Comparison of methods for accurate end-point detection of potentiometric titrations

NASA Astrophysics Data System (ADS)

Villela, R. L. A.; Borges, P. P.; Vyskočil, L.

2015-01-01

Detection of the end point in potentiometric titrations has wide application on experiments that demand very low measurement uncertainties mainly for certifying reference materials. Simulations of experimental coulometric titration data and consequential error analysis of the end-point values were conducted using a programming code. These simulations revealed that the Levenberg-Marquardt method is in general more accurate than the traditional second derivative technique used currently as end-point detection for potentiometric titrations. Performance of the methods will be compared and presented in this paper.

Rapid and accurate peripheral nerve detection using multipoint Raman imaging (Conference Presentation)

NASA Astrophysics Data System (ADS)

Kumamoto, Yasuaki; Minamikawa, Takeo; Kawamura, Akinori; Matsumura, Junichi; Tsuda, Yuichiro; Ukon, Juichiro; Harada, Yoshinori; Tanaka, Hideo; Takamatsu, Tetsuro

2017-02-01

Nerve-sparing surgery is essential to avoid functional deficits of the limbs and organs. Raman scattering, a label-free, minimally invasive, and accurate modality, is one of the best candidate technologies to detect nerves for nerve-sparing surgery. However, Raman scattering imaging is too time-consuming to be employed in surgery. Here we present a rapid and accurate nerve visualization method using a multipoint Raman imaging technique that has enabled simultaneous spectra measurement from different locations (n=32) of a sample. Five sec is sufficient for measuring n=32 spectra with good S/N from a given tissue. Principal component regression discriminant analysis discriminated spectra obtained from peripheral nerves (n=863 from n=161 myelinated nerves) and connective tissue (n=828 from n=121 tendons) with sensitivity and specificity of 88.3% and 94.8%, respectively. To compensate the spatial information of a multipoint-Raman-derived tissue discrimination image that is too sparse to visualize nerve arrangement, we used morphological information obtained from a bright-field image. When merged with the sparse tissue discrimination image, a morphological image of a sample shows what portion of Raman measurement points in arbitrary structure is determined as nerve. Setting a nerve detection criterion on the portion of "nerve" points in the structure as 40% or more, myelinated nerves (n=161) and tendons (n=121) were discriminated with sensitivity and specificity of 97.5%. The presented technique utilizing a sparse multipoint Raman image and a bright-field image has enabled rapid, safe, and accurate detection of peripheral nerves.

A highly accurate wireless digital sun sensor based on profile detecting and detector multiplexing technologies

NASA Astrophysics Data System (ADS)

Wei, Minsong; Xing, Fei; You, Zheng

2017-01-01

The advancing growth of micro- and nano-satellites requires miniaturized sun sensors which could be conveniently applied in the attitude determination subsystem. In this work, a profile detecting technology based high accurate wireless digital sun sensor was proposed, which could transform a two-dimensional image into two-linear profile output so that it can realize a high update rate under a very low power consumption. A multiple spots recovery approach with an asymmetric mask pattern design principle was introduced to fit the multiplexing image detector method for accuracy improvement of the sun sensor within a large Field of View (FOV). A FOV determination principle based on the concept of FOV region was also proposed to facilitate both sub-FOV analysis and the whole FOV determination. A RF MCU, together with solar cells, was utilized to achieve the wireless and self-powered functionality. The prototype of the sun sensor is approximately 10 times lower in size and weight compared with the conventional digital sun sensor (DSS). Test results indicated that the accuracy of the prototype was 0.01° within a cone FOV of 100°. Such an autonomous DSS could be equipped flexibly on a micro- or nano-satellite, especially for highly accurate remote sensing applications.

Breaking Snake Camouflage: Humans Detect Snakes More Accurately than Other Animals under Less Discernible Visual Conditions.

PubMed

Kawai, Nobuyuki; He, Hongshen

2016-01-01

Humans and non-human primates are extremely sensitive to snakes as exemplified by their ability to detect pictures of snakes more quickly than those of other animals. These findings are consistent with the Snake Detection Theory, which hypothesizes that as predators, snakes were a major source of evolutionary selection that favored expansion of the visual system of primates for rapid snake detection. Many snakes use camouflage to conceal themselves from both prey and their own predators, making it very challenging to detect them. If snakes have acted as a selective pressure on primate visual systems, they should be more easily detected than other animals under difficult visual conditions. Here we tested whether humans discerned images of snakes more accurately than those of non-threatening animals (e.g., birds, cats, or fish) under conditions of less perceptual information by presenting a series of degraded images with the Random Image Structure Evolution technique (interpolation of random noise). We find that participants recognize mosaic images of snakes, which were regarded as functionally equivalent to camouflage, more accurately than those of other animals under dissolved conditions. The present study supports the Snake Detection Theory by showing that humans have a visual system that accurately recognizes snakes under less discernible visual conditions.

Breaking Snake Camouflage: Humans Detect Snakes More Accurately than Other Animals under Less Discernible Visual Conditions

PubMed Central

He, Hongshen

2016-01-01

Humans and non-human primates are extremely sensitive to snakes as exemplified by their ability to detect pictures of snakes more quickly than those of other animals. These findings are consistent with the Snake Detection Theory, which hypothesizes that as predators, snakes were a major source of evolutionary selection that favored expansion of the visual system of primates for rapid snake detection. Many snakes use camouflage to conceal themselves from both prey and their own predators, making it very challenging to detect them. If snakes have acted as a selective pressure on primate visual systems, they should be more easily detected than other animals under difficult visual conditions. Here we tested whether humans discerned images of snakes more accurately than those of non-threatening animals (e.g., birds, cats, or fish) under conditions of less perceptual information by presenting a series of degraded images with the Random Image Structure Evolution technique (interpolation of random noise). We find that participants recognize mosaic images of snakes, which were regarded as functionally equivalent to camouflage, more accurately than those of other animals under dissolved conditions. The present study supports the Snake Detection Theory by showing that humans have a visual system that accurately recognizes snakes under less discernible visual conditions. PMID:27783686

Towards Accurate Node-Based Detection of P2P Botnets

PubMed Central

2014-01-01

Botnets are a serious security threat to the current Internet infrastructure. In this paper, we propose a novel direction for P2P botnet detection called node-based detection. This approach focuses on the network characteristics of individual nodes. Based on our model, we examine node's flows and extract the useful features over a given time period. We have tested our approach on real-life data sets and achieved detection rates of 99-100% and low false positives rates of 0–2%. Comparison with other similar approaches on the same data sets shows that our approach outperforms the existing approaches. PMID:25089287

Accurate detection of blood vessels improves the detection of exudates in color fundus images.

PubMed

Youssef, Doaa; Solouma, Nahed H

2012-12-01

Exudates are one of the earliest and most prevalent symptoms of diseases leading to blindness such as diabetic retinopathy and macular degeneration. Certain areas of the retina with such conditions are to be photocoagulated by laser to stop the disease progress and prevent blindness. Outlining these areas is dependent on outlining the lesions and the anatomic structures of the retina. In this paper, we provide a new method for the detection of blood vessels that improves the detection of exudates in fundus photographs. The method starts with an edge detection algorithm which results in a over segmented image. Then the new feature-based algorithm can be used to accurately detect the blood vessels. This algorithm considers the characteristics of a retinal blood vessel such as its width range, intensities and orientations for the purpose of selective segmentation. Because of its bulb shape and its color similarity with exudates, the optic disc can be detected using the common Hough transform technique. The extracted blood vessel tree and optic disc could be subtracted from the over segmented image to get an initial estimate of exudates. The final estimation of exudates can then be obtained by morphological reconstruction based on the appearance of exudates. This method is shown to be promising since it increases the sensitivity and specificity of exudates detection to 80% and 100% respectively. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Parkinsonian rest tremor can be detected accurately based on neuronal oscillations recorded from the subthalamic nucleus.

PubMed

Hirschmann, J; Schoffelen, J M; Schnitzler, A; van Gerven, M A J

2017-10-01

To investigate the possibility of tremor detection based on deep brain activity. We re-analyzed recordings of local field potentials (LFPs) from the subthalamic nucleus in 10 PD patients (12 body sides) with spontaneously fluctuating rest tremor. Power in several frequency bands was estimated and used as input to Hidden Markov Models (HMMs) which classified short data segments as either tremor-free rest or rest tremor. HMMs were compared to direct threshold application to individual power features. Applying a threshold directly to band-limited power was insufficient for tremor detection (mean area under the curve [AUC] of receiver operating characteristic: 0.64, STD: 0.19). Multi-feature HMMs, in contrast, allowed for accurate detection (mean AUC: 0.82, STD: 0.15), using four power features obtained from a single contact pair. Within-patient training yielded better accuracy than across-patient training (0.84vs. 0.78, p=0.03), yet tremor could often be detected accurately with either approach. High frequency oscillations (>200Hz) were the best performing individual feature. LFP-based markers of tremor are robust enough to allow for accurate tremor detection in short data segments, provided that appropriate statistical models are used. LFP-based markers of tremor could be useful control signals for closed-loop deep brain stimulation. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

SpotCaliper: fast wavelet-based spot detection with accurate size estimation.

PubMed

Püspöki, Zsuzsanna; Sage, Daniel; Ward, John Paul; Unser, Michael

2016-04-15

SpotCaliper is a novel wavelet-based image-analysis software providing a fast automatic detection scheme for circular patterns (spots), combined with the precise estimation of their size. It is implemented as an ImageJ plugin with a friendly user interface. The user is allowed to edit the results by modifying the measurements (in a semi-automated way), extract data for further analysis. The fine tuning of the detections includes the possibility of adjusting or removing the original detections, as well as adding further spots. The main advantage of the software is its ability to capture the size of spots in a fast and accurate way. http://bigwww.epfl.ch/algorithms/spotcaliper/ zsuzsanna.puspoki@epfl.ch Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Fast and accurate spectral estimation for online detection of partial broken bar in induction motors

NASA Astrophysics Data System (ADS)

Samanta, Anik Kumar; Naha, Arunava; Routray, Aurobinda; Deb, Alok Kanti

2018-01-01

In this paper, an online and real-time system is presented for detecting partial broken rotor bar (BRB) of inverter-fed squirrel cage induction motors under light load condition. This system with minor modifications can detect any fault that affects the stator current. A fast and accurate spectral estimator based on the theory of Rayleigh quotient is proposed for detecting the spectral signature of BRB. The proposed spectral estimator can precisely determine the relative amplitude of fault sidebands and has low complexity compared to available high-resolution subspace-based spectral estimators. Detection of low-amplitude fault components has been improved by removing the high-amplitude fundamental frequency using an extended-Kalman based signal conditioner. Slip is estimated from the stator current spectrum for accurate localization of the fault component. Complexity and cost of sensors are minimal as only a single-phase stator current is required. The hardware implementation has been carried out on an Intel i7 based embedded target ported through the Simulink Real-Time. Evaluation of threshold and detectability of faults with different conditions of load and fault severity are carried out with empirical cumulative distribution function.

Improved Detection System Description and New Method for Accurate Calibration of Micro-Channel Plate Based Instruments and Its Use in the Fast Plasma Investigation on NASA's Magnetospheric MultiScale Mission

NASA Technical Reports Server (NTRS)

Gliese, U.; Avanov, L. A.; Barrie, A. C.; Kujawski, J. T.; Mariano, A. J.; Tucker, C. J.; Chornay, D. J.; Cao, N. T.; Gershman, D. J.; Dorelli, J. C.;

New Method for Accurate Calibration of Micro-Channel Plate based Detection Systems and its use in the Fast Plasma Investigation of NASA's Magnetospheric MultiScale Mission

NASA Astrophysics Data System (ADS)

Gliese, U.; Avanov, L. A.; Barrie, A.; Kujawski, J. T.; Mariano, A. J.; Tucker, C. J.; Chornay, D. J.; Cao, N. T.; Zeuch, M.; Pollock, C. J.; Jacques, A. D.

2013-12-01

The Fast Plasma Investigation (FPI) of the NASA Magnetospheric MultiScale (MMS) mission employs 16 Dual Electron Spectrometers (DESs) and 16 Dual Ion Spectrometers (DISs) with 4 of each type on each of 4 spacecraft to enable fast (30ms for electrons; 150ms for ions) and spatially differentiated measurements of full the 3D particle velocity distributions. This approach presents a new and challenging aspect to the calibration and operation of these instruments on ground and in flight. The response uniformity and reliability of their calibration and the approach to handling any temporal evolution of these calibrated characteristics all assume enhanced importance in this application, where we attempt to understand the meaning of particle distributions within the ion and electron diffusion regions. Traditionally, the micro-channel plate (MCP) based detection systems for electrostatic particle spectrometers have been calibrated by setting a fixed detection threshold and, subsequently, measuring a detection system count rate plateau curve to determine the MCP voltage that ensures the count rate has reached a constant value independent of further variation in the MCP voltage. This is achieved when most of the MCP pulse height distribution (PHD) is located at higher values (larger pulses) than the detection amplifier threshold. This method is adequate in single-channel detection systems and in multi-channel detection systems with very low crosstalk between channels. However, in dense multi-channel systems, it can be inadequate. Furthermore, it fails to fully and individually characterize each of the fundamental parameters of the detection system. We present a new detection system calibration method that enables accurate and repeatable measurement and calibration of MCP gain, MCP efficiency, signal loss due to variation in gain and efficiency, crosstalk from effects both above and below the MCP, noise margin, and stability margin in one single measurement. The fundamental

Lung ultrasound accurately detects pneumothorax in a preterm newborn lamb model.

PubMed

Blank, Douglas A; Hooper, Stuart B; Binder-Heschl, Corinna; Kluckow, Martin; Gill, Andrew W; LaRosa, Domenic A; Inocencio, Ishmael M; Moxham, Alison; Rodgers, Karyn; Zahra, Valerie A; Davis, Peter G; Polglase, Graeme R

2016-06-01

Pneumothorax is a common emergency affecting extremely preterm. In adult studies, lung ultrasound has performed better than chest x-ray in the diagnosis of pneumothorax. The purpose of this study was to determine the efficacy of lung ultrasound (LUS) examination to detect pneumothorax using a preterm animal model. This was a prospective, observational study using newborn Border-Leicester lambs at gestational age = 126 days (equivalent to gestational age = 26 weeks in humans) receiving mechanical ventilation from birth to 2 h of life. At the conclusion of the experiment, LUS was performed, the lambs were then euthanised and a post-mortem exam was immediately performed. We used previously published ultrasound techniques to identify pneumothorax. Test characteristics of LUS to detect pneumothorax were calculated, using the post-mortem exam as the 'gold standard' test. Nine lambs (18 lungs) were examined. Four lambs had a unilateral pneumothorax, all of which were identified by LUS with no false positives. This was the first study to use post-mortem findings to test the efficacy of LUS to detect pneumothorax in a newborn animal model. Lung ultrasound accurately detected pneumothorax, verified by post-mortem exam, in premature, newborn lambs. © 2016 Paediatrics and Child Health Division (The Royal Australasian College of Physicians).

Accurate Fall Detection in a Top View Privacy Preserving Configuration.

PubMed

Ricciuti, Manola; Spinsante, Susanna; Gambi, Ennio

2018-05-29

Fall detection is one of the most investigated themes in the research on assistive solutions for aged people. In particular, a false-alarm-free discrimination between falls and non-falls is indispensable, especially to assist elderly people living alone. Current technological solutions designed to monitor several types of activities in indoor environments can guarantee absolute privacy to the people that decide to rely on them. Devices integrating RGB and depth cameras, such as the Microsoft Kinect, can ensure privacy and anonymity, since the depth information is considered to extract only meaningful information from video streams. In this paper, we propose an accurate fall detection method investigating the depth frames of the human body using a single device in a top-view configuration, with the subjects located under the device inside a room. Features extracted from depth frames train a classifier based on a binary support vector machine learning algorithm. The dataset includes 32 falls and 8 activities considered for comparison, for a total of 800 sequences performed by 20 adults. The system showed an accuracy of 98.6% and only one false positive.

An Accurate Framework for Arbitrary View Pedestrian Detection in Images

NASA Astrophysics Data System (ADS)

Fan, Y.; Wen, G.; Qiu, S.

