water with graphite and aluminum electrode was studied, and combined with plasma restraint device for improving the sensitivity of detection and reducing the limit of detection (LOD) of elements. For aluminum electrode enrichment, the LODs of Cd, Pb and Ni can be as low as several ppb. For graphite enrichment, the measurement time can be less than 3 min. The results showed that the graphite enrichment and aluminum electrode enrichment method can effectively improve the LIBS detection ability. The graphite enrichment method combined with plasma spatial confinement is more suitable for on-line monitoring of industrial waste water, the aluminum electrode enrichment method can be used for trace heavy metal detection in water. A LIBS method and device for soil heavy metals analysis was also developed, and a mobile LIBS system was tested in outfield. The measurement results deduced from LIBS and ICP-MS had a good consistency. The results provided an important application support for rapid and on-site monitoring of heavy metals in soil. (Left: the mobile LIBS system for analysis of heavy metals in soils. Top right: the spatial confinement device. Bottom right: automatic graphite enrichment device for on0line analysis of heavy metals in water).

Protocol vulnerability detection based on network traffic analysis and binary reverse engineering.

PubMed

Wen, Shameng; Meng, Qingkun; Feng, Chao; Tang, Chaojing

2017-01-01

Network protocol vulnerability detection plays an important role in many domains, including protocol security analysis, application security, and network intrusion detection. In this study, by analyzing the general fuzzing method of network protocols, we propose a novel approach that combines network traffic analysis with the binary reverse engineering method. For network traffic analysis, the block-based protocol description language is introduced to construct test scripts, while the binary reverse engineering method employs the genetic algorithm with a fitness function designed to focus on code coverage. This combination leads to a substantial improvement in fuzz testing for network protocols. We build a prototype system and use it to test several real-world network protocol implementations. The experimental results show that the proposed approach detects vulnerabilities more efficiently and effectively than general fuzzing methods such as SPIKE.

Detection of titanium in human tissues after craniofacial surgery.

PubMed

Jorgenson, D S; Mayer, M H; Ellenbogen, R G; Centeno, J A; Johnson, F B; Mullick, F G; Manson, P N

1997-04-01

Generally, titanium fixation plates are not removed after osteosynthesis, because they have high biocompatability and high corrosion resistance characteristics. Experiments with laboratory animals, and limited studies of analyses of human tissues, have reported evidence of titanium release into local and distant tissues. This study summarizes our results of the analysis of soft tissues for titanium in four patients with titanium microfixation plates. Energy dispersive x-ray analysis, scanning electron microscopy, and electrothermal atomic absorption spectrophotometry were used to detect trace amounts of titanium in surrounding soft tissues. A single metal inclusion was detected by scanning electron microscopy and energy dispersive x-ray analysis in one patient, whereas, electrothermal atomic absorption spectrophotometry analyses revealed titanium present in three of four specimens in levels ranging from 7.92 to 31.8 micrograms/gm of dry tissue. Results from this study revealed trace amounts of titanium in tissues surrounding craniofacial plates. At the atomic level, electrothermal atomic absorption spectrophotometry appears to be a sensitive tool to quantitatively detect ultra-trace amounts of metal in human tissue.

Threshold-free high-power methods for the ontological analysis of genome-wide gene-expression studies

PubMed Central

Nilsson, Björn; Håkansson, Petra; Johansson, Mikael; Nelander, Sven; Fioretos, Thoas

2007-01-01

Ontological analysis facilitates the interpretation of microarray data. Here we describe new ontological analysis methods which, unlike existing approaches, are threshold-free and statistically powerful. We perform extensive evaluations and introduce a new concept, detection spectra, to characterize methods. We show that different ontological analysis methods exhibit distinct detection spectra, and that it is critical to account for this diversity. Our results argue strongly against the continued use of existing methods, and provide directions towards an enhanced approach. PMID:17488501

SNIa detection in the SNLS photometric analysis using Morphological Component Analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Möller, A.; Ruhlmann-Kleider, V.; Neveu, J.

2015-04-01

Detection of supernovae (SNe) and, more generally, of transient events in large surveys can provide numerous false detections. In the case of a deferred processing of survey images, this implies reconstructing complete light curves for all detections, requiring sizable processing time and resources. Optimizing the detection of transient events is thus an important issue for both present and future surveys. We present here the optimization done in the SuperNova Legacy Survey (SNLS) for the 5-year data deferred photometric analysis. In this analysis, detections are derived from stacks of subtracted images with one stack per lunation. The 3-year analysis provided 300,000more » detections dominated by signals of bright objects that were not perfectly subtracted. Allowing these artifacts to be detected leads not only to a waste of resources but also to possible signal coordinate contamination. We developed a subtracted image stack treatment to reduce the number of non SN-like events using morphological component analysis. This technique exploits the morphological diversity of objects to be detected to extract the signal of interest. At the level of our subtraction stacks, SN-like events are rather circular objects while most spurious detections exhibit different shapes. A two-step procedure was necessary to have a proper evaluation of the noise in the subtracted image stacks and thus a reliable signal extraction. We also set up a new detection strategy to obtain coordinates with good resolution for the extracted signal. SNIa Monte-Carlo (MC) generated images were used to study detection efficiency and coordinate resolution. When tested on SNLS 3-year data this procedure decreases the number of detections by a factor of two, while losing only 10% of SN-like events, almost all faint ones. MC results show that SNIa detection efficiency is equivalent to that of the original method for bright events, while the coordinate resolution is improved.« less

Surface Management System Departure Event Data Analysis

NASA Technical Reports Server (NTRS)

Monroe, Gilena A.

2010-01-01

This paper presents a data analysis of the Surface Management System (SMS) performance of departure events, including push-back and runway departure events.The paper focuses on the detection performance, or the ability to detect departure events, as well as the prediction performance of SMS. The results detail a modest overall detection performance of push-back events and a significantly high overall detection performance of runway departure events. The overall detection performance of SMS for push-back events is approximately 55%.The overall detection performance of SMS for runway departure events nears 100%. This paper also presents the overall SMS prediction performance for runway departure events as well as the timeliness of the Aircraft Situation Display for Industry data source for SMS predictions.

Detection of Fatty Acids from Intact Microorganisms by Molecular Beam Static Secondary Ion Mass Spectrometry

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ingram, Jani Cheri; Lehman, Richard Michael; Bauer, William Francis

We report the use of a surface analysis approach, static secondary ion mass spectrometry (SIMS) equipped with a molecular (ReO4-) ion primary beam, to analyze the surface of intact microbial cells. SIMS spectra of 28 microorganisms were compared to fatty acid profiles determined by gas chromatographic analysis of transesterfied fatty acids extracted from the same organisms. The results indicate that surface bombardment using the molecular primary beam cleaved the ester linkage characteristic of bacteria at the glycerophosphate backbone of the phospholipid components of the cell membrane. This cleavage enables direct detection of the fatty acid conjugate base of intact microorganismsmore » by static SIMS. The limit of detection for this approach is approximately 107 bacterial cells/cm2. Multivariate statistical methods were applied in a graded approach to the SIMS microbial data. The results showed that the full data set could initially be statistically grouped based upon major differences in biochemical composition of the cell wall. The gram-positive bacteria were further statistically analyzed, followed by final analysis of a specific bacterial genus that was successfully grouped by species. Additionally, the use of SIMS to detect microbes on mineral surfaces is demonstrated by an analysis of Shewanella oneidensis on crushed hematite. The results of this study provide evidence for the potential of static SIMS to rapidly detect bacterial species based on ion fragments originating from cell membrane lipids directly from sample surfaces.« less

Applications of ERTS-1 data to landscape change in eastern Tennessee

NASA Technical Reports Server (NTRS)

Rehder, J. B. (Principal Investigator)

1973-01-01

The author has identified the following significant results. The analysis of landscape change in eastern Tennessee from ERTS-1 data is being derived from three avenues of experimentation and analysis: (1) a multi-stage sampling procedure utilizing ground and aircraft imagery for ground truth and control; (2) a densitometric and computer analytical experiment for the analysis of gray tone signatures and comparisons for landscape change detection and monitoring; and (3) an ERTS image enhancement procedure for the detection and analysis of photomorphic regions. Significant results include: maps of strip mining changes and forest inventory, watershed identification and delimitation, and agricultural regions derived from spring plowing patterns appearing on the ERTS-1 imagery.

A New System to Monitor Data Analyses and Results of Physics Data Validation Between Pulses at DIII-D

NASA Astrophysics Data System (ADS)

Flanagan, S.; Schachter, J. M.; Schissel, D. P.

2001-10-01

A Data Analysis Monitoring (DAM) system has been developed to monitor between pulse physics analysis at the DIII-D National Fusion Facility. The system allows for rapid detection of discrepancies in diagnostic measurements or the results from physics analysis codes. This enables problems to be detected and possibly fixed between pulses as opposed to after the experimental run has concluded thus increasing the efficiency of experimental time. An example of a consistency check is comparing the stored energy from integrating the measured kinetic profiles to that calculated from magnetic measurements by EFIT. This new system also tracks the progress of MDSplus dispatching of software for data analysis and the loading of analyzed data into MDSplus. DAM uses a Java Servlet to receive messages, Clips to implement expert system logic, and displays its results to multiple web clients via HTML. If an error is detected by DAM, users can view more detailed information so that steps can be taken to eliminate the error for the next pulse. A demonstration of this system including a simulated DIII-D pulse cycle will be presented.

Optimization of data analysis for the in vivo neutron activation analysis of aluminum in bone.

PubMed

Mohseni, H K; Matysiak, W; Chettle, D R; Byun, S H; Priest, N; Atanackovic, J; Prestwich, W V

2016-10-01

An existing system at McMaster University has been used for the in vivo measurement of aluminum in human bone. Precise and detailed analysis approaches are necessary to determine the aluminum concentration because of the low levels of aluminum found in the bone and the challenges associated with its detection. Phantoms resembling the composition of the human hand with varying concentrations of aluminum were made for testing the system prior to the application to human studies. A spectral decomposition model and a photopeak fitting model involving the inverse-variance weighted mean and a time-dependent analysis were explored to analyze the results and determine the model with the best performance and lowest minimum detection limit. The results showed that the spectral decomposition and the photopeak fitting model with the inverse-variance weighted mean both provided better results compared to the other methods tested. The spectral decomposition method resulted in a marginally lower detection limit (5μg Al/g Ca) compared to the inverse-variance weighted mean (5.2μg Al/g Ca), rendering both equally applicable to human measurements. Copyright © 2016 Elsevier Ltd. All rights reserved.

A Pulsed Thermographic Imaging System for Detection and Identification of Cotton Foreign Matter

PubMed Central

Kuzy, Jesse; Li, Changying

2017-01-01

Detection of foreign matter in cleaned cotton is instrumental to accurately grading cotton quality, which in turn impacts the marketability of the cotton. Current grading systems return estimates of the amount of foreign matter present, but provide no information about the identity of the contaminants. This paper explores the use of pulsed thermographic analysis to detect and identify cotton foreign matter. The design and implementation of a pulsed thermographic analysis system is described. A sample set of 240 foreign matter and cotton lint samples were collected. Hand-crafted waveform features and frequency-domain features were extracted and analyzed for statistical significance. Classification was performed on these features using linear discriminant analysis and support vector machines. Using waveform features and support vector machine classifiers, detection of cotton foreign matter was performed with 99.17% accuracy. Using frequency-domain features and linear discriminant analysis, identification was performed with 90.00% accuracy. These results demonstrate that pulsed thermographic imaging analysis produces data which is of significant utility for the detection and identification of cotton foreign matter. PMID:28273848

Evaluation of bone marrow specimens with acute myelogenous leukemia for CD34, CD15, CD117, and myeloperoxidase.

PubMed

Dunphy, C H; Polski, J M; Evans, H L; Gardner, L J

2001-08-01

Immunophenotyping of bone marrow (BM) specimens with acute myelogenous leukemia (AML) may be performed by flow cytometric (FC) or immunohistochemical (IH) techniques. Some markers (CD34, CD15, and CD117) are available for both techniques. Myeloperoxidase (MPO) analysis may be performed by enzyme cytochemical (EC) or IH techniques. To determine the reliability of these markers and MPO by these techniques, we designed a study to compare the results of analyses of these markers and MPO by FC (CD34, CD15, and CD117), EC (MPO), and IH (CD34, CD15, CD117, and MPO) techniques. Twenty-nine AMLs formed the basis of the study. These AMLs all had been immunophenotyped previously by FC analysis; 27 also had had EC analysis performed. Of the AMLs, 29 had BM core biopsies and 26 had BM clots that could be evaluated. The paraffin blocks of the 29 BM core biopsies and 26 BM clots were stained for CD34, CD117, MPO, and CD15. These results were compared with results by FC analysis (CD34, CD15, and CD117) and EC analysis (MPO). Immunodetection of CD34 expression in AML had a similar sensitivity by FC and IH techniques. Immunodetection of CD15 and CD117 had a higher sensitivity by FC analysis than by IH analysis. Detection of MPO by IH analysis was more sensitive than by EC analysis. There was no correlation of French-American-British (FAB) subtype of AML with CD34 or CD117 expression. Expression of CD15 was associated with AMLs with a monocytic component. Myeloperoxidase reactivity by IH analysis was observed in AMLs originally FAB subtyped as M0. CD34 can be equally detected by FC and IH techniques. CD15 and CD117 are better detected by FC analysis and MPO is better detected by IH analysis.

Spiral Bevel Gear Damage Detection Using Decision Fusion Analysis

NASA Technical Reports Server (NTRS)

Dempsey, Paula J.; Handschuh, Robert F.; Afjeh, Abdollah A.

2002-01-01

A diagnostic tool for detecting damage to spiral bevel gears was developed. Two different monitoring technologies, oil debris analysis and vibration, were integrated using data fusion into a health monitoring system for detecting surface fatigue pitting damage on gears. This integrated system showed improved detection and decision-making capabilities as compared to using individual monitoring technologies. This diagnostic tool was evaluated by collecting vibration and oil debris data from fatigue tests performed in the NASA Glenn Spiral Bevel Gear Fatigue Rigs. Data was collected during experiments performed in this test rig when pitting damage occurred. Results show that combining the vibration and oil debris measurement technologies improves the detection of pitting damage on spiral bevel gears.

Integrated software for the detection of epileptogenic zones in refractory epilepsy.

PubMed

Mottini, Alejandro; Miceli, Franco; Albin, Germán; Nuñez, Margarita; Ferrándo, Rodolfo; Aguerrebere, Cecilia; Fernandez, Alicia

2010-01-01

In this paper we present an integrated software designed to help nuclear medicine physicians in the detection of epileptogenic zones (EZ) by means of ictal-interictal SPECT and MR images. This tool was designed to be flexible, friendly and efficient. A novel detection method was included (A-contrario) along with the classical detection method (Subtraction analysis). The software's performance was evaluated with two separate sets of validation studies: visual interpretation of 12 patient images by an experimented observer and objective analysis of virtual brain phantom experiments by proposed numerical observers. Our results support the potential use of the proposed software to help nuclear medicine physicians in the detection of EZ in clinical practice.

A Hybrid Color Space for Skin Detection Using Genetic Algorithm Heuristic Search and Principal Component Analysis Technique

PubMed Central

2015-01-01

Color is one of the most prominent features of an image and used in many skin and face detection applications. Color space transformation is widely used by researchers to improve face and skin detection performance. Despite the substantial research efforts in this area, choosing a proper color space in terms of skin and face classification performance which can address issues like illumination variations, various camera characteristics and diversity in skin color tones has remained an open issue. This research proposes a new three-dimensional hybrid color space termed SKN by employing the Genetic Algorithm heuristic and Principal Component Analysis to find the optimal representation of human skin color in over seventeen existing color spaces. Genetic Algorithm heuristic is used to find the optimal color component combination setup in terms of skin detection accuracy while the Principal Component Analysis projects the optimal Genetic Algorithm solution to a less complex dimension. Pixel wise skin detection was used to evaluate the performance of the proposed color space. We have employed four classifiers including Random Forest, Naïve Bayes, Support Vector Machine and Multilayer Perceptron in order to generate the human skin color predictive model. The proposed color space was compared to some existing color spaces and shows superior results in terms of pixel-wise skin detection accuracy. Experimental results show that by using Random Forest classifier, the proposed SKN color space obtained an average F-score and True Positive Rate of 0.953 and False Positive Rate of 0.0482 which outperformed the existing color spaces in terms of pixel wise skin detection accuracy. The results also indicate that among the classifiers used in this study, Random Forest is the most suitable classifier for pixel wise skin detection applications. PMID:26267377

Detection of contamination on selected apple cultivars using reflectance hyperspectral and multispectral analysis

NASA Astrophysics Data System (ADS)

Mehl, Patrick M.; Chao, Kevin; Kim, Moon S.; Chen, Yud-Ren

2001-03-01

Presence of natural or exogenous contaminations on apple cultivars is a food safety and quality concern touching the general public and strongly affecting this commodity market. Accumulations of human pathogens are usually observed on surface lesions of commodities. Detections of either lesions or directly of the pathogens are essential for assuring the quality and safety of commodities. We are presenting the application of hyperspectral image analysis towards the development of multispectral techniques for the detection of defects on chosen apple cultivars, such as Golden Delicious, Red Delicious, and Gala apples. Separate apple cultivars possess different spectral characteristics leading to different approaches for analysis. General preprocessing analysis with morphological treatments is followed by different image treatments and condition analysis for highlighting lesions and contaminations on the apple cultivars. Good isolations of scabs, fungal and soil contaminations and bruises are observed with hyperspectral imaging processing either using principal component analysis or utilizing the chlorophyll absorption peak. Applications of hyperspectral results to a multispectral detection are limited by the spectral capabilities of our RGB camera using either specific band pass filters and using direct neutral filters. Good separations of defects are obtained for Golden Delicious apples. It is however limited for the other cultivars. Having an extra near infrared channel will increase the detection level utilizing the chlorophyll absorption band for detection as demonstrated by the present hyperspectral imaging analysis

Theoretical Analysis of Penalized Maximum-Likelihood Patlak Parametric Image Reconstruction in Dynamic PET for Lesion Detection.

PubMed

Yang, Li; Wang, Guobao; Qi, Jinyi

2016-04-01

Detecting cancerous lesions is a major clinical application of emission tomography. In a previous work, we studied penalized maximum-likelihood (PML) image reconstruction for lesion detection in static PET. Here we extend our theoretical analysis of static PET reconstruction to dynamic PET. We study both the conventional indirect reconstruction and direct reconstruction for Patlak parametric image estimation. In indirect reconstruction, Patlak parametric images are generated by first reconstructing a sequence of dynamic PET images, and then performing Patlak analysis on the time activity curves (TACs) pixel-by-pixel. In direct reconstruction, Patlak parametric images are estimated directly from raw sinogram data by incorporating the Patlak model into the image reconstruction procedure. PML reconstruction is used in both the indirect and direct reconstruction methods. We use a channelized Hotelling observer (CHO) to assess lesion detectability in Patlak parametric images. Simplified expressions for evaluating the lesion detectability have been derived and applied to the selection of the regularization parameter value to maximize detection performance. The proposed method is validated using computer-based Monte Carlo simulations. Good agreements between the theoretical predictions and the Monte Carlo results are observed. Both theoretical predictions and Monte Carlo simulation results show the benefit of the indirect and direct methods under optimized regularization parameters in dynamic PET reconstruction for lesion detection, when compared with the conventional static PET reconstruction.

Laser-induced fluorescence microscopic system using an optical parametric oscillator for tunable detection in microchip analysis.

PubMed

Kumemura, Momoko; Odake, Tamao; Korenaga, Takashi

2005-06-01

A laser-induced fluorescence microscopic system based on optical parametric oscillation has been constructed as a tunable detector for microchip analysis. The detection limit of sulforhodamine B (Ex. 520 nm, Em. 570 nm) was 0.2 mumol, which was approximately eight orders of magnitude better than with a conventional fluorophotometer. The system was applied to the determination of fluorescence-labeled DNA (Ex. 494 nm, Em. 519 nm) in a microchannel and the detection limit reached a single molecule. These results showed the feasibility of this system as a highly sensitive and tunable fluorescence detector for microchip analysis.

Detecting a periodic signal in the terrestrial cratering record

NASA Technical Reports Server (NTRS)

Grieve, Richard A. F.; Rupert, James D.; Goodacre, Alan K.; Sharpton, Virgil L.

1988-01-01

A time-series analysis of model periodic data, where the period and phase are known, has been performed in order to investigate whether a significant period can be detected consistently from a mix of random and periodic impacts. Special attention is given to the effect of age uncertainties and random ages in the detection of a periodic signal. An equivalent analysis is performed with observed data on crater ages and compared with the model data, and the effects of the temporal distribution of crater ages on the results from the time-series analysis are studied. Evidence for a consistent 30-m.y. period is found to be weak.

A mathematical analysis of the Janus combat simulation weather effects models and sensitivity analysis of sky-to-ground brightness ratio on target detection

NASA Astrophysics Data System (ADS)

Shorts, Vincient F.

1994-09-01

The Janus combat simulation offers the user a wide variety of weather effects options to employ during the execution of any simulation run, which can directly influence detection of opposing forces. Realistic weather effects are required if the simulation is to accurately reproduce 'real world' results. This thesis examines the mathematics of the Janus weather effects models. A weather effect option in Janus is the sky-to-ground brightness ratio (SGR). SGR affects an optical sensor's ability to detect targets. It is a measure of the sun angle in relation to the horizon. A review of the derivation of SGR is performed and an analysis of SGR's affect on the number of optical detections and detection ranges is performed using an unmanned aerial vehicle (UAV) search scenario. For comparison, the UAV's are equipped with a combination of optical and thermal sensors.

Determination of dasatinib in the tablet dosage form by ultra high performance liquid chromatography, capillary zone electrophoresis, and sequential injection analysis.

PubMed

Gonzalez, Aroa Garcia; Taraba, Lukáš; Hraníček, Jakub; Kozlík, Petr; Coufal, Pavel

2017-01-01

Dasatinib is a novel oral prescription drug proposed for treating adult patients with chronic myeloid leukemia. Three analytical methods, namely ultra high performance liquid chromatography, capillary zone electrophoresis, and sequential injection analysis, were developed, validated, and compared for determination of the drug in the tablet dosage form. The total analysis time of optimized ultra high performance liquid chromatography and capillary zone electrophoresis methods was 2.0 and 2.2 min, respectively. Direct ultraviolet detection with detection wavelength of 322 nm was employed in both cases. The optimized sequential injection analysis method was based on spectrophotometric detection of dasatinib after a simple colorimetric reaction with folin ciocalteau reagent forming a blue-colored complex with an absorbance maximum at 745 nm. The total analysis time was 2.5 min. The ultra high performance liquid chromatography method provided the lowest detection and quantitation limits and the most precise and accurate results. All three newly developed methods were demonstrated to be specific, linear, sensitive, precise, and accurate, providing results satisfactorily meeting the requirements of the pharmaceutical industry, and can be employed for the routine determination of the active pharmaceutical ingredient in the tablet dosage form. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

MPI Runtime Error Detection with MUST: Advances in Deadlock Detection

DOE PAGES

Hilbrich, Tobias; Protze, Joachim; Schulz, Martin; ...

2013-01-01

The widely used Message Passing Interface (MPI) is complex and rich. As a result, application developers require automated tools to avoid and to detect MPI programming errors. We present the Marmot Umpire Scalable Tool (MUST) that detects such errors with significantly increased scalability. We present improvements to our graph-based deadlock detection approach for MPI, which cover future MPI extensions. Our enhancements also check complex MPI constructs that no previous graph-based detection approach handled correctly. Finally, we present optimizations for the processing of MPI operations that reduce runtime deadlock detection overheads. Existing approaches often require ( p ) analysis time permore » MPI operation, for p processes. We empirically observe that our improvements lead to sub-linear or better analysis time per operation for a wide range of real world applications.« less

Sniper detection using infrared camera: technical possibilities and limitations

NASA Astrophysics Data System (ADS)

Kastek, M.; Dulski, R.; Trzaskawka, P.; Bieszczad, G.

2010-04-01

The paper discusses technical possibilities to build an effective system for sniper detection using infrared cameras. Descriptions of phenomena which make it possible to detect sniper activities in infrared spectra as well as analysis of physical limitations were performed. Cooled and uncooled detectors were considered. Three phases of sniper activities were taken into consideration: before, during and after the shot. On the basis of experimental data the parameters defining the target were determined which are essential in assessing the capability of infrared camera to detect sniper activity. A sniper body and muzzle flash were analyzed as targets. The simulation of detection ranges was done for the assumed scenario of sniper detection task. The infrared sniper detection system was discussed, capable of fulfilling the requirements. The discussion of the results of analysis and simulations was finally presented.

Detection of β-Lactams and Chloramphenicol Residues in Raw Milk-Development and Application of an HPLC-DAD Method in Comparison with Microbial Inhibition Assays.

PubMed

Karageorgou, Eftychia; Christoforidou, Sofia; Ioannidou, Maria; Psomas, Evdoxios; Samouris, Georgios

2018-06-01

The present study was carried out to assess the detection sensitivity of four microbial inhibition assays (MIAs) in comparison with the results obtained by the High Performance Liquid Chromatography with Diode-Array Detection (HPLC-DAD) method for antibiotics of the β-lactam group and chloramphenicol in fortified raw milk samples. MIAs presented fairly good results when detecting β-lactams, whereas none were able to detect chloramphenicol at or above the permissible limits. HPLC analysis revealed high recoveries of examined compounds, whereas all detection limits observed were lower than their respective maximum residue limits (MRL) values. The extraction and clean-up procedure of antibiotics was performed by a modified matrix solid phase dispersion procedure using a mixture of Plexa by Agilent and QuEChERS as a sorbent. The HPLC method developed was validated, determining the accuracy, precision, linearity, decision limit, and detection capability. Both methods were used to monitor raw milk samples of several cows and sheep, obtained from producers in different regions of Greece, for the presence of examined antibiotic residues. Results obtained showed that MIAs could be used effectively and routinely to detect antibiotic residues in several milk types. However, in some cases, spoilage of milk samples revealed that the kits' sensitivity could be strongly affected, whereas this fact does not affect the effectiveness of HPLC-DAD analysis.

Combining Randomized and Non-Randomized Evidence in Clinical Research: A Review of Methods and Applications

ERIC Educational Resources Information Center

Verde, Pablo E.; Ohmann, Christian

2015-01-01

Researchers may have multiple motivations for combining disparate pieces of evidence in a meta-analysis, such as generalizing experimental results or increasing the power to detect an effect that a single study is not able to detect. However, while in meta-analysis, the main question may be simple, the structure of evidence available to answer it…

Automatic Road Gap Detection Using Fuzzy Inference System

NASA Astrophysics Data System (ADS)

Hashemi, S.; Valadan Zoej, M. J.; Mokhtarzadeh, M.

2011-09-01

Automatic feature extraction from aerial and satellite images is a high-level data processing which is still one of the most important research topics of the field. In this area, most of the researches are focused on the early step of road detection, where road tracking methods, morphological analysis, dynamic programming and snakes, multi-scale and multi-resolution methods, stereoscopic and multi-temporal analysis, hyper spectral experiments, are some of the mature methods in this field. Although most researches are focused on detection algorithms, none of them can extract road network perfectly. On the other hand, post processing algorithms accentuated on the refining of road detection results, are not developed as well. In this article, the main is to design an intelligent method to detect and compensate road gaps remained on the early result of road detection algorithms. The proposed algorithm consists of five main steps as follow: 1) Short gap coverage: In this step, a multi-scale morphological is designed that covers short gaps in a hierarchical scheme. 2) Long gap detection: In this step, the long gaps, could not be covered in the previous stage, are detected using a fuzzy inference system. for this reason, a knowledge base consisting of some expert rules are designed which are fired on some gap candidates of the road detection results. 3) Long gap coverage: In this stage, detected long gaps are compensated by two strategies of linear and polynomials for this reason, shorter gaps are filled by line fitting while longer ones are compensated by polynomials.4) Accuracy assessment: In order to evaluate the obtained results, some accuracy assessment criteria are proposed. These criteria are obtained by comparing the obtained results with truly compensated ones produced by a human expert. The complete evaluation of the obtained results whit their technical discussions are the materials of the full paper.

A SVM-based quantitative fMRI method for resting-state functional network detection.

PubMed

Song, Xiaomu; Chen, Nan-kuei

2014-09-01

Resting-state functional magnetic resonance imaging (fMRI) aims to measure baseline neuronal connectivity independent of specific functional tasks and to capture changes in the connectivity due to neurological diseases. Most existing network detection methods rely on a fixed threshold to identify functionally connected voxels under the resting state. Due to fMRI non-stationarity, the threshold cannot adapt to variation of data characteristics across sessions and subjects, and generates unreliable mapping results. In this study, a new method is presented for resting-state fMRI data analysis. Specifically, the resting-state network mapping is formulated as an outlier detection process that is implemented using one-class support vector machine (SVM). The results are refined by using a spatial-feature domain prototype selection method and two-class SVM reclassification. The final decision on each voxel is made by comparing its probabilities of functionally connected and unconnected instead of a threshold. Multiple features for resting-state analysis were extracted and examined using an SVM-based feature selection method, and the most representative features were identified. The proposed method was evaluated using synthetic and experimental fMRI data. A comparison study was also performed with independent component analysis (ICA) and correlation analysis. The experimental results show that the proposed method can provide comparable or better network detection performance than ICA and correlation analysis. The method is potentially applicable to various resting-state quantitative fMRI studies. Copyright © 2014 Elsevier Inc. All rights reserved.

Comparing GWAS Results of Complex Traits Using Full Genetic Model and Additive Models for Revealing Genetic Architecture

PubMed Central

Monir, Md. Mamun; Zhu, Jun

2017-01-01

Most of the genome-wide association studies (GWASs) for human complex diseases have ignored dominance, epistasis and ethnic interactions. We conducted comparative GWASs for total cholesterol using full model and additive models, which illustrate the impacts of the ignoring genetic variants on analysis results and demonstrate how genetic effects of multiple loci could differ across different ethnic groups. There were 15 quantitative trait loci with 13 individual loci and 3 pairs of epistasis loci identified by full model, whereas only 14 loci (9 common loci and 5 different loci) identified by multi-loci additive model. Again, 4 full model detected loci were not detected using multi-loci additive model. PLINK-analysis identified two loci and GCTA-analysis detected only one locus with genome-wide significance. Full model identified three previously reported genes as well as several new genes. Bioinformatics analysis showed some new genes are related with cholesterol related chemicals and/or diseases. Analyses of cholesterol data and simulation studies revealed that the full model performs were better than the additive-model performs in terms of detecting power and unbiased estimations of genetic variants of complex traits. PMID:28079101

Advancing the detection of steady-state visual evoked potentials in brain-computer interfaces

NASA Astrophysics Data System (ADS)

Abu-Alqumsan, Mohammad; Peer, Angelika

2016-06-01

Objective. Spatial filtering has proved to be a powerful pre-processing step in detection of steady-state visual evoked potentials and boosted typical detection rates both in offline analysis and online SSVEP-based brain-computer interface applications. State-of-the-art detection methods and the spatial filters used thereby share many common foundations as they all build upon the second order statistics of the acquired Electroencephalographic (EEG) data, that is, its spatial autocovariance and cross-covariance with what is assumed to be a pure SSVEP response. The present study aims at highlighting the similarities and differences between these methods. Approach. We consider the canonical correlation analysis (CCA) method as a basis for the theoretical and empirical (with real EEG data) analysis of the state-of-the-art detection methods and the spatial filters used thereby. We build upon the findings of this analysis and prior research and propose a new detection method (CVARS) that combines the power of the canonical variates and that of the autoregressive spectral analysis in estimating the signal and noise power levels. Main results. We found that the multivariate synchronization index method and the maximum contrast combination method are variations of the CCA method. All three methods were found to provide relatively unreliable detections in low signal-to-noise ratio (SNR) regimes. CVARS and the minimum energy combination methods were found to provide better estimates for different SNR levels. Significance. Our theoretical and empirical results demonstrate that the proposed CVARS method outperforms other state-of-the-art detection methods when used in an unsupervised fashion. Furthermore, when used in a supervised fashion, a linear classifier learned from a short training session is able to estimate the hidden user intention, including the idle state (when the user is not attending to any stimulus), rapidly, accurately and reliably.

An Improved Time-Frequency Analysis Method in Interference Detection for GNSS Receivers

PubMed Central

Sun, Kewen; Jin, Tian; Yang, Dongkai

2015-01-01

In this paper, an improved joint time-frequency (TF) analysis method based on a reassigned smoothed pseudo Wigner–Ville distribution (RSPWVD) has been proposed in interference detection for Global Navigation Satellite System (GNSS) receivers. In the RSPWVD, the two-dimensional low-pass filtering smoothing function is introduced to eliminate the cross-terms present in the quadratic TF distribution, and at the same time, the reassignment method is adopted to improve the TF concentration properties of the auto-terms of the signal components. This proposed interference detection method is evaluated by experiments on GPS L1 signals in the disturbing scenarios compared to the state-of-the-art interference detection approaches. The analysis results show that the proposed interference detection technique effectively overcomes the cross-terms problem and also preserves good TF localization properties, which has been proven to be effective and valid to enhance the interference detection performance of the GNSS receivers, particularly in the jamming environments. PMID:25905704

Nondestructive Analysis of Tumor-Associated Membrane Protein Integrating Imaging and Amplified Detection in situ Based on Dual-Labeled DNAzyme.

PubMed

Chen, Xiaoxia; Zhao, Jing; Chen, Tianshu; Gao, Tao; Zhu, Xiaoli; Li, Genxi

2018-01-01

Comprehensive analysis of the expression level and location of tumor-associated membrane proteins (TMPs) is of vital importance for the profiling of tumor cells. Currently, two kinds of independent techniques, i.e. ex situ detection and in situ imaging, are usually required for the quantification and localization of TMPs respectively, resulting in some inevitable problems. Methods: Herein, based on a well-designed and fluorophore-labeled DNAzyme, we develop an integrated and facile method, in which imaging and quantification of TMPs in situ are achieved simultaneously in a single system. The labeled DNAzyme not only produces localized fluorescence for the visualization of TMPs but also catalyzes the cleavage of a substrate to produce quantitative fluorescent signals that can be collected from solution for the sensitive detection of TMPs. Results: Results from the DNAzyme-based in situ imaging and quantification of TMPs match well with traditional immunofluorescence and western blotting. In addition to the advantage of two-in-one, the DNAzyme-based method is highly sensitivity, allowing the detection of TMPs in only 100 cells. Moreover, the method is nondestructive. Cells after analysis could retain their physiological activity and could be cultured for other applications. Conclusion: The integrated system provides solid results for both imaging and quantification of TMPs, making it a competitive method over some traditional techniques for the analysis of TMPs, which offers potential application as a toolbox in the future.

Detection of rebar delamination using modal analysis

NASA Astrophysics Data System (ADS)

Blodgett, David W.

2003-08-01

A non-destructive method for early detection of reinforcement steel bars (re-bar) delamination in concrete structures has been developed. This method, termed modal analysis, has been shown effective in both laboratory and field experiments. In modal analysis, an audio speaker is used to generate flexural resonant modes in the re-bar in reinforced concrete structures. Vibrations associated with these modes are coupled to the surrounding concrete and propagate to the surface where they are detected using a laser vibrometer and/or accelerometer. Monitoring both the frequency and amplitude of these vibrations provides information on the bonding state of the embedded re-bar. Laboratory measurements were performed on several specially prepared concrete blocks with re-bar of varying degrees of simulated corrosion. Field measurements were performed on an old bridge about to be torn down in Howard County, Maryland and the results compared with those obtained using destructive analysis of the bridge after demolition. Both laboratory and field test results show this technique to be sensitive to re-bar delamination.

An Android malware detection system based on machine learning

NASA Astrophysics Data System (ADS)

Wen, Long; Yu, Haiyang

2017-08-01

The Android smartphone, with its open source character and excellent performance, has attracted many users. However, the convenience of the Android platform also has motivated the development of malware. The traditional method which detects the malware based on the signature is unable to detect unknown applications. The article proposes a machine learning-based lightweight system that is capable of identifying malware on Android devices. In this system we extract features based on the static analysis and the dynamitic analysis, then a new feature selection approach based on principle component analysis (PCA) and relief are presented in the article to decrease the dimensions of the features. After that, a model will be constructed with support vector machine (SVM) for classification. Experimental results show that our system provides an effective method in Android malware detection.

The Impact of the Implementation of Edge Detection Methods on the Accuracy of Automatic Voltage Reading

NASA Astrophysics Data System (ADS)

Sidor, Kamil; Szlachta, Anna

2017-04-01

The article presents the impact of the edge detection method in the image analysis on the reading accuracy of the measured value. In order to ensure the automatic reading of the measured value by an analog meter, a standard webcam and the LabVIEW programme were applied. NI Vision Development tools were used. The Hough transform was used to detect the indicator. The programme output was compared during the application of several methods of edge detection. Those included: the Prewitt operator, the Roberts cross, the Sobel operator and the Canny edge detector. The image analysis was made for an analog meter indicator with the above-mentioned methods, and the results of that analysis were compared with each other and presented.

18F-fluorodeoxyglucose positron emission tomography in the diagnosis of malignancy in patients with paraneoplastic neurological syndrome: a systematic review and meta-analysis.

PubMed

García Vicente, Ana María; Delgado-Bolton, Roberto C; Amo-Salas, Mariano; López-Fidalgo, Jesús; Caresia Aróztegui, Ana Paula; García Garzón, José Ramón; Orcajo Rincón, Javier; García Velloso, María José; de Arcocha Torres, María; Alvárez Ruíz, Soledad

2017-08-01

The detection of occult cancer in patients suspected of having a paraneoplastic neurological syndrome (PNS) poses a diagnostic challenge. The aim of our study was to perform a systematic review and meta-analysis to assess the diagnostic performance of FDG PET for the detection of occult malignant disease responsible for PNS. A systematic review of the literature (MEDLINE, EMBASE, Cochrane, and DARE) was undertaken to identify studies published in any language. The search strategy was structured after addressing clinical questions regarding the validity or usefulness of the test, following the PICO framework. Inclusion criteria were studies involving patients with PNS in whom FDG PET was performed to detect malignancy, and which reported sufficient primary data to allow calculation of diagnostic accuracy parameters. When possible, a meta-analysis was performed to calculate the joint sensitivity, specificity, and detection rate for malignancy (with 95% confidence intervals [CIs]), as well as a subgroup analysis based on patient characteristics (antibodies, syndrome). The comprehensive literature search revealed 700 references. Sixteen studies met the inclusion criteria and were ultimately selected. Most of the studies were retrospective (12/16). For the quality assessment, the QUADAS-2 tool was applied to assess the risk of bias. Across 16 studies (793 patients), the joint sensitivity, specificity, and detection rate for malignancy with FDG PET were 0.87 (95% CI: 0.80-0.93), 0.86 (95% CI: 0.83-0.89), and 14.9% (95% CI: 11.5-18.7), respectively. The area under the curve (AUC) of the summary ROC curve was 0.917. Homogeneity of results was observed for sensitivity but not for specificity. Some of the individual studies showed large 95% CIs as a result of small sample size. The results of our meta-analysis reveal high diagnostic performance of FDG PET in the detection of malignancy responsible for PNS, not affected by the presence of onconeural antibodies or clinical characteristics.

Detecting phase separation of freeze-dried binary amorphous systems using pair-wise distribution function and multivariate data analysis.

PubMed

Chieng, Norman; Trnka, Hjalte; Boetker, Johan; Pikal, Michael; Rantanen, Jukka; Grohganz, Holger

2013-09-15

The purpose of this study is to investigate the use of multivariate data analysis for powder X-ray diffraction-pair-wise distribution function (PXRD-PDF) data to detect phase separation in freeze-dried binary amorphous systems. Polymer-polymer and polymer-sugar binary systems at various ratios were freeze-dried. All samples were analyzed by PXRD, transformed to PDF and analyzed by principal component analysis (PCA). These results were validated by differential scanning calorimetry (DSC) through characterization of glass transition of the maximally freeze-concentrate solute (Tg'). Analysis of PXRD-PDF data using PCA provides a more clear 'miscible' or 'phase separated' interpretation through the distribution pattern of samples on a score plot presentation compared to residual plot method. In a phase separated system, samples were found to be evenly distributed around the theoretical PDF profile. For systems that were miscible, a clear deviation of samples away from the theoretical PDF profile was observed. Moreover, PCA analysis allows simultaneous analysis of replicate samples. Comparatively, the phase behavior analysis from PXRD-PDF-PCA method was in agreement with the DSC results. Overall, the combined PXRD-PDF-PCA approach improves the clarity of the PXRD-PDF results and can be used as an alternative explorative data analytical tool in detecting phase separation in freeze-dried binary amorphous systems. Copyright © 2013 Elsevier B.V. All rights reserved.

Bearing failure detection of micro wind turbine via power spectral density analysis for stator current signals spectrum

NASA Astrophysics Data System (ADS)

Mahmood, Faleh H.; Kadhim, Hussein T.; Resen, Ali K.; Shaban, Auday H.

2018-05-01

The failure such as air gap weirdness, rubbing, and scrapping between stator and rotor generator arise unavoidably and may cause extremely terrible results for a wind turbine. Therefore, we should pay more attention to detect and identify its cause-bearing failure in wind turbine to improve the operational reliability. The current paper tends to use of power spectral density analysis method of detecting internal race and external race bearing failure in micro wind turbine by estimation stator current signal of the generator. The failure detector method shows that it is well suited and effective for bearing failure detection.

Vibrations Detection in Industrial Pumps Based on Spectral Analysis to Increase Their Efficiency

NASA Astrophysics Data System (ADS)

Rachid, Belhadef; Hafaifa, Ahmed; Boumehraz, Mohamed

2016-03-01

Spectral analysis is the key tool for the study of vibration signals in rotating machinery. In this work, the vibration analysis applied for conditional preventive maintenance of such machines is proposed, as part of resolved problems related to vibration detection on the organs of these machines. The vibration signal of a centrifugal pump was treated to mount the benefits of the approach proposed. The obtained results present the signal estimation of a pump vibration using Fourier transform technique compared by the spectral analysis methods based on Prony approach.

Initial experiences in the photoacoustic detection of melanoma metastases in resected lymph nodes

NASA Astrophysics Data System (ADS)

Grootendorst, D.; Jose, J.; Van der Jagt, P.; Van der Weg, W.; Nagel, K.; Wouters, M.; Van Boven, H.; Van Leeuwen, T. G.; Steenbergen, W.; Ruers, T.; Manohar, S.

2011-03-01

Accurate lymph node analysis is essential to determine the prognosis and treatment of patients suffering from melanoma. The initial results of a tomographic photoacoustic modality to detect melanoma metastases in resected lymph nodes are presented based on phantom models and a human lymph node. The results show melanoma metastases detection is feasible and the setup is capable of distinguishing absorbing structures down to 1 mm. In addition, the use of longer laser wavelengths could result in an image containing a higher contrast ratio. Future research shall be focused on using the melanin characteristics to improve contrast and detection possibilities.

Stray light suppression of optical and mechanical system for telescope detection

NASA Astrophysics Data System (ADS)

Wang, Lei; Ma, Wenli

2013-09-01

During telescope detection, there is atmosphere overflow and other stray light affecting the system which leads to background disturbance. Thus reduce the detection capability of the system. So it is very necessary to design mechanical structure to suppress the stray light for the telescope detection system. It can both improve the signal-to-noise and contrast of the object. This paper designs the optical and mechanical structure of the 400mm telescope. And then the main baffle, baffle vane, field stop and coating technology are used to eliminate the effect of stray light on the optical and mechanical system. Finally, software is used to analyze and simulate stray light on the whole optical and mechanical system. Using PST as the evaluating standard, separate and integrated analysis of the suppressing effect of main baffle, baffle vane and field aperture is completed. And also get the results of PST before and after eliminating the stray light. Meanwhile, the results of stray light analysis can be used to guide the design of the optical and mechanical structure. The analysis results demonstrate that reasonable optical and mechanical structure and stray light suppression measure can highly reduce the PST and also improve the detection capability of the telescope system, and the designed outside baffle, inside baffle, vanes and coating technique etc. can decrease the PST approximately 1 to 3 level.

Network hydraulics inclusion in water quality event detection using multiple sensor stations data.

PubMed

Oliker, Nurit; Ostfeld, Avi

2015-09-01

Event detection is one of the current most challenging topics in water distribution systems analysis: how regular on-line hydraulic (e.g., pressure, flow) and water quality (e.g., pH, residual chlorine, turbidity) measurements at different network locations can be efficiently utilized to detect water quality contamination events. This study describes an integrated event detection model which combines multiple sensor stations data with network hydraulics. To date event detection modelling is likely limited to single sensor station location and dataset. Single sensor station models are detached from network hydraulics insights and as a result might be significantly exposed to false positive alarms. This work is aimed at decreasing this limitation through integrating local and spatial hydraulic data understanding into an event detection model. The spatial analysis complements the local event detection effort through discovering events with lower signatures by exploring the sensors mutual hydraulic influences. The unique contribution of this study is in incorporating hydraulic simulation information into the overall event detection process of spatially distributed sensors. The methodology is demonstrated on two example applications using base runs and sensitivity analyses. Results show a clear advantage of the suggested model over single-sensor event detection schemes. Copyright © 2015 Elsevier Ltd. All rights reserved.

Support vector machine as a binary classifier for automated object detection in remotely sensed data

NASA Astrophysics Data System (ADS)

Wardaya, P. D.

2014-02-01

In the present paper, author proposes the application of Support Vector Machine (SVM) for the analysis of satellite imagery. One of the advantages of SVM is that, with limited training data, it may generate comparable or even better results than the other methods. The SVM algorithm is used for automated object detection and characterization. Specifically, the SVM is applied in its basic nature as a binary classifier where it classifies two classes namely, object and background. The algorithm aims at effectively detecting an object from its background with the minimum training data. The synthetic image containing noises is used for algorithm testing. Furthermore, it is implemented to perform remote sensing image analysis such as identification of Island vegetation, water body, and oil spill from the satellite imagery. It is indicated that SVM provides the fast and accurate analysis with the acceptable result.

Central sleep apnea detection from ECG-derived respiratory signals. Application of multivariate recurrence plot analysis.

PubMed

Maier, C; Dickhaus, H

2010-01-01

This study examines the suitability of recurrence plot analysis for the problem of central sleep apnea (CSA) detection and delineation from ECG-derived respiratory (EDR) signals. A parameter describing the average length of vertical line structures in recurrence plots is calculated at a time resolution of 1 s as 'instantaneous trapping time'. Threshold comparison of this parameter is used to detect ongoing CSA. In data from 26 patients (duration 208 h) we assessed sensitivity for detection of CSA and mixed apnea (MSA) events by comparing the results obtained from 8-channel Holter ECGs to the annotations (860 CSA, 480 MSA) of simultaneously registered polysomnograms. Multivariate combination of the EDR from different ECG leads improved the detection accuracy significantly. When all eight leads were considered, an average instantaneous vertical line length above 5 correctly identified 1126 of the 1340 events (sensitivity 84%) with a total number of 1881 positive detections. We conclude that recurrence plot analysis is a promising tool for detection and delineation of CSA epochs from EDR signals with high time resolution. Moreover, the approach is likewise applicable to directly measured respiratory signals.

[A comparative analysis of the passive electric probe detection and spectrum diagnosis of laser-induced plasma].

PubMed

Liu, Tong; Yang, Li-Jun; Wang, Li-Jun; Wang, Lang-Ping

2014-02-01

An approach to detecting laser-induced plasma using passive probe was brought up. The plasma of laser welding was studied by using a synchronous electric and spectral information acquisition system, the laser-induced plasma was detected by a passive electric probe and fiber spectrometer, the electrical signal was analyzed on the basis of the theory of plasma sheath, and the temperature of laser-induced plasma was calculated by using the method of relative spectral intensity. The analysis results from electrical signal and spectral one were compared. Calculation results of three kinds of surface circumstances, which were respectively coated by KF, TiO2 and without coating, were compared. The factors affecting the detection accuracy were studied. The results indicated that the results calculated by passive probe matched that by spectral signal basically, and the accuracy was affected by ions mass of the plasma. The designed passive electric probe can be used to reflect the continuous fluctuation of electron temperature of the generated plasma, and monitor the laser-induced plasma.

Updating Landsat-derived land-cover maps using change detection and masking techniques

NASA Technical Reports Server (NTRS)

Likens, W.; Maw, K.

1982-01-01

The California Integrated Remote Sensing System's San Bernardino County Project was devised to study the utilization of a data base at a number of jurisdictional levels. The present paper discusses the implementation of change-detection and masking techniques in the updating of Landsat-derived land-cover maps. A baseline landcover classification was first created from a 1976 image, then the adjusted 1976 image was compared with a 1979 scene by the techniques of (1) multidate image classification, (2) difference image-distribution tails thresholding, (3) difference image classification, and (4) multi-dimensional chi-square analysis of a difference image. The union of the results of methods 1, 3 and 4 was used to create a mask of possible change areas between 1976 and 1979, which served to limit analysis of the update image and reduce comparison errors in unchanged areas. The techniques of spatial smoothing of change-detection products, and of combining results of difference change-detection algorithms are also shown to improve Landsat change-detection accuracies.

An application of LOH analysis for detecting the genetic influences of space environmental radiation

NASA Astrophysics Data System (ADS)

Yatagai, F.; Umebayashi, Y.; Honma, M.; Abe, T.; Suzuki, H.; Shimazu, T.; Ishioka, N.; Iwaki, M.

To detect the genetic influence of space environmental radiation at the chromosome level we proposed an application of loss of heterozygosity LOH analysis system for the mutations induced in human lymphoblastoid TK6 cells Surprisingly we succeeded the mutation detection in the frozen dells which were exposed to a low-dose 10 cGy of carbon-ion beam irradiation Mutation assays were performed within a few days or after about one month preservation at --80 r C following irradiation The results showed an increase in mutation frequency at the thymidine kinase TK gene locus 1 6-fold 2 5 X 10 -6 to 3 9 X 10 -6 and 2 1-fold 2 5 X 10 -6 to 5 3 X 10 -6 respectively Although the relative distributions of mutation classes were not changed by the radiation exposure in either assay an interesting characteristic was detected using this LOH analysis system two TK locus markers and eleven microsatellite loci spanning chromosome 17 The radiation-specific patterns of interstitial deletions were observed in the hemizygous LOH mutants which were considered as a result of end-joining repair of carbon ion-induced DNA double-strand breaks These results clearly demonstrate that this analysis can be used for the detection of low-dose ionizing radiation effects in the frozen cells In addition we performed so called adaptive response experiments in which TK6 cells were pre-irradiated with low-dose 2 5 sim 10 cGy of X-ray and then exposed to challenging dose 2Gy of X-rays Interestingly the

Bladed wheels damage detection through Non-Harmonic Fourier Analysis improved algorithm

NASA Astrophysics Data System (ADS)

Neri, P.

2017-05-01

Recent papers introduced the Non-Harmonic Fourier Analysis for bladed wheels damage detection. This technique showed its potential in estimating the frequency of sinusoidal signals even when the acquisition time is short with respect to the vibration period, provided that some hypothesis are fulfilled. Anyway, previously proposed algorithms showed severe limitations in cracks detection at their early stage. The present paper proposes an improved algorithm which allows to detect a blade vibration frequency shift due to a crack whose size is really small compared to the blade width. Such a technique could be implemented for condition-based maintenance, allowing to use non-contact methods for vibration measurements. A stator-fixed laser sensor could monitor all the blades as they pass in front of the spot, giving precious information about the wheel health. This configuration determines an acquisition time for each blade which become shorter as the machine rotational speed increases. In this situation, traditional Discrete Fourier Transform analysis results in poor frequency resolution, being not suitable for small frequency shift detection. Non-Harmonic Fourier Analysis instead showed high reliability in vibration frequency estimation even with data samples collected in a short time range. A description of the improved algorithm is provided in the paper, along with a comparison with the previous one. Finally, a validation of the method is presented, based on finite element simulations results.

PROBLEMS OF RADIOLOGICAL PROTECTION IN FLAW DETECTION (in Polish)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Domanus, J.; Wolski, M.

1962-01-01

All industrial flaw detection laboratories are covered, with respect to their radiological protection, by the supervision of the Inst. of Electrotechnics. A discussion is given of the results of this action, especially the cases of exceeding the admissible doses. The analysis of endangerment by radiation of employees of flaw detection laboratories is given. (auth)

Genomic profiling of plasma cell disorders in a clinical setting: integration of microarray and FISH, after CD138 selection of bone marrow

PubMed Central

Berry, Nadine Kaye; Bain, Nicole L; Enjeti, Anoop K; Rowlings, Philip

2014-01-01

Aim To evaluate the role of whole genome comparative genomic hybridisation microarray (array-CGH) in detecting genomic imbalances as compared to conventional karyotype (GTG-analysis) or myeloma specific fluorescence in situ hybridisation (FISH) panel in a diagnostic setting for plasma cell dyscrasia (PCD). Methods A myeloma-specific interphase FISH (i-FISH) panel was carried out on CD138 PC-enriched bone marrow (BM) from 20 patients having BM biopsies for evaluation of PCD. Whole genome array-CGH was performed on reference (control) and neoplastic (test patient) genomic DNA extracted from CD138 PC-enriched BM and analysed. Results Comparison of techniques demonstrated a much higher detection rate of genomic imbalances using array-CGH. Genomic imbalances were detected in 1, 19 and 20 patients using GTG-analysis, i-FISH and array-CGH, respectively. Genomic rearrangements were detected in one patient using GTG-analysis and seven patients using i-FISH, while none were detected using array-CGH. I-FISH was the most sensitive method for detecting gene rearrangements and GTG-analysis was the least sensitive method overall. All copy number aberrations observed in GTG-analysis were detected using array-CGH and i-FISH. Conclusions We show that array-CGH performed on CD138-enriched PCs significantly improves the detection of clinically relevant and possibly novel genomic abnormalities in PCD, and thus could be considered as a standard diagnostic technique in combination with IGH rearrangement i-FISH. PMID:23969274

Preliminary Comparisons of the Information Content and Utility of TM Versus MSS Data

NASA Technical Reports Server (NTRS)

Markham, B. L.

1984-01-01

Comparisons were made between subscenes from the first TM scene acquired of the Washington, D.C. area and a MSS scene acquired approximately one year earlier. Three types of analyses were conducted to compare TM and MSS data: a water body analysis, a principal components analysis and a spectral clustering analysis. The water body analysis compared the capability of the TM to the MSS for detecting small uniform targets. Of the 59 ponds located on aerial photographs 34 (58%) were detected by the TM with six commission errors (15%) and 13 (22%) were detected by the MSS with three commission errors (19%). The smallest water body detected by the TM was 16 meters; the smallest detected by the MSS was 40 meters. For the principal components analysis, means and covariance matrices were calculated for each subscene, and principal components images generated and characterized. In the spectral clustering comparison each scene was independently clustered and the clusters were assigned to informational classes. The preliminary comparison indicated that TM data provides enhancements over MSS in terms of (1) small target detection and (2) data dimensionality (even with 4-band data). The extra dimension, partially resultant from TM band 1, appears useful for built-up/non-built-up area separation.

Fragment analysis represents a suitable approach for the detection of hotspot c.7541_7542delCT NOTCH1 mutation in chronic lymphocytic leukemia.

PubMed

Vavrova, Eva; Kantorova, Barbara; Vonkova, Barbara; Kabathova, Jitka; Skuhrova-Francova, Hana; Diviskova, Eva; Letocha, Ondrej; Kotaskova, Jana; Brychtova, Yvona; Doubek, Michael; Mayer, Jiri; Pospisilova, Sarka

2017-09-01

The hotspot c.7541_7542delCT NOTCH1 mutation has been proven to have a negative clinical impact in chronic lymphocytic leukemia (CLL). However, an optimal method for its detection has not yet been specified. The aim of our study was to examine the presence of the NOTCH1 mutation in CLL using three commonly used molecular methods. Sanger sequencing, fragment analysis and allele-specific PCR were compared in the detection of the c.7541_7542delCT NOTCH1 mutation in 201 CLL patients. In 7 patients with inconclusive mutational analysis results, the presence of the NOTCH1 mutation was also confirmed using ultra-deep next generation sequencing. The NOTCH1 mutation was detected in 15% (30/201) of examined patients. Only fragment analysis was able to identify all 30 NOTCH1-mutated patients. Sanger sequencing and allele-specific PCR showed a lower detection efficiency, determining 93% (28/30) and 80% (24/30) of the present NOTCH1 mutations, respectively. Considering these three most commonly used methodologies for c.7541_7542delCT NOTCH1 mutation screening in CLL, we defined fragment analysis as the most suitable approach for detecting the hotspot NOTCH1 mutation. Copyright © 2017 Elsevier Ltd. All rights reserved.

A Multimodal Emotion Detection System during Human-Robot Interaction

PubMed Central

Alonso-Martín, Fernando; Malfaz, María; Sequeira, João; Gorostiza, Javier F.; Salichs, Miguel A.

2013-01-01

In this paper, a multimodal user-emotion detection system for social robots is presented. This system is intended to be used during human–robot interaction, and it is integrated as part of the overall interaction system of the robot: the Robotics Dialog System (RDS). Two modes are used to detect emotions: the voice and face expression analysis. In order to analyze the voice of the user, a new component has been developed: Gender and Emotion Voice Analysis (GEVA), which is written using the Chuck language. For emotion detection in facial expressions, the system, Gender and Emotion Facial Analysis (GEFA), has been also developed. This last system integrates two third-party solutions: Sophisticated High-speed Object Recognition Engine (SHORE) and Computer Expression Recognition Toolbox (CERT). Once these new components (GEVA and GEFA) give their results, a decision rule is applied in order to combine the information given by both of them. The result of this rule, the detected emotion, is integrated into the dialog system through communicative acts. Hence, each communicative act gives, among other things, the detected emotion of the user to the RDS so it can adapt its strategy in order to get a greater satisfaction degree during the human–robot dialog. Each of the new components, GEVA and GEFA, can also be used individually. Moreover, they are integrated with the robotic control platform ROS (Robot Operating System). Several experiments with real users were performed to determine the accuracy of each component and to set the final decision rule. The results obtained from applying this decision rule in these experiments show a high success rate in automatic user emotion recognition, improving the results given by the two information channels (audio and visual) separately. PMID:24240598

Automatic zebrafish heartbeat detection and analysis for zebrafish embryos.

PubMed

Pylatiuk, Christian; Sanchez, Daniela; Mikut, Ralf; Alshut, Rüdiger; Reischl, Markus; Hirth, Sofia; Rottbauer, Wolfgang; Just, Steffen

2014-08-01

A fully automatic detection and analysis method of heartbeats in videos of nonfixed and nonanesthetized zebrafish embryos is presented. This method reduces the manual workload and time needed for preparation and imaging of the zebrafish embryos, as well as for evaluating heartbeat parameters such as frequency, beat-to-beat intervals, and arrhythmicity. The method is validated by a comparison of the results from automatic and manual detection of the heart rates of wild-type zebrafish embryos 36-120 h postfertilization and of embryonic hearts with bradycardia and pauses in the cardiac contraction.

Direct surface analysis of pesticides on soil, leaves, grass, and stainless steel by static secondary ion mass spectrometry

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ingram, J.C.; Groenewold, G.S.; Appelhans, A.D.

1997-02-01

Direct surface analyses by static secondary ion mass spectrometry (SIMS) were performed for the following pesticides adsorbed on dandelion leaves, grass, soil, and stainless steel samples: alachlor, atrazine, captan, carbofuran, chlorpyrifos, chlorosulfuron, chlorthal-dimethyl, cypermethrin, 2,4-D, diuron, glyphosate, malathion, methomyl, methyl arsonic acid, mocap, norflurazon, oxyfluorfen, paraquat, temik, and trifluralin. The purpose of this study was to evaluate static SIMS as a tool for pesticide analysis, principally for use in screening samples for pesticides. The advantage of direct surface analysis compared with conventional pesticide analysis methods is the elimination of sample pretreatment including extraction, which streamlines the analysis substantially; total analysismore » time for SIMS analysis was ca. 10 min/sample. Detection of 16 of the 20 pesticides on all four substrates was achieved. Of the remaining four pesticides, only one (trifluralin) was not detected on any of the samples. The minimum detectable quantity was determined for paraquat on soil in order to evaluate the efficacy of using SIMS as a screening tool. Paraquat was detected at 3 pg/mm{sup 2} (c.a. 0.005 monolayers). The results of these studies suggest that SIMS is capable of direct surface detection of a range of pesticides, with low volatility, polar pesticides being the most easily detected. 25 refs., 2 figs., 2 tabs.« less

The value of the first trimester ultrasound in the era of cell free DNA screening.

PubMed

Rao, Rashmi R; Valderramos, Stephanie G; Silverman, Neil S; Han, Christina S; Platt, Lawrence D

2016-12-01

To describe the clinically relevant findings detected by the first trimester ultrasound (FTU) and to determine the additional value of the FTU compared to cell free DNA (cfDNA) alone. Retrospective cohort study of patients undergoing a FTU at a maternal-fetal medicine referral practice. Fetal, gynecologic, and placental findings detected by ultrasound were analyzed with available cfDNA and diagnostic testing results. A subgroup analysis of positive ultrasound findings and cfDNA results was performed to assess the additional benefit of ultrasound evaluation in FT prenatal screening. There were 1906 FTU between 1 October 2013 and 1 October 2014. CfDNA results were available for 959 (50%) patients. FTU detected: 42 fetal (2.2%), 286 gynecologic (15.0%), and 317 placental (16.6%) findings. CfDNA results were discordant with invasive testing results in 8/61 cases (13%) and with ultrasound findings in 18/42 (42%) cases. There were six false positive and two false negative cfDNA results confirmed by diagnostic testing. Subgroup analysis revealed that cfDNA as the sole method of prenatal screening in the FT would miss 95% of the fetal findings detected with ultrasound. The comprehensive FTU provides valuable clinical information about fetal and maternal anatomy that cannot be detected with cfDNA alone. © 2016 John Wiley & Sons, Ltd. © 2016 John Wiley & Sons, Ltd.

Optical Coherence Tomography Minimum Intensity as an Objective Measure for the Detection of Hydroxychloroquine Toxicity

PubMed Central

Allahdina, Ali M.; Stetson, Paul F.; Vitale, Susan; Wong, Wai T.; Chew, Emily Y.; Ferris, Fredrick L.; Sieving, Paul A.