2018-01-01

We consider the problem of detect pedestrian under from images collected under various viewpoints. This paper utilizes a novel framework called locality-constrained affine subspace coding (LASC). Firstly, the positive training samples are clustered into similar entities which represent similar viewpoint. Then Principal Component Analysis (PCA) is used to obtain the shared feature of each viewpoint. Finally, the samples that can be reconstructed by linear approximation using their top- k nearest shared feature with a small error are regarded as a correct detection. No negative samples are required for our method. Histograms of orientated gradient (HOG) features are used as the feature descriptors, and the sliding window scheme is adopted to detect humans in images. The proposed method exploits the sparse property of intrinsic information and the correlations among the multiple-views samples. Experimental results on the INRIA and SDL human datasets show that the proposed method achieves a higher performance than the state-of-the-art methods in form of effect and efficiency.

Data-Mining Techniques in Detecting Factors Linked to Academic Achievement

ERIC Educational Resources Information Center

Martínez Abad, Fernando; Chaparro Caso López, Alicia A.

2017-01-01

In light of the emergence of statistical analysis techniques based on data mining in education sciences, and the potential they offer to detect non-trivial information in large databases, this paper presents a procedure used to detect factors linked to academic achievement in large-scale assessments. The study is based on a non-experimental,…

Radio Detection of Cosmic Rays-Achievements and Future Potential

NASA Astrophysics Data System (ADS)

Huege, Tim

When modern efforts for radio detection of cosmic rays started about a decade ago, hopes were high but the true potential was unknown. Since then, we have achieved a detailed understanding of the radio emission physics and have consequently succeeded in developing sophisticated detection schemes and analysis approaches. In particular, we have demonstrated that the important air-shower parameters arrival direction, particle energy and depth of shower maximum can be reconstructed reliably from radio measurements, with a precision that is comparable with that of other detection techniques. At the same time, limitations inherent to the radio-emission mechanisms have become apparent. In this article, I shortly review the capabilities of radio detection in the very high-frequency band, and discuss the potential for future application in existing and new experiments for cosmic-ray detection.

Methodological Guidelines for Accurate Detection of Viruses in Wild Plant Species

PubMed Central

Renner, Kurra; Cole, Ellen; Seabloom, Eric W.; Borer, Elizabeth T.; Malmstrom, Carolyn M.

2016-01-01

Ecological understanding of disease risk, emergence, and dynamics and of the efficacy of control strategies relies heavily on efficient tools for microorganism identification and characterization. Misdetection, such as the misclassification of infected hosts as healthy, can strongly bias estimates of disease prevalence and lead to inaccurate conclusions. In natural plant ecosystems, interest in assessing microbial dynamics is increasing exponentially, but guidelines for detection of microorganisms in wild plants remain limited, particularly so for plant viruses. To address this gap, we explored issues and solutions associated with virus detection by serological and molecular methods in noncrop plant species as applied to the globally important Barley yellow dwarf virus PAV (Luteoviridae), which infects wild native plants as well as crops. With enzyme-linked immunosorbent assays (ELISA), we demonstrate how virus detection in a perennial wild plant species may be much greater in stems than in leaves, although leaves are most commonly sampled, and may also vary among tillers within an individual, thereby highlighting the importance of designing effective sampling strategies. With reverse transcription-PCR (RT-PCR), we demonstrate how inhibitors in tissues of perennial wild hosts can suppress virus detection but can be overcome with methods and products that improve isolation and amplification of nucleic acids. These examples demonstrate the paramount importance of testing and validating survey designs and virus detection methods for noncrop plant communities to ensure accurate ecological surveys and reliable assumptions about virus dynamics in wild hosts. PMID:26773088

Methodological Guidelines for Accurate Detection of Viruses in Wild Plant Species.

PubMed

Lacroix, Christelle; Renner, Kurra; Cole, Ellen; Seabloom, Eric W; Borer, Elizabeth T; Malmstrom, Carolyn M

2016-01-15

Ecological understanding of disease risk, emergence, and dynamics and of the efficacy of control strategies relies heavily on efficient tools for microorganism identification and characterization. Misdetection, such as the misclassification of infected hosts as healthy, can strongly bias estimates of disease prevalence and lead to inaccurate conclusions. In natural plant ecosystems, interest in assessing microbial dynamics is increasing exponentially, but guidelines for detection of microorganisms in wild plants remain limited, particularly so for plant viruses. To address this gap, we explored issues and solutions associated with virus detection by serological and molecular methods in noncrop plant species as applied to the globally important Barley yellow dwarf virus PAV (Luteoviridae), which infects wild native plants as well as crops. With enzyme-linked immunosorbent assays (ELISA), we demonstrate how virus detection in a perennial wild plant species may be much greater in stems than in leaves, although leaves are most commonly sampled, and may also vary among tillers within an individual, thereby highlighting the importance of designing effective sampling strategies. With reverse transcription-PCR (RT-PCR), we demonstrate how inhibitors in tissues of perennial wild hosts can suppress virus detection but can be overcome with methods and products that improve isolation and amplification of nucleic acids. These examples demonstrate the paramount importance of testing and validating survey designs and virus detection methods for noncrop plant communities to ensure accurate ecological surveys and reliable assumptions about virus dynamics in wild hosts. Copyright © 2016, American Society for Microbiology. All Rights Reserved.

Accurate Sybil Attack Detection Based on Fine-Grained Physical Channel Information.

PubMed

Wang, Chundong; Zhu, Likun; Gong, Liangyi; Zhao, Zhentang; Yang, Lei; Liu, Zheli; Cheng, Xiaochun

2018-03-15

With the development of the Internet-of-Things (IoT), wireless network security has more and more attention paid to it. The Sybil attack is one of the famous wireless attacks that can forge wireless devices to steal information from clients. These forged devices may constantly attack target access points to crush the wireless network. In this paper, we propose a novel Sybil attack detection based on Channel State Information (CSI). This detection algorithm can tell whether the static devices are Sybil attackers by combining a self-adaptive multiple signal classification algorithm with the Received Signal Strength Indicator (RSSI). Moreover, we develop a novel tracing scheme to cluster the channel characteristics of mobile devices and detect dynamic attackers that change their channel characteristics in an error area. Finally, we experiment on mobile and commercial WiFi devices. Our algorithm can effectively distinguish the Sybil devices. The experimental results show that our Sybil attack detection system achieves high accuracy for both static and dynamic scenarios. Therefore, combining the phase and similarity of channel features, the multi-dimensional analysis of CSI can effectively detect Sybil nodes and improve the security of wireless networks.

Accurate Sybil Attack Detection Based on Fine-Grained Physical Channel Information

PubMed Central

Wang, Chundong; Zhao, Zhentang; Yang, Lei; Liu, Zheli; Cheng, Xiaochun

2018-01-01

With the development of the Internet-of-Things (IoT), wireless network security has more and more attention paid to it. The Sybil attack is one of the famous wireless attacks that can forge wireless devices to steal information from clients. These forged devices may constantly attack target access points to crush the wireless network. In this paper, we propose a novel Sybil attack detection based on Channel State Information (CSI). This detection algorithm can tell whether the static devices are Sybil attackers by combining a self-adaptive multiple signal classification algorithm with the Received Signal Strength Indicator (RSSI). Moreover, we develop a novel tracing scheme to cluster the channel characteristics of mobile devices and detect dynamic attackers that change their channel characteristics in an error area. Finally, we experiment on mobile and commercial WiFi devices. Our algorithm can effectively distinguish the Sybil devices. The experimental results show that our Sybil attack detection system achieves high accuracy for both static and dynamic scenarios. Therefore, combining the phase and similarity of channel features, the multi-dimensional analysis of CSI can effectively detect Sybil nodes and improve the security of wireless networks. PMID:29543773

Detection of multiple damages employing best achievable eigenvectors under Bayesian inference

NASA Astrophysics Data System (ADS)

Prajapat, Kanta; Ray-Chaudhuri, Samit

2018-05-01

A novel approach is presented in this work to localize simultaneously multiple damaged elements in a structure along with the estimation of damage severity for each of the damaged elements. For detection of damaged elements, a best achievable eigenvector based formulation has been derived. To deal with noisy data, Bayesian inference is employed in the formulation wherein the likelihood of the Bayesian algorithm is formed on the basis of errors between the best achievable eigenvectors and the measured modes. In this approach, the most probable damage locations are evaluated under Bayesian inference by generating combinations of various possible damaged elements. Once damage locations are identified, damage severities are estimated using a Bayesian inference Markov chain Monte Carlo simulation. The efficiency of the proposed approach has been demonstrated by carrying out a numerical study involving a 12-story shear building. It has been found from this study that damage scenarios involving as low as 10% loss of stiffness in multiple elements are accurately determined (localized and severities quantified) even when 2% noise contaminated modal data are utilized. Further, this study introduces a term parameter impact (evaluated based on sensitivity of modal parameters towards structural parameters) to decide the suitability of selecting a particular mode, if some idea about the damaged elements are available. It has been demonstrated here that the accuracy and efficiency of the Bayesian quantification algorithm increases if damage localization is carried out a-priori. An experimental study involving a laboratory scale shear building and different stiffness modification scenarios shows that the proposed approach is efficient enough to localize the stories with stiffness modification.

Accurate clinical detection of exon copy number variants in a targeted NGS panel using DECoN.

PubMed

Fowler, Anna; Mahamdallie, Shazia; Ruark, Elise; Seal, Sheila; Ramsay, Emma; Clarke, Matthew; Uddin, Imran; Wylie, Harriet; Strydom, Ann; Lunter, Gerton; Rahman, Nazneen

2016-11-25

Background: Targeted next generation sequencing (NGS) panels are increasingly being used in clinical genomics to increase capacity, throughput and affordability of gene testing. Identifying whole exon deletions or duplications (termed exon copy number variants, 'exon CNVs') in exon-targeted NGS panels has proved challenging, particularly for single exon CNVs.  Methods: We developed a tool for the Detection of Exon Copy Number variants (DECoN), which is optimised for analysis of exon-targeted NGS panels in the clinical setting. We evaluated DECoN performance using 96 samples with independently validated exon CNV data. We performed simulations to evaluate DECoN detection performance of single exon CNVs and to evaluate performance using different coverage levels and sample numbers. Finally, we implemented DECoN in a clinical laboratory that tests BRCA1 and BRCA2 with the TruSight Cancer Panel (TSCP). We used DECoN to analyse 1,919 samples, validating exon CNV detections by multiplex ligation-dependent probe amplification (MLPA).  Results: In the evaluation set, DECoN achieved 100% sensitivity and 99% specificity for BRCA exon CNVs, including identification of 8 single exon CNVs. DECoN also identified 14/15 exon CNVs in 8 other genes. Simulations of all possible BRCA single exon CNVs gave a mean sensitivity of 98% for deletions and 95% for duplications. DECoN performance remained excellent with different levels of coverage and sample numbers; sensitivity and specificity was >98% with the typical NGS run parameters. In the clinical pipeline, DECoN automatically analyses pools of 48 samples at a time, taking 24 minutes per pool, on average. DECoN detected 24 BRCA exon CNVs, of which 23 were confirmed by MLPA, giving a false discovery rate of 4%. Specificity was 99.7%.  Conclusions: DECoN is a fast, accurate, exon CNV detection tool readily implementable in research and clinical NGS pipelines. It has high sensitivity and specificity and acceptable false discovery rate

Tissue resonance interaction accurately detects colon lesions: A double-blind pilot study.

PubMed

Dore, Maria P; Tufano, Marcello O; Pes, Giovanni M; Cuccu, Marianna; Farina, Valentina; Manca, Alessandra; Graham, David Y

2015-07-07

To investigated the performance of the tissue resonance interaction method (TRIM) for the non-invasive detection of colon lesions. We performed a prospective single-center blinded pilot study of consecutive adults undergoing colonoscopy at the University Hospital in Sassari, Italy. Before patients underwent colonoscopy, they were examined by the TRIMprobe which detects differences in electromagnetic properties between pathological and normal tissues. All patients had completed the polyethylene glycol-containing bowel prep for the colonoscopy procedure before being screened. During the procedure the subjects remained fully dressed. A hand-held probe was moved over the abdomen and variations in electromagnetic signals were recorded for 3 spectral lines (462-465 MHz, 930 MHz, and 1395 MHz). A single investigator, blind to any clinical information, performed the test using the TRIMprob system. Abnormal signals were identified and recorded as malignant or benign (adenoma or hyperplastic polyps). Findings were compared with those from colonoscopy with histologic confirmation. Statistical analysis was performed by χ(2) test. A total of 305 consecutive patients fulfilling the inclusion criteria were enrolled over a period of 12 months. The most frequent indication for colonoscopy was abdominal pain (33%). The TRIMprob was well accepted by all patients; none spontaneously complained about the procedure, and no adverse effects were observed. TRIM proved inaccurate for polyp detection in patients with inflammatory bowel disease (IBD) and they were excluded leaving 281 subjects (mean age 59 ± 13 years; 107 males). The TRIM detected and accurately characterized all 12 adenocarcinomas and 135/137 polyps (98.5%) including 64 adenomatous (100%) found. The method identified cancers and polyps with 98.7% sensitivity, 96.2% specificity, and 97.5% diagnostic accuracy, compared to colonoscopy and histology analyses. The positive predictive value was 96.7% and the negative predictive

Tissue resonance interaction accurately detects colon lesions: A double-blind pilot study

PubMed Central

Dore, Maria P; Tufano, Marcello O; Pes, Giovanni M; Cuccu, Marianna; Farina, Valentina; Manca, Alessandra; Graham, David Y

2015-01-01

AIM: To investigated the performance of the tissue resonance interaction method (TRIM) for the non-invasive detection of colon lesions. METHODS: We performed a prospective single-center blinded pilot study of consecutive adults undergoing colonoscopy at the University Hospital in Sassari, Italy. Before patients underwent colonoscopy, they were examined by the TRIMprobe which detects differences in electromagnetic properties between pathological and normal tissues. All patients had completed the polyethylene glycol-containing bowel prep for the colonoscopy procedure before being screened. During the procedure the subjects remained fully dressed. A hand-held probe was moved over the abdomen and variations in electromagnetic signals were recorded for 3 spectral lines (462-465 MHz, 930 MHz, and 1395 MHz). A single investigator, blind to any clinical information, performed the test using the TRIMprob system. Abnormal signals were identified and recorded as malignant or benign (adenoma or hyperplastic polyps). Findings were compared with those from colonoscopy with histologic confirmation. Statistical analysis was performed by χ2 test. RESULTS: A total of 305 consecutive patients fulfilling the inclusion criteria were enrolled over a period of 12 months. The most frequent indication for colonoscopy was abdominal pain (33%). The TRIMprob was well accepted by all patients; none spontaneously complained about the procedure, and no adverse effects were observed. TRIM proved inaccurate for polyp detection in patients with inflammatory bowel disease (IBD) and they were excluded leaving 281 subjects (mean age 59 ± 13 years; 107 males). The TRIM detected and accurately characterized all 12 adenocarcinomas and 135/137 polyps (98.5%) including 64 adenomatous (100%) found. The method identified cancers and polyps with 98.7% sensitivity, 96.2% specificity, and 97.5% diagnostic accuracy, compared to colonoscopy and histology analyses. The positive predictive value was 96.7% and the

Accurate SERS detection of malachite green in aquatic products on basis of graphene wrapped flexible sensor.