2018-01-01

Purpose As optical coherence tomography (OCT) minimum intensity (MI) analysis provides a quantitative assessment of changes in the outer nuclear layer (ONL), we evaluated the ability of OCT-MI analysis to detect hydroxychloroquine toxicity. Methods Fifty-seven predominantly female participants (91.2% female; mean age, 55.7 ± 10.4 years; mean time on hydroxychloroquine, 15.0 ± 7.5 years) were enrolled in a case-control study and categorized into affected (i.e., with toxicity, n = 19) and unaffected (n = 38) groups using objective multifocal electroretinographic (mfERG) criteria. Spectral-domain OCT scans of the macula were analyzed and OCT-MI values quantitated for each subfield of the Early Treatment Diabetic Retinopathy Study (ETDRS) grid. A two-sample U-test and a cross-validation approach were used to assess the sensitivity and specificity of toxicity detection according to OCT-MI criteria. Results The medians of the OCT-MI values in all nine of the ETDRS subfields were significantly elevated in the affected group relative to the unaffected group (P < 0.005 for all comparisons), with the largest difference found for the inner inferior subfield (P < 0.0001). The receiver operating characteristic analysis of median MI values of the inner inferior subfields showed high sensitivity and high specificity in the detection of toxicity with area under the curve = 0.99. Conclusions Retinal changes secondary to hydroxychloroquine toxicity result in increased OCT reflectivity in the ONL that can be detected and quantitated using OCT-MI analysis. Analysis of OCT-MI values demonstrates high sensitivity and specificity for detecting the presence of hydroxychloroquine toxicity in this cohort and may contribute additionally to current screening practices. PMID:29677357

Analysis and segmentation of images in case of solving problems of detecting and tracing objects on real-time video

NASA Astrophysics Data System (ADS)

Ezhova, Kseniia; Fedorenko, Dmitriy; Chuhlamov, Anton

2016-04-01

The article deals with the methods of image segmentation based on color space conversion, and allow the most efficient way to carry out the detection of a single color in a complex background and lighting, as well as detection of objects on a homogeneous background. The results of the analysis of segmentation algorithms of this type, the possibility of their implementation for creating software. The implemented algorithm is very time-consuming counting, making it a limited application for the analysis of the video, however, it allows us to solve the problem of analysis of objects in the image if there is no dictionary of images and knowledge bases, as well as the problem of choosing the optimal parameters of the frame quantization for video analysis.

Analytical performance of benchtop total reflection X-ray fluorescence instrumentation for multielemental analysis of wine samples

NASA Astrophysics Data System (ADS)

Dalipi, Rogerta; Marguí, Eva; Borgese, Laura; Bilo, Fabjola; Depero, Laura E.

2016-06-01

Recent technological improvements have led to a widespread adoption of benchtop total reflection X-ray fluorescence systems (TXRF) for analysis of liquid samples. However, benchtop TXRF systems usually present limited sensitivity compared with high-scale instrumentation which can restrict its application in some fields. The aim of the present work was to evaluate and compare the analytical capabilities of two TXRF systems, equipped with low power Mo and W target X-ray tubes, for multielemental analysis of wine samples. Using the Mo-TXRF system, the detection limits for most elements were one order of magnitude lower than those attained using the W-TXRF system. For the detection of high Z elements like Cd and Ag, however, W-TXRF remains a very good option due to the possibility of K-Lines detection. Accuracy and precision of the obtained results have been evaluated analyzing spiked real wine samples and comparing the TXRF results with those obtained by inductively coupled plasma emission spectroscopy (ICP-OES). In general, good agreement was obtained between ICP-OES and TXRF results for the analysis of both red and white wine samples except for light elements (i.e., K) which TXRF concentrations were underestimated. However, a further achievement of analytical quality of TXRF results can be achieved if wine analysis is performed after dilution of the sample with de-ionized water.

Detection of Focal Cortical Dysplasia Lesions in MRI Using Textural Features

NASA Astrophysics Data System (ADS)

Loyek, Christian; Woermann, Friedrich G.; Nattkemper, Tim W.

Focal cortical dysplasia (FCD) is a frequent cause of medically refractory partial epilepsy. The visual identification of FCD lesions on magnetic resonance images (MRI) is a challenging task in standard radiological analysis. Quantitative image analysis which tries to assist in the diagnosis of FCD lesions is an active field of research. In this work we investigate the potential of different texture features, in order to explore to what extent they are suitable for detecting lesional tissue. As a result we can show first promising results based on segmentation and texture classification.

The visual analysis of emotional actions.

PubMed

Chouchourelou, Arieta; Matsuka, Toshihiko; Harber, Kent; Shiffrar, Maggie

2006-01-01

Is the visual analysis of human actions modulated by the emotional content of those actions? This question is motivated by a consideration of the neuroanatomical connections between visual and emotional areas. Specifically, the superior temporal sulcus (STS), known to play a critical role in the visual detection of action, is extensively interconnected with the amygdala, a center for emotion processing. To the extent that amygdala activity influences STS activity, one would expect to find systematic differences in the visual detection of emotional actions. A series of psychophysical studies tested this prediction. Experiment 1 identified point-light walker movies that convincingly depicted five different emotional states: happiness, sadness, neutral, anger, and fear. In Experiment 2, participants performed a walker detection task with these movies. Detection performance was systematically modulated by the emotional content of the gaits. Participants demonstrated the greatest visual sensitivity to angry walkers. The results of Experiment 3 suggest that local velocity cues to anger may account for high false alarm rates to the presence of angry gaits. These results support the hypothesis that the visual analysis of human action depends upon emotion processes.

Health economics evaluation of a gastric cancer early detection and treatment program in China.

PubMed

Li, Dan; Yuan, Yuan; Sun, Li-Ping; Fang, Xue; Zhou, Bao-Sen

2014-01-01

To use health economics methodology to assess the screening program on gastric cancer in Zhuanghe, China, so as to provide the basis for health decision on expanding the program of early detection and treatment. The expense of an early detection and treatment program for gastric cancer in patients found by screening, and also costs of traditional treatment in a hospital of Zhuanghe were assessed. Three major techniques of medical economics, namely cost-effective analysis (CEA), cost-benefit analysis (CBA) and cost-utility analysis (CUA), were used to assess the screening program. RESULTS from CEA showed that investing every 25, 235 Yuan on screening program in Zhuanghe area, one gastric cancer patient could be saved. Data from CUA showed that it was cost 1, 370 Yuan per QALY saved. RESULTS from CBA showed that: the total cost was 1,945,206 Yuan with a benefit as 8,669,709 Yuan and an CBR of 4.46. The early detection and treatment program of gastric cancer appears economic and society-beneficial. We suggest that it should be carry out in more high risk areas for gastric cancer.

Comparison of human and algorithmic target detection in passive infrared imagery

NASA Astrophysics Data System (ADS)

Weber, Bruce A.; Hutchinson, Meredith

2003-09-01

We have designed an experiment that compares the performance of human observers and a scale-insensitive target detection algorithm that uses pixel level information for the detection of ground targets in passive infrared imagery. The test database contains targets near clutter whose detectability ranged from easy to very difficult. Results indicate that human observers detect more "easy-to-detect" targets, and with far fewer false alarms, than the algorithm. For "difficult-to-detect" targets, human and algorithm detection rates are considerably degraded, and algorithm false alarms excessive. Analysis of detections as a function of observer confidence shows that algorithm confidence attribution does not correspond to human attribution, and does not adequately correlate with correct detections. The best target detection score for any human observer was 84%, as compared to 55% for the algorithm for the same false alarm rate. At 81%, the maximum detection score for the algorithm, the same human observer had 6 false alarms per frame as compared to 29 for the algorithm. Detector ROC curves and observer-confidence analysis benchmarks the algorithm and provides insights into algorithm deficiencies and possible paths to improvement.

The use of real-time polymerase chain reaction with high resolution melting (real-time PCR-HRM) analysis for the detection and discrimination of nematodes Bursaphelenchus xylophilus and Bursaphelenchus mucronatus.

PubMed

Filipiak, Anna; Hasiów-Jaroszewska, Beata

2016-04-01

The real-time PCR-HRM analysis was developed for the detection and discrimination of the quarantine nematode Bursaphelenchus xylophilus and Bursaphelenchus mucronatus. A set of primers was designed to target the ITS region of rDNA. The results have demonstrated that this analysis is a valuable tool for differentiation of these both species. Copyright © 2016 Elsevier Ltd. All rights reserved.

[EXPERIENCE OF STUDY AND POSSIBLE WAYS OF ELIMINATION OF FALSE POSITIVE AND FALSE NEGATIVE RESULTS DURING EXECUTION OF POLYMERASE CHAIN REACTION ON AN EXAMPLE OF JUNIN VIRUS RNA DETECTION].

PubMed

Sizikova, T E; Lebedev, V N; Pantyukhov, V B; Borisevich, S V; Merkulov, V A

2015-01-01

Experience of study and possible ways of elimination of false positive and false negative results during execution of polymerase chain reaction on an example of Junin virus RNA detection. MATERIALSS AND METHODS: Junin virus--causative agent of Argentine hemorrhagic fever (AHF) strain XJpR37/5787 was obtained from the State collection of pathogenicity group I causative agents of the 48th Central Research Institute. Reagent kit for detection of Junin virus RNA by RT-PCR was developed in the Institute and consists of 4 sets: for isolation of RNA, execution of reverse-transcription reaction, execution of PCR and electrophoretic detection of PCR products. RT-PCR was carried out by a standard technique. Continuous cell cultures of African green monkey Vero B, GMK-AH-1(D) were obtained from the museum of cell culture department of the Centre. An experimental study of the effect of various factors of impact on the sample under investigation ("thawing-freezing", presence of formaldehyde, heparin) on the obtaining of false negative results during Junin virus RNA detection by using RT-PCR was studied. Addition of 0.01% heparin to the samples was shown to completely inhibit PCR. Addition of 0.05% formaldehyde significantly reduces sensitivity of the method. A possibility of reduction of analysis timeframe from 15 to 5 days was shown during detection of the causative agent in samples with low concentration of the latter by growing the samples and subsequent analysis of the material obtained by using RT-PCR. During detection of causative agent by using RT-PCR false negative results could appear in the presence of formaldehyde and heparin in the sample. A possibility of elimination of false negative PCR results due to concentration of the causative agent in the sample under investigation at a level below sensitivity threshold was shown on the example of Junin virus RNA detection by using growing of the pathogen in appropriate accumulation system with subsequent analysis of the material obtained using PCR.

Fault detection of gearbox using time-frequency method

NASA Astrophysics Data System (ADS)

Widodo, A.; Satrijo, Dj.; Prahasto, T.; Haryanto, I.

2017-04-01

This research deals with fault detection and diagnosis of gearbox by using vibration signature. In this work, fault detection and diagnosis are approached by employing time-frequency method, and then the results are compared with cepstrum analysis. Experimental work has been conducted for data acquisition of vibration signal thru self-designed gearbox test rig. This test-rig is able to demonstrate normal and faulty gearbox i.e., wears and tooth breakage. Three accelerometers were used for vibration signal acquisition from gearbox, and optical tachometer was used for shaft rotation speed measurement. The results show that frequency domain analysis using fast-fourier transform was less sensitive to wears and tooth breakage condition. However, the method of short-time fourier transform was able to monitor the faults in gearbox. Wavelet Transform (WT) method also showed good performance in gearbox fault detection using vibration signal after employing time synchronous averaging (TSA).

Deep Learning Nuclei Detection in Digitized Histology Images by Superpixels

PubMed Central

Sornapudi, Sudhir; Stanley, Ronald Joe; Stoecker, William V.; Almubarak, Haidar; Long, Rodney; Antani, Sameer; Thoma, George; Zuna, Rosemary; Frazier, Shelliane R.

2018-01-01

Background: Advances in image analysis and computational techniques have facilitated automatic detection of critical features in histopathology images. Detection of nuclei is critical for squamous epithelium cervical intraepithelial neoplasia (CIN) classification into normal, CIN1, CIN2, and CIN3 grades. Methods: In this study, a deep learning (DL)-based nuclei segmentation approach is investigated based on gathering localized information through the generation of superpixels using a simple linear iterative clustering algorithm and training with a convolutional neural network. Results: The proposed approach was evaluated on a dataset of 133 digitized histology images and achieved an overall nuclei detection (object-based) accuracy of 95.97%, with demonstrated improvement over imaging-based and clustering-based benchmark techniques. Conclusions: The proposed DL-based nuclei segmentation Method with superpixel analysis has shown improved segmentation results in comparison to state-of-the-art methods. PMID:29619277

Improved computer-aided detection of small polyps in CT colonography using interpolation for curvature estimationa

PubMed Central

Liu, Jiamin; Kabadi, Suraj; Van Uitert, Robert; Petrick, Nicholas; Deriche, Rachid; Summers, Ronald M.

2011-01-01

Purpose: Surface curvatures are important geometric features for the computer-aided analysis and detection of polyps in CT colonography (CTC). However, the general kernel approach for curvature computation can yield erroneous results for small polyps and for polyps that lie on haustral folds. Those erroneous curvatures will reduce the performance of polyp detection. This paper presents an analysis of interpolation’s effect on curvature estimation for thin structures and its application on computer-aided detection of small polyps in CTC. Methods: The authors demonstrated that a simple technique, image interpolation, can improve the accuracy of curvature estimation for thin structures and thus significantly improve the sensitivity of small polyp detection in CTC. Results: Our experiments showed that the merits of interpolating included more accurate curvature values for simulated data, and isolation of polyps near folds for clinical data. After testing on a large clinical data set, it was observed that sensitivities with linear, quadratic B-spline and cubic B-spline interpolations significantly improved the sensitivity for small polyp detection. Conclusions: The image interpolation can improve the accuracy of curvature estimation for thin structures and thus improve the computer-aided detection of small polyps in CTC. PMID:21859029

On the Detectability of Interstellar Objects Like 1I/'Oumuamua

NASA Astrophysics Data System (ADS)

Ragozzine, Darin

2018-04-01

Almost since Oort's 1950 hypothesis of a tenuously bound cloud of comets, planetary formation theorists have realized that the process of planet formation must have ejected very large numbers of planetesimals into interstellar space. Unforunately, these objects are distributed over galactic volumes, while they are only likely to be detectable if they pass within a few AU of Earth, resulting in an incredibly sparse detectable population. Furthermore, hypotheses for the formation and distribution of these bodies allows for uncertainties of orders of magnitude in the expected detection rate: our analysis suggested LSST would discover 0.01-100 objects during its lifetime (Cook et al. 2016). The discovery of 1I/'Oumuamua by a survey less powerful that LSST indicates either a low probability event and/or that properties of this population are on the more favorable end of the spectrum. We revisit the detailed detection analysis of Cook et al. 2016 in light of the detection of 1I/'Oumuamua. We use these results to better understand 1I/'Oumuamua and to update our assessment of future detections of interstellar objects. We highlight some key questions that can be answered only by additional discoveries.

Cumulative meta-analysis: a new tool for detection of temporal trends and publication bias in ecology.

PubMed Central

Leimu, Roosa; Koricheva, Julia

2004-01-01

Temporal changes in the magnitude of research findings have recently been recognized as a general phenomenon in ecology, and have been attributed to the delayed publication of non-significant results and disconfirming evidence. Here we introduce a method of cumulative meta-analysis which allows detection of both temporal trends and publication bias in the ecological literature. To illustrate the application of the method, we used two datasets from recently conducted meta-analyses of studies testing two plant defence theories. Our results revealed three phases in the evolution of the treatment effects. Early studies strongly supported the hypothesis tested, but the magnitude of the effect decreased considerably in later studies. In the latest studies, a trend towards an increase in effect size was observed. In one of the datasets, a cumulative meta-analysis revealed publication bias against studies reporting disconfirming evidence; such studies were published in journals with a lower impact factor compared to studies with results supporting the hypothesis tested. Correlation analysis revealed neither temporal trends nor evidence of publication bias in the datasets analysed. We thus suggest that cumulative meta-analysis should be used as a visual aid to detect temporal trends and publication bias in research findings in ecology in addition to the correlative approach. PMID:15347521

Consistency of influenza A virus detection test results across respiratory specimen collection methods using real-time reverse transcription-PCR.

PubMed

Spencer, Sarah; Gaglani, Manjusha; Naleway, Allison; Reynolds, Sue; Ball, Sarah; Bozeman, Sam; Henkle, Emily; Meece, Jennifer; Vandermause, Mary; Clipper, Lydia; Thompson, Mark

2013-11-01

In our prospective cohort study, we compared the performance of nasopharyngeal, oropharyngeal, and nasal swabs for the detection of influenza virus using real-time reverse transcription-PCR assay. Joint consideration of results from oropharyngeal and nasal swabs was as effective as consideration of results from nasopharyngeal swabs alone, as measured by sensitivity and noninferiority analysis.

It's the deceiver, not the receiver: No individual differences when detecting deception in a foreign and a native language.

PubMed

Law, Marvin K H; Jackson, Simon A; Aidman, Eugene; Geiger, Mattis; Olderbak, Sally; Kleitman, Sabina

2018-01-01

Individual differences in lie detection remain poorly understood. Bond and DePaulo's meta-analysis examined judges (receivers) who were ascertaining lies from truths and senders (deceiver) who told these lies and truths. Bond and DePaulo found that the accuracy of detecting deception depended more on the characteristics of senders rather than the judges' ability to detect lies/truths. However, for many studies in this meta-analysis, judges could hear and understand senders. This made language comprehension a potential confound. This paper presents the results of two studies. Extending previous work, in Study 1, we removed language comprehension as a potential confound by having English-speakers (N = 126, mean age = 19.86) judge the veracity of German speakers (n = 12) in a lie detection task. The twelve lie-detection stimuli included emotional and non-emotional content, and were presented in three modalities-audio only, video only, and audio and video together. The intelligence (General, Auditory, Emotional) and personality (Dark Triads and Big 6) of participants was also assessed. In Study 2, a native German-speaking sample (N = 117, mean age = 29.10) were also tested on a similar lie detection task to provide a control condition. Despite significantly extending research design and the selection of constructs employed to capture individual differences, both studies replicated Bond and DePaulo's findings. The results of Study1 indicated that removing language comprehension did not amplify individual differences in judge's ability to ascertain lies from truths. Study 2 replicated these results confirming a lack of individual differences in judge's ability to detect lies. The results of both studies suggest that Sender (deceiver) characteristics exerted a stronger influence on the outcomes of lie detection than the judge's attributes.

Point-of-care mobile digital microscopy and deep learning for the detection of soil-transmitted helminths and Schistosoma haematobium

PubMed Central

Holmström, Oscar; Linder, Nina; Ngasala, Billy; Mårtensson, Andreas; Linder, Ewert; Lundin, Mikael; Moilanen, Hannu; Suutala, Antti; Diwan, Vinod; Lundin, Johan

2017-01-01

ABSTRACT Background: Microscopy remains the gold standard in the diagnosis of neglected tropical diseases. As resource limited, rural areas often lack laboratory equipment and trained personnel, new diagnostic techniques are needed. Low-cost, point-of-care imaging devices show potential in the diagnosis of these diseases. Novel, digital image analysis algorithms can be utilized to automate sample analysis. Objective: Evaluation of the imaging performance of a miniature digital microscopy scanner for the diagnosis of soil-transmitted helminths and Schistosoma haematobium, and training of a deep learning-based image analysis algorithm for automated detection of soil-transmitted helminths in the captured images. Methods: A total of 13 iodine-stained stool samples containing Ascaris lumbricoides, Trichuris trichiura and hookworm eggs and 4 urine samples containing Schistosoma haematobium were digitized using a reference whole slide-scanner and the mobile microscopy scanner. Parasites in the images were identified by visual examination and by analysis with a deep learning-based image analysis algorithm in the stool samples. Results were compared between the digital and visual analysis of the images showing helminth eggs. Results: Parasite identification by visual analysis of digital slides captured with the mobile microscope was feasible for all analyzed parasites. Although the spatial resolution of the reference slide-scanner is higher, the resolution of the mobile microscope is sufficient for reliable identification and classification of all parasites studied. Digital image analysis of stool sample images captured with the mobile microscope showed high sensitivity for detection of all helminths studied (range of sensitivity = 83.3–100%) in the test set (n = 217) of manually labeled helminth eggs. Conclusions: In this proof-of-concept study, the imaging performance of a mobile, digital microscope was sufficient for visual detection of soil-transmitted helminths and Schistosoma haematobium. Furthermore, we show that deep learning-based image analysis can be utilized for the automated detection and classification of helminths in the captured images. PMID:28838305

Inflight and Preflight Detection of Pitot Tube Anomalies

NASA Technical Reports Server (NTRS)

Mitchell, Darrell W.

2014-01-01

The health and integrity of aircraft sensors play a critical role in aviation safety. Inaccurate or false readings from these sensors can lead to improper decision making, resulting in serious and sometimes fatal consequences. This project demonstrated the feasibility of using advanced data analysis techniques to identify anomalies in Pitot tubes resulting from blockage such as icing, moisture, or foreign objects. The core technology used in this project is referred to as noise analysis because it relates sensors' response time to the dynamic component (noise) found in the signal of these same sensors. This analysis technique has used existing electrical signals of Pitot tube sensors that result from measured processes during inflight conditions and/or induced signals in preflight conditions to detect anomalies in the sensor readings. Analysis and Measurement Services Corporation (AMS Corp.) has routinely used this technology to determine the health of pressure transmitters in nuclear power plants. The application of this technology for the detection of aircraft anomalies is innovative. Instead of determining the health of process monitoring at a steady-state condition, this technology will be used to quickly inform the pilot when an air-speed indication becomes faulty under any flight condition as well as during preflight preparation.

A NEW SYSTEM TO MONITOR DATA ANALYSES AND RESULTS OF PHYSICS DATA VALIDATION BETWEEN PULSES AT DIII-D

DOE Office of Scientific and Technical Information (OSTI.GOV)

FLANAGAN,A; SCHACHTER,J.M; SCHISSEL,D.P

2003-02-01

A Data Analysis Monitoring (DAM) system has been developed to monitor between pulse physics analysis at the DIII-D National Fusion Facility (http://nssrv1.gat.com:8000/dam). The system allows for rapid detection of discrepancies in diagnostic measurements or the results from physics analysis codes. This enables problems to be detected and possibly fixed between pulses as opposed to after the experimental run has concluded thus increasing the efficiency of experimental time. An example of a consistency check is comparing the experimentally measured neutron rate and the expected neutron emission, RDD0D. A significant difference between these two values could indicate a problem with one ormore » more diagnostics, or the presence of unanticipated phenomena in the plasma. This new system also tracks the progress of MDSplus dispatched data analysis software and the loading of analyzed data into MDSplus. DAM uses a Java Servlet to receive messages, CLIPS to implement expert system logic, and displays its results to multiple web clients via HTML. If an error is detected by DAM, users can view more detailed information so that steps can be taken to eliminate the error for the next pulse.« less

System to monitor data analyses and results of physics data validation between pulses at DIII-D

NASA Astrophysics Data System (ADS)

Flanagan, S.; Schachter, J. M.; Schissel, D. P.

2004-06-01

A data analysis monitoring (DAM) system has been developed to monitor between pulse physics analysis at the DIII-D National Fusion Facility (http://nssrv1.gat.com:8000/dam). The system allows for rapid detection of discrepancies in diagnostic measurements or the results from physics analysis codes. This enables problems to be detected and possibly fixed between pulses as opposed to after the experimental run has concluded, thus increasing the efficiency of experimental time. An example of a consistency check is comparing the experimentally measured neutron rate and the expected neutron emission, RDD0D. A significant difference between these two values could indicate a problem with one or more diagnostics, or the presence of unanticipated phenomena in the plasma. This system also tracks the progress of MDSplus dispatched data analysis software and the loading of analyzed data into MDSplus. DAM uses a Java Servlet to receive messages, C Language Integrated Production System to implement expert system logic, and displays its results to multiple web clients via Hypertext Markup Language. If an error is detected by DAM, users can view more detailed information so that steps can be taken to eliminate the error for the next pulse.

Is HPV DNA testing specificity comparable to that of cytological testing in primary cervical cancer screening? Results of a meta-analysis of randomized controlled trials.

PubMed

Pileggi, Claudia; Flotta, Domenico; Bianco, Aida; Nobile, Carmelo G A; Pavia, Maria

2014-07-01

Human-papillomavirus (HPV) DNA testing has been proposed as an alternative to primary cervical cancer screening using cytological testing. Review of the evidence shows that available data are conflicting for some aspects. The overall goal of the study is to update the performance of HPV DNA as stand-alone testing in primary cervical cancer screening, focusing particularly on the aspects related to the specificity profile of the HPV DNA testing in respect to cytology. We performed a meta-analysis of randomized controlled clinical trials. Eight articles were included in the meta-analysis. Three outcomes have been investigated: relative detection, relative specificity, and relative positive predictive value (PPV) of HPV DNA testing versus cytology. Overall evaluation of relative detection showed a significantly higher detection of CIN2+ and CIN3+ for HPV DNA testing versus cytology. Meta-analyses that considered all age groups showed a relative specificity that favored the cytology in detecting both CIN2+ and CIN3+ lesions whereas, in the ≥30 years' group, specificity of HPV DNA and cytology tests was similar in detecting both CIN2+ and CIN3+ lesions. Results of the pooled analysis on relative PPV showed a not significantly lower PPV of HPV DNA test over cytology. A main key finding of the study is that in women aged ≥30, has been found an almost overlapping specificity between the two screening tests in detecting CIN2 and above-grade lesions. Therefore, primary screening of cervical cancer by HPV DNA testing appears to offer the right balance between maximum detection of CIN2+ and adequate specificity, if performed in the age group ≥30 years. © 2013 UICC.

Evaluation of Hemifield Sector Analysis Protocol in Multifocal Visual Evoked Potential Objective Perimetry for the Diagnosis and Early Detection of Glaucomatous Field Defects

PubMed Central

Mousa, Mohammad F.; Cubbidge, Robert P.; Al-Mansouri, Fatima

2014-01-01

Purpose Multifocal visual evoked potential (mfVEP) is a newly introduced method used for objective visual field assessment. Several analysis protocols have been tested to identify early visual field losses in glaucoma patients using the mfVEP technique, some were successful in detection of field defects, which were comparable to the standard automated perimetry (SAP) visual field assessment, and others were not very informative and needed more adjustment and research work. In this study we implemented a novel analysis approach and evaluated its validity and whether it could be used effectively for early detection of visual field defects in glaucoma. Methods Three groups were tested in this study; normal controls (38 eyes), glaucoma patients (36 eyes) and glaucoma suspect patients (38 eyes). All subjects had a two standard Humphrey field analyzer (HFA) test 24-2 and a single mfVEP test undertaken in one session. Analysis of the mfVEP results was done using the new analysis protocol; the hemifield sector analysis (HSA) protocol. Analysis of the HFA was done using the standard grading system. Results Analysis of mfVEP results showed that there was a statistically significant difference between the three groups in the mean signal to noise ratio (ANOVA test, p < 0.001 with a 95% confidence interval). The difference between superior and inferior hemispheres in all subjects were statistically significant in the glaucoma patient group in all 11 sectors (t-test, p < 0.001), partially significant in 5 / 11 (t-test, p < 0.01), and no statistical difference in most sectors of the normal group (1 / 11 sectors was significant, t-test, p < 0.9). Sensitivity and specificity of the HSA protocol in detecting glaucoma was 97% and 86%, respectively, and for glaucoma suspect patients the values were 89% and 79%, respectively. Conclusions The new HSA protocol used in the mfVEP testing can be applied to detect glaucomatous visual field defects in both glaucoma and glaucoma suspect patients. Using this protocol can provide information about focal visual field differences across the horizontal midline, which can be utilized to differentiate between glaucoma and normal subjects. Sensitivity and specificity of the mfVEP test showed very promising results and correlated with other anatomical changes in glaucoma field loss. PMID:24511212

Real-time detection of organic contamination events in water distribution systems by principal components analysis of ultraviolet spectral data.

PubMed

Zhang, Jian; Hou, Dibo; Wang, Ke; Huang, Pingjie; Zhang, Guangxin; Loáiciga, Hugo

2017-05-01

The detection of organic contaminants in water distribution systems is essential to protect public health from potential harmful compounds resulting from accidental spills or intentional releases. Existing methods for detecting organic contaminants are based on quantitative analyses such as chemical testing and gas/liquid chromatography, which are time- and reagent-consuming and involve costly maintenance. This study proposes a novel procedure based on discrete wavelet transform and principal component analysis for detecting organic contamination events from ultraviolet spectral data. Firstly, the spectrum of each observation is transformed using discrete wavelet with a coiflet mother wavelet to capture the abrupt change along the wavelength. Principal component analysis is then employed to approximate the spectra based on capture and fusion features. The significant value of Hotelling's T 2 statistics is calculated and used to detect outliers. An alarm of contamination event is triggered by sequential Bayesian analysis when the outliers appear continuously in several observations. The effectiveness of the proposed procedure is tested on-line using a pilot-scale setup and experimental data.

Analysis on Target Detection and Classification in LTE Based Passive Forward Scattering Radar.

PubMed

Raja Abdullah, Raja Syamsul Azmir; Abdul Aziz, Noor Hafizah; Abdul Rashid, Nur Emileen; Ahmad Salah, Asem; Hashim, Fazirulhisyam

2016-09-29

The passive bistatic radar (PBR) system can utilize the illuminator of opportunity to enhance radar capability. By utilizing the forward scattering technique and procedure into the specific mode of PBR can provide an improvement in target detection and classification. The system is known as passive Forward Scattering Radar (FSR). The passive FSR system can exploit the peculiar advantage of the enhancement in forward scatter radar cross section (FSRCS) for target detection. Thus, the aim of this paper is to show the feasibility of passive FSR for moving target detection and classification by experimental analysis and results. The signal source is coming from the latest technology of 4G Long-Term Evolution (LTE) base station. A detailed explanation on the passive FSR receiver circuit, the detection scheme and the classification algorithm are given. In addition, the proposed passive FSR circuit employs the self-mixing technique at the receiver; hence the synchronization signal from the transmitter is not required. The experimental results confirm the passive FSR system's capability for ground target detection and classification. Furthermore, this paper illustrates the first classification result in the passive FSR system. The great potential in the passive FSR system provides a new research area in passive radar that can be used for diverse remote monitoring applications.

Analysis on Target Detection and Classification in LTE Based Passive Forward Scattering Radar

PubMed Central

Raja Abdullah, Raja Syamsul Azmir; Abdul Aziz, Noor Hafizah; Abdul Rashid, Nur Emileen; Ahmad Salah, Asem; Hashim, Fazirulhisyam

2016-01-01

The passive bistatic radar (PBR) system can utilize the illuminator of opportunity to enhance radar capability. By utilizing the forward scattering technique and procedure into the specific mode of PBR can provide an improvement in target detection and classification. The system is known as passive Forward Scattering Radar (FSR). The passive FSR system can exploit the peculiar advantage of the enhancement in forward scatter radar cross section (FSRCS) for target detection. Thus, the aim of this paper is to show the feasibility of passive FSR for moving target detection and classification by experimental analysis and results. The signal source is coming from the latest technology of 4G Long-Term Evolution (LTE) base station. A detailed explanation on the passive FSR receiver circuit, the detection scheme and the classification algorithm are given. In addition, the proposed passive FSR circuit employs the self-mixing technique at the receiver; hence the synchronization signal from the transmitter is not required. The experimental results confirm the passive FSR system’s capability for ground target detection and classification. Furthermore, this paper illustrates the first classification result in the passive FSR system. The great potential in the passive FSR system provides a new research area in passive radar that can be used for diverse remote monitoring applications. PMID:27690051

Network Anomaly Detection Based on Wavelet Analysis

NASA Astrophysics Data System (ADS)

Lu, Wei; Ghorbani, Ali A.

2008-12-01

Signal processing techniques have been applied recently for analyzing and detecting network anomalies due to their potential to find novel or unknown intrusions. In this paper, we propose a new network signal modelling technique for detecting network anomalies, combining the wavelet approximation and system identification theory. In order to characterize network traffic behaviors, we present fifteen features and use them as the input signals in our system. We then evaluate our approach with the 1999 DARPA intrusion detection dataset and conduct a comprehensive analysis of the intrusions in the dataset. Evaluation results show that the approach achieves high-detection rates in terms of both attack instances and attack types. Furthermore, we conduct a full day's evaluation in a real large-scale WiFi ISP network where five attack types are successfully detected from over 30 millions flows.

Spatiotemporal Change Detection Using Landsat Imagery: the Case Study of Karacabey Flooded Forest, Bursa, Turkey

NASA Astrophysics Data System (ADS)

Akay, A. E.; Gencal, B.; Taş, İ.

2017-11-01

This short paper aims to detect spatiotemporal detection of land use/land cover change within Karacabey Flooded Forest region. Change detection analysis applied to Landsat 5 TM images representing July 2000 and a Landsat 8 OLI representing June 2017. Various image processing tools were implemented using ERDAS 9.2, ArcGIS 10.4.1, and ENVI programs to conduct spatiotemporal change detection over these two images such as band selection, corrections, subset, classification, recoding, accuracy assessment, and change detection analysis. Image classification revealed that there are five significant land use/land cover types, including forest, flooded forest, swamp, water, and other lands (i.e. agriculture, sand, roads, settlement, and open areas). The results indicated that there was increase in flooded forest, water, and other lands, while the cover of forest and swamp decreased.

Comparing power and precision of within-breed and multibreed genome-wide association studies of production traits using whole-genome sequence data for 5 French and Danish dairy cattle breeds.

PubMed

van den Berg, Irene; Boichard, Didier; Lund, Mogens Sandø

2016-11-01

The objective of this study was to compare mapping precision and power of within-breed and multibreed genome-wide association studies (GWAS) and to compare the results obtained by the multibreed GWAS with 3 meta-analysis methods. The multibreed GWAS was expected to improve mapping precision compared with a within-breed GWAS because linkage disequilibrium is conserved over shorter distances across breeds than within breeds. The multibreed GWAS was also expected to increase detection power for quantitative trait loci (QTL) segregating across breeds. GWAS were performed for production traits in dairy cattle, using imputed full genome sequences of 16,031 bulls, originating from 6 French and Danish dairy cattle populations. Our results show that a multibreed GWAS can be a valuable tool for the detection and fine mapping of quantitative trait loci. The number of QTL detected with the multibreed GWAS was larger than the number detected by the within-breed GWAS, indicating an increase in power, especially when the 2 Holstein populations were combined. The largest number of QTL was detected when all populations were combined. The analysis combining all breeds was, however, dominated by Holstein, and QTL segregating in other breeds but not in Holstein were sometimes overshadowed by larger QTL segregating in Holstein. Therefore, the GWAS combining all breeds except Holstein was useful to detect such peaks. Combining all breeds except Holstein resulted in smaller QTL intervals on average, but this outcome was not the case when the Holstein populations were included in the analysis. Although no decrease in the average QTL size was observed, mapping precision did improve for several QTL. Out of 3 different multibreed meta-analysis methods, the weighted z-scores model resulted in the most similar results to the full multibreed GWAS and can be useful as an alternative to a full multibreed GWAS. Differences between the multibreed GWAS and the meta-analyses were larger when different breeds were combined than when the 2 Holstein populations were combined. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Real-time obstructive sleep apnea detection from frequency analysis of EDR and HRV using Lomb Periodogram.

PubMed

Fan, Shu-Han; Chou, Chia-Ching; Chen, Wei-Chen; Fang, Wai-Chi

2015-01-01

In this study, an effective real-time obstructive sleep apnea (OSA) detection method from frequency analysis of ECG-derived respiratory (EDR) and heart rate variability (HRV) is proposed. Compared to traditional Polysomnography (PSG) which needs several physiological signals measured from patients, the proposed OSA detection method just only use ECG signals to determine the time interval of OSA. In order to be feasible to be implemented in hardware to achieve the real-time detection and portable application, the simplified Lomb Periodogram is utilized to perform the frequency analysis of EDR and HRV in this study. The experimental results of this work indicate that the overall accuracy can be effectively increased with values of Specificity (Sp) of 91%, Sensitivity (Se) of 95.7%, and Accuracy of 93.2% by integrating the EDR and HRV indexes.

Identification of faulty sensor using relative partial decomposition via independent component analysis

NASA Astrophysics Data System (ADS)

Wang, Z.; Quek, S. T.

2015-07-01

Performance of any structural health monitoring algorithm relies heavily on good measurement data. Hence, it is necessary to employ robust faulty sensor detection approaches to isolate sensors with abnormal behaviour and exclude the highly inaccurate data in the subsequent analysis. The independent component analysis (ICA) is implemented to detect the presence of sensors showing abnormal behaviour. A normalized form of the relative partial decomposition contribution (rPDC) is proposed to identify the faulty sensor. Both additive and multiplicative types of faults are addressed and the detectability illustrated using a numerical and an experimental example. An empirical method to establish control limits for detecting and identifying the type of fault is also proposed. The results show the effectiveness of the ICA and rPDC method in identifying faulty sensor assuming that baseline cases are available.

Innovative Tools and Technology for Analysis of Single Cells and Cell-Cell Interaction.

PubMed

Konry, Tania; Sarkar, Saheli; Sabhachandani, Pooja; Cohen, Noa

2016-07-11

Heterogeneity in single-cell responses and intercellular interactions results from complex regulation of cell-intrinsic and environmental factors. Single-cell analysis allows not only detection of individual cellular characteristics but also correlation of genetic content with phenotypic traits in the same cell. Technological advances in micro- and nanofabrication have benefited single-cell analysis by allowing precise control of the localized microenvironment, cell manipulation, and sensitive detection capabilities. Additionally, microscale techniques permit rapid, high-throughput, multiparametric screening that has become essential for -omics research. This review highlights innovative applications of microscale platforms in genetic, proteomic, and metabolic detection in single cells; cell sorting strategies; and heterotypic cell-cell interaction. We discuss key design aspects of single-cell localization and isolation in microfluidic systems, dynamic and endpoint analyses, and approaches that integrate highly multiplexed detection of various intracellular species.

Multi-dimensional TOF-SIMS analysis for effective profiling of disease-related ions from the tissue surface.

PubMed

Park, Ji-Won; Jeong, Hyobin; Kang, Byeongsoo; Kim, Su Jin; Park, Sang Yoon; Kang, Sokbom; Kim, Hark Kyun; Choi, Joon Sig; Hwang, Daehee; Lee, Tae Geol

2015-06-05

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) emerges as a promising tool to identify the ions (small molecules) indicative of disease states from the surface of patient tissues. In TOF-SIMS analysis, an enhanced ionization of surface molecules is critical to increase the number of detected ions. Several methods have been developed to enhance ionization capability. However, how these methods improve identification of disease-related ions has not been systematically explored. Here, we present a multi-dimensional SIMS (MD-SIMS) that combines conventional TOF-SIMS and metal-assisted SIMS (MetA-SIMS). Using this approach, we analyzed cancer and adjacent normal tissues first by TOF-SIMS and subsequently by MetA-SIMS. In total, TOF- and MetA-SIMS detected 632 and 959 ions, respectively. Among them, 426 were commonly detected by both methods, while 206 and 533 were detected uniquely by TOF- and MetA-SIMS, respectively. Of the 426 commonly detected ions, 250 increased in their intensities by MetA-SIMS, whereas 176 decreased. The integrated analysis of the ions detected by the two methods resulted in an increased number of discriminatory ions leading to an enhanced separation between cancer and normal tissues. Therefore, the results show that MD-SIMS can be a useful approach to provide a comprehensive list of discriminatory ions indicative of disease states.

Multi-dimensional TOF-SIMS analysis for effective profiling of disease-related ions from the tissue surface

PubMed Central

Park, Ji-Won; Jeong, Hyobin; Kang, Byeongsoo; Kim, Su Jin; Park, Sang Yoon; Kang, Sokbom; Kim, Hark Kyun; Choi, Joon Sig; Hwang, Daehee; Lee, Tae Geol

2015-01-01

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) emerges as a promising tool to identify the ions (small molecules) indicative of disease states from the surface of patient tissues. In TOF-SIMS analysis, an enhanced ionization of surface molecules is critical to increase the number of detected ions. Several methods have been developed to enhance ionization capability. However, how these methods improve identification of disease-related ions has not been systematically explored. Here, we present a multi-dimensional SIMS (MD-SIMS) that combines conventional TOF-SIMS and metal-assisted SIMS (MetA-SIMS). Using this approach, we analyzed cancer and adjacent normal tissues first by TOF-SIMS and subsequently by MetA-SIMS. In total, TOF- and MetA-SIMS detected 632 and 959 ions, respectively. Among them, 426 were commonly detected by both methods, while 206 and 533 were detected uniquely by TOF- and MetA-SIMS, respectively. Of the 426 commonly detected ions, 250 increased in their intensities by MetA-SIMS, whereas 176 decreased. The integrated analysis of the ions detected by the two methods resulted in an increased number of discriminatory ions leading to an enhanced separation between cancer and normal tissues. Therefore, the results show that MD-SIMS can be a useful approach to provide a comprehensive list of discriminatory ions indicative of disease states. PMID:26046669

Raman Spectroscopic Detection for Simulants of Chemical Warfare Agents Using a Spatial Heterodyne Spectrometer.

PubMed

Hu, Guangxiao; Xiong, Wei; Luo, Haiyan; Shi, Hailiang; Li, Zhiwei; Shen, Jing; Fang, Xuejing; Xu, Biao; Zhang, Jicheng

2018-01-01

Raman spectroscopic detection is one of the suitable methods for the detection of chemical warfare agents (CWAs) and simulants. Since the 1980s, many researchers have been dedicated to the research of chemical characteristic of CWAs and simulants and instrumental improvement for their analysis and detection. The spatial heterodyne Raman spectrometer (SHRS) is a new developing instrument for Raman detection that appeared in 2011. It is already well-known that SHRS has the characteristics of high spectral resolution, a large field-of-view, and high throughput. Thus, it is inherently suitable for the analysis and detection of these toxic chemicals and simulants. The in situ and standoff detection of some typical simulants of CWAs, such as dimethyl methylphosphonate (DMMP), diisopropyl methylphosphonate (DIMP), triethylphosphate (TEP), diethyl malonate (DEM), methyl salicylate (MES), 2-chloroethyl ethyl sulfide (CEES), and malathion, were tried. The achieved results show that SHRS does have the ability of in situ analysis or standoff detection for simulants of CWAs. When the laser power was set to as low as 26 mW, the SHRS still has a signal-to-noise ratio higher than 5 in in situ detection. The standoff Raman spectra detection of CWAs simulants was realized at a distance of 11 m. The potential feasibility of standoff detection of SHRS for CWAs simulants has been proved.

Algorithm for automatic analysis of electro-oculographic data

PubMed Central

2013-01-01

Background Large amounts of electro-oculographic (EOG) data, recorded during electroencephalographic (EEG) measurements, go underutilized. We present an automatic, auto-calibrating algorithm that allows efficient analysis of such data sets. Methods The auto-calibration is based on automatic threshold value estimation. Amplitude threshold values for saccades and blinks are determined based on features in the recorded signal. The performance of the developed algorithm was tested by analyzing 4854 saccades and 213 blinks recorded in two different conditions: a task where the eye movements were controlled (saccade task) and a task with free viewing (multitask). The results were compared with results from a video-oculography (VOG) device and manually scored blinks. Results The algorithm achieved 93% detection sensitivity for blinks with 4% false positive rate. The detection sensitivity for horizontal saccades was between 98% and 100%, and for oblique saccades between 95% and 100%. The classification sensitivity for horizontal and large oblique saccades (10 deg) was larger than 89%, and for vertical saccades larger than 82%. The duration and peak velocities of the detected horizontal saccades were similar to those in the literature. In the multitask measurement the detection sensitivity for saccades was 97% with a 6% false positive rate. Conclusion The developed algorithm enables reliable analysis of EOG data recorded both during EEG and as a separate metrics. PMID:24160372

Prognostic value of bone marrow involvement by clonal immunoglobulin gene rearrangements in follicular lymphoma

PubMed Central

Berget, Ellen; Helgeland, Lars; Liseth, Knut; Løkeland, Turid; Molven, Anders; Vintermyr, Olav Karsten

2014-01-01

Aims We aimed to evaluate the prognostic value of routine use of PCR amplification of immunoglobulin gene rearrangements in bone marrow (BM) staging in patients with follicular lymphoma (FL). Methods Clonal rearrangements were assessed by immunoglobulin heavy and light-chain gene rearrangement analysis in BM aspirates from 96 patients diagnosed with FL and related to morphological detection of BM involvement in biopsies. In 71 patients, results were also compared with concurrent flow cytometry analysis. Results BM involvement was detected by PCR in 34.4% (33/96) of patients. The presence of clonal rearrangements by PCR was associated with advanced clinical stage (I–III vs IV; p<0.001), high FL International Prognostic Index (FLIPI) score (0–1, 2 vs ≥3; p=0.003), and detection of BM involvement by morphology and flow cytometry analysis (p<0.001 for both). PCR-positive patients had a significantly poorer survival than PCR-negative patients (p=0.001, log-rank test). Thirteen patients positive by PCR but without morphologically detectable BM involvement, had significantly poorer survival than patients with negative morphology and negative PCR result (p=0.002). The poor survival associated with BM involvement by PCR was independent of the FLIPI score (p=0.007, Cox regression). BM involvement by morphology or flow cytometry did not show a significant impact on survival. Conclusions Our results showed that routine use of PCR-based clonality analysis significantly improved the prognostic impact of BM staging in patients with FL. BM involvement by PCR was also an independent adverse prognostic factor. PMID:25233852

Erythrocyte membrane analysis for type II diabetes detection using Raman spectroscopy in high-wavenumber region

NASA Astrophysics Data System (ADS)

Lin, Jinyong; Zeng, Yongyi; Lin, Juqiang; Wang, Jing; Li, Ling; Huang, Zufang; Li, Buhong; Zeng, Haishan; Chen, Rong

2014-03-01

Raman spectroscopy was employed to detect lipid variation occurring in type II diabetic erythrocyte membrane (EM) without using exogenous reagents. In high-wavenumber (HW) region, significant Raman spectral differences between diabetic and normal EM are observed at 2850, 2873, 2885, 2935, and 2965 cm-1, which are mainly related to lipid in EM. Based on principal component analysis, the diagnostic accuracy of HW region for diabetes detection is 98.8%, which is much higher than that of low-wavenumber region (82.9%). The results suggest that EM HW Raman region has great promise for the reagent-free and non-invasive detection of type II diabetes.

State of the art in hair analysis for detection of drug and alcohol abuse.

PubMed

Pragst, Fritz; Balikova, Marie A

2006-08-01

Hair differs from other materials used for toxicological analysis because of its unique ability to serve as a long-term storage of foreign substances with respect to the temporal appearance in blood. Over the last 20 years, hair testing has gained increasing attention and recognition for the retrospective investigation of chronic drug abuse as well as intentional or unintentional poisoning. In this paper, we review the physiological basics of hair growth, mechanisms of substance incorporation, analytical methods, result interpretation and practical applications of hair analysis for drugs and other organic substances. Improved chromatographic-mass spectrometric techniques with increased selectivity and sensitivity and new methods of sample preparation have improved detection limits from the ng/mg range to below pg/mg. These technical advances have substantially enhanced the ability to detect numerous drugs and other poisons in hair. For example, it was possible to detect previous administration of a single very low dose in drug-facilitated crimes. In addition to its potential application in large scale workplace drug testing and driving ability examination, hair analysis is also used for detection of gestational drug exposure, cases of criminal liability of drug addicts, diagnosis of chronic intoxication and in postmortem toxicology. Hair has only limited relevance in therapy compliance control. Fatty acid ethyl esters and ethyl glucuronide in hair have proven to be suitable markers for alcohol abuse. Hair analysis for drugs is, however, not a simple routine procedure and needs substantial guidelines throughout the testing process, i.e., from sample collection to results interpretation.

Analysis of Poly-β-Hydroxybutyrate in Rhizobium japonicum Bacteroids by Ion-Exclusion High-Pressure Liquid Chromatography and UV Detection †

PubMed Central

Karr, Dale B.; Waters, James K.; Emerich, David W.

1983-01-01

Ion-exclusion high-pressure liquid chromatography (HPLC) was used to measure poly-β-hydroxybutyrate (PHB) in Rhizobium japonicum bacteroids. The products in the acid digest of PHB-containing material were fractionated by HPLC on Aminex HPX-87H ion-exclusion resin for organic acid analysis. Crotonic acid formed from PHB during acid digestion was detected by its intense absorbance at 210 nm. The Aminex-HPLC method provides a rapid and simple chromatographic technique for routine analysis of organic acids. Results of PHB analysis by Aminex-HPLC were confirmed by gas chromatography and spectrophotometric analysis. PMID:16346443

Live face detection based on the analysis of Fourier spectra

NASA Astrophysics Data System (ADS)

Li, Jiangwei; Wang, Yunhong; Tan, Tieniu; Jain, Anil K.

2004-08-01

Biometrics is a rapidly developing technology that is to identify a person based on his or her physiological or behavioral characteristics. To ensure the correction of authentication, the biometric system must be able to detect and reject the use of a copy of a biometric instead of the live biometric. This function is usually termed "liveness detection". This paper describes a new method for live face detection. Using structure and movement information of live face, an effective live face detection algorithm is presented. Compared to existing approaches, which concentrate on the measurement of 3D depth information, this method is based on the analysis of Fourier spectra of a single face image or face image sequences. Experimental results show that the proposed method has an encouraging performance.

Highly sensitive protein detection by biospecific AFM-based fishing with pulsed electrical stimulation.

PubMed

Pleshakova, Tatyana O; Malsagova, Kristina A; Kaysheva, Anna L; Kopylov, Arthur T; Tatur, Vadim Yu; Ziborov, Vadim S; Kanashenko, Sergey L; Galiullin, Rafael A; Ivanov, Yuri D

2017-08-01

We report here the highly sensitive detection of protein in solution at concentrations from 10 -15 to 10 -18 m using the combination of atomic force microscopy (AFM) and mass spectrometry. Biospecific detection of biotinylated bovine serum albumin was carried out by fishing out the protein onto the surface of AFM chips with immobilized avidin, which determined the specificity of the analysis. Electrical stimulation was applied to enhance the fishing efficiency. A high sensitivity of detection was achieved by application of nanosecond electric pulses to highly oriented pyrolytic graphite placed under the AFM chip. A peristaltic pump-based flow system, which is widely used in routine bioanalytical assays, was employed throughout the analysis. These results hold promise for the development of highly sensitive protein detection methods using nanosensor devices.

Directional analysis and filtering for dust storm detection in NOAA-AVHRR imagery

NASA Astrophysics Data System (ADS)

Janugani, S.; Jayaram, V.; Cabrera, S. D.; Rosiles, J. G.; Gill, T. E.; Rivera Rivera, N.

2009-05-01

In this paper, we propose spatio-spectral processing techniques for the detection of dust storms and automatically finding its transport direction in 5-band NOAA-AVHRR imagery. Previous methods that use simple band math analysis have produced promising results but have drawbacks in producing consistent results when low signal to noise ratio (SNR) images are used. Moreover, in seeking to automate the dust storm detection, the presence of clouds in the vicinity of the dust storm creates a challenge in being able to distinguish these two types of image texture. This paper not only addresses the detection of the dust storm in the imagery, it also attempts to find the transport direction and the location of the sources of the dust storm. We propose a spatio-spectral processing approach with two components: visualization and automation. Both approaches are based on digital image processing techniques including directional analysis and filtering. The visualization technique is intended to enhance the image in order to locate the dust sources. The automation technique is proposed to detect the transport direction of the dust storm. These techniques can be used in a system to provide timely warnings of dust storms or hazard assessments for transportation, aviation, environmental safety, and public health.

Validation of a near infrared microscopy method for the detection of animal products in feedingstuffs: results of a collaborative study.

PubMed

Boix, A; Fernández Pierna, J A; von Holst, C; Baeten, V

2012-01-01

The performance characteristics of a near infrared microscopy (NIRM) method, when applied to the detection of animal products in feedingstuffs, were determined via a collaborative study. The method delivers qualitative results in terms of the presence or absence of animal particles in feed and differentiates animal from vegetable feed ingredients on the basis of the evaluation of near infrared spectra obtained from individual particles present in the sample. The specificity ranged from 86% to 100%. The limit of detection obtained on the analysis of the sediment fraction, prepared as for the European official method, was 0.1% processed animal proteins (PAPs) in feed, since all laboratories correctly identified the positive samples. This limit has to be increased up to 2% for the analysis of samples which are not sedimented. The required sensitivity for the official control is therefore achieved in the analysis of the sediment fraction of the samples where the method can be applied for the detection of the presence of animal meal. Criteria for the classification of samples, when fewer than five spectra are found, as being of animal origin needs to be set up in order to harmonise the approach taken by the laboratories when applying NIRM for the detection of the presence of animal meal in feed.

Pesticide analysis using nanoceria-coated paper-based devices as a detection platform.

PubMed

Nouanthavong, Souksanh; Nacapricha, Duangjai; Henry, Charles S; Sameenoi, Yupaporn

2016-03-07

We report the first use of a paper-based device coated with nanoceria as a simple, low-cost and rapid detection platform for the analysis of organophosphate (OP) pesticides using an enzyme inhibition assay with acetylcholinesterase (AChE) and choline oxidase (ChOX). In the presence of acetylcholine, AChE and ChOX catalyze the formation of H2O2, which is detected colorimetrically by a nanoceria-coated device resulting in the formation of a yellow color. After incubation with OP pesticides, the AChE activity was inhibited, producing less H2O2, and a reduction in the yellow intensity. The assay is able to analyze OP pesticides without the use of sophisticated instruments and gives detection limits of 18 ng mL(-1) and 5.3 ng mL(-1) for methyl-paraoxon and chlorpyrifos-oxon, respectively. The developed method was successfully applied to detect methyl-paraoxon in spiked vegetables (cabbage) and a dried seafood product (dried green mussel), obtaining ∼95% recovery values for both sample types. The spiked samples were also analyzed using LC-MS/MS as a comparison to the developed method and similar values were obtained, indicating that the developed method gives accurate results and is suitable for OP analysis in real samples.

A Flexible Analysis Tool for the Quantitative Acoustic Assessment of Infant Cry

PubMed Central

Reggiannini, Brian; Sheinkopf, Stephen J.; Silverman, Harvey F.; Li, Xiaoxue; Lester, Barry M.

2015-01-01

Purpose In this article, the authors describe and validate the performance of a modern acoustic analyzer specifically designed for infant cry analysis. Method Utilizing known algorithms, the authors developed a method to extract acoustic parameters describing infant cries from standard digital audio files. They used a frame rate of 25 ms with a frame advance of 12.5 ms. Cepstral-based acoustic analysis proceeded in 2 phases, computing frame-level data and then organizing and summarizing this information within cry utterances. Using signal detection methods, the authors evaluated the accuracy of the automated system to determine voicing and to detect fundamental frequency (F0) as compared to voiced segments and pitch periods manually coded from spectrogram displays. Results The system detected F0 with 88% to 95% accuracy, depending on tolerances set at 10 to 20 Hz. Receiver operating characteristic analyses demonstrated very high accuracy at detecting voicing characteristics in the cry samples. Conclusions This article describes an automated infant cry analyzer with high accuracy to detect important acoustic features of cry. A unique and important aspect of this work is the rigorous testing of the system’s accuracy as compared to ground-truth manual coding. The resulting system has implications for basic and applied research on infant cry development. PMID:23785178

Fusing Symbolic and Numerical Diagnostic Computations

NASA Technical Reports Server (NTRS)

James, Mark

2007-01-01

X-2000 Anomaly Detection Language denotes a developmental computing language, and the software that establishes and utilizes the language, for fusing two diagnostic computer programs, one implementing a numerical analysis method, the other implementing a symbolic analysis method into a unified event-based decision analysis software system for realtime detection of events (e.g., failures) in a spacecraft, aircraft, or other complex engineering system. The numerical analysis method is performed by beacon-based exception analysis for multi-missions (BEAMs), which has been discussed in several previous NASA Tech Briefs articles. The symbolic analysis method is, more specifically, an artificial-intelligence method of the knowledge-based, inference engine type, and its implementation is exemplified by the Spacecraft Health Inference Engine (SHINE) software. The goal in developing the capability to fuse numerical and symbolic diagnostic components is to increase the depth of analysis beyond that previously attainable, thereby increasing the degree of confidence in the computed results. In practical terms, the sought improvement is to enable detection of all or most events, with no or few false alarms.

Potential of non-invasive esophagus cancer detection based on urine surface-enhanced Raman spectroscopy

NASA Astrophysics Data System (ADS)

Huang, Shaohua; Wang, Lan; Chen, Weisheng; Feng, Shangyuan; Lin, Juqiang; Huang, Zufang; Chen, Guannan; Li, Buhong; Chen, Rong

2014-11-01

Non-invasive esophagus cancer detection based on urine surface-enhanced Raman spectroscopy (SERS) analysis was presented. Urine SERS spectra were measured on esophagus cancer patients (n = 56) and healthy volunteers (n = 36) for control analysis. Tentative assignments of the urine SERS spectra indicated some interesting esophagus cancer-specific biomolecular changes, including a decrease in the relative content of urea and an increase in the percentage of uric acid in the urine of esophagus cancer patients compared to that of healthy subjects. Principal component analysis (PCA) combined with linear discriminant analysis (LDA) was employed to analyze and differentiate the SERS spectra between normal and esophagus cancer urine. The diagnostic algorithms utilizing a multivariate analysis method achieved a diagnostic sensitivity of 89.3% and specificity of 83.3% for separating esophagus cancer samples from normal urine samples. These results from the explorative work suggested that silver nano particle-based urine SERS analysis coupled with PCA-LDA multivariate analysis has potential for non-invasive detection of esophagus cancer.

Diagnosis of 25 genotypes of human papillomaviruses for their physical statuses in cervical precancerous/cancerous lesions: a comparison of E2/E6E7 ratio-based vs. multiple E1-L1/E6E7 ratio-based detection techniques.

PubMed

Zhang, Rong; He, Yi-feng; Chen, Mo; Chen, Chun-mei; Zhu, Qiu-jing; Lu, Huan; Wei, Zhen-hong; Li, Fang; Zhang, Xiao-xin; Xu, Cong-jian; Yu, Long

2014-10-02

Cervical lesions caused by integrated human papillomavirus (HPV) infection are highly dangerous because they can quickly develop into invasive cancers. However, clinicians are currently hampered by the lack of a quick, convenient and precise technique to detect integrated/mixed infections of various genotypes of HPVs in the cervix. This study aimed to develop a practical tool to determine the physical status of different HPVs and evaluate its clinical significance. The target population comprised 1162 women with an HPV infection history of > six months and an abnormal cervical cytological finding. The multiple E1-L1/E6E7 ratio analysis, a novel technique, was developed based on determining the ratios of E1/E6E7, E2/E6E7, E4E5/E6E7, L2/E6E7 and L1/E6E7 within the viral genome. Any imbalanced ratios indicate integration. Its diagnostic and predictive performances were compared with those of E2/E6E7 ratio analysis. The detection accuracy of both techniques was evaluated using the gold-standard technique "detection of integrated papillomavirus sequences" (DIPS). To realize a multigenotypic detection goal, a primer and probe library was established. The integration rate of a particular genotype of HPV was correlated with its tumorigenic potential and women with higher lesion grades often carried lower viral loads. The E1-L1/E6E7 ratio analysis achieved 92.7% sensitivity and 99.0% specificity in detecting HPV integration, while the E2/E6E7 ratio analysis showed a much lower sensitivity (75.6%) and a similar specificity (99.3%). Interference due to episomal copies was observed in both techniques, leading to false-negative results. However, some positive results of E1-L1/E6E7 ratio analysis were missed by DIPS due to its stochastic detection nature. The E1-L1/E6E7 ratio analysis is more efficient than E2/E6E7 ratio analysis and DIPS in predicting precancerous/cancerous lesions, in which both positive predictive values (36.7%-82.3%) and negative predictive values (75.9%-100%) were highest (based on the results of three rounds of biopsies). The multiple E1-L1/E6E7 ratio analysis is more sensitive and predictive than E2/E6E7 ratio analysis as a triage test for detecting HPV integration. It can effectively narrow the range of candidates for colposcopic examination and cervical biopsy, thereby lowering the expense of cervical cancer prevention.

[Research on a non-invasive pulse wave detection and analysis system].

PubMed

Li, Ting; Yu, Gang

2008-10-01

A novel non-invasive pulse wave detection and analysis system has been developed, including the software and the hardware. Bi-channel signals can be acquired, stored and shown on the screen dynamically at the same time. Pulse wave can be reshown and printed after pulse wave analysis and pulse wave velocity analysis. This system embraces a computer which is designed for fast data saving, analyzing and processing, and a portable data sampling machine which is based on a singlechip. Experimental results have shown that the system is stable and easy to use, and the parameters are calculated accurately.

[The application of wavelet analysis of remote detection of pollution clouds].

PubMed

Zhang, J; Jiang, F

2001-08-01

The discrete wavelet transform (DWT) is used to analyse the spectra of pollution clouds in complicated environment and extract the small-features. The DWT is a time-frequency analysis technology, which detects the subtle small changes in the target spectrum. The results show that the DWT is a quite effective method to extract features of target-cloud and improve the reliability of monitoring alarm system.

Regional interpretation of PSInSAR(TM) data for landslide investigations

NASA Astrophysics Data System (ADS)

Meisina, Claudia; Zucca, Francesco; Notti, Davide

2010-05-01

The work presents the results of a PSInSAR™ analysis carried out in a wide area of North-Western Italy (Lombardia and Piemonte regions) with an extension of about 40.000 km2. The regional study of PS data was part of a project concerning the geological interpretation of PSInSAR™ data, carried out in collaboration with local regional environment agencies (ARPA Piemonte and Lombardia Region). A set of SAR scenes acquired between May 1992 and December 2000 by the ERS sensor of the European Space Agency along ascending orbits and a set of scenes acquired between April 2003 and June 2007 by RADARSAT sensor along descending and ascending orbits were used by TeleRilevamento-Europa for a Standard PS Analysis. The RADARSAT images cover only a sector of Lombardia Region (Varese, Brescia, Bergamo, Sondrio provinces). At regional scale the aims were: 1) to check the capability of the technique in the detection of the landslides in different geological and geomorphological environments using different sensors; 2) to verify how the PSInSAR™ technique can improve the results of the landslide database IFFI (the Italian Landslide Inventory) in terms of landslide areal extent evaluation and unmapped phenomena detection. A database containing the areas where the SAR data showed anomalous movement (the so called anomalous areas) was built. This analysis takes into account the influence of the types of sensors (RADARSAT and ERS), of the different geometries of acquisition (ascending and descending) and of the geological and geomorphological characteristics of the main environments (Alps, Langhe Hills, Apennines) on landslide detection. The results of the research have showed that the PSInSAR™ analysis for slope movements is limited to very slow landslides with constant movement (particularly DSGSD) that represent a small percentage of all landslides. Problems related with lack of scatterers and topographic effects also limit the number of landslides suitable for studies. However regional landslide inventories traditionally based on aerial photo interpretation and field surveys can be improved by coupling with PSInSAR™ interferometry. The integration of the outcomes of the conventional geological, geomorphological studies with the results of the PSInSAR™ analysis improve the landslide inventory in terms of landslide areal extent evaluation and unmapped phenomena detection (about 60 landslides nor reported in the IFFI database were detected in the study area). Nevertheless, the integration with traditional methods and field surveys is still necessary for a correct interpretation of the PSInSAR™ results. The analysis of PS data also showed that the landslide detection depends by some factors: - scatterer distribution: the debris (rock fall, glacial, etc… deposits) in Alpine area is a good reflector and this increases the number of landslides detected in this area. Nevertheless, it is difficult to discriminate ground deformation due to different processes, as local settlement of man-made structure (e.g. Apennine and Langhe) or the shallow deformations caused by seasonal processes in debris (Alps); - type of sensor: RADARSAT data allow to detect an higher number of landslides than ERS. - geometry of acquisition: using two geometries of acquisition (ascending and descending) it is possible to detect more landslides.