PubMed

Ouyang, Lei; Yao, Ling; Zhou, Taohong; Zhu, Lihua

2018-10-16

Malachite Green (MG) is a banned pesticide for aquaculture products. As a required inspection item, its fast and accurate determination before the products' accessing market is very important. Surface enhanced Raman scattering (SERS) is a promising tool for MG sensing, but it requires the overcoming of several problems such as fairly poor sensitivity and reproducibility, especially laser induced chemical conversion and photo-bleaching during SERS observation. By using a graphene wrapped Ag array based flexible membrane sensor, a modified SERS strategy was proposed for the sensitive and accurate detection of MG. The graphene layer functioned as an inert protector for impeding chemical transferring of the bioproduct Leucomalachite Green (LMG) to MG during the SERS detection, and as a heat transmitter for preventing laser induced photo-bleaching, which enables the separate detection of MG and LMG in fish extracts. The combination of the Ag array and the graphene cover also produced plentiful densely and uniformly distributed hot spots, leading to analytical enhancement factor up to 3.9 × 10 8 and excellent reproducibility (relative standard deviation low to 5.8% for 70 runs). The proposed method was easily used for MG detection with limit of detection (LOD) as low as 2.7 × 10 -11  mol L -1 . The flexibility of the sensor enable it have a merit for in-field fast detection of MG residues on the scale of a living fish through a surface extraction and paste transferring manner. The developed strategy was successfully applied in the analysis of real samples, showing good prospects for both the fast inspection and quantitative detection of MG. Copyright © 2018 Elsevier B.V. All rights reserved.

Natural Language Processing Accurately Calculates Adenoma and Sessile Serrated Polyp Detection Rates.

PubMed

Nayor, Jennifer; Borges, Lawrence F; Goryachev, Sergey; Gainer, Vivian S; Saltzman, John R

2018-07-01

ADR is a widely used colonoscopy quality indicator. Calculation of ADR is labor-intensive and cumbersome using current electronic medical databases. Natural language processing (NLP) is a method used to extract meaning from unstructured or free text data. (1) To develop and validate an accurate automated process for calculation of adenoma detection rate (ADR) and serrated polyp detection rate (SDR) on data stored in widely used electronic health record systems, specifically Epic electronic health record system, Provation ® endoscopy reporting system, and Sunquest PowerPath pathology reporting system. Screening colonoscopies performed between June 2010 and August 2015 were identified using the Provation ® reporting tool. An NLP pipeline was developed to identify adenomas and sessile serrated polyps (SSPs) on pathology reports corresponding to these colonoscopy reports. The pipeline was validated using a manual search. Precision, recall, and effectiveness of the natural language processing pipeline were calculated. ADR and SDR were then calculated. We identified 8032 screening colonoscopies that were linked to 3821 pathology reports (47.6%). The NLP pipeline had an accuracy of 100% for adenomas and 100% for SSPs. Mean total ADR was 29.3% (range 14.7-53.3%); mean male ADR was 35.7% (range 19.7-62.9%); and mean female ADR was 24.9% (range 9.1-51.0%). Mean total SDR was 4.0% (0-9.6%). We developed and validated an NLP pipeline that accurately and automatically calculates ADRs and SDRs using data stored in Epic, Provation ® and Sunquest PowerPath. This NLP pipeline can be used to evaluate colonoscopy quality parameters at both individual and practice levels.

Highly accurate surface maps from profilometer measurements

NASA Astrophysics Data System (ADS)

Medicus, Kate M.; Nelson, Jessica D.; Mandina, Mike P.

2013-04-01

Many aspheres and free-form optical surfaces are measured using a single line trace profilometer which is limiting because accurate 3D corrections are not possible with the single trace. We show a method to produce an accurate fully 2.5D surface height map when measuring a surface with a profilometer using only 6 traces and without expensive hardware. The 6 traces are taken at varying angular positions of the lens, rotating the part between each trace. The output height map contains low form error only, the first 36 Zernikes. The accuracy of the height map is ±10% of the actual Zernike values and within ±3% of the actual peak to valley number. The calculated Zernike values are affected by errors in the angular positioning, by the centering of the lens, and to a small effect, choices made in the processing algorithm. We have found that the angular positioning of the part should be better than 1?, which is achievable with typical hardware. The centering of the lens is essential to achieving accurate measurements. The part must be centered to within 0.5% of the diameter to achieve accurate results. This value is achievable with care, with an indicator, but the part must be edged to a clean diameter.

A Comprehensive Strategy for Accurate Mutation Detection of the Highly Homologous PMS2.

PubMed

Li, Jianli; Dai, Hongzheng; Feng, Yanming; Tang, Jia; Chen, Stella; Tian, Xia; Gorman, Elizabeth; Schmitt, Eric S; Hansen, Terah A A; Wang, Jing; Plon, Sharon E; Zhang, Victor Wei; Wong, Lee-Jun C

2015-09-01

Germline mutations in the DNA mismatch repair gene PMS2 underlie the cancer susceptibility syndrome, Lynch syndrome. However, accurate molecular testing of PMS2 is complicated by a large number of highly homologous sequences. To establish a comprehensive approach for mutation detection of PMS2, we have designed a strategy combining targeted capture next-generation sequencing (NGS), multiplex ligation-dependent probe amplification, and long-range PCR followed by NGS to simultaneously detect point mutations and copy number changes of PMS2. Exonic deletions (E2 to E9, E5 to E9, E8, E10, E14, and E1 to E15), duplications (E11 to E12), and a nonsense mutation, p.S22*, were identified. Traditional multiplex ligation-dependent probe amplification and Sanger sequencing approaches cannot differentiate the origin of the exonic deletions in the 3' region when PMS2 and PMS2CL share identical sequences as a result of gene conversion. Our approach allows unambiguous identification of mutations in the active gene with a straightforward long-range-PCR/NGS method. Breakpoint analysis of multiple samples revealed that recurrent exon 14 deletions are mediated by homologous Alu sequences. Our comprehensive approach provides a reliable tool for accurate molecular analysis of genes containing multiple copies of highly homologous sequences and should improve PMS2 molecular analysis for patients with Lynch syndrome. Copyright © 2015 American Society for Investigative Pathology and the Association for Molecular Pathology. Published by Elsevier Inc. All rights reserved.

Automated selected reaction monitoring software for accurate label-free protein quantification.

PubMed

Teleman, Johan; Karlsson, Christofer; Waldemarson, Sofia; Hansson, Karin; James, Peter; Malmström, Johan; Levander, Fredrik

2012-07-06

Selected reaction monitoring (SRM) is a mass spectrometry method with documented ability to quantify proteins accurately and reproducibly using labeled reference peptides. However, the use of labeled reference peptides becomes impractical if large numbers of peptides are targeted and when high flexibility is desired when selecting peptides. We have developed a label-free quantitative SRM workflow that relies on a new automated algorithm, Anubis, for accurate peak detection. Anubis efficiently removes interfering signals from contaminating peptides to estimate the true signal of the targeted peptides. We evaluated the algorithm on a published multisite data set and achieved results in line with manual data analysis. In complex peptide mixtures from whole proteome digests of Streptococcus pyogenes we achieved a technical variability across the entire proteome abundance range of 6.5-19.2%, which was considerably below the total variation across biological samples. Our results show that the label-free SRM workflow with automated data analysis is feasible for large-scale biological studies, opening up new possibilities for quantitative proteomics and systems biology.

Obtaining Accurate Probabilities Using Classifier Calibration

ERIC Educational Resources Information Center

Pakdaman Naeini, Mahdi

2016-01-01

Learning probabilistic classification and prediction models that generate accurate probabilities is essential in many prediction and decision-making tasks in machine learning and data mining. One way to achieve this goal is to post-process the output of classification models to obtain more accurate probabilities. These post-processing methods are…

Tools for Accurate and Efficient Analysis of Complex Evolutionary Mechanisms in Microbial Genomes. Final Report

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

Nakhleh, Luay

I proposed to develop computationally efficient tools for accurate detection and reconstruction of microbes' complex evolutionary mechanisms, thus enabling rapid and accurate annotation, analysis and understanding of their genomes. To achieve this goal, I proposed to address three aspects. (1) Mathematical modeling. A major challenge facing the accurate detection of HGT is that of distinguishing between these two events on the one hand and other events that have similar "effects." I proposed to develop a novel mathematical approach for distinguishing among these events. Further, I proposed to develop a set of novel optimization criteria for the evolutionary analysis of microbialmore » genomes in the presence of these complex evolutionary events. (2) Algorithm design. In this aspect of the project, I proposed to develop an array of e cient and accurate algorithms for analyzing microbial genomes based on the formulated optimization criteria. Further, I proposed to test the viability of the criteria and the accuracy of the algorithms in an experimental setting using both synthetic as well as biological data. (3) Software development. I proposed the nal outcome to be a suite of software tools which implements the mathematical models as well as the algorithms developed.« less

VarDetect: a nucleotide sequence variation exploratory tool

PubMed Central

Ngamphiw, Chumpol; Kulawonganunchai, Supasak; Assawamakin, Anunchai; Jenwitheesuk, Ekachai; Tongsima, Sissades

2008-01-01

Background Single nucleotide polymorphisms (SNPs) are the most commonly studied units of genetic variation. The discovery of such variation may help to identify causative gene mutations in monogenic diseases and SNPs associated with predisposing genes in complex diseases. Accurate detection of SNPs requires software that can correctly interpret chromatogram signals to nucleotides. Results We present VarDetect, a stand-alone nucleotide variation exploratory tool that automatically detects nucleotide variation from fluorescence based chromatogram traces. Accurate SNP base-calling is achieved using pre-calculated peak content ratios, and is enhanced by rules which account for common sequence reading artifacts. The proposed software tool is benchmarked against four other well-known SNP discovery software tools (PolyPhred, novoSNP, Genalys and Mutation Surveyor) using fluorescence based chromatograms from 15 human genes. These chromatograms were obtained from sequencing 16 two-pooled DNA samples; a total of 32 individual DNA samples. In this comparison of automatic SNP detection tools, VarDetect achieved the highest detection efficiency. Availability VarDetect is compatible with most major operating systems such as Microsoft Windows, Linux, and Mac OSX. The current version of VarDetect is freely available at . PMID:19091032

Fluctuation localization imaging-based fluorescence in situ hybridization (fliFISH) for accurate detection and counting of RNA copies in single cells

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

Cui, Yi; Hu, Dehong; Markillie, Lye Meng

Quantitative gene expression analysis in intact single cells can be achieved using single molecule- based fluorescence in situ hybridization (smFISH). This approach relies on fluorescence intensity to distinguish between true signals, emitted from an RNA copy hybridized with multiple FISH sub-probes, and background noise. Thus, the precision in smFISH is often compromised by partial or nonspecific binding of sub-probes and tissue autofluorescence, limiting its accuracy. Here we provide an accurate approach for setting quantitative thresholds between true and false signals, which relies on blinking frequencies of photoswitchable dyes. This fluctuation localization imaging-based FISH (fliFISH) uses blinking frequency patterns, emitted frommore » a transcript bound to multiple sub-probes, which are distinct from blinking patterns emitted from partial or nonspecifically bound sub-probes and autofluorescence. Using multicolor fliFISH, we identified radial gene expression patterns in mouse pancreatic islets for insulin, the transcription factor, NKX2-2, and their ratio (Nkx2-2/Ins2). These radial patterns, showing higher values in β cells at the islet core and lower values in peripheral cells, were lost in diabetic mouse islets. In summary, fliFISH provides an accurate, quantitative approach for detecting and counting true RNA copies and rejecting false signals by their distinct blinking frequency patterns, laying the foundation for reliable single-cell transcriptomics.« less

How Accurately Can Your Wrist Device Recognize Daily Activities and Detect Falls?

PubMed Central

Gjoreski, Martin; Gjoreski, Hristijan; Luštrek, Mitja; Gams, Matjaž

2016-01-01

Although wearable accelerometers can successfully recognize activities and detect falls, their adoption in real life is low because users do not want to wear additional devices. A possible solution is an accelerometer inside a wrist device/smartwatch. However, wrist placement might perform poorly in terms of accuracy due to frequent random movements of the hand. In this paper we perform a thorough, large-scale evaluation of methods for activity recognition and fall detection on four datasets. On the first two we showed that the left wrist performs better compared to the dominant right one, and also better compared to the elbow and the chest, but worse compared to the ankle, knee and belt. On the third (Opportunity) dataset, our method outperformed the related work, indicating that our feature-preprocessing creates better input data. And finally, on a real-life unlabeled dataset the recognized activities captured the subject’s daily rhythm and activities. Our fall-detection method detected all of the fast falls and minimized the false positives, achieving 85% accuracy on the first dataset. Because the other datasets did not contain fall events, only false positives were evaluated, resulting in 9 for the second, 1 for the third and 15 for the real-life dataset (57 days data). PMID:27258282

A Robust Motion Artifact Detection Algorithm for Accurate Detection of Heart Rates From Photoplethysmographic Signals Using Time-Frequency Spectral Features.

PubMed

Dao, Duy; Salehizadeh, S M A; Noh, Yeonsik; Chong, Jo Woon; Cho, Chae Ho; McManus, Dave; Darling, Chad E; Mendelson, Yitzhak; Chon, Ki H

2017-09-01

Motion and noise artifacts (MNAs) impose limits on the usability of the photoplethysmogram (PPG), particularly in the context of ambulatory monitoring. MNAs can distort PPG, causing erroneous estimation of physiological parameters such as heart rate (HR) and arterial oxygen saturation (SpO2). In this study, we present a novel approach, "TifMA," based on using the time-frequency spectrum of PPG to first detect the MNA-corrupted data and next discard the nonusable part of the corrupted data. The term "nonusable" refers to segments of PPG data from which the HR signal cannot be recovered accurately. Two sequential classification procedures were included in the TifMA algorithm. The first classifier distinguishes between MNA-corrupted and MNA-free PPG data. Once a segment of data is deemed MNA-corrupted, the next classifier determines whether the HR can be recovered from the corrupted segment or not. A support vector machine (SVM) classifier was used to build a decision boundary for the first classification task using data segments from a training dataset. Features from time-frequency spectra of PPG were extracted to build the detection model. Five datasets were considered for evaluating TifMA performance: (1) and (2) were laboratory-controlled PPG recordings from forehead and finger pulse oximeter sensors with subjects making random movements, (3) and (4) were actual patient PPG recordings from UMass Memorial Medical Center with random free movements and (5) was a laboratory-controlled PPG recording dataset measured at the forehead while the subjects ran on a treadmill. The first dataset was used to analyze the noise sensitivity of the algorithm. Datasets 2-4 were used to evaluate the MNA detection phase of the algorithm. The results from the first phase of the algorithm (MNA detection) were compared to results from three existing MNA detection algorithms: the Hjorth, kurtosis-Shannon entropy, and time-domain variability-SVM approaches. This last is an approach

Accurate detection of hierarchical communities in complex networks based on nonlinear dynamical evolution

NASA Astrophysics Data System (ADS)

Zhuo, Zhao; Cai, Shi-Min; Tang, Ming; Lai, Ying-Cheng

2018-04-01

One of the most challenging problems in network science is to accurately detect communities at distinct hierarchical scales. Most existing methods are based on structural analysis and manipulation, which are NP-hard. We articulate an alternative, dynamical evolution-based approach to the problem. The basic principle is to computationally implement a nonlinear dynamical process on all nodes in the network with a general coupling scheme, creating a networked dynamical system. Under a proper system setting and with an adjustable control parameter, the community structure of the network would "come out" or emerge naturally from the dynamical evolution of the system. As the control parameter is systematically varied, the community hierarchies at different scales can be revealed. As a concrete example of this general principle, we exploit clustered synchronization as a dynamical mechanism through which the hierarchical community structure can be uncovered. In particular, for quite arbitrary choices of the nonlinear nodal dynamics and coupling scheme, decreasing the coupling parameter from the global synchronization regime, in which the dynamical states of all nodes are perfectly synchronized, can lead to a weaker type of synchronization organized as clusters. We demonstrate the existence of optimal choices of the coupling parameter for which the synchronization clusters encode accurate information about the hierarchical community structure of the network. We test and validate our method using a standard class of benchmark modular networks with two distinct hierarchies of communities and a number of empirical networks arising from the real world. Our method is computationally extremely efficient, eliminating completely the NP-hard difficulty associated with previous methods. The basic principle of exploiting dynamical evolution to uncover hidden community organizations at different scales represents a "game-change" type of approach to addressing the problem of community

Detection theory for accurate and non-invasive skin cancer diagnosis using dynamic thermal imaging

PubMed Central

Godoy, Sebastián E.; Hayat, Majeed M.; Ramirez, David A.; Myers, Stephen A.; Padilla, R. Steven; Krishna, Sanjay

2017-01-01

Skin cancer is the most common cancer in the United States with over 3.5M annual cases. Presently, visual inspection by a dermatologist has good sensitivity (> 90%) but poor specificity (< 10%), especially for melanoma, which leads to a high number of unnecessary biopsies. Here we use dynamic thermal imaging (DTI) to demonstrate a rapid, accurate and non-invasive imaging system for detection of skin cancer. In DTI, the lesion is cooled down and the thermal recovery is recorded using infrared imaging. The thermal recovery curves of the suspected lesions are then utilized in the context of continuous-time detection theory in order to define an optimal statistical decision rule such that the sensitivity of the algorithm is guaranteed to be at a maximum for every prescribed false-alarm probability. The proposed methodology was tested in a pilot study including 140 human subjects demonstrating a sensitivity in excess of 99% for a prescribed specificity in excess of 99% for detection of skin cancer. To the best of our knowledge, this is the highest reported accuracy for any non-invasive skin cancer diagnosis method. PMID:28736673

COPS: A Sensitive and Accurate Tool for Detecting Somatic Copy Number Alterations Using Short-Read Sequence Data from Paired Samples

PubMed Central

Krishnan, Neeraja M.; Gaur, Prakhar; Chaudhary, Rakshit; Rao, Arjun A.; Panda, Binay

2012-01-01

Copy Number Alterations (CNAs) such as deletions and duplications; compose a larger percentage of genetic variations than single nucleotide polymorphisms or other structural variations in cancer genomes that undergo major chromosomal re-arrangements. It is, therefore, imperative to identify cancer-specific somatic copy number alterations (SCNAs), with respect to matched normal tissue, in order to understand their association with the disease. We have devised an accurate, sensitive, and easy-to-use tool, COPS, COpy number using Paired Samples, for detecting SCNAs. We rigorously tested the performance of COPS using short sequence simulated reads at various sizes and coverage of SCNAs, read depths, read lengths and also with real tumor:normal paired samples. We found COPS to perform better in comparison to other known SCNA detection tools for all evaluated parameters, namely, sensitivity (detection of true positives), specificity (detection of false positives) and size accuracy. COPS performed well for sequencing reads of all lengths when used with most upstream read alignment tools. Additionally, by incorporating a downstream boundary segmentation detection tool, the accuracy of SCNA boundaries was further improved. Here, we report an accurate, sensitive and easy to use tool in detecting cancer-specific SCNAs using short-read sequence data. In addition to cancer, COPS can be used for any disease as long as sequence reads from both disease and normal samples from the same individual are available. An added boundary segmentation detection module makes COPS detected SCNA boundaries more specific for the samples studied. COPS is available at ftp://115.119.160.213 with username “cops” and password “cops”. PMID:23110103

Real-time 3D change detection of IEDs

NASA Astrophysics Data System (ADS)

Wathen, Mitch; Link, Norah; Iles, Peter; Jinkerson, John; Mrstik, Paul; Kusevic, Kresimir; Kovats, David

2012-06-01

Road-side bombs are a real and continuing threat to soldiers in theater. CAE USA recently developed a prototype Volume based Intelligence Surveillance Reconnaissance (VISR) sensor platform for IED detection. This vehicle-mounted, prototype sensor system uses a high data rate LiDAR (1.33 million range measurements per second) to generate a 3D mapping of roadways. The mapped data is used as a reference to generate real-time change detection on future trips on the same roadways. The prototype VISR system is briefly described. The focus of this paper is the methodology used to process the 3D LiDAR data, in real-time, to detect small changes on and near the roadway ahead of a vehicle traveling at moderate speeds with sufficient warning to stop the vehicle at a safe distance from the threat. The system relies on accurate navigation equipment to geo-reference the reference run and the change-detection run. Since it was recognized early in the project that detection of small changes could not be achieved with accurate navigation solutions alone, a scene alignment algorithm was developed to register the reference run with the change detection run prior to applying the change detection algorithm. Good success was achieved in simultaneous real time processing of scene alignment plus change detection.

Simple, Sensitive and Accurate Multiplex Detection of Clinically Important Melanoma DNA Mutations in Circulating Tumour DNA with SERS Nanotags

PubMed Central

Wee, Eugene J.H.; Wang, Yuling; Tsao, Simon Chang-Hao; Trau, Matt

2016-01-01

Sensitive and accurate identification of specific DNA mutations can influence clinical decisions. However accurate diagnosis from limiting samples such as circulating tumour DNA (ctDNA) is challenging. Current approaches based on fluorescence such as quantitative PCR (qPCR) and more recently, droplet digital PCR (ddPCR) have limitations in multiplex detection, sensitivity and the need for expensive specialized equipment. Herein we describe an assay capitalizing on the multiplexing and sensitivity benefits of surface-enhanced Raman spectroscopy (SERS) with the simplicity of standard PCR to address the limitations of current approaches. This proof-of-concept method could reproducibly detect as few as 0.1% (10 copies, CV < 9%) of target sequences thus demonstrating the high sensitivity of the method. The method was then applied to specifically detect three important melanoma mutations in multiplex. Finally, the PCR/SERS assay was used to genotype cell lines and ctDNA from serum samples where results subsequently validated with ddPCR. With ddPCR-like sensitivity and accuracy yet at the convenience of standard PCR, we believe this multiplex PCR/SERS method could find wide applications in both diagnostics and research. PMID:27446486

Simple, Sensitive and Accurate Multiplex Detection of Clinically Important Melanoma DNA Mutations in Circulating Tumour DNA with SERS Nanotags.

PubMed

Wee, Eugene J H; Wang, Yuling; Tsao, Simon Chang-Hao; Trau, Matt

2016-01-01

Sensitive and accurate identification of specific DNA mutations can influence clinical decisions. However accurate diagnosis from limiting samples such as circulating tumour DNA (ctDNA) is challenging. Current approaches based on fluorescence such as quantitative PCR (qPCR) and more recently, droplet digital PCR (ddPCR) have limitations in multiplex detection, sensitivity and the need for expensive specialized equipment. Herein we describe an assay capitalizing on the multiplexing and sensitivity benefits of surface-enhanced Raman spectroscopy (SERS) with the simplicity of standard PCR to address the limitations of current approaches. This proof-of-concept method could reproducibly detect as few as 0.1% (10 copies, CV < 9%) of target sequences thus demonstrating the high sensitivity of the method. The method was then applied to specifically detect three important melanoma mutations in multiplex. Finally, the PCR/SERS assay was used to genotype cell lines and ctDNA from serum samples where results subsequently validated with ddPCR. With ddPCR-like sensitivity and accuracy yet at the convenience of standard PCR, we believe this multiplex PCR/SERS method could find wide applications in both diagnostics and research.

A simplified and accurate detection of the genetically modified wheat MON71800 with one calibrator plasmid.

PubMed

Kim, Jae-Hwan; Park, Saet-Byul; Roh, Hyo-Jeong; Park, Sunghoon; Shin, Min-Ki; Moon, Gui Im; Hong, Jin-Hwan; Kim, Hae-Yeong

2015-06-01

With the increasing number of genetically modified (GM) events, unauthorized GMO releases into the food market have increased dramatically, and many countries have developed detection tools for them. This study described the qualitative and quantitative detection methods of unauthorized the GM wheat MON71800 with a reference plasmid (pGEM-M71800). The wheat acetyl-CoA carboxylase (acc) gene was used as the endogenous gene. The plasmid pGEM-M71800, which contains both the acc gene and the event-specific target MON71800, was constructed as a positive control for the qualitative and quantitative analyses. The limit of detection in the qualitative PCR assay was approximately 10 copies. In the quantitative PCR assay, the standard deviation and relative standard deviation repeatability values ranged from 0.06 to 0.25 and from 0.23% to 1.12%, respectively. This study supplies a powerful and very simple but accurate detection strategy for unauthorized GM wheat MON71800 that utilizes a single calibrator plasmid. Copyright © 2014 Elsevier Ltd. All rights reserved.

Recommendations for Achieving Accurate Numerical Simulation of Tip Clearance Flows in Transonic Compressor Rotors

NASA Technical Reports Server (NTRS)

VanZante, Dale E.; Strazisar, Anthony J.; Wood, Jerry R,; Hathaway, Michael D.; Okiishi, Theodore H.

2000-01-01

The tip clearance flows of transonic compressor rotors are important because they have a significant impact on rotor and stage performance. While numerical simulations of these flows are quite sophisticated. they are seldom verified through rigorous comparisons of numerical and measured data because these kinds of measurements are rare in the detail necessary to be useful in high-speed machines. In this paper we compare measured tip clearance flow details (e.g. trajectory and radial extent) with corresponding data obtained from a numerical simulation. Recommendations for achieving accurate numerical simulation of tip clearance flows are presented based on this comparison. Laser Doppler Velocimeter (LDV) measurements acquired in a transonic compressor rotor, NASA Rotor 35, are used. The tip clearance flow field of this transonic rotor was simulated using a Navier-Stokes turbomachinery solver that incorporates an advanced k-epsilon turbulence model derived for flows that are not in local equilibrium. Comparison between measured and simulated results indicates that simulation accuracy is primarily dependent upon the ability of the numerical code to resolve important details of a wall-bounded shear layer formed by the relative motion between the over-tip leakage flow and the shroud wall. A simple method is presented for determining the strength of this shear layer.

Fluctuation localization imaging-based fluorescence in situ hybridization (fliFISH) for accurate detection and counting of RNA copies in single cells

DOE PAGES

Cui, Yi; Hu, Dehong; Markillie, Lye Meng; ...

2017-10-04

Here, quantitative gene expression analysis in intact single cells can be achieved using single molecule-based fluorescence in situ hybridization (smFISH). This approach relies on fluorescence intensity to distinguish between true signals, emitted from an RNA copy hybridized with multiple oligonucleotide probes, and background noise. Thus, the precision in smFISH is often compromised by partial or nonspecific probe binding and tissue autofluorescence, especially when only a small number of probes can be fitted to the target transcript. Here we provide an accurate approach for setting quantitative thresholds between true and false signals, which relies on on-off duty cycles of photoswitchable dyes.more » This fluctuation localization imaging-based FISH (fliFISH) uses on-time fractions (measured over a series of exposures) collected from transcripts bound to as low as 8 probes, which are distinct from on-time fractions collected from nonspecifically bound probes or autofluorescence. Using multicolor fliFISH, we identified radial gene expression patterns in mouse pancreatic islets for insulin, the transcription factor, NKX2-2 and their ratio ( Nkx2- 2/Ins2). These radial patterns, showing higher values in β cells at the islet core and lower values in peripheral cells, were lost in diabetic mouse islets. In summary, fliFISH provides an accurate, quantitative approach for detecting and counting true RNA copies and rejecting false signals by their distinct on-time fractions, laying the foundation for reliable single-cell transcriptomics.« less

Fluctuation localization imaging-based fluorescence in situ hybridization (fliFISH) for accurate detection and counting of RNA copies in single cells

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

Cui, Yi; Hu, Dehong; Markillie, Lye Meng

Here, quantitative gene expression analysis in intact single cells can be achieved using single molecule-based fluorescence in situ hybridization (smFISH). This approach relies on fluorescence intensity to distinguish between true signals, emitted from an RNA copy hybridized with multiple oligonucleotide probes, and background noise. Thus, the precision in smFISH is often compromised by partial or nonspecific probe binding and tissue autofluorescence, especially when only a small number of probes can be fitted to the target transcript. Here we provide an accurate approach for setting quantitative thresholds between true and false signals, which relies on on-off duty cycles of photoswitchable dyes.more » This fluctuation localization imaging-based FISH (fliFISH) uses on-time fractions (measured over a series of exposures) collected from transcripts bound to as low as 8 probes, which are distinct from on-time fractions collected from nonspecifically bound probes or autofluorescence. Using multicolor fliFISH, we identified radial gene expression patterns in mouse pancreatic islets for insulin, the transcription factor, NKX2-2 and their ratio ( Nkx2- 2/Ins2). These radial patterns, showing higher values in β cells at the islet core and lower values in peripheral cells, were lost in diabetic mouse islets. In summary, fliFISH provides an accurate, quantitative approach for detecting and counting true RNA copies and rejecting false signals by their distinct on-time fractions, laying the foundation for reliable single-cell transcriptomics.« less

Quality detection system and method of micro-accessory based on microscopic vision

NASA Astrophysics Data System (ADS)

Li, Dongjie; Wang, Shiwei; Fu, Yu

2017-10-01

Considering that the traditional manual detection of micro-accessory has some problems, such as heavy workload, low efficiency and large artificial error, a kind of quality inspection system of micro-accessory has been designed. Micro-vision technology has been used to inspect quality, which optimizes the structure of the detection system. The stepper motor is used to drive the rotating micro-platform to transfer quarantine device and the microscopic vision system is applied to get graphic information of micro-accessory. The methods of image processing and pattern matching, the variable scale Sobel differential edge detection algorithm and the improved Zernike moments sub-pixel edge detection algorithm are combined in the system in order to achieve a more detailed and accurate edge of the defect detection. The grade at the edge of the complex signal can be achieved accurately by extracting through the proposed system, and then it can distinguish the qualified products and unqualified products with high precision recognition.

Development for equipment of the milk macromolecules content detection

NASA Astrophysics Data System (ADS)

Ding, Guochao; Li, Weimin; Shang, Tingyi; Xi, Yang; Gao, Yunli; Zhou, Zhen

Developed an experimental device for rapid and accurate detection of milk macromolecular content. This device developed based on laser scattered through principle, the principle use of the ingredients of the scattered light and transmitted light ratio characterization of macromolecules. Peristaltic pump to achieve automatic input and output of the milk samples, designing weak signal detection amplifier circuit for detecting the ratio with ICL7650. Real-time operating system μC / OS-II is the core design of the software part of the whole system. The experimental data prove that the device can achieve a fast real-time measurement of milk macromolecules.

Detecting Cancer Quickly and Accurately

NASA Astrophysics Data System (ADS)

Gourley, Paul; McDonald, Anthony; Hendricks, Judy; Copeland, Guild; Hunter, John; Akhil, Ohmar; Capps, Heather; Curry, Marc; Skirboll, Steve

2000-03-01

We present a new technique for high throughput screening of tumor cells in a sensitive nanodevice that has the potential to quickly identify a cell population that has begun the rapid protein synthesis and mitosis characteristic of cancer cell proliferation. Currently, pathologists rely on microscopic examination of cell morphology using century-old staining methods that are labor-intensive, time-consuming and frequently in error. New micro-analytical methods for automated, real time screening without chemical modification are critically needed to advance pathology and improve diagnoses. We have teamed scientists with physicians to create a microlaser biochip (based upon our R&D award winning bio-laser concept)1 which evaluates tumor cells by quantifying their growth kinetics. The key new discovery was demonstrating that the lasing spectra are sensitive to the biomolecular mass in the cell, which changes the speed of light in the laser microcavity. Initial results with normal and cancerous human brain cells show that only a few hundred cells -- the equivalent of a billionth of a liter -- are required to detect abnormal growth. The ability to detect cancer in such a minute tissue sample is crucial for resecting a tumor margin or grading highly localized tumor malignancy. 1. P. L. Gourley, NanoLasers, Scientific American, March 1998, pp. 56-61. This work supported under DOE contract DE-AC04-94AL85000 and the Office of Basic Energy Sciences.

Individuals Achieve More Accurate Results with Meters That Are Codeless and Employ Dynamic Electrochemistry

PubMed Central

Rao, Anoop; Wiley, Meg; Iyengar, Sridhar; Nadeau, Dan; Carnevale, Julie

2010-01-01

Background Studies have shown that controlling blood glucose can reduce the onset and progression of the long-term microvascular and neuropathic complications associated with the chronic course of diabetes mellitus. Improved glycemic control can be achieved by frequent testing combined with changes in medication, exercise, and diet. Technological advancements have enabled improvements in analytical accuracy of meters, and this paper explores two such parameters to which that accuracy can be attributed. Methods Four blood glucose monitoring systems (with or without dynamic electrochemistry algorithms, codeless or requiring coding prior to testing) were evaluated and compared with respect to their accuracy. Results Altogether, 108 blood glucose values were obtained for each system from 54 study participants and compared with the reference values. The analysis depicted in the International Organization for Standardization table format indicates that the devices with dynamic electrochemistry and the codeless feature had the highest proportion of acceptable results overall (System A, 101/103). Results were significant when compared at the 10% bias level with meters that were codeless and utilized static electrochemistry (p = .017) or systems that had static electrochemistry but needed coding (p = .008). Conclusions Analytical performance of these blood glucose meters differed significantly depending on their technologic features. Meters that utilized dynamic electrochemistry and did not require coding were more accurate than meters that used static electrochemistry or required coding. PMID:20167178

Individuals achieve more accurate results with meters that are codeless and employ dynamic electrochemistry.