Automatic Implementation of Prony Analysis for Electromechanical Mode Identification from Phasor Measurements

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhou, Ning; Huang, Zhenyu; Tuffner, Francis K.

2010-07-31

Small signal stability problems are one of the major threats to grid stability and reliability. Prony analysis has been successfully applied on ringdown data to monitor electromechanical modes of a power system using phasor measurement unit (PMU) data. To facilitate an on-line application of mode estimation, this paper developed a recursive algorithm for implementing Prony analysis and proposed an oscillation detection method to detect ringdown data in real time. By automatically detect ringdown data, the proposed method helps guarantee that Prony analysis is applied properly and timely on the ringdown data. Thus, the mode estimation results can be performed reliablymore » and timely. The proposed method is tested using Monte Carlo simulations based on a 17-machine model and is shown to be able to properly identify the oscillation data for on-line application of Prony analysis.« less

Detection of incipient defects in cables by partial discharge signal analysis

NASA Astrophysics Data System (ADS)

Martzloff, F. D.; Simmon, E.; Steiner, J. P.; Vanbrunt, R. J.

1992-07-01

As one of the objectives of a program aimed at assessing test methods for in-situ detection of incipient defects in cables due to aging, a laboratory test system was implemented to demonstrate that the partial discharge analysis method can be successfully applied to low-voltage cables. Previous investigations generally involved cables rated 5 kV or higher, while the objective of the program focused on the lower voltages associated with the safety systems of nuclear power plants. The defect detection system implemented for the project was based on commercially available signal analysis hardware and software packages, customized for the specific purposes of the project. The test specimens included several cables of the type found in nuclear power plants, including artificial defects introduced at various points of the cable. The results indicate that indeed, partial discharge analysis is capable of detecting incipient defects in low-voltage cables. There are, however, some limitations of technical and non-technical nature that need further exploration before this method can be accepted in the industry.

Fast and sensitive trace analysis of malachite green using a surface-enhanced Raman microfluidic sensor.

PubMed

Lee, Sangyeop; Choi, Junghyun; Chen, Lingxin; Park, Byungchoon; Kyong, Jin Burm; Seong, Gi Hun; Choo, Jaebum; Lee, Yeonjung; Shin, Kyung-Hoon; Lee, Eun Kyu; Joo, Sang-Woo; Lee, Kyeong-Hee

2007-05-08

A rapid and highly sensitive trace analysis technique for determining malachite green (MG) in a polydimethylsiloxane (PDMS) microfluidic sensor was investigated using surface-enhanced Raman spectroscopy (SERS). A zigzag-shaped PDMS microfluidic channel was fabricated for efficient mixing between MG analytes and aggregated silver colloids. Under the optimal condition of flow velocity, MG molecules were effectively adsorbed onto silver nanoparticles while flowing along the upper and lower zigzag-shaped PDMS channel. A quantitative analysis of MG was performed based on the measured peak height at 1615 cm(-1) in its SERS spectrum. The limit of detection, using the SERS microfluidic sensor, was found to be below the 1-2 ppb level and this low detection limit is comparable to the result of the LC-Mass detection method. In the present study, we introduce a new conceptual detection technology, using a SERS microfluidic sensor, for the highly sensitive trace analysis of MG in water.

Detecting and Estimating Contamination of Human DNA Samples in Sequencing and Array-Based Genotype Data

PubMed Central

Jun, Goo; Flickinger, Matthew; Hetrick, Kurt N.; Romm, Jane M.; Doheny, Kimberly F.; Abecasis, Gonçalo R.; Boehnke, Michael; Kang, Hyun Min

2012-01-01

DNA sample contamination is a serious problem in DNA sequencing studies and may result in systematic genotype misclassification and false positive associations. Although methods exist to detect and filter out cross-species contamination, few methods to detect within-species sample contamination are available. In this paper, we describe methods to identify within-species DNA sample contamination based on (1) a combination of sequencing reads and array-based genotype data, (2) sequence reads alone, and (3) array-based genotype data alone. Analysis of sequencing reads allows contamination detection after sequence data is generated but prior to variant calling; analysis of array-based genotype data allows contamination detection prior to generation of costly sequence data. Through a combination of analysis of in silico and experimentally contaminated samples, we show that our methods can reliably detect and estimate levels of contamination as low as 1%. We evaluate the impact of DNA contamination on genotype accuracy and propose effective strategies to screen for and prevent DNA contamination in sequencing studies. PMID:23103226

SU-G-JeP4-03: Anomaly Detection of Respiratory Motion by Use of Singular Spectrum Analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kotoku, J; Kumagai, S; Nakabayashi, S

Purpose: The implementation and realization of automatic anomaly detection of respiratory motion is a very important technique to prevent accidental damage during radiation therapy. Here, we propose an automatic anomaly detection method using singular value decomposition analysis. Methods: The anomaly detection procedure consists of four parts:1) measurement of normal respiratory motion data of a patient2) calculation of a trajectory matrix representing normal time-series feature3) real-time monitoring and calculation of a trajectory matrix of real-time data.4) calculation of an anomaly score from the similarity of the two feature matrices. Patient motion was observed by a marker-less tracking system using a depthmore » camera. Results: Two types of motion e.g. cough and sudden stop of breathing were successfully detected in our real-time application. Conclusion: Automatic anomaly detection of respiratory motion using singular spectrum analysis was successful in the cough and sudden stop of breathing. The clinical use of this algorithm will be very hopeful. This work was supported by JSPS KAKENHI Grant Number 15K08703.« less

Statistical power analysis of cardiovascular safety pharmacology studies in conscious rats.

PubMed

Bhatt, Siddhartha; Li, Dingzhou; Flynn, Declan; Wisialowski, Todd; Hemkens, Michelle; Steidl-Nichols, Jill

2016-01-01

Cardiovascular (CV) toxicity and related attrition are a major challenge for novel therapeutic entities and identifying CV liability early is critical for effective derisking. CV safety pharmacology studies in rats are a valuable tool for early investigation of CV risk. Thorough understanding of data analysis techniques and statistical power of these studies is currently lacking and is imperative for enabling sound decision-making. Data from 24 crossover and 12 parallel design CV telemetry rat studies were used for statistical power calculations. Average values of telemetry parameters (heart rate, blood pressure, body temperature, and activity) were logged every 60s (from 1h predose to 24h post-dose) and reduced to 15min mean values. These data were subsequently binned into super intervals for statistical analysis. A repeated measure analysis of variance was used for statistical analysis of crossover studies and a repeated measure analysis of covariance was used for parallel studies. Statistical power analysis was performed to generate power curves and establish relationships between detectable CV (blood pressure and heart rate) changes and statistical power. Additionally, data from a crossover CV study with phentolamine at 4, 20 and 100mg/kg are reported as a representative example of data analysis methods. Phentolamine produced a CV profile characteristic of alpha adrenergic receptor antagonism, evidenced by a dose-dependent decrease in blood pressure and reflex tachycardia. Detectable blood pressure changes at 80% statistical power for crossover studies (n=8) were 4-5mmHg. For parallel studies (n=8), detectable changes at 80% power were 6-7mmHg. Detectable heart rate changes for both study designs were 20-22bpm. Based on our results, the conscious rat CV model is a sensitive tool to detect and mitigate CV risk in early safety studies. Furthermore, these results will enable informed selection of appropriate models and study design for early stage CV studies. Copyright © 2016 Elsevier Inc. All rights reserved.

Direct Detection and Identification of Prosthetic Joint Infection Pathogens in Synovial Fluid by Metagenomic Shotgun Sequencing.

PubMed

Ivy, Morgan I; Thoendel, Matthew J; Jeraldo, Patricio R; Greenwood-Quaintance, Kerryl E; Hanssen, Arlen D; Abdel, Matthew P; Chia, Nicholas; Yao, Janet Z; Tande, Aaron J; Mandrekar, Jayawant N; Patel, Robin

2018-05-30

Background: Metagenomic shotgun sequencing has the potential to transform how serious infections are diagnosed by offering universal, culture-free pathogen detection. This may be especially advantageous for microbial diagnosis of prosthetic joint infection (PJI) by synovial fluid analysis, since synovial fluid cultures are not universally positive, and synovial fluid is easily obtained pre-operatively. We applied a metagenomics-based approach to synovial fluid in an attempt to detect microorganisms in 168 failed total knee arthroplasties. Results: Genus- and species-level analysis of metagenomic sequencing yielded the known pathogen in 74 (90%) and 68 (83%) of the 82 culture-positive PJIs analyzed, respectively, with testing of two (2%) and three (4%) samples, respectively, yielding additional pathogens not detected by culture. For the 25 culture-negative PJIs tested, genus- and species-level analysis yielded 19 (76%) and 21 (84%) samples with insignificant findings, respectively, and 6 (24%) and 4 (16%) with potential pathogens detected, respectively. Genus- and species-level analysis of the 60 culture-negative aseptic failure cases yielded 53 (88.3%) and 56 (93.3%) cases with insignificant findings, and 7 (11.7%) and 4 (6.7%) with potential clinically-significant organisms detected, respectively. There was one case of aseptic failure with synovial fluid culture growth; metagenomic analysis showed insignificant findings, suggesting possible synovial fluid culture contamination. Conclusion: Metagenomic shotgun sequencing can detect pathogens involved in PJI when applied to synovial fluid and may be particularly useful for culture-negative cases. Copyright © 2018 American Society for Microbiology.

Consistency of Influenza A Virus Detection Test Results across Respiratory Specimen Collection Methods Using Real-Time Reverse Transcription-PCR

PubMed Central

Gaglani, Manjusha; Naleway, Allison; Reynolds, Sue; Ball, Sarah; Bozeman, Sam; Henkle, Emily; Meece, Jennifer; Vandermause, Mary; Clipper, Lydia; Thompson, Mark

2013-01-01

In our prospective cohort study, we compared the performance of nasopharyngeal, oropharyngeal, and nasal swabs for the detection of influenza virus using real-time reverse transcription-PCR assay. Joint consideration of results from oropharyngeal and nasal swabs was as effective as consideration of results from nasopharyngeal swabs alone, as measured by sensitivity and noninferiority analysis. PMID:24108606

Conflict Detection Performance Analysis for Function Allocation Using Time-Shifted Recorded Traffic Data

NASA Technical Reports Server (NTRS)

Guerreiro, Nelson M.; Butler, Ricky W.; Maddalon, Jeffrey M.; Hagen, George E.; Lewis, Timothy A.

2015-01-01

The performance of the conflict detection function in a separation assurance system is dependent on the content and quality of the data available to perform that function. Specifically, data quality and data content available to the conflict detection function have a direct impact on the accuracy of the prediction of an aircraft's future state or trajectory, which, in turn, impacts the ability to successfully anticipate potential losses of separation (detect future conflicts). Consequently, other separation assurance functions that rely on the conflict detection function - namely, conflict resolution - are prone to negative performance impacts. The many possible allocations and implementations of the conflict detection function between centralized and distributed systems drive the need to understand the key relationships that impact conflict detection performance, with respect to differences in data available. This paper presents the preliminary results of an analysis technique developed to investigate the impacts of data quality and data content on conflict detection performance. Flight track data recorded from a day of the National Airspace System is time-shifted to create conflicts not present in the un-shifted data. A methodology is used to smooth and filter the recorded data to eliminate sensor fusion noise, data drop-outs and other anomalies in the data. The metrics used to characterize conflict detection performance are presented and a set of preliminary results is discussed.

Edge detection and localization with edge pattern analysis and inflection characterization

NASA Astrophysics Data System (ADS)

Jiang, Bo

2012-05-01

In general edges are considered to be abrupt changes or discontinuities in two dimensional image signal intensity distributions. The accuracy of front-end edge detection methods in image processing impacts the eventual success of higher level pattern analysis downstream. To generalize edge detectors designed from a simple ideal step function model to real distortions in natural images, research on one dimensional edge pattern analysis to improve the accuracy of edge detection and localization proposes an edge detection algorithm, which is composed by three basic edge patterns, such as ramp, impulse, and step. After mathematical analysis, general rules for edge representation based upon the classification of edge types into three categories-ramp, impulse, and step (RIS) are developed to reduce detection and localization errors, especially reducing "double edge" effect that is one important drawback to the derivative method. But, when applying one dimensional edge pattern in two dimensional image processing, a new issue is naturally raised that the edge detector should correct marking inflections or junctions of edges. Research on human visual perception of objects and information theory pointed out that a pattern lexicon of "inflection micro-patterns" has larger information than a straight line. Also, research on scene perception gave an idea that contours have larger information are more important factor to determine the success of scene categorization. Therefore, inflections or junctions are extremely useful features, whose accurate description and reconstruction are significant in solving correspondence problems in computer vision. Therefore, aside from adoption of edge pattern analysis, inflection or junction characterization is also utilized to extend traditional derivative edge detection algorithm. Experiments were conducted to test my propositions about edge detection and localization accuracy improvements. The results support the idea that these edge detection method improvements are effective in enhancing the accuracy of edge detection and localization.

A cloud shadow detection method combined with cloud height iteration and spectral analysis for Landsat 8 OLI data

NASA Astrophysics Data System (ADS)

Sun, Lin; Liu, Xinyan; Yang, Yikun; Chen, TingTing; Wang, Quan; Zhou, Xueying

2018-04-01

Although enhanced over prior Landsat instruments, Landsat 8 OLI can obtain very high cloud detection precisions, but for the detection of cloud shadows, it still faces great challenges. Geometry-based cloud shadow detection methods are considered the most effective and are being improved constantly. The Function of Mask (Fmask) cloud shadow detection method is one of the most representative geometry-based methods that has been used for cloud shadow detection with Landsat 8 OLI. However, the Fmask method estimates cloud height employing fixed temperature rates, which are highly uncertain, and errors of large area cloud shadow detection can be caused by errors in estimations of cloud height. This article improves the geometry-based cloud shadow detection method for Landsat OLI from the following two aspects. (1) Cloud height no longer depends on the brightness temperature of the thermal infrared band but uses a possible dynamic range from 200 m to 12,000 m. In this case, cloud shadow is not a specific location but a possible range. Further analysis was carried out in the possible range based on the spectrum to determine cloud shadow location. This effectively avoids the cloud shadow leakage caused by the error in the height determination of a cloud. (2) Object-based and pixel spectral analyses are combined to detect cloud shadows, which can realize cloud shadow detection from two aspects of target scale and pixel scale. Based on the analysis of the spectral differences between the cloud shadow and typical ground objects, the best cloud shadow detection bands of Landsat 8 OLI were determined. The combined use of spectrum and shape can effectively improve the detection precision of cloud shadows produced by thin clouds. Several cloud shadow detection experiments were carried out, and the results were verified by the results of artificial recognition. The results of these experiments indicated that this method can identify cloud shadows in different regions with correct accuracy exceeding 80%, approximately 5% of the areas were wrongly identified, and approximately 10% of the cloud shadow areas were missing. The accuracy of this method is obviously higher than the recognition accuracy of Fmask, which has correct accuracy lower than 60%, and the missing recognition is approximately 40%.

Phenology satellite experiment

NASA Technical Reports Server (NTRS)

Dethier, B. E. (Principal Investigator)

1973-01-01

The author has identified the following significant results. The detection of a phenological event (the Brown Wave-vegetation sensescence) for specific forest and crop types using ERTS-1 imagery is described. Data handling techniques including computer analysis and photointerpretation procedures are explained. Computer analysis of multspectral scanner digital tapes in all bands was used to give the relative changes of spectral reflectance with time of forests and specified crops. These data were obtained for a number of the twenty-four sites located within four north-south corridors across the United States. Analysis of ground observation photography and ERTS-1 imagery for sites in the Appalachian Corridor and Mississippi Valley Corridor indicates that the recession of vegetation development can be detected very well. Tentative conclusions are that specific phenological events such as crop maturity or leaf fall can be mapped for specific sites and possible for different regions. Preliminary analysis based on a number of samples in mixed deciduous hardwood stands indicates that as senescence proceeds both the rate of change and differences in color among species can be detected. The results to data show the feasibility of the development and refinement of phenoclimatic models.

Glial brain tumor detection by using symmetry analysis

NASA Astrophysics Data System (ADS)

Pedoia, Valentina; Binaghi, Elisabetta; Balbi, Sergio; De Benedictis, Alessandro; Monti, Emanuele; Minotto, Renzo

2012-02-01

In this work a fully automatic algorithm to detect brain tumors by using symmetry analysis is proposed. In recent years a great effort of the research in field of medical imaging was focused on brain tumors segmentation. The quantitative analysis of MRI brain tumor allows to obtain useful key indicators of disease progression. The complex problem of segmenting tumor in MRI can be successfully addressed by considering modular and multi-step approaches mimicking the human visual inspection process. The tumor detection is often an essential preliminary phase to solvethe segmentation problem successfully. In visual analysis of the MRI, the first step of the experts cognitive process, is the detection of an anomaly respect the normal tissue, whatever its nature. An healthy brain has a strong sagittal symmetry, that is weakened by the presence of tumor. The comparison between the healthy and ill hemisphere, considering that tumors are generally not symmetrically placed in both hemispheres, was used to detect the anomaly. A clustering method based on energy minimization through Graph-Cut is applied on the volume computed as a difference between the left hemisphere and the right hemisphere mirrored across the symmetry plane. Differential analysis involves the loss the knowledge of the tumor side. Through an histogram analysis the ill hemisphere is recognized. Many experiments are performed to assess the performance of the detection strategy on MRI volumes in presence of tumors varied in terms of shapes positions and intensity levels. The experiments showed good results also in complex situations.

Gas-phase detection of solid-state fission product complexes for post-detonation nuclear forensic analysis

DOE PAGES

Stratz, S. Adam; Jones, Steven A.; Oldham, Colton J.; ...

2016-06-27

This study presents the first known detection of fission products commonly found in post-detonation nuclear debris samples using solid sample introduction and a uniquely coupled gas chromatography inductively-coupled plasma time-of-flight mass spectrometer. Rare earth oxides were chemically altered to incorporate a ligand that enhances the volatility of the samples. These samples were injected (as solids) into the aforementioned instrument and detected for the first time. Repeatable results indicate the validity of the methodology, and this capability, when refined, will prove to be a valuable asset for rapid post-detonation nuclear forensic analysis.

Interactive-predictive detection of handwritten text blocks

NASA Astrophysics Data System (ADS)

Ramos Terrades, O.; Serrano, N.; Gordó, A.; Valveny, E.; Juan, A.

2010-01-01

A method for text block detection is introduced for old handwritten documents. The proposed method takes advantage of sequential book structure, taking into account layout information from pages previously transcribed. This glance at the past is used to predict the position of text blocks in the current page with the help of conventional layout analysis methods. The method is integrated into the GIDOC prototype: a first attempt to provide integrated support for interactive-predictive page layout analysis, text line detection and handwritten text transcription. Results are given in a transcription task on a 764-page Spanish manuscript from 1891.

Gas-phase detection of solid-state fission product complexes for post-detonation nuclear forensic analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stratz, S. Adam; Jones, Steven A.; Oldham, Colton J.

This study presents the first known detection of fission products commonly found in post-detonation nuclear debris samples using solid sample introduction and a uniquely coupled gas chromatography inductively-coupled plasma time-of-flight mass spectrometer. Rare earth oxides were chemically altered to incorporate a ligand that enhances the volatility of the samples. These samples were injected (as solids) into the aforementioned instrument and detected for the first time. Repeatable results indicate the validity of the methodology, and this capability, when refined, will prove to be a valuable asset for rapid post-detonation nuclear forensic analysis.

[A new HPLC procedure for cyclamate in food with pre-chromatographic derivatization].

PubMed

Schwedt, G; Hauck, M

1988-08-01

A high-pressure liquid chromatography (HPLC) procedure for the detection of cyclamate in liquid and solid samples is presented, which depends on oxidation and the reaction of cyclohexylamine with o-phthaldialdehyde to form a condensation product. The results of the HPLC analysis, using an RP-C 18 separation system with UV detection at 242 nm are reported. Contents, from 2 to 400 mg/l, can be detected in less than 2 h (HPLC analysis within 20 min) with relative standard deviations of 4%. Only for cucumber infusions were incomplete recoveries of 68% obtained.

Non-invasive detection of the freezing of gait in Parkinson's disease using spectral and wavelet features.

PubMed

Nazarzadeh, Kimia; Arjunan, Sridhar P; Kumar, Dinesh K; Das, Debi Prasad

2016-08-01

In this study, we have analyzed the accelerometer data recorded during gait analysis of Parkinson disease patients for detecting freezing of gait (FOG) episodes. The proposed method filters the recordings for noise reduction of the leg movement changes and computes the wavelet coefficients to detect FOG events. Publicly available FOG database was used and the technique was evaluated using receiver operating characteristic (ROC) analysis. Results show a higher performance of the wavelet feature in discrimination of the FOG events from the background activity when compared with the existing technique.

Efficacy of a novel PCR- and microarray-based method in diagnosis of a prosthetic joint infection

PubMed Central

2014-01-01

Background and purpose Polymerase chain reaction (PCR) methods enable detection and species identification of many pathogens. We assessed the efficacy of a new PCR and microarray-based platform for detection of bacteria in prosthetic joint infections (PJIs). Methods This prospective study involved 61 suspected PJIs in hip and knee prostheses and 20 negative controls. 142 samples were analyzed by Prove-it Bone and Joint assay. The laboratory staff conducting the Prove-it analysis were not aware of the results of microbiological culture and clinical findings. The results of the analysis were compared with diagnosis of PJIs defined according to the Musculoskeletal Infection Society (MSIS) criteria and with the results of microbiological culture. Results 38 of 61 suspected PJIs met the definition of PJI according to the MSIS criteria. Of the 38 patients, the PCR detected bacteria in 31 whereas bacterial culture was positive in 28 patients. 15 of the PJI patients were undergoing antimicrobial treatment as the samples for analysis were obtained. When antimicrobial treatment had lasted 4 days or more, PCR detected bacteria in 6 of the 9 patients, but positive cultures were noted in only 2 of the 9 patients. All PCR results for the controls were negative. Of the 61 suspected PJIs, there were false-positive PCR results in 6 cases. Interpretation The Prove-it assay was helpful in PJI diagnostics during ongoing antimicrobial treatment. Without preceding treatment with antimicrobials, PCR and microarray-based assay did not appear to give any additional information over culture. PMID:24564748

TargetVue: Visual Analysis of Anomalous User Behaviors in Online Communication Systems.

PubMed

Cao, Nan; Shi, Conglei; Lin, Sabrina; Lu, Jie; Lin, Yu-Ru; Lin, Ching-Yung

2016-01-01

Users with anomalous behaviors in online communication systems (e.g. email and social medial platforms) are potential threats to society. Automated anomaly detection based on advanced machine learning techniques has been developed to combat this issue; challenges remain, though, due to the difficulty of obtaining proper ground truth for model training and evaluation. Therefore, substantial human judgment on the automated analysis results is often required to better adjust the performance of anomaly detection. Unfortunately, techniques that allow users to understand the analysis results more efficiently, to make a confident judgment about anomalies, and to explore data in their context, are still lacking. In this paper, we propose a novel visual analysis system, TargetVue, which detects anomalous users via an unsupervised learning model and visualizes the behaviors of suspicious users in behavior-rich context through novel visualization designs and multiple coordinated contextual views. Particularly, TargetVue incorporates three new ego-centric glyphs to visually summarize a user's behaviors which effectively present the user's communication activities, features, and social interactions. An efficient layout method is proposed to place these glyphs on a triangle grid, which captures similarities among users and facilitates comparisons of behaviors of different users. We demonstrate the power of TargetVue through its application in a social bot detection challenge using Twitter data, a case study based on email records, and an interview with expert users. Our evaluation shows that TargetVue is beneficial to the detection of users with anomalous communication behaviors.

Balancing precision and risk: should multiple detection methods be analyzed separately in N-mixture models?

USGS Publications Warehouse

Graves, Tabitha A.; Royle, J. Andrew; Kendall, Katherine C.; Beier, Paul; Stetz, Jeffrey B.; Macleod, Amy C.

2012-01-01

Using multiple detection methods can increase the number, kind, and distribution of individuals sampled, which may increase accuracy and precision and reduce cost of population abundance estimates. However, when variables influencing abundance are of interest, if individuals detected via different methods are influenced by the landscape differently, separate analysis of multiple detection methods may be more appropriate. We evaluated the effects of combining two detection methods on the identification of variables important to local abundance using detections of grizzly bears with hair traps (systematic) and bear rubs (opportunistic). We used hierarchical abundance models (N-mixture models) with separate model components for each detection method. If both methods sample the same population, the use of either data set alone should (1) lead to the selection of the same variables as important and (2) provide similar estimates of relative local abundance. We hypothesized that the inclusion of 2 detection methods versus either method alone should (3) yield more support for variables identified in single method analyses (i.e. fewer variables and models with greater weight), and (4) improve precision of covariate estimates for variables selected in both separate and combined analyses because sample size is larger. As expected, joint analysis of both methods increased precision as well as certainty in variable and model selection. However, the single-method analyses identified different variables and the resulting predicted abundances had different spatial distributions. We recommend comparing single-method and jointly modeled results to identify the presence of individual heterogeneity between detection methods in N-mixture models, along with consideration of detection probabilities, correlations among variables, and tolerance to risk of failing to identify variables important to a subset of the population. The benefits of increased precision should be weighed against those risks. The analysis framework presented here will be useful for other species exhibiting heterogeneity by detection method.

Improved biliary detection and diagnosis through intelligent machine analysis.

PubMed

Logeswaran, Rajasvaran

2012-09-01

This paper reports on work undertaken to improve automated detection of bile ducts in magnetic resonance cholangiopancreatography (MRCP) images, with the objective of conducting preliminary classification of the images for diagnosis. The proposed I-BDeDIMA (Improved Biliary Detection and Diagnosis through Intelligent Machine Analysis) scheme is a multi-stage framework consisting of successive phases of image normalization, denoising, structure identification, object labeling, feature selection and disease classification. A combination of multiresolution wavelet, dynamic intensity thresholding, segment-based region growing, region elimination, statistical analysis and neural networks, is used in this framework to achieve good structure detection and preliminary diagnosis. Tests conducted on over 200 clinical images with known diagnosis have shown promising results of over 90% accuracy. The scheme outperforms related work in the literature, making it a viable framework for computer-aided diagnosis of biliary diseases. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Detecting spatial regimes in ecosystems | Science Inventory ...

EPA Pesticide Factsheets

Research on early warning indicators has generally focused on assessing temporal transitions with limited application of these methods to detecting spatial regimes. Traditional spatial boundary detection procedures that result in ecoregion maps are typically based on ecological potential (i.e. potential vegetation), and often fail to account for ongoing changes due to stressors such as land use change and climate change and their effects on plant and animal communities. We use Fisher information, an information theory based method, on both terrestrial and aquatic animal data (US Breeding Bird Survey and marine zooplankton) to identify ecological boundaries, and compare our results to traditional early warning indicators, conventional ecoregion maps, and multivariate analysis such as nMDS (non-metric Multidimensional Scaling) and cluster analysis. We successfully detect spatial regimes and transitions in both terrestrial and aquatic systems using Fisher information. Furthermore, Fisher information provided explicit spatial information about community change that is absent from other multivariate approaches. Our results suggest that defining spatial regimes based on animal communities may better reflect ecological reality than do traditional ecoregion maps, especially in our current era of rapid and unpredictable ecological change. Use an information theory based method to identify ecological boundaries and compare our results to traditional early warning

Karyotype versus microarray testing for genetic abnormalities after stillbirth.

PubMed

Reddy, Uma M; Page, Grier P; Saade, George R; Silver, Robert M; Thorsten, Vanessa R; Parker, Corette B; Pinar, Halit; Willinger, Marian; Stoll, Barbara J; Heim-Hall, Josefine; Varner, Michael W; Goldenberg, Robert L; Bukowski, Radek; Wapner, Ronald J; Drews-Botsch, Carolyn D; O'Brien, Barbara M; Dudley, Donald J; Levy, Brynn

2012-12-06

Genetic abnormalities have been associated with 6 to 13% of stillbirths, but the true prevalence may be higher. Unlike karyotype analysis, microarray analysis does not require live cells, and it detects small deletions and duplications called copy-number variants. The Stillbirth Collaborative Research Network conducted a population-based study of stillbirth in five geographic catchment areas. Standardized postmortem examinations and karyotype analyses were performed. A single-nucleotide polymorphism array was used to detect copy-number variants of at least 500 kb in placental or fetal tissue. Variants that were not identified in any of three databases of apparently unaffected persons were then classified into three groups: probably benign, clinical significance unknown, or pathogenic. We compared the results of karyotype and microarray analyses of samples obtained after delivery. In our analysis of samples from 532 stillbirths, microarray analysis yielded results more often than did karyotype analysis (87.4% vs. 70.5%, P<0.001) and provided better detection of genetic abnormalities (aneuploidy or pathogenic copy-number variants, 8.3% vs. 5.8%; P=0.007). Microarray analysis also identified more genetic abnormalities among 443 antepartum stillbirths (8.8% vs. 6.5%, P=0.02) and 67 stillbirths with congenital anomalies (29.9% vs. 19.4%, P=0.008). As compared with karyotype analysis, microarray analysis provided a relative increase in the diagnosis of genetic abnormalities of 41.9% in all stillbirths, 34.5% in antepartum stillbirths, and 53.8% in stillbirths with anomalies. Microarray analysis is more likely than karyotype analysis to provide a genetic diagnosis, primarily because of its success with nonviable tissue, and is especially valuable in analyses of stillbirths with congenital anomalies or in cases in which karyotype results cannot be obtained. (Funded by the Eunice Kennedy Shriver National Institute of Child Health and Human Development.).

Organic chemical analysis on a microscopic scale using two-step laser desorption/laser ionization mass spectrometry

NASA Technical Reports Server (NTRS)

Kovalenko, L. J.; Philippoz, J.-M.; Bucenell, J. R.; Zenobi, R.; Zare, R. N.

1991-01-01

The distribution of PAHs in the Allende meteorite has been measured using two-step laser desorption and laser multiphoton-ionization mass spectrometry. This method enables in situ analysis (with a spatial resolution of 1 mm or better) of selected organic molecules. Results show that PAH concentrations are locally high compared to the average concentration found by analysis of pulverized samples, and are found primarily in the fine-grained matrix; no PAHs were detected in the interiors of individual chondrules at the detection limit (about 0.05 ppm).

Formal Analysis of Extended Well-Clear Boundaries for Unmanned Aircraft

NASA Technical Reports Server (NTRS)

Munoz, Cesar; Narkawicz, Anthony

2016-01-01

This paper concerns the application of formal methods to the definition of a detect and avoid concept for unmanned aircraft systems (UAS). In particular, it illustrates how formal analysis was used to explain and correct unexpected behaviors of the logic that issues alerts when two aircraft are predicted not to be well clear from one another. As a result of this analysis, a recommendation was proposed to, and subsequently adopted by, the US standards organization that defines the minimum operational requirements for the UAS detect and avoid concept.

Detecting position using ARKit

NASA Astrophysics Data System (ADS)

Dilek, Ufuk; Erol, Mustafa

2018-03-01

Developed by using ARKit, a novel app which can be used to detect position in physics experiments was introduced. The ARKit relies on a new technique. The result of the experiment presented in this study was satisfactory, suggesting that the new technique can be employed in position detection experiments/demonstrations that are conducted using mobile technology. This technique has several promising advantages over video analysis.

Classification of functional interactions from multi-electrodes data using conditional modularity analysis

NASA Astrophysics Data System (ADS)

Makhtar, Siti Noormiza; Senik, Mohd Harizal

2018-02-01

The availability of massive amount of neuronal signals are attracting widespread interest in functional connectivity analysis. Functional interactions estimated by multivariate partial coherence analysis in the frequency domain represent the connectivity strength in this study. Modularity is a network measure for the detection of community structure in network analysis. The discovery of community structure for the functional neuronal network was implemented on multi-electrode array (MEA) signals recorded from hippocampal regions in isoflurane-anaesthetized Lister-hooded rats. The analysis is expected to show modularity changes before and after local unilateral kainic acid (KA)-induced epileptiform activity. The result is presented using color-coded graphic of conditional modularity measure for 19 MEA nodes. This network is separated into four sub-regions to show the community detection within each sub-region. The results show that classification of neuronal signals into the inter- and intra-modular nodes is feasible using conditional modularity analysis. Estimation of segregation properties using conditional modularity analysis may provide further information about functional connectivity from MEA data.

A targeted change-detection procedure by combining change vector analysis and post-classification approach

NASA Astrophysics Data System (ADS)

Ye, Su; Chen, Dongmei; Yu, Jie

2016-04-01

In remote sensing, conventional supervised change-detection methods usually require effective training data for multiple change types. This paper introduces a more flexible and efficient procedure that seeks to identify only the changes that users are interested in, here after referred to as "targeted change detection". Based on a one-class classifier "Support Vector Domain Description (SVDD)", a novel algorithm named "Three-layer SVDD Fusion (TLSF)" is developed specially for targeted change detection. The proposed algorithm combines one-class classification generated from change vector maps, as well as before- and after-change images in order to get a more reliable detecting result. In addition, this paper introduces a detailed workflow for implementing this algorithm. This workflow has been applied to two case studies with different practical monitoring objectives: urban expansion and forest fire assessment. The experiment results of these two case studies show that the overall accuracy of our proposed algorithm is superior (Kappa statistics are 86.3% and 87.8% for Case 1 and 2, respectively), compared to applying SVDD to change vector analysis and post-classification comparison.

Detection of Toxoplasma gondii DNA in Brazilian oysters (Crassostrea rhizophorae).

PubMed

Ribeiro, L A; Santos, L K N S S; Brito, P A; Maciel, B M; Da Silva, A V; Albuquerque, G R

2015-05-04

The aim of this study was to detect evidence of Toxoplasma gondii using polymerase chain reaction (PCR)-based techniques in oysters (Crassostrea rhizophorae) obtained from the southern coastal region of Bahia, Brazil. A total of 624 oysters were collected, and the gills and digestive glands were dissected. Each tissue sample was separated into pools containing tissues (of the same type) from three animals, leading to a total of 416 experimental samples for analysis (208 samples each from the gills and digestive glands). Molecular analysis using PCR-based detection of the T. gondii AF 146527 repetitive fragment yielded negative results for all samples. However, when nested-PCR was used for detection of the T. gondii SAG-1 gene, 17 samples were positive, with the gills being the tissue with maximal detection of the parasite. These positive results were confirmed by sample sequencing. It is therefore suggested that C. rhizophorae oysters are capable of filtering and retaining T. gondii oocysts in their tissue. This represents a risk to public health because they are traditionally ingested in natura.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shull, D.

This report documents the initial feasibility tests performed using a commercial acoustic emission instrument for the purpose of detecting beetles in Department of Energy 9975 shipping packages. The device selected for this testing was a commercial handheld instrument and probe developed for the detection of termites, weevils, beetles and other insect infestations in wooden structures, trees, plants and soil. The results of two rounds of testing are presented. The first tests were performed by the vendor using only the hand-held instrument’s indications and real-time operator analysis of the audio signal content. The second tests included hands-free positioning of the instrumentmore » probe and post-collection analysis of the recorded audio signal content including audio background comparisons. The test results indicate that the system is promising for detecting the presence of drugstore beetles, however, additional work would be needed to improve the ease of detection and to automate the signal processing to eliminate the need for human interpretation. Mechanisms for hands-free positioning of the probe and audio background discrimination are also necessary for reliable detection and to reduce potential operator dose in radiation environments.« less

Assessing bat detectability and occupancy with multiple automated echolocation detectors

USGS Publications Warehouse

Gorresen, P.M.; Miles, A.C.; Todd, C.M.; Bonaccorso, F.J.; Weller, T.J.

2008-01-01

Occupancy analysis and its ability to account for differential detection probabilities is important for studies in which detecting echolocation calls is used as a measure of bat occurrence and activity. We examined the feasibility of remotely acquiring bat encounter histories to estimate detection probability and occupancy. We used echolocation detectors coupled to digital recorders operating at a series of proximate sites on consecutive nights in 2 trial surveys for the Hawaiian hoary bat (Lasiurus cinereus semotus). Our results confirmed that the technique is readily amenable for use in occupancy analysis. We also conducted a simulation exercise to assess the effects of sampling effort on parameter estimation. The results indicated that the precision and bias of parameter estimation were often more influenced by the number of sites sampled than number of visits. Acceptable accuracy often was not attained until at least 15 sites or 15 visits were used to estimate detection probability and occupancy. The method has significant potential for use in monitoring trends in bat activity and in comparative studies of habitat use. ?? 2008 American Society of Mammalogists.

Rapid determination of trace copper in animal feed based on micro-plate colorimetric reaction and statistical partitioning correction.

PubMed

Niu, Yiming; Wang, Jiayi; Zhang, Chi; Chen, Yiqiang

2017-04-15

The objective of this study was to develop a micro-plate based colorimetric assay for rapid and high-throughput detection of copper in animal feed. Copper ion in animal feed was extracted by trichloroacetic acid solution and reduced to cuprous ion by hydroxylamine. The cuprous ion can chelate with 2,2'-bicinchoninic acid to form a Cu-BCA complex which was detected with high sensitivity by micro-plate reader at 354nm. The whole assay procedure can be completed within 20min. To eliminate matrix interference, a statistical partitioning correction approach was proposed, which makes the detection of copper in complex samples possible. The limit of detection was 0.035μg/mL and the detection range was 0.1-10μg/mL of copper in buffer solution. Actual sample analysis indicated that this colorimetric assay produced results consistent with atomic absorption spectrometry analysis. These results demonstrated that the developed assay can be used for rapid determination of copper in animal feed. Copyright © 2016 Elsevier Ltd. All rights reserved.

Dynamic biochemical tissue analysis detects functional L-selectin ligands on colon cancer tissues

PubMed Central

Carlson, Grady E.; Martin, Eric W.; Shirure, Venktesh S.; Malgor, Ramiro; Resto, Vicente A.; Goetz, Douglas J.; Burdick, Monica M.

2017-01-01

A growing body of evidence suggests that L-selectin ligands presented on circulating tumor cells facilitate metastasis by binding L-selectin presented on leukocytes. Commonly used methods for detecting L-selectin ligands on tissues, e.g., immunostaining, are performed under static, no-flow conditions. However, such analysis does not assay for functional L-selectin ligands, specifically those ligands that promote adhesion under shear flow conditions. Recently our lab developed a method, termed dynamic biochemical tissue analysis (DBTA), to detect functional selectin ligands in situ by probing tissues with L-selectin-coated microspheres under hemodynamic flow conditions. In this investigation, DBTA was used to probe human colon tissues for L-selectin ligand activity. The detection of L-selectin ligands using DBTA was highly specific. Furthermore, DBTA reproducibly detected functional L-selectin ligands on diseased, e.g., cancerous or inflamed, tissues but not on noncancerous tissues. In addition, DBTA revealed a heterogeneous distribution of functional L-selectin ligands on colon cancer tissues. Most notably, detection of L-selectin ligands by immunostaining using HECA-452 antibody only partially correlated with functional L-selectin ligands detected by DBTA. In summation, the results of this study demonstrate that DBTA detects functional selectin ligands to provide a unique characterization of pathological tissue. PMID:28282455

Cascaded image analysis for dynamic crack detection in material testing

NASA Astrophysics Data System (ADS)

Hampel, U.; Maas, H.-G.

Concrete probes in civil engineering material testing often show fissures or hairline-cracks. These cracks develop dynamically. Starting at a width of a few microns, they usually cannot be detected visually or in an image of a camera imaging the whole probe. Conventional image analysis techniques will detect fissures only if they show a width in the order of one pixel. To be able to detect and measure fissures with a width of a fraction of a pixel at an early stage of their development, a cascaded image analysis approach has been developed, implemented and tested. The basic idea of the approach is to detect discontinuities in dense surface deformation vector fields. These deformation vector fields between consecutive stereo image pairs, which are generated by cross correlation or least squares matching, show a precision in the order of 1/50 pixel. Hairline-cracks can be detected and measured by applying edge detection techniques such as a Sobel operator to the results of the image matching process. Cracks will show up as linear discontinuities in the deformation vector field and can be vectorized by edge chaining. In practical tests of the method, cracks with a width of 1/20 pixel could be detected, and their width could be determined at a precision of 1/50 pixel.

Dynamic biochemical tissue analysis detects functional L-selectin ligands on colon cancer tissues.

PubMed

Carlson, Grady E; Martin, Eric W; Shirure, Venktesh S; Malgor, Ramiro; Resto, Vicente A; Goetz, Douglas J; Burdick, Monica M

2017-01-01

A growing body of evidence suggests that L-selectin ligands presented on circulating tumor cells facilitate metastasis by binding L-selectin presented on leukocytes. Commonly used methods for detecting L-selectin ligands on tissues, e.g., immunostaining, are performed under static, no-flow conditions. However, such analysis does not assay for functional L-selectin ligands, specifically those ligands that promote adhesion under shear flow conditions. Recently our lab developed a method, termed dynamic biochemical tissue analysis (DBTA), to detect functional selectin ligands in situ by probing tissues with L-selectin-coated microspheres under hemodynamic flow conditions. In this investigation, DBTA was used to probe human colon tissues for L-selectin ligand activity. The detection of L-selectin ligands using DBTA was highly specific. Furthermore, DBTA reproducibly detected functional L-selectin ligands on diseased, e.g., cancerous or inflamed, tissues but not on noncancerous tissues. In addition, DBTA revealed a heterogeneous distribution of functional L-selectin ligands on colon cancer tissues. Most notably, detection of L-selectin ligands by immunostaining using HECA-452 antibody only partially correlated with functional L-selectin ligands detected by DBTA. In summation, the results of this study demonstrate that DBTA detects functional selectin ligands to provide a unique characterization of pathological tissue.

SmartMal: a service-oriented behavioral malware detection framework for mobile devices.

PubMed

Wang, Chao; Wu, Zhizhong; Li, Xi; Zhou, Xuehai; Wang, Aili; Hung, Patrick C K

2014-01-01

This paper presents SmartMal--a novel service-oriented behavioral malware detection framework for vehicular and mobile devices. The highlight of SmartMal is to introduce service-oriented architecture (SOA) concepts and behavior analysis into the malware detection paradigms. The proposed framework relies on client-server architecture, the client continuously extracts various features and transfers them to the server, and the server's main task is to detect anomalies using state-of-art detection algorithms. Multiple distributed servers simultaneously analyze the feature vector using various detectors and information fusion is used to concatenate the results of detectors. We also propose a cycle-based statistical approach for mobile device anomaly detection. We accomplish this by analyzing the users' regular usage patterns. Empirical results suggest that the proposed framework and novel anomaly detection algorithm are highly effective in detecting malware on Android devices.

Standoff detection of explosives: a challenging approach for optical technologies

NASA Astrophysics Data System (ADS)

Désilets, S.; Hô, N.; Mathieu, P.; Simard, J. R.; Puckrin, E.; Thériault, J. M.; Lavoie, H.; Théberge, F.; Babin, F.; Gay, D.; Forest, R.; Maheux, J.; Roy, G.; Châteauneuf, M.

2011-06-01

Standoff detection of explosives residues on surfaces at few meters was made using optical technologies based on Raman scattering, Laser-Induced Breakdown Spectroscopy (LIBS) and passive standoff FTIR radiometry. By comparison, detection and analysis of nanogram samples of different explosives was made with a microscope system where Raman scattering from a micron-size single point illuminated crystal of explosive was observed. Results from standoff detection experiments using a telescope were compared to experiments using a microscope to find out important parameters leading to the detection. While detection and spectral identification of the micron-size explosive particles was possible with a microscope, standoff detection of these particles was very challenging due to undesired light reflected and produced by the background surface or light coming from other contaminants. Results illustrated the challenging approach of detecting at a standoff distance the presence of low amount of micron or submicron explosive particles.

SmartMal: A Service-Oriented Behavioral Malware Detection Framework for Mobile Devices

PubMed Central

Wu, Zhizhong; Li, Xi; Zhou, Xuehai; Wang, Aili; Hung, Patrick C. K.

2014-01-01

This paper presents SmartMal—a novel service-oriented behavioral malware detection framework for vehicular and mobile devices. The highlight of SmartMal is to introduce service-oriented architecture (SOA) concepts and behavior analysis into the malware detection paradigms. The proposed framework relies on client-server architecture, the client continuously extracts various features and transfers them to the server, and the server's main task is to detect anomalies using state-of-art detection algorithms. Multiple distributed servers simultaneously analyze the feature vector using various detectors and information fusion is used to concatenate the results of detectors. We also propose a cycle-based statistical approach for mobile device anomaly detection. We accomplish this by analyzing the users' regular usage patterns. Empirical results suggest that the proposed framework and novel anomaly detection algorithm are highly effective in detecting malware on Android devices. PMID:25165729

Explosive detection using high-volume vapor sampling and analysis by trained canines and ultra-trace detection equipment

NASA Astrophysics Data System (ADS)

Fisher, Mark; Sikes, John; Prather, Mark

2004-09-01

The dog's nose is an effective, highly-mobile sampling system, while the canine olfactory organs are an extremely sensitive detector. Having been trained to detect a wide variety of substances with exceptional results, canines are widely regarded as the 'gold standard' in chemical vapor detection. Historically, attempts to mimic the ability of dogs to detect vapors of explosives using electronic 'dogs noses' has proven difficult. However, recent advances in technology have resulted in development of detection (i.e., sampling and sensor) systems with performance that is rapidly approaching that of trained canines. The Nomadics Fido was the first sensor to demonstrate under field conditions the detection of landmines with performance approaching that of canines. More recently, comparative testing of Fido against canines has revealed that electronic vapor detection, when coupled with effective sampling methods, can produce results comparable to that of highly-trained canines. The results of these comparative tests will be presented, as will recent test results in which explosives hidden in cargo were detected using Fido with a high-volume sampling technique. Finally, the use of canines along with electronic sensors will be discussed as a means of improving the performance and expanding the capabilities of both methods.

[Comparison of Discriminant Analysis and Decision Trees for the Detection of Subclinical Keratoconus].

PubMed

Kleinhans, Sonja; Herrmann, Eva; Kohnen, Thomas; Bühren, Jens

2017-08-15

Background Iatrogenic keratectasia is one of the most dreaded complications of refractive surgery. In most cases, keratectasia develops after refractive surgery of eyes suffering from subclinical stages of keratoconus with few or no signs. Unfortunately, there has been no reliable procedure for the early detection of keratoconus. In this study, we used binary decision trees (recursive partitioning) to assess their suitability for discrimination between normal eyes and eyes with subclinical keratoconus. Patients and Methods The method of decision tree analysis was compared with discriminant analysis which has shown good results in previous studies. Input data were 32 eyes of 32 patients with newly diagnosed keratoconus in the contralateral eye and preoperative data of 10 eyes of 5 patients with keratectasia after laser in-situ keratomileusis (LASIK). The control group was made up of 245 normal eyes after LASIK and 12-month follow-up without any signs of iatrogenic keratectasia. Results Decision trees gave better accuracy and specificity than did discriminant analysis. The sensitivity of decision trees was lower than the sensitivity of discriminant analysis. Conclusion On the basis of the patient population of this study, decision trees did not prove to be superior to linear discriminant analysis for the detection of subclinical keratoconus. Georg Thieme Verlag KG Stuttgart · New York.

[Identification of a HPGD mutation in three families affected with primary hypertrophic osteoarthropathy].

PubMed

Zhang, Wanying; Wang, Tao; Huang, Shuaiwu; Zhao, Xiuli

2018-04-10

To detect mutation of HPGD gene among three pedigrees affected with primary hypertrophic osteoarthropathy (PHO) by DNA sequencing and high-resolution melting (HRM) analysis. Genomic DNA was extracted from peripheral blood samples collected from the pedigrees. PCR and direct sequencing were carried out to identify potential mutations of the HPGD gene. Amplicons containing the mutation spot were generated by nested PCR. The products were then subjected to HRM analysis using the HR-1 instrument. Direct sequencing was carried out in family members and healthy individuals to confirm the result of HRM analysis. A homozygous mutation c.310_311delCT was detected in 2 affected probands, while a heterozygous mutation c.310_311delCT was detected in the third proband. HRM analysis of the fragments encompassing HPGD exon 3 showed 3 curve patterns representing three different genotypes, i.e., the wild type, the c.310_311delCT homozygote, and the c.310_311delCT heterozygote. Result of DNA sequencing was consistent with that of the HRM analysis and phenotype of the subjects. The c.310_311delCT mutation may be the most prevalent mutation among Chinese population. HRM analysis has provided an optimized method for genetic testing of HPGD mutation for its simplicity, rapid turnover and high sensitivity.

Fault detection in digital and analog circuits using an i(DD) temporal analysis technique

NASA Technical Reports Server (NTRS)

Beasley, J.; Magallanes, D.; Vridhagiri, A.; Ramamurthy, Hema; Deyong, Mark

1993-01-01

An i(sub DD) temporal analysis technique which is used to detect defects (faults) and fabrication variations in both digital and analog IC's by pulsing the power supply rails and analyzing the temporal data obtained from the resulting transient rail currents is presented. A simple bias voltage is required for all the inputs, to excite the defects. Data from hardware tests supporting this technique are presented.

Microfluidic lab-on-a-foil for nucleic acid analysis based on isothermal recombinase polymerase amplification (RPA).

PubMed

Lutz, Sascha; Weber, Patrick; Focke, Max; Faltin, Bernd; Hoffmann, Jochen; Müller, Claas; Mark, Daniel; Roth, Günter; Munday, Peter; Armes, Niall; Piepenburg, Olaf; Zengerle, Roland; von Stetten, Felix

2010-04-07

For the first time we demonstrate a self-sufficient lab-on-a-foil system for the fully automated analysis of nucleic acids which is based on the recently available isothermal recombinase polymerase amplification (RPA). The system consists of a novel, foil-based centrifugal microfluidic cartridge including prestored liquid and dry reagents, and a commercially available centrifugal analyzer for incubation at 37 degrees C and real-time fluorescence detection. The system was characterized with an assay for the detection of the antibiotic resistance gene mecA of Staphylococcus aureus. The limit of detection was <10 copies and time-to-result was <20 min. Microfluidic unit operations comprise storage and release of liquid reagents, reconstitution of lyophilized reagents, aliquoting the sample into < or = 30 independent reaction cavities, and mixing of reagents with the DNA samples. The foil-based cartridge was produced by blow-molding and sealed with a self-adhesive tape. The demonstrated system excels existing PCR based lab-on-a-chip platforms in terms of energy efficiency and time-to-result. Applications are suggested in the field of mobile point-of-care analysis, B-detection, or in combination with continuous monitoring systems.

A comparative analysis of the effectiveness of cytogenetic and molecular genetic methods in the detection of Down syndrome.

PubMed

Mačkić-Đurović, Mirela; Projić, Petar; Ibrulj, Slavka; Cakar, Jasmina; Marjanović, Damir

2014-05-01

The goal of this study was to examine the effectiveness of 6 STR markers application (D21S1435, D21S11, D21S1270, D21S1411, D21S226 and IFNAR) in molecular genetic diagnostics of Down syndrome (DS) and to compare it with cytogenetic method. Testing was performed on 73 children, with the previously cytogenetically confirmed Down syndrome. DNA isolated from the buccal swab was used. Previously mentioned loci located on chromosome 21 were simultaneously amplified using quantitative fluorescence PCR (QF PCR). Using this method, 60 previously cytogenetically diagnosed DS with standard type of trisomy 21 were confirmed. Furthermore, six of eight children with mosaic type of DS were detected. Two false negative results for mosaic type of DS were obtained. Finally, five children with the translocation type of Down syndrome were also confirmed with this molecular test. In conclusion, molecular genetic analysis of STR loci is fast, cheap and simple method that could be used in detection of DS. Regarding possible false results detected for certain number of mosaic types, cytogenetic analysis should be used as a confirmatory test.

PSK Shift Timing Information Detection Using Image Processing and a Matched Filter

DTIC Science & Technology

2009-09-01

phase shifts are enhanced.  Develop, design, and test the resulting phase shift identification scheme. xx  Develop, design, and test an optional...and the resulting phase shift identification algorithm is investigated for SNR levels in the range -2dB to 12 dB. Detection performances are derived...test the resulting phase shift identification scheme.  Develop, design, and test an optional analysis window overlapping technique to improve phase

Presumptive Sources of Fecal Contamination in Four Tributaries to the New River Gorge National River, West Virginia, 2004

USGS Publications Warehouse

Mathes, Melvin V.; O'Brien, Tara L.; Strickler, Kriston M.; Hardy, Joshua J.; Schill, William B.; Lukasik, Jerzy; Scott, Troy M.; Bailey, David E.; Fenger, Terry L.

2007-01-01

Several methods were used to determine the sources of fecal contamination in water samples collected during September and October 2004 from four tributaries to the New River Gorge National River -- Arbuckle Creek, Dunloup Creek, Keeney Creek, and Wolf Creek. All four tributaries historically have had elevated levels of fecal coliform bacteria. The source-tracking methods used yielded various results, possibly because one or more methods failed. Sourcing methods used in this study included the detection of several human-specific and animal-specific biological or molecular markers, and library-dependent pulsed-field gel electrophoresis analysis that attempted to associate Escherichia coli bacteria obtained from water samples with animal sources by matching DNA-fragment banding patterns. Evaluation of the results of quality-control analysis indicated that pulsed-field gel electrophoresis analysis was unable to identify known-source bacteria isolates. Increasing the size of the known-source library did not improve the results for quality-control samples. A number of emerging methods, using markers in Enterococcus, human urine, Bacteroidetes, and host mitochondrial DNA, demonstrated some potential in associating fecal contamination with human or animal sources in a limited analysis of quality-control samples. All four of the human-specific markers were detected in water samples from Keeney Creek, a watershed with no centralized municipal wastewater-treatment facilities, thus indicating human sources of fecal contamination. The human-specific Bacteroidetes and host mitochondrial DNA markers were detected in water samples from Dunloup Creek, Wolf Creek, and to a lesser degree Arbuckle Creek. Results of analysis for wastewater compounds indicate that the September 27 sample from Arbuckle Creek contained numerous human tracer compounds likely from sewage. Dog, horse, chicken, and pig host mitochondrial DNA were detected in some of the water samples with the exception of the October 5 sample from Dunloup Creek. Cow, white-tailed deer, and Canada goose DNA were not detected in any of the samples collected from the four tributaries, despite the presence of these animals in the watersheds. Future studies with more rigorous quality-control analyses are needed to investigate the potential applicability and use of these emerging methods. Because many of the detections for the various methods could vary over time and with flow conditions, repeated sampling during both base flow and storm events would be necessary to more definitively determine the sources of fecal contamination for each watershed.

Effects of visual erotic stimulation on vibrotactile detection thresholds in men.

PubMed

Jiao, Chuanshu; Knight, Peter K; Weerakoon, Patricia; Turman, A Bulent

2007-12-01

This study examined the effects of sexual arousal on vibration detection thresholds in the right index finger of 30 healthy, heterosexual males who reported no sexual dysfunction. Vibrotactile detection thresholds at frequencies of 30, 60, and 100 Hz were assessed before and after watching erotic and control videos using a forced-choice, staircase method. A mechanical stimulator was used to produce the vibratory stimulus. Results were analyzed using repeated measures analysis of variance. After watching the erotic video, the vibrotactile detection thresholds at 30, 60, and 100 Hz were significantly reduced (p < .01). No changes in thresholds were detected at any frequency following exposure to the non-erotic stimulus. The results show that sexual arousal resulted in an increase in vibrotactile sensitivity to low frequency stimuli in the index finger of sexually functional men.

Performance and sensitivity analysis of the generalized likelihood ratio method for failure detection. M.S. Thesis

NASA Technical Reports Server (NTRS)

Bueno, R. A.

1977-01-01

Results of the generalized likelihood ratio (GLR) technique for the detection of failures in aircraft application are presented, and its relationship to the properties of the Kalman-Bucy filter is examined. Under the assumption that the system is perfectly modeled, the detectability and distinguishability of four failure types are investigated by means of analysis and simulations. Detection of failures is found satisfactory, but problems in identifying correctly the mode of a failure may arise. These issues are closely examined as well as the sensitivity of GLR to modeling errors. The advantages and disadvantages of this technique are discussed, and various modifications are suggested to reduce its limitations in performance and computational complexity.

Detection of multiple chemicals based on external cavity quantum cascade laser spectroscopy

NASA Astrophysics Data System (ADS)

Sun, Juan; Ding, Junya; Liu, Ningwu; Yang, Guangxiang; Li, Jingsong

2018-02-01

A laser spectroscopy system based on a broadband tunable external cavity quantum cascade laser (ECQCL) and a mini quartz crystal tuning fork (QCTF) detector was developed for standoff detection of volatile organic compounds (VOCs). The self-established spectral analysis model based on multiple algorithms for quantitative and qualitative analysis of VOC components (i.e. ethanol and acetone) was detailedly investigated in both closed cell and open path configurations. A good agreement was obtained between the experimentally observed spectra and the standard reference spectra. For open path detection of VOCs, the sensor system was demonstrated at a distance of 30 m. The preliminary laboratory results show that standoff detection of VOCs at a distance of over 100 m is very promising.

Why conventional detection methods fail in identifying the existence of contamination events.

PubMed

Liu, Shuming; Li, Ruonan; Smith, Kate; Che, Han

2016-04-15

Early warning systems are widely used to safeguard water security, but their effectiveness has raised many questions. To understand why conventional detection methods fail to identify contamination events, this study evaluates the performance of three contamination detection methods using data from a real contamination accident and two artificial datasets constructed using a widely applied contamination data construction approach. Results show that the Pearson correlation Euclidean distance (PE) based detection method performs better for real contamination incidents, while the Euclidean distance method (MED) and linear prediction filter (LPF) method are more suitable for detecting sudden spike-like variation. This analysis revealed why the conventional MED and LPF methods failed to identify existence of contamination events. The analysis also revealed that the widely used contamination data construction approach is misleading. Copyright © 2016 Elsevier Ltd. All rights reserved.

Transmission Bearing Damage Detection Using Decision Fusion Analysis

NASA Technical Reports Server (NTRS)

Dempsey, Paula J.; Lewicki, David G.; Decker, Harry J.

2004-01-01

A diagnostic tool was developed for detecting fatigue damage to rolling element bearings in an OH-58 main rotor transmission. Two different monitoring technologies, oil debris analysis and vibration, were integrated using data fusion into a health monitoring system for detecting bearing surface fatigue pitting damage. This integrated system showed improved detection and decision-making capabilities as compared to using individual monitoring technologies. This diagnostic tool was evaluated by collecting vibration and oil debris data from tests performed in the NASA Glenn 500 hp Helicopter Transmission Test Stand. Data was collected during experiments performed in this test rig when two unanticipated bearing failures occurred. Results show that combining the vibration and oil debris measurement technologies improves the detection of pitting damage on spiral bevel gears duplex ball bearings and spiral bevel pinion triplex ball bearings in a main rotor transmission.

Expression analysis of polyphenol oxidase isozymes by active staining method and tissue browning of head lettuce (Lactuca sativa L.).

PubMed

Noda, Takahiro; Iimure, Kazuhiko; Okamoto, Shunsuke; Saito, Akira

2017-08-01

Browning of plant tissue is generally considered attributable to enzymatic oxidation by polyphenol oxidase (PPO). Electrophoresis followed by activity staining has been used as an effective procedure to visually detect and isolate isozymes; however, it has not been applied for examination of various PPO isozymes in lettuce. Our study demonstrated that different lettuce PPO isozymes could be detected at different pH in active staining, and multiple isozymes were detected only under alkaline conditions. As a result, we concluded that activity staining with approximately pH 8 enabled to detect various PPO isozymes in lettuce. By expression analysis of the PPO isozymes after wounding, PPO isozymes that correlated with time-course of tissue browning were detected. The wound-induced PPO may play a key role in enzymatic browning.

An evaluation of computer-aided disproportionality analysis for post-marketing signal detection.

PubMed

Lehman, H P; Chen, J; Gould, A L; Kassekert, R; Beninger, P R; Carney, R; Goldberg, M; Goss, M A; Kidos, K; Sharrar, R G; Shields, K; Sweet, A; Wiholm, B E; Honig, P K

2007-08-01

To understand the value of computer-aided disproportionality analysis (DA) in relation to current pharmacovigilance signal detection methods, four products were retrospectively evaluated by applying an empirical Bayes method to Merck's post-marketing safety database. Findings were compared with the prior detection of labeled post-marketing adverse events. Disproportionality ratios (empirical Bayes geometric mean lower 95% bounds for the posterior distribution (EBGM05)) were generated for product-event pairs. Overall (1993-2004 data, EBGM05> or =2, individual terms) results of signal detection using DA compared to standard methods were sensitivity, 31.1%; specificity, 95.3%; and positive predictive value, 19.9%. Using groupings of synonymous labeled terms, sensitivity improved (40.9%). More of the adverse events detected by both methods were detected earlier using DA and grouped (versus individual) terms. With 1939-2004 data, diagnostic properties were similar to those from 1993 to 2004. DA methods using Merck's safety database demonstrate sufficient sensitivity and specificity to be considered for use as an adjunct to conventional signal detection methods.

A Comparison of Rule-based Analysis with Regression Methods in Understanding the Risk Factors for Study Withdrawal in a Pediatric Study.

PubMed

Haghighi, Mona; Johnson, Suzanne Bennett; Qian, Xiaoning; Lynch, Kristian F; Vehik, Kendra; Huang, Shuai

2016-08-26

Regression models are extensively used in many epidemiological studies to understand the linkage between specific outcomes of interest and their risk factors. However, regression models in general examine the average effects of the risk factors and ignore subgroups with different risk profiles. As a result, interventions are often geared towards the average member of the population, without consideration of the special health needs of different subgroups within the population. This paper demonstrates the value of using rule-based analysis methods that can identify subgroups with heterogeneous risk profiles in a population without imposing assumptions on the subgroups or method. The rules define the risk pattern of subsets of individuals by not only considering the interactions between the risk factors but also their ranges. We compared the rule-based analysis results with the results from a logistic regression model in The Environmental Determinants of Diabetes in the Young (TEDDY) study. Both methods detected a similar suite of risk factors, but the rule-based analysis was superior at detecting multiple interactions between the risk factors that characterize the subgroups. A further investigation of the particular characteristics of each subgroup may detect the special health needs of the subgroup and lead to tailored interventions.

Pneumothorax detection in chest radiographs using local and global texture signatures

NASA Astrophysics Data System (ADS)

Geva, Ofer; Zimmerman-Moreno, Gali; Lieberman, Sivan; Konen, Eli; Greenspan, Hayit

2015-03-01

A novel framework for automatic detection of pneumothorax abnormality in chest radiographs is presented. The suggested method is based on a texture analysis approach combined with supervised learning techniques. The proposed framework consists of two main steps: at first, a texture analysis process is performed for detection of local abnormalities. Labeled image patches are extracted in the texture analysis procedure following which local analysis values are incorporated into a novel global image representation. The global representation is used for training and detection of the abnormality at the image level. The presented global representation is designed based on the distinctive shape of the lung, taking into account the characteristics of typical pneumothorax abnormalities. A supervised learning process was performed on both the local and global data, leading to trained detection system. The system was tested on a dataset of 108 upright chest radiographs. Several state of the art texture feature sets were experimented with (Local Binary Patterns, Maximum Response filters). The optimal configuration yielded sensitivity of 81% with specificity of 87%. The results of the evaluation are promising, establishing the current framework as a basis for additional improvements and extensions.

Gravitational Wave Detection in the Introductory Lab

NASA Astrophysics Data System (ADS)

Burko, Lior M.

2017-01-01

Great physics breakthroughs are rarely included in the introductory physics course. General relativity and binary black hole coalescence are no different, and can be included in the introductory course only in a very limited sense. However, we can design activities that directly involve the detection of GW150914, the designation of the Gravitation Wave signal detected on September 14, 2015, thereby engage the students in this exciting discovery directly. The activities naturally do not include the construction of a detector or the detection of gravitational waves. Instead, we design it to include analysis of the data from GW150914, which includes some interesting analysis activities for students of the introductory course. The same activities can be assigned either as a laboratory exercise or as a computational project for the same population of students. The analysis tools used here are simple and available to the intended student population. It does not include the sophisticated analysis tools, which were used by LIGO to carefully analyze the detected signal. However, these simple tools are sufficient to allow the student to get important results. We have successfully assigned this lab project for students of the introductory course with calculus at Georgia Gwinnett College.

Process fault detection and nonlinear time series analysis for anomaly detection in safeguards

DOE Office of Scientific and Technical Information (OSTI.GOV)

Burr, T.L.; Mullen, M.F.; Wangen, L.E.

In this paper we discuss two advanced techniques, process fault detection and nonlinear time series analysis, and apply them to the analysis of vector-valued and single-valued time-series data. We investigate model-based process fault detection methods for analyzing simulated, multivariate, time-series data from a three-tank system. The model-predictions are compared with simulated measurements of the same variables to form residual vectors that are tested for the presence of faults (possible diversions in safeguards terminology). We evaluate two methods, testing all individual residuals with a univariate z-score and testing all variables simultaneously with the Mahalanobis distance, for their ability to detect lossmore » of material from two different leak scenarios from the three-tank system: a leak without and with replacement of the lost volume. Nonlinear time-series analysis tools were compared with the linear methods popularized by Box and Jenkins. We compare prediction results using three nonlinear and two linear modeling methods on each of six simulated time series: two nonlinear and four linear. The nonlinear methods performed better at predicting the nonlinear time series and did as well as the linear methods at predicting the linear values.« less

Cascade detection for the extraction of localized sequence features; specificity results for HIV-1 protease and structure-function results for the Schellman loop.

PubMed

Newell, Nicholas E

2011-12-15

The extraction of the set of features most relevant to function from classified biological sequence sets is still a challenging problem. A central issue is the determination of expected counts for higher order features so that artifact features may be screened. Cascade detection (CD), a new algorithm for the extraction of localized features from sequence sets, is introduced. CD is a natural extension of the proportional modeling techniques used in contingency table analysis into the domain of feature detection. The algorithm is successfully tested on synthetic data and then applied to feature detection problems from two different domains to demonstrate its broad utility. An analysis of HIV-1 protease specificity reveals patterns of strong first-order features that group hydrophobic residues by side chain geometry and exhibit substantial symmetry about the cleavage site. Higher order results suggest that favorable cooperativity is weak by comparison and broadly distributed, but indicate possible synergies between negative charge and hydrophobicity in the substrate. Structure-function results for the Schellman loop, a helix-capping motif in proteins, contain strong first-order features and also show statistically significant cooperativities that provide new insights into the design of the motif. These include a new 'hydrophobic staple' and multiple amphipathic and electrostatic pair features. CD should prove useful not only for sequence analysis, but also for the detection of multifactor synergies in cross-classified data from clinical studies or other sources. Windows XP/7 application and data files available at: https://sites.google.com/site/cascadedetect/home. nacnewell@comcast.net Supplementary information is available at Bioinformatics online.

Optical detection of glyphosate in water

NASA Astrophysics Data System (ADS)

de Góes, R. E.; Possetti, G. R. C.; Muller, M.; Fabris, J. L.

2017-04-01

This work shows preliminary results of the detection of Glyphosate in water by using optical fiber spectroscopy. A colloid with citrate-caped silver nanoparticles was employed as substrate for the measurements. A cross analysis between optical absorption and inelastic scattering evidenced a controlled aggregation of the sample constituents, leading to the possibility of quantitative detection of the analyte. The estimate limit of detection for Glyphosate in water for the proposed sensing scheme was about 1.7 mg/L.

Adaptive Locally Optimum Processing for Interference Suppression from Communication and Undersea Surveillance Signals

DTIC Science & Technology

1994-07-01

1993. "Analysis of the 1730-1732. Track - Before - Detect Approach to Target Detection using Pixel Statistics", to appear in IEEE Transactions Scholz, J...large surveillance arrays. One approach to combining energy in different spatial cells is track - before - detect . References to examples appear in the next... track - before - detect problem. The results obtained are not expected to depend strongly on model details. In particular, the structure of the tracking

Effectively Identifying eQTLs from Multiple Tissues by Combining Mixed Model and Meta-analytic Approaches

PubMed Central

Choi, Ted; Eskin, Eleazar

2013-01-01

Gene expression data, in conjunction with information on genetic variants, have enabled studies to identify expression quantitative trait loci (eQTLs) or polymorphic locations in the genome that are associated with expression levels. Moreover, recent technological developments and cost decreases have further enabled studies to collect expression data in multiple tissues. One advantage of multiple tissue datasets is that studies can combine results from different tissues to identify eQTLs more accurately than examining each tissue separately. The idea of aggregating results of multiple tissues is closely related to the idea of meta-analysis which aggregates results of multiple genome-wide association studies to improve the power to detect associations. In principle, meta-analysis methods can be used to combine results from multiple tissues. However, eQTLs may have effects in only a single tissue, in all tissues, or in a subset of tissues with possibly different effect sizes. This heterogeneity in terms of effects across multiple tissues presents a key challenge to detect eQTLs. In this paper, we develop a framework that leverages two popular meta-analysis methods that address effect size heterogeneity to detect eQTLs across multiple tissues. We show by using simulations and multiple tissue data from mouse that our approach detects many eQTLs undetected by traditional eQTL methods. Additionally, our method provides an interpretation framework that accurately predicts whether an eQTL has an effect in a particular tissue. PMID:23785294

The mouse lymphoma assay detects recombination, deletion, and aneuploidy.

PubMed

Wang, Jianyong; Sawyer, Jeffrey R; Chen, Ling; Chen, Tao; Honma, Masamitsu; Mei, Nan; Moore, Martha M

2009-05-01

The mouse lymphoma assay (MLA) uses the thymidine kinase (Tk) gene of the L5178Y/Tk(+/-)-3.7.2C mouse lymphoma cell line as a reporter gene to evaluate the mutagenicity of chemical and physical agents. The MLA is recommended by both the United States Food and Drug Administration and the United States Environmental Protection Agency as the preferred in vitro mammalian cell mutation assay for genetic toxicology screening because it detects a wide range of genetic alterations, including both point mutations and chromosomal mutations. However, the specific types of chromosomal mutations that can be detected by the MLA need further clarification. For this purpose, three chemicals, including two clastogens and an aneugen (3'-azido-3'-deoxythymidine, mitomycin C, and taxol), were used to induce Tk mutants. Loss of heterozygosity (LOH) analysis was used to select mutants that could be informative as to whether they resulted from deletion, mitotic recombination, or aneuploidy. A combination of additional methods, G-banding analysis, chromosome painting, and a real-time PCR method to detect the copy number (CN) of the Tk gene was then used to provide a detailed analysis. LOH involving at least 25% of chromosome 11, a normal karyotype, and a Tk CN of 2 would indicate that the mutant resulted from recombination, whereas LOH combined with a karyotypically visible deletion of chromosome 11 and a Tk CN of 1 would indicate a deletion. Aneuploidy was confirmed using G-banding combined with chromosome painting analysis for mutants showing LOH at every microsatellite marker on chromosome 11. From this analysis, it is clear that mouse lymphoma Tk mutants can result from recombination, deletion, and aneuploidy.

Theoretical detection limit of PIXE analysis using 20 MeV proton beams

NASA Astrophysics Data System (ADS)

Ishii, Keizo; Hitomi, Keitaro

2018-02-01

Particle-induced X-ray emission (PIXE) analysis is usually performed using proton beams with energies in the range 2∼3 MeV because at these energies, the detection limit is low. The detection limit of PIXE analysis depends on the X-ray production cross-section, the continuous background of the PIXE spectrum and the experimental parameters such as the beam currents and the solid angle and detector efficiency of X-ray detector. Though the continuous background increases as the projectile energy increases, the cross-section of the X-ray increases as well. Therefore, the detection limit of high energy proton PIXE is not expected to increase significantly. We calculated the cross sections of continuous X-rays produced in several bremsstrahlung processes and estimated the detection limit of a 20 MeV proton PIXE analysis by modelling the Compton tail of the γ-rays produced in the nuclear reactions, and the escape effect on the secondary electron bremsstrahlung. We found that the Compton tail does not affect the detection limit when a thin X-ray detector is used, but the secondary electron bremsstrahlung escape effect does have an impact. We also confirmed that the detection limit of the PIXE analysis, when used with 4 μm polyethylene backing film and an integrated beam current of 1 μC, is 0.4∼2.0 ppm for proton energies in the range 10∼30 MeV and elements with Z = 16-90. This result demonstrates the usefulness of several 10 MeV cyclotrons for performing PIXE analysis. Cyclotrons with these properties are currently installed in positron emission tomography (PET) centers.

Real-time supervised detection of pink areas in dermoscopic images of melanoma: importance of color shades, texture and location.

PubMed

Kaur, R; Albano, P P; Cole, J G; Hagerty, J; LeAnder, R W; Moss, R H; Stoecker, W V

2015-11-01

Early detection of malignant melanoma is an important public health challenge. In the USA, dermatologists are seeing more melanomas at an early stage, before classic melanoma features have become apparent. Pink color is a feature of these early melanomas. If rapid and accurate automatic detection of pink color in these melanomas could be accomplished, there could be significant public health benefits. Detection of three shades of pink (light pink, dark pink, and orange pink) was accomplished using color analysis techniques in five color planes (red, green, blue, hue, and saturation). Color shade analysis was performed using a logistic regression model trained with an image set of 60 dermoscopic images of melanoma that contained pink areas. Detected pink shade areas were further analyzed with regard to the location within the lesion, average color parameters over the detected areas, and histogram texture features. Logistic regression analysis of a separate set of 128 melanomas and 128 benign images resulted in up to 87.9% accuracy in discriminating melanoma from benign lesions measured using area under the receiver operating characteristic curve. The accuracy in this model decreased when parameters for individual shades, texture, or shade location within the lesion were omitted. Texture, color, and lesion location analysis applied to multiple shades of pink can assist in melanoma detection. When any of these three details: color location, shade analysis, or texture analysis were omitted from the model, accuracy in separating melanoma from benign lesions was lowered. Separation of colors into shades and further details that enhance the characterization of these color shades are needed for optimal discrimination of melanoma from benign lesions. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Detection of banned meat and bone meal in feedstuffs by near-infrared microscopic analysis of the dense sediment fraction.

PubMed

Baeten, Vincent; von Holst, Christoph; Garrido, Ana; Vancutsem, Jeroen; Michotte Renier, Antoine; Dardenne, Pierre

2005-05-01

In this paper we present an alternative method for detection of meat and bone meal (MBM) in feedstuffs by near-infrared microscopic (NIRM) analysis of the particles in the sediment fraction (dense fraction (d >1.62) from dichloroethylene) of compound feeds. To apply this method the particles of the sediment fraction are spread on a sample holder and presented to the NIR microscope. By using the pointer of the microscope the infrared beam is focussed on each particle and the NIR spectrum (1112-2500 nm) is collected. This method can be used to detect the presence of MBM at concentrations as low as 0.05% mass fraction. When results from the NIRM method were compared with the classical microscopic method, a coefficient of determination (R2) of 0.87 was obtained. The results of this study demonstrated that this method could be proposed as a complementary tool for the detection of banned MBM in feedstuffs by reinforcement of the monitoring of feeds.

Item response theory detects differential item functioning between healthy and ill children in QoL measures

PubMed Central

Langer, Michelle M.; Hill, Cheryl D.; Thissen, David; Burwinkle, Tasha M.; Varni, James W.; DeWalt, Darren A.

2008-01-01

Objective To demonstrate the value of item response theory (IRT) and differential item functioning (DIF) methods in examining a health-related quality of life (HRQOL) measure in children and adolescents. Study Design and Setting This illustration uses data from 5,429 children using the four subscales of the PedsQL™ 4.0 Generic Core Scales. The IRT model-based likelihood ratio test was used to detect and evaluate DIF between healthy children and children with a chronic condition. Results DIF was detected for a majority of items but cancelled out at the total test score level due to opposing directions of DIF. Post-hoc analysis indicated that this pattern of results may be due to multidimensionality. We discuss issues in detecting and handling DIF. Conclusion This paper describes how to perform DIF analyses in validating a questionnaire to ensure that scores have equivalent meaning across subgroups. It offers insight into ways information gained through the analysis can be used to evaluate an existing scale. PMID:18226750

Using multi-scale entropy and principal component analysis to monitor gears degradation via the motor current signature analysis

NASA Astrophysics Data System (ADS)

Aouabdi, Salim; Taibi, Mahmoud; Bouras, Slimane; Boutasseta, Nadir

2017-06-01

This paper describes an approach for identifying localized gear tooth defects, such as pitting, using phase currents measured from an induction machine driving the gearbox. A new tool of anomaly detection based on multi-scale entropy (MSE) algorithm SampEn which allows correlations in signals to be identified over multiple time scales. The motor current signature analysis (MCSA) in conjunction with principal component analysis (PCA) and the comparison of observed values with those predicted from a model built using nominally healthy data. The Simulation results show that the proposed method is able to detect gear tooth pitting in current signals.

Computerized Lung Sound Analysis as diagnostic aid for the detection of abnormal lung sounds: a systematic review and meta-analysis

PubMed Central

Gurung, Arati; Scrafford, Carolyn G; Tielsch, James M; Levine, Orin S; Checkley, William

2011-01-01

Rationale The standardized use of a stethoscope for chest auscultation in clinical research is limited by its inherent inter-listener variability. Electronic auscultation and automated classification of recorded lung sounds may help prevent some these shortcomings. Objective We sought to perform a systematic review and meta-analysis of studies implementing computerized lung sounds analysis (CLSA) to aid in the detection of abnormal lung sounds for specific respiratory disorders. Methods We searched for articles on CLSA in MEDLINE, EMBASE, Cochrane Library and ISI Web of Knowledge through July 31, 2010. Following qualitative review, we conducted a meta-analysis to estimate the sensitivity and specificity of CLSA for the detection of abnormal lung sounds. Measurements and Main Results Of 208 articles identified, we selected eight studies for review. Most studies employed either electret microphones or piezoelectric sensors for auscultation, and Fourier Transform and Neural Network algorithms for analysis and automated classification of lung sounds. Overall sensitivity for the detection of wheezes or crackles using CLSA was 80% (95% CI 72–86%) and specificity was 85% (95% CI 78–91%). Conclusions While quality data on CLSA are relatively limited, analysis of existing information suggests that CLSA can provide a relatively high specificity for detecting abnormal lung sounds such as crackles and wheezes. Further research and product development could promote the value of CLSA in research studies or its diagnostic utility in clinical setting. PMID:21676606

Advancing the detection of steady-state visual evoked potentials in brain-computer interfaces.

PubMed

Abu-Alqumsan, Mohammad; Peer, Angelika

2016-06-01

Spatial filtering has proved to be a powerful pre-processing step in detection of steady-state visual evoked potentials and boosted typical detection rates both in offline analysis and online SSVEP-based brain-computer interface applications. State-of-the-art detection methods and the spatial filters used thereby share many common foundations as they all build upon the second order statistics of the acquired Electroencephalographic (EEG) data, that is, its spatial autocovariance and cross-covariance with what is assumed to be a pure SSVEP response. The present study aims at highlighting the similarities and differences between these methods. We consider the canonical correlation analysis (CCA) method as a basis for the theoretical and empirical (with real EEG data) analysis of the state-of-the-art detection methods and the spatial filters used thereby. We build upon the findings of this analysis and prior research and propose a new detection method (CVARS) that combines the power of the canonical variates and that of the autoregressive spectral analysis in estimating the signal and noise power levels. We found that the multivariate synchronization index method and the maximum contrast combination method are variations of the CCA method. All three methods were found to provide relatively unreliable detections in low signal-to-noise ratio (SNR) regimes. CVARS and the minimum energy combination methods were found to provide better estimates for different SNR levels. Our theoretical and empirical results demonstrate that the proposed CVARS method outperforms other state-of-the-art detection methods when used in an unsupervised fashion. Furthermore, when used in a supervised fashion, a linear classifier learned from a short training session is able to estimate the hidden user intention, including the idle state (when the user is not attending to any stimulus), rapidly, accurately and reliably.

Use of Standing Gold Nanorods for Detection of Malachite Green and Crystal Violet in Fish by SERS.

PubMed

Chen, Xiaowei; Nguyen, Trang H D; Gu, Liqun; Lin, Mengshi

2017-07-01

With growing consumption of aquaculture products, there is increasing demand on rapid and sensitive techniques that can detect prohibited substances in the seafood products. This study aimed to develop a novel surface-enhanced Raman spectroscopy (SERS) method coupled with simplified extraction protocol and novel gold nanorod (AuNR) substrates to detect banned aquaculture substances (malachite green [MG] and crystal violet [CV]) and their mixture (1:1) in aqueous solution and fish samples. Multivariate statistical tools such as principal component analysis (PCA) and partial least squares regression (PLSR) were used in data analysis. PCA results demonstrate that SERS can distinguish MG, CV and their mixture (1:1) in aqueous solution and in fish samples. The detection limit of SERS coupled with standing AuNR substrates is 1 ppb for both MG and CV in fish samples. A good linear relationship between the actual concentration and predicted concentration of analytes based on PLSR models with R 2 values from 0.87 to 0.99 were obtained, indicating satisfactory quantification results of this method. These results demonstrate that the SERS method coupled with AuNR substrates can be used for rapid and accurate detection of MG and CV in fish samples. © 2017 Institute of Food Technologists®.

Simulation of Wake Vortex Radiometric Detection via Jet Exhaust Proxy

NASA Technical Reports Server (NTRS)

Daniels, Taumi S.

2015-01-01

This paper describes an analysis of the potential of an airborne hyperspectral imaging IR instrument to infer wake vortices via turbine jet exhaust as a proxy. The goal was to determine the requirements for an imaging spectrometer or radiometer to effectively detect the exhaust plume, and by inference, the location of the wake vortices. The effort examines the gas spectroscopy of the various major constituents of turbine jet exhaust and their contributions to the modeled detectable radiance. Initially, a theoretical analysis of wake vortex proxy detection by thermal radiation was realized in a series of simulations. The first stage used the SLAB plume model to simulate turbine jet exhaust plume characteristics, including exhaust gas transport dynamics and concentrations. The second stage used these plume characteristics as input to the Line By Line Radiative Transfer Model (LBLRTM) to simulate responses from both an imaging IR hyperspectral spectrometer or radiometer. These numerical simulations generated thermal imagery that was compared with previously reported wake vortex temperature data. This research is a continuation of an effort to specify the requirements for an imaging IR spectrometer or radiometer to make wake vortex measurements. Results of the two-stage simulation will be reported, including instrument specifications for wake vortex thermal detection. These results will be compared with previously reported results for IR imaging spectrometer performance.

Censoring approach to the detection limits in X-ray fluorescence analysis

NASA Astrophysics Data System (ADS)

Pajek, M.; Kubala-Kukuś, A.

2004-10-01

We demonstrate that the effect of detection limits in the X-ray fluorescence analysis (XRF), which limits the determination of very low concentrations of trace elements and results in appearance of the so-called "nondetects", can be accounted for using the statistical concept of censoring. More precisely, the results of such measurements can be viewed as the left random censored data, which can further be analyzed using the Kaplan-Meier method correcting the data for the presence of nondetects. Using this approach, the results of measured, detection limit censored concentrations can be interpreted in a nonparametric manner including the correction for the nondetects, i.e. the measurements in which the concentrations were found to be below the actual detection limits. Moreover, using the Monte Carlo simulation technique we show that by using the Kaplan-Meier approach the corrected mean concentrations for a population of the samples can be estimated within a few percent uncertainties with respect of the simulated, uncensored data. This practically means that the final uncertainties of estimated mean values are limited in fact by the number of studied samples and not by the correction procedure itself. The discussed random-left censoring approach was applied to analyze the XRF detection-limit-censored concentration measurements of trace elements in biomedical samples.

Information theoretic analysis of canny edge detection in visual communication

NASA Astrophysics Data System (ADS)

Jiang, Bo; Rahman, Zia-ur

2011-06-01

In general edge detection evaluation, the edge detectors are examined, analyzed, and compared either visually or with a metric for specific an application. This analysis is usually independent of the characteristics of the image-gathering, transmission and display processes that do impact the quality of the acquired image and thus, the resulting edge image. We propose a new information theoretic analysis of edge detection that unites the different components of the visual communication channel and assesses edge detection algorithms in an integrated manner based on Shannon's information theory. The edge detection algorithm here is considered to achieve high performance only if the information rate from the scene to the edge approaches the maximum possible. Thus, by setting initial conditions of the visual communication system as constant, different edge detection algorithms could be evaluated. This analysis is normally limited to linear shift-invariant filters so in order to examine the Canny edge operator in our proposed system, we need to estimate its "power spectral density" (PSD). Since the Canny operator is non-linear and shift variant, we perform the estimation for a set of different system environment conditions using simulations. In our paper we will first introduce the PSD of the Canny operator for a range of system parameters. Then, using the estimated PSD, we will assess the Canny operator using information theoretic analysis. The information-theoretic metric is also used to compare the performance of the Canny operator with other edge-detection operators. This also provides a simple tool for selecting appropriate edgedetection algorithms based on system parameters, and for adjusting their parameters to maximize information throughput.

Data in support of the detection of genetically modified organisms (GMOs) in food and feed samples.

PubMed

Alasaad, Noor; Alzubi, Hussein; Kader, Ahmad Abdul

2016-06-01

Food and feed samples were randomly collected from different sources, including local and imported materials from the Syrian local market. These included maize, barley, soybean, fresh food samples and raw material. GMO detection was conducted by PCR and nested PCR-based techniques using specific primers for the most used foreign DNA commonly used in genetic transformation procedures, i.e., 35S promoter, T-nos, epsps, cryIA(b) gene and nptII gene. The results revealed for the first time in Syria the presence of GM foods and feeds with glyphosate-resistant trait of P35S promoter and NOS terminator in the imported soybean samples with high frequency (5 out of the 6 imported soybean samples). While, tests showed negative results for the local samples. Also, tests revealed existence of GMOs in two imported maize samples detecting the presence of 35S promoter and nos terminator. Nested PCR results using two sets of primers confirmed our data. The methods applied in the brief data are based on DNA analysis by Polymerase Chain Reaction (PCR). This technique is specific, practical, reproducible and sensitive enough to detect up to 0.1% GMO in food and/or feedstuffs. Furthermore, all of the techniques mentioned are economic and can be applied in Syria and other developing countries. For all these reasons, the DNA-based analysis methods were chosen and preferred over protein-based analysis.

Cell-Free DNA Analysis in Maternal Blood: Differences in Estimates between Laboratories with Different Methodologies Using a Propensity Score Approach.

PubMed

Bevilacqua, Elisa; Jani, Jacques C; Letourneau, Alexandra; Duiella, Silvia F; Kleinfinger, Pascale; Lohmann, Laurence; Resta, Serena; Cos Sanchez, Teresa; Fils, Jean-François; Mirra, Marilyn; Benachi, Alexandra; Costa, Jean-Marc

2018-06-13

To evaluate the failure rate and performance of cell-free DNA (cfDNA) testing, mainly in terms of detection rates for trisomy 21, performed by 2 laboratories using different analytical methods. cfDNA testing was performed on 2,870 pregnancies with the HarmonyTM Prenatal Test using the targeted digital analysis of selected regions (DANSR) method, and on 2,635 pregnancies with the "Cerba test" using the genome-wide massively parallel sequencing (GW-MPS) method, with available outcomes. Propensity score analysis was used to match patients between the 2 groups. A comparison of the detection rates for trisomy 21 between the 2 laboratories was made. In all, 2,811 patients in the Harmony group and 2,530 patients in the Cerba group had no trisomy 21, 18, or 13. Postmatched comparisons of the patient characteristics indicated a higher no-result rate in the Harmony group (1.30%) than in the Cerba group (0.75%; p = 0.039). All 41 cases of trisomy 21 in the Harmony group and 93 cases in the Cerba group were detected. Both methods of cfDNA testing showed low no-result rates and a comparable performance in detecting trisomy 21; yet GW-MPS had a slightly lower no-result rate than the DANSR method. © 2018 S. Karger AG, Basel.

Infrared emission contrast for the visualization of subsurface graphical features in artworks

NASA Astrophysics Data System (ADS)

Mercuri, Fulvio; Paoloni, Stefano; Cicero, Cristina; Zammit, Ugo; Orazi, Noemi

2018-03-01

In this paper a method is presented based on the use of active infrared thermography for the detection of subsurface graphical features in artworks. A theoretical model for the thermographic signal describing the physical mechanisms which allow the identification of the buried features has been proposed and thereafter it has been applied to the analysis of the results obtained on specifically made test samples. It is shown that the proposed model predictions adequately describe the experimental results obtained on the test samples. A comparative analysis between the proposed technique and infrared reflectography is also presented. The comparison shows that active thermography can be more effective in the detection of features buried below infrared translucent layers and, in addition, that it can provide information about the depth of the detected features, particularly in highly IR diffusing materials.

Evaluation of hemifield sector analysis protocol in multifocal visual evoked potential objective perimetry for the diagnosis and early detection of glaucomatous field defects.

PubMed

Mousa, Mohammad F; Cubbidge, Robert P; Al-Mansouri, Fatima; Bener, Abdulbari

2014-02-01

Multifocal visual evoked potential (mfVEP) is a newly introduced method used for objective visual field assessment. Several analysis protocols have been tested to identify early visual field losses in glaucoma patients using the mfVEP technique, some were successful in detection of field defects, which were comparable to the standard automated perimetry (SAP) visual field assessment, and others were not very informative and needed more adjustment and research work. In this study we implemented a novel analysis approach and evaluated its validity and whether it could be used effectively for early detection of visual field defects in glaucoma. Three groups were tested in this study; normal controls (38 eyes), glaucoma patients (36 eyes) and glaucoma suspect patients (38 eyes). All subjects had a two standard Humphrey field analyzer (HFA) test 24-2 and a single mfVEP test undertaken in one session. Analysis of the mfVEP results was done using the new analysis protocol; the hemifield sector analysis (HSA) protocol. Analysis of the HFA was done using the standard grading system. Analysis of mfVEP results showed that there was a statistically significant difference between the three groups in the mean signal to noise ratio (ANOVA test, p < 0.001 with a 95% confidence interval). The difference between superior and inferior hemispheres in all subjects were statistically significant in the glaucoma patient group in all 11 sectors (t-test, p < 0.001), partially significant in 5 / 11 (t-test, p < 0.01), and no statistical difference in most sectors of the normal group (1 / 11 sectors was significant, t-test, p < 0.9). Sensitivity and specificity of the HSA protocol in detecting glaucoma was 97% and 86%, respectively, and for glaucoma suspect patients the values were 89% and 79%, respectively. The new HSA protocol used in the mfVEP testing can be applied to detect glaucomatous visual field defects in both glaucoma and glaucoma suspect patients. Using this protocol can provide information about focal visual field differences across the horizontal midline, which can be utilized to differentiate between glaucoma and normal subjects. Sensitivity and specificity of the mfVEP test showed very promising results and correlated with other anatomical changes in glaucoma field loss.

Integrated analysis of copy number alteration and RNA expression profiles of cancer using a high-resolution whole-genome oligonucleotide array.

PubMed

Jung, Seung-Hyun; Shin, Seung-Hun; Yim, Seon-Hee; Choi, Hye-Sun; Lee, Sug-Hyung; Chung, Yeun-Jun

2009-07-31

Recently, microarray-based comparative genomic hybridization (array-CGH) has emerged as a very efficient technology with higher resolution for the genome-wide identification of copy number alterations (CNA). Although CNAs are thought to affect gene expression, there is no platform currently available for the integrated CNA-expression analysis. To achieve high-resolution copy number analysis integrated with expression profiles, we established human 30k oligoarray-based genome-wide copy number analysis system and explored the applicability of this system for integrated genome and transcriptome analysis using MDA-MB-231 cell line. We compared the CNAs detected by the oligoarray with those detected by the 3k BAC array for validation. The oligoarray identified the single copy difference more accurately and sensitively than the BAC array. Seventeen CNAs detected by both platforms in MDA-MB-231 such as gains of 5p15.33-13.1, 8q11.22-8q21.13, 17p11.2, and losses of 1p32.3, 8p23.3-8p11.21, and 9p21 were consistently identified in previous studies on breast cancer. There were 122 other small CNAs (mean size 1.79 mb) that were detected by oligoarray only, not by BAC-array. We performed genomic qPCR targeting 7 CNA regions, detected by oligoarray only, and one non-CNA region to validate the oligoarray CNA detection. All qPCR results were consistent with the oligoarray-CGH results. When we explored the possibility of combined interpretation of both DNA copy number and RNA expression profiles, mean DNA copy number and RNA expression levels showed a significant correlation. In conclusion, this 30k oligoarray-CGH system can be a reasonable choice for analyzing whole genome CNAs and RNA expression profiles at a lower cost.

QESA: Quarantine Extraterrestrial Sample Analysis Methodology

NASA Astrophysics Data System (ADS)

Simionovici, A.; Lemelle, L.; Beck, P.; Fihman, F.; Tucoulou, R.; Kiryukhina, K.; Courtade, F.; Viso, M.

2018-04-01

Our nondestructive, nm-sized, hyperspectral analysis methodology of combined X-rays/Raman/IR probes in BSL4 quarantine, renders our patented mini-sample holder ideal for detecting extraterrestrial life. Our Stardust and Archean results validate it.

Researches on Position Detection for Vacuum Switch Electrode

NASA Astrophysics Data System (ADS)

Dong, Huajun; Guo, Yingjie; Li, Jie; Kong, Yihan

2018-03-01

Form and transformation character of vacuum arc is important influencing factor on the vacuum switch performance, and the dynamic separations of electrode is the chief effecting factor on the transformation of vacuum arcs forms. Consequently, how to detect the position of electrode to calculate the separations in the arcs image is of great significance. However, gray level distribution of vacuum arcs image isn’t even, the gray level of burning arcs is high, but the gray level of electrode is low, meanwhile, the forms of vacuum arcs changes sharply, the problems above restrict electrode position detection precisely. In this paper, algorithm of detecting electrode position base on vacuum arcs image was proposed. The digital image processing technology was used in vacuum switch arcs image analysis, the upper edge and lower edge were detected respectively, then linear fitting was done using the result of edge detection, the fitting result was the position of electrode, thus, accurate position detection of electrode was realized. From the experimental results, we can see that: algorithm described in this paper detected upper and lower edge of arcs successfully and the position of electrode was obtained through calculation.

NanoDLSay: a new platform technology for biomolecular detection and analysis using gold nanoparticle probes coupled with dynamic light scattering

NASA Astrophysics Data System (ADS)

Bogdanovic, Jelena; Huo, Qun

2010-04-01

Most analytical techniques that are routinely used in biomedical research for detection and quantification of biomolecules are time-consuming, expensive and labor-intensive, and there is always a need for rapid, affordable and convenient methods. Recently we have developed a new platform technology for biomolecular detection and analysis: NanoDLSay. NanoDLSay employs antibody-coated gold nanoparticles (GNPs) and dynamic light scattering, and correlates the specific increase in particle size after antigen-antibody interaction to the target antigen concentration. We applied this technology to develop an assay for rapid detection of actin, a protein widely used as a loading control in Western Blot analysis. GNPs were coated with two types of polyclonal anti-actin antibodies, and used in the assay to detect two types of actin: β- and bovine skeletal muscle actin in RIPA buffer. The results of our study revealed some complex aspects of actin binding characteristics, which depended on the type of actin reagent and anti-actin antibody used. A surprising finding was a reverse dose-response relationship between the actin concentration and the average particle size in the assay solution, which we attributed to the effect of RIPA buffer. Our results indicate that RIPA may also interfere in other types of nanoparticle-based assays, and that this interference deserves further study.

Tissue or blood: which is more suitable for detection of EGFR mutations in non-small cell lung cancer?

PubMed

Biaoxue, Rong; Shuanying, Yang

2018-01-01

Many studies have evaluated the accuracy of EGFR mutation status in blood against that in tumor tissues as the reference. We conducted this systematic review and meta-analysis to assess whether blood can be used as a substitute for tumor tissue in detecting EGFR mutations. Investigations that provided data on EGFR mutation status in blood were searched in the databases of Medline, Embase, Ovid Technologies and Web of Science. The detect efficiency of EGFR mutations in paired blood and tissues was compared using a random-effects model of meta-analysis. Pooled sensitivity and specificity and diagnostic accuracy were calculated by receiver operating characteristic curve. A total of 19 studies with 2,922 individuals were involved in this meta-analysis. The pooled results showed the positive detection rate of EGFR mutations in lung cancer tissues was remarkably higher than that of paired blood samples (odds ratio [OR] = 1.47, p<0.001). The pooled sensitivity and specificity of blood were 0.65 and 0.91, respectively, and the area under the receiver operating characteristic curve was 0.89. Although blood had a better specificity for detecting EGFR mutations, the absence of blood positivity should not necessarily be construed as confirmed negativity. Patients with negative results for blood should decidedly undergo further biopsies to ascertain EGFR mutations.

Occurrence and sources of antibiotics and their metabolites in river water, WWTPs, and swine wastewater in Jiulongjiang River basin, south China.

PubMed

Jiang, Hongyou; Zhang, Dandan; Xiao, Shichang; Geng, Chunnv; Zhang, Xian

2013-12-01

In this study, the occurrence and sources of five cataloged antibiotics and metabolites were studied in Jiulongjiang River basin, south China. Nineteen antibiotics and 13 metabolites were detected in water samples from 16 river sampling sites, wastewater from 5 swine-raising facilities, and effluent from 5 wastewater treatment plants (WWTPs). The results showed that 12 antibiotics and 6 metabolites were detected in river water samples. Sulfonamides (SAs) and their metabolites were detected at high concentrations (8.59-158.94 ng/L). Tetracyclines (TCs) and their metabolites were frequently detected in swine wastewater, and the maximum concentration was up to the level in milligram per liter. Macrolides (MLs) and β-lactams (β-Ls) were found in all WWTP effluent samples and some river samples, while they were never found in any of the swine wastewater samples. SAs and quinolones (QNs) were detected in all samples. Hierarchical cluster analysis of 16 surface water samples was applied to achieve the spatial distribution characteristics of antibiotics in the Jiulongjiang River. As a result, two categories were obviously obtained. Principal component analysis and redundancy analysis showed that TCs and SAs as well as their metabolites were the major antibiotics in Jiulongjiang River, and they mainly originated from swine wastewater, while the QNs, MLs, and β-Ls in the Jiulongjiang River came from WWTP effluent.

Breath analysis based on micropreconcentrator for early cancer diagnosis

NASA Astrophysics Data System (ADS)

Lee, Sang-Seok

2018-02-01

We are developing micropreconcentrators based on micro/nanotechnology to detect trace levels of volatile organic compound (VOC) gases contained in human and canine exhaled breath. The possibility of using exhaled VOC gases as biomarkers for various cancer diagnoses has been previously discussed. For early cancer diagnosis, detection of trace levels of VOC gas is indispensable. Using micropreconcentrators based on MEMS technology or nanotechnology is very promising for detection of VOC gas. A micropreconcentrator based breath analysis technique also has advantages from the viewpoints of cost performance and availability for various cancers diagnosis. In this paper, we introduce design, fabrication and evaluation results of our MEMS and nanotechnology based micropreconcentrators. In the MEMS based device, we propose a flower leaf type Si microstructure, and its shape and configuration are optimized quantitatively by finite element method simulation. The nanotechnology based micropreconcentrator consists of carbon nanotube (CNT) structures. As a result, we achieve ppb level VOC gas detection with our micropreconcentrators and usual gas chromatography system that can detect on the order of ppm VOC in gas samples. In performance evaluation, we also confirm that the CNT based micropreconcentrator shows 115 times better concentration ratio than that of the Si based micropreconcentrator. Moreover, we discuss a commercialization idea for new cancer diagnosis using breath analysis. Future work and preliminary clinical testing in dogs is also discussed.

Detecting transitions in protein dynamics using a recurrence quantification analysis based bootstrap method.

PubMed

Karain, Wael I

2017-11-28

Proteins undergo conformational transitions over different time scales. These transitions are closely intertwined with the protein's function. Numerous standard techniques such as principal component analysis are used to detect these transitions in molecular dynamics simulations. In this work, we add a new method that has the ability to detect transitions in dynamics based on the recurrences in the dynamical system. It combines bootstrapping and recurrence quantification analysis. We start from the assumption that a protein has a "baseline" recurrence structure over a given period of time. Any statistically significant deviation from this recurrence structure, as inferred from complexity measures provided by recurrence quantification analysis, is considered a transition in the dynamics of the protein. We apply this technique to a 132 ns long molecular dynamics simulation of the β-Lactamase Inhibitory Protein BLIP. We are able to detect conformational transitions in the nanosecond range in the recurrence dynamics of the BLIP protein during the simulation. The results compare favorably to those extracted using the principal component analysis technique. The recurrence quantification analysis based bootstrap technique is able to detect transitions between different dynamics states for a protein over different time scales. It is not limited to linear dynamics regimes, and can be generalized to any time scale. It also has the potential to be used to cluster frames in molecular dynamics trajectories according to the nature of their recurrence dynamics. One shortcoming for this method is the need to have large enough time windows to insure good statistical quality for the recurrence complexity measures needed to detect the transitions.

A fast automatic target detection method for detecting ships in infrared scenes

NASA Astrophysics Data System (ADS)

Özertem, Kemal Arda

2016-05-01

Automatic target detection in infrared scenes is a vital task for many application areas like defense, security and border surveillance. For anti-ship missiles, having a fast and robust ship detection algorithm is crucial for overall system performance. In this paper, a straight-forward yet effective ship detection method for infrared scenes is introduced. First, morphological grayscale reconstruction is applied to the input image, followed by an automatic thresholding onto the suppressed image. For the segmentation step, connected component analysis is employed to obtain target candidate regions. At this point, it can be realized that the detection is defenseless to outliers like small objects with relatively high intensity values or the clouds. To deal with this drawback, a post-processing stage is introduced. For the post-processing stage, two different methods are used. First, noisy detection results are rejected with respect to target size. Second, the waterline is detected by using Hough transform and the detection results that are located above the waterline with a small margin are rejected. After post-processing stage, there are still undesired holes remaining, which cause to detect one object as multi objects or not to detect an object as a whole. To improve the detection performance, another automatic thresholding is implemented only to target candidate regions. Finally, two detection results are fused and post-processing stage is repeated to obtain final detection result. The performance of overall methodology is tested with real world infrared test data.

Vision-based method for detecting driver drowsiness and distraction in driver monitoring system

NASA Astrophysics Data System (ADS)

Jo, Jaeik; Lee, Sung Joo; Jung, Ho Gi; Park, Kang Ryoung; Kim, Jaihie

2011-12-01

Most driver-monitoring systems have attempted to detect either driver drowsiness or distraction, although both factors should be considered for accident prevention. Therefore, we propose a new driver-monitoring method considering both factors. We make the following contributions. First, if the driver is looking ahead, drowsiness detection is performed; otherwise, distraction detection is performed. Thus, the computational cost and eye-detection error can be reduced. Second, we propose a new eye-detection algorithm that combines adaptive boosting, adaptive template matching, and blob detection with eye validation, thereby reducing the eye-detection error and processing time significantly, which is hardly achievable using a single method. Third, to enhance eye-detection accuracy, eye validation is applied after initial eye detection, using a support vector machine based on appearance features obtained by principal component analysis (PCA) and linear discriminant analysis (LDA). Fourth, we propose a novel eye state-detection algorithm that combines appearance features obtained using PCA and LDA, with statistical features such as the sparseness and kurtosis of the histogram from the horizontal edge image of the eye. Experimental results showed that the detection accuracies of the eye region and eye states were 99 and 97%, respectively. Both driver drowsiness and distraction were detected with a success rate of 98%.

Novel image processing approach to detect malaria

NASA Astrophysics Data System (ADS)

Mas, David; Ferrer, Belen; Cojoc, Dan; Finaurini, Sara; Mico, Vicente; Garcia, Javier; Zalevsky, Zeev

2015-09-01

In this paper we present a novel image processing algorithm providing good preliminary capabilities for in vitro detection of malaria. The proposed concept is based upon analysis of the temporal variation of each pixel. Changes in dark pixels mean that inter cellular activity happened, indicating the presence of the malaria parasite inside the cell. Preliminary experimental results involving analysis of red blood cells being either healthy or infected with malaria parasites, validated the potential benefit of the proposed numerical approach.

MR 201104: Evaluation of Discrimination Technologies and Classification Results and MR 201157: Demonstration of MetalMapper Static Data Acquisition and Data Analysis

DTIC Science & Technology

2016-09-23

Acquisition and Data Analysis). EMI sensors, MetalMapper, man-portable Time-domain Electromagnetic Multi-sensor Towed Array Detection System (TEMTADS...California Department of Toxic Substances Control EM61 EM61-MK2 EMI electromagnetic induction ESTCP Environmental Security Technology Certification...SOP Standard Operating Procedure v TEMTADS Time-domain Electromagnetic Multi-sensor Towed Array Detection System man-portable 2x2 TOI target(s

Quantification of the methylation status of the PWS/AS imprinted region: comparison of two approaches based on bisulfite sequencing and methylation-sensitive MLPA.

PubMed

Dikow, Nicola; Nygren, Anders Oh; Schouten, Jan P; Hartmann, Carolin; Krämer, Nikola; Janssen, Bart; Zschocke, Johannes

2007-06-01

Standard methods used for genomic methylation analysis allow the detection of complete absence of either methylated or non-methylated alleles but are usually unable to detect changes in the proportion of methylated and unmethylated alleles. We compare two methods for quantitative methylation analysis, using the chromosome 15q11-q13 imprinted region as model. Absence of the non-methylated paternal allele in this region leads to Prader-Willi syndrome (PWS) whilst absence of the methylated maternal allele results in Angelman syndrome (AS). A proportion of AS is caused by mosaic imprinting defects which may be missed with standard methods and require quantitative analysis for their detection. Sequence-based quantitative methylation analysis (SeQMA) involves quantitative comparison of peaks generated through sequencing reactions after bisulfite treatment. It is simple, cost-effective and can be easily established for a large number of genes. However, our results support previous suggestions that methods based on bisulfite treatment may be problematic for exact quantification of methylation status. Methylation-specific multiplex ligation-dependent probe amplification (MS-MLPA) avoids bisulfite treatment. It detects changes in both CpG methylation as well as copy number of up to 40 chromosomal sequences in one simple reaction. Once established in a laboratory setting, the method is more accurate, reliable and less time consuming.

Predicting neuropathic ulceration: analysis of static temperature distributions in thermal images

NASA Astrophysics Data System (ADS)

Kaabouch, Naima; Hu, Wen-Chen; Chen, Yi; Anderson, Julie W.; Ames, Forrest; Paulson, Rolf

2010-11-01

Foot ulcers affect millions of Americans annually. Conventional methods used to assess skin integrity, including inspection and palpation, may be valuable approaches, but they usually do not detect changes in skin integrity until an ulcer has already developed. We analyze the feasibility of thermal imaging as a technique to assess the integrity of the skin and its many layers. Thermal images are analyzed using an asymmetry analysis, combined with a genetic algorithm, to examine the infrared images for early detection of foot ulcers. Preliminary results show that the proposed technique can reliably and efficiently detect inflammation and hence effectively predict potential ulceration.

Identification and detection of anomalies through SSME data analysis

NASA Technical Reports Server (NTRS)

Pereira, Lisa; Ali, Moonis

1990-01-01

The goal of the ongoing research described in this paper is to analyze real-time ground test data in order to identify patterns associated with the anomalous engine behavior, and on the basis of this analysis to develop an expert system which detects anomalous engine behavior in the early stages of fault development. A prototype of the expert system has been developed and tested on the high frequency data of two SSME tests, namely Test #901-0516 and Test #904-044. The comparison of our results with the post-test analyses indicates that the expert system detected the presence of the anomalies in a significantly early stage of fault development.

Multi-complexity ensemble measures for gait time series analysis: application to diagnostics, monitoring and biometrics.

PubMed

Gavrishchaka, Valeriy; Senyukova, Olga; Davis, Kristina

2015-01-01

Previously, we have proposed to use complementary complexity measures discovered by boosting-like ensemble learning for the enhancement of quantitative indicators dealing with necessarily short physiological time series. We have confirmed robustness of such multi-complexity measures for heart rate variability analysis with the emphasis on detection of emerging and intermittent cardiac abnormalities. Recently, we presented preliminary results suggesting that such ensemble-based approach could be also effective in discovering universal meta-indicators for early detection and convenient monitoring of neurological abnormalities using gait time series. Here, we argue and demonstrate that these multi-complexity ensemble measures for gait time series analysis could have significantly wider application scope ranging from diagnostics and early detection of physiological regime change to gait-based biometrics applications.

Derivative Analysis of AVIRIS Data for Crop Stress Detection

NASA Technical Reports Server (NTRS)

Estep, Lee; Carter, Gregory A.; Berglund, Judith

2003-01-01

Low-altitude Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral imagery of a cornfield in Nebraska was used to determine whether derivative analysis methods provided enhanced plant stress detection compared with narrow-band ratios. The field was divided into 20 plots representing 4 replicates each of 5 nitrogen (N) fertilization treatments that ranged from 0 to 200 kg N/ha in 50 kg/ha increments. The imagery yielded a 3 m ground pixel size for 224 spectral bands. Derivative analysis provided no advantage in stress detection compared with the performance of narrow-band indices derived from the literature. This result was attributed to a high leaf area index at the time of overflight (LAI approx. equal to 5 to 6t) and the high signal-to-noise character of the narrow AVIRIS bands.

Analysis of the restricting factors of laser countermeasure active detection technology

NASA Astrophysics Data System (ADS)

Zhang, Yufa; Sun, Xiaoquan

2016-07-01

The detection effect of laser active detection system is affected by various kinds of factors. In view of the application requirement of laser active detection, the influence factors for laser active detection are analyzed. The mathematical model of cat eye target detection distance has been built, influence of the parameters of laser detection system and the environment on detection range and the detection efficiency are analyzed. Various parameters constraint detection performance is simulated. The results show that the discovery distance of laser active detection is affected by the laser divergence angle, the incident angle and the visibility of the atmosphere. For a given detection range, the laser divergence angle and the detection efficiency are mutually restricted. Therefore, in view of specific application environment, it is necessary to select appropriate laser detection parameters to achieve optimal detection effect.

High-throughput quantitation of amino acids in rat and mouse biological matrices using stable isotope labeling and UPLC-MS/MS analysis.

PubMed

Takach, Edward; O'Shea, Thomas; Liu, Hanlan

2014-08-01

Quantifying amino acids in biological matrices is typically performed using liquid chromatography (LC) coupled with fluorescent detection (FLD), requiring both derivatization and complete baseline separation of all amino acids. Due to its high specificity and sensitivity, the use of UPLC-MS/MS eliminates the derivatization step and allows for overlapping amino acid retention times thereby shortening the analysis time. Furthermore, combining UPLC-MS/MS with stable isotope labeling (e.g., isobaric tag for relative and absolute quantitation, i.e., iTRAQ) of amino acids enables quantitation while maintaining sensitivity, selectivity and speed of analysis. In this study, we report combining UPLC-MS/MS analysis with iTRAQ labeling of amino acids resulting in the elution and quantitation of 44 amino acids within 5 min demonstrating the speed and convenience of this assay over established approaches. This chromatographic analysis time represented a 5-fold improvement over the conventional HPLC-MS/MS method developed in our laboratory. In addition, the UPLC-MS/MS method demonstrated improvements in both specificity and sensitivity without loss of precision. In comparing UPLC-MS/MS and HPLC-MS/MS results of 32 detected amino acids, only 2 amino acids exhibited imprecision (RSD) >15% using UPLC-MS/MS, while 9 amino acids exhibited RSD >15% using HPLC-MS/MS. Evaluating intra- and inter-assay precision over 3 days, the quantitation range for 32 detected amino acids in rat plasma was 0.90-497 μM, with overall mean intra-day precision of less than 15% and mean inter-day precision of 12%. This UPLC-MS/MS assay was successfully implemented for the quantitative analysis of amino acids in rat and mouse plasma, along with mouse urine and tissue samples, resulting in the following concentration ranges: 0.98-431 μM in mouse plasma for 32 detected amino acids; 0.62-443 μM in rat plasma for 32 detected amino acids; 0.44-8590μM in mouse liver for 33 detected amino acids; 0.61-1241 μM in mouse kidney for 37 detected amino acids; and 1.39-1,681 μM in rat urine for 34 detected amino acids. The utility of the assay was further demonstrated by measuring and comparing plasma amino acid levels between pre-diabetic Zucker diabetic fatty rats (ZDF/Gmi fa/fa) and their lean littermates (ZDF/Gmi fa/?). Significant differences (P<0.001) in 9 amino acid concentrations were observed, with the majority ranging from a 2- to 5-fold increase in pre-diabetic ZDF rats on comparison with ZDF lean rats, consistent with previous literature reports. Copyright © 2014 Elsevier B.V. All rights reserved.

Escherichia coli biosensors for environmental, food industry and biological warfare agent detection

NASA Astrophysics Data System (ADS)

Allil, R. C. S. B.; Werneck, M. M.; da Silva-Neto, J. L.; Miguel, M. A. L.; Rodrigues, D. M. C.; Wandermur, G. L.; Rambauske, D. C.

2013-06-01

This work has the objective to research and develop a plastic optical fiber biosensor based taper and mPOF LPG techniques to detect Escherichia coli by measurements of index of refraction. Generally, cell detection is crucial in microbiological analysis of clinical, food, water or environmental samples. However, methods current employed are time consuming, taking at least 72 hours in order to produce reliable responses as they depend on sample collection and cell culture in controlled conditions. The delay in obtaining the results of the analysis can result in contamination of a great number of consumers. Plastic Optical Fiber (POF) biosensors consist in a viable alternative for rapid and inexpensive scheme for cells detection. A study the sensitivity of these sensors for microbiological detection, fiber Tapers and Long Period Grating (LPG) both in poly-methyl-methacrylate (PMMA) were realized as possible candidates to take part of a biosensor system to detect Escherichia coli in water samples. In this work we adopted the immunocapture technique, which consists of quantifying bacteria in a liquid sample, attract-ing and fixing the bacteria on the surface of the polymer optical fiber, by the antigen-antibody reaction. The results were obtained by optical setup that consists in a side of the fiber a LED coupled to a photodetector through a POF with the taper in the middle of it. On the other side of the POF a photodetector receives this light producting a photocurrent. The output voltage is fed into the microcontroller A/D input port and its output data is sent via USB to a LabView software running in a microcomputer. The results showed the possibility of the POF in biosensor application capable to detect E. coli for environmental and food industry and for detecting and identifying biological-warfare agents using a very rapid response sensor, applicable to field detection prototypes.

Analytical and Experimental Vibration Analysis of a Faulty Gear System.

DTIC Science & Technology

1994-10-01

Wigner - Ville Distribution ( WVD ) was used to give a comprehensive comparison of the predicted and...experimental results. The WVD method applied to the experimental results were also compared to other fault detection techniques to verify the WVD’s ability to...of the damaged test gear and the predicted vibration from the model with simulated gear tooth pitting damage. Results also verified that the WVD method can successfully detect and locate gear tooth wear and pitting damage.

Principal component analysis for the early detection of mastitis and lameness in dairy cows.

PubMed

Miekley, Bettina; Traulsen, Imke; Krieter, Joachim

2013-08-01

This investigation analysed the applicability of principal component analysis (PCA), a latent variable method, for the early detection of mastitis and lameness. Data used were recorded on the Karkendamm dairy research farm between August 2008 and December 2010. For mastitis and lameness detection, data of 338 and 315 cows in their first 200 d in milk were analysed, respectively. Mastitis as well as lameness were specified according to veterinary treatments. Diseases were defined as disease blocks. The different definitions used (two for mastitis, three for lameness) varied solely in the sequence length of the blocks. Only the days before the treatment were included in the blocks. Milk electrical conductivity, milk yield and feeding patterns (feed intake, number of feeding visits and time at the trough) were used for recognition of mastitis. Pedometer activity and feeding patterns were utilised for lameness detection. To develop and verify the PCA model, the mastitis and the lameness datasets were divided into training and test datasets. PCA extracted uncorrelated principle components (PC) by linear transformations of the raw data so that the first few PCs captured most of the variations in the original dataset. For process monitoring and disease detection, these resulting PCs were applied to the Hotelling's T 2 chart and to the residual control chart. The results show that block sensitivity of mastitis detection ranged from 77·4 to 83·3%, whilst specificity was around 76·7%. The error rates were around 98·9%. For lameness detection, the block sensitivity ranged from 73·8 to 87·8% while the obtained specificities were between 54·8 and 61·9%. The error rates varied from 87·8 to 89·2%. In conclusion, PCA seems to be not yet transferable into practical usage. Results could probably be improved if different traits and more informative sensor data are included in the analysis.

Shilling Attacks Detection in Recommender Systems Based on Target Item Analysis

PubMed Central

Zhou, Wei; Wen, Junhao; Koh, Yun Sing; Xiong, Qingyu; Gao, Min; Dobbie, Gillian; Alam, Shafiq

2015-01-01

Recommender systems are highly vulnerable to shilling attacks, both by individuals and groups. Attackers who introduce biased ratings in order to affect recommendations, have been shown to negatively affect collaborative filtering (CF) algorithms. Previous research focuses only on the differences between genuine profiles and attack profiles, ignoring the group characteristics in attack profiles. In this paper, we study the use of statistical metrics to detect rating patterns of attackers and group characteristics in attack profiles. Another question is that most existing detecting methods are model specific. Two metrics, Rating Deviation from Mean Agreement (RDMA) and Degree of Similarity with Top Neighbors (DegSim), are used for analyzing rating patterns between malicious profiles and genuine profiles in attack models. Building upon this, we also propose and evaluate a detection structure called RD-TIA for detecting shilling attacks in recommender systems using a statistical approach. In order to detect more complicated attack models, we propose a novel metric called DegSim’ based on DegSim. The experimental results show that our detection model based on target item analysis is an effective approach for detecting shilling attacks. PMID:26222882

Ground-based deep-space LADAR for satellite detection: A parametric study

NASA Astrophysics Data System (ADS)

Davey, Kevin F.

1989-12-01

The minimum performance requirements are determined of a ground based infrared LADAR designed to detect deep space satellites, and a candidate sensor design is presented based on current technology. The research examines LADAR techniques and detection methods to determine the optimum LADAR configuration, and then assesses the effects of atmospheric transmission, background radiance, and turbulence across the infrared region to find the optimum laser wavelengths. Diffraction theory is then used in a parametric analysis of the transmitted laser beam and received signal, using a Cassegrainian telescope design and heterodyne detection. The effects of beam truncation and obscuration, heterodyne misalignment, off-boresight detection, and image-pixel geometry are also included in the analysis. The derived equations are then used to assess the feasibility of several candidate designs under a wide range of detection conditions including daylight operation through cirrus. The results show that successful detection is theoretically possible under most conditions by transmitting a high power frequency modulated pulse train from an isotopic 13CO2 laser radiating at 11.17 micrometers, and utilizing post-detection integration and pulse compression techniques.

Electrochemical and Infrared Absorption Spectroscopy Detection of SF₆ Decomposition Products.

PubMed

Dong, Ming; Zhang, Chongxing; Ren, Ming; Albarracín, Ricardo; Ye, Rixin

2017-11-15

Sulfur hexafluoride (SF₆) gas-insulated electrical equipment is widely used in high-voltage (HV) and extra-high-voltage (EHV) power systems. Partial discharge (PD) and local heating can occur in the electrical equipment because of insulation faults, which results in SF₆ decomposition and ultimately generates several types of decomposition products. These SF₆ decomposition products can be qualitatively and quantitatively detected with relevant detection methods, and such detection contributes to diagnosing the internal faults and evaluating the security risks of the equipment. At present, multiple detection methods exist for analyzing the SF₆ decomposition products, and electrochemical sensing (ES) and infrared (IR) spectroscopy are well suited for application in online detection. In this study, the combination of ES with IR spectroscopy is used to detect SF₆ gas decomposition. First, the characteristics of these two detection methods are studied, and the data analysis matrix is established. Then, a qualitative and quantitative analysis ES-IR model is established by adopting a two-step approach. A SF₆ decomposition detector is designed and manufactured by combining an electrochemical sensor and IR spectroscopy technology. The detector is used to detect SF₆ gas decomposition and is verified to reliably and accurately detect the gas components and concentrations.

Detection of compatibility between baclofen and excipients with aid of infrared spectroscopy and chemometry

NASA Astrophysics Data System (ADS)

Rojek, Barbara; Wesolowski, Marek; Suchacz, Bogdan

2013-12-01

In the paper infrared (IR) spectroscopy and multivariate exploration techniques: principal component analysis (PCA) and cluster analysis (CA) were applied as supportive methods for the detection of physicochemical incompatibilities between baclofen and excipients. In the course of research, the most useful rotational strategy in PCA proved to be varimax normalized, while in CA Ward's hierarchical agglomeration with Euclidean distance measure enabled to yield the most interpretable results. Chemometrical calculations confirmed the suitability of PCA and CA as the auxiliary methods for interpretation of infrared spectra in order to recognize whether compatibilities or incompatibilities between active substance and excipients occur. On the basis of IR spectra and the results of PCA and CA it was possible to demonstrate that the presence of lactose, β-cyclodextrin and meglumine in binary mixtures produce interactions with baclofen. The results were verified using differential scanning calorimetry, differential thermal analysis, thermogravimetry/differential thermogravimetry and X-ray powder diffraction analyses.

Content Analysis of Measures for Identification of Elder Abuse.

ERIC Educational Resources Information Center

Sengstock, Mary C.; And Others

Measures designed to detect elder abuse lack uniformity as a result of having been designed in isolation. To develop and test a uniform index for the identification of elder abuse victims, an analysis of existing abuse identification instruments was conducted. Initially, seven elder abuse identification measures were content analyzed, resulting in…

DNA nanomechanics allows direct digital detection of complementary DNA and microRNA targets.

PubMed

Husale, Sudhir; Persson, Henrik H J; Sahin, Ozgur

2009-12-24

Techniques to detect and quantify DNA and RNA molecules in biological samples have had a central role in genomics research. Over the past decade, several techniques have been developed to improve detection performance and reduce the cost of genetic analysis. In particular, significant advances in label-free methods have been reported. Yet detection of DNA molecules at concentrations below the femtomolar level requires amplified detection schemes. Here we report a unique nanomechanical response of hybridized DNA and RNA molecules that serves as an intrinsic molecular label. Nanomechanical measurements on a microarray surface have sufficient background signal rejection to allow direct detection and counting of hybridized molecules. The digital response of the sensor provides a large dynamic range that is critical for gene expression profiling. We have measured differential expressions of microRNAs in tumour samples; such measurements have been shown to help discriminate between the tissue origins of metastatic tumours. Two hundred picograms of total RNA is found to be sufficient for this analysis. In addition, the limit of detection in pure samples is found to be one attomolar. These results suggest that nanomechanical read-out of microarrays promises attomolar-level sensitivity and large dynamic range for the analysis of gene expression, while eliminating biochemical manipulations, amplification and labelling.

A Mass Spectrometric Analysis Method Based on PPCA and SVM for Early Detection of Ovarian Cancer.

PubMed

Wu, Jiang; Ji, Yanju; Zhao, Ling; Ji, Mengying; Ye, Zhuang; Li, Suyi

2016-01-01

Background. Surfaced-enhanced laser desorption-ionization-time of flight mass spectrometry (SELDI-TOF-MS) technology plays an important role in the early diagnosis of ovarian cancer. However, the raw MS data is highly dimensional and redundant. Therefore, it is necessary to study rapid and accurate detection methods from the massive MS data. Methods. The clinical data set used in the experiments for early cancer detection consisted of 216 SELDI-TOF-MS samples. An MS analysis method based on probabilistic principal components analysis (PPCA) and support vector machine (SVM) was proposed and applied to the ovarian cancer early classification in the data set. Additionally, by the same data set, we also established a traditional PCA-SVM model. Finally we compared the two models in detection accuracy, specificity, and sensitivity. Results. Using independent training and testing experiments 10 times to evaluate the ovarian cancer detection models, the average prediction accuracy, sensitivity, and specificity of the PCA-SVM model were 83.34%, 82.70%, and 83.88%, respectively. In contrast, those of the PPCA-SVM model were 90.80%, 92.98%, and 88.97%, respectively. Conclusions. The PPCA-SVM model had better detection performance. And the model combined with the SELDI-TOF-MS technology had a prospect in early clinical detection and diagnosis of ovarian cancer.

GMDD: a database of GMO detection methods

PubMed Central

Dong, Wei; Yang, Litao; Shen, Kailin; Kim, Banghyun; Kleter, Gijs A; Marvin, Hans JP; Guo, Rong; Liang, Wanqi; Zhang, Dabing

2008-01-01

Background Since more than one hundred events of genetically modified organisms (GMOs) have been developed and approved for commercialization in global area, the GMO analysis methods are essential for the enforcement of GMO labelling regulations. Protein and nucleic acid-based detection techniques have been developed and utilized for GMOs identification and quantification. However, the information for harmonization and standardization of GMO analysis methods at global level is needed. Results GMO Detection method Database (GMDD) has collected almost all the previous developed and reported GMOs detection methods, which have been grouped by different strategies (screen-, gene-, construct-, and event-specific), and also provide a user-friendly search service of the detection methods by GMO event name, exogenous gene, or protein information, etc. In this database, users can obtain the sequences of exogenous integration, which will facilitate PCR primers and probes design. Also the information on endogenous genes, certified reference materials, reference molecules, and the validation status of developed methods is included in this database. Furthermore, registered users can also submit new detection methods and sequences to this database, and the newly submitted information will be released soon after being checked. Conclusion GMDD contains comprehensive information of GMO detection methods. The database will make the GMOs analysis much easier. PMID:18522755

Analysis of glycosaminoglycan-derived disaccharides by capillary electrophoresis using laser-induced fluorescence detection

PubMed Central

Chang, Yuqing; Yang, Bo; Zhao, Xue; Linhardt, Robert J.