PubMed

Rao, Anoop; Wiley, Meg; Iyengar, Sridhar; Nadeau, Dan; Carnevale, Julie

2010-01-01

Studies have shown that controlling blood glucose can reduce the onset and progression of the long-term microvascular and neuropathic complications associated with the chronic course of diabetes mellitus. Improved glycemic control can be achieved by frequent testing combined with changes in medication, exercise, and diet. Technological advancements have enabled improvements in analytical accuracy of meters, and this paper explores two such parameters to which that accuracy can be attributed. Four blood glucose monitoring systems (with or without dynamic electrochemistry algorithms, codeless or requiring coding prior to testing) were evaluated and compared with respect to their accuracy. Altogether, 108 blood glucose values were obtained for each system from 54 study participants and compared with the reference values. The analysis depicted in the International Organization for Standardization table format indicates that the devices with dynamic electrochemistry and the codeless feature had the highest proportion of acceptable results overall (System A, 101/103). Results were significant when compared at the 10% bias level with meters that were codeless and utilized static electrochemistry (p = .017) or systems that had static electrochemistry but needed coding (p = .008). Analytical performance of these blood glucose meters differed significantly depending on their technologic features. Meters that utilized dynamic electrochemistry and did not require coding were more accurate than meters that used static electrochemistry or required coding. 2010 Diabetes Technology Society.

Modeling inter-signal arrival times for accurate detection of CAN bus signal injection attacks

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

Moore, Michael Roy; Bridges, Robert A; Combs, Frank L

Modern vehicles rely on hundreds of on-board electronic control units (ECUs) communicating over in-vehicle networks. As external interfaces to the car control networks (such as the on-board diagnostic (OBD) port, auxiliary media ports, etc.) become common, and vehicle-to-vehicle / vehicle-to-infrastructure technology is in the near future, the attack surface for vehicles grows, exposing control networks to potentially life-critical attacks. This paper addresses the need for securing the CAN bus by detecting anomalous traffic patterns via unusual refresh rates of certain commands. While previous works have identified signal frequency as an important feature for CAN bus intrusion detection, this paper providesmore » the first such algorithm with experiments on five attack scenarios. Our data-driven anomaly detection algorithm requires only five seconds of training time (on normal data) and achieves true positive / false discovery rates of 0.9998/0.00298, respectively (micro-averaged across the five experimental tests).« less

Panel-based Genetic Diagnostic Testing for Inherited Eye Diseases is Highly Accurate and Reproducible and More Sensitive for Variant Detection Than Exome Sequencing

PubMed Central

Bujakowska, Kinga M.; Sousa, Maria E.; Fonseca-Kelly, Zoë D.; Taub, Daniel G.; Janessian, Maria; Wang, Dan Yi; Au, Elizabeth D.; Sims, Katherine B.; Sweetser, David A.; Fulton, Anne B.; Liu, Qin; Wiggs, Janey L.; Gai, Xiaowu; Pierce, Eric A.

2015-01-01

Purpose Next-generation sequencing (NGS) based methods are being adopted broadly for genetic diagnostic testing, but the performance characteristics of these techniques have not been fully defined with regard to test accuracy and reproducibility. Methods We developed a targeted enrichment and NGS approach for genetic diagnostic testing of patients with inherited eye disorders, including inherited retinal degenerations, optic atrophy and glaucoma. In preparation for providing this Genetic Eye Disease (GEDi) test on a CLIA-certified basis, we performed experiments to measure the sensitivity, specificity, reproducibility as well as the clinical sensitivity of the test. Results The GEDi test is highly reproducible and accurate, with sensitivity and specificity for single nucleotide variant detection of 97.9% and 100%, respectively. The sensitivity for variant detection was notably better than the 88.3% achieved by whole exome sequencing (WES) using the same metrics, due to better coverage of targeted genes in the GEDi test compared to commercially available exome capture sets. Prospective testing of 192 patients with IRDs indicated that the clinical sensitivity of the GEDi test is high, with a diagnostic rate of 51%. Conclusion The data suggest that based on quantified performance metrics, selective targeted enrichment is preferable to WES for genetic diagnostic testing. PMID:25412400

Accurate Natural Trail Detection Using a Combination of a Deep Neural Network and Dynamic Programming.

PubMed

Adhikari, Shyam Prasad; Yang, Changju; Slot, Krzysztof; Kim, Hyongsuk

2018-01-10

This paper presents a vision sensor-based solution to the challenging problem of detecting and following trails in highly unstructured natural environments like forests, rural areas and mountains, using a combination of a deep neural network and dynamic programming. The deep neural network (DNN) concept has recently emerged as a very effective tool for processing vision sensor signals. A patch-based DNN is trained with supervised data to classify fixed-size image patches into "trail" and "non-trail" categories, and reshaped to a fully convolutional architecture to produce trail segmentation map for arbitrary-sized input images. As trail and non-trail patches do not exhibit clearly defined shapes or forms, the patch-based classifier is prone to misclassification, and produces sub-optimal trail segmentation maps. Dynamic programming is introduced to find an optimal trail on the sub-optimal DNN output map. Experimental results showing accurate trail detection for real-world trail datasets captured with a head mounted vision system are presented.

COSMOS: accurate detection of somatic structural variations through asymmetric comparison between tumor and normal samples

PubMed Central

Yamagata, Koichi; Yamanishi, Ayako; Kokubu, Chikara; Takeda, Junji; Sese, Jun

2016-01-01

An important challenge in cancer genomics is precise detection of structural variations (SVs) by high-throughput short-read sequencing, which is hampered by the high false discovery rates of existing analysis tools. Here, we propose an accurate SV detection method named COSMOS, which compares the statistics of the mapped read pairs in tumor samples with isogenic normal control samples in a distinct asymmetric manner. COSMOS also prioritizes the candidate SVs using strand-specific read-depth information. Performance tests on modeled tumor genomes revealed that COSMOS outperformed existing methods in terms of F-measure. We also applied COSMOS to an experimental mouse cell-based model, in which SVs were induced by genome engineering and gamma-ray irradiation, followed by polymerase chain reaction-based confirmation. The precision of COSMOS was 84.5%, while the next best existing method was 70.4%. Moreover, the sensitivity of COSMOS was the highest, indicating that COSMOS has great potential for cancer genome analysis. PMID:26833260

Photogrammetry: an accurate and reliable tool to detect thoracic musculoskeletal abnormalities in preterm infants.

PubMed

Davidson, Josy; dos Santos, Amelia Miyashiro N; Garcia, Kessey Maria B; Yi, Liu C; João, Priscila C; Miyoshi, Milton H; Goulart, Ana Lucia

2012-09-01

To analyse the accuracy and reproducibility of photogrammetry in detecting thoracic abnormalities in infants born prematurely. Cross-sectional study. The Premature Clinic at the Federal University of São Paolo. Fifty-eight infants born prematurely in their first year of life. Measurement of the manubrium/acromion/trapezius angle (degrees) and the deepest thoracic retraction (cm). Digitised photographs were analysed by two blinded physiotherapists using a computer program (SAPO; http://SAPO.incubadora.fapesp.br) to detect shoulder elevation and thoracic retraction. Physical examinations performed independently by two physiotherapists were used to assess the accuracy of the new tool. Thoracic alterations were detected in 39 (67%) and in 40 (69%) infants by Physiotherapists 1 and 2, respectively (kappa coefficient=0.80). Using a receiver operating characteristic curve, measurement of the manubrium/acromion/trapezius angle and the deepest thoracic retraction indicated accuracy of 0.79 and 0.91, respectively. For measurement of the manubrium/acromion/trapezius angle, the Bland and Altman limits of agreement were -6.22 to 7.22° [mean difference (d)=0.5] for repeated measures by one physiotherapist, and -5.29 to 5.79° (d=0.75) between two physiotherapists. For thoracic retraction, the intra-rater limits of agreement were -0.14 to 0.18cm (d=0.02) and the inter-rater limits of agreement were -0.20 to -0.17cm (d=0.02). SAPO provided an accurate and reliable tool for the detection of thoracic abnormalities in preterm infants. Copyright © 2011 Chartered Society of Physiotherapy. Published by Elsevier Ltd. All rights reserved.

Accurate method for luminous transmittance and signal detection quotients measurements in sunglasses lenses

NASA Astrophysics Data System (ADS)

Loureiro, A. D.; Gomes, L. M.; Ventura, L.

2018-02-01

The international standard ISO 12312-1 proposes transmittance tests that quantify how dark sunglasses lenses are and whether or not they are suitable for driving. To perform these tests a spectrometer is required. In this study, we present and analyze theoretically an accurate alternative method for performing these measurements using simple components. Using three LEDs and a four-channel sensor we generated weighting functions similar to the standard ones for luminous and traffic lights transmittances. From 89 sunglasses lens spectroscopy data, we calculated luminous transmittance and signal detection quotients using our obtained weighting functions and the standard ones. Mean-difference Tukey plots were used to compare the results. All tested sunglasses lenses were classified in the right category and correctly as suitable or not for driving. The greatest absolute errors for luminous transmittance and red, yellow, green and blue signal detection quotients were 0.15%, 0.17, 0.06, 0.04 and 0.18, respectively. This method will be used in a device capable to perform transmittance tests (visible, traffic lights and ultraviolet (UV)) according to the standard. It is important to measure rightly luminous transmittance and relative visual attenuation quotients to report correctly whether or not sunglasses are suitable for driving. Moreover, standard UV requirements depend on luminous transmittance.

Accurate Monitoring and Fault Detection in Wind Measuring Devices through Wireless Sensor Networks

PubMed Central

Khan, Komal Saifullah; Tariq, Muhammad

2014-01-01

Many wind energy projects report poor performance as low as 60% of the predicted performance. The reason for this is poor resource assessment and the use of new untested technologies and systems in remote locations. Predictions about the potential of an area for wind energy projects (through simulated models) may vary from the actual potential of the area. Hence, introducing accurate site assessment techniques will lead to accurate predictions of energy production from a particular area. We solve this problem by installing a Wireless Sensor Network (WSN) to periodically analyze the data from anemometers installed in that area. After comparative analysis of the acquired data, the anemometers transmit their readings through a WSN to the sink node for analysis. The sink node uses an iterative algorithm which sequentially detects any faulty anemometer and passes the details of the fault to the central system or main station. We apply the proposed technique in simulation as well as in practical implementation and study its accuracy by comparing the simulation results with experimental results to analyze the variation in the results obtained from both simulation model and implemented model. Simulation results show that the algorithm indicates faulty anemometers with high accuracy and low false alarm rate when as many as 25% of the anemometers become faulty. Experimental analysis shows that anemometers incorporating this solution are better assessed and performance level of implemented projects is increased above 86% of the simulated models. PMID:25421739

Multislice Computed Tomography Accurately Detects Stenosis in Coronary Artery Bypass Conduits

PubMed Central

Duran, Cihan; Sagbas, Ertan; Caynak, Baris; Sanisoglu, Ilhan; Akpinar, Belhhan; Gulbaran, Murat

2007-01-01

The aim of this study was to evaluate the accuracy of multislice computed tomography in detecting graft stenosis or occlusion after coronary artery bypass grafting, using coronary angiography as the standard. From January 2005 through May 2006, 25 patients (19 men and 6 women; mean age, 54 ± 11.3 years) underwent diagnostic investigation of their bypass grafts by multislice computed tomography within 1 month of coronary angiography. The mean time elapsed after coronary artery bypass grafting was 6.2 years. In these 25 patients, we examined 65 bypass conduits (24 arterial and 41 venous) and 171 graft segments (the shaft, proximal anastomosis, and distal anastomosis). Compared with coronary angiography, the segment-based sensitivity, specificity, and positive and negative predictive values of multislice computed tomography in the evaluation of stenosis were 89%, 100%, 100%, and 99%, respectively. The patency rate for multislice compu-ted tomography was 85% (55/65: 3 arterial and 7 venous grafts were occluded), with 100% sensitivity and specificity. From these data, we conclude that multislice computed tomography can accurately evaluate the patency and stenosis of bypass grafts during outpatient follow-up. PMID:17948078

Maximized Inter-Class Weighted Mean for Fast and Accurate Mitosis Cells Detection in Breast Cancer Histopathology Images.

PubMed

Nateghi, Ramin; Danyali, Habibollah; Helfroush, Mohammad Sadegh

2017-08-14

Based on the Nottingham criteria, the number of mitosis cells in histopathological slides is an important factor in diagnosis and grading of breast cancer. For manual grading of mitosis cells, histopathology slides of the tissue are examined by pathologists at 40× magnification for each patient. This task is very difficult and time-consuming even for experts. In this paper, a fully automated method is presented for accurate detection of mitosis cells in histopathology slide images. First a method based on maximum-likelihood is employed for segmentation and extraction of mitosis cell. Then a novel Maximized Inter-class Weighted Mean (MIWM) method is proposed that aims at reducing the number of extracted non-mitosis candidates that results in reducing the false positive mitosis detection rate. Finally, segmented candidates are classified into mitosis and non-mitosis classes by using a support vector machine (SVM) classifier. Experimental results demonstrate a significant improvement in accuracy of mitosis cells detection in different grades of breast cancer histopathological images.

Structural damage detection using deep learning of ultrasonic guided waves

NASA Astrophysics Data System (ADS)

Melville, Joseph; Alguri, K. Supreet; Deemer, Chris; Harley, Joel B.

2018-04-01

Structural health monitoring using ultrasonic guided waves relies on accurate interpretation of guided wave propagation to distinguish damage state indicators. However, traditional physics based models do not provide an accurate representation, and classic data driven techniques, such as a support vector machine, are too simplistic to capture the complex nature of ultrasonic guide waves. To address this challenge, this paper uses a deep learning interpretation of ultrasonic guided waves to achieve fast, accurate, and automated structural damaged detection. To achieve this, full wavefield scans of thin metal plates are used, half from the undamaged state and half from the damaged state. This data is used to train our deep network to predict the damage state of a plate with 99.98% accuracy given signals from just 10 spatial locations on the plate, as compared to that of a support vector machine (SVM), which achieved a 62% accuracy.

Retinal nerve fibre thickness measured with optical coherence tomography accurately detects confirmed glaucomatous damage.

PubMed

Hood, D C; Harizman, N; Kanadani, F N; Grippo, T M; Baharestani, S; Greenstein, V C; Liebmann, J M; Ritch, R

2007-07-01

To assess the accuracy of optical coherence tomography (OCT) in detecting damage to a hemifield, patients with hemifield defects confirmed on both static automated perimetry (SAP) and multifocal visual evoked potentials (mfVEP) were studied. Eyes of 40 patients with concomitant SAP and mfVEP glaucomatous loss and 25 controls underwent OCT retinal nerve fibre layer (RNFL), mfVEP and 24-2 SAP tests. For the mfVEP and 24-2 SAP, a hemifield was defined as abnormal based upon cluster criteria. On OCT, a hemifield was considered abnormal if one of the five clock hour sectors (3 and 9 o'clock excluded) was at <1% (red) or two were at <5% (yellow). Seventy seven (43%) of the hemifields were abnormal on both mfVEP and SAP tests. The OCT was abnormal for 73 (95%) of these. Only 1 (1%) of the 100 hemifields of the controls was abnormal on OCT. Sensitivity/specificity (one eye per person) was 95/98%. The OCT RNFL test accurately detects abnormal hemifields confirmed on both subjective and objective functional tests. Identifying abnormal hemifields with a criterion of 1 red (1%) or 2 yellow (5%) clock hours may prove useful in clinical practice.