2012-01-01

A quantitative and highly sensitive method for the analysis of glycosaminoglycan (GAG)-derived disaccharides is presented that relies on capillary electrophoresis (CE) with laser-induced fluorescence (LIF) detection. This method enables complete separation of seventeen GAG-derived disaccharides in a single run. Unsaturated disaccharides were derivatized with 2-aminoacridone (AMAC) to improve sensitivity. The limit of detection was at the attomole level and about 100-fold more sensitive than traditional CE-ultraviolet detection. A CE separation timetable was developed to achieve complete resolution and shorten analysis time. The RSD of migration time and peak areas at both low and high concentrations of unsaturated disaccharides are all less than 2.7% and 3.2%, respectively, demonstrating that this is a reproducible method. This analysis was successfully applied to cultured Chinese hamster ovary cell samples for determination of GAG disaccharides. The current method simplifies GAG extraction steps, and reduces inaccuracy in calculating ratios of heparin/heparan sulfate to chondroitin sulfate/dermatan sulfate, resulting from the separate analyses of a single sample. PMID:22609076

A wavelet transform algorithm for peak detection and application to powder x-ray diffraction data.

PubMed

Gregoire, John M; Dale, Darren; van Dover, R Bruce

2011-01-01

Peak detection is ubiquitous in the analysis of spectral data. While many noise-filtering algorithms and peak identification algorithms have been developed, recent work [P. Du, W. Kibbe, and S. Lin, Bioinformatics 22, 2059 (2006); A. Wee, D. Grayden, Y. Zhu, K. Petkovic-Duran, and D. Smith, Electrophoresis 29, 4215 (2008)] has demonstrated that both of these tasks are efficiently performed through analysis of the wavelet transform of the data. In this paper, we present a wavelet-based peak detection algorithm with user-defined parameters that can be readily applied to the application of any spectral data. Particular attention is given to the algorithm's resolution of overlapping peaks. The algorithm is implemented for the analysis of powder diffraction data, and successful detection of Bragg peaks is demonstrated for both low signal-to-noise data from theta-theta diffraction of nanoparticles and combinatorial x-ray diffraction data from a composition spread thin film. These datasets have different types of background signals which are effectively removed in the wavelet-based method, and the results demonstrate that the algorithm provides a robust method for automated peak detection.

Development and evaluation of a microdevice for amino acid biomarker detection and analysis on Mars

PubMed Central

Skelley, Alison M.; Scherer, James R.; Aubrey, Andrew D.; Grover, William H.; Ivester, Robin H. C.; Ehrenfreund, Pascale; Grunthaner, Frank J.; Bada, Jeffrey L.; Mathies, Richard A.

2005-01-01

The Mars Organic Analyzer (MOA), a microfabricated capillary electrophoresis (CE) instrument for sensitive amino acid biomarker analysis, has been developed and evaluated. The microdevice consists of a four-wafer sandwich combining glass CE separation channels, microfabricated pneumatic membrane valves and pumps, and a nanoliter fluidic network. The portable MOA instrument integrates high voltage CE power supplies, pneumatic controls, and fluorescence detection optics necessary for field operation. The amino acid concentration sensitivities range from micromolar to 0.1 nM, corresponding to part-per-trillion sensitivity. The MOA was first used in the lab to analyze soil extracts from the Atacama Desert, Chile, detecting amino acids ranging from 10–600 parts per billion. Field tests of the MOA in the Panoche Valley, CA, successfully detected amino acids at 70 parts per trillion to 100 parts per billion in jarosite, a sulfate-rich mineral associated with liquid water that was recently detected on Mars. These results demonstrate the feasibility of using the MOA to perform sensitive in situ amino acid biomarker analysis on soil samples representative of a Mars-like environment. PMID:15657130

Development and evaluation of a microdevice for amino acid biomarker detection and analysis on Mars.

PubMed

Skelley, Alison M; Scherer, James R; Aubrey, Andrew D; Grover, William H; Ivester, Robin H C; Ehrenfreund, Pascale; Grunthaner, Frank J; Bada, Jeffrey L; Mathies, Richard A

2005-01-25

The Mars Organic Analyzer (MOA), a microfabricated capillary electrophoresis (CE) instrument for sensitive amino acid biomarker analysis, has been developed and evaluated. The microdevice consists of a four-wafer sandwich combining glass CE separation channels, microfabricated pneumatic membrane valves and pumps, and a nanoliter fluidic network. The portable MOA instrument integrates high voltage CE power supplies, pneumatic controls, and fluorescence detection optics necessary for field operation. The amino acid concentration sensitivities range from micromolar to 0.1 nM, corresponding to part-per-trillion sensitivity. The MOA was first used in the lab to analyze soil extracts from the Atacama Desert, Chile, detecting amino acids ranging from 10-600 parts per billion. Field tests of the MOA in the Panoche Valley, CA, successfully detected amino acids at 70 parts per trillion to 100 parts per billion in jarosite, a sulfate-rich mineral associated with liquid water that was recently detected on Mars. These results demonstrate the feasibility of using the MOA to perform sensitive in situ amino acid biomarker analysis on soil samples representative of a Mars-like environment.

Clinical Application of Volatile Organic Compound Analysis for Detecting Infectious Diseases

PubMed Central

Nanda, Ranjan; Chakraborty, Trinad

2013-01-01

SUMMARY This review article introduces the significance of testing of volatile organic compounds (VOCs) in clinical samples and summarizes important features of some of the technologies. Compared to other human diseases such as cancer, studies on VOC analysis in cases of infectious diseases are limited. Here, we have described results of studies which have used some of the appropriate technologies to evaluate VOC biomarkers and biomarker profiles associated with infections. The publications reviewed include important infections of the respiratory tract, gastrointestinal tract, urinary tract, and nasal cavity. The results highlight the use of VOC biomarker profiles resulting from certain infectious diseases in discriminating between infected and healthy subjects. Infection-related VOC profiles measured in exhaled breath as well as from headspaces of feces or urine samples are a source of information with respect to disease detection. The volatiles emitted in clinical matrices may on the one hand represent metabolites of the infecting pathogen or on the other hand reflect pathogen-induced host responses or, indeed, a combination of both. Because exhaled-breath samples are easy to collect and online instruments are commercially available, VOC analysis in exhaled breath appears to be a promising tool for noninvasive detection and monitoring of infectious diseases. PMID:23824368

An online sleep apnea detection method based on recurrence quantification analysis.

PubMed

Nguyen, Hoa Dinh; Wilkins, Brek A; Cheng, Qi; Benjamin, Bruce Allen

2014-07-01

This paper introduces an online sleep apnea detection method based on heart rate complexity as measured by recurrence quantification analysis (RQA) statistics of heart rate variability (HRV) data. RQA statistics can capture nonlinear dynamics of a complex cardiorespiratory system during obstructive sleep apnea. In order to obtain a more robust measurement of the nonstationarity of the cardiorespiratory system, we use different fixed amount of neighbor thresholdings for recurrence plot calculation. We integrate a feature selection algorithm based on conditional mutual information to select the most informative RQA features for classification, and hence, to speed up the real-time classification process without degrading the performance of the system. Two types of binary classifiers, i.e., support vector machine and neural network, are used to differentiate apnea from normal sleep. A soft decision fusion rule is developed to combine the results of these classifiers in order to improve the classification performance of the whole system. Experimental results show that our proposed method achieves better classification results compared with the previous recurrence analysis-based approach. We also show that our method is flexible and a strong candidate for a real efficient sleep apnea detection system.

An Investigation of Document Partitions.

ERIC Educational Resources Information Center

Shaw, W. M., Jr.

1986-01-01

Empirical significance of document partitions is investigated as a function of index term-weight and similarity thresholds. Results show the same empirically preferred partitions can be detected by two independent strategies: an analysis of cluster-based retrieval analysis and an analysis of regularities in the underlying structure of the document…

Cost-Effectiveness Analysis of Three Leprosy Case Detection Methods in Northern Nigeria

PubMed Central

Ezenduka, Charles; Post, Erik; John, Steven; Suraj, Abdulkarim; Namadi, Abdulahi; Onwujekwe, Obinna

2012-01-01

Background Despite several leprosy control measures in Nigeria, child proportion and disability grade 2 cases remain high while new cases have not significantly reduced, suggesting continuous spread of the disease. Hence, there is the need to review detection methods to enhance identification of early cases for effective control and prevention of permanent disability. This study evaluated the cost-effectiveness of three leprosy case detection methods in Northern Nigeria to identify the most cost-effective approach for detection of leprosy. Methods A cross-sectional study was carried out to evaluate the additional benefits of using several case detection methods in addition to routine practice in two north-eastern states of Nigeria. Primary and secondary data were collected from routine practice records and the Nigerian Tuberculosis and Leprosy Control Programme of 2009. The methods evaluated were Rapid Village Survey (RVS), Household Contact Examination (HCE) and Traditional Healers incentive method (TH). Effectiveness was measured as number of new leprosy cases detected and cost-effectiveness was expressed as cost per case detected. Costs were measured from both providers' and patients' perspectives. Additional costs and effects of each method were estimated by comparing each method against routine practise and expressed as incremental cost-effectiveness ratio (ICER). All costs were converted to the U.S. dollar at the 2010 exchange rate. Univariate sensitivity analysis was used to evaluate uncertainties around the ICER. Results The ICER for HCE was $142 per additional case detected at all contact levels and it was the most cost-effective method. At ICER of $194 per additional case detected, THs method detected more cases at a lower cost than the RVS, which was not cost-effective at $313 per additional case detected. Sensitivity analysis showed that varying the proportion of shared costs and subsistent wage for valuing unpaid time did not significantly change the results. Conclusion Complementing routine practice with household contact examination is the most cost-effective approach to identify new leprosy cases and we recommend that, depending on acceptability and feasibility, this intervention is introduced for improved case detection in Northern Nigeria. PMID:23029580

Evaluation of a Real-Time PCR Test for the Detection and Discrimination of Theileria Species in the African Buffalo (Syncerus caffer)

PubMed Central

Chaisi, Mamohale E.; Janssens, Michiel E.; Vermeiren, Lieve; Oosthuizen, Marinda C.; Collins, Nicola E.; Geysen, Dirk

2013-01-01

A quantitative real-time PCR (qPCR) assay based on the cox III gene was evaluated for the simultaneous detection and discrimination of Theileria species in buffalo and cattle blood samples from South Africa and Mozambique using melting curve analysis. The results obtained were compared to those of the reverse line blot (RLB) hybridization assay for the simultaneous detection and differentiation of Theileria spp. in mixed infections, and to the 18S rRNA qPCR assay results for the specific detection of Theileria parva. Theileria parva, Theileria sp. (buffalo), Theileria taurotragi, Theileria buffeli and Theileria mutans were detected by the cox III assay. Theileria velifera was not detected from any of the samples analysed. Seventeen percent of the samples had non-species specific melting peaks and 4.5% of the samples were negative or below the detection limit of the assay. The cox III assay identified more T. parva and Theileria sp. (buffalo) positive samples than the RLB assay, and also detected more T. parva infections than the 18S assay. However, only a small number of samples were positive for the benign Theileria spp. To our knowledge T. taurotragi has never been identified from the African buffalo, its identification in some samples by the qPCR assay was unexpected. Because of these discrepancies in the results, cox III qPCR products were cloned and sequenced. Sequence analysis indicated extensive inter- and intra-species variations in the probe target regions of the cox III gene sequences of the benign Theileria spp. and therefore explains their low detection. The cox III assay is specific for the detection of T. parva infections in cattle and buffalo. Sequence data generated from this study can be used for the development of a more inclusive assay for detection and differentiation of all variants of the mildly pathogenic and benign Theileria spp. of buffalo and cattle. PMID:24146782

Evaluation of a real-time PCR test for the detection and discrimination of theileria species in the African buffalo (Syncerus caffer).

PubMed

Chaisi, Mamohale E; Janssens, Michiel E; Vermeiren, Lieve; Oosthuizen, Marinda C; Collins, Nicola E; Geysen, Dirk

2013-01-01

A quantitative real-time PCR (qPCR) assay based on the cox III gene was evaluated for the simultaneous detection and discrimination of Theileria species in buffalo and cattle blood samples from South Africa and Mozambique using melting curve analysis. The results obtained were compared to those of the reverse line blot (RLB) hybridization assay for the simultaneous detection and differentiation of Theileria spp. in mixed infections, and to the 18S rRNA qPCR assay results for the specific detection of Theileria parva. Theileria parva, Theileria sp. (buffalo), Theileria taurotragi, Theileria buffeli and Theileria mutans were detected by the cox III assay. Theileria velifera was not detected from any of the samples analysed. Seventeen percent of the samples had non-species specific melting peaks and 4.5% of the samples were negative or below the detection limit of the assay. The cox III assay identified more T. parva and Theileria sp. (buffalo) positive samples than the RLB assay, and also detected more T. parva infections than the 18S assay. However, only a small number of samples were positive for the benign Theileria spp. To our knowledge T. taurotragi has never been identified from the African buffalo, its identification in some samples by the qPCR assay was unexpected. Because of these discrepancies in the results, cox III qPCR products were cloned and sequenced. Sequence analysis indicated extensive inter- and intra-species variations in the probe target regions of the cox III gene sequences of the benign Theileria spp. and therefore explains their low detection. The cox III assay is specific for the detection of T. parva infections in cattle and buffalo. Sequence data generated from this study can be used for the development of a more inclusive assay for detection and differentiation of all variants of the mildly pathogenic and benign Theileria spp. of buffalo and cattle.

Clinical experience with a computer-aided diagnosis system for automatic detection of pulmonary nodules at spiral CT of the chest

NASA Astrophysics Data System (ADS)

Wormanns, Dag; Fiebich, Martin; Saidi, Mustafa; Diederich, Stefan; Heindel, Walter

2001-05-01

The purpose of the study was to evaluate a computer aided diagnosis (CAD) workstation with automatic detection of pulmonary nodules at low-dose spiral CT in a clinical setting for early detection of lung cancer. Two radiologists in consensus reported 88 consecutive spiral CT examinations. All examinations were reviewed using a UNIX-based CAD workstation with a self-developed algorithm for automatic detection of pulmonary nodules. The algorithm was designed to detect nodules with at least 5 mm diameter. The results of automatic nodule detection were compared to the consensus reporting of two radiologists as gold standard. Additional CAD findings were regarded as nodules initially missed by the radiologists or as false positive results. A total of 153 nodules were detected with all modalities (diameter: 85 nodules <5mm, 63 nodules 5-9 mm, 5 nodules >= 10 mm). Reasons for failure of automatic nodule detection were assessed. Sensitivity of radiologists for nodules >=5 mm was 85%, sensitivity of CAD was 38%. For nodules >=5 mm without pleural contact sensitivity was 84% for radiologists at 45% for CAD. CAD detected 15 (10%) nodules not mentioned in the radiologist's report but representing real nodules, among them 10 (15%) nodules with a diameter $GREW5 mm. Reasons for nodules missed by CAD include: exclusion because of morphological features during region analysis (33%), nodule density below the detection threshold (26%), pleural contact (33%), segmentation errors (5%) and other reasons (2%). CAD improves detection of pulmonary nodules at spiral CT significantly and is a valuable second opinion in a clinical setting for lung cancer screening. Optimization of region analysis and an appropriate density threshold have a potential for further improvement of automatic nodule detection.

Automated clinical system for chromosome analysis

NASA Technical Reports Server (NTRS)

Castleman, K. R.; Friedan, H. J.; Johnson, E. T.; Rennie, P. A.; Wall, R. J. (Inventor)

1978-01-01

An automatic chromosome analysis system is provided wherein a suitably prepared slide with chromosome spreads thereon is placed on the stage of an automated microscope. The automated microscope stage is computer operated to move the slide to enable detection of chromosome spreads on the slide. The X and Y location of each chromosome spread that is detected is stored. The computer measures the chromosomes in a spread, classifies them by group or by type and also prepares a digital karyotype image. The computer system can also prepare a patient report summarizing the result of the analysis and listing suspected abnormalities.

Algorithm Summary and Evaluation: Automatic Implementation of Ringdown Analysis for Electromechanical Mode Identification from Phasor Measurements

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhou, Ning; Huang, Zhenyu; Tuffner, Francis K.

2010-02-28

Small signal stability problems are one of the major threats to grid stability and reliability. Prony analysis has been successfully applied on ringdown data to monitor electromechanical modes of a power system using phasor measurement unit (PMU) data. To facilitate an on-line application of mode estimation, this paper develops a recursive algorithm for implementing Prony analysis and proposed an oscillation detection method to detect ringdown data in real time. By automatically detecting ringdown data, the proposed method helps guarantee that Prony analysis is applied properly and timely on the ringdown data. Thus, the mode estimation results can be performed reliablymore » and timely. The proposed method is tested using Monte Carlo simulations based on a 17-machine model and is shown to be able to properly identify the oscillation data for on-line application of Prony analysis. In addition, the proposed method is applied to field measurement data from WECC to show the performance of the proposed algorithm.« less

Retinal imaging analysis based on vessel detection.

PubMed

Jamal, Arshad; Hazim Alkawaz, Mohammed; Rehman, Amjad; Saba, Tanzila

2017-07-01

With an increase in the advancement of digital imaging and computing power, computationally intelligent technologies are in high demand to be used in ophthalmology cure and treatment. In current research, Retina Image Analysis (RIA) is developed for optometrist at Eye Care Center in Management and Science University. This research aims to analyze the retina through vessel detection. The RIA assists in the analysis of the retinal images and specialists are served with various options like saving, processing and analyzing retinal images through its advanced interface layout. Additionally, RIA assists in the selection process of vessel segment; processing these vessels by calculating its diameter, standard deviation, length, and displaying detected vessel on the retina. The Agile Unified Process is adopted as the methodology in developing this research. To conclude, Retina Image Analysis might help the optometrist to get better understanding in analyzing the patient's retina. Finally, the Retina Image Analysis procedure is developed using MATLAB (R2011b). Promising results are attained that are comparable in the state of art. © 2017 Wiley Periodicals, Inc.

The Researches on Damage Detection Method for Truss Structures

NASA Astrophysics Data System (ADS)

Wang, Meng Hong; Cao, Xiao Nan

2018-06-01

This paper presents an effective method to detect damage in truss structures. Numerical simulation and experimental analysis were carried out on a damaged truss structure under instantaneous excitation. The ideal excitation point and appropriate hammering method were determined to extract time domain signals under two working conditions. The frequency response function and principal component analysis were used for data processing, and the angle between the frequency response function vectors was selected as a damage index to ascertain the location of a damaged bar in the truss structure. In the numerical simulation, the time domain signal of all nodes was extracted to determine the location of the damaged bar. In the experimental analysis, the time domain signal of a portion of the nodes was extracted on the basis of an optimal sensor placement method based on the node strain energy coefficient. The results of the numerical simulation and experimental analysis showed that the damage detection method based on the frequency response function and principal component analysis could locate the damaged bar accurately.

Comparison of detection methods for HPV status as a prognostic marker for loco-regional control after radiochemotherapy in patients with HNSCC.

PubMed

Linge, Annett; Schötz, Ulrike; Löck, Steffen; Lohaus, Fabian; von Neubeck, Cläre; Gudziol, Volker; Nowak, Alexander; Tinhofer, Inge; Budach, Volker; Sak, Ali; Stuschke, Martin; Balermpas, Panagiotis; Rödel, Claus; Bunea, Hatice; Grosu, Anca-Ligia; Abdollahi, Amir; Debus, Jürgen; Ganswindt, Ute; Lauber, Kirsten; Pigorsch, Steffi; Combs, Stephanie E; Mönnich, David; Zips, Daniel; Baretton, Gustavo B; Buchholz, Frank; Krause, Mechthild; Belka, Claus; Baumann, Michael

2018-04-01

To compare six HPV detection methods in pre-treatment FFPE tumour samples from patients with locally advanced head and neck squamous cell carcinoma (HNSCC) who received postoperative (N = 175) or primary (N = 90) radiochemotherapy. HPV analyses included detection of (i) HPV16 E6/E7 RNA, (ii) HPV16 DNA (PCR-based arrays, A-PCR), (iii) HPV DNA (GP5+/GP6+ qPCR, (GP-PCR)), (iv) p16 (immunohistochemistry, p16 IHC), (v) combining p16 IHC and the A-PCR result and (vi) combining p16 IHC and the GP-PCR result. Differences between HPV positive and negative subgroups were evaluated for the primary endpoint loco-regional control (LRC) using Cox regression. Correlation between the HPV detection methods was high (chi-squared test, p < 0.001). While p16 IHC analysis resulted in several false positive classifications, A-PCR, GP-PCR and the combination of p16 IHC and A-PCR or GP-PCR led to results comparable to RNA analysis. In both cohorts, Cox regression analyses revealed significantly prolonged LRC for patients with HPV positive tumours irrespective of the detection method. The most stringent classification was obtained by detection of HPV16 RNA, or combining p16 IHC with A-PCR or GP-PCR. This approach revealed the lowest rate of recurrence in patients with tumours classified as HPV positive and therefore appears most suited for patient stratification in HPV-based clinical studies. Copyright © 2017 Elsevier B.V. All rights reserved.

Quality assurance using outlier detection on an automatic segmentation method for the cerebellar peduncles

NASA Astrophysics Data System (ADS)

Li, Ke; Ye, Chuyang; Yang, Zhen; Carass, Aaron; Ying, Sarah H.; Prince, Jerry L.

2016-03-01

Cerebellar peduncles (CPs) are white matter tracts connecting the cerebellum to other brain regions. Automatic segmentation methods of the CPs have been proposed for studying their structure and function. Usually the performance of these methods is evaluated by comparing segmentation results with manual delineations (ground truth). However, when a segmentation method is run on new data (for which no ground truth exists) it is highly desirable to efficiently detect and assess algorithm failures so that these cases can be excluded from scientific analysis. In this work, two outlier detection methods aimed to assess the performance of an automatic CP segmentation algorithm are presented. The first one is a univariate non-parametric method using a box-whisker plot. We first categorize automatic segmentation results of a dataset of diffusion tensor imaging (DTI) scans from 48 subjects as either a success or a failure. We then design three groups of features from the image data of nine categorized failures for failure detection. Results show that most of these features can efficiently detect the true failures. The second method—supervised classification—was employed on a larger DTI dataset of 249 manually categorized subjects. Four classifiers—linear discriminant analysis (LDA), logistic regression (LR), support vector machine (SVM), and random forest classification (RFC)—were trained using the designed features and evaluated using a leave-one-out cross validation. Results show that the LR performs worst among the four classifiers and the other three perform comparably, which demonstrates the feasibility of automatically detecting segmentation failures using classification methods.

Novel Method For Low-Rate Ddos Attack Detection

NASA Astrophysics Data System (ADS)

Chistokhodova, A. A.; Sidorov, I. D.

2018-05-01

The relevance of the work is associated with an increasing number of advanced types of DDoS attacks, in particular, low-rate HTTP-flood. Last year, the power and complexity of such attacks increased significantly. The article is devoted to the analysis of DDoS attacks detecting methods and their modifications with the purpose of increasing the accuracy of DDoS attack detection. The article details low-rate attacks features in comparison with conventional DDoS attacks. During the analysis, significant shortcomings of the available method for detecting low-rate DDoS attacks were found. Thus, the result of the study is an informal description of a new method for detecting low-rate denial-of-service attacks. The architecture of the stand for approbation of the method is developed. At the current stage of the study, it is possible to improve the efficiency of an already existing method by using a classifier with memory, as well as additional information.

Fringe projection application for surface variation analysis on helical shaped silicon breast

NASA Astrophysics Data System (ADS)

Vairavan, R.; Ong, N. R.; Sauli, Z.; Shahimin, M. M.; Kirtsaeng, S.; Sakuntasathien, S.; Alcain, J. B.; Paitong, P.; Retnasamy, V.

2017-09-01

Breast carcinoma is rated as a second collective cause of cancer associated death among adult females. Detection of the disease at an early stage would enhance the chance for survival. Established detection methods such as mammography, ultrasound and MRI are classified as non invasive breast cancer detection modality, but however they are not entire non-invasive as physical contact still occurs to the breast. Thus requirement for a complete non invasive and non contact is evident. Therefore, in this work, a novel application of digital fringe projection for early detection of breast cancer based on breast surface analysis is reported. Phase shift fringe projection technique and pixel tracing method was utilized to analyze the breast surface change due to the incidence of breast lump. Results have shown that the digital fringe projection is capable in detecting the existence of 1 cm sized lump within the breast sample.

Hidden corrosion detection in aircraft aluminum structures using laser ultrasonics and wavelet transform signal analysis.

PubMed

Silva, M Z; Gouyon, R; Lepoutre, F

2003-06-01

Preliminary results of hidden corrosion detection in aircraft aluminum structures using a noncontact laser based ultrasonic technique are presented. A short laser pulse focused to a line spot is used as a broadband source of ultrasonic guided waves in an aluminum 2024 sample cut from an aircraft structure and prepared with artificially corroded circular areas on its back surface. The out of plane surface displacements produced by the propagating ultrasonic waves were detected with a heterodyne Mach-Zehnder interferometer. Time-frequency analysis of the signals using a continuous wavelet transform allowed the identification of the generated Lamb modes by comparison with the calculated dispersion curves. The presence of back surface corrosion was detected by noting the loss of the S(1) mode near its cutoff frequency. This method is applicable to fast scanning inspection techniques and it is particularly suited for early corrosion detection.

A Small Leak Detection Method Based on VMD Adaptive De-Noising and Ambiguity Correlation Classification Intended for Natural Gas Pipelines.

PubMed

Xiao, Qiyang; Li, Jian; Bai, Zhiliang; Sun, Jiedi; Zhou, Nan; Zeng, Zhoumo

2016-12-13

In this study, a small leak detection method based on variational mode decomposition (VMD) and ambiguity correlation classification (ACC) is proposed. The signals acquired from sensors were decomposed using the VMD, and numerous components were obtained. According to the probability density function (PDF), an adaptive de-noising algorithm based on VMD is proposed for noise component processing and de-noised components reconstruction. Furthermore, the ambiguity function image was employed for analysis of the reconstructed signals. Based on the correlation coefficient, ACC is proposed to detect the small leak of pipeline. The analysis of pipeline leakage signals, using 1 mm and 2 mm leaks, has shown that proposed detection method can detect a small leak accurately and effectively. Moreover, the experimental results have shown that the proposed method achieved better performances than support vector machine (SVM) and back propagation neural network (BP) methods.

A Small Leak Detection Method Based on VMD Adaptive De-Noising and Ambiguity Correlation Classification Intended for Natural Gas Pipelines

PubMed Central

Xiao, Qiyang; Li, Jian; Bai, Zhiliang; Sun, Jiedi; Zhou, Nan; Zeng, Zhoumo

2016-01-01

In this study, a small leak detection method based on variational mode decomposition (VMD) and ambiguity correlation classification (ACC) is proposed. The signals acquired from sensors were decomposed using the VMD, and numerous components were obtained. According to the probability density function (PDF), an adaptive de-noising algorithm based on VMD is proposed for noise component processing and de-noised components reconstruction. Furthermore, the ambiguity function image was employed for analysis of the reconstructed signals. Based on the correlation coefficient, ACC is proposed to detect the small leak of pipeline. The analysis of pipeline leakage signals, using 1 mm and 2 mm leaks, has shown that proposed detection method can detect a small leak accurately and effectively. Moreover, the experimental results have shown that the proposed method achieved better performances than support vector machine (SVM) and back propagation neural network (BP) methods. PMID:27983577

Enzymatic signal amplification for sensitive detection of intracellular antigens by flow cytometry.

PubMed

Karkmann, U; Radbruch, A; Hölzel, V; Scheffold, A

1999-11-19

Flow cytometry is the method of choice for the analysis of single cells with respect to the expression of specific antigens. Antigens can be detected with specific antibodies either on the cell surface or within the cells, after fixation and permeabilization of the cell membrane. Using conventional fluorochrome-labeled antibodies several thousand antigens are required for clear-cut separation of positive and negative cells. More sensitive reagents, e.g., magnetofluorescent liposomes conjugated to specific antibodies permit the detection of less than 200 molecules per cell but cannot be used for the detection of intracellular antigens. Here, we describe an enzymatic amplification technique (intracellular tyramine-based signal amplification, ITSA) for the sensitive cytometric analysis of intracellular cytokines by immunofluorescence. This approach results in a 10 to 15-fold improvement of the signal-to-noise ratio compared to conventional fluorochrome labeled antibodies and permits the detection of as few as 300-400 intracellular antigens per cell.

Automated detection of fundus photographic red lesions in diabetic retinopathy.

PubMed

Larsen, Michael; Godt, Jannik; Larsen, Nicolai; Lund-Andersen, Henrik; Sjølie, Anne Katrin; Agardh, Elisabet; Kalm, Helle; Grunkin, Michael; Owens, David R

2003-02-01

To compare a fundus image-analysis algorithm for automated detection of hemorrhages and microaneurysms with visual detection of retinopathy in patients with diabetes. Four hundred fundus photographs (35-mm color transparencies) were obtained in 200 eyes of 100 patients with diabetes who were randomly selected from the Welsh Community Diabetic Retinopathy Study. A gold standard reference was defined by classifying each patient as having or not having diabetic retinopathy based on overall visual grading of the digitized transparencies. A single-lesion visual grading was made independently, comprising meticulous outlining of all single lesions in all photographs and used to develop the automated red lesion detection system. A comparison of visual and automated single-lesion detection in replicating the overall visual grading was then performed. Automated red lesion detection demonstrated a specificity of 71.4% and a resulting sensitivity of 96.7% in detecting diabetic retinopathy when applied at a tentative threshold setting for use in diabetic retinopathy screening. The accuracy of 79% could be raised to 85% by adjustment of a single user-supplied parameter determining the balance between the screening priorities, for which a considerable range of options was demonstrated by the receiver-operating characteristic (area under the curve 90.3%). The agreement of automated lesion detection with overall visual grading (0.659) was comparable to the mean agreement of six ophthalmologists (0.648). Detection of diabetic retinopathy by automated detection of single fundus lesions can be achieved with a performance comparable to that of experienced ophthalmologists. The results warrant further investigation of automated fundus image analysis as a tool for diabetic retinopathy screening.

Prediction of topographic and bathymetric measurement performance of airborne low-SNR lidar systems

NASA Astrophysics Data System (ADS)

Cossio, Tristan

Low signal-to-noise ratio (LSNR) lidar (light detection and ranging) is an alternative paradigm to traditional lidar based on the detection of return signals at the single photoelectron level. The objective of this work was to predict low altitude (600 m) LSNR lidar system performance with regards to elevation measurement and target detection capability in topographic (dry land) and bathymetric (shallow water) scenarios. A modular numerical sensor model has been developed to provide data for further analysis due to the dearth of operational low altitude LSNR lidar systems. This simulator tool is described in detail, with consideration given to atmospheric effects, surface conditions, and the effects of laser phenomenology. Measurement performance analysis of the simulated topographic data showed results comparable to commercially available lidar systems, with a standard deviation of less than 12 cm for calculated elevation values. Bathymetric results, although dependent largely on water turbidity, were indicative of meter-scale horizontal data spacing for sea depths less than 5 m. The high prevalence of noise in LSNR lidar data introduces significant difficulties in data analysis. Novel algorithms to reduce noise are described, with particular focus on their integration into an end-to-end target detection classifier for both dry and submerged targets (cube blocks, 0.5 m to 1.0 m on a side). The key characteristic exploited to discriminate signal and noise is the temporal coherence of signal events versus the random distribution of noise events. Target detection performance over dry earth was observed to be robust, reliably detecting over 90% of targets with a minimal false alarm rate. Comparable results were observed in waters of high clarity, where the investigated system was generally able to detect more than 70% of targets to a depth of 5 m. The results of the study show that CATS, the University of Florida's LSNR lidar prototype, is capable of high fidelity (decimeter-scale) coverage of the topographic zone with limited applicability to shallow waters less than 5 m deep. To increase the spatial-temporal contrast between signal and noise events, laser pulse rate is the optimal system characteristic to improve in future LSNR lidar units.

Droplet-Based Segregation and Extraction of Concentrated Samples

DOE Office of Scientific and Technical Information (OSTI.GOV)

Buie, C R; Buckley, P; Hamilton, J

2007-02-23

Microfluidic analysis often requires sample concentration and separation techniques to isolate and detect analytes of interest. Complex or scarce samples may also require an orthogonal separation and detection method or off-chip analysis to confirm results. To perform these additional steps, the concentrated sample plug must be extracted from the primary microfluidic channel with minimal sample loss and dilution. We investigated two extraction techniques; injection of immiscible fluid droplets into the sample stream (''capping'''') and injection of the sample into an immiscible fluid stream (''extraction''). From our results we conclude that capping is the more effective partitioning technique. Furthermore, this functionalitymore » enables additional off-chip post-processing procedures such as DNA/RNA microarray analysis, realtime polymerase chain reaction (RT-PCR), and culture growth to validate chip performance.« less

Cocaine use during pregnancy assessed by hair analysis in a Canary Islands cohort

PubMed Central

2012-01-01

Background Drug use during pregnancy is difficult to ascertain, and maternal reports are likely to be inaccurate. The aim of this study was to estimate the prevalence of illicit drug use among pregnant women by using maternal hair analysis. Methods A toxicological analysis of hair was used to detect chronic recreational drug use during pregnancy. In 2007, 347 mother-infant dyads were included from the Hospital La Candelaria, Santa Cruz de Tenerife, Canary Islands (Spain). Data on socioeconomic characteristics and on substance misuse during pregnancy were collected using a structured questionnaire. Drugs of abuse: opiates, cocaine, cannabinoids and amphetamines were detected in maternal hair by immunoassay followed by gas chromatography-mass spectrometry for confirmation and quantitation. Results Hair analysis revealed 2.6% positivity for cocaine and its metabolites. Use of cocaine during pregnancy was associated with unusual behaviour with potentially harmful effects on the baby. Conclusions The results of the study demonstrate significant cocaine use by pregnant women in Canary Islands. The data should be used for the purpose of preventive health and policy strategies aimed to detect and possibly to avoid in the future prenatal exposure to drugs of abuse. PMID:22230295

Regression analysis for LED color detection of visual-MIMO system

NASA Astrophysics Data System (ADS)

Banik, Partha Pratim; Saha, Rappy; Kim, Ki-Doo

2018-04-01

Color detection from a light emitting diode (LED) array using a smartphone camera is very difficult in a visual multiple-input multiple-output (visual-MIMO) system. In this paper, we propose a method to determine the LED color using a smartphone camera by applying regression analysis. We employ a multivariate regression model to identify the LED color. After taking a picture of an LED array, we select the LED array region, and detect the LED using an image processing algorithm. We then apply the k-means clustering algorithm to determine the number of potential colors for feature extraction of each LED. Finally, we apply the multivariate regression model to predict the color of the transmitted LEDs. In this paper, we show our results for three types of environmental light condition: room environmental light, low environmental light (560 lux), and strong environmental light (2450 lux). We compare the results of our proposed algorithm from the analysis of training and test R-Square (%) values, percentage of closeness of transmitted and predicted colors, and we also mention about the number of distorted test data points from the analysis of distortion bar graph in CIE1931 color space.

Applications of independent component analysis in SAR images

NASA Astrophysics Data System (ADS)

Huang, Shiqi; Cai, Xinhua; Hui, Weihua; Xu, Ping

2009-07-01

The detection of faint, small and hidden targets in synthetic aperture radar (SAR) image is still an issue for automatic target recognition (ATR) system. How to effectively separate these targets from the complex background is the aim of this paper. Independent component analysis (ICA) theory can enhance SAR image targets and improve signal clutter ratio (SCR), which benefits to detect and recognize faint targets. Therefore, this paper proposes a new SAR image target detection algorithm based on ICA. In experimental process, the fast ICA (FICA) algorithm is utilized. Finally, some real SAR image data is used to test the method. The experimental results verify that the algorithm is feasible, and it can improve the SCR of SAR image and increase the detection rate for the faint small targets.

High efficiency processing for reduced amplitude zones detection in the HRECG signal

NASA Astrophysics Data System (ADS)

Dugarte, N.; Álvarez, A.; Balacco, J.; Mercado, G.; Gonzalez, A.; Dugarte, E.; Olivares, A.

2016-04-01

Summary - This article presents part of a more detailed research proposed in the medium to long term, with the intention of establishing a new philosophy of electrocardiogram surface analysis. This research aims to find indicators of cardiovascular disease in its early stage that may go unnoticed with conventional electrocardiography. This paper reports the development of a software processing which collect some existing techniques and incorporates novel methods for detection of reduced amplitude zones (RAZ) in high resolution electrocardiographic signal (HRECG).The algorithm consists of three stages, an efficient processing for QRS detection, averaging filter using correlation techniques and a step for RAZ detecting. Preliminary results show the efficiency of system and point to incorporation of techniques new using signal analysis with involving 12 leads.

Analysis of genetically modified organisms by pyrosequencing on a portable photodiode-based bioluminescence sequencer.

PubMed

Song, Qinxin; Wei, Guijiang; Zhou, Guohua

2014-07-01

A portable bioluminescence analyser for detecting the DNA sequence of genetically modified organisms (GMOs) was developed by using a photodiode (PD) array. Pyrosequencing on eight genes (zSSIIb, Bt11 and Bt176 gene of genetically modified maize; Lectin, 35S-CTP4, CP4EPSPS, CaMV35S promoter and NOS terminator of the genetically modified Roundup ready soya) was successfully detected with this instrument. The corresponding limit of detection (LOD) was 0.01% with 35 PCR cycles. The maize and soya available from three different provenances in China were detected. The results indicate that pyrosequencing using the small size of the detector is a simple, inexpensive, and reliable way in a farm/field test of GMO analysis. Copyright © 2014 Elsevier Ltd. All rights reserved.

Hyperspectral imaging using a color camera and its application for pathogen detection

USDA-ARS?s Scientific Manuscript database

This paper reports the results of a feasibility study for the development of a hyperspectral image recovery (reconstruction) technique using a RGB color camera and regression analysis in order to detect and classify colonies of foodborne pathogens. The target bacterial pathogens were the six represe...

Guided Wave Delamination Detection and Quantification With Wavefield Data Analysis

NASA Technical Reports Server (NTRS)

Tian, Zhenhua; Campbell Leckey, Cara A.; Seebo, Jeffrey P.; Yu, Lingyu

2014-01-01

Unexpected damage can occur in aerospace composites due to impact events or material stress during off-nominal loading events. In particular, laminated composites are susceptible to delamination damage due to weak transverse tensile and inter-laminar shear strengths. Developments of reliable and quantitative techniques to detect delamination damage in laminated composites are imperative for safe and functional optimally-designed next-generation composite structures. In this paper, we investigate guided wave interactions with delamination damage and develop quantification algorithms by using wavefield data analysis. The trapped guided waves in the delamination region are observed from the wavefield data and further quantitatively interpreted by using different wavenumber analysis methods. The frequency-wavenumber representation of the wavefield shows that new wavenumbers are present and correlate to trapped waves in the damage region. These new wavenumbers are used to detect and quantify the delamination damage through the wavenumber analysis, which can show how the wavenumber changes as a function of wave propagation distance. The location and spatial duration of the new wavenumbers can be identified, providing a useful means not only for detecting the presence of delamination damage but also allowing for estimation of the delamination size. Our method has been applied to detect and quantify real delamination damage with complex geometry (grown using a quasi-static indentation technique). The detection and quantification results show the location, size, and shape of the delamination damage.

CAFNA{reg{underscore}sign}, coded aperture fast neutron analysis for contraband detection: Preliminary results

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, L.; Lanza, R.C.

1999-12-01

The authors have developed a near field coded aperture imaging system for use with fast neutron techniques as a tool for the detection of contraband and hidden explosives through nuclear elemental analysis. The technique relies on the prompt gamma rays produced by fast neutron interactions with the object being examined. The position of the nuclear elements is determined by the location of the gamma emitters. For existing fast neutron techniques, in Pulsed Fast Neutron Analysis (PFNA), neutrons are used with very low efficiency; in Fast Neutron Analysis (FNS), the sensitivity for detection of the signature gamma rays is very low.more » For the Coded Aperture Fast Neutron Analysis (CAFNA{reg{underscore}sign}) the authors have developed, the efficiency for both using the probing fast neutrons and detecting the prompt gamma rays is high. For a probed volume of n{sup 3} volume elements (voxels) in a cube of n resolution elements on a side, they can compare the sensitivity with other neutron probing techniques. As compared to PFNA, the improvement for neutron utilization is n{sup 2}, where the total number of voxels in the object being examined is n{sup 3}. Compared to FNA, the improvement for gamma-ray imaging is proportional to the total open area of the coded aperture plane; a typical value is n{sup 2}/2, where n{sup 2} is the number of total detector resolution elements or the number of pixels in an object layer. It should be noted that the actual signal to noise ratio of a system depends also on the nature and distribution of background events and this comparison may reduce somewhat the effective sensitivity of CAFNA. They have performed analysis, Monte Carlo simulations, and preliminary experiments using low and high energy gamma-ray sources. The results show that a high sensitivity 3-D contraband imaging and detection system can be realized by using CAFNA.« less

[Comparative research into sensitivity and specificity of immune-enzyme analysis with chemiluminescence and colorimetric detection for detecting antigens and antibodies to avian influenza viruses and newcastle disease].

PubMed

Vitkova, O N; Kapustina, T P; Mikhailova, V V; Safonov, G A; Vlasova, N N; Belousova, R V

2015-01-01

The goal of this work was to demonstrate the results of the development of the enzyme-linked immunosorbent tests with chemiluminescence detection and colorimetric detection of specific viral antigens and antibodies for identifying the avian influenza and the Newcastle disease viruses: high sensitivity and specificity of the immuno- chemiluminescence assay, which are 10-50 times higher than those of the ELISA colorimetric method. The high effectiveness of the results and the automation of the process of laboratory testing (using a luminometer) allow these methods to be recommended for including in primary screening tests for these infectious diseases.

[Comparative evaluation of the sensitivity of Acinetobacter to colistin, using the prediffusion and minimum inhibitory concentration methods: detection of heteroresistant isolates].

PubMed

Herrera, Melina E; Mobilia, Liliana N; Posse, Graciela R

2011-01-01

The objective of this study is to perform a comparative evaluation of the prediffusion and minimum inhibitory concentration (MIC) methods for the detection of sensitivity to colistin, and to detect Acinetobacter baumanii-calcoaceticus complex (ABC) heteroresistant isolates to colistin. We studied 75 isolates of ABC recovered from clinically significant samples obtained from various centers. Sensitivity to colistin was determined by prediffusion as well as by MIC. All the isolates were sensitive to colistin, with MIC = 2µg/ml. The results were analyzed by dispersion graph and linear regression analysis, revealing that the prediffusion method did not correlate with the MIC values for isolates sensitive to colistin (r² = 0.2017). Detection of heteroresistance to colistin was determined by plaque efficiency of all the isolates with the same initial MICs of 2, 1, and 0.5 µg/ml, which resulted in 14 of them with a greater than 8-fold increase in the MIC in some cases. When the sensitivity of these resistant colonies was determined by prediffusion, the resulting dispersion graph and linear regression analysis yielded an r² = 0.604, which revealed a correlation between the methodologies used.

Laser spectrum detection methods for substance of Mars surface

NASA Astrophysics Data System (ADS)

Zhang, Dan; Xue, Bin; Zhao, Yi-yi

2014-11-01

The chemical element and mineral rock's abundance and distribution are the basic material of planetary geology evolution research [1], hence preterit detection for composition of Mars surface substance contains both elements sorts and mineral ingredients. This article introduced new ways to detect Mars elements and mineral components, Laser Induced Breakdown Spectroscopy (LIBS) and Raman Spectroscopy (RS) which have distinct advantages, such as work over a long distance, detect rapidly, accuratly and nondestructively. LIBS and RS both use laser excitation to shoot the substance of Mars exciting new wavelengths. The techniques of LIBS and RS in laboratory are mature, besides the technique of LIBS is being used in MSL (Chemcam) now and RS will be used in ExoMars. Comparing LIBS and RS's detection results with XRF and APXS, Mossbauer spectrometer, these existed Mars surface material detection instruments,and the Infrared spectrometer, Mid-IR, they have more accurate detection results. So LIBS and RS are competent for Mars surface substance detection instead of X-ray spectrometer and Mossbauer spectrometer which were already used in 'Viking 1' and 'Opportunity'. Only accurate detection results about Mars surface substance can lead to scientist's right analysis in inversing geological evolution of the planet.

Sequential analysis as a tool for detection of amikacin ototoxicity in the treatment of multidrug-resistant tuberculosis.

PubMed

Vasconcelos, Karla Anacleto de; Frota, Silvana Maria Monte Coelho; Ruffino-Netto, Antonio; Kritski, Afrânio Lineu

2018-04-01

To investigate early detection of amikacin-induced ototoxicity in a population treated for multidrug-resistant tuberculosis (MDR-TB), by means of three different tests: pure-tone audiometry (PTA); high-frequency audiometry (HFA); and distortion-product otoacoustic emission (DPOAE) testing. This was a longitudinal prospective cohort study involving patients aged 18-69 years with a diagnosis of MDR-TB who had to receive amikacin for six months as part of their antituberculosis drug regimen for the first time. Hearing was assessed before treatment initiation and at two and six months after treatment initiation. Sequential statistics were used to analyze the results. We included 61 patients, but the final population consisted of 10 patients (7 men and 3 women) because of sequential analysis. Comparison of the test results obtained at two and six months after treatment initiation with those obtained at baseline revealed that HFA at two months and PTA at six months detected hearing threshold shifts consistent with ototoxicity. However, DPOAE testing did not detect such shifts. The statistical method used in this study makes it possible to conclude that, over the six-month period, amikacin-associated hearing threshold shifts were detected by HFA and PTA, and that DPOAE testing was not efficient in detecting such shifts.

Automatic telangiectasia analysis in dermoscopy images using adaptive critic design.

PubMed

Cheng, B; Stanley, R J; Stoecker, W V; Hinton, K

2012-11-01

Telangiectasia, tiny skin vessels, are important dermoscopy structures used to discriminate basal cell carcinoma (BCC) from benign skin lesions. This research builds off of previously developed image analysis techniques to identify vessels automatically to discriminate benign lesions from BCCs. A biologically inspired reinforcement learning approach is investigated in an adaptive critic design framework to apply action-dependent heuristic dynamic programming (ADHDP) for discrimination based on computed features using different skin lesion contrast variations to promote the discrimination process. Lesion discrimination results for ADHDP are compared with multilayer perception backpropagation artificial neural networks. This study uses a data set of 498 dermoscopy skin lesion images of 263 BCCs and 226 competitive benign images as the input sets. This data set is extended from previous research [Cheng et al., Skin Research and Technology, 2011, 17: 278]. Experimental results yielded a diagnostic accuracy as high as 84.6% using the ADHDP approach, providing an 8.03% improvement over a standard multilayer perception method. We have chosen BCC detection rather than vessel detection as the endpoint. Although vessel detection is inherently easier, BCC detection has potential direct clinical applications. Small BCCs are detectable early by dermoscopy and potentially detectable by the automated methods described in this research. © 2011 John Wiley & Sons A/S.

Detection, Mapping, and Quantification of Single Walled Carbon Nanotubes in Histological Specimens with Photoacoustic Microscopy

PubMed Central

Mikos, Antonios G.; Jansen, John A.; Shroyer, Kenneth R.; Wang, Lihong V.; Sitharaman, Balaji

2012-01-01

Aims In the present study, the efficacy of multi-scale photoacoustic microscopy (PAM) was investigated to detect, map, and quantify trace amounts [nanograms (ng) to micrograms (µg)] of SWCNTs in a variety of histological tissue specimens consisting of cancer and benign tissue biopsies (histological specimens from implanted tissue engineering scaffolds). Materials and Methods Optical-resolution (OR) and acoustic-resolution (AR) - Photoacoustic microscopy (PAM) was employed to detect, map and quantify the SWCNTs in a variety of tissue histological specimens and compared with other optical techniques (bright-field optical microscopy, Raman microscopy, near infrared (NIR) fluorescence microscopy). Results Both optical-resolution and acoustic-resolution PAM, allow the detection and quantification of SWCNTs in histological specimens with scalable spatial resolution and depth penetration. The noise-equivalent detection sensitivity to SWCNTs in the specimens was calculated to be as low as ∼7 pg. Image processing analysis further allowed the mapping, distribution, and quantification of the SWCNTs in the histological sections. Conclusions The results demonstrate the potential of PAM as a promising imaging technique to detect, map, and quantify SWCNTs in histological specimens, and could complement the capabilities of current optical and electron microscopy techniques in the analysis of histological specimens containing SWCNTs. PMID:22496892

Nanoporous Glass Integrated in Volumetric Bar-Chart Chip for Point-of-Care Diagnostics of Non-Small Cell Lung Cancer.

PubMed

Li, Ying; Xuan, Jie; Song, Yujun; Qi, Wenjin; He, Bangshun; Wang, Ping; Qin, Lidong

2016-01-26

Point-of-care (POC) testing has the potential to enable rapid, low-cost, and large-scale screening. POC detection of a multiplexed biomarker panel can facilitate the early diagnosis of non-small cell lung cancer (NSCLC) and, thus, may allow for more timely surgical intervention for life-saving treatment. Herein, we report the nanoporous glass (NPG) integrated volumetric bar-chart chip (V-Chip) for POC detection of the three NSCLC biomarkers CEA, CYFRA 21-1, and SCCA, by the naked eye. The 3D nanostructures in the NPG membrane efficiently increase the number of binding sites for antibodies and decrease the diffusion distance between antibody and antigen, enabling the low detection limit and rapid analysis time of the NPG-V-Chip. We utilized the NPG-V-Chip to test the NSCLC biomarker panel and found that the limit of detection can reach 50 pg/mL (10-fold improvement over the original V-Chip), and the total assay time can be decreased from 4 to 0.5 h. We then detected CEA in 21 serum samples from patients with common cancers, and the on-chip results showed good correlation with the clinical results. We further assayed 10 lung cancer samples using the device and confirmed the results obtained using conventional ELISA methods. In summary, the NPG-V-Chip platform has the ability of multiplex, low detection limit, low cost, lack of need for accessory equipment, and rapid analysis time, which may render the V-Chip a useful platform for quantitative POC detection in resource-limited settings and personalized diagnostics.

Comparison of Abbott RealTime High-Risk HPV and Hybrid Capture 2 Assays for Detection of HPV Infection.

PubMed

Ko, Kiwoong; Yu, Shinae; Lee, Eun Hee; Park, Hyosoon; Woo, Hee-Yeon; Kwon, Min-Jung

2016-09-01

Various assays for detecting high-risk human papillomavirus (HR HPV) have been introduced recently, including the Abbott RealTime High-Risk HPV assay. We sought to compare the performance of Abbott PCR to Hybrid Capture 2 for the detection of HR HPV. A total of 941 cervical swab specimens were obtained. We submitted all specimens for HR HPV detection with HC2 and Abbott PCR, and then additionally analyzed discordant and concordant positive results using restriction fragment mass polymorphism (RFMP) genotyping analysis. HC2 detected one of 13 HR HPV types in 12.3% (116/941) of cases, while Abbott PCR detected one of 14 detectable HR HPV types in 12.9% (121/941) of cases. The overall agreement rate was 97.3% with a kappa coefficient of 0.879. Discordant results between these two assays were observed in 25 cases. HC2 showed a sensitivity of 90.0% and specificity of 95.9%, while Abbott PCR showed a sensitivity of 98.0% and specificity of 96.8% when using RFMP results as the gold standard. For HPV 16/18 detection, Abbott PCR showed 95.8%/88.9% sensitivity and 99.2%/99.8% specificity, respectively. The overall coinfection rate between HPV 16, 18 and non-16/18 was 9.9% (12/121) in Abbott PCR analysis. Considering its high agreement rate with HC2, higher sensitivity/specificity compared to HC2, and ability to differentiate HPV 16/18 from other HPV types, Abbott PCR could be a reliable laboratory testing method for the screening of HPV infections. © 2016 by the Association of Clinical Scientists, Inc.

Addressing multi-label imbalance problem of surgical tool detection using CNN.

PubMed

Sahu, Manish; Mukhopadhyay, Anirban; Szengel, Angelika; Zachow, Stefan

2017-06-01

A fully automated surgical tool detection framework is proposed for endoscopic video streams. State-of-the-art surgical tool detection methods rely on supervised one-vs-all or multi-class classification techniques, completely ignoring the co-occurrence relationship of the tools and the associated class imbalance. In this paper, we formulate tool detection as a multi-label classification task where tool co-occurrences are treated as separate classes. In addition, imbalance on tool co-occurrences is analyzed and stratification techniques are employed to address the imbalance during convolutional neural network (CNN) training. Moreover, temporal smoothing is introduced as an online post-processing step to enhance runtime prediction. Quantitative analysis is performed on the M2CAI16 tool detection dataset to highlight the importance of stratification, temporal smoothing and the overall framework for tool detection. The analysis on tool imbalance, backed by the empirical results, indicates the need and superiority of the proposed framework over state-of-the-art techniques.

Food Forensics: Using Mass Spectrometry To Detect Foodborne Protein Contaminants, as Exemplified by Shiga Toxin Variants and Prion Strains.

PubMed

Silva, Christopher J

2018-06-13

Food forensicists need a variety of tools to detect the many possible food contaminants. As a result of its analytical flexibility, mass spectrometry is one of those tools. Use of the multiple reaction monitoring (MRM) method expands its use to quantitation as well as detection of infectious proteins (prions) and protein toxins, such as Shiga toxins. The sample processing steps inactivate prions and Shiga toxins; the proteins are digested with proteases to yield peptides suitable for MRM-based analysis. Prions are detected by their distinct physicochemical properties and differential covalent modification. Shiga toxin analysis is based on detecting peptides derived from the five identical binding B subunits comprising the toxin. 15 N-labeled internal standards are prepared from cloned proteins. These examples illustrate the power of MRM, in that the same instrument can be used to safely detect and quantitate protein toxins, prions, and small molecules that might contaminate our food.

Detection of soft tissue densities from digital breast tomosynthesis: comparison of conventional and deep learning approaches

NASA Astrophysics Data System (ADS)

Fotin, Sergei V.; Yin, Yin; Haldankar, Hrishikesh; Hoffmeister, Jeffrey W.; Periaswamy, Senthil

2016-03-01

Computer-aided detection (CAD) has been used in screening mammography for many years and is likely to be utilized for digital breast tomosynthesis (DBT). Higher detection performance is desirable as it may have an impact on radiologist's decisions and clinical outcomes. Recently the algorithms based on deep convolutional architectures have been shown to achieve state of the art performance in object classification and detection. Similarly, we trained a deep convolutional neural network directly on patches sampled from two-dimensional mammography and reconstructed DBT volumes and compared its performance to a conventional CAD algorithm that is based on computation and classification of hand-engineered features. The detection performance was evaluated on the independent test set of 344 DBT reconstructions (GE SenoClaire 3D, iterative reconstruction algorithm) containing 328 suspicious and 115 malignant soft tissue densities including masses and architectural distortions. Detection sensitivity was measured on a region of interest (ROI) basis at the rate of five detection marks per volume. Moving from conventional to deep learning approach resulted in increase of ROI sensitivity from 0:832 +/- 0:040 to 0:893 +/- 0:033 for suspicious ROIs; and from 0:852 +/- 0:065 to 0:930 +/- 0:046 for malignant ROIs. These results indicate the high utility of deep feature learning in the analysis of DBT data and high potential of the method for broader medical image analysis tasks.

Clinical Evaluation of a Loop-Mediated Amplification Kit for Diagnosis of Imported Malaria

PubMed Central

Polley, Spencer D.; González, Iveth J.; Mohamed, Deqa; Daly, Rosemarie; Bowers, Kathy; Watson, Julie; Mewse, Emma; Armstrong, Margaret; Gray, Christen; Perkins, Mark D.; Bell, David; Kanda, Hidetoshi; Tomita, Norihiro; Kubota, Yutaka; Mori, Yasuyoshi; Chiodini, Peter L.; Sutherland, Colin J.

2013-01-01

Background. Diagnosis of malaria relies on parasite detection by microscopy or antigen detection; both fail to detect low-density infections. New tests providing rapid, sensitive diagnosis with minimal need for training would enhance both malaria diagnosis and malaria control activities. We determined the diagnostic accuracy of a new loop-mediated amplification (LAMP) kit in febrile returned travelers. Methods. The kit was evaluated in sequential blood samples from returned travelers sent for pathogen testing to a specialist parasitology laboratory. Microscopy was performed, and then malaria LAMP was performed using Plasmodium genus and Plasmodium falciparum–specific tests in parallel. Nested polymerase chain reaction (PCR) was performed on all samples as the reference standard. Primary outcome measures for diagnostic accuracy were sensitivity and specificity of LAMP results, compared with those of nested PCR. Results. A total of 705 samples were tested in the primary analysis. Sensitivity and specificity were 98.4% and 98.1%, respectively, for the LAMP P. falciparum primers and 97.0% and 99.2%, respectively, for the Plasmodium genus primers. Post hoc repeat PCR analysis of all 15 tests with discrepant results resolved 4 results in favor of LAMP, suggesting that the primary analysis had underestimated diagnostic accuracy. Conclusions. Malaria LAMP had a diagnostic accuracy similar to that of nested PCR, with a greatly reduced time to result, and was superior to expert microscopy. PMID:23633403

Know the Star, Know the Planet

DTIC Science & Technology

2011-11-01

Survey ( 2MASS ) and finally Section 5 summarizes our results. 2. DATA COLLECTION AND ANALYSIS Observations were made using the AEOS 3.6m telescope and...difficult to detect a companion with the expected dynamic range at that separation. 4. 2MASS ANALYSIS A check was made against the 2MASS Point Source...companion was found at 187.◦5 and 5.′′70, with ΔJ = 3.59. Mugrauer et al. (2005) detected this companion with AO in 2002 and noted the match with the 2MASS

A comparison of computer-assisted detection (CAD) programs for the identification of colorectal polyps: performance and sensitivity analysis, current limitations and practical tips for radiologists.

PubMed

Bell, L T O; Gandhi, S

2018-06-01

To directly compare the accuracy and speed of analysis of two commercially available computer-assisted detection (CAD) programs in detecting colorectal polyps. In this retrospective single-centre study, patients who had colorectal polyps identified on computed tomography colonography (CTC) and subsequent lower gastrointestinal endoscopy, were analysed using two commercially available CAD programs (CAD1 and CAD2). Results were compared against endoscopy to ascertain sensitivity and positive predictive value (PPV) for colorectal polyps. Time taken for CAD analysis was also calculated. CAD1 demonstrated a sensitivity of 89.8%, PPV of 17.6% and mean analysis time of 125.8 seconds. CAD2 demonstrated a sensitivity of 75.5%, PPV of 44.0% and mean analysis time of 84.6 seconds. The sensitivity and PPV for colorectal polyps and CAD analysis times can vary widely between current commercially available CAD programs. There is still room for improvement. Generally, there is a trade-off between sensitivity and PPV, and so further developments should aim to optimise both. Information on these factors should be made routinely available, so that an informed choice on their use can be made. This information could also potentially influence the radiologist's use of CAD results. Copyright © 2018 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

BP fusion model for the detection of oil spills on the sea by remote sensing

NASA Astrophysics Data System (ADS)

Chen, Weiwei; An, Jubai; Zhang, Hande; Lin, Bin

2003-06-01

Oil spills are very serious marine pollution in many countries. In order to detect and identify the oil-spilled on the sea by remote sensor, scientists have to conduct a research work on the remote sensing image. As to the detection of oil spills on the sea, edge detection is an important technology in image processing. There are many algorithms of edge detection developed for image processing. These edge detection algorithms always have their own advantages and disadvantages in the image processing. Based on the primary requirements of edge detection of the oil spills" image on the sea, computation time and detection accuracy, we developed a fusion model. The model employed a BP neural net to fuse the detection results of simple operators. The reason we selected BP neural net as the fusion technology is that the relation between simple operators" result of edge gray level and the image"s true edge gray level is nonlinear, while BP neural net is good at solving the nonlinear identification problem. Therefore in this paper we trained a BP neural net by some oil spill images, then applied the BP fusion model on the edge detection of other oil spill images and obtained a good result. In this paper the detection result of some gradient operators and Laplacian operator are also compared with the result of BP fusion model to analysis the fusion effect. At last the paper pointed out that the fusion model has higher accuracy and higher speed in the processing oil spill image"s edge detection.

Periodic component analysis as a spatial filter for SSVEP-based brain-computer interface.

PubMed

Kiran Kumar, G R; Reddy, M Ramasubba

2018-06-08

Traditional Spatial filters used for steady-state visual evoked potential (SSVEP) extraction such as minimum energy combination (MEC) require the estimation of the background electroencephalogram (EEG) noise components. Even though this leads to improved performance in low signal to noise ratio (SNR) conditions, it makes such algorithms slow compared to the standard detection methods like canonical correlation analysis (CCA) due to the additional computational cost. In this paper, Periodic component analysis (πCA) is presented as an alternative spatial filtering approach to extract the SSVEP component effectively without involving extensive modelling of the noise. The πCA can separate out components corresponding to a given frequency of interest from the background electroencephalogram (EEG) by capturing the temporal information and does not generalize SSVEP based on rigid templates. Data from ten test subjects were used to evaluate the proposed method and the results demonstrate that the periodic component analysis acts as a reliable spatial filter for SSVEP extraction. Statistical tests were performed to validate the results. The experimental results show that πCA provides significant improvement in accuracy compared to standard CCA and MEC in low SNR conditions. The results demonstrate that πCA provides better detection accuracy compared to CCA and on par with that of MEC at a lower computational cost. Hence πCA is a reliable and efficient alternative detection algorithm for SSVEP based brain-computer interface (BCI). Copyright © 2018. Published by Elsevier B.V.

Discrepancy between culture and DNA probe analysis for the detection of periodontal bacteria.

PubMed

van Steenbergen, T J; Timmerman, M F; Mikx, F H; de Quincey, G; van der Weijden, G A; van der Velden, U; de Graaff, J

1996-10-01

The purpose of this study was to compare a commercially available DNA probe technique with conventional cultural techniques for the detection of Actinobacillus actinomycetemcomitans, Porphyromonas gingivalis and Prevotella intermedia in subgingival plaque samples. Samples from 20 patients with moderate to severe periodontitis were evaluated at baseline and during a 15 months period of periodontal treatment. Paperpoints from 4 periodontal pockets per patient were forwarded to Omnigene for DNA probe analysis, and simultaneously inserted paperpoints from the same pockets were analyzed by standard culture techniques. In addition, mixed bacterial samples were constructed harbouring known proportions of 25 strains of A. actinomycetemcomitans, P. gingivalis and P. intermedia each. A relatively low concordance was found between both methods. At baseline a higher detection frequency was found for A. actinomycetemcomitans and P. gingivalis for the DNA probe technique; for P. intermedia the detection frequency by culture was higher. For A. actinomycetemcomitans, 21% of the culture positive samples was positive with the DNA probe. Testing the constructed bacterial samples with the DNA probe method resulted in about 16% false positive results for the 3 species tested. Furthermore, 40% of P. gingivalis strains were not detected by the DNA probe. The present data suggest that at least part of the discrepancies found between the DNA probe technique used and cultural methods are caused by false positive and false negative DNA probe results. Therefore, the value of this DNA probe method for the detection of periodontal pathogens is questionable.

[Analysis and experimental verification of sensitivity and SNR of laser warning receiver].

PubMed

Zhang, Ji-Long; Wang, Ming; Tian, Er-Ming; Li, Xiao; Wang, Zhi-Bin; Zhang, Yue

2009-01-01

In order to countermeasure increasingly serious threat from hostile laser in modern war, it is urgent to do research on laser warning technology and system, and the sensitivity and signal to noise ratio (SNR) are two important performance parameters in laser warning system. In the present paper, based on the signal statistical detection theory, a method for calculation of the sensitivity and SNR in coherent detection laser warning receiver (LWR) has been proposed. Firstly, the probabilities of the laser signal and receiver noise were analyzed. Secondly, based on the threshold detection theory and Neyman-Pearson criteria, the signal current equation was established by introducing detection probability factor and false alarm rate factor, then, the mathematical expressions of sensitivity and SNR were deduced. Finally, by using method, the sensitivity and SNR of the sinusoidal grating laser warning receiver developed by our group were analyzed, and the theoretic calculation and experimental results indicate that the SNR analysis method is feasible, and can be used in performance analysis of LWR.

Rapid Assessment of Genotoxicity by Flow Cytometric Detection of Cell Cycle Alterations.

PubMed

Bihari, Nevenka

2017-01-01

Flow cytometry is a convenient method for the determination of genotoxic effects of environmental pollution and can reveal genotoxic compounds in unknown environmental mixtures. It is especially suitable for the analyses of large numbers of samples during monitoring programs. The speed of detection is one of the advantages of this technique which permits the acquisition of 10 4 -10 5 cells per sample in 5 min. This method can rapidly detect cell cycle alterations resulting from DNA damage. The outcome of such an analysis is a diagram of DNA content across the cell cycle which indicates cell proliferation, G 2 arrests, G 1 delays, apoptosis, and ploidy.Here, we present the flow cytometric procedure for rapid assessment of genotoxicity via detection of cell cycle alterations. The described protocol simplifies the analysis of genotoxic effects in marine environments and is suitable for monitoring purposes. It uses marine mussel cells in the analysis and can be adapted to investigations on a broad range of marine invertebrates.

Fault detection and classification in electrical power transmission system using artificial neural network.

PubMed

Jamil, Majid; Sharma, Sanjeev Kumar; Singh, Rajveer

2015-01-01

This paper focuses on the detection and classification of the faults on electrical power transmission line using artificial neural networks. The three phase currents and voltages of one end are taken as inputs in the proposed scheme. The feed forward neural network along with back propagation algorithm has been employed for detection and classification of the fault for analysis of each of the three phases involved in the process. A detailed analysis with varying number of hidden layers has been performed to validate the choice of the neural network. The simulation results concluded that the present method based on the neural network is efficient in detecting and classifying the faults on transmission lines with satisfactory performances. The different faults are simulated with different parameters to check the versatility of the method. The proposed method can be extended to the Distribution network of the Power System. The various simulations and analysis of signals is done in the MATLAB(®) environment.

Spectroscopic signatures of PETN in contact with sand particles

NASA Astrophysics Data System (ADS)

Ballesteros, Luz M.; Herrera, Gloria M.; Castro, Miguel E.; Briano, Julio; Mina, Nairmen; Hernandez-Rivera, Samuel P.

2005-06-01

The detection of explosive materials is not only important as an issue in landmines but also for global security reasons, unexploded ordnance, and Improvised Explosive Devices detection. In such areas, explosives detection has played a central role in ensuring the safety of the lives of citizens in many countries. Raman Spectroscopy is a well established tool for vibrational spectroscopic analysis and can be applied to the field of explosives identification and detection. The analysis of PETN is important because it is used in laminar form or mixed with RDX to manufacture Semtex plastic explosive and in the fabrication of Improvised Explosive Devices (IEDs). Our investigation is focused on the study of spectroscopic signatures of PETN in contact with soil. Ottawa sand mixed in different proportions with PETN together with the study of the influence of pH, temperature, humidity, and UV light on the vibrational signatures of the mixtures constitute the core of the investigation. The results reveal that the characteristic bands of PETN are not significantly shifted but rather appear constant with respect of the ubiquitous band of sand (~463 cm-1). These results will make possible the development of highly sensitive sensors for detection of explosives materials and IDEs.

Sweet-spot training for early esophageal cancer detection

NASA Astrophysics Data System (ADS)

van der Sommen, Fons; Zinger, Svitlana; Schoon, Erik J.; de With, Peter H. N.

2016-03-01

Over the past decade, the imaging tools for endoscopists have improved drastically. This has enabled physicians to visually inspect the intestinal tissue for early signs of malignant lesions. Besides this, recent studies show the feasibility of supportive image analysis for endoscopists, but the analysis problem is typically approached as a segmentation task where binary ground truth is employed. In this study, we show that the detection of early cancerous tissue in the gastrointestinal tract cannot be approached as a binary segmentation problem and it is crucial and clinically relevant to involve multiple experts for annotating early lesions. By employing the so-called sweet spot for training purposes as a metric, a much better detection performance can be achieved. Furthermore, a multi-expert-based ground truth, i.e. a golden standard, enables an improved validation of the resulting delineations. For this purpose, besides the sweet spot we also propose another novel metric, the Jaccard Golden Standard (JIGS) that can handle multiple ground-truth annotations. Our experiments involving these new metrics and based on the golden standard show that the performance of a detection algorithm of early neoplastic lesions in Barrett's esophagus can be increased significantly, demonstrating a 10 percent point increase in the resulting F1 detection score.

A pilot study on the Chinese Minnesota Multiphasic Personality Inventory-2 in detecting feigned mental disorders: Simulators classified by using the Structured Interview of Reported Symptoms.

PubMed

Chang, Yi-Ting; Tam, Wai-Cheong C; Shiah, Yung-Jong; Chiang, Shih-Kuang

2017-09-01

The Minnesota Multiphasic Personality Inventory-2 (MMPI-2) is often used in forensic psychological/psychiatric assessment. This was a pilot study on the utility of the Chinese MMPI-2 in detecting feigned mental disorders. The sample consisted of 194 university students who were either simulators (informed or uninformed) or controls. All the participants were administered the Chinese MMPI-2 and the Structured Interview of Reported Symptoms-2 (SIRS-2). The results of the SIRS-2 were utilized to classify the participants into the feigning or control groups. The effectiveness of eight detection indices was investigated by using item analysis, multivariate analysis of covariance (MANCOVA), and receiver operating characteristic (ROC) analysis. Results indicated that informed-simulating participants with prior knowledge of mental disorders did not perform better in avoiding feigning detection than uninformed-simulating participants. In addition, the eight detection indices of the Chinese MMPI-2 were effective in discriminating participants in the feigning and control groups, and the best cut-off scores of three of the indices were higher than those obtained from the studies using the English MMPI-2. Thus, in this sample of university students, the utility of the Chinese MMPI-2 in detecting feigned mental disorders was tentatively supported, and the Chinese Infrequency Scale (ICH), a scale developed specifically for the Chinese MMPI-2, was also supported as a valid scale for validity checking. © 2017 The Institute of Psychology, Chinese Academy of Sciences and John Wiley & Sons Australia, Ltd.

Diagnostic Value of PCR Analysis of Bacteria and Fungi from Blood in Empiric-Therapy-Resistant Febrile Neutropenia ▿

PubMed Central

Nakamura, Akiko; Sugimoto, Yuka; Ohishi, Kohshi; Sugawara, Yumiko; Fujieda, Atsushi; Monma, Fumihiko; Suzuki, Kei; Masuya, Masahiro; Nakase, Kazunori; Matsushima, Yoshiko; Wada, Hideo; Katayama, Naoyuki; Nobori, Tsutomu

2010-01-01

This study aimed to assess the clinical utility of PCR for the analysis of bacteria and fungi from blood for the management of febrile neutropenic patients with hematologic malignancies. Using a PCR system able to detect a broad range of bacteria and fungi, we conducted a prospective pilot study of periodic analyses of blood from patients following intensive chemotherapy. When fever occurred, it was treated with empirical antibiotic therapy, basically without knowledge of the PCR results. In 23 febrile episodes during the neutropenic period, bacteria were detected by PCR in 11 cases, while the same species were identified by blood culture in 3 cases. In 10 out of 11 PCR-positive cases, fever could be managed by empirical therapy. In the empirical-therapy-resistant case, the identification of Stenotrophomonas maltophilia by PCR led to improvement of fever. No fungi were detected by PCR in febrile cases, while Aspergillus fumigatus was detected in one afebrile patient, several days before a clinical diagnosis was made. In subsequent sporadic PCR analyses in 15 cases of febrile neutropenia, bacteria were detected by both PCR and blood culture in 7 cases and by PCR alone in 6. Fungi were not detected. While fever was improved by empirical therapy in 12 out of the 13 PCR-positive cases, the identification of Pseudomonas aeruginosa by PCR in one therapy-resistant case contributed to the successful treatment of persistent fever. Our results indicate that PCR analysis of bacteria from blood provides essential information for managing empirical-therapy-resistant febrile neutropenia. PMID:20392911

Cross-reactivity of stimulants found in sports drug testing by two fluorescence polarization immunoassays.

PubMed

de la Torre, R; Badia, R; Gonzàlez, G; García, M; Pretel, M J; Farré, M; Segura, J

1996-01-01

We investigated the usefulness of immunological methods for presumptive detection of stimulants found in sports drug testing. The ingestion of substances that show no cross-reactivity in tests commercially available for the detection of amphetamines can produce positive results in the urine. Human metabolism contributes to the positive results of some urine samples when the parent compound does not cross-react with the antibodies of the assay. Urine samples from healthy volunteers given stimulants were tested by chromatographic methods and by two different fluorescence polarization immunoassays (FPIA) from Abbott Laboratories for the analysis of amphetamines. According to the results obtained, we classified stimulants into four groups: detectable stimulants that gave rise to amphetamine by human metabolism (group 1); detectable ephedrines and related compounds, appearing in the urine either as parent compounds or originated by metabolism (group 2); detectable stimulants that displayed actual cross-reactivity with amphetamine tests (group 3); and stimulants not detected by FPIA (group 4). Most of the true doping cases due to the ingestion of stimulants may be detected by FPIA. The specificity of the results may be increased by combining immunological assays with different antibodies.

Application of PCR-LDR-nucleic acid detection strip in detection of YMDD mutation in hepatitis B patients treated with lamivudine.

PubMed

Xu, Gaolian; You, Qimin; Pickerill, Sam; Zhong, Huayan; Wang, Hongying; Shi, Jian; Luo, Ying; You, Paul; Kong, Huimin; Lu, Fengmin; Hu, Lin

2010-07-01

Chronic hepatitis B virus (CHBV) infection causes cirrhosis and hepatocellular carcinoma. Lamivudine (LAM) has been successfully used to treat CHBV infections but prolonged use leads to the emergence of drug-resistant variants. This is primarily linked to a mutation in the tyrosine-methionine-aspartate-aspartate (YMDD) motif of the HBV polymerase gene at position 204. Rapid diagnosis of drug-resistant HBV is necessary for a prompt treatment response. Common diagnostic methods such as sequencing and restriction fragment length polymorphism (RFLP) analysis lack sensitivity and require significant processing. The aim of this study was to demonstrate the usefulness of a novel diagnostic method that combines polymerase chain reaction (PCR), ligase detection reaction (LDR) and a nucleic acid detection strip (NADS) in detecting site-specific mutations related to HBV LAM resistance. We compared this method (PLNA) to direct sequencing and RFLP analysis in 50 clinical samples from HBV infected patients. There was 90% concordance between all three results. PLNA detected more samples containing mutant variants than both sequencing and RFLP analysis and was more sensitive in detecting mixed variant populations. Plasmid standards indicated that the sensitivity of PLNA is at or below 3,000 copies per ml and that it can detect a minor variant at 5% of the total viral population. This warrants its further development and suggests that the PLNA method could be a useful tool in detecting LAM resistance. (c) 2010 Wiley-Liss, Inc.

Analysis of variation in oil pressure in lubricating system

NASA Astrophysics Data System (ADS)

Sharma, Sumit; Upreti, Mritunjay; Sharma, Bharat; Poddar, Keshav

2018-05-01

Automotive Maintenance for an engine contributes to its reliability, energy efficiency and repair cost reduction. Modeling of engine performance and fault detection require large amount of data, which are usually obtained on test benches. This report presents a methodical study on analysis of variation in lubrication system of various medium speed engines. Further this study is limited to the influence of Engine Oil Pressure on frictional losses, Torque analysis for various Oil Pressures and an analytical analysis of engine Lubrication System. The data collected from various Engines under diagnostics is represented graphically. Finally the illustrated results were used as a viable source for detection and troubleshooting of faults in Lubrication System of regular passenger vehicle.

Islanding detection technique using wavelet energy in grid-connected PV system

NASA Astrophysics Data System (ADS)

Kim, Il Song

2016-08-01

This paper proposes a new islanding detection method using wavelet energy in a grid-connected photovoltaic system. The method detects spectral changes in the higher-frequency components of the point of common coupling voltage and obtains wavelet coefficients by multilevel wavelet analysis. The autocorrelation of the wavelet coefficients can clearly identify islanding detection, even in the variations of the grid voltage harmonics during normal operating conditions. The advantage of the proposed method is that it can detect islanding condition the conventional under voltage/over voltage/under frequency/over frequency methods fail to detect. The theoretical method to obtain wavelet energies is evolved and verified by the experimental result.

Herbicide Orange Site Characterization Study Naval Construction Battalion Center

DTIC Science & Technology

1987-01-01

U.S. Testing Laboratories for analysis. Over 200 additional analyses were performed for a variety of quality assurance criteria. The resultant data...TABLE 9. NCBC PERFORMANCE AUDIT SAMPLE ANALYSIS SUNMARYa (SERIES 1) TCDD Sppb ) Reported Detection Relative b Sample Number Concentration Limit...limit rather than estimating the variance of the results. The sample results were transformed using the natural logarithm. The Shapiro-Wilk W test

Implementation of a data packet generator using pattern matching for wearable ECG monitoring systems.

PubMed

Noh, Yun Hong; Jeong, Do Un

2014-07-15

In this paper, a packet generator using a pattern matching algorithm for real-time abnormal heartbeat detection is proposed. The packet generator creates a very small data packet which conveys sufficient crucial information for health condition analysis. The data packet envelopes real time ECG signals and transmits them to a smartphone via Bluetooth. An Android application was developed specifically to decode the packet and extract ECG information for health condition analysis. Several graphical presentations are displayed and shown on the smartphone. We evaluate the performance of abnormal heartbeat detection accuracy using the MIT/BIH Arrhythmia Database and real time experiments. The experimental result confirm our finding that abnormal heart beat detection is practically possible. We also performed data compression ratio and signal restoration performance evaluations to establish the usefulness of the proposed packet generator and the results were excellent.

Antibody biosensors for spoilage yeast detection based on impedance spectroscopy.

PubMed

Tubía, I; Paredes, J; Pérez-Lorenzo, E; Arana, S

2018-04-15

Brettanomyces is a yeast species responsible for wine and cider spoilage, producing volatile phenols that result in off-odors and loss of fruity sensorial qualities. Current commercial detection methods for these spoilage species are liable to frequent false positives, long culture times and fungal contamination. In this work, an interdigitated (IDE) biosensor was created to detect Brettanomyces using immunological reactions and impedance spectroscopy analysis. To promote efficient antibody immobilization on the electrodes' surface and to decrease non-specific adsorption, a Self-Assembled Monolayer (SAM) was developed. An impedance spectroscopy analysis, over four yeast strains, confirmed our device's increased efficacy. Compared to label-free sensors, antibody biosensors showed a higher relative impedance. The results also suggested that these biosensors could be a promising method to monitor some spoilage yeasts, offering an efficient alternative to the laborious and expensive traditional methods. Copyright © 2017 Elsevier B.V. All rights reserved.

Laboratory practice guidelines for detecting and reporting JAK2 and MPL mutations in myeloproliferative neoplasms: a report of the Association for Molecular Pathology.

PubMed

Gong, Jerald Z; Cook, James R; Greiner, Timothy C; Hedvat, Cyrus; Hill, Charles E; Lim, Megan S; Longtine, Janina A; Sabath, Daniel; Wang, Y Lynn

2013-11-01

Recurrent mutations in JAK2 and MPL genes are genetic hallmarks of BCR-ABL1-negative myeloproliferative neoplasms. Detection of JAK2 and MPL mutations has been incorporated into routine diagnostic algorithms for these diseases. This Special Article summarizes results from a nationwide laboratory survey of JAK2 and MPL mutation analysis. Based on the current practice pattern and the literature, this Special Article provides recommendations and guidelines for laboratory practice for detection of mutations in the JAK2 and MPL genes, including clinical manifestations for prompting the mutation analysis, current and recommended methodologies for testing the mutations, and standardization for reporting the test results. This Special Article also points to future directions for genomic testing in BCR-ABL1-negative myeloproliferative neoplasms. Copyright © 2013 American Society for Investigative Pathology and the Association for Molecular Pathology. Published by Elsevier Inc. All rights reserved.

Water Vapor Uptake of Ultrathin Films of Biologically Derived Nanocrystals: Quantitative Assessment with Quartz Crystal Microbalance and Spectroscopic Ellipsometry.

PubMed

Niinivaara, Elina; Faustini, Marco; Tammelin, Tekla; Kontturi, Eero

2015-11-10

Despite the relevance of water interactions, explicit analysis of vapor adsorption on biologically derived surfaces is often difficult. Here, a system was introduced to study the vapor uptake on a native polysaccharide surface; namely, cellulose nanocrystal (CNC) ultrathin films were examined with a quartz crystal microbalance with dissipation monitoring (QCM-D) and spectroscopic ellipsometry (SE). A significant mass uptake of water vapor by the CNC films was detected using the QCM-D upon increasing relative humidity. In addition, thickness changes proportional to changes in relative humidity were detected using SE. Quantitative analysis of the results attained indicated that in preference to being soaked by water at the point of hydration each individual CNC in the film became enveloped by a 1 nm thick layer of adsorbed water vapor, resulting in the detected thickness response.

Signal injection as a fault detection technique.

PubMed

Cusidó, Jordi; Romeral, Luis; Ortega, Juan Antonio; Garcia, Antoni; Riba, Jordi

2011-01-01

Double frequency tests are used for evaluating stator windings and analyzing the temperature. Likewise, signal injection on induction machines is used on sensorless motor control fields to find out the rotor position. Motor Current Signature Analysis (MCSA), which focuses on the spectral analysis of stator current, is the most widely used method for identifying faults in induction motors. Motor faults such as broken rotor bars, bearing damage and eccentricity of the rotor axis can be detected. However, the method presents some problems at low speed and low torque, mainly due to the proximity between the frequencies to be detected and the small amplitude of the resulting harmonics. This paper proposes the injection of an additional voltage into the machine being tested at a frequency different from the fundamental one, and then studying the resulting harmonics around the new frequencies appearing due to the composition between injected and main frequencies.

Signal Injection as a Fault Detection Technique

PubMed Central

Cusidó, Jordi; Romeral, Luis; Ortega, Juan Antonio; Garcia, Antoni; Riba, Jordi

2011-01-01

Double frequency tests are used for evaluating stator windings and analyzing the temperature. Likewise, signal injection on induction machines is used on sensorless motor control fields to find out the rotor position. Motor Current Signature Analysis (MCSA), which focuses on the spectral analysis of stator current, is the most widely used method for identifying faults in induction motors. Motor faults such as broken rotor bars, bearing damage and eccentricity of the rotor axis can be detected. However, the method presents some problems at low speed and low torque, mainly due to the proximity between the frequencies to be detected and the small amplitude of the resulting harmonics. This paper proposes the injection of an additional voltage into the machine being tested at a frequency different from the fundamental one, and then studying the resulting harmonics around the new frequencies appearing due to the composition between injected and main frequencies. PMID:22163801

LAMP-based group specific detection of aflatoxin producers within Aspergillus section Flavi in food raw materials, spices, and dried fruit using neutral red for visible-light signal detection.

PubMed

Niessen, Ludwig; Bechtner, Julia; Fodil, Sihem; Taniwaki, Marta H; Vogel, Rudi F

2018-02-02

Aflatoxins can be produced by 21 species within sections Flavi (16 species), Ochraceorosei (2), and Nidulantes (3) of the fungal genus Aspergillus. They pose risks to human and animal health due to high toxicity and carcinogenicity. Detecting aflatoxin producers can help to assess toxicological risks associated with contaminated commodities. Species specific molecular assays (PCR and LAMP) are available for detection of major producers, but fail to detect species of minor importance. To enable rapid and sensitive detection of several aflatoxin producing species in a single analysis, a nor1 gene-specific LAMP assay was developed. Specificity testing showed that among 128 fungal species from 28 genera, 15 aflatoxigenic species in section Flavi were detected, including synonyms of A. flavus and A. parasiticus. No cross reactions were found with other tested species. The detection limit of the assay was 9.03pg of A. parasiticus genomic DNA per reaction. Visual detection of positive LAMP reactions under daylight conditions was facilitated using neutral red to allow unambiguous distinction between positive and negative assay results. Application of the assay to the detection of A. parasiticus conidia revealed a detection limit of 211 conidia per reaction after minimal sample preparation. The usefulness of the assay was demonstrated in the analysis of aflatoxinogenic species in samples of rice, nuts, raisins, dried figs, as well as powdered spices. Comparison of LAMP results with presence/absence of aflatoxins and aflatoxin producing fungi in 50 rice samples showed good correlation between these parameters. Our study suggests that the developed LAMP assay is a rapid, sensitive and user-friendly tool for surveillance and quality control in our food industry. Copyright © 2017 Elsevier B.V. All rights reserved.

A feasibility study of stateful automaton packet inspection for streaming application detection systems

NASA Astrophysics Data System (ADS)

Tseng, Kuo-Kun; Lo, Jiao; Liu, Yiming; Chang, Shih-Hao; Merabti, Madjid; Ng, Felix, C. K.; Wu, C. H.

2017-10-01

The rapid development of the internet has brought huge benefits and social impacts; however, internet security has also become a great problem for users, since traditional approaches to packet classification cannot achieve satisfactory detection performance due to their low accuracy and efficiency. In this paper, a new stateful packet inspection method is introduced, which can be embedded in the network gateway and used by a streaming application detection system. This new detection method leverages the inexact automaton approach, using part of the header field and part of the application layer data of a packet. Based on this approach, an advanced detection system is proposed for streaming applications. The workflow of the system involves two stages: the training stage and the detection stage. In the training stage, the system initially captures characteristic patterns from a set of application packet flows. After this training is completed, the detection stage allows the user to detect the target application by capturing new application flows. This new detection approach is also evaluated using experimental analysis; the results of this analysis show that this new approach not only simplifies the management of the state detection system, but also improves the accuracy of data flow detection, making it feasible for real-world network applications.

Resequencing Pathogen Microarray (RPM) for prospective detection and identification of emergent pathogen strains and variants

NASA Astrophysics Data System (ADS)

Tibbetts, Clark; Lichanska, Agnieszka M.; Borsuk, Lisa A.; Weslowski, Brian; Morris, Leah M.; Lorence, Matthew C.; Schafer, Klaus O.; Campos, Joseph; Sene, Mohamadou; Myers, Christopher A.; Faix, Dennis; Blair, Patrick J.; Brown, Jason; Metzgar, David

2010-04-01

High-density resequencing microarrays support simultaneous detection and identification of multiple viral and bacterial pathogens. Because detection and identification using RPM is based upon multiple specimen-specific target pathogen gene sequences generated in the individual test, the test results enable both a differential diagnostic analysis and epidemiological tracking of detected pathogen strains and variants from one specimen to the next. The RPM assay enables detection and identification of pathogen sequences that share as little as 80% sequence similarity to prototype target gene sequences represented as detector tiles on the array. This capability enables the RPM to detect and identify previously unknown strains and variants of a detected pathogen, as in sentinel cases associated with an infectious disease outbreak. We illustrate this capability using assay results from testing influenza A virus vaccines configured with strains that were first defined years after the design of the RPM microarray. Results are also presented from RPM-Flu testing of three specimens independently confirmed to the positive for the 2009 Novel H1N1 outbreak strain of influenza virus.

Comparing synthetic imagery with real imagery for visible signature analysis: human observer results

NASA Astrophysics Data System (ADS)

Culpepper, Joanne B.; Richards, Noel; Madden, Christopher S.; Winter, Neal; Wheaton, Vivienne C.

2017-10-01

Synthetic imagery could potentially enhance visible signature analysis by providing a wider range of target images in differing environmental conditions than would be feasible to collect in field trials. Achieving this requires a method for generating synthetic imagery that is both verified to be realistic and produces the same visible signature analysis results as real images. Is target detectability as measured by image metrics the same for real images and synthetic images of the same scene? Is target detectability as measured by human observer trials the same for real images and synthetic images of the same scene, and how realistic do the synthetic images need to be? In this paper we present the results of a small scale exploratory study on the second question: a photosimulation experiment conducted using digital photographs and synthetic images generated of the same scene. Two sets of synthetic images were created: a high fidelity set created using an image generation tool, E-on Vue, and a low fidelity set created using a gaming engine, Unity 3D. The target detection results obtained using digital photographs were compared with those obtained using the two sets of synthetic images. There was a moderate correlation between the high fidelity synthetic image set and the real images in both the probability of correct detection (Pd: PCC = 0.58, SCC = 0.57) and mean search time (MST: PCC = 0.63, SCC = 0.61). There was no correlation between the low fidelity synthetic image set and the real images for the Pd, but a moderate correlation for MST (PCC = 0.67, SCC = 0.55).

Algorithm for automatic analysis of electro-oculographic data.

PubMed

Pettersson, Kati; Jagadeesan, Sharman; Lukander, Kristian; Henelius, Andreas; Haeggström, Edward; Müller, Kiti

2013-10-25

Large amounts of electro-oculographic (EOG) data, recorded during electroencephalographic (EEG) measurements, go underutilized. We present an automatic, auto-calibrating algorithm that allows efficient analysis of such data sets. The auto-calibration is based on automatic threshold value estimation. Amplitude threshold values for saccades and blinks are determined based on features in the recorded signal. The performance of the developed algorithm was tested by analyzing 4854 saccades and 213 blinks recorded in two different conditions: a task where the eye movements were controlled (saccade task) and a task with free viewing (multitask). The results were compared with results from a video-oculography (VOG) device and manually scored blinks. The algorithm achieved 93% detection sensitivity for blinks with 4% false positive rate. The detection sensitivity for horizontal saccades was between 98% and 100%, and for oblique saccades between 95% and 100%. The classification sensitivity for horizontal and large oblique saccades (10 deg) was larger than 89%, and for vertical saccades larger than 82%. The duration and peak velocities of the detected horizontal saccades were similar to those in the literature. In the multitask measurement the detection sensitivity for saccades was 97% with a 6% false positive rate. The developed algorithm enables reliable analysis of EOG data recorded both during EEG and as a separate metrics.

Use of next-generation sequencing to detect LDLR gene copy number variation in familial hypercholesterolemia[S

PubMed Central

Iacocca, Michael A.; Wang, Jian; Dron, Jacqueline S.; Robinson, John F.; McIntyre, Adam D.; Cao, Henian

2017-01-01

Familial hypercholesterolemia (FH) is a heritable condition of severely elevated LDL cholesterol, caused predominantly by autosomal codominant mutations in the LDL receptor gene (LDLR). In providing a molecular diagnosis for FH, the current procedure often includes targeted next-generation sequencing (NGS) panels for the detection of small-scale DNA variants, followed by multiplex ligation-dependent probe amplification (MLPA) in LDLR for the detection of whole-exon copy number variants (CNVs). The latter is essential because ∼10% of FH cases are attributed to CNVs in LDLR; accounting for them decreases false negative findings. Here, we determined the potential of replacing MLPA with bioinformatic analysis applied to NGS data, which uses depth-of-coverage analysis as its principal method to identify whole-exon CNV events. In analysis of 388 FH patient samples, there was 100% concordance in LDLR CNV detection between these two methods: 38 reported CNVs identified by MLPA were also successfully detected by our NGS method, while 350 samples negative for CNVs by MLPA were also negative by NGS. This result suggests that MLPA can be removed from the routine diagnostic screening for FH, significantly reducing associated costs, resources, and analysis time, while promoting more widespread assessment of this important class of mutations across diagnostic laboratories. PMID:28874442

Smart ECG Monitoring Patch with Built-in R-Peak Detection for Long-Term HRV Analysis.

PubMed

Lee, W K; Yoon, H; Park, K S

2016-07-01

Since heart rate variability (HRV) analysis is widely used to evaluate the physiological status of the human body, devices specifically designed for such applications are needed. To this end, we developed a smart electrocardiography (ECG) patch. The smart patch measures ECG using three electrodes integrated into the patch, filters the measured signals to minimize noise, performs analog-to-digital conversion, and detects R-peaks. The measured raw ECG data and the interval between the detected R-peaks can be recorded to enable long-term HRV analysis. Experiments were performed to evaluate the performance of the built-in R-wave detection, robustness of the device under motion, and applicability to the evaluation of mental stress. The R-peak detection results obtained with the device exhibited a sensitivity of 99.29%, a positive predictive value of 100.00%, and an error of 0.71%. The device also exhibited less motional noise than conventional ECG recording, being stable up to a walking speed of 5 km/h. When applied to mental stress analysis, the device evaluated the variation in HRV parameters in the same way as a normal ECG, with very little difference. This device can help users better understand their state of health and provide physicians with more reliable data for objective diagnosis.

Intrusion detection system using Online Sequence Extreme Learning Machine (OS-ELM) in advanced metering infrastructure of smart grid.

PubMed

Li, Yuancheng; Qiu, Rixuan; Jing, Sitong

2018-01-01

Advanced Metering Infrastructure (AMI) realizes a two-way communication of electricity data through by interconnecting with a computer network as the core component of the smart grid. Meanwhile, it brings many new security threats and the traditional intrusion detection method can't satisfy the security requirements of AMI. In this paper, an intrusion detection system based on Online Sequence Extreme Learning Machine (OS-ELM) is established, which is used to detecting the attack in AMI and carrying out the comparative analysis with other algorithms. Simulation results show that, compared with other intrusion detection methods, intrusion detection method based on OS-ELM is more superior in detection speed and accuracy.

ALK rearrangement in a large series of consecutive non-small cell lung cancers: comparison between a new immunohistochemical approach and fluorescence in situ hybridization for the screening of patients eligible for crizotinib treatment.

PubMed

Alì, Greta; Proietti, Agnese; Pelliccioni, Serena; Niccoli, Cristina; Lupi, Cristiana; Sensi, Elisa; Giannini, Riccardo; Borrelli, Nicla; Menghi, Maura; Chella, Antonio; Ribechini, Alessandro; Cappuzzo, Federico; Melfi, Franca; Lucchi, Marco; Mussi, Alfredo; Fontanini, Gabriella

2014-11-01

Echinoderm microtubule associated proteinlike 4-anaplastic lymphoma receptor tyrosine kinase (EML4-ALK) translocation has been described in a subset of patients with non-small cell lung cancer (NSCLC) and has been shown to have oncogenic activity. Fluorescence in situ hybridization (FISH) is used to detect ALK-positive NSCLC, but it is expensive, time-consuming, and difficult for routine application. To evaluate the potential role of immunohistochemistry (IHC) as a screening tool to identify candidate cases for FISH analysis and for ALK inhibitor therapy in NSCLC. We performed FISH and IHC for ALK and mutational analysis for epidermal growth factor receptor (EGFR) and KRAS in 523 NSCLC specimens. We conducted IHC analysis with the monoclonal antibody D5F3 (Ventana Medical Systems, Tucson, Arizona) and a highly sensitive detection system. We also performed a MassARRAY-based analysis (Sequenom, San Diego, California) in a small subset of 11 samples to detect EML4-ALK rearrangement. Of the 523 NSCLC specimens, 20 (3.8%) were positive for ALK rearrangement by FISH analysis. EGFR and KRAS mutations were identified in 70 (13.4%) and 124 (23.7%) of the 523 tumor samples, respectively. ALK rearrangement and EGFR and KRAS mutations were mutually exclusive. Of 523 tumor samples analyzed, 18 (3.4%) were ALK(+) by IHC, 18 samples (3.4%) had concordant IHC and FISH results, and 2 ALK(+) cases (0.3%) by FISH failed to show ALK protein expression. In the 2 discrepant cases, we did not detect any mass peaks for the EML4-ALK variants by MassARRAY. Our results show that IHC may be a useful technique for selecting NSCLC cases to undergo ALK FISH analysis.

The use of droplet digital PCR in liquid biopsies: A highly sensitive technique for MYD88 p.(L265P) detection in cerebrospinal fluid.

PubMed

Hiemcke-Jiwa, Laura S; Minnema, Monique C; Radersma-van Loon, Joyce H; Jiwa, N Mehdi; de Boer, Mirthe; Leguit, Roos J; de Weger, Roel A; Huibers, Manon M H

2018-04-01

The gold standard for diagnosis of central nervous system lymphomas still regards a stereotactic brain biopsy, with the risk of major complications for the patient. As tumor cells can be detected in cerebrospinal fluid (CSF), CSF analysis can be used as an alternative. In this respect, mutation analysis in CSF can be of added value to other diagnostic parameters such a cytomorphology and clonality analysis. A well-known example of targeted mutation analysis entails MYD88 p.(L265P) detection, which is present in the majority of Bing Neel syndrome and primary central nervous system lymphoma (PCNSL) patients. Unfortunately, tumor yield in CSF can be very low. Therefore, use of the highly sensitive droplet digital PCR (ddPCR) might be a suitable analysis strategy for targeted mutation detection. We analyzed 26 formalin fixed paraffin embedded (FFPE) samples (8 positive and 18 negative for MYD88 p.(L265P) mutation) by ddPCR, of which the results were compared with next generation sequencing (NGS). Subsequently, 32 CSF samples were analyzed by ddPCR. ddPCR and NGS results on FFPE material showed 100% concordance. Among the 32 CSF samples, 9 belonged to patients with lymphoplasmacytic lymphoma (LPL) and clinical suspicion of Bing Neel syndrome, and 3 belonged to patients with PCNSL. Nine of these samples tested positive for MYD88 p.(L265P) (8 LPL and 1 PCNSL). This study shows that sensitive MYD88 mutation analysis by ddPCR in CSF is highly reliable and can be applied even when DNA input is low. Therefore, ddPCR is of added value to current diagnostic parameters, especially when the available amount of DNA is limited. Copyright © 2017 John Wiley & Sons, Ltd.

Preliminary scientific results from the first six months of the Infrared Astronomical Satellite (IRAS)

NASA Technical Reports Server (NTRS)

Soifer, B. T.; Neugebauer, G.; Beichman, C. A.; Houck, J. R.; Rowan-Robinson, M.

1984-01-01

The Infrared Astronomical Satellite (IRAS) was successfully launched on January 25, 1983. This paper presents results based on analysis of early scientific data returned from IRAS. Among the early results of IRAS are the discovery of comet IRAS-Araki-Alcock, evidence for a shell of large particles around the nearby bright star Vega, detection of stars in the process of formation, and detection of many infrared bright galaxies. These early results demonstrate that the IRAS data will be a treasure chest for astronomers for years to come.

Preliminary scientific results from the first six months of the Infrared Astronomical Satellite (IRAS)

NASA Astrophysics Data System (ADS)

Soifer, B. T.; Beichman, C. A.; Houck, J. R.; Neugebauer, G.; Rowan-Robinson, M.

1984-04-01

The Infrared Astronomical Satellite (IRAS) was successfully launched on January 25, 1983. This paper presents results based on analysis of early scientific data returned from IRAS. Among the early results of IRAS are the discovery of comet IRAS-Araki-Alcock, evidence for a shell of large particles around the nearby bright star Vega, detection of stars in the process of formation, and detection of many infrared bright galaxies. These early results demonstrate that the IRAS data will be a treasure chest for astronomers for years to come.

Defect analysis and detection of micro nano structured optical thin film

NASA Astrophysics Data System (ADS)

Xu, Chang; Shi, Nuo; Zhou, Lang; Shi, Qinfeng; Yang, Yang; Li, Zhuo

2017-10-01

This paper focuses on developing an automated method for detecting defects on our wavelength conversion thin film. We analyzes the operating principle of our wavelength conversion Micro/Nano thin film which absorbing visible light and emitting infrared radiation, indicates the relationship between the pixel's pattern and the radiation of the thin film, and issues the principle of defining blind pixels and their categories due to the calculated and experimental results. An effective method is issued for the automated detection based on wavelet transform and template matching. The results reveal that this method has desired accuracy and processing speed.

Fast and simultaneous determination of 12 polyphenols in apple peel and pulp by using chemometrics-assisted high-performance liquid chromatography with diode array detection.

PubMed

Wang, Tong; Wu, Hai-Long; Xie, Li-Xia; Zhu, Li; Liu, Zhi; Sun, Xiao-Dong; Xiao, Rong; Yu, Ru-Qin

2017-04-01

In this work, a smart chemometrics-enhanced strategy, high-performance liquid chromatography, and diode array detection coupled with second-order calibration method based on alternating trilinear decomposition algorithm was proposed to simultaneously quantify 12 polyphenols in different kinds of apple peel and pulp samples. The proposed strategy proved to be a powerful tool to solve the problems of coelution, unknown interferences, and chromatographic shifts in the process of high-performance liquid chromatography analysis, making it possible for the determination of 12 polyphenols in complex apple matrices within 10 min under simple conditions of elution. The average recoveries with standard deviations, and figures of merit including sensitivity, selectivity, limit of detection, and limit of quantitation were calculated to validate the accuracy of the proposed method. Compared to the quantitative analysis results from the classic high-performance liquid chromatography method, the statistical and graphical analysis showed that our proposed strategy obtained more reliable results. All results indicated that our proposed method used in the quantitative analysis of apple polyphenols was an accurate, fast, universal, simple, and green one, and it was expected to be developed as an attractive alternative method for simultaneous determination of multitargeted analytes in complex matrices. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Magnetic force microscopy method and apparatus to detect and image currents in integrated circuits

DOEpatents

Campbell, Ann. N.; Anderson, Richard E.; Cole, Jr., Edward I.

1995-01-01

A magnetic force microscopy method and improved magnetic tip for detecting and quantifying internal magnetic fields resulting from current of integrated circuits. Detection of the current is used for failure analysis, design verification, and model validation. The interaction of the current on the integrated chip with a magnetic field can be detected using a cantilevered magnetic tip. Enhanced sensitivity for both ac and dc current and voltage detection is achieved with voltage by an ac coupling or a heterodyne technique. The techniques can be used to extract information from analog circuits.

Magnetic force microscopy method and apparatus to detect and image currents in integrated circuits

DOEpatents

Campbell, A.N.; Anderson, R.E.; Cole, E.I. Jr.

1995-11-07

A magnetic force microscopy method and improved magnetic tip for detecting and quantifying internal magnetic fields resulting from current of integrated circuits are disclosed. Detection of the current is used for failure analysis, design verification, and model validation. The interaction of the current on the integrated chip with a magnetic field can be detected using a cantilevered magnetic tip. Enhanced sensitivity for both ac and dc current and voltage detection is achieved with voltage by an ac coupling or a heterodyne technique. The techniques can be used to extract information from analog circuits. 17 figs.

Deep learning on temporal-spectral data for anomaly detection

NASA Astrophysics Data System (ADS)

Ma, King; Leung, Henry; Jalilian, Ehsan; Huang, Daniel

2017-05-01

Detecting anomalies is important for continuous monitoring of sensor systems. One significant challenge is to use sensor data and autonomously detect changes that cause different conditions to occur. Using deep learning methods, we are able to monitor and detect changes as a result of some disturbance in the system. We utilize deep neural networks for sequence analysis of time series. We use a multi-step method for anomaly detection. We train the network to learn spectral and temporal features from the acoustic time series. We test our method using fiber-optic acoustic data from a pipeline.

Method for combined biometric and chemical analysis of human fingerprints.

PubMed

Staymates, Jessica L; Orandi, Shahram; Staymates, Matthew E; Gillen, Greg

This paper describes a method for combining direct chemical analysis of latent fingerprints with subsequent biometric analysis within a single sample. The method described here uses ion mobility spectrometry (IMS) as a chemical detection method for explosives and narcotics trace contamination. A collection swab coated with a high-temperature adhesive has been developed to lift latent fingerprints from various surfaces. The swab is then directly inserted into an IMS instrument for a quick chemical analysis. After the IMS analysis, the lifted print remains intact for subsequent biometric scanning and analysis using matching algorithms. Several samples of explosive-laden fingerprints were successfully lifted and the explosives detected with IMS. Following explosive detection, the lifted fingerprints remained of sufficient quality for positive match scores using a prepared gallery consisting of 60 fingerprints. Based on our results ( n  = 1200), there was no significant decrease in the quality of the lifted print post IMS analysis. In fact, for a small subset of lifted prints, the quality was improved after IMS analysis. The described method can be readily applied to domestic criminal investigations, transportation security, terrorist and bombing threats, and military in-theatre settings.

Noninvasive Computed Tomography–based Risk Stratification of Lung Adenocarcinomas in the National Lung Screening Trial

PubMed Central

Maldonado, Fabien; Duan, Fenghai; Raghunath, Sushravya M.; Rajagopalan, Srinivasan; Karwoski, Ronald A.; Garg, Kavita; Greco, Erin; Nath, Hrudaya; Robb, Richard A.; Bartholmai, Brian J.

2015-01-01

Rationale: Screening for lung cancer using low-dose computed tomography (CT) reduces lung cancer mortality. However, in addition to a high rate of benign nodules, lung cancer screening detects a large number of indolent cancers that generally belong to the adenocarcinoma spectrum. Individualized management of screen-detected adenocarcinomas would be facilitated by noninvasive risk stratification. Objectives: To validate that Computer-Aided Nodule Assessment and Risk Yield (CANARY), a novel image analysis software, successfully risk stratifies screen-detected lung adenocarcinomas based on clinical disease outcomes. Methods: We identified retrospective 294 eligible patients diagnosed with lung adenocarcinoma spectrum lesions in the low-dose CT arm of the National Lung Screening Trial. The last low-dose CT scan before the diagnosis of lung adenocarcinoma was analyzed using CANARY blinded to clinical data. Based on their parametric CANARY signatures, all the lung adenocarcinoma nodules were risk stratified into three groups. CANARY risk groups were compared using survival analysis for progression-free survival. Measurements and Main Results: A total of 294 patients were included in the analysis. Kaplan-Meier analysis of all the 294 adenocarcinoma nodules stratified into the Good, Intermediate, and Poor CANARY risk groups yielded distinct progression-free survival curves (P < 0.0001). This observation was confirmed in the unadjusted and adjusted (age, sex, race, and smoking status) progression-free survival analysis of all stage I cases. Conclusions: CANARY allows the noninvasive risk stratification of lung adenocarcinomas into three groups with distinct post-treatment progression-free survival. Our results suggest that CANARY could ultimately facilitate individualized management of incidentally or screen-detected lung adenocarcinomas. PMID:26052977

A new method for detection and discrimination of Pepino mosaic virus isolates using high resolution melting analysis of the triple gene block 3.

PubMed

Hasiów-Jaroszewska, Beata; Komorowska, Beata

2013-10-01

Diagnostic methods distinguished different Pepino mosaic virus (PepMV) genotypes but the methods do not detect sequence variation in particular gene segments. The necrotic and non-necrotic isolates (pathotypes) of PepMV share a 99% sequence similarity. These isolates differ from each other at one nucleotide site in the triple gene block 3. In this study, a combination of real-time reverse transcription polymerase chain reaction and high resolution melting curve analysis of triple gene block 3 was developed for simultaneous detection and differentiation of PepMV pathotypes. The triple gene block 3 region carrying a transition A → G was amplified using two primer pairs from twelve virus isolates, and was subjected to high resolution melting curve analysis. The results showed two distinct melting curve profiles related to each pathotype. The results also indicated that the high resolution melting method could readily differentiate between necrotic and non-necrotic PepMV pathotypes. Copyright © 2013 Elsevier B.V. All rights reserved.

Analysis of the detection materials as resonant pads for attaching the measuring arm of the interferometer when sensing mechanical vibrations

NASA Astrophysics Data System (ADS)

Nedoma, Jan; Fajkus, Marcel; Martinek, Radek; Zboril, Ondrej; Bednarek, Lukas; Novak, Martin; Witas, Karel; Vasinek, Vladimir

2017-05-01

Fiber-optic sensors (FOS), today among the most widespread measuring sensors and during various types of measuring, are irreplaceable. Among the distinctive features include immunity to electromagnetic interference, passivity regarding power supply and high sensitivity. One of the representatives FOS is the interferometric sensors working on the principle of interference of light. Authors of this article focused on the analysis of the detection material as resonant pads for attaching the measuring arm of the interferometer when sensing mechanical vibrations (low frequencies). A typical example is the use of interferometer sensors in automobile traffic while sensing a vibration response from the roadway while passing the cars. For analysis was used sensor with Mach-Zehnder interferometer. Defined were different detection materials about different size and thickness. We analyzed the influence on the sensitivity (amplitude response) of the interferometer. Based on the results we have defined the best material for sensing mechanical vibrations. The signal was processed by applications created in LabView development environment. The results were verified by repeated testing in laboratory conditions.

Online anomaly detection in wireless body area networks for reliable healthcare monitoring.

PubMed

Salem, Osman; Liu, Yaning; Mehaoua, Ahmed; Boutaba, Raouf

2014-09-01

In this paper, we propose a lightweight approach for online detection of faulty measurements by analyzing the data collected from medical wireless body area networks. The proposed framework performs sequential data analysis using a smart phone as a base station, and takes into account the constrained resources of the smart phone, such as processing power and storage capacity. The main objective is to raise alarms only when patients enter in an emergency situation, and to discard false alarms triggered by faulty measurements or ill-behaved sensors. The proposed approach is based on the Haar wavelet decomposition, nonseasonal Holt-Winters forecasting, and the Hampel filter for spatial analysis, and on for temporal analysis. Our objective is to reduce false alarms resulting from unreliable measurements and to reduce unnecessary healthcare intervention. We apply our proposed approach on real physiological dataset. Our experimental results prove the effectiveness of our approach in achieving good detection accuracy with a low false alarm rate. The simplicity and the processing speed of our proposed framework make it useful and efficient for real time diagnosis.

Assessment of Gamma-Ray Spectra Analysis Method Utilizing the Fireworks Algorithm for various Error Measures

DOE Office of Scientific and Technical Information (OSTI.GOV)

Alamaniotis, Miltiadis; Tsoukalas, Lefteri H.

2018-01-01

Significant role in enhancing nuclear nonproliferation plays the analysis of obtained data and the inference of the presence or not of special nuclear materials in them. Among various types of measurements, gamma-ray spectra is the widest used type of data utilized for analysis in nonproliferation. In this chapter, a method that employs the fireworks algorithm (FWA) for analyzing gamma-ray spectra aiming at detecting gamma signatures is presented. In particular FWA is utilized to fit a set of known signatures to a measured spectrum by optimizing an objective function, with non-zero coefficients expressing the detected signatures. FWA is tested on amore » set of experimentally obtained measurements and various objective functions -MSE, RMSE, Theil-2, MAE, MAPE, MAP- with results exhibiting its potential in providing high accuracy and high precision of detected signatures. Furthermore, FWA is benchmarked against genetic algorithms, and multiple linear regression with results exhibiting its superiority over the rest tested algorithms with respect to precision for MAE, MAPE and MAP measures.« less

Application of clone library analysis and real-time PCR for comparison of microbial communities in a low-grade copper sulfide ore bioheap leachate.

PubMed

Bowei, Chen; Xingyu, Liu; Wenyan, Liu; Jiankang, Wen

2009-11-01

The microbial communities of leachate from a bioleaching heap located in China were analyzed using the 16S rRNA gene clone library and real-time quantitative PCR. Both methods showed that Leptospirillum spp. were the dominant bacteria, and Ferroplasma acidiphilum were the only archaea detected in the leachate. Clone library results indicated that nine operational taxonomic units (OTUs) were obtained, which fell into four divisions, the Nitrospirae (74%), the gamma-Proteobacteria (14%), the Actinobacteria (6%) and the Euryarchaeota (6%). The results obtained by real-time PCR in some ways were the same as clone library analysis. Furthermore, Sulfobacillus spp., detected only by real-time PCR, suggests that real-time PCR was a reliable technology to study the microbial communities in bioleaching environments. It is a useful tool to assist clone library analysis, to further understand microbial consortia and to have comprehensive and exact microbiological information about bioleaching environments. Finally, the interactions among the microorganisms detected in the leachate were summarized according to the characteristics of these species.

Robust Cell Detection of Histopathological Brain Tumor Images Using Sparse Reconstruction and Adaptive Dictionary Selection

PubMed Central

Su, Hai; Xing, Fuyong; Yang, Lin

2016-01-01

Successful diagnostic and prognostic stratification, treatment outcome prediction, and therapy planning depend on reproducible and accurate pathology analysis. Computer aided diagnosis (CAD) is a useful tool to help doctors make better decisions in cancer diagnosis and treatment. Accurate cell detection is often an essential prerequisite for subsequent cellular analysis. The major challenge of robust brain tumor nuclei/cell detection is to handle significant variations in cell appearance and to split touching cells. In this paper, we present an automatic cell detection framework using sparse reconstruction and adaptive dictionary learning. The main contributions of our method are: 1) A sparse reconstruction based approach to split touching cells; 2) An adaptive dictionary learning method used to handle cell appearance variations. The proposed method has been extensively tested on a data set with more than 2000 cells extracted from 32 whole slide scanned images. The automatic cell detection results are compared with the manually annotated ground truth and other state-of-the-art cell detection algorithms. The proposed method achieves the best cell detection accuracy with a F1 score = 0.96. PMID:26812706

Output-Based Structural Damage Detection by Using Correlation Analysis Together with Transmissibility

PubMed Central

Cao, Hongyou; Liu, Quanmin; Wahab, Magd Abdel

2017-01-01

Output-based structural damage detection is becoming increasingly appealing due to its potential in real engineering applications without any restriction regarding excitation measurements. A new transmissibility-based damage detection approach is presented in this study by combining transmissibility with correlation analysis in order to strengthen its performance in discriminating damaged from undamaged scenarios. From this perspective, damage detection strategies are hereafter established by constructing damage-sensitive indicators from a derived transmissibility. A cantilever beam is numerically analyzed to verify the feasibility of the proposed damage detection procedure, and an ASCE (American Society of Civil Engineers) benchmark is henceforth used in the validation for its application in engineering structures. The results of both studies reveal a good performance of the proposed methodology in identifying damaged states from intact states. The comparison between the proposed indicator and the existing indicator also affirms its applicability in damage detection, which might be adopted in further structural health monitoring systems as a discrimination criterion. This study contributed an alternative criterion for transmissibility-based damage detection in addition to the conventional ones. PMID:28773218

78 FR 54387 - Airworthiness Directives; The Boeing Company Airplanes

Federal Register 2010, 2011, 2012, 2013, 2014

2013-09-04

... at S-6L and S-6R on several airplanes, and subsequent analysis results that indicated that the... reach a critical length. We are issuing this AD to detect and correct cracking at the upper row of... and S-6R on several airplanes, and subsequent analysis results that indicated that the protruding head...

Feasibility analysis of EDXRF method to detect heavy metal pollution in ecological environment

NASA Astrophysics Data System (ADS)

Hao, Zhixu; Qin, Xulei

2018-02-01

The change of heavy metal content in water environment, soil and plant can reflect the change of heavy metal pollution in ecological environment, and it is important to monitor the trend of heavy metal pollution in eco-environment by using water environment, soil and heavy metal content in plant. However, the content of heavy metals in nature is very low, the background elements of water environment, soil and plant samples are complex, and there are many interfering factors in the EDXRF system that will affect the spectral analysis results and reduce the detection accuracy. Through the contrastive analysis of several heavy metal elements detection methods, it is concluded that the EDXRF method is superior to other chemical methods in testing accuracy and method feasibility when the heavy metal pollution in soil is tested in ecological environment.

Wear detection by means of wavelet-based acoustic emission analysis

NASA Astrophysics Data System (ADS)

Baccar, D.; Söffker, D.

2015-08-01

Wear detection and monitoring during operation are complex and difficult tasks especially for materials under sliding conditions. Due to the permanent contact and repetitive motion, the material surface remains during tests non-accessible for optical inspection so that attrition of the contact partners cannot be easily detected. This paper introduces the relevant scientific components of reliable and efficient condition monitoring system for online detection and automated classification of wear phenomena by means of acoustic emission (AE) and advanced signal processing approaches. The related experiments were performed using a tribological system consisting of two martensitic plates, sliding against each other. High sensitive piezoelectric transducer was used to provide the continuous measurement of AE signals. The recorded AE signals were analyzed mainly by time-frequency analysis. A feature extraction module using a novel combination of Short-Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) were used for the first time. A detailed correlation analysis between complex signal characteristics and the surface damage resulting from contact fatigue was investigated. Three wear process stages were detected and could be distinguished. To obtain quantitative and detailed information about different wear phases, the AE energy was calculated using STFT and decomposed into a suitable number of frequency levels. The individual energy distribution and the cumulative AE energy of each frequency components were analyzed using CWT. Results show that the behavior of individual frequency component changes when the wear state changes. Here, specific frequency ranges are attributed to the different wear states. The study reveals that the application of the STFT-/CWT-based AE analysis is an appropriate approach to distinguish and to interpret the different damage states occurred during sliding contact. Based on this results a new generation of condition monitoring systems can be build, able to evaluate automatically the surface condition of machine components with sliding surfaces.

Molecular identification and phylogenetic analysis of Wuchereria bancrofti from human blood samples in Egypt.

PubMed

Abdel-Shafi, Iman R; Shoieb, Eman Y; Attia, Samar S; Rubio, José M; Ta-Tang, Thuy-Huong; El-Badry, Ayman A

2017-03-01

Lymphatic filariasis (LF) is a serious vector-borne health problem, and Wuchereria bancrofti (W.b) is the major cause of LF worldwide and is focally endemic in Egypt. Identification of filarial infection using traditional morphologic and immunological criteria can be difficult and lead to misdiagnosis. The aim of the present study was molecular detection of W.b in residents in endemic areas in Egypt, sequence variance analysis, and phylogenetic analysis of W.b DNA. Collected blood samples from residents in filariasis endemic areas in five governorates were subjected to semi-nested PCR targeting repeated DNA sequence, for detection of W.b DNA. PCR products were sequenced; subsequently, a phylogenetic analysis of the obtained sequences was performed. Out of 300 blood samples, W.b DNA was identified in 48 (16%). Sequencing analysis confirmed PCR results identifying only W.b species. Sequence alignment and phylogenetic analysis indicated genetically distinct clusters of W.b among the study population. Study results demonstrated that the semi-nested PCR proved to be an effective diagnostic tool for accurate and rapid detection of W.b infections in nano-epidemics and is applicable for samples collected in the daytime as well as the night time. PCR products sequencing and phylogenitic analysis revealed three different nucleotide sequences variants. Further genetic studies of W.b in Egypt and other endemic areas are needed to distinguish related strains and the various ecological as well as drug effects exerted on them to support W.b elimination.

Dependency of human target detection performance on clutter and quality of supporting image analysis algorithms in a video surveillance task

NASA Astrophysics Data System (ADS)

Huber, Samuel; Dunau, Patrick; Wellig, Peter; Stein, Karin

2017-10-01

Background: In target detection, the success rates depend strongly on human observer performances. Two prior studies tested the contributions of target detection algorithms and prior training sessions. The aim of this Swiss-German cooperation study was to evaluate the dependency of human observer performance on the quality of supporting image analysis algorithms. Methods: The participants were presented 15 different video sequences. Their task was to detect all targets in the shortest possible time. Each video sequence showed a heavily cluttered simulated public area from a different viewing angle. In each video sequence, the number of avatars in the area was altered to 100, 150 and 200 subjects. The number of targets appearing was kept at 10%. The number of marked targets varied from 0, 5, 10, 20 up to 40 marked subjects while keeping the positive predictive value of the detection algorithm at 20%. During the task, workload level was assessed by applying an acoustic secondary task. Detection rates and detection times for the targets were analyzed using inferential statistics. Results: The study found Target Detection Time to increase and Target Detection Rates to decrease with increasing numbers of avatars. The same is true for the Secondary Task Reaction Time while there was no effect on Secondary Task Hit Rate. Furthermore, we found a trend for a u-shaped correlation between the numbers of markings and RTST indicating increased workload. Conclusion: The trial results may indicate useful criteria for the design of training and support of observers in observational tasks.

Preparation of miniantibodies to Azospirillum brasilense Sp245 surface antigens and their use for bacterial detection.

PubMed

Dykman, Lev A; Staroverov, Sergei A; Guliy, Olga I; Ignatov, Oleg V; Fomin, Alexander S; Vidyasheva, Irina V; Karavaeva, Olga A; Bunin, Viktor D; Burygin, Gennady L

2012-01-01

This article reports the first preparation of miniantibodies to Azospirillum brasilense Sp245 surface antigens by using a combinatorial phage library of sheep antibodies. The prepared phage antibodies were used for the first time for lipopolysaccharide and flagellin detection by dot assay, electro-optical analysis of cell suspensions, and transmission electron microscopy. Interaction of A. brasilense Sp245 with antilipopolysaccharide and antiflagellin phage-displayed miniantibodies caused the magnitude of the electro-optical signal to change considerably. The electro-optical results were in good agreement with the electron microscopic data. This is the first reported possibility of employing phage-displayed miniantibodies in bacterial detection aided by electro-optical analysis of cell suspensions.

Research on artificial neural network intrusion detection photochemistry based on the improved wavelet analysis and transformation

NASA Astrophysics Data System (ADS)

Li, Hong; Ding, Xue

2017-03-01

This paper combines wavelet analysis and wavelet transform theory with artificial neural network, through the pretreatment on point feature attributes before in intrusion detection, to make them suitable for improvement of wavelet neural network. The whole intrusion classification model gets the better adaptability, self-learning ability, greatly enhances the wavelet neural network for solving the problem of field detection invasion, reduces storage space, contributes to improve the performance of the constructed neural network, and reduces the training time. Finally the results of the KDDCup99 data set simulation experiment shows that, this method reduces the complexity of constructing wavelet neural network, but also ensures the accuracy of the intrusion classification.

Portable device for the detection of colorimetric assays

PubMed Central

Nowak, E.; Kawchuk, J.; Hoorfar, M.; Najjaran, H.

2017-01-01

In this work, a low-cost, portable device is developed to detect colorimetric assays for in-field and point-of-care (POC) analysis. The device can rapidly detect both pH values and nitrite concentrations of five different samples, simultaneously. After mixing samples with specific reagents, a high-resolution digital camera collects a picture of the sample, and a single-board computer processes the image in real time to identify the hue–saturation–value coordinates of the image. An internal light source reduces the effect of any ambient light so the device can accurately determine the corresponding pH values or nitrite concentrations. The device was purposefully designed to be low-cost, yet versatile, and the accuracy of the results have been compared to those from a conventional method. The results obtained for pH values have a mean standard deviation of 0.03 and a correlation coefficient R2 of 0.998. The detection of nitrites is between concentrations of 0.4–1.6 mg l−1, with a low detection limit of 0.2 mg l−1, and has a mean standard deviation of 0.073 and an R2 value of 0.999. The results represent great potential of the proposed portable device as an excellent analytical tool for POC colorimetric analysis and offer broad accessibility in resource-limited settings. PMID:29291093

On-line damage detection in rotating machinery

NASA Astrophysics Data System (ADS)

Alkhalifa, Tareq Jawad

This work is concerned with a set of techniques to detect internal defects in uniform circular discs (rotors). An internal defect is intentionally manufactured in stereolithographic discs by a rapid prototyping process using cured resin SL 5170 material. The analysis and results presented here are limited to a uniform circular disc, with internal defects, mounted on a uniform flexible circular shaft. The setup is comprised of a Bently Nevada rotor kit connected to a data acquisition system. The rotor consists of a disc and shaft that is supported by journal bearings and is coupled to a motor by a rubber joint. Damage produces localized changes in the strain energy, which is quantified to characterize the damage. Based on previous research, the Strain Energy Damage Index (SEDI) is utilized to localize the damage due to strain energy differences between damaged and undamaged modes. To accomplish the objective, this work covers three types of analysis: finite element analysis, vibration analysis, and experimental modal analysis. Finite element analysis (using SDRC Ideas software) is performed to develop a multi-degree-of-freedom (MDOF) rotor system with internal damage, and its dynamic characteristics are investigated. The analysis is performed for two different types damage cases: radial damage and circular damage. Parametric study for radial damage and random noise to undamaged disc have been investigated to predict the effect of noise in the damage detection. The developed on-line damage detection technique for rotating equipment incorporates and couples both vibration analysis and experimental modal analysis. The dynamic investigation of the rotating discs (with and without defect) is conducted by vibration signal analysis (using proximity sensors, data acquisition and LabView). The vibration analysis provides a unique vibration signature for the damaged disc, which indicates the existence of the damage. The vibration data are acquired at different running speeds (1000, 2500, 5000 rpm). Then the dynamic investigation of non-rotating discs (with and without defect) is conducted by experimental modal analysis (using STAR software). While the vibration analysis detects and indicates the existence of damage while the disc is rotating, experimental modal analysis (using STAR and MATLAB software) provides the localization of damage through the modal parameters for a non-rotating disc. Both of the experimental diagnostic algorithms are based on measurement of the dynamic behavior of the damaged disc. The results are compared with the reference, or baseline, one, obtained initially for an undamaged disc. (Abstract shortened by UMI.)

Comparison of fMRI analysis methods for heterogeneous BOLD responses in block design studies

PubMed Central

Bernal-Casas, David; Fang, Zhongnan; Lee, Jin Hyung

2017-01-01

A large number of fMRI studies have shown that the temporal dynamics of evoked BOLD responses can be highly heterogeneous. Failing to model heterogeneous responses in statistical analysis can lead to significant errors in signal detection and characterization and alter the neurobiological interpretation. However, to date it is not clear that, out of a large number of options, which methods are robust against variability in the temporal dynamics of BOLD responses in block-design studies. Here, we used rodent optogenetic fMRI data with heterogeneous BOLD responses and simulations guided by experimental data as a means to investigate different analysis methods’ performance against heterogeneous BOLD responses. Evaluations are carried out within the general linear model (GLM) framework and consist of standard basis sets as well as independent component analysis (ICA). Analyses show that, in the presence of heterogeneous BOLD responses, conventionally used GLM with a canonical basis set leads to considerable errors in the detection and characterization of BOLD responses. Our results suggest that the 3rd and 4th order gamma basis sets, the 7th to 9th order finite impulse response (FIR) basis sets, the 5th to 9th order B-spline basis sets, and the 2nd to 5th order Fourier basis sets are optimal for good balance between detection and characterization, while the 1st order Fourier basis set (coherence analysis) used in our earlier studies show good detection capability. ICA has mostly good detection and characterization capabilities, but detects a large volume of spurious activation with the control fMRI data. PMID:27993672

Applicability of computer-aided comprehensive tool (LINDA: LINeament Detection and Analysis) and shaded digital elevation model for characterizing and interpreting morphotectonic features from lineaments

NASA Astrophysics Data System (ADS)

Masoud, Alaa; Koike, Katsuaki

2017-09-01

Detection and analysis of linear features related to surface and subsurface structures have been deemed necessary in natural resource exploration and earth surface instability assessment. Subjectivity in choosing control parameters required in conventional methods of lineament detection may cause unreliable results. To reduce this ambiguity, we developed LINDA (LINeament Detection and Analysis), an integrated tool with graphical user interface in Visual Basic. This tool automates processes of detection and analysis of linear features from grid data of topography (digital elevation model; DEM), gravity and magnetic surfaces, as well as data from remote sensing imagery. A simple interface with five display windows forms a user-friendly interactive environment. The interface facilitates grid data shading, detection and grouping of segments, lineament analyses for calculating strike and dip and estimating fault type, and interactive viewing of lineament geometry. Density maps of the center and intersection points of linear features (segments and lineaments) are also included. A systematic analysis of test DEMs and Landsat 7 ETM+ imagery datasets in the North and South Eastern Deserts of Egypt is implemented to demonstrate the capability of LINDA and correct use of its functions. Linear features from the DEM are superior to those from the imagery in terms of frequency, but both linear features agree with location and direction of V-shaped valleys and dykes and reference fault data. Through the case studies, LINDA applicability is demonstrated to highlight dominant structural trends, which can aid understanding of geodynamic frameworks in any region.