Accurate, reliable prototype earth horizon sensor head

NASA Technical Reports Server (NTRS)

Schwarz, F.; Cohen, H.

1973-01-01

The design and performance is described of an accurate and reliable prototype earth sensor head (ARPESH). The ARPESH employs a detection logic 'locator' concept and horizon sensor mechanization which should lead to high accuracy horizon sensing that is minimally degraded by spatial or temporal variations in sensing attitude from a satellite in orbit around the earth at altitudes in the 500 km environ 1,2. An accuracy of horizon location to within 0.7 km has been predicted, independent of meteorological conditions. This corresponds to an error of 0.015 deg-at 500 km altitude. Laboratory evaluation of the sensor indicates that this accuracy is achieved. First, the basic operating principles of ARPESH are described; next, detailed design and construction data is presented and then performance of the sensor under laboratory conditions in which the sensor is installed in a simulator that permits it to scan over a blackbody source against background representing the earth space interface for various equivalent plant temperatures.

COSMOS: accurate detection of somatic structural variations through asymmetric comparison between tumor and normal samples.

PubMed

Yamagata, Koichi; Yamanishi, Ayako; Kokubu, Chikara; Takeda, Junji; Sese, Jun

2016-05-05

An important challenge in cancer genomics is precise detection of structural variations (SVs) by high-throughput short-read sequencing, which is hampered by the high false discovery rates of existing analysis tools. Here, we propose an accurate SV detection method named COSMOS, which compares the statistics of the mapped read pairs in tumor samples with isogenic normal control samples in a distinct asymmetric manner. COSMOS also prioritizes the candidate SVs using strand-specific read-depth information. Performance tests on modeled tumor genomes revealed that COSMOS outperformed existing methods in terms of F-measure. We also applied COSMOS to an experimental mouse cell-based model, in which SVs were induced by genome engineering and gamma-ray irradiation, followed by polymerase chain reaction-based confirmation. The precision of COSMOS was 84.5%, while the next best existing method was 70.4%. Moreover, the sensitivity of COSMOS was the highest, indicating that COSMOS has great potential for cancer genome analysis. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Simplified intraoperative sentinel-node detection performed by the urologist accurately determines lymph-node stage in prostate cancer.

PubMed

Kjölhede, Henrik; Bratt, Ola; Gudjonsson, Sigurdur; Sundqvist, Pernilla; Liedberg, Fredrik

2015-04-01

The reference standard for lymph-node staging in prostate cancer is currently an extended pelvic lymph-node dissection (ePLND), which detects most, but not all, regional lymph-node metastases. As an alternative to ePLND, sentinel-node dissection with preoperative isotope injection and imaging has been reported. The objective was to determine whether intraoperative sentinel-node detection with a simplified protocol can accurately determine lymph-node stage in prostate cancer patients. Patients with biopsy-verified high-risk prostate cancer with tumour stage T2-3 were included in the study. All patients underwent both ePLND and sentinel-node detection. (99m)Tc-marked nanocolloid was injected peritumourally by the operating urologist after induction of anaesthesia just before surgery. Sentinel nodes were detected both in vivo and ex vivo intraoperatively using a gamma probe. Sentinel nodes and metastases and their locations were recorded. Sensitivity and specificity were calculated. At least one sentinel node was detected in 72 (87%) of the 83 patients. In 13 (18%) of these 72 patients sentinel nodes were detected outside the ePLND template. In six of these 13 patients, the Sentinel nodes from outside the template contained metastases, which proved to be the only metastases in two. For 12 patients the only metastatic deposit found was a micrometastasis (≤2 mm) in a sentinel node. In the 72 patients with detectable sentinel nodes, pathological analysis of the sentinel node correctly categorized 71 and ePLND 70 patients. This protocol yielded results comparable to the commonly used technique of sentinel-node detection, but with more cases of non-detection.

Accurate Orientation Estimation Using AHRS under Conditions of Magnetic Distortion

PubMed Central

Yadav, Nagesh; Bleakley, Chris

2014-01-01

Low cost, compact attitude heading reference systems (AHRS) are now being used to track human body movements in indoor environments by estimation of the 3D orientation of body segments. In many of these systems, heading estimation is achieved by monitoring the strength of the Earth's magnetic field. However, the Earth's magnetic field can be locally distorted due to the proximity of ferrous and/or magnetic objects. Herein, we propose a novel method for accurate 3D orientation estimation using an AHRS, comprised of an accelerometer, gyroscope and magnetometer, under conditions of magnetic field distortion. The system performs online detection and compensation for magnetic disturbances, due to, for example, the presence of ferrous objects. The magnetic distortions are detected by exploiting variations in magnetic dip angle, relative to the gravity vector, and in magnetic strength. We investigate and show the advantages of using both magnetic strength and magnetic dip angle for detecting the presence of magnetic distortions. The correction method is based on a particle filter, which performs the correction using an adaptive cost function and by adapting the variance during particle resampling, so as to place more emphasis on the results of dead reckoning of the gyroscope measurements and less on the magnetometer readings. The proposed method was tested in an indoor environment in the presence of various magnetic distortions and under various accelerations (up to 3 g). In the experiments, the proposed algorithm achieves <2° static peak-to-peak error and <5° dynamic peak-to-peak error, significantly outperforming previous methods. PMID:25347584

Insar Unwrapping Error Correction Based on Quasi-Accurate Detection of Gross Errors (quad)

NASA Astrophysics Data System (ADS)

Kang, Y.; Zhao, C. Y.; Zhang, Q.; Yang, C. S.

2018-04-01

Unwrapping error is a common error in the InSAR processing, which will seriously degrade the accuracy of the monitoring results. Based on a gross error correction method, Quasi-accurate detection (QUAD), the method for unwrapping errors automatic correction is established in this paper. This method identifies and corrects the unwrapping errors by establishing a functional model between the true errors and interferograms. The basic principle and processing steps are presented. Then this method is compared with the L1-norm method with simulated data. Results show that both methods can effectively suppress the unwrapping error when the ratio of the unwrapping errors is low, and the two methods can complement each other when the ratio of the unwrapping errors is relatively high. At last the real SAR data is tested for the phase unwrapping error correction. Results show that this new method can correct the phase unwrapping errors successfully in the practical application.

Retinal nerve fibre thickness measured with optical coherence tomography accurately detects confirmed glaucomatous damage

PubMed Central

Hood, D C; Harizman, N; Kanadani, F N; Grippo, T M; Baharestani, S; Greenstein, V C; Liebmann, J M; Ritch, R

2007-01-01

Aim To assess the accuracy of optical coherence tomography (OCT) in detecting damage to a hemifield, patients with hemifield defects confirmed on both static automated perimetry (SAP) and multifocal visual evoked potentials (mfVEP) were studied. Methods Eyes of 40 patients with concomitant SAP and mfVEP glaucomatous loss and 25 controls underwent OCT retinal nerve fibre layer (RNFL), mfVEP and 24‐2 SAP tests. For the mfVEP and 24‐2 SAP, a hemifield was defined as abnormal based upon cluster criteria. On OCT, a hemifield was considered abnormal if one of the five clock hour sectors (3 and 9 o'clock excluded) was at <1% (red) or two were at <5% (yellow). Results Seventy seven (43%) of the hemifields were abnormal on both mfVEP and SAP tests. The OCT was abnormal for 73 (95%) of these. Only 1 (1%) of the 100 hemifields of the controls was abnormal on OCT. Sensitivity/specificity (one eye per person) was 95/98%. Conclusions The OCT RNFL test accurately detects abnormal hemifields confirmed on both subjective and objective functional tests. Identifying abnormal hemifields with a criterion of 1 red (1%) or 2 yellow (5%) clock hours may prove useful in clinical practice. PMID:17301118

An object detection and tracking system for unmanned surface vehicles

NASA Astrophysics Data System (ADS)

Yang, Jian; Xiao, Yang; Fang, Zhiwen; Zhang, Naiwen; Wang, Li; Li, Tao

2017-10-01

Object detection and tracking are critical parts of unmanned surface vehicles(USV) to achieve automatic obstacle avoidance. Off-the-shelf object detection methods have achieved impressive accuracy in public datasets, though they still meet bottlenecks in practice, such as high time consumption and low detection quality. In this paper, we propose a novel system for USV, which is able to locate the object more accurately while being fast and stable simultaneously. Firstly, we employ Faster R-CNN to acquire several initial raw bounding boxes. Secondly, the image is segmented to a few superpixels. For each initial box, the superpixels inside will be grouped into a whole according to a combination strategy, and a new box is thereafter generated as the circumscribed bounding box of the final superpixel. Thirdly, we utilize KCF to track these objects after several frames, Faster-RCNN is again used to re-detect objects inside tracked boxes to prevent tracking failure as well as remove empty boxes. Finally, we utilize Faster R-CNN to detect objects in the next image, and refine object boxes by repeating the second module of our system. The experimental results demonstrate that our system is fast, robust and accurate, which can be applied to USV in practice.

DeepMitosis: Mitosis detection via deep detection, verification and segmentation networks.

PubMed

Li, Chao; Wang, Xinggang; Liu, Wenyu; Latecki, Longin Jan

2018-04-01

Mitotic count is a critical predictor of tumor aggressiveness in the breast cancer diagnosis. Nowadays mitosis counting is mainly performed by pathologists manually, which is extremely arduous and time-consuming. In this paper, we propose an accurate method for detecting the mitotic cells from histopathological slides using a novel multi-stage deep learning framework. Our method consists of a deep segmentation network for generating mitosis region when only a weak label is given (i.e., only the centroid pixel of mitosis is annotated), an elaborately designed deep detection network for localizing mitosis by using contextual region information, and a deep verification network for improving detection accuracy by removing false positives. We validate the proposed deep learning method on two widely used Mitosis Detection in Breast Cancer Histological Images (MITOSIS) datasets. Experimental results show that we can achieve the highest F-score on the MITOSIS dataset from ICPR 2012 grand challenge merely using the deep detection network. For the ICPR 2014 MITOSIS dataset that only provides the centroid location of mitosis, we employ the segmentation model to estimate the bounding box annotation for training the deep detection network. We also apply the verification model to eliminate some false positives produced from the detection model. By fusing scores of the detection and verification models, we achieve the state-of-the-art results. Moreover, our method is very fast with GPU computing, which makes it feasible for clinical practice. Copyright © 2018 Elsevier B.V. All rights reserved.

Achieving the Heisenberg limit in quantum metrology using quantum error correction.

PubMed

Zhou, Sisi; Zhang, Mengzhen; Preskill, John; Jiang, Liang

2018-01-08

Quantum metrology has many important applications in science and technology, ranging from frequency spectroscopy to gravitational wave detection. Quantum mechanics imposes a fundamental limit on measurement precision, called the Heisenberg limit, which can be achieved for noiseless quantum systems, but is not achievable in general for systems subject to noise. Here we study how measurement precision can be enhanced through quantum error correction, a general method for protecting a quantum system from the damaging effects of noise. We find a necessary and sufficient condition for achieving the Heisenberg limit using quantum probes subject to Markovian noise, assuming that noiseless ancilla systems are available, and that fast, accurate quantum processing can be performed. When the sufficient condition is satisfied, a quantum error-correcting code can be constructed that suppresses the noise without obscuring the signal; the optimal code, achieving the best possible precision, can be found by solving a semidefinite program.

Detection limits of organic compounds achievable with intense, short-pulse lasers.

PubMed

Miles, Jordan; De Camillis, Simone; Alexander, Grace; Hamilton, Kathryn; Kelly, Thomas J; Costello, John T; Zepf, Matthew; Williams, Ian D; Greenwood, Jason B

2015-06-21

Many organic molecules have strong absorption bands which can be accessed by ultraviolet short pulse lasers to produce efficient ionization. This resonant multiphoton ionization scheme has already been exploited as an ionization source in time-of-flight mass spectrometers used for environmental trace analysis. In the present work we quantify the ultimate potential of this technique by measuring absolute ion yields produced from the interaction of 267 nm femtosecond laser pulses with the organic molecules indole and toluene, and gases Xe, N2 and O2. Using multiphoton ionization cross sections extracted from these results, we show that the laser pulse parameters required for real-time detection of aromatic molecules at concentrations of one part per trillion in air and a limit of detection of a few attomoles are achievable with presently available commercial laser systems. The potential applications for the analysis of human breath, blood and tissue samples are discussed.

Aberrant Learning Achievement Detection Based on Person-Fit Statistics in Personalized e-Learning Systems

ERIC Educational Resources Information Center

Liu, Ming-Tsung; Yu, Pao-Ta

2011-01-01

A personalized e-learning service provides learning content to fit learners' individual differences. Learning achievements are influenced by cognitive as well as non-cognitive factors such as mood, motivation, interest, and personal styles. This paper proposes the Learning Caution Indexes (LCI) to detect aberrant learning patterns. The philosophy…

A unique charge-coupled device/xenon arc lamp based imaging system for the accurate detection and quantitation of multicolour fluorescence.

PubMed

Spibey, C A; Jackson, P; Herick, K

2001-03-01

In recent years the use of fluorescent dyes in biological applications has dramatically increased. The continual improvement in the capabilities of these fluorescent dyes demands increasingly sensitive detection systems that provide accurate quantitation over a wide linear dynamic range. In the field of proteomics, the detection, quantitation and identification of very low abundance proteins are of extreme importance in understanding cellular processes. Therefore, the instrumentation used to acquire an image of such samples, for spot picking and identification by mass spectrometry, must be sensitive enough to be able, not only, to maximise the sensitivity and dynamic range of the staining dyes but, as importantly, adapt to the ever changing portfolio of fluorescent dyes as they become available. Just as the available fluorescent probes are improving and evolving so are the users application requirements. Therefore, the instrumentation chosen must be flexible to address and adapt to those changing needs. As a result, a highly competitive market for the supply and production of such dyes and the instrumentation for their detection and quantitation have emerged. The instrumentation currently available is based on either laser/photomultiplier tube (PMT) scanning or lamp/charge-coupled device (CCD) based mechanisms. This review briefly discusses the advantages and disadvantages of both System types for fluorescence imaging, gives a technical overview of CCD technology and describes in detail a unique xenon/are lamp CCD based instrument, from PerkinElmer Life Sciences. The Wallac-1442 ARTHUR is unique in its ability to scan both large areas at high resolution and give accurate selectable excitation over the whole of the UV/visible range. It operates by filtering both the excitation and emission wavelengths, providing optimal and accurate measurement and quantitation of virtually any available dye and allows excellent spectral resolution between different fluorophores

Methods to achieve accurate projection of regional and global raster databases

USGS Publications Warehouse

Usery, E. Lynn; Seong, Jeong Chang; Steinwand, Dan

2002-01-01

Modeling regional and global activities of climatic and human-induced change requires accurate geographic data from which we can develop mathematical and statistical tabulations of attributes and properties of the environment. Many of these models depend on data formatted as raster cells or matrices of pixel values. Recently, it has been demonstrated that regional and global raster datasets are subject to significant error from mathematical projection and that these errors are of such magnitude that model results may be jeopardized (Steinwand, et al., 1995; Yang, et al., 1996; Usery and Seong, 2001; Seong and Usery, 2001). There is a need to develop methods of projection that maintain the accuracy of these datasets to support regional and global analyses and modeling

Accurate Determination of the Q Quality Factor in Magnetoelastic Resonant Platforms for Advanced Biological Detection

PubMed Central

Lopes, Ana Catarina; Sagasti, Ariane; Lasheras, Andoni; Muto, Virginia; Gutiérrez, Jon; Kouzoudis, Dimitris; Barandiarán, José Manuel

2018-01-01

The main parameters of magnetoelastic resonators in the detection of chemical (i.e., salts, gases, etc.) or biological (i.e., bacteria, phages, etc.) agents are the sensitivity S (or external agent change magnitude per Hz change in the resonance frequency) and the quality factor Q of the resonance. We present an extensive study on the experimental determination of the Q factor in such magnetoelastic resonant platforms, using three different strategies: (a) analyzing the real and imaginary components of the susceptibility at resonance; (b) numerical fitting of the modulus of the susceptibility; (c) using an exact mathematical expression for the real part of the susceptibility. Q values obtained by the three methods are analyzed and discussed, aiming to establish the most adequate one to accurately determine the quality factor of the magnetoelastic resonance. PMID:29547578

Accurate Determination of the Q Quality Factor in Magnetoelastic Resonant Platforms for Advanced Biological Detection.