Analysis of the 3’ untranslated regions of α-tubulin and S-crystallin mRNA and the identification of CPEB in dark- and light-adapted octopus retinas

PubMed Central

Kelly, Shannan; Yamamoto, Hideki

2008-01-01

Purpose We previously reported the differential expression and translation of mRNA and protein in dark- and light-adapted octopus retinas, which may result from cytoplasmic polyadenylation element (CPE)–dependent mRNA masking and unmasking. Here we investigate the presence of CPEs in α-tubulin and S-crystallin mRNA and report the identification of cytoplasmic polyadenylation element binding protein (CPEB) in light- and dark-adapted octopus retinas. Methods 3’-RACE and sequencing were used to isolate and analyze the 3’-UTRs of α-tubulin and S-crystallin mRNA. Total retinal protein isolated from light- and dark-adapted octopus retinas was subjected to western blot analysis followed by CPEB antibody detection, PEP-171 inhibition of CPEB, and dephosphorylation of CPEB. Results The following CPE-like sequence was detected in the 3’-UTR of isolated long S-crystallin mRNA variants: UUUAACA. No CPE or CPE-like sequences were detected in the 3’-UTRs of α-tubulin mRNA or of the short S-crystallin mRNA variants. Western blot analysis detected CPEB as two putative bands migrating between 60-80 kDa, while a third band migrated below 30 kDa in dark- and light-adapted retinas. Conclusions The detection of CPEB and the identification of the putative CPE-like sequences in the S-crystallin 3’-UTR suggest that CPEB may be involved in the activation of masked S-crystallin mRNA, but not in the regulation of α-tubulin mRNA, resulting in increased S-crystallin protein synthesis in dark-adapted octopus retinas. PMID:18682811

Fluorescence spectroscopy for rapid detection and classification of bacterial pathogens.

PubMed

Sohn, Miryeong; Himmelsbach, David S; Barton, Franklin E; Fedorka-Cray, Paula J

2009-11-01

This study deals with the rapid detection and differentiation of Escherichia coli, Salmonella, and Campylobacter, which are the most commonly identified commensal and pathogenic bacteria in foods, using fluorescence spectroscopy and multivariate analysis. Each bacterial sample cultured under controlled conditions was diluted in physiologic saline for analysis. Fluorescence spectra were collected over a range of 200-700 nm with 0.5 nm intervals on the PerkinElmer Fluorescence Spectrometer. The synchronous scan technique was employed to find the optimum excitation (lambda(ex)) and emission (lambda(em)) wavelengths for individual bacteria with the wavelength interval (Deltalambda) being varied from 10 to 200 nm. The synchronous spectra and two-dimensional plots showed two maximum lambda(ex) values at 225 nm and 280 nm and one maximum lambda(em) at 335-345 nm (lambda(em) = lambda(ex) + Deltalambda), which correspond to the lambda(ex) = 225 nm, Deltalambda = 110-120 nm, and lambda(ex) = 280 nm, Deltalambda = 60-65 nm. For all three bacterial genera, the same synchronous scan results were obtained. The emission spectra from the three bacteria groups were very similar, creating difficulty in classification. However, the application of principal component analysis (PCA) to the fluorescence spectra resulted in successful classification of the bacteria by their genus as well as determining their concentration. The detection limit was approximately 10(3)-10(4) cells/mL for each bacterial sample. These results demonstrated that fluorescence spectroscopy, when coupled with PCA processing, has the potential to detect and to classify bacterial pathogens in liquids. The methodology is rapid (>10 min), inexpensive, and requires minimal sample preparation compared to standard analytical methods for bacterial detection.

Microarray-based comparative genomic hybridization analysis in neonates with congenital anomalies: detection of chromosomal imbalances.

PubMed

Emy Dorfman, Luiza; Leite, Júlio César L; Giugliani, Roberto; Riegel, Mariluce

2015-01-01

To identify chromosomal imbalances by whole-genome microarray-based comparative genomic hybridization (array-CGH) in DNA samples of neonates with congenital anomalies of unknown cause from a birth defects monitoring program at a public maternity hospital. A blind genomic analysis was performed retrospectively in 35 stored DNA samples of neonates born between July of 2011 and December of 2012. All potential DNA copy number variations detected (CNVs) were matched with those reported in public genomic databases, and their clinical significance was evaluated. Out of a total of 35 samples tested, 13 genomic imbalances were detected in 12/35 cases (34.3%). In 4/35 cases (11.4%), chromosomal imbalances could be defined as pathogenic; in 5/35 (14.3%) cases, DNA CNVs of uncertain clinical significance were identified; and in 4/35 cases (11.4%), normal variants were detected. Among the four cases with results considered causally related to the clinical findings, two of the four (50%) showed causative alterations already associated with well-defined microdeletion syndromes. In two of the four samples (50%), the chromosomal imbalances found, although predicted as pathogenic, had not been previously associated with recognized clinical entities. Array-CGH analysis allowed for a higher rate of detection of chromosomal anomalies, and this determination is especially valuable in neonates with congenital anomalies of unknown etiology, or in cases in which karyotype results cannot be obtained. Moreover, although the interpretation of the results must be refined, this method is a robust and precise tool that can be used in the first-line investigation of congenital anomalies, and should be considered for prospective/retrospective analyses of DNA samples by birth defect monitoring programs. Copyright © 2014 Sociedade Brasileira de Pediatria. Published by Elsevier Editora Ltda. All rights reserved.

Metal oxide nanoparticles for latent fingerprint visualization and analysis of small drug molecules using surface-assisted laser desorption/ionization mass spectrometry.

PubMed

Amin, Mohamed O; Madkour, Metwally; Al-Hetlani, Entesar

2018-05-17

We explored the applicability of different metal oxide nanoparticles (NPs; ZnO, TiO 2 , Fe 2 O 3 , and CeO 2 ) for the optical imaging and mass spectrometric determination of small drug molecules in latent fingerprints (LFPs). Optical imaging was achieved using a dry method-simply dusting the LFPs with a minute amount of NP powder-and still images were captured using a digital microscope and a smartphone camera. Mass spectrometric determination was performed using the NPs as substrates for surface-assisted laser desorption ionization/mass spectrometry (SALDI-MS), which enabled the detection of small drug molecules with high signal intensities. The reproducibility of the results was studied by calculating the % error, SD, and RSD in the results obtained with the various metal oxide NPs. Collectively, the findings showed that using NPs can boost the intensity of the detected signal while minimizing background noise which is an issue predominantly associated with conventional organic matrices of MALDI-MS. Among the four metal oxide NPs, utilization of the Fe 2 O 3 NPs led to the best SALDI performance and the highest detection sensitivity for the analytes of interest. The study was then extended by investigating the influence of time elapsed since the generation of the LFP on the detection of drug molecules in the LFP. The results demonstrated that this method allows the analysis of drug molecules after as long as one week at low and intermediate temperatures (0 and 25 °C). Therefore, the SALDI analysis of small molecules using inorganic NPs, which can be implemented in forensic laboratories for screening and detection purposes, as a powerful alternative to the use of organic matrices. Graphical abstract ᅟ.

The detection of earth orbiting objects by IRAS

NASA Technical Reports Server (NTRS)

Dow, Kimberly L.; Sykes, Mark V.; Low, Frank J.; Vilas, Faith

1990-01-01

A systematic examination of 1836 images of the sky constructed from scans made by the Infrared Astronomical Satellite has resulted in the detection of 466 objects which are shown to be in earth orbit. Analysis of the spatial and size distribution and thermal properties of these objets, which may include payloads, rocket bodies and debris particles, is being conducted as one step in a feasibility study for space-based debris detection technologies.

Detection and Analysis of the Quality of Ibuprofen Granules

NASA Astrophysics Data System (ADS)

Yu-bin, Ji; Xin, LI; Guo-song, Xin; Qin-bing, Xue

2017-12-01

The Ibuprofen Granules comprehensive quality testing to ensure that it is in accordance with the provisions of Chinese pharmacopoeia. With reference of Chinese pharmacopoeia, the Ibuprofen Granules is tested by UV, HPLC, in terms of grain size checking, volume deviation, weight loss on drying detection, dissolution rate detection, and quality evaluation. Results indicated that Ibuprofen Granules conform to the standards. The Ibuprofen Granules are qualified and should be permitted to be marketed.

Simultaneous detection of landmarks and key-frame in cardiac perfusion MRI using a joint spatial-temporal context model

NASA Astrophysics Data System (ADS)

Lu, Xiaoguang; Xue, Hui; Jolly, Marie-Pierre; Guetter, Christoph; Kellman, Peter; Hsu, Li-Yueh; Arai, Andrew; Zuehlsdorff, Sven; Littmann, Arne; Georgescu, Bogdan; Guehring, Jens

2011-03-01

Cardiac perfusion magnetic resonance imaging (MRI) has proven clinical significance in diagnosis of heart diseases. However, analysis of perfusion data is time-consuming, where automatic detection of anatomic landmarks and key-frames from perfusion MR sequences is helpful for anchoring structures and functional analysis of the heart, leading toward fully automated perfusion analysis. Learning-based object detection methods have demonstrated their capabilities to handle large variations of the object by exploring a local region, i.e., context. Conventional 2D approaches take into account spatial context only. Temporal signals in perfusion data present a strong cue for anchoring. We propose a joint context model to encode both spatial and temporal evidence. In addition, our spatial context is constructed not only based on the landmark of interest, but also the landmarks that are correlated in the neighboring anatomies. A discriminative model is learned through a probabilistic boosting tree. A marginal space learning strategy is applied to efficiently learn and search in a high dimensional parameter space. A fully automatic system is developed to simultaneously detect anatomic landmarks and key frames in both RV and LV from perfusion sequences. The proposed approach was evaluated on a database of 373 cardiac perfusion MRI sequences from 77 patients. Experimental results of a 4-fold cross validation show superior landmark detection accuracies of the proposed joint spatial-temporal approach to the 2D approach that is based on spatial context only. The key-frame identification results are promising.

Feasibility of clinical detection of cervical dysplasia using angle-resolved low coherence interferometry measurements of depth-resolved nuclear morphology.

PubMed

Ho, Derek; Drake, Tyler K; Smith-McCune, Karen K; Darragh, Teresa M; Hwang, Loris Y; Wax, Adam

2017-03-15

This study sought to establish the feasibility of using in situ depth-resolved nuclear morphology measurements for detection of cervical dysplasia. Forty enrolled patients received routine cervical colposcopy with angle-resolved low coherence interferometry (a/LCI) measurements of nuclear morphology. a/LCI scans from 63 tissue sites were compared to histopathological analysis of co-registered biopsy specimens which were classified as benign, low-grade squamous intraepithelial lesion (LSIL), or high-grade squamous intraepithelial lesion (HSIL). Results were dichotomized as dysplastic (LSIL/HSIL) versus non-dysplastic and HSIL versus LSIL/benign to determine both accuracy and potential clinical utility of a/LCI nuclear morphology measurements. Analysis of a/LCI data was conducted using both traditional Mie theory based processing and a new hybrid algorithm that provides improved processing speed to ascertain the feasibility of real-time measurements. Analysis of depth-resolved nuclear morphology data revealed a/LCI was able to detect a significant increase in the nuclear diameter at the depth bin containing the basal layer of the epithelium for dysplastic versus non-dysplastic and HSIL versus LSIL/Benign biopsy sites (both p < 0.001). Both processing techniques resulted in high sensitivity and specificity (>0.80) in identifying dysplastic biopsies and HSIL. The hybrid algorithm demonstrated a threefold decrease in processing time at a slight cost in classification accuracy. The results demonstrate the feasibility of using a/LCI as an adjunctive clinical tool for detecting cervical dysplasia and guiding the identification of optimal biopsy sites. The faster speed from the hybrid algorithm offers a promising approach for real-time clinical analysis. © 2016 UICC.

Feasibility of clinical detection of cervical dysplasia using angle-resolved low coherence interferometry measurements of depth-resolved nuclear morphology

PubMed Central

Ho, Derek; Drake, Tyler K.; Smith-McCune, Karen K.; Darragh, Teresa M.; Hwang, Loris Y.; Wax, Adam

2017-01-01

This study sought to establish the feasibility of using in situ depth-resolved nuclear morphology measurements for detection of cervical dysplasia. Forty (40) enrolled patients received routine cervical colposcopy with angle-resolved low coherence interferometry (a/LCI) measurements of nuclear morphology. a/LCI scans from 63 tissue sites were compared to histopathological analysis of co-registered biopsy specimens which were classified as benign, low-grade squamous intraepithelial lesion (LSIL), or high-grade squamous intraepithelial lesion (HSIL). Results were dichotomized as dysplastic (LSIL/HSIL) versus non-dysplastic and HSIL versus LSIL/benign to determine both accuracy and potential clinical utility of a/LCI nuclear morphology measurements. Analysis of a/LCI data was conducted using both traditional Mie theory based processing and a new hybrid algorithm that provides improved processing speed to ascertain the feasibility of real-time measurements. Analysis of depth-resolved nuclear morphology data revealed a/LCI was able to detect a significant increase in the nuclear diameter at the depth bin containing the basal layer of the epithelium for dysplastic versus non-dysplastic and HSIL versus LSIL/Benign biopsy sites (both p < 0.001). Both processing techniques resulted in high sensitivity and specificity (> 0.80) in identifying dysplastic biopsies and HSIL. The hybrid algorithm demonstrated a threefold decrease in processing time at a slight cost in classification accuracy. The results demonstrate the feasibility of using a/LCI as an adjunctive clinical tool for detecting cervical dysplasia and guiding the identification of optimal biopsy sites. The faster speed from the hybrid algorithm offers a promising approach for real-time clinical analysis. PMID:27883177

Differentiation of live and dead salmonella cells using fourier transform infrared (FTIR) spectroscopy and principle component analysis (PCA) technique

USDA-ARS?s Scientific Manuscript database

Various technologies have been developed for pathogen detection using optical, electrochemical, biochemical and physical properties. Conventional microbiological methods need time from days to week to get the result. Though this method is very sensitive and accurate, a rapid detection of pathogens i...

75 FR 15357 - Airworthiness Directives; The Boeing Company Model 767 Airplanes

Federal Register 2010, 2011, 2012, 2013, 2014

2010-03-29

... airplanes. This proposed AD would require repetitive inspections to detect fatigue cracking in the upper... this AD to detect and correct fatigue cracking in the upper wing skin at the fastener holes common to... October 28, 1999. Further Boeing analysis has determined the cracks to be a result of fatigue due to...

Radiometric Short-Term Fourier Transform analysis of photonic Doppler velocimetry recordings and detectivity limit

NASA Astrophysics Data System (ADS)

Prudhomme, G.; Berthe, L.; Bénier, J.; Bozier, O.; Mercier, P.

2017-01-01

Photonic Doppler Velocimetry is a plug-and-play and versatile diagnostic used in dynamic physic experiments to measure velocities. When signals are analyzed using a Short-Time Fourier Transform, multiple velocities can be distinguished: for example, the velocities of moving particle-cloud appear on spectrograms. In order to estimate the back-scattering fluxes of target, we propose an original approach "PDV Radiometric analysis" resulting in an expression of time-velocity spectrograms coded in power units. Experiments involving micron-sized particles raise the issue of detection limit; particle-size limit is very difficult to evaluate. From the quantification of noise sources, we derive an estimation of the spectrogram noise leading to a detectivity limit, which may be compared to the fraction of the incoming power which has been back-scattered by the particle and then collected by the probe. This fraction increases with their size. At last, some results from laser-shock accelerated particles using two different PDV systems are compared: it shows the improvement of detectivity with respect to the Effective Number of Bits (ENOB) of the digitizer.

Detection of heat wave using Kalpana-1 VHRR land surface temperature product over India

NASA Astrophysics Data System (ADS)

Shah, Dhiraj; Pandya, Mehul R.; Pathak, Vishal N.; Darji, Nikunj P.; Trivedi, Himanshu J.

2016-05-01

Heat Waves can have notable impacts on human mortality, ecosystem, economics and energy supply. The effect of heat wave is much more intense during summer than the other seasons. During the period of April to June, spells of very hot weather occur over certain regions of India and global warming scenario may result in further increases of such temperature anomalies and corresponding heat waves conditions. In this paper, satellite observations have been used to detect the heat wave conditions prevailing over India for the period of May-June 2015. The Kalpana-1 VHRR derived land surface temperature (LST) products have been used in the analysis to detect the heat wave affected regions over India. Results from the analysis shows the detection of heat wave affected pixels over Indian land mass. It can be seen that during the study period the parts of the west India, Indo-gangetic plane, Telangana and part of Vidarbh was under severe heat wave conditions which is also confirmed with Automatic Weather Station (AWS) air temperature observations.

Field-Effect Biosensors for On-Site Detection: Recent Advances and Promising Targets.

PubMed

Choi, Jaebin; Seong, Tae Wha; Jeun, Minhong; Lee, Kwan Hyi

2017-10-01

There is an explosive interest in the immediate and cost-effective analysis of field-collected biological samples, as many advanced biodetection tools are highly sensitive, yet immobile. On-site biosensors are portable and convenient sensors that provide detection results at the point of care. They are designed to secure precision in highly ionic and heterogeneous solutions with minimal hardware. Among various methods that are capable of such analysis, field-effect biosensors are promising candidates due to their unique sensitivity, manufacturing scalability, and integrability with computational circuitry. Recent developments in nanotechnological surface modification show promising results in sensing from blood, serum, and urine. This report gives a particular emphasis on the on-site efficacy of recently published field-effect biosensors, specifically, detection limits in physiological solutions, response times, and scalability. The survey of the properties and existing detection methods of four promising biotargets, exosomes, bacteria, viruses, and metabolites, aims at providing a roadmap for future field-effect and other on-site biosensors. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Highly sensitive and selective dynamic light-scattering assay for TNT detection using p-ATP attached gold nanoparticle.

PubMed

Dasary, Samuel S R; Senapati, Dulal; Singh, Anant Kumar; Anjaneyulu, Yerramilli; Yu, Hongtao; Ray, Paresh Chandra

2010-12-01

TNT is one of the most commonly used nitro aromatic explosives for landmines of military and terrorist activities. As a result, there is an urgent need for rapid and reliable methods for the detection of trace amount of TNT for screenings in airport, analysis of forensic samples, and environmental analysis. Driven by the need to detect trace amounts of TNT from environmental samples, this article demonstrates a label-free, highly selective, and ultrasensitive para-aminothiophenol (p-ATP) modified gold nanoparticle based dynamic light scattering (DLS) probe for TNT recognition in 100 pico molar (pM) level from ethanol:acetonitile mixture solution. Because of the formation of strong π-donor-acceptor interaction between TNT and p-ATP, para-aminothiophenol attached gold nanoparticles undergo aggregation in the presence of TNT, which changes the DLS intensity tremendously. A detailed mechanism for significant DLS intensity change has been discussed. Our experimental results show that TNT can be detected quickly and accurately without any dye tagging in 100 pM level with excellent discrimination against other nitro compounds.

An interferometric imaging biosensor using weighted spectrum analysis to confirm DNA monolayer films with attogram sensitivity.

PubMed

Fu, Rongxin; Li, Qi; Wang, Ruliang; Xue, Ning; Lin, Xue; Su, Ya; Jiang, Kai; Jin, Xiangyu; Lin, Rongzan; Gan, Wupeng; Lu, Ying; Huang, Guoliang

2018-05-01

Interferometric imaging biosensors are powerful and convenient tools for confirming the existence of DNA monolayer films on silicon microarray platforms. However, their accuracy and sensitivity need further improvement because DNA molecules contribute to an inconspicuous interferometric signal both in thickness and size. Such weaknesses result in poor performance of these biosensors for low DNA content analyses and point mutation tests. In this paper, an interferometric imaging biosensor with weighted spectrum analysis is presented to confirm DNA monolayer films. The interferometric signal of DNA molecules can be extracted and then quantitative detection results for DNA microarrays can be reconstructed. With the proposed strategy, the relative error of thickness detection was reduced from 88.94% to merely 4.15%. The mass sensitivity per unit area of the proposed biosensor reached 20 attograms (ag). Therefore, the sample consumption per unit area of the target DNA content was only 62.5 zeptomoles (zm), with the volume of 0.25 picolitres (pL). Compared with the fluorescence resonance energy transfer (FRET), the measurement veracity of the interferometric imaging biosensor with weighted spectrum analysis is free to the changes in spotting concentration and DNA length. The detection range was more than 1µm. Moreover, single nucleotide mismatch could be pointed out combined with specific DNA ligation. A mutation experiment for lung cancer detection proved the high selectivity and accurate analysis capability of the presented biosensor. Copyright © 2017 Elsevier B.V. All rights reserved.

Detection of Lung Cancer by Sensor Array Analyses of Exhaled Breath

PubMed Central

Machado, Roberto F.; Laskowski, Daniel; Deffenderfer, Olivia; Burch, Timothy; Zheng, Shuo; Mazzone, Peter J.; Mekhail, Tarek; Jennings, Constance; Stoller, James K.; Pyle, Jacqueline; Duncan, Jennifer; Dweik, Raed A.; Erzurum, Serpil C.

2005-01-01

Rationale: Electronic noses are successfully used in commercial applications, including detection and analysis of volatile organic compounds in the food industry. Objectives: We hypothesized that the electronic nose could identify and discriminate between lung diseases, especially bronchogenic carcinoma. Methods: In a discovery and training phase, exhaled breath of 14 individuals with bronchogenic carcinoma and 45 healthy control subjects or control subjects without cancer was analyzed. Principal components and canonic discriminant analysis of the sensor data was used to determine whether exhaled gases could discriminate between cancer and noncancer. Discrimination between classes was performed using Mahalanobis distance. Support vector machine analysis was used to create and apply a cancer prediction model prospectively in a separate group of 76 individuals, 14 with and 62 without cancer. Main Results: Principal components and canonic discriminant analysis demonstrated discrimination between samples from patients with lung cancer and those from other groups. In the validation study, the electronic nose had 71.4% sensitivity and 91.9% specificity for detecting lung cancer; positive and negative predictive values were 66.6 and 93.4%, respectively. In this population with a lung cancer prevalence of 18%, positive and negative predictive values were 66.6 and 94.5%, respectively. Conclusion: The exhaled breath of patients with lung cancer has distinct characteristics that can be identified with an electronic nose. The results provide feasibility to the concept of using the electronic nose for managing and detecting lung cancer. PMID:15750044

Analysis of anabolic steroids in human hair using LC-MS/MS.

PubMed

Deshmukh, Nawed; Hussain, Iltaf; Barker, James; Petroczi, Andrea; Naughton, Declan P

2010-10-01

New highly sensitive, specific, reliable, reproducible and robust LC-MS/MS methods were developed to detect the anabolic steroids, nandrolone and stanozolol, in human hair for the first time. Hair samples from 180 participants (108 males, 72 females, 62% athletes) were screened using ELISA which revealed 16 athletes as positive for stanozolol and 3 for nandrolone. Positive samples were confirmed on LC-MS/MS in selective reaction monitoring (SRM) mode. The assays for stanozolol and nandrolone showed good linearity in the range 1-400pg/mg and 5-400pg/mg, respectively. The methods were validated for LLOD, interday precision, intraday precision, specificity, extraction recovery and accuracy. The assays were capable of detecting 0.5pg stanozolol and 3.0pg nandrolone per mg of hair, when approximately 20mg of hair were processed. Analysis using LC-MS/MS confirmed 11 athletes' positive for stanozolol (5.0pg/mg to 86.3pg/mg) and 1 for nandrolone (14.0pg/mg) thus avoiding false results from ELISA screening. The results obtained demonstrate the application of these hair analysis methods to detect both steroids at low concentrations, hence reducing the amount of hair required significantly. The new methods complement urinalysis or blood testing and facilitate improved doping testing regimes. Hair analysis benefits from non-invasiveness, negligible risk of infection and facile sample storage and collection, whilst reducing risks of tampering and cross-contamination. Owing to the wide detection window, this approach may also offer an alternative approach for out-of-competition testing.

Classification of the medicinal plants of the genus Atractylodes using high-performance liquid chromatography with diode array and tandem mass spectrometry detection combined with multivariate statistical analysis.

PubMed

Cho, Hyun-Deok; Kim, Unyong; Suh, Joon Hyuk; Eom, Han Young; Kim, Junghyun; Lee, Seul Gi; Choi, Yong Seok; Han, Sang Beom

2016-04-01

Analytical methods using high-performance liquid chromatography with diode array and tandem mass spectrometry detection were developed for the discrimination of the rhizomes of four Atractylodes medicinal plants: A. japonica, A. macrocephala, A. chinensis, and A. lancea. A quantitative study was performed, selecting five bioactive components, including atractylenolide I, II, III, eudesma-4(14),7(11)-dien-8-one and atractylodin, on twenty-six Atractylodes samples of various origins. Sample extraction was optimized to sonication with 80% methanol for 40 min at room temperature. High-performance liquid chromatography with diode array detection was established using a C18 column with a water/acetonitrile gradient system at a flow rate of 1.0 mL/min, and the detection wavelength was set at 236 nm. Liquid chromatography with tandem mass spectrometry was applied to certify the reliability of the quantitative results. The developed methods were validated by ensuring specificity, linearity, limit of quantification, accuracy, precision, recovery, robustness, and stability. Results showed that cangzhu contained higher amounts of atractylenolide I and atractylodin than baizhu, and especially atractylodin contents showed the greatest variation between baizhu and cangzhu. Multivariate statistical analysis, such as principal component analysis and hierarchical cluster analysis, were also employed for further classification of the Atractylodes plants. The established method was suitable for quality control of the Atractylodes plants. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Fusion of an Ensemble of Augmented Image Detectors for Robust Object Detection

PubMed Central

Wei, Pan; Anderson, Derek T.

2018-01-01

A significant challenge in object detection is accurate identification of an object’s position in image space, whereas one algorithm with one set of parameters is usually not enough, and the fusion of multiple algorithms and/or parameters can lead to more robust results. Herein, a new computational intelligence fusion approach based on the dynamic analysis of agreement among object detection outputs is proposed. Furthermore, we propose an online versus just in training image augmentation strategy. Experiments comparing the results both with and without fusion are presented. We demonstrate that the augmented and fused combination results are the best, with respect to higher accuracy rates and reduction of outlier influences. The approach is demonstrated in the context of cone, pedestrian and box detection for Advanced Driver Assistance Systems (ADAS) applications. PMID:29562609

Detection of anabolic androgenic steroid abuse in doping control using mammalian reporter gene bioassays.

PubMed

Houtman, Corine J; Sterk, Saskia S; van de Heijning, Monique P M; Brouwer, Abraham; Stephany, Rainer W; van der Burg, Bart; Sonneveld, Edwin

2009-04-01

Anabolic androgenic steroids (AAS) are a class of steroid hormones related to the male hormone testosterone. They are frequently detected as drugs in sport doping control. Being similar to or derived from natural male hormones, AAS share the activation of the androgen receptor (AR) as common mechanism of action. The mammalian androgen responsive reporter gene assay (AR CALUX bioassay), measuring compounds interacting with the AR can be used for the analysis of AAS without the necessity of knowing their chemical structure beforehand, whereas current chemical-analytical approaches may have difficulty in detecting compounds with unknown structures, such as designer steroids. This study demonstrated that AAS prohibited in sports and potential designer AAS can be detected with this AR reporter gene assay, but that also additional steroid activities of AAS could be found using additional mammalian bioassays for other types of steroid hormones. Mixtures of AAS were found to behave additively in the AR reporter gene assay showing that it is possible to use this method for complex mixtures as are found in doping control samples, including mixtures that are a result of multi drug use. To test if mammalian reporter gene assays could be used for the detection of AAS in urine samples, background steroidal activities were measured. AAS-spiked urine samples, mimicking doping positive samples, showed significantly higher androgenic activities than unspiked samples. GC-MS analysis of endogenous androgens and AR reporter gene assay analysis of urine samples showed how a combined chemical-analytical and bioassay approach can be used to identify samples containing AAS. The results indicate that the AR reporter gene assay, in addition to chemical-analytical methods, can be a valuable tool for the analysis of AAS for doping control purposes.

Separate modal analysis for tumor detection with a digital image elasto tomography (DIET) breast cancer screening system.

PubMed

Kashif, Amer S; Lotz, Thomas F; Heeren, Adrianus M W; Chase, James G

2013-11-01

It is estimated that every year, 1 × 10(6) women are diagnosed with breast cancer, and more than 410,000 die annually worldwide. Digital Image Elasto Tomography (DIET) is a new noninvasive breast cancer screening modality that induces mechanical vibrations in the breast and images its surface motion with digital cameras to detect changes in stiffness. This research develops a new automated approach for diagnosing breast cancer using DIET based on a modal analysis model. The first and second natural frequency of silicone phantom breasts is analyzed. Separate modal analysis is performed for each region of the phantom to estimate the modal parameters using imaged motion data over several input frequencies. Statistical methods are used to assess the likelihood of a frequency shift, which can indicate tumor location. Phantoms with 5, 10, and 20 mm stiff inclusions are tested, as well as a homogeneous (healthy) phantom. Inclusions are located at four locations with different depth. The second natural frequency proves to be a reliable metric with the potential to clearly distinguish lesion like inclusions of different stiffness, as well as providing an approximate location for the tumor like inclusions. The 10 and 20 mm inclusions are always detected regardless of depth. The 5 mm inclusions are only detected near the surface. The homogeneous phantom always yields a negative result, as expected. Detection is based on a statistical likelihood analysis to determine the presence of significantly different frequency response over the phantom, which is a novel approach to this problem. The overall results show promise and justify proof of concept trials with human subjects.

Noise-robust unsupervised spike sorting based on discriminative subspace learning with outlier handling

NASA Astrophysics Data System (ADS)

Keshtkaran, Mohammad Reza; Yang, Zhi

2017-06-01

Objective. Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. Most of the feature extraction and dimensionality reduction techniques that have been used for spike sorting give a projection subspace which is not necessarily the most discriminative one. Therefore, the clusters which appear inherently separable in some discriminative subspace may overlap if projected using conventional feature extraction approaches leading to a poor sorting accuracy especially when the noise level is high. In this paper, we propose a noise-robust and unsupervised spike sorting algorithm based on learning discriminative spike features for clustering. Approach. The proposed algorithm uses discriminative subspace learning to extract low dimensional and most discriminative features from the spike waveforms and perform clustering with automatic detection of the number of the clusters. The core part of the algorithm involves iterative subspace selection using linear discriminant analysis and clustering using Gaussian mixture model with outlier detection. A statistical test in the discriminative subspace is proposed to automatically detect the number of the clusters. Main results. Comparative results on publicly available simulated and real in vivo datasets demonstrate that our algorithm achieves substantially improved cluster distinction leading to higher sorting accuracy and more reliable detection of clusters which are highly overlapping and not detectable using conventional feature extraction techniques such as principal component analysis or wavelets. Significance. By providing more accurate information about the activity of more number of individual neurons with high robustness to neural noise and outliers, the proposed unsupervised spike sorting algorithm facilitates more detailed and accurate analysis of single- and multi-unit activities in neuroscience and brain machine interface studies.

Extraction of the number of peroxisomes in yeast cells by automated image analysis.

PubMed

Niemistö, Antti; Selinummi, Jyrki; Saleem, Ramsey; Shmulevich, Ilya; Aitchison, John; Yli-Harja, Olli

2006-01-01

An automated image analysis method for extracting the number of peroxisomes in yeast cells is presented. Two images of the cell population are required for the method: a bright field microscope image from which the yeast cells are detected and the respective fluorescent image from which the number of peroxisomes in each cell is found. The segmentation of the cells is based on clustering the local mean-variance space. The watershed transformation is thereafter employed to separate cells that are clustered together. The peroxisomes are detected by thresholding the fluorescent image. The method is tested with several images of a budding yeast Saccharomyces cerevisiae population, and the results are compared with manually obtained results.

Zipf's Law Application To Oil Spill Detection In The Ocean

NASA Astrophysics Data System (ADS)

Platonov, A.; Redondo, J. M.

One of the results of the CLEAN SEAS European Union project using SAR imaging of European Coastal Waters was the statistical analysis and detection of thousands of oil spills and slicks in the three compared sections, Baltic Sea, North Sea and N.W. Mediterranean. The results of another European Project, OIL WATCH together with the past 30 years of recorded mayor tanker accidental oil spills have been used in a predictive scheme that subject to spatial and temporal normalization of these two different scale processes clearly shows that the annual probability of the occurence of an oil spill follows Zipf's law. Local deviations from the law may be also explained in terms of multifractal analysis.

On-chip wavelength multiplexed detection of cancer DNA biomarkers in blood

PubMed Central

Cai, H.; Stott, M. A.; Ozcelik, D.; Parks, J. W.; Hawkins, A. R.; Schmidt, H.

2016-01-01

We have developed an optofluidic analysis system that processes biomolecular samples starting from whole blood and then analyzes and identifies multiple targets on a silicon-based molecular detection platform. We demonstrate blood filtration, sample extraction, target enrichment, and fluorescent labeling using programmable microfluidic circuits. We detect and identify multiple targets using a spectral multiplexing technique based on wavelength-dependent multi-spot excitation on an antiresonant reflecting optical waveguide chip. Specifically, we extract two types of melanoma biomarkers, mutated cell-free nucleic acids —BRAFV600E and NRAS, from whole blood. We detect and identify these two targets simultaneously using the spectral multiplexing approach with up to a 96% success rate. These results point the way toward a full front-to-back chip-based optofluidic compact system for high-performance analysis of complex biological samples. PMID:28058082

Near-infrared high-resolution real-time omnidirectional imaging platform for drone detection

NASA Astrophysics Data System (ADS)

Popovic, Vladan; Ott, Beat; Wellig, Peter; Leblebici, Yusuf

2016-10-01

Recent technological advancements in hardware systems have made higher quality cameras. State of the art panoramic systems use them to produce videos with a resolution of 9000 x 2400 pixels at a rate of 30 frames per second (fps).1 Many modern applications use object tracking to determine the speed and the path taken by each object moving through a scene. The detection requires detailed pixel analysis between two frames. In fields like surveillance systems or crowd analysis, this must be achieved in real time.2 In this paper, we focus on the system-level design of multi-camera sensor acquiring near-infrared (NIR) spectrum and its ability to detect mini-UAVs in a representative rural Swiss environment. The presented results show the UAV detection from the trial that we conducted during a field trial in August 2015.

Deep Learning Nuclei Detection in Digitized Histology Images by Superpixels.

PubMed

Sornapudi, Sudhir; Stanley, Ronald Joe; Stoecker, William V; Almubarak, Haidar; Long, Rodney; Antani, Sameer; Thoma, George; Zuna, Rosemary; Frazier, Shelliane R

2018-01-01

Advances in image analysis and computational techniques have facilitated automatic detection of critical features in histopathology images. Detection of nuclei is critical for squamous epithelium cervical intraepithelial neoplasia (CIN) classification into normal, CIN1, CIN2, and CIN3 grades. In this study, a deep learning (DL)-based nuclei segmentation approach is investigated based on gathering localized information through the generation of superpixels using a simple linear iterative clustering algorithm and training with a convolutional neural network. The proposed approach was evaluated on a dataset of 133 digitized histology images and achieved an overall nuclei detection (object-based) accuracy of 95.97%, with demonstrated improvement over imaging-based and clustering-based benchmark techniques. The proposed DL-based nuclei segmentation Method with superpixel analysis has shown improved segmentation results in comparison to state-of-the-art methods.

Electrochemical and Infrared Absorption Spectroscopy Detection of SF6 Decomposition Products

PubMed Central

Dong, Ming; Ren, Ming; Ye, Rixin

2017-01-01

Sulfur hexafluoride (SF6) gas-insulated electrical equipment is widely used in high-voltage (HV) and extra-high-voltage (EHV) power systems. Partial discharge (PD) and local heating can occur in the electrical equipment because of insulation faults, which results in SF6 decomposition and ultimately generates several types of decomposition products. These SF6 decomposition products can be qualitatively and quantitatively detected with relevant detection methods, and such detection contributes to diagnosing the internal faults and evaluating the security risks of the equipment. At present, multiple detection methods exist for analyzing the SF6 decomposition products, and electrochemical sensing (ES) and infrared (IR) spectroscopy are well suited for application in online detection. In this study, the combination of ES with IR spectroscopy is used to detect SF6 gas decomposition. First, the characteristics of these two detection methods are studied, and the data analysis matrix is established. Then, a qualitative and quantitative analysis ES-IR model is established by adopting a two-step approach. A SF6 decomposition detector is designed and manufactured by combining an electrochemical sensor and IR spectroscopy technology. The detector is used to detect SF6 gas decomposition and is verified to reliably and accurately detect the gas components and concentrations. PMID:29140268

Performance Analysis of Iterative Channel Estimation and Multiuser Detection in Multipath DS-CDMA Channels

NASA Astrophysics Data System (ADS)

Li, Husheng; Betz, Sharon M.; Poor, H. Vincent

2007-05-01

This paper examines the performance of decision feedback based iterative channel estimation and multiuser detection in channel coded aperiodic DS-CDMA systems operating over multipath fading channels. First, explicit expressions describing the performance of channel estimation and parallel interference cancellation based multiuser detection are developed. These results are then combined to characterize the evolution of the performance of a system that iterates among channel estimation, multiuser detection and channel decoding. Sufficient conditions for convergence of this system to a unique fixed point are developed.

Biochemia Medica has started using the CrossCheck plagiarism detection software powered by iThenticate

PubMed Central

Šupak-Smolčić, Vesna; Šimundić, Ana-Maria

2013-01-01

In February 2013, Biochemia Medica has joined CrossRef, which enabled us to implement CrossCheck plagiarism detection service. Therefore, all manuscript submitted to Biochemia Medica are now first assigned to Research integrity editor (RIE), before sending the manuscript for peer-review. RIE submits the text to CrossCheck analysis and is responsible for reviewing the results of the text similarity analysis. Based on the CrossCheck analysis results, RIE subsequently provides a recommendation to the Editor-in-chief (EIC) on whether the manuscript should be forwarded to peer-review, corrected for suspected parts prior to peer-review or immediately rejected. Final decision on the manuscript is, however, with the EIC. We hope that our new policy and manuscript processing algorithm will help us to further increase the overall quality of our Journal. PMID:23894858

Lidar point density analysis: implications for identifying water bodies

USGS Publications Warehouse

Worstell, Bruce B.; Poppenga, Sandra K.; Evans, Gayla A.; Prince, Sandra

2014-01-01

Most airborne topographic light detection and ranging (lidar) systems operate within the near-infrared spectrum. Laser pulses from these systems frequently are absorbed by water and therefore do not generate reflected returns on water bodies in the resulting void regions within the lidar point cloud. Thus, an analysis of lidar voids has implications for identifying water bodies. Data analysis techniques to detect reduced lidar return densities were evaluated for test sites in Blackhawk County, Iowa, and Beltrami County, Minnesota, to delineate contiguous areas that have few or no lidar returns. Results from this study indicated a 5-meter radius moving window with fewer than 23 returns (28 percent of the moving window) was sufficient for delineating void regions. Techniques to provide elevation values for void regions to flatten water features and to force channel flow in the downstream direction also are presented.

Structural models used in real-time biosurveillance outbreak detection and outbreak curve isolation from noisy background morbidity levels

PubMed Central

Cheng, Karen Elizabeth; Crary, David J; Ray, Jaideep; Safta, Cosmin

2013-01-01

Objective We discuss the use of structural models for the analysis of biosurveillance related data. Methods and results Using a combination of real and simulated data, we have constructed a data set that represents a plausible time series resulting from surveillance of a large scale bioterrorist anthrax attack in Miami. We discuss the performance of anomaly detection with structural models for these data using receiver operating characteristic (ROC) and activity monitoring operating characteristic (AMOC) analysis. In addition, we show that these techniques provide a method for predicting the level of the outbreak valid for approximately 2 weeks, post-alarm. Conclusions Structural models provide an effective tool for the analysis of biosurveillance data, in particular for time series with noisy, non-stationary background and missing data. PMID:23037798

Detection of small-size solder ball defects through heat conduction analysis

NASA Astrophysics Data System (ADS)

Zhou, Xiuyun; Chen, Yaqiu; Lu, Xiaochuan

2018-02-01

Aiming to solve the defect detection problem of a small-size solder ball in the high density chip, heat conduction analysis based on eddy current pulsed thermography is put forward to differentiate various defects. With establishing the 3D finite element model about induction heating, defects such as cracks and void can be distinguished by temperature difference resulting from heat conduction. Furthermore, the experiment of 0.4 mm-diameter solder balls with different defects is carried out to prove that crack and void solder can be distinguished. Three kinds of crack length on a gull-wing pin are selected, including 0.24 mm, 1.2 mm, and 2.16 mm, to verify that the small defect can be discriminated. Both the simulation study and experiment result show that the heat conduction analysis method is reliable and convenient.

Double Aneuploidy Detected by Cell-Free DNA Testing and Confirmed by Fetal Tissue Analysis.

PubMed

Echague, Charlene G; Petersen, Scott M

2016-06-01

Double aneuploidies account for 0.21-2.8% of spontaneous abortions resulting from chromosomal abnormalities. Rarely, cell-free DNA testing detects multiple aneuploidies; however, to discern among maternal, placental, and fetal origin, further evaluation is required. A 49-year-old woman, gravida 5 para 0, underwent cell-free DNA testing at 11 4/7 weeks of gestation, which revealed a fetus that was high risk for trisomies 18 and 21. On ultrasonography at 14 weeks of gestation, she was diagnosed with a missed abortion and underwent surgical management. Fetal and placental tissues were sent for analysis and were positive for trisomies 18 and 21, confirming the results of cell-free DNA testing. Our case highlights the ability of cell-free DNA testing to recognize a double aneuploidy confirmed by fetal tissue analysis.

Usefulness of MLPA in the detection of SHOX deletions.

PubMed

Funari, Mariana F A; Jorge, Alexander A L; Souza, Silvia C A L; Billerbeck, Ana E C; Arnhold, Ivo J P; Mendonca, Berenice B; Nishi, Mirian Y

2010-01-01

SHOX haploinsufficiency causes a wide spectrum of short stature phenotypes, such as Leri-Weill dyschondrosteosis (LWD) and disproportionate short stature (DSS). SHOX deletions are responsible for approximately two thirds of isolated haploinsufficiency; therefore, it is important to determine the most appropriate methodology for detection of gene deletion. In this study, three methodologies for the detection of SHOX deletions were compared: the fluorescence in situ hybridization (FISH), microsatellite analysis and multiplex ligation-dependent probe amplification (MLPA). Forty-four patients (8 LWD and 36 DSS) were analyzed. The cosmid LLNOYCO3'M'34F5 was used as a probe for the FISH analysis and microsatellite analysis were performed using three intragenic microsatellite markers. MLPA was performed using commercial kits. Twelve patients (8 LWD and 4 DSS) had deletions in SHOX area detected by MLPA and 2 patients generated discordant results with the other methodologies. In the first case, the deletion was not detected by FISH. In the second case, both FISH and microsatellite analyses were unable to identify the intragenic deletion. In conclusion, MLPA was more sensitive, less expensive and less laborious; therefore, it should be used as the initial molecular method for the detection of SHOX gene deletion. Copyright © 2010 Elsevier Masson SAS. All rights reserved.

Research of the fluorescence detection apparatus for nutrients

NASA Astrophysics Data System (ADS)

Wang, Yu; Yan, Huimin; Ni, Xuxiang; Xu, Xiaoyi; Chen, Shibing

2015-10-01

The research of the multifunctional analyzer of Clinical Nutrition, which integrates the absorbance, luminescence, fluorescence and other optical detection methods, can overcome the functional limitations of a single technology on human nutrition analysis, and realize a rapid and accurate analysis of the nutrients. This article focuses on the design of fluorescence detection module that uses a photomultiplier tube(PMT) to detect weak fluorescence, and utilizes the single photon counting method to measure the fluorescence intensity, and then according to the relationship between the fluorescent marker and fluorescence intensity, the concentration of the analyte can be derived. Using fluorescein isothiocyanate(FITC, the most widely used fluorescein currently)to mark antibodies in the experiment, therefore, according to the maximum absorption wavelength and the maximum emission wavelength of the fluorescein isothiocyanate, to select the appropriate filters to set up the optical path. In addition, the fluorescence detection apparatus proposed in this paper uses an aspherical lens with large numerical aperture, in order to improve the capacity of signal acquisition more effectively, and the selective adoption of flexible optical fiber can realize a compact opto-mechanical structure, which is also conducive to the miniaturization of the device. The experimental results show that this apparatus has a high sensitivity, can be used for the detection and analysis of human nutrition.

CHARACTERIZATION OF THE COMPLETE FIBER NETWORK TOPOLOGY OF PLANAR FIBROUS TISSUES AND SCAFFOLDS

PubMed Central

D'Amore, Antonio; Stella, John A.; Wagner, William R.; Sacks, Michael S.

2010-01-01

Understanding how engineered tissue scaffold architecture affects cell morphology, metabolism, phenotypic expression, as well as predicting material mechanical behavior have recently received increased attention. In the present study, an image-based analysis approach that provides an automated tool to characterize engineered tissue fiber network topology is presented. Micro-architectural features that fully defined fiber network topology were detected and quantified, which include fiber orientation, connectivity, intersection spatial density, and diameter. Algorithm performance was tested using scanning electron microscopy (SEM) images of electrospun poly(ester urethane)urea (ES-PEUU) scaffolds. SEM images of rabbit mesenchymal stem cell (MSC) seeded collagen gel scaffolds and decellularized rat carotid arteries were also analyzed to further evaluate the ability of the algorithm to capture fiber network morphology regardless of scaffold type and the evaluated size scale. The image analysis procedure was validated qualitatively and quantitatively, comparing fiber network topology manually detected by human operators (n=5) with that automatically detected by the algorithm. Correlation values between manual detected and algorithm detected results for the fiber angle distribution and for the fiber connectivity distribution were 0.86 and 0.93 respectively. Algorithm detected fiber intersections and fiber diameter values were comparable (within the mean ± standard deviation) with those detected by human operators. This automated approach identifies and quantifies fiber network morphology as demonstrated for three relevant scaffold types and provides a means to: (1) guarantee objectivity, (2) significantly reduce analysis time, and (3) potentiate broader analysis of scaffold architecture effects on cell behavior and tissue development both in vitro and in vivo. PMID:20398930

High-resolution melting (HRM) re-analysis of a polyposis patients cohort reveals previously undetected heterozygous and mosaic APC gene mutations.

PubMed

Out, Astrid A; van Minderhout, Ivonne J H M; van der Stoep, Nienke; van Bommel, Lysette S R; Kluijt, Irma; Aalfs, Cora; Voorendt, Marsha; Vossen, Rolf H A M; Nielsen, Maartje; Vasen, Hans F A; Morreau, Hans; Devilee, Peter; Tops, Carli M J; Hes, Frederik J

2015-06-01

Familial adenomatous polyposis is most frequently caused by pathogenic variants in either the APC gene or the MUTYH gene. The detection rate of pathogenic variants depends on the severity of the phenotype and sensitivity of the screening method, including sensitivity for mosaic variants. For 171 patients with multiple colorectal polyps without previously detectable pathogenic variant, APC was reanalyzed in leukocyte DNA by one uniform technique: high-resolution melting (HRM) analysis. Serial dilution of heterozygous DNA resulted in a lowest detectable allelic fraction of 6% for the majority of variants. HRM analysis and subsequent sequencing detected pathogenic fully heterozygous APC variants in 10 (6%) of the patients and pathogenic mosaic variants in 2 (1%). All these variants were previously missed by various conventional scanning methods. In parallel, HRM APC scanning was applied to DNA isolated from polyp tissue of two additional patients with apparently sporadic polyposis and without detectable pathogenic APC variant in leukocyte DNA. In both patients a pathogenic mosaic APC variant was present in multiple polyps. The detection of pathogenic APC variants in 7% of the patients, including mosaics, illustrates the usefulness of a complete APC gene reanalysis of previously tested patients, by a supplementary scanning method. HRM is a sensitive and fast pre-screening method for reliable detection of heterozygous and mosaic variants, which can be applied to leukocyte and polyp derived DNA.

Lesion detection performance: comparative analysis of low-dose CT data of the chest on two hybrid imaging systems.

PubMed

Jessop, Maryam; Thompson, John D; Coward, Joanne; Sanderud, Audun; Jorge, José; de Groot, Martijn; Lança, Luís; Hogg, Peter

2015-03-01

Incidental findings on low-dose CT images obtained during hybrid imaging are an increasing phenomenon as CT technology advances. Understanding the diagnostic value of incidental findings along with the technical limitations is important when reporting image results and recommending follow-up, which may result in an additional radiation dose from further diagnostic imaging and an increase in patient anxiety. This study assessed lesions incidentally detected on CT images acquired for attenuation correction on two SPECT/CT systems. An anthropomorphic chest phantom containing simulated lesions of varying size and density was imaged on an Infinia Hawkeye 4 and a Symbia T6 using the low-dose CT settings applied for attenuation correction acquisitions in myocardial perfusion imaging. Twenty-two interpreters assessed 46 images from each SPECT/CT system (15 normal images and 31 abnormal images; 41 lesions). Data were evaluated using a jackknife alternative free-response receiver-operating-characteristic analysis (JAFROC). JAFROC analysis showed a significant difference (P < 0.0001) in lesion detection, with the figures of merit being 0.599 (95% confidence interval, 0.568, 0.631) and 0.810 (95% confidence interval, 0.781, 0.839) for the Infinia Hawkeye 4 and Symbia T6, respectively. Lesion detection on the Infinia Hawkeye 4 was generally limited to larger, higher-density lesions. The Symbia T6 allowed improved detection rates for midsized lesions and some lower-density lesions. However, interpreters struggled to detect small (5 mm) lesions on both image sets, irrespective of density. Lesion detection is more reliable on low-dose CT images from the Symbia T6 than from the Infinia Hawkeye 4. This phantom-based study gives an indication of potential lesion detection in the clinical context as shown by two commonly used SPECT/CT systems, which may assist the clinician in determining whether further diagnostic imaging is justified. © 2015 by the Society of Nuclear Medicine and Molecular Imaging, Inc.

Optimizing a neural network for detection of moving vehicles in video

NASA Astrophysics Data System (ADS)

Fischer, Noëlle M.; Kruithof, Maarten C.; Bouma, Henri

2017-10-01

In the field of security and defense, it is extremely important to reliably detect moving objects, such as cars, ships, drones and missiles. Detection and analysis of moving objects in cameras near borders could be helpful to reduce illicit trading, drug trafficking, irregular border crossing, trafficking in human beings and smuggling. Many recent benchmarks have shown that convolutional neural networks are performing well in the detection of objects in images. Most deep-learning research effort focuses on classification or detection on single images. However, the detection of dynamic changes (e.g., moving objects, actions and events) in streaming video is extremely relevant for surveillance and forensic applications. In this paper, we combine an end-to-end feedforward neural network for static detection with a recurrent Long Short-Term Memory (LSTM) network for multi-frame analysis. We present a practical guide with special attention to the selection of the optimizer and batch size. The end-to-end network is able to localize and recognize the vehicles in video from traffic cameras. We show an efficient way to collect relevant in-domain data for training with minimal manual labor. Our results show that the combination with LSTM improves performance for the detection of moving vehicles.

A method for evaluating the performance of computer-aided detection of pulmonary nodules in lung cancer CT screening: detection limit for nodule size and density

PubMed Central

Kobayashi, Hajime; Ohkubo, Masaki; Narita, Akihiro; Marasinghe, Janaka C; Murao, Kohei; Matsumoto, Toru; Sone, Shusuke

2017-01-01

Objective: We propose the application of virtual nodules to evaluate the performance of computer-aided detection (CAD) of lung nodules in cancer screening using low-dose CT. Methods: The virtual nodules were generated based on the spatial resolution measured for a CT system used in an institution providing cancer screening and were fused into clinical lung images obtained at that institution, allowing site specificity. First, we validated virtual nodules as an alternative to artificial nodules inserted into a phantom. In addition, we compared the results of CAD analysis between the real nodules (n = 6) and the corresponding virtual nodules. Subsequently, virtual nodules of various sizes and contrasts between nodule density and background density (ΔCT) were inserted into clinical images (n = 10) and submitted for CAD analysis. Results: In the validation study, 46 of 48 virtual nodules had the same CAD results as artificial nodules (kappa coefficient = 0.913). Real nodules and the corresponding virtual nodules showed the same CAD results. The detection limits of the tested CAD system were determined in terms of size and density of peripheral lung nodules; we demonstrated that a nodule with a 5-mm diameter was detected when the nodule had a ΔCT > 220 HU. Conclusion: Virtual nodules are effective in evaluating CAD performance using site-specific scan/reconstruction conditions. Advances in knowledge: Virtual nodules can be an effective means of evaluating site-specific CAD performance. The methodology for guiding the detection limit for nodule size/density might be a useful evaluation strategy. PMID:27897029

Comparison of formant detection methods used in speech processing applications

NASA Astrophysics Data System (ADS)

Belean, Bogdan

2013-11-01

The paper describes time frequency representations of speech signal together with the formant significance in speech processing applications. Speech formants can be used in emotion recognition, sex discrimination or diagnosing different neurological diseases. Taking into account the various applications of formant detection in speech signal, two methods for detecting formants are presented. First, the poles resulted after a complex analysis of LPC coefficients are used for formants detection. The second approach uses the Kalman filter for formant prediction along the speech signal. Results are presented for both approaches on real life speech spectrograms. A comparison regarding the features of the proposed methods is also performed, in order to establish which method is more suitable in case of different speech processing applications.

Saliency-Guided Change Detection of Remotely Sensed Images Using Random Forest

NASA Astrophysics Data System (ADS)

Feng, W.; Sui, H.; Chen, X.

2018-04-01

Studies based on object-based image analysis (OBIA) representing the paradigm shift in change detection (CD) have achieved remarkable progress in the last decade. Their aim has been developing more intelligent interpretation analysis methods in the future. The prediction effect and performance stability of random forest (RF), as a new kind of machine learning algorithm, are better than many single predictors and integrated forecasting method. In this paper, we present a novel CD approach for high-resolution remote sensing images, which incorporates visual saliency and RF. First, highly homogeneous and compact image super-pixels are generated using super-pixel segmentation, and the optimal segmentation result is obtained through image superimposition and principal component analysis (PCA). Second, saliency detection is used to guide the search of interest regions in the initial difference image obtained via the improved robust change vector analysis (RCVA) algorithm. The salient regions within the difference image that correspond to the binarized saliency map are extracted, and the regions are subject to the fuzzy c-means (FCM) clustering to obtain the pixel-level pre-classification result, which can be used as a prerequisite for superpixel-based analysis. Third, on the basis of the optimal segmentation and pixel-level pre-classification results, different super-pixel change possibilities are calculated. Furthermore, the changed and unchanged super-pixels that serve as the training samples are automatically selected. The spectral features and Gabor features of each super-pixel are extracted. Finally, superpixel-based CD is implemented by applying RF based on these samples. Experimental results on Ziyuan 3 (ZY3) multi-spectral images show that the proposed method outperforms the compared methods in the accuracy of CD, and also confirm the feasibility and effectiveness of the proposed approach.

Document reconstruction by layout analysis of snippets

NASA Astrophysics Data System (ADS)

Kleber, Florian; Diem, Markus; Sablatnig, Robert

2010-02-01

Document analysis is done to analyze entire forms (e.g. intelligent form analysis, table detection) or to describe the layout/structure of a document. Also skew detection of scanned documents is performed to support OCR algorithms that are sensitive to skew. In this paper document analysis is applied to snippets of torn documents to calculate features for the reconstruction. Documents can either be destroyed by the intention to make the printed content unavailable (e.g. tax fraud investigation, business crime) or due to time induced degeneration of ancient documents (e.g. bad storage conditions). Current reconstruction methods for manually torn documents deal with the shape, inpainting and texture synthesis techniques. In this paper the possibility of document analysis techniques of snippets to support the matching algorithm by considering additional features are shown. This implies a rotational analysis, a color analysis and a line detection. As a future work it is planned to extend the feature set with the paper type (blank, checked, lined), the type of the writing (handwritten vs. machine printed) and the text layout of a snippet (text size, line spacing). Preliminary results show that these pre-processing steps can be performed reliably on a real dataset consisting of 690 snippets.

Rapid detection of microbes in the dialysis solution by the microcolony fluorescence staining method (Millflex quantum).

PubMed

Osono, Eiichi; Kobayashi, Eiko; Inoue, Yuki; Honda, Kazumi; Kumagai, Takuya; Negishi, Hideki; Okamatsu, Kentaro; Ichimura, Kyoko; Kamano, Chisako; Suzuki, Fumi; Norose, Yoshihiko; Takahashi, Megumi; Takaku, Shun; Fujioka, Noriaki; Hayama, Naoaki; Takizawa, Hideaki

2014-01-01

A chemiluminescence system, Milliflex Quantum (MFQ), to detect microcolonies, has been used in the pharmaceutical field. In this study, we investigated aquatic bacteria in hemodialysis solutions sampled from bioburden areas in 4 dialysis faculties. Using MFQ, microcolonies could be detected after a short incubation period. The colony count detected with MFQ after a 48-hour incubation was 92% ± 39%, compared to that after the conventionally used 7-14-day incubation period; in addition, the results also showed a linear correlation. Moreover, MFQ-based analysis allowed the visualization of damaged cells and of the high density due to the excessive amount of bacteria. These results suggested that MFQ had adequate sensitivity to detect microbacteria in dialysis solutions, and it was useful for validating the conditions of conventional culture methods.

Signal processing for the detection of explosive residues on varying substrates using laser-induced breakdown spectroscopy

NASA Astrophysics Data System (ADS)

Morton, Kenneth D., Jr.; Torrione, Peter A.; Collins, Leslie

2011-05-01

Laser induced breakdown spectroscopy (LIBS) can provide rapid, minimally destructive, chemical analysis of substances with the benefit of little to no sample preparation. Therefore, LIBS is a viable technology for the detection of substances of interest in near real-time fielded remote sensing scenarios. Of particular interest to military and security operations is the detection of explosive residues on various surfaces. It has been demonstrated that LIBS is capable of detecting such residues, however, the surface or substrate on which the residue is present can alter the observed spectra. Standard chemometric techniques such as principal components analysis and partial least squares discriminant analysis have previously been applied to explosive residue detection, however, the classification techniques developed on such data perform best against residue/substrate pairs that were included in model training but do not perform well when the residue/substrate pairs are not in the training set. Specifically residues in the training set may not be correctly detected if they are presented on a previously unseen substrate. In this work, we explicitly model LIBS spectra resulting from the residue and substrate to attempt to separate the response from each of the two components. This separation process is performed jointly with classifier design to ensure that the classifier that is developed is able to detect residues of interest without being confused by variations in the substrates. We demonstrate that the proposed classification algorithm provides improved robustness to variations in substrate compared to standard chemometric techniques for residue detection.

Analysis of the discriminative methods for diagnosis of benign and malignant solitary pulmonary nodules based on serum markers.

PubMed

Wang, Wanping; Liu, Mingyue; Wang, Jing; Tian, Rui; Dong, Junqiang; Liu, Qi; Zhao, Xianping; Wang, Yuanfang

2014-01-01

Screening indexes of tumor serum markers for benign and malignant solitary pulmonary nodules (SPNs) were analyzed to find the optimum method for diagnosis. Enzyme-linked immunosorbent assays, an automatic immune analyzer and radioimmunoassay methods were used to examine the levels of 8 serum markers in 164 SPN patients, and the sensitivity for differential diagnosis of malignant or benign SPN was compared for detection using a single plasma marker or a combination of markers. The results for serological indicators that closely relate to benign and malignant SPNs were screened using the Fisher discriminant analysis and a non-conditional logistic regression analysis method, respectively. The results were then verified by the k-means clustering analysis method. The sensitivity when using a combination of serum markers to detect SPN was higher than that using a single marker. By Fisher discriminant analysis, cytokeratin 19 fragments (CYFRA21-1), carbohydrate antigen 125 (CA125), squamous cell carcinoma antigen (SCC) and breast cancer antigen (CA153), which relate to the benign and malignant SPNs, were screened. Through non-conditional logistic regression analysis, CYFRA21-1, SCC and CA153 were obtained. Using the k-means clustering analysis, the cophenetic correlation coefficient (0.940) obtained by the Fisher discriminant analysis was higher than that obtained with logistic regression analysis (0.875). This study indicated that the Fisher discriminant analysis functioned better in screening out serum markers to recognize the benign and malignant SPN. The combined detection of CYFRA21-1, CA125, SCC and CA153 is an effective way to distinguish benign and malignant SPN, and will find an important clinical application in the early diagnosis of SPN. © 2014 S. Karger GmbH, Freiburg.

Change Detection Analysis of Water Pollution in Coimbatore Region using Different Color Models

NASA Astrophysics Data System (ADS)

Jiji, G. Wiselin; Devi, R. Naveena

2017-12-01

The data acquired through remote sensing satellites furnish facts about the land and water at varying resolutions and has been widely used for several change detection studies. Apart from the existence of many change detection methodologies and techniques, emergence of new ones continues to subsist. Existing change detection techniques exploit images that are either in gray scale or RGB color model. In this paper we introduced color models for performing change detection for water pollution. Here the polluted lakes are classified and post-classification change detection techniques are applied to RGB images and results obtained are analysed for changes to exist or not. Furthermore RGB images obtained after classification when converted to any of the two color models YCbCr and YIQ is found to produce the same results as that of the RGB model images. Thus it can be concluded that other color models like YCbCr, YIQ can be used as substitution to RGB color model for analysing change detection with regard to water pollution.

Calculation of the detection limits for radionuclides identified in gamma-ray spectra based on post-processing peak analysis results.