PubMed

Lopes, Ana Catarina; Sagasti, Ariane; Lasheras, Andoni; Muto, Virginia; Gutiérrez, Jon; Kouzoudis, Dimitris; Barandiarán, José Manuel

2018-03-16

The main parameters of magnetoelastic resonators in the detection of chemical (i.e., salts, gases, etc.) or biological (i.e., bacteria, phages, etc.) agents are the sensitivity S (or external agent change magnitude per Hz change in the resonance frequency) and the quality factor Q of the resonance. We present an extensive study on the experimental determination of the Q factor in such magnetoelastic resonant platforms, using three different strategies: (a) analyzing the real and imaginary components of the susceptibility at resonance; (b) numerical fitting of the modulus of the susceptibility; (c) using an exact mathematical expression for the real part of the susceptibility. Q values obtained by the three methods are analyzed and discussed, aiming to establish the most adequate one to accurately determine the quality factor of the magnetoelastic resonance.

A non-contact method based on multiple signal classification algorithm to reduce the measurement time for accurately heart rate detection

NASA Astrophysics Data System (ADS)

Bechet, P.; Mitran, R.; Munteanu, M.

2013-08-01

Non-contact methods for the assessment of vital signs are of great interest for specialists due to the benefits obtained in both medical and special applications, such as those for surveillance, monitoring, and search and rescue. This paper investigates the possibility of implementing a digital processing algorithm based on the MUSIC (Multiple Signal Classification) parametric spectral estimation in order to reduce the observation time needed to accurately measure the heart rate. It demonstrates that, by proper dimensioning the signal subspace, the MUSIC algorithm can be optimized in order to accurately assess the heart rate during an 8-28 s time interval. The validation of the processing algorithm performance was achieved by minimizing the mean error of the heart rate after performing simultaneous comparative measurements on several subjects. In order to calculate the error the reference value of heart rate was measured using a classic measurement system through direct contact.

3D surface voxel tracing corrector for accurate bone segmentation.

PubMed

Guo, Haoyan; Song, Sicong; Wang, Jinke; Guo, Maozu; Cheng, Yuanzhi; Wang, Yadong; Tamura, Shinichi

2018-06-18

For extremely close bones, their boundaries are weak and diffused due to strong interaction between adjacent surfaces. These factors prevent the accurate segmentation of bone structure. To alleviate these difficulties, we propose an automatic method for accurate bone segmentation. The method is based on a consideration of the 3D surface normal direction, which is used to detect the bone boundary in 3D CT images. Our segmentation method is divided into three main stages. Firstly, we consider a surface tracing corrector combined with Gaussian standard deviation [Formula: see text] to improve the estimation of normal direction. Secondly, we determine an optimal value of [Formula: see text] for each surface point during this normal direction correction. Thirdly, we construct the 1D signal and refining the rough boundary along the corrected normal direction. The value of [Formula: see text] is used in the first directional derivative of the Gaussian to refine the location of the edge point along accurate normal direction. Because the normal direction is corrected and the value of [Formula: see text] is optimized, our method is robust to noise images and narrow joint space caused by joint degeneration. We applied our method to 15 wrists and 50 hip joints for evaluation. In the wrist segmentation, Dice overlap coefficient (DOC) of [Formula: see text]% was obtained by our method. In the hip segmentation, fivefold cross-validations were performed for two state-of-the-art methods. Forty hip joints were used for training in two state-of-the-art methods, 10 hip joints were used for testing and performing comparisons. The DOCs of [Formula: see text], [Formula: see text]%, and [Formula: see text]% were achieved by our method for the pelvis, the left femoral head and the right femoral head, respectively. Our method was shown to improve segmentation accuracy for several specific challenging cases. The results demonstrate that our approach achieved a superior accuracy over two

Variational mode decomposition based approach for accurate classification of color fundus images with hemorrhages

NASA Astrophysics Data System (ADS)

Lahmiri, Salim; Shmuel, Amir

2017-11-01

Diabetic retinopathy is a disease that can cause a loss of vision. An early and accurate diagnosis helps to improve treatment of the disease and prognosis. One of the earliest characteristics of diabetic retinopathy is the appearance of retinal hemorrhages. The purpose of this study is to design a fully automated system for the detection of hemorrhages in a retinal image. In the first stage of our proposed system, a retinal image is processed with variational mode decomposition (VMD) to obtain the first variational mode, which captures the high frequency components of the original image. In the second stage, four texture descriptors are extracted from the first variational mode. Finally, a classifier trained with all computed texture descriptors is used to distinguish between images of healthy and unhealthy retinas with hemorrhages. Experimental results showed evidence of the effectiveness of the proposed system for detection of hemorrhages in the retina, since a perfect detection rate was achieved. Our proposed system for detecting diabetic retinopathy is simple and easy to implement. It requires only short processing time, and it yields higher accuracy in comparison with previously proposed methods for detecting diabetic retinopathy.

Research on the Rapid and Accurate Positioning and Orientation Approach for Land Missile-Launching Vehicle

PubMed Central

Li, Kui; Wang, Lei; Lv, Yanhong; Gao, Pengyu; Song, Tianxiao

2015-01-01

Getting a land vehicle’s accurate position, azimuth and attitude rapidly is significant for vehicle based weapons’ combat effectiveness. In this paper, a new approach to acquire vehicle’s accurate position and orientation is proposed. It uses biaxial optical detection platform (BODP) to aim at and lock in no less than three pre-set cooperative targets, whose accurate positions are measured beforehand. Then, it calculates the vehicle’s accurate position, azimuth and attitudes by the rough position and orientation provided by vehicle based navigation systems and no less than three couples of azimuth and pitch angles measured by BODP. The proposed approach does not depend on Global Navigation Satellite System (GNSS), thus it is autonomous and difficult to interfere. Meanwhile, it only needs a rough position and orientation as algorithm’s iterative initial value, consequently, it does not have high performance requirement for Inertial Navigation System (INS), odometer and other vehicle based navigation systems, even in high precise applications. This paper described the system’s working procedure, presented theoretical deviation of the algorithm, and then verified its effectiveness through simulation and vehicle experiments. The simulation and experimental results indicate that the proposed approach can achieve positioning and orientation accuracy of 0.2 m and 20″ respectively in less than 3 min. PMID:26492249

Research on the rapid and accurate positioning and orientation approach for land missile-launching vehicle.

PubMed

Li, Kui; Wang, Lei; Lv, Yanhong; Gao, Pengyu; Song, Tianxiao

2015-10-20

Getting a land vehicle's accurate position, azimuth and attitude rapidly is significant for vehicle based weapons' combat effectiveness. In this paper, a new approach to acquire vehicle's accurate position and orientation is proposed. It uses biaxial optical detection platform (BODP) to aim at and lock in no less than three pre-set cooperative targets, whose accurate positions are measured beforehand. Then, it calculates the vehicle's accurate position, azimuth and attitudes by the rough position and orientation provided by vehicle based navigation systems and no less than three couples of azimuth and pitch angles measured by BODP. The proposed approach does not depend on Global Navigation Satellite System (GNSS), thus it is autonomous and difficult to interfere. Meanwhile, it only needs a rough position and orientation as algorithm's iterative initial value, consequently, it does not have high performance requirement for Inertial Navigation System (INS), odometer and other vehicle based navigation systems, even in high precise applications. This paper described the system's working procedure, presented theoretical deviation of the algorithm, and then verified its effectiveness through simulation and vehicle experiments. The simulation and experimental results indicate that the proposed approach can achieve positioning and orientation accuracy of 0.2 m and 20″ respectively in less than 3 min.

Achieving Accurate Automatic Sleep Staging on Manually Pre-processed EEG Data Through Synchronization Feature Extraction and Graph Metrics.

PubMed

Chriskos, Panteleimon; Frantzidis, Christos A; Gkivogkli, Polyxeni T; Bamidis, Panagiotis D; Kourtidou-Papadeli, Chrysoula

2018-01-01

Sleep staging, the process of assigning labels to epochs of sleep, depending on the stage of sleep they belong, is an arduous, time consuming and error prone process as the initial recordings are quite often polluted by noise from different sources. To properly analyze such data and extract clinical knowledge, noise components must be removed or alleviated. In this paper a pre-processing and subsequent sleep staging pipeline for the sleep analysis of electroencephalographic signals is described. Two novel methods of functional connectivity estimation (Synchronization Likelihood/SL and Relative Wavelet Entropy/RWE) are comparatively investigated for automatic sleep staging through manually pre-processed electroencephalographic recordings. A multi-step process that renders signals suitable for further analysis is initially described. Then, two methods that rely on extracting synchronization features from electroencephalographic recordings to achieve computerized sleep staging are proposed, based on bivariate features which provide a functional overview of the brain network, contrary to most proposed methods that rely on extracting univariate time and frequency features. Annotation of sleep epochs is achieved through the presented feature extraction methods by training classifiers, which are in turn able to accurately classify new epochs. Analysis of data from sleep experiments on a randomized, controlled bed-rest study, which was organized by the European Space Agency and was conducted in the "ENVIHAB" facility of the Institute of Aerospace Medicine at the German Aerospace Center (DLR) in Cologne, Germany attains high accuracy rates, over 90% based on ground truth that resulted from manual sleep staging by two experienced sleep experts. Therefore, it can be concluded that the above feature extraction methods are suitable for semi-automatic sleep staging.

Achieving Accurate Automatic Sleep Staging on Manually Pre-processed EEG Data Through Synchronization Feature Extraction and Graph Metrics

PubMed Central

Chriskos, Panteleimon; Frantzidis, Christos A.; Gkivogkli, Polyxeni T.; Bamidis, Panagiotis D.; Kourtidou-Papadeli, Chrysoula

2018-01-01

Sleep staging, the process of assigning labels to epochs of sleep, depending on the stage of sleep they belong, is an arduous, time consuming and error prone process as the initial recordings are quite often polluted by noise from different sources. To properly analyze such data and extract clinical knowledge, noise components must be removed or alleviated. In this paper a pre-processing and subsequent sleep staging pipeline for the sleep analysis of electroencephalographic signals is described. Two novel methods of functional connectivity estimation (Synchronization Likelihood/SL and Relative Wavelet Entropy/RWE) are comparatively investigated for automatic sleep staging through manually pre-processed electroencephalographic recordings. A multi-step process that renders signals suitable for further analysis is initially described. Then, two methods that rely on extracting synchronization features from electroencephalographic recordings to achieve computerized sleep staging are proposed, based on bivariate features which provide a functional overview of the brain network, contrary to most proposed methods that rely on extracting univariate time and frequency features. Annotation of sleep epochs is achieved through the presented feature extraction methods by training classifiers, which are in turn able to accurately classify new epochs. Analysis of data from sleep experiments on a randomized, controlled bed-rest study, which was organized by the European Space Agency and was conducted in the “ENVIHAB” facility of the Institute of Aerospace Medicine at the German Aerospace Center (DLR) in Cologne, Germany attains high accuracy rates, over 90% based on ground truth that resulted from manual sleep staging by two experienced sleep experts. Therefore, it can be concluded that the above feature extraction methods are suitable for semi-automatic sleep staging. PMID:29628883

Mechanism for accurate, protein-assisted DNA annealing by Deinococcus radiodurans DdrB

PubMed Central

Sugiman-Marangos, Seiji N.; Weiss, Yoni M.; Junop, Murray S.

2016-01-01

Accurate pairing of DNA strands is essential for repair of DNA double-strand breaks (DSBs). How cells achieve accurate annealing when large regions of single-strand DNA are unpaired has remained unclear despite many efforts focused on understanding proteins, which mediate this process. Here we report the crystal structure of a single-strand annealing protein [DdrB (DNA damage response B)] in complex with a partially annealed DNA intermediate to 2.2 Å. This structure and supporting biochemical data reveal a mechanism for accurate annealing involving DdrB-mediated proofreading of strand complementarity. DdrB promotes high-fidelity annealing by constraining specific bases from unauthorized association and only releases annealed duplex when bound strands are fully complementary. To our knowledge, this mechanism provides the first understanding for how cells achieve accurate, protein-assisted strand annealing under biological conditions that would otherwise favor misannealing. PMID:27044084

Creation of an Accurate Algorithm to Detect Snellen Best Documented Visual Acuity from Ophthalmology Electronic Health Record Notes.

PubMed

Mbagwu, Michael; French, Dustin D; Gill, Manjot; Mitchell, Christopher; Jackson, Kathryn; Kho, Abel; Bryar, Paul J

2016-05-04

Visual acuity is the primary measure used in ophthalmology to determine how well a patient can see. Visual acuity for a single eye may be recorded in multiple ways for a single patient visit (eg, Snellen vs. Jäger units vs. font print size), and be recorded for either distance or near vision. Capturing the best documented visual acuity (BDVA) of each eye in an individual patient visit is an important step for making electronic ophthalmology clinical notes useful in research. Currently, there is limited methodology for capturing BDVA in an efficient and accurate manner from electronic health record (EHR) notes. We developed an algorithm to detect BDVA for right and left eyes from defined fields within electronic ophthalmology clinical notes. We designed an algorithm to detect the BDVA from defined fields within 295,218 ophthalmology clinical notes with visual acuity data present. About 5668 unique responses were identified and an algorithm was developed to map all of the unique responses to a structured list of Snellen visual acuities. Visual acuity was captured from a total of 295,218 ophthalmology clinical notes during the study dates. The algorithm identified all visual acuities in the defined visual acuity section for each eye and returned a single BDVA for each eye. A clinician chart review of 100 random patient notes showed a 99% accuracy detecting BDVA from these records and 1% observed error. Our algorithm successfully captures best documented Snellen distance visual acuity from ophthalmology clinical notes and transforms a variety of inputs into a structured Snellen equivalent list. Our work, to the best of our knowledge, represents the first attempt at capturing visual acuity accurately from large numbers of electronic ophthalmology notes. Use of this algorithm can benefit research groups interested in assessing visual acuity for patient centered outcome. All codes used for this study are currently available, and will be made available online at https://phekb.org.

Creation of an Accurate Algorithm to Detect Snellen Best Documented Visual Acuity from Ophthalmology Electronic Health Record Notes

PubMed Central

French, Dustin D; Gill, Manjot; Mitchell, Christopher; Jackson, Kathryn; Kho, Abel; Bryar, Paul J

2016-01-01

Background Visual acuity is the primary measure used in ophthalmology to determine how well a patient can see. Visual acuity for a single eye may be recorded in multiple ways for a single patient visit (eg, Snellen vs. Jäger units vs. font print size), and be recorded for either distance or near vision. Capturing the best documented visual acuity (BDVA) of each eye in an individual patient visit is an important step for making electronic ophthalmology clinical notes useful in research. Objective Currently, there is limited methodology for capturing BDVA in an efficient and accurate manner from electronic health record (EHR) notes. We developed an algorithm to detect BDVA for right and left eyes from defined fields within electronic ophthalmology clinical notes. Methods We designed an algorithm to detect the BDVA from defined fields within 295,218 ophthalmology clinical notes with visual acuity data present. About 5668 unique responses were identified and an algorithm was developed to map all of the unique responses to a structured list of Snellen visual acuities. Results Visual acuity was captured from a total of 295,218 ophthalmology clinical notes during the study dates. The algorithm identified all visual acuities in the defined visual acuity section for each eye and returned a single BDVA for each eye. A clinician chart review of 100 random patient notes showed a 99% accuracy detecting BDVA from these records and 1% observed error. Conclusions Our algorithm successfully captures best documented Snellen distance visual acuity from ophthalmology clinical notes and transforms a variety of inputs into a structured Snellen equivalent list. Our work, to the best of our knowledge, represents the first attempt at capturing visual acuity accurately from large numbers of electronic ophthalmology notes. Use of this algorithm can benefit research groups interested in assessing visual acuity for patient centered outcome. All codes used for this study are currently

An accurate and inexpensive color-based assay for detecting severe anemia in a limited-resource setting

PubMed Central

McGann, Patrick T.; Tyburski, Erika A.; de Oliveira, Vysolela; Santos, Brigida; Ware, Russell E.; Lam, Wilbur A.

2016-01-01

Severe anemia is an important cause of morbidity and mortality among children in resource-poor settings, but laboratory diagnostics are often limited in these locations. To address this need, we developed a simple, inexpensive, and color-based point-of-care (POC) assay to detect severe anemia. The purpose of this study was to evaluate the accuracy of this novel POC assay to detect moderate and severe anemia in a limited-resource setting. The study was a cross-sectional study conducted on children with sickle cell anemia in Luanda, Angola. The hemoglobin concentrations obtained by the POC assay were compared to reference values measured by a calibrated automated hematology analyzer. A total of 86 samples were analyzed (mean hemoglobin concentration 6.6 g/dL). There was a strong correlation between the hemoglobin concentrations obtained by the POC assay and reference values obtained from an automated hematology analyzer (r=0.88, P<0.0001). The POC assay demonstrated excellent reproducibility (r=0.93, P<0.0001) and the reagents appeared to be durable in a tropical setting (r=0.93, P<0.0001). For the detection of severe anemia that may require blood transfusion (hemoglobin <5 g/dL), the POC assay had sensitivity of 88.9% and specificity of 98.7%. These data demonstrate that an inexpensive (<$0.25 USD) POC assay accurately estimates low hemoglobin concentrations and has the potential to become a transformational diagnostic tool for severe anemia in limited-resource settings. PMID:26317494

Accurate, multi-kb reads resolve complex populations and detect rare microorganisms.

PubMed

Sharon, Itai; Kertesz, Michael; Hug, Laura A; Pushkarev, Dmitry; Blauwkamp, Timothy A; Castelle, Cindy J; Amirebrahimi, Mojgan; Thomas, Brian C; Burstein, David; Tringe, Susannah G; Williams, Kenneth H; Banfield, Jillian F

2015-04-01

Accurate evaluation of microbial communities is essential for understanding global biogeochemical processes and can guide bioremediation and medical treatments. Metagenomics is most commonly used to analyze microbial diversity and metabolic potential, but assemblies of the short reads generated by current sequencing platforms may fail to recover heterogeneous strain populations and rare organisms. Here we used short (150-bp) and long (multi-kb) synthetic reads to evaluate strain heterogeneity and study microorganisms at low abundance in complex microbial communities from terrestrial sediments. The long-read data revealed multiple (probably dozens of) closely related species and strains from previously undescribed Deltaproteobacteria and Aminicenantes (candidate phylum OP8). Notably, these are the most abundant organisms in the communities, yet short-read assemblies achieved only partial genome coverage, mostly in the form of short scaffolds (N50 = ∼ 2200 bp). Genome architecture and metabolic potential for these lineages were reconstructed using a new synteny-based method. Analysis of long-read data also revealed thousands of species whose abundances were <0.1% in all samples. Most of the organisms in this "long tail" of rare organisms belong to phyla that are also represented by abundant organisms. Genes encoding glycosyl hydrolases are significantly more abundant than expected in rare genomes, suggesting that rare species may augment the capability for carbon turnover and confer resilience to changing environmental conditions. Overall, the study showed that a diversity of closely related strains and rare organisms account for a major portion of the communities. These are probably common features of many microbial communities and can be effectively studied using a combination of long and short reads. © 2015 Sharon et al.; Published by Cold Spring Harbor Laboratory Press.

Multimodal Spatial Calibration for Accurately Registering EEG Sensor Positions

PubMed Central

Chen, Shengyong; Xiao, Gang; Li, Xiaoli

2014-01-01

This paper proposes a fast and accurate calibration method to calibrate multiple multimodal sensors using a novel photogrammetry system for fast localization of EEG sensors. The EEG sensors are placed on human head and multimodal sensors are installed around the head to simultaneously obtain all EEG sensor positions. A multiple views' calibration process is implemented to obtain the transformations of multiple views. We first develop an efficient local repair algorithm to improve the depth map, and then a special calibration body is designed. Based on them, accurate and robust calibration results can be achieved. We evaluate the proposed method by corners of a chessboard calibration plate. Experimental results demonstrate that the proposed method can achieve good performance, which can be further applied to EEG source localization applications on human brain. PMID:24803954

A dedicated on-line detecting system for auto air dryers

NASA Astrophysics Data System (ADS)

Shi, Chao-yu; Luo, Zai

2013-10-01

According to the correlative automobile industry standard and the requirements of manufacturer, this dedicated on-line detecting system is designed against the shortage of low degree automatic efficiency and detection precision of auto air dryer in the domestic. Fast automatic detection is achieved by combining the technology of computer control, mechatronics and pneumatics. This system can detect the speciality performance of pressure regulating valve and sealability of auto air dryer, in which online analytical processing of test data is available, at the same time, saving and inquiring data is achieved. Through some experimental analysis, it is indicated that efficient and accurate detection of the performance of auto air dryer is realized, and the test errors are less than 3%. Moreover, we carry out the type A evaluation of uncertainty in test data based on Bayesian theory, and the results show that the test uncertainties of all performance parameters are less than 0.5kPa, which can meet the requirements of operating industrial site absolutely.

[Study on Accurately Controlling Discharge Energy Method Used in External Defibrillator].

PubMed

Song, Biao; Wang, Jianfei; Jin, Lian; Wu, Xiaomei

2016-01-01

This paper introduces a new method which controls discharge energy accurately. It is achieved by calculating target voltage based on transthoracic impedance and accurately controlling charging voltage and discharge pulse width. A new defibrillator is designed and programmed using this method. The test results show that this method is valid and applicable to all kinds of external defibrillators.

A robust recognition and accurate locating method for circular coded diagonal target

NASA Astrophysics Data System (ADS)

Bao, Yunna; Shang, Yang; Sun, Xiaoliang; Zhou, Jiexin

2017-10-01

As a category of special control points which can be automatically identified, artificial coded targets have been widely developed in the field of computer vision, photogrammetry, augmented reality, etc. In this paper, a new circular coded target designed by RockeTech technology Corp. Ltd is analyzed and studied, which is called circular coded diagonal target (CCDT). A novel detection and recognition method with good robustness is proposed in the paper, and implemented on Visual Studio. In this algorithm, firstly, the ellipse features of the center circle are used for rough positioning. Then, according to the characteristics of the center diagonal target, a circular frequency filter is designed to choose the correct center circle and eliminates non-target noise. The precise positioning of the coded target is done by the correlation coefficient fitting extreme value method. Finally, the coded target recognition is achieved by decoding the binary sequence in the outer ring of the extracted target. To test the proposed algorithm, this paper has carried out simulation experiments and real experiments. The results show that the CCDT recognition and accurate locating method proposed in this paper can robustly recognize and accurately locate the targets in complex and noisy background.

Accurate antemortem diagnosis of equine protozoal myeloencephalitis (EPM) based on detecting intrathecal antibodies against Sarcocystis neurona using the SnSAG2 and SnSAG4/3 ELISAs.

PubMed

Reed, S M; Howe, D K; Morrow, J K; Graves, A; Yeargan, M R; Johnson, A L; MacKay, R J; Furr, M; Saville, W J A; Williams, N M

2013-01-01

Recent work demonstrated the value of antigen-specific antibody indices (AI and C-value) to detect intrathecal antibody production against Sarcocystis neurona for antemortem diagnosis of equine protozoal myeloencephalitis (EPM). The study was conducted to assess whether the antigen-specific antibody indices can be reduced to a simple serum : cerebrospinal fluid (CSF) titer ratio to achieve accurate EPM diagnosis. Paired serum and CSF samples from 128 horses diagnosed by postmortem examination. The sample set included 44 EPM cases, 35 cervical-vertebral malformation (CVM) cases, 39 neurologic cases other than EPM or CVM, and 10 non-neurologic cases. Antibodies against S. neurona were measured in serum and CSF pairs using the SnSAG2 and SnSAG4/3 (SnSAG2, 4/3) ELISAs, and the ratio of each respective serum titer to CSF titer was determined. Likelihood ratios and diagnostic sensitivity and specificity were calculated based on serum titers, CSF titers, and serum : CSF titer ratios. Excellent diagnostic sensitivity and specificity was obtained from the SnSAG2, 4/3 serum : CSF titer ratio. Sensitivity and specificity of 93.2 and 81.1%, respectively, were achieved using a ratio cutoff of ≤100, whereas sensitivity and specificity were 86.4 and 95.9%, respectively, if a more rigorous cutoff of ≤50 was used. Antibody titers in CSF also provided good diagnostic accuracy. Serum antibody titers alone yielded much lower sensitivity and specificity. The study confirms the value of detecting intrathecal antibody production for antemortem diagnosis of EPM, and they further show that the antigen-specific antibody indices can be reduced in practice to a simple serum : CSF titer ratio. Copyright © 2013 by the American College of Veterinary Internal Medicine.

Four Types of Pulse Oximeters Accurately Detect Hypoxia during Low Perfusion and Motion.

PubMed

Louie, Aaron; Feiner, John R; Bickler, Philip E; Rhodes, Laura; Bernstein, Michael; Lucero, Jennifer

2018-03-01

Pulse oximeter performance is degraded by motion artifacts and low perfusion. Manufacturers developed algorithms to improve instrument performance during these challenges. There have been no independent comparisons of these devices. We evaluated the performance of four pulse oximeters (Masimo Radical-7, USA; Nihon Kohden OxyPal Neo, Japan; Nellcor N-600, USA; and Philips Intellivue MP5, USA) in 10 healthy adult volunteers. Three motions were evaluated: tapping, pseudorandom, and volunteer-generated rubbing, adjusted to produce photoplethsmogram disturbance similar to arterial pulsation amplitude. During motion, inspired gases were adjusted to achieve stable target plateaus of arterial oxygen saturation (SaO2) at 75%, 88%, and 100%. Pulse oximeter readings were compared with simultaneous arterial blood samples to calculate bias (oxygen saturation measured by pulse oximetry [SpO2] - SaO2), mean, SD, 95% limits of agreement, and root mean square error. Receiver operating characteristic curves were determined to detect mild (SaO2 < 90%) and severe (SaO2 < 80%) hypoxemia. Pulse oximeter readings corresponding to 190 blood samples were analyzed. All oximeters detected hypoxia but motion and low perfusion degraded performance. Three of four oximeters (Masimo, Nellcor, and Philips) had root mean square error greater than 3% for SaO2 70 to 100% during any motion, compared to a root mean square error of 1.8% for the stationary control. A low perfusion index increased error. All oximeters detected hypoxemia during motion and low-perfusion conditions, but motion impaired performance at all ranges, with less accuracy at lower SaO2. Lower perfusion degraded performance in all but the Nihon Kohden instrument. We conclude that different types of pulse oximeters can be similarly effective in preserving sensitivity to clinically relevant hypoxia.

Automated detection of photoreceptor disruption in mild diabetic retinopathy on volumetric optical coherence tomography

PubMed Central

Wang, Zhuo; Camino, Acner; Zhang, Miao; Wang, Jie; Hwang, Thomas S.; Wilson, David J.; Huang, David; Li, Dengwang; Jia, Yali

2017-01-01

Diabetic retinopathy is a pathology where microvascular circulation abnormalities ultimately result in photoreceptor disruption and, consequently, permanent loss of vision. Here, we developed a method that automatically detects photoreceptor disruption in mild diabetic retinopathy by mapping ellipsoid zone reflectance abnormalities from en face optical coherence tomography images. The algorithm uses a fuzzy c-means scheme with a redefined membership function to assign a defect severity level on each pixel and generate a probability map of defect category affiliation. A novel scheme of unsupervised clustering optimization allows accurate detection of the affected area. The achieved accuracy, sensitivity and specificity were about 90% on a population of thirteen diseased subjects. This method shows potential for accurate and fast detection of early biomarkers in diabetic retinopathy evolution. PMID:29296475

Automated detection of photoreceptor disruption in mild diabetic retinopathy on volumetric optical coherence tomography.

PubMed

Wang, Zhuo; Camino, Acner; Zhang, Miao; Wang, Jie; Hwang, Thomas S; Wilson, David J; Huang, David; Li, Dengwang; Jia, Yali

2017-12-01

Diabetic retinopathy is a pathology where microvascular circulation abnormalities ultimately result in photoreceptor disruption and, consequently, permanent loss of vision. Here, we developed a method that automatically detects photoreceptor disruption in mild diabetic retinopathy by mapping ellipsoid zone reflectance abnormalities from en face optical coherence tomography images. The algorithm uses a fuzzy c-means scheme with a redefined membership function to assign a defect severity level on each pixel and generate a probability map of defect category affiliation. A novel scheme of unsupervised clustering optimization allows accurate detection of the affected area. The achieved accuracy, sensitivity and specificity were about 90% on a population of thirteen diseased subjects. This method shows potential for accurate and fast detection of early biomarkers in diabetic retinopathy evolution.

Automatic laser beam alignment using blob detection for an environment monitoring spectroscopy

NASA Astrophysics Data System (ADS)

Khidir, Jarjees; Chen, Youhua; Anderson, Gary

2013-05-01

This paper describes a fully automated system to align an infra-red laser beam with a small retro-reflector over a wide range of distances. The component development and test were especially used for an open-path spectrometer gas detection system. Using blob detection under OpenCV library, an automatic alignment algorithm was designed to achieve fast and accurate target detection in a complex background environment. Test results are presented to show that the proposed algorithm has been successfully applied to various target distances and environment conditions.

Fast and accurate inference of local ancestry in Latino populations

PubMed Central

Baran, Yael; Pasaniuc, Bogdan; Sankararaman, Sriram; Torgerson, Dara G.; Gignoux, Christopher; Eng, Celeste; Rodriguez-Cintron, William; Chapela, Rocio; Ford, Jean G.; Avila, Pedro C.; Rodriguez-Santana, Jose; Burchard, Esteban Gonzàlez; Halperin, Eran

2012-01-01

Motivation: It is becoming increasingly evident that the analysis of genotype data from recently admixed populations is providing important insights into medical genetics and population history. Such analyses have been used to identify novel disease loci, to understand recombination rate variation and to detect recent selection events. The utility of such studies crucially depends on accurate and unbiased estimation of the ancestry at every genomic locus in recently admixed populations. Although various methods have been proposed and shown to be extremely accurate in two-way admixtures (e.g. African Americans), only a few approaches have been proposed and thoroughly benchmarked on multi-way admixtures (e.g. Latino populations of the Americas). Results: To address these challenges we introduce here methods for local ancestry inference which leverage the structure of linkage disequilibrium in the ancestral population (LAMP-LD), and incorporate the constraint of Mendelian segregation when inferring local ancestry in nuclear family trios (LAMP-HAP). Our algorithms uniquely combine hidden Markov models (HMMs) of haplotype diversity within a novel window-based framework to achieve superior accuracy as compared with published methods. Further, unlike previous methods, the structure of our HMM does not depend on the number of reference haplotypes but on a fixed constant, and it is thereby capable of utilizing large datasets while remaining highly efficient and robust to over-fitting. Through simulations and analysis of real data from 489 nuclear trio families from the mainland US, Puerto Rico and Mexico, we demonstrate that our methods achieve superior accuracy compared with published methods for local ancestry inference in Latinos. Availability: http://lamp.icsi.berkeley.edu/lamp/lampld/ Contact: bpasaniu@hsph.harvard.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:22495753

Accurate determination of the geoid undulation N

NASA Astrophysics Data System (ADS)

Lambrou, E.; Pantazis, G.; Balodimos, D. D.

2003-04-01

This work is related to the activities of the CERGOP Study Group Geodynamics of the Balkan Peninsula, presents a method for the determination of the variation ΔN and, indirectly, of the geoid undulation N with an accuracy of a few millimeters. It is based on the determination of the components xi, eta of the deflection of the vertical using modern geodetic instruments (digital total station and GPS receiver). An analysis of the method is given. Accuracy of the order of 0.01arcsec in the estimated values of the astronomical coordinates Φ and Δ is achieved. The result of applying the proposed method in an area around Athens is presented. In this test application, a system is used which takes advantage of the capabilities of modern geodetic instruments. The