PubMed

Korun, M; Vodenik, B; Zorko, B

2018-03-01

A new method for calculating the detection limits of gamma-ray spectrometry measurements is presented. The method is applicable for gamma-ray emitters, irrespective of the influences of the peaked background, the origin of the background and the overlap with other peaks. It offers the opportunity for multi-gamma-ray emitters to calculate the common detection limit, corresponding to more peaks. The detection limit is calculated by approximating the dependence of the uncertainty in the indication on its value with a second-order polynomial. In this approach the relation between the input quantities and the detection limit are described by an explicit expression and can be easy investigated. The detection limit is calculated from the data usually provided by the reports of peak-analyzing programs: the peak areas and their uncertainties. As a result, the need to use individual channel contents for calculating the detection limit is bypassed. Copyright © 2017 Elsevier Ltd. All rights reserved.

On the performance of energy detection-based CR with SC diversity over IG channel

NASA Astrophysics Data System (ADS)

Verma, Pappu Kumar; Soni, Sanjay Kumar; Jain, Priyanka

2017-12-01

Cognitive radio (CR) is a viable 5G technology to address the scarcity of the spectrum. Energy detection-based sensing is known to be the simplest method as far as hardware complexity is concerned. In this paper, the performance of spectrum sensing-based energy detection technique in CR networks over inverse Gaussian channel for selection combining diversity technique is analysed. More specifically, accurate analytical expressions for the average detection probability under different detection scenarios such as single channel (no diversity) and with diversity reception are derived and evaluated. Further, the detection threshold parameter is optimised by minimising the probability of error over several diversity branches. The results clearly show the significant improvement in the probability of detection when optimised threshold parameter is applied. The impact of shadowing parameters on the performance of energy detector is studied in terms of complimentary receiver operating characteristic curve. To verify the correctness of our analysis, the derived analytical expressions are corroborated via exact result and Monte Carlo simulations.

A Smart Toy to Enhance the Decision-Making Process at Children’s Psychomotor Delay Screenings: A Pilot Study

PubMed Central

2017-01-01

Background EDUCERE (“Ubiquitous Detection Ecosystem to Care and Early Stimulation for Children with Developmental Disorders”) is an ecosystem for ubiquitous detection, care, and early stimulation of children with developmental disorders. The objectives of this Spanish government-funded research and development project are to investigate, develop, and evaluate innovative solutions to detect changes in psychomotor development through the natural interaction of children with toys and everyday objects, and perform stimulation and early attention activities in real environments such as home and school. Thirty multidisciplinary professionals and three nursery schools worked in the EDUCERE project between 2014 and 2017 and they obtained satisfactory results. Related to EDUCERE, we found studies based on providing networks of connected smart objects and the interaction between toys and social networks. Objective This research includes the design, implementation, and validation of an EDUCERE smart toy aimed to automatically detect delays in psychomotor development. The results from initial tests led to enhancing the effectiveness of the original design and deployment. The smart toy, based on stackable cubes, has a data collector module and a smart system for detection of developmental delays, called the EDUCERE developmental delay screening system (DDSS). Methods The pilot study involved 65 toddlers aged between 23 and 37 months (mean=29.02, SD 3.81) who built a tower with five stackable cubes, designed by following the EDUCERE smart toy model. As toddlers made the tower, sensors in the cubes sent data to a collector module through a wireless connection. All trials were video-recorded for further analysis by child development experts. After watching the videos, experts scored the performance of the trials to compare and fine-tune the interpretation of the data automatically gathered by the toy-embedded sensors. Results Judges were highly reliable in an interrater agreement analysis (intraclass correlation 0.961, 95% CI 0.937-0.967), suggesting that the process was successful to separate different levels of performance. A factor analysis of collected data showed that three factors, trembling, speed, and accuracy, accounted for 76.79% of the total variance, but only two of them were predictors of performance in a regression analysis: accuracy (P=.001) and speed (P=.002). The other factor, trembling (P=.79), did not have a significant effect on this dependent variable. Conclusions The EDUCERE DDSS is ready to use the regression equation obtained for the dependent variable “performance” as an algorithm for the automatic detection of psychomotor developmental delays. The results of the factor analysis are valuable to simplify the design of the smart toy by taking into account only the significant variables in the collector module. The fine-tuning of the toy process module will be carried out by following the specifications resulting from the analysis of the data to improve the efficiency and effectiveness of the product. PMID:28526666

Aqueous nitrite ion determination by selective reduction and gas phase nitric oxide chemiluminescence

NASA Technical Reports Server (NTRS)

Dunham, A. J.; Barkley, R. M.; Sievers, R. E.; Clarkson, T. W. (Principal Investigator)

1995-01-01

An improved method of flow injection analysis for aqueous nitrite ion exploits the sensitivity and selectivity of the nitric oxide (NO) chemilluminescence detector. Trace analysis of nitrite ion in a small sample (5-160 microL) is accomplished by conversion of nitrite ion to NO by aqueous iodide in acid. The resulting NO is transported to the gas phase through a semipermeable membrane and subsequently detected by monitoring the photoemission of the reaction between NO and ozone (O3). Chemiluminescence detection is selective for measurement of NO, and, since the detection occurs in the gas-phase, neither sample coloration nor turbidity interfere. The detection limit for a 100-microL sample is 0.04 ppb of nitrite ion. The precision at the 10 ppb level is 2% relative standard deviation, and 60-180 samples can be analyzed per hour. Samples of human saliva and food extracts were analyzed; the results from a standard colorimetric measurement are compared with those from the new chemiluminescence method in order to further validate the latter method. A high degree of selectivity is obtained due to the three discriminating steps in the process: (1) the nitrite ion to NO conversion conditions are virtually specific for nitrite ion, (2) only volatile products of the conversion will be swept to the gas phase (avoiding turbidity or color in spectrophotometric methods), and (3) the NO chemiluminescence detector selectively detects the emission from the NO + O3 reaction. The method is free of interferences, offers detection limits of low parts per billion of nitrite ion, and allows the analysis of up to 180 microL-sized samples per hour, with little sample preparation and no chromatographic separation. Much smaller samples can be analyzed by this method than in previously reported batch analysis methods, which typically require 5 mL or more of sample and often need chromatographic separations as well.

A randomized approach to speed up the analysis of large-scale read-count data in the application of CNV detection.

PubMed

Wang, WeiBo; Sun, Wei; Wang, Wei; Szatkiewicz, Jin

2018-03-01

The application of high-throughput sequencing in a broad range of quantitative genomic assays (e.g., DNA-seq, ChIP-seq) has created a high demand for the analysis of large-scale read-count data. Typically, the genome is divided into tiling windows and windowed read-count data is generated for the entire genome from which genomic signals are detected (e.g. copy number changes in DNA-seq, enrichment peaks in ChIP-seq). For accurate analysis of read-count data, many state-of-the-art statistical methods use generalized linear models (GLM) coupled with the negative-binomial (NB) distribution by leveraging its ability for simultaneous bias correction and signal detection. However, although statistically powerful, the GLM+NB method has a quadratic computational complexity and therefore suffers from slow running time when applied to large-scale windowed read-count data. In this study, we aimed to speed up substantially the GLM+NB method by using a randomized algorithm and we demonstrate here the utility of our approach in the application of detecting copy number variants (CNVs) using a real example. We propose an efficient estimator, the randomized GLM+NB coefficients estimator (RGE), for speeding up the GLM+NB method. RGE samples the read-count data and solves the estimation problem on a smaller scale. We first theoretically validated the consistency and the variance properties of RGE. We then applied RGE to GENSENG, a GLM+NB based method for detecting CNVs. We named the resulting method as "R-GENSENG". Based on extensive evaluation using both simulated and empirical data, we concluded that R-GENSENG is ten times faster than the original GENSENG while maintaining GENSENG's accuracy in CNV detection. Our results suggest that RGE strategy developed here could be applied to other GLM+NB based read-count analyses, i.e. ChIP-seq data analysis, to substantially improve their computational efficiency while preserving the analytic power.

Multi-modality analysis of glucose aqueous solution using photoacoustic and dielectric spectroscopy for non-invasive glucose monitoring

NASA Astrophysics Data System (ADS)

Tajima, Takuro; Tanaka, Yujiro; Nakamura, Masahito; Seyama, Michiko

2017-03-01

Quantitative analysis of glucose using conventional optical spectroscopy suffers from a lack of repeatability due to high optical scattering in skin tissue. Here we present a multi-modality analysis of glucose aqueous solution using photoacoustic spectroscopy (PAS) and broadband dielectric spectroscopy (BDS). These techniques involve the direct detection of the acoustic and electromagnetic waves propagating through or reflecting from tissue without their being scattered. They therefore have potential for better tolerance to the variation of scattering. For PAS, to differentiate signals induced by water absorption, we select another laser wavelength (1.38 μm) that exhibits the same absorbance for water at 1.61 μm. Furthermore, one of the two photoacoustic signals is used to normalize the variations of acoustic properties in differential signal. Measured results for glucose solutions (0-2 g/dL) showed that the differential signal has a sensitivity of 1.61%/g·dL-1 and a detection limit of 120 mg/dL. We also tested glucose detection with BDS (500 MHz to 50 GHz) by detecting glucose hydration bonding at around 10-20 GHz. Using a partial least square analysis and first derivation on broadband spectra, we obtained an RMS error 19 mg/dL and a detection limit of 59 mg/dL. Using both the low-scattering ultrasonic and microwave detection techniques, we successfully captured the glucose footprint in the physiological range.

Detection of Glaucoma Using Image Processing Techniques: A Critique.

PubMed

Kumar, B Naveen; Chauhan, R P; Dahiya, Nidhi

2018-01-01

The primary objective of this article is to present a summary of different types of image processing methods employed for the detection of glaucoma, a serious eye disease. Glaucoma affects the optic nerve in which retinal ganglion cells become dead, and this leads to loss of vision. The principal cause is the increase in intraocular pressure, which occurs in open-angle and angle-closure glaucoma, the two major types affecting the optic nerve. In the early stages of glaucoma, no perceptible symptoms appear. As the disease progresses, vision starts to become hazy, leading to blindness. Therefore, early detection of glaucoma is needed for prevention. Manual analysis of ophthalmic images is fairly time-consuming and accuracy depends on the expertise of the professionals. Automatic analysis of retinal images is an important tool. Automation aids in the detection, diagnosis, and prevention of risks associated with the disease. Fundus images obtained from a fundus camera have been used for the analysis. Requisite pre-processing techniques have been applied to the image and, depending upon the technique, various classifiers have been used to detect glaucoma. The techniques mentioned in the present review have certain advantages and disadvantages. Based on this study, one can determine which technique provides an optimum result.

Elemental composition of edible nuts: fast optimization and validation procedure of an ICP-OES method.

PubMed

Tošić, Snežana B; Mitić, Snežana S; Velimirović, Dragan S; Stojanović, Gordana S; Pavlović, Aleksandra N; Pecev-Marinković, Emilija T

2015-08-30

An inductively coupled plasma-optical emission spectrometry method for the speedy simultaneous detection of 19 elements in edible nuts (walnuts: Juglans nigra; almonds: Prunus dulcis; hazelnuts: Corylus avellana; Brazil nuts: Bertholletia excelsa; cashews: Anacardium occidentalle; pistachios: Pistacia vera; and peanuts: Arachis hypogaea) available on the Serbian markets, was optimized and validated through the selection of instrumental parameters and analytical lines free from spectral interference and with the lowest matrix effects. The analysed macro-elements were present in the following descending order: Na > Mg > Ca > K. Of all the trace elements, the tested samples showed the highest content of Fe. The micro-element Se was detected in all the samples of nuts. The toxic elements As, Cd and Pb were either not detected or the contents were below the limit of detection. One-way analysis of variance, Student's t-test, Tukey's HSD post hoc test and hierarchical agglomerative cluster analysis were applied in the statistical analysis of the results. Based on the detected content of analysed elements it can be concluded that nuts may be a good additional source of minerals as micronutrients. © 2014 Society of Chemical Industry.

Comparative analysis of peak-detection techniques for comprehensive two-dimensional chromatography.

PubMed

Latha, Indu; Reichenbach, Stephen E; Tao, Qingping

2011-09-23

Comprehensive two-dimensional gas chromatography (GC×GC) is a powerful technology for separating complex samples. The typical goal of GC×GC peak detection is to aggregate data points of analyte peaks based on their retention times and intensities. Two techniques commonly used for two-dimensional peak detection are the two-step algorithm and the watershed algorithm. A recent study [4] compared the performance of the two-step and watershed algorithms for GC×GC data with retention-time shifts in the second-column separations. In that analysis, the peak retention-time shifts were corrected while applying the two-step algorithm but the watershed algorithm was applied without shift correction. The results indicated that the watershed algorithm has a higher probability of erroneously splitting a single two-dimensional peak than the two-step approach. This paper reconsiders the analysis by comparing peak-detection performance for resolved peaks after correcting retention-time shifts for both the two-step and watershed algorithms. Simulations with wide-ranging conditions indicate that when shift correction is employed with both algorithms, the watershed algorithm detects resolved peaks with greater accuracy than the two-step method. Copyright © 2011 Elsevier B.V. All rights reserved.

Targeted and non-targeted detection of lemon juice adulteration by LC-MS and chemometrics.

PubMed

Wang, Zhengfang; Jablonski, Joseph E

2016-01-01

Economically motivated adulteration (EMA) of lemon juice was detected by LC-MS and principal component analysis (PCA). Twenty-two batches of freshly squeezed lemon juice were adulterated by adding an aqueous solution containing 5% citric acid and 6% sucrose to pure lemon juice to obtain 30%, 60% and 100% lemon juice samples. Their total titratable acidities, °Brix and pH values were measured, and then all the lemon juice samples were subject to LC-MS analysis. Concentrations of hesperidin and eriocitrin, major phenolic components of lemon juice, were quantified. The PCA score plots for LC-MS datasets were used to preview the classification of pure and adulterated lemon juice samples. Results showed a large inherent variability in the chemical properties among 22 batches of 100% lemon juice samples. Measurement or quantitation of one or several chemical properties (targeted detection) was not effective in detecting lemon juice adulteration. However, by using the LC-MS datasets, including both chromatographic and mass spectrometric information, 100% lemon juice samples were successfully differentiated from adulterated samples containing 30% lemon juice in the PCA score plot. LC-MS coupled with chemometric analysis can be a complement to existing methods for detecting juice adulteration.

Evaluation of tools for highly variable gene discovery from single-cell RNA-seq data.

PubMed

Yip, Shun H; Sham, Pak Chung; Wang, Junwen

2018-02-21

Traditional RNA sequencing (RNA-seq) allows the detection of gene expression variations between two or more cell populations through differentially expressed gene (DEG) analysis. However, genes that contribute to cell-to-cell differences are not discoverable with RNA-seq because RNA-seq samples are obtained from a mixture of cells. Single-cell RNA-seq (scRNA-seq) allows the detection of gene expression in each cell. With scRNA-seq, highly variable gene (HVG) discovery allows the detection of genes that contribute strongly to cell-to-cell variation within a homogeneous cell population, such as a population of embryonic stem cells. This analysis is implemented in many software packages. In this study, we compare seven HVG methods from six software packages, including BASiCS, Brennecke, scLVM, scran, scVEGs and Seurat. Our results demonstrate that reproducibility in HVG analysis requires a larger sample size than DEG analysis. Discrepancies between methods and potential issues in these tools are discussed and recommendations are made.

Change Analysis in Structural Laser Scanning Point Clouds: The Baseline Method

PubMed Central

Shen, Yueqian; Lindenbergh, Roderik; Wang, Jinhu

2016-01-01

A method is introduced for detecting changes from point clouds that avoids registration. For many applications, changes are detected between two scans of the same scene obtained at different times. Traditionally, these scans are aligned to a common coordinate system having the disadvantage that this registration step introduces additional errors. In addition, registration requires stable targets or features. To avoid these issues, we propose a change detection method based on so-called baselines. Baselines connect feature points within one scan. To analyze changes, baselines connecting corresponding points in two scans are compared. As feature points either targets or virtual points corresponding to some reconstructable feature in the scene are used. The new method is implemented on two scans sampling a masonry laboratory building before and after seismic testing, that resulted in damages in the order of several centimeters. The centres of the bricks of the laboratory building are automatically extracted to serve as virtual points. Baselines connecting virtual points and/or target points are extracted and compared with respect to a suitable structural coordinate system. Changes detected from the baseline analysis are compared to a traditional cloud to cloud change analysis demonstrating the potential of the new method for structural analysis. PMID:28029121

Change Analysis in Structural Laser Scanning Point Clouds: The Baseline Method.

PubMed

Shen, Yueqian; Lindenbergh, Roderik; Wang, Jinhu

2016-12-24

A method is introduced for detecting changes from point clouds that avoids registration. For many applications, changes are detected between two scans of the same scene obtained at different times. Traditionally, these scans are aligned to a common coordinate system having the disadvantage that this registration step introduces additional errors. In addition, registration requires stable targets or features. To avoid these issues, we propose a change detection method based on so-called baselines. Baselines connect feature points within one scan. To analyze changes, baselines connecting corresponding points in two scans are compared. As feature points either targets or virtual points corresponding to some reconstructable feature in the scene are used. The new method is implemented on two scans sampling a masonry laboratory building before and after seismic testing, that resulted in damages in the order of several centimeters. The centres of the bricks of the laboratory building are automatically extracted to serve as virtual points. Baselines connecting virtual points and/or target points are extracted and compared with respect to a suitable structural coordinate system. Changes detected from the baseline analysis are compared to a traditional cloud to cloud change analysis demonstrating the potential of the new method for structural analysis.

Analysis of phosphorus trends and evaluation of sampling designs in the Quinebaug River Basin, Connecticut

USGS Publications Warehouse

Todd Trench, Elaine C.

2004-01-01

A time-series analysis approach developed by the U.S. Geological Survey was used to analyze trends in total phosphorus and evaluate optimal sampling designs for future trend detection, using long-term data for two water-quality monitoring stations on the Quinebaug River in eastern Connecticut. Trend-analysis results for selected periods of record during 1971?2001 indicate that concentrations of total phosphorus in the Quinebaug River have varied over time, but have decreased significantly since the 1970s and 1980s. Total phosphorus concentrations at both stations increased in the late 1990s and early 2000s, but were still substantially lower than historical levels. Drainage areas for both stations are primarily forested, but water quality at both stations is affected by point discharges from municipal wastewater-treatment facilities. Various designs with sampling frequencies ranging from 4 to 11 samples per year were compared to the trend-detection power of the monthly (12-sample) design to determine the most efficient configuration of months to sample for a given annual sampling frequency. Results from this evaluation indicate that the current (2004) 8-sample schedule for the two Quinebaug stations, with monthly sampling from May to September and bimonthly sampling for the remainder of the year, is not the most efficient 8-sample design for future detection of trends in total phosphorus. Optimal sampling schedules for the two stations differ, but in both cases, trend-detection power generally is greater among 8-sample designs that include monthly sampling in fall and winter. Sampling designs with fewer than 8 samples per year generally provide a low level of probability for detection of trends in total phosphorus. Managers may determine an acceptable level of probability for trend detection within the context of the multiple objectives of the state?s water-quality management program and the scientific understanding of the watersheds in question. Managers may identify a threshold of probability for trend detection that is high enough to justify the agency?s investment in the water-quality sampling program. Results from an analysis of optimal sampling designs can provide an important component of information for the decision-making process in which sampling schedules are periodically reviewed and revised. Results from the study described in this report and previous studies indicate that optimal sampling schedules for trend detection may differ substantially for different stations and constituents. A more comprehensive statewide evaluation of sampling schedules for key stations and constituents could provide useful information for any redesign of the schedule for water-quality monitoring in the Quinebaug River Basin and elsewhere in the state.

NPE 2010 results - Independent performance assessment by simulated CTBT violation scenarios

NASA Astrophysics Data System (ADS)

Ross, O.; Bönnemann, C.; Ceranna, L.; Gestermann, N.; Hartmann, G.; Plenefisch, T.

2012-04-01

For verification of compliance to the Comprehensive Nuclear-Test-Ban Treaty (CTBT) the global International Monitoring System (IMS) is currently being built up. The IMS is designed to detect nuclear explosions through their seismic, hydroacoustic, infrasound, and radionuclide signature. The IMS data are collected, processed to analysis products, and distributed to the state signatories by the International Data Centre (IDC) in Vienna. The state signatories themselves may operate National Data Centers (NDC) giving technical advice concerning CTBT verification to the government. NDC Preparedness Exercises (NPE) are regularly performed to practice the verification procedures for the detection of nuclear explosions in the framework of CTBT monitoring. The initial focus of the NPE 2010 was on the component of radionuclide detections and the application of Atmospheric Transport Modeling (ATM) for defining the source region of a radionuclide event. The exercise was triggered by fictitious radioactive noble gas detections which were calculated beforehand secretly by forward ATM for a hypothetical xenon release scenario starting at location and time of a real seismic event. The task for the exercise participants was to find potential source events by atmospheric backtracking and to analyze in the following promising candidate events concerning their waveform signals. The study shows one possible way of solution for NPE 2010 as it was performed at German NDC by a team without precedent knowledge of the selected event and release scenario. The ATM Source Receptor Sensitivity (SRS) fields as provided by the IDC were evaluated in a logical approach in order to define probable source regions for several days before the first reported fictitious radioactive xenon finding. Additional information on likely event times was derived from xenon isotopic ratios where applicable. Of the considered seismic events in the potential source region all except one could be identified as earthquakes by seismological analysis. The remaining event at Black Thunder Mine, Wyoming, on 23 Oct at 21:15 UTC showed clear explosion characteristics. It caused also Infrasound detections at one station in Canada. An infrasonic one station localization algorithm led to event localization results comparable in precision to the teleseismic localization. However, the analysis of regional seismological stations gave the most accurate result giving an error ellipse of about 60 square kilometer. Finally a forward ATM simulation was performed with the candidate event as source in order to reproduce the original detection scenario. The ATM results showed a simulated station fingerprint in the IMS very similar to the fictitious detections given in the NPE 2010 scenario which is an additional confirmation that the event was correctly identified. The shown event analysis of the NPE 2010 serves as successful example for Data Fusion between the technology of radionuclide detection supported by ATM and seismological methodology as well as infrasound signal processing.

Statistical colour models: an automated digital image analysis method for quantification of histological biomarkers.

PubMed

Shu, Jie; Dolman, G E; Duan, Jiang; Qiu, Guoping; Ilyas, Mohammad

2016-04-27

Colour is the most important feature used in quantitative immunohistochemistry (IHC) image analysis; IHC is used to provide information relating to aetiology and to confirm malignancy. Statistical modelling is a technique widely used for colour detection in computer vision. We have developed a statistical model of colour detection applicable to detection of stain colour in digital IHC images. Model was first trained by massive colour pixels collected semi-automatically. To speed up the training and detection processes, we removed luminance channel, Y channel of YCbCr colour space and chose 128 histogram bins which is the optimal number. A maximum likelihood classifier is used to classify pixels in digital slides into positively or negatively stained pixels automatically. The model-based tool was developed within ImageJ to quantify targets identified using IHC and histochemistry. The purpose of evaluation was to compare the computer model with human evaluation. Several large datasets were prepared and obtained from human oesophageal cancer, colon cancer and liver cirrhosis with different colour stains. Experimental results have demonstrated the model-based tool achieves more accurate results than colour deconvolution and CMYK model in the detection of brown colour, and is comparable to colour deconvolution in the detection of pink colour. We have also demostrated the proposed model has little inter-dataset variations. A robust and effective statistical model is introduced in this paper. The model-based interactive tool in ImageJ, which can create a visual representation of the statistical model and detect a specified colour automatically, is easy to use and available freely at http://rsb.info.nih.gov/ij/plugins/ihc-toolbox/index.html . Testing to the tool by different users showed only minor inter-observer variations in results.

Evaluation of usefulness of fine-needle aspiration cytology in the diagnosis of tumours of the accessory parotid gland: a preliminary analysis of a case series in Japan.

PubMed

Iguchi, Hiroyoshi; Wada, Tadashi; Matsushita, Naoki; Oishi, Masahiro; Teranishi, Yuichi; Yamane, Hideo

2014-07-01

The accuracy and sensitivity of fine-needle aspiration cytology (FNAC) in this analysis were not satisfactory, and the false-negative rate seemed to be higher than for parotid tumours. The possibility of low-grade malignancy should be considered in the surgical treatment of accessory parotid gland (APG) tumours, even if the preoperative results of FNAC suggest that the tumour is benign. Little is known about the usefulness of FNAC in the preoperative evaluation of APG tumours, probably due to the paucity of APG tumour cases. We examined the usefulness of FNAC in the detection of malignant APG tumours. We conducted a retrospective analysis of 3 cases from our hospital, along with 18 previously reported Japanese cases. We compared the preoperative FNAC results with postoperative histopathological diagnoses of APG tumours and evaluated the accuracy, sensitivity, specificity and false-negative rates of FNAC in detecting malignant APG tumours. There were four false-negative cases (19.0%), three of mucoepidermoid carcinomas and one of malignant lymphoma. One false-positive result was noted in the case of a myoepithelioma, which was cytologically diagnosed as suspected adenoid cystic carcinoma. The accuracy, sensitivity and specificity of FNAC in detecting malignant tumours were 76.2%, 60.0% and 90.9%, respectively.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Donnelly, H.; Fullwood, R.; Glancy, J.

This is the second volume of a two volume report on the VISA method for evaluating safeguards at fixed-site facilities. This volume contains appendices that support the description of the VISA concept and the initial working version of the method, VISA-1, presented in Volume I. The information is separated into four appendices, each describing details of one of the four analysis modules that comprise the analysis sections of the method. The first appendix discusses Path Analysis methodology, applies it to a Model Fuel Facility, and describes the computer codes that are being used. Introductory material on Path Analysis given inmore » Chapter 3.2.1 and Chapter 4.2.1 of Volume I. The second appendix deals with Detection Analysis, specifically the schemes used in VISA-1 for classifying adversaries and the methods proposed for evaluating individual detection mechanisms in order to build the data base required for detection analysis. Examples of evaluations on identity-access systems, SNM portal monitors, and intrusion devices are provided. The third appendix describes the Containment Analysis overt-segment path ranking, the Monte Carlo engagement model, the network simulation code, the delay mechanism data base, and the results of a sensitivity analysis. The last appendix presents general equations used in Interruption Analysis for combining covert-overt segments and compares them with equations given in Volume I, Chapter 3.« less

Detecting long-term growth trends using tree rings: a critical evaluation of methods.

PubMed

Peters, Richard L; Groenendijk, Peter; Vlam, Mart; Zuidema, Pieter A

2015-05-01

Tree-ring analysis is often used to assess long-term trends in tree growth. A variety of growth-trend detection methods (GDMs) exist to disentangle age/size trends in growth from long-term growth changes. However, these detrending methods strongly differ in approach, with possible implications for their output. Here, we critically evaluate the consistency, sensitivity, reliability and accuracy of four most widely used GDMs: conservative detrending (CD) applies mathematical functions to correct for decreasing ring widths with age; basal area correction (BAC) transforms diameter into basal area growth; regional curve standardization (RCS) detrends individual tree-ring series using average age/size trends; and size class isolation (SCI) calculates growth trends within separate size classes. First, we evaluated whether these GDMs produce consistent results applied to an empirical tree-ring data set of Melia azedarach, a tropical tree species from Thailand. Three GDMs yielded similar results - a growth decline over time - but the widely used CD method did not detect any change. Second, we assessed the sensitivity (probability of correct growth-trend detection), reliability (100% minus probability of detecting false trends) and accuracy (whether the strength of imposed trends is correctly detected) of these GDMs, by applying them to simulated growth trajectories with different imposed trends: no trend, strong trends (-6% and +6% change per decade) and weak trends (-2%, +2%). All methods except CD, showed high sensitivity, reliability and accuracy to detect strong imposed trends. However, these were considerably lower in the weak or no-trend scenarios. BAC showed good sensitivity and accuracy, but low reliability, indicating uncertainty of trend detection using this method. Our study reveals that the choice of GDM influences results of growth-trend studies. We recommend applying multiple methods when analysing trends and encourage performing sensitivity and reliability analysis. Finally, we recommend SCI and RCS, as these methods showed highest reliability to detect long-term growth trends. © 2014 John Wiley & Sons Ltd.

Comparing methods for analysis of biomedical hyperspectral image data

NASA Astrophysics Data System (ADS)

Leavesley, Silas J.; Sweat, Brenner; Abbott, Caitlyn; Favreau, Peter F.; Annamdevula, Naga S.; Rich, Thomas C.

2017-02-01

Over the past 2 decades, hyperspectral imaging technologies have been adapted to address the need for molecule-specific identification in the biomedical imaging field. Applications have ranged from single-cell microscopy to whole-animal in vivo imaging and from basic research to clinical systems. Enabling this growth has been the availability of faster, more effective hyperspectral filtering technologies and more sensitive detectors. Hence, the potential for growth of biomedical hyperspectral imaging is high, and many hyperspectral imaging options are already commercially available. However, despite the growth in hyperspectral technologies for biomedical imaging, little work has been done to aid users of hyperspectral imaging instruments in selecting appropriate analysis algorithms. Here, we present an approach for comparing the effectiveness of spectral analysis algorithms by combining experimental image data with a theoretical "what if" scenario. This approach allows us to quantify several key outcomes that characterize a hyperspectral imaging study: linearity of sensitivity, positive detection cut-off slope, dynamic range, and false positive events. We present results of using this approach for comparing the effectiveness of several common spectral analysis algorithms for detecting weak fluorescent protein emission in the midst of strong tissue autofluorescence. Results indicate that this approach should be applicable to a very wide range of applications, allowing a quantitative assessment of the effectiveness of the combined biology, hardware, and computational analysis for detecting a specific molecular signature.

A comparative analysis of pixel- and object-based detection of landslides from very high-resolution images

NASA Astrophysics Data System (ADS)

Keyport, Ren N.; Oommen, Thomas; Martha, Tapas R.; Sajinkumar, K. S.; Gierke, John S.

2018-02-01

A comparative analysis of landslides detected by pixel-based and object-oriented analysis (OOA) methods was performed using very high-resolution (VHR) remotely sensed aerial images for the San Juan La Laguna, Guatemala, which witnessed widespread devastation during the 2005 Hurricane Stan. A 3-band orthophoto of 0.5 m spatial resolution together with a 115 field-based landslide inventory were used for the analysis. A binary reference was assigned with a zero value for landslide and unity for non-landslide pixels. The pixel-based analysis was performed using unsupervised classification, which resulted in 11 different trial classes. Detection of landslides using OOA includes 2-step K-means clustering to eliminate regions based on brightness; elimination of false positives using object properties such as rectangular fit, compactness, length/width ratio, mean difference of objects, and slope angle. Both overall accuracy and F-score for OOA methods outperformed pixel-based unsupervised classification methods in both landslide and non-landslide classes. The overall accuracy for OOA and pixel-based unsupervised classification was 96.5% and 94.3%, respectively, whereas the best F-score for landslide identification for OOA and pixel-based unsupervised methods: were 84.3% and 77.9%, respectively.Results indicate that the OOA is able to identify the majority of landslides with a few false positive when compared to pixel-based unsupervised classification.

Intrusion detection system using Online Sequence Extreme Learning Machine (OS-ELM) in advanced metering infrastructure of smart grid

PubMed Central

Li, Yuancheng; Jing, Sitong

2018-01-01

Advanced Metering Infrastructure (AMI) realizes a two-way communication of electricity data through by interconnecting with a computer network as the core component of the smart grid. Meanwhile, it brings many new security threats and the traditional intrusion detection method can’t satisfy the security requirements of AMI. In this paper, an intrusion detection system based on Online Sequence Extreme Learning Machine (OS-ELM) is established, which is used to detecting the attack in AMI and carrying out the comparative analysis with other algorithms. Simulation results show that, compared with other intrusion detection methods, intrusion detection method based on OS-ELM is more superior in detection speed and accuracy. PMID:29485990

Broadband external cavity quantum cascade laser based sensor for gasoline detection

NASA Astrophysics Data System (ADS)

Ding, Junya; He, Tianbo; Zhou, Sheng; Li, Jinsong

2018-02-01

A new type of tunable diode spectroscopy sensor based on an external cavity quantum cascade laser (ECQCL) and a quartz crystal tuning fork (QCTF) were used for quantitative analysis of volatile organic compounds. In this work, the sensor system had been tested on different gasoline sample analysis. For signal processing, the self-established interpolation algorithm and multiple linear regression algorithm model were used for quantitative analysis of major volatile organic compounds in gasoline samples. The results were very consistent with that of the standard spectra taken from the Pacific Northwest National Laboratory (PNNL) database. In future, The ECQCL sensor will be used for trace explosive, chemical warfare agent, and toxic industrial chemical detection and spectroscopic analysis, etc.

Direct spectral analysis of tea samples using 266 nm UV pulsed laser-induced breakdown spectroscopy and cross validation of LIBS results with ICP-MS.

PubMed

Gondal, M A; Habibullah, Y B; Baig, Umair; Oloore, L E

2016-05-15

Tea is one of the most common and popular beverages spanning vast array of cultures all over the world. The main nutritional benefits of drinking tea are its anti-oxidant properties, presumed protection against certain cancers, inhibition of inflammation and possible protective effects against diabetes. Laser induced breakdown spectrometer (LIBS) was assembled as a powerful tool for qualitative and quantitative analysis of various brands of tea samples using 266 nm pulsed UV laser. LIBS spectra for six brands of tea samples in the wavelength range of 200-900 nm was recorded and all elements present in our tea samples were identified. The major toxic elements detected in several brands of tea samples were bromine, chromium and minerals like iron, calcium, potassium and silicon. The spectral assignment was conducted prior to the determination of concentration of each element. For quantitative analysis, calibration curves were drawn for each element using standard samples prepared in known concentration in the tea matrix. The plasma parameters (electron temperature and electron density) were also determined prior to the tea samples spectroscopic analysis. The concentration of iron, chromium, potassium, bromine, copper, silicon and calcium detected in all tea samples was between 378-656, 96-124, 1421-6785, 99-1476, 17-36, 2-11 and 92-130 mg L(-1) respectively. The limits of detection estimated for Fe, Cr, K, Br, Cu, Si, Ca in tea samples were 22, 12, 14, 11, 6, 1 and 12 mg L(-1) respectively. To further confirm the accuracy of our LIBS results, we determined the concentration of each element present in tea samples by using standard analytical technique like ICP-MS. The concentrations detected with our LIBS system are in excellent agreement with ICP-MS results. The system assembled for spectral analysis in this work could be highly applicable for testing the quality and purity of food and also pharmaceuticals products. Copyright © 2016 Elsevier B.V. All rights reserved.

Differences in Looking at Own- and Other-Race Faces Are Subtle and Analysis-Dependent: An Account of Discrepant Reports.

PubMed

Arizpe, Joseph; Kravitz, Dwight J; Walsh, Vincent; Yovel, Galit; Baker, Chris I

2016-01-01

The Other-Race Effect (ORE) is the robust and well-established finding that people are generally poorer at facial recognition of individuals of another race than of their own race. Over the past four decades, much research has focused on the ORE because understanding this phenomenon is expected to elucidate fundamental face processing mechanisms and the influence of experience on such mechanisms. Several recent studies of the ORE in which the eye-movements of participants viewing own- and other-race faces were tracked have, however, reported highly conflicting results regarding the presence or absence of differential patterns of eye-movements to own- versus other-race faces. This discrepancy, of course, leads to conflicting theoretical interpretations of the perceptual basis for the ORE. Here we investigate fixation patterns to own- versus other-race (African and Chinese) faces for Caucasian participants using different analysis methods. While we detect statistically significant, though subtle, differences in fixation pattern using an Area of Interest (AOI) approach, we fail to detect significant differences when applying a spatial density map approach. Though there were no significant differences in the spatial density maps, the qualitative patterns matched the results from the AOI analyses reflecting how, in certain contexts, Area of Interest (AOI) analyses can be more sensitive in detecting the differential fixation patterns than spatial density analyses, due to spatial pooling of data with AOIs. AOI analyses, however, also come with the limitation of requiring a priori specification. These findings provide evidence that the conflicting reports in the prior literature may be at least partially accounted for by the differences in the statistical sensitivity associated with the different analysis methods employed across studies. Overall, our results suggest that detection of differences in eye-movement patterns can be analysis-dependent and rests on the assumptions inherent in the given analysis.

Differences in Looking at Own- and Other-Race Faces Are Subtle and Analysis-Dependent: An Account of Discrepant Reports

PubMed Central

Arizpe, Joseph; Kravitz, Dwight J.; Walsh, Vincent; Yovel, Galit; Baker, Chris I.

2016-01-01

The Other-Race Effect (ORE) is the robust and well-established finding that people are generally poorer at facial recognition of individuals of another race than of their own race. Over the past four decades, much research has focused on the ORE because understanding this phenomenon is expected to elucidate fundamental face processing mechanisms and the influence of experience on such mechanisms. Several recent studies of the ORE in which the eye-movements of participants viewing own- and other-race faces were tracked have, however, reported highly conflicting results regarding the presence or absence of differential patterns of eye-movements to own- versus other-race faces. This discrepancy, of course, leads to conflicting theoretical interpretations of the perceptual basis for the ORE. Here we investigate fixation patterns to own- versus other-race (African and Chinese) faces for Caucasian participants using different analysis methods. While we detect statistically significant, though subtle, differences in fixation pattern using an Area of Interest (AOI) approach, we fail to detect significant differences when applying a spatial density map approach. Though there were no significant differences in the spatial density maps, the qualitative patterns matched the results from the AOI analyses reflecting how, in certain contexts, Area of Interest (AOI) analyses can be more sensitive in detecting the differential fixation patterns than spatial density analyses, due to spatial pooling of data with AOIs. AOI analyses, however, also come with the limitation of requiring a priori specification. These findings provide evidence that the conflicting reports in the prior literature may be at least partially accounted for by the differences in the statistical sensitivity associated with the different analysis methods employed across studies. Overall, our results suggest that detection of differences in eye-movement patterns can be analysis-dependent and rests on the assumptions inherent in the given analysis. PMID:26849447

Heartbeat detection system using piezoelectric transducer

NASA Astrophysics Data System (ADS)

Hamonangan, Yosua; Purnamaningsih, Wigajatri

2017-02-01

This paper presents a simple piezoelectric based heartbeat detection system. The signal produced by the piezoelectric will undergo signal conditioning and then converted into digital data by Arduino Nano. Using serial communication, the data will be sent to a computer for display and further analysis. The detection of heartbeat is carried out on three locations; wrist, chest, and diaphragm. From the measurement results, it is shown that the system work best when the piezoelectric is placed on wrist.

Wind profiler signal detection improvements

NASA Technical Reports Server (NTRS)

Hart, G. F.; Divis, Dale H.

1992-01-01

Research is described on potential improvements to the software used with the NASA 49.25 MHz wind profiler located at Kennedy Space Center. In particular, the analysis and results are provided of a study to (1) identify preferred mathematical techniques for the detection of atmospheric signals that provide wind velocities which are obscured by natural and man-made sources, and (2) to analyze one or more preferred techniques to demonstrate proof of the capability to improve the detection of wind velocities.

A Vibration-Based Approach for Stator Winding Fault Diagnosis of Induction Motors: Application of Envelope Analysis

DTIC Science & Technology

2014-10-02

takes it either as auxiliary to magnetic flux, or is not able to detect the winding faults unless severity is already quite significant. This paper...different loads, speeds and severity levels. The experimental results show that the proposed method was able to detect inter-turn faults in the...maintenance strategy requires the technologies of: (a) on- line condition monitoring, (b) fault detection and diagnosis, and (c) prognostics. Figure 1

A Resonant Synchronous Vibration Based Approach for Rotor Imbalance Detection

NASA Technical Reports Server (NTRS)

Luo, Huangeng; Rodriquez, Hector; Hallman, Darren; Lewicki, David G.

2006-01-01

This paper presents a methodology of detecting rotor imbalances, such as mass imbalance and crack-induced imbalance, using shaft synchronous vibrations. An iterative scheme is developed to identify parameters from measured synchronous vibration data. A detection system is integrated by using state-of-the-art commercial analysis equipment. A laboratory rotor test rig is used to verify the system integration and algorithm validation. A real engine test has been carried out and the results are reported.

Projective techniques and the detection of child sexual abuse.

PubMed

Garb, H N; Wood, J M; Nezworski, M T

2000-05-01

Projective techniques (e.g., the Rorschach, Human Figure Drawings) are sometimes used to detect child sexual abuse. West recently conducted a meta-analysis on this topic, but she systematically excluded nonsignificant results. In this article, a reanalysis of her data is presented. The authors conclude that projective techniques should not be used to detect child sexual abuse. Many of the studies purportedly demonstrating validity are flawed, and none of the projective test scores have been well replicated.

Optimized Seizure Detection Algorithm: A Fast Approach for Onset of Epileptic in EEG Signals Using GT Discriminant Analysis and K-NN Classifier

PubMed Central

Rezaee, Kh.; Azizi, E.; Haddadnia, J.

2016-01-01

Background Epilepsy is a severe disorder of the central nervous system that predisposes the person to recurrent seizures. Fifty million people worldwide suffer from epilepsy; after Alzheimer’s and stroke, it is the third widespread nervous disorder. Objective In this paper, an algorithm to detect the onset of epileptic seizures based on the analysis of brain electrical signals (EEG) has been proposed. 844 hours of EEG were recorded form 23 pediatric patients consecutively with 163 occurrences of seizures. Signals had been collected from Children’s Hospital Boston with a sampling frequency of 256 Hz through 18 channels in order to assess epilepsy surgery. By selecting effective features from seizure and non-seizure signals of each individual and putting them into two categories, the proposed algorithm detects the onset of seizures quickly and with high sensitivity. Method In this algorithm, L-sec epochs of signals are displayed in form of a third-order tensor in spatial, spectral and temporal spaces by applying wavelet transform. Then, after applying general tensor discriminant analysis (GTDA) on tensors and calculating mapping matrix, feature vectors are extracted. GTDA increases the sensitivity of the algorithm by storing data without deleting them. Finally, K-Nearest neighbors (KNN) is used to classify the selected features. Results The results of simulating algorithm on algorithm standard dataset shows that the algorithm is capable of detecting 98 percent of seizures with an average delay of 4.7 seconds and the average error rate detection of three errors in 24 hours. Conclusion Today, the lack of an automated system to detect or predict the seizure onset is strongly felt. PMID:27672628

Qualitative metabolome analysis of human cerebrospinal fluid by 13C-/12C-isotope dansylation labeling combined with liquid chromatography Fourier transform ion cyclotron resonance mass spectrometry.

PubMed

Guo, Kevin; Bamforth, Fiona; Li, Liang

2011-02-01

Metabolome analysis of human cerebrospinal fluid (CSF) is challenging because of low abundance of metabolites present in a small volume of sample. We describe and apply a sensitive isotope labeling LC-MS technique for qualitative analysis of the CSF metabolome. After a CSF sample is divided into two aliquots, they are labeled by (13)C-dansyl and (12)C-dansyl chloride, respectively. The differentially labeled aliquots are then mixed and subjected to LC-MS using Fourier-transform ion cyclotron resonance mass spectrometry (FTICR MS). Dansylation offers significant improvement in the performance of chromatography separation and detection sensitivity. Moreover, peaks detected in the mass spectra can be readily analyzed for ion pair recognition and database search based on accurate mass and/or retention time information. It is shown that about 14,000 features can be detected in a 25-min LC-FTICR MS run of a dansyl-labeled CSF sample, from which about 500 metabolites can be profiled. Results from four CSF samples are compared to gauge the detectability of metabolites by this method. About 261 metabolites are commonly detected in replicate runs of four samples. In total, 1132 unique metabolite ion pairs are detected and 347 pairs (31%) matched with at least one metabolite in the Human Metabolome Database. We also report a dansylation library of 220 standard compounds and, using this library, about 85 metabolites can be positively identified. Among them, 21 metabolites have never been reported to be associated with CSF. These results illustrate that the dansylation LC-FTICR MS method can be used to analyze the CSF metabolome in a more comprehensive manner. © American Society for Mass Spectrometry, 2011

Multivariate image analysis of laser-induced photothermal imaging used for detection of caries tooth

NASA Astrophysics Data System (ADS)

El-Sherif, Ashraf F.; Abdel Aziz, Wessam M.; El-Sharkawy, Yasser H.

2010-08-01

Time-resolved photothermal imaging has been investigated to characterize tooth for the purpose of discriminating between normal and caries areas of the hard tissue using thermal camera. Ultrasonic thermoelastic waves were generated in hard tissue by the absorption of fiber-coupled Q-switched Nd:YAG laser pulses operating at 1064 nm in conjunction with a laser-induced photothermal technique used to detect the thermal radiation waves for diagnosis of human tooth. The concepts behind the use of photo-thermal techniques for off-line detection of caries tooth features were presented by our group in earlier work. This paper illustrates the application of multivariate image analysis (MIA) techniques to detect the presence of caries tooth. MIA is used to rapidly detect the presence and quantity of common caries tooth features as they scanned by the high resolution color (RGB) thermal cameras. Multivariate principal component analysis is used to decompose the acquired three-channel tooth images into a two dimensional principal components (PC) space. Masking score point clusters in the score space and highlighting corresponding pixels in the image space of the two dominant PCs enables isolation of caries defect pixels based on contrast and color information. The technique provides a qualitative result that can be used for early stage caries tooth detection. The proposed technique can potentially be used on-line or real-time resolved to prescreen the existence of caries through vision based systems like real-time thermal camera. Experimental results on the large number of extracted teeth as well as one of the thermal image panoramas of the human teeth voltanteer are investigated and presented.

Qualitative Metabolome Analysis of Human Cerebrospinal Fluid by 13C-/12C-Isotope Dansylation Labeling Combined with Liquid Chromatography Fourier Transform Ion Cyclotron Resonance Mass Spectrometry

NASA Astrophysics Data System (ADS)

Guo, Kevin; Bamforth, Fiona; Li, Liang

2011-02-01

Metabolome analysis of human cerebrospinal fluid (CSF) is challenging because of low abundance of metabolites present in a small volume of sample. We describe and apply a sensitive isotope labeling LC-MS technique for qualitative analysis of the CSF metabolome. After a CSF sample is divided into two aliquots, they are labeled by 13C-dansyl and 12C-dansyl chloride, respectively. The differentially labeled aliquots are then mixed and subjected to LC-MS using Fourier-transform ion cyclotron resonance mass spectrometry (FTICR MS). Dansylation offers significant improvement in the performance of chromatography separation and detection sensitivity. Moreover, peaks detected in the mass spectra can be readily analyzed for ion pair recognition and database search based on accurate mass and/or retention time information. It is shown that about 14,000 features can be detected in a 25-min LC-FTICR MS run of a dansyl-labeled CSF sample, from which about 500 metabolites can be profiled. Results from four CSF samples are compared to gauge the detectability of metabolites by this method. About 261 metabolites are commonly detected in replicate runs of four samples. In total, 1132 unique metabolite ion pairs are detected and 347 pairs (31%) matched with at least one metabolite in the Human Metabolome Database. We also report a dansylation library of 220 standard compounds and, using this library, about 85 metabolites can be positively identified. Among them, 21 metabolites have never been reported to be associated with CSF. These results illustrate that the dansylation LC-FTICR MS method can be used to analyze the CSF metabolome in a more comprehensive manner.

Development and validation of an universal interface for compound-specific stable isotope analysis of chlorine (37Cl/35Cl) by GC-high-temperature conversion (HTC)-MS/IRMS.

PubMed

Renpenning, Julian; Hitzfeld, Kristina L; Gilevska, Tetyana; Nijenhuis, Ivonne; Gehre, Matthias; Richnow, Hans-Hermann

2015-03-03

A universal application of compound-specific isotope analysis of chlorine was thus far limited by the availability of suitable analysis techniques. In this study, gas chromatography in combination with a high-temperature conversion interface (GC-HTC), converting organic chlorine in the presence of H2 to gaseous HCl, was coupled to a dual-detection system, combining an ion trap mass spectrometer (MS) and isotope-ratio mass spectrometer (IRMS). The combination of the MS/IRMS detection enabled a detailed characterization, optimization, and online monitoring of the high-temperature conversion process via ion trap MS as well as a simultaneous chlorine isotope analysis by the IRMS. Using GC-HTC-MS/IRMS, chlorine isotope analysis at optimized conversion conditions resulted in very accurate isotope values (δ(37)Cl(SMOC)) for measured reference material with known isotope composition, including chlorinated ethylene, chloromethane, hexachlorocyclohexane, and trichloroacetic acids methyl ester. Respective detection limits were determined to be <15 nmol Cl on column with achieved precision of <0.3‰.

Quality evaluation of moluodan concentrated pill using high-performance liquid chromatography fingerprinting coupled with chemometrics.

PubMed

Tao, Lingyan; Zhang, Qing; Wu, Yongjiang; Liu, Xuesong

2016-12-01

In this study, a fast and effective high-performance liquid chromatography method was developed to obtain a fingerprint chromatogram and quantitative analysis simultaneously of four indexes including gallic acid, chlorogenic acid, albiflorin and paeoniflorin of the traditional Chinese medicine Moluodan Concentrated Pill. The method was performed by using a Waters X-bridge C 18 reversed phase column on an Agilent 1200S high-performance liquid chromatography system coupled with diode array detection. The mobile phase of the high-performance liquid chromatography method was composed of 20 mmol/L phosphate solution and acetonitrile with a 1 mL/min eluent velocity, under a detection temperature of 30°C and a UV detection wavelength of 254 nm. After the methodology validation, 16 batches of Moluodan Concentrated Pill were analyzed by this high-performance liquid chromatography method and both qualitative and quantitative evaluation results were achieved by similarity analysis, principal component analysis and hierarchical cluster analysis. The results of these three chemometrics were in good agreement and all indicated that batch 10 and batch 16 showed significant differences with the other 14 batches. This suggested that the developed high-performance liquid chromatography method could be applied in the quality evaluation of Moluodan Concentrated Pill. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

An epidemiological survey of Theileria infections in small ruminants in central China.

PubMed

Li, Youquan; Zhang, Xiao; Liu, Zhijie; Chen, Ze; Yang, Jifei; He, Haining; Guan, Guiquan; Liu, Aihong; Ren, Qiaoyun; Niu, Qingli; Liu, Junlong; Luo, Jianxun; Yin, Hong

2014-02-24

Here, we conducted an epidemiological study in five regions in central China to assess the impact of theileriosis on small ruminants. PCR analysis and microscopic evaluations of blood smears to detect ovine and caprine theileriosis was conducted, in which 256 blood samples and 250 ticks were collected from sheep and goats, and tested for Theileria uilenbergi, T. luwenshuni, and T. ovis. The 18S rRNA gene sequences were deduced from positive samples and used for phylogenetic analysis. The results showed that T. luwenshuni was found most frequently in the five investigated regions and the prevalence of T. luwenshuni was found to be very high by PCR analysis. In contrast, T. uilenbergi and T. ovis infections were not detected in these regions. Phylogenetic tree analysis showed that all of the newly isolated Theileria spp. was in the same clade as T. luwenshuni. Haemaphysalis longicornis, which can transmit T. luwenshuni, was also detected in the sampled sheep and goats in these regions. Our results provide important data to increase the understanding of the epidemiology of ovine and caprine theileriosis, and will aid in the implementation of measures to control theileriosis transmission to small ruminants in central China. Copyright © 2013. Published by Elsevier B.V.

Molecular detection and genetic characterization of circulating measles virus in northern Italy.

PubMed

Piccirilli, Giulia; Chiereghin, Angela; Pascucci, Maria Grazia; Frasca, Gabriella; Zuntini, Roberta; Ferrari, Simona; Gabrielli, Liliana; Landini, Maria Paola; Lazzarotto, Tiziana

2016-08-01

Laboratory diagnosis of measles virus (MV) infection and genetic characterization of circulating MV play an essential role in measles surveillance, allowing proper interventions to interrupt endemic transmission. We describe results obtained using serological and molecular methods to confirm MV infection among suspected cases reported in a large region in the north of Italy during 2010-2014 and the genotyping of the MV strains detected. Three hundred seventy-two samples (361 urine and 11 oral fluids) were tested for MV-RNA detection. In 281 cases, the serological results for MV-IgM detection were also available. A total of 276 cases were classified as confirmed measles and MV-RNA detection resulted positive for 239/276 cases. Nucleotide sequence analysis revealed sporadic cases of genotypes D9 and different circulations of endemic MV strains (D8, D4 and B3). This data suggests that there is still an unvaccinated part of the population maintaining the endemic circulation of MV in Italy. Copyright © 2016 Elsevier B.V. All rights reserved.

Detecting non-orthology in the COGs database and other approaches grouping orthologs using genome-specific best hits.

PubMed

Dessimoz, Christophe; Boeckmann, Brigitte; Roth, Alexander C J; Gonnet, Gaston H

2006-01-01

Correct orthology assignment is a critical prerequisite of numerous comparative genomics procedures, such as function prediction, construction of phylogenetic species trees and genome rearrangement analysis. We present an algorithm for the detection of non-orthologs that arise by mistake in current orthology classification methods based on genome-specific best hits, such as the COGs database. The algorithm works with pairwise distance estimates, rather than computationally expensive and error-prone tree-building methods. The accuracy of the algorithm is evaluated through verification of the distribution of predicted cases, case-by-case phylogenetic analysis and comparisons with predictions from other projects using independent methods. Our results show that a very significant fraction of the COG groups include non-orthologs: using conservative parameters, the algorithm detects non-orthology in a third of all COG groups. Consequently, sequence analysis sensitive to correct orthology assignments will greatly benefit from these findings.

Efficient Enrichment and Analysis of Vicinal-Diol-Containing Flavonoid Molecules Using Boronic-Acid-Functionalized Particles and Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry.

PubMed

Kim, Eunjin; Kang, Hyunook; Choi, Insung; Song, Jihyeon; Mok, Hyejung; Jung, Woong; Yeo, Woon-Seok

2018-05-09

Detection and quantitation of flavonoids are relatively difficult compared to those of other small-molecule analytes because flavonoids undergo rapid metabolic processes, resulting in their elimination from the body. Here, we report an efficient enrichment method for facilitating the analysis of vicinal-diol-containing flavonoid molecules using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. In our strategy, boronic-acid-functionalized polyacrylamide particles were used, where boronic acids bound to vicinal diols to form boronate monoesters at basic pH. This complex remained intact during the enrichment processes, and the vicinal-diol-containing flavonoids were easily separated by centrifugation and subsequent acidic treatments. The selectivity and limit of detection of our strategy were confirmed by mass spectrometry analysis, and the validity was assessed by performing the detection and quantitation of quercetin in mouse organs.

Weak Fault Feature Extraction of Rolling Bearings Based on an Improved Kurtogram.

PubMed

Chen, Xianglong; Feng, Fuzhou; Zhang, Bingzhi

2016-09-13

Kurtograms have been verified to be an efficient tool in bearing fault detection and diagnosis because of their superiority in extracting transient features. However, the short-time Fourier Transform is insufficient in time-frequency analysis and kurtosis is deficient in detecting cyclic transients. Those factors weaken the performance of the original kurtogram in extracting weak fault features. Correlated Kurtosis (CK) is then designed, as a more effective solution, in detecting cyclic transients. Redundant Second Generation Wavelet Packet Transform (RSGWPT) is deemed to be effective in capturing more detailed local time-frequency description of the signal, and restricting the frequency aliasing components of the analysis results. The authors in this manuscript, combining the CK with the RSGWPT, propose an improved kurtogram to extract weak fault features from bearing vibration signals. The analysis of simulation signals and real application cases demonstrate that the proposed method is relatively more accurate and effective in extracting weak fault features.

Influence of Rack Design and Disease Prevalence on Detection of Rodent Pathogens in Exhaust Debris Samples from Individually Ventilated Caging Systems.

PubMed

Bauer, Beth A; Besch-Williford, Cynthia; Livingston, Robert S; Crim, Marcus J; Riley, Lela K; Myles, Matthew H

2016-11-01

Sampling of bedding debris within the exhaust systems of ventilated racks may be a mechanism for detecting murine pathogens in colony animals. This study examined the effectiveness of detecting pathogens by PCR analysis of exhaust debris samples collected from ventilated racks of 2 different rack designs, one with unfiltered air flow from within the cage to the air-exhaust pathway, and the other had a filter between the cage and the air-exhaust pathway. For 12 wk, racks were populated with either 1 or 5 cages of mice (3 mice per cage) infected with one of the following pathogens: mouse norovirus (MNV), mouse parvovirus (MPV), mouse hepatitis virus (MHV), Helicobacter spp., Pasteurella pneumotropica, pinworms, Entamoeba muris, Tritrichomonas muris, and fur mites. Pathogen shedding by infected mice was monitored throughout the study. In the filter-containing rack, PCR testing of exhaust plenums yielded negative results for all pathogens at all time points of the study. In the rack with open air flow, pathogens detected by PCR analysis of exhaust debris included MHV, Helicobacter spp., P. pneumotropica, pinworms, enteric protozoa, and fur mites; these pathogens were detected in racks housing either 1 or 5 cages of infected mice. Neither MPV nor MNV was detected in exhaust debris, even though prolonged viral shedding was confirmed. These results demonstrate that testing rack exhaust debris from racks with unfiltered air flow detected MHV, enteric bacteria and parasites, and fur mites. However, this method failed to reliably detect MNV or MPV infection of colony animals.

Influence of Rack Design and Disease Prevalence on Detection of Rodent Pathogens in Exhaust Debris Samples from Individually Ventilated Caging Systems

PubMed Central

Bauer, Beth A; Besch-Williford, Cynthia; Livingston, Robert S; Crim, Marcus J; Riley, Lela K; Myles, Matthew H

2016-01-01

Sampling of bedding debris within the exhaust systems of ventilated racks may be a mechanism for detecting murine pathogens in colony animals. This study examined the effectiveness of detecting pathogens by PCR analysis of exhaust debris samples collected from ventilated racks of 2 different rack designs, one with unfiltered air flow from within the cage to the air-exhaust pathway, and the other had a filter between the cage and the air-exhaust pathway. For 12 wk, racks were populated with either 1 or 5 cages of mice (3 mice per cage) infected with one of the following pathogens: mouse norovirus (MNV), mouse parvovirus (MPV), mouse hepatitis virus (MHV), Helicobacter spp., Pasteurella pneumotropica, pinworms, Entamoeba muris, Tritrichomonas muris, and fur mites. Pathogen shedding by infected mice was monitored throughout the study. In the filter-containing rack, PCR testing of exhaust plenums yielded negative results for all pathogens at all time points of the study. In the rack with open air flow, pathogens detected by PCR analysis of exhaust debris included MHV, Helicobacter spp., P. pneumotropica, pinworms, enteric protozoa, and fur mites; these pathogens were detected in racks housing either 1 or 5 cages of infected mice. Neither MPV nor MNV was detected in exhaust debris, even though prolonged viral shedding was confirmed. These results demonstrate that testing rack exhaust debris from racks with unfiltered air flow detected MHV, enteric bacteria and parasites, and fur mites. However, this method failed to reliably detect MNV or MPV infection of colony animals. PMID:27931317

Spoofing detection on facial images recognition using LBP and GLCM combination

NASA Astrophysics Data System (ADS)

Sthevanie, F.; Ramadhani, K. N.

2018-03-01

The challenge for the facial based security system is how to detect facial image falsification such as facial image spoofing. Spoofing occurs when someone try to pretend as a registered user to obtain illegal access and gain advantage from the protected system. This research implements facial image spoofing detection method by analyzing image texture. The proposed method for texture analysis combines the Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) method. The experimental results show that spoofing detection using LBP and GLCM combination achieves high detection rate compared to that of using only LBP feature or GLCM feature.

Design study of beam position monitors for measuring second-order moments of charged particle beams

NASA Astrophysics Data System (ADS)

Yanagida, Kenichi; Suzuki, Shinsuke; Hanaki, Hirofumi

2012-01-01

This paper presents a theoretical investigation on the multipole moments of charged particle beams in two-dimensional polar coordinates. The theoretical description of multipole moments is based on a single-particle system that is expanded to a multiparticle system by superposition, i.e., summing over all single-particle results. This paper also presents an analysis and design method for a beam position monitor (BPM) that detects higher-order (multipole) moments of a charged particle beam. To calculate the electric fields, a numerical analysis based on the finite difference method was created and carried out. Validity of the numerical analysis was proven by comparing the numerical with the analytical results for a BPM with circular cross section. Six-electrode BPMs with circular and elliptical cross sections were designed for the SPring-8 linac. The results of the numerical calculations show that the second-order moment can be detected for beam sizes ≧420μm (circular) and ≧550μm (elliptical).

Standoff Detection of Uranium and its Isotopes by Femtosecond Filament Laser Ablation Molecular Isotopic Spectrometry

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hartig, Kyle C.; Ghebregziabher, Isaac; Jovanovic, Igor

The ability to perform not only elementally but also isotopically sensitive detection and analysis at standoff distances is important for remote sensing applications in diverse ares, such as nuclear nonproliferation, environmental monitoring, geophysics, and planetary science. We demonstrate isotopically sensitive real-time standoff detection of uranium by the use of femtosecond filament-induced laser ablation molecular isotopic spectrometry. A uranium oxide molecular emission isotope shift of 0.05 ± 0.007 nm is reported at 593.6 nm. We implement both spectroscopic and acoustic diagnostics to characterize the properties of uranium plasma generated at different filament- uranium interaction points. The resulting uranium oxide emission exhibitsmore » a nearly constant signal-to-background ratio over the length of the filament, unlike the uranium atomic and ionic emission, for which the signal-to-background ratio varies significantly along the filament propagation. This is explained by the different rates of increase of plasma density and uranium oxide density along the filament length resulting from spectral and temporal evolution of the filament along its propagation. Lastly, the results provide a basis for the optimal use of filaments for standoff detection and analysis of uranium isotopes and indicate the potential of the technique for a wider range of remote sensing applications that require isotopic sensitivity.« less

Standoff Detection of Uranium and its Isotopes by Femtosecond Filament Laser Ablation Molecular Isotopic Spectrometry

PubMed Central

Hartig, Kyle C.; Ghebregziabher, Isaac; Jovanovic, Igor

2017-01-01

The ability to perform not only elementally but also isotopically sensitive detection and analysis at standoff distances is impor-tant for remote sensing applications in diverse ares, such as nuclear nonproliferation, environmental monitoring, geophysics, and planetary science. We demonstrate isotopically sensitive real-time standoff detection of uranium by the use of femtosecond filament-induced laser ablation molecular isotopic spectrometry. A uranium oxide molecular emission isotope shift of 0.05 ± 0.007 nm is reported at 593.6 nm. We implement both spectroscopic and acoustic diagnostics to characterize the properties of uranium plasma generated at different filament-uranium interaction points. The resulting uranium oxide emis-sion exhibits a nearly constant signal-to-background ratio over the length of the filament, unlike the uranium atomic and ionic emission, for which the signal-to-background ratio varies significantly along the filament propagation. This is explained by the different rates of increase of plasma density and uranium oxide density along the filament length resulting from spectral and temporal evolution of the filament along its propagation. The results provide a basis for the optimal use of filaments for standoff detection and analysis of uranium isotopes and indicate the potential of the technique for a wider range of remote sensing applications that require isotopic sensitivity. PMID:28272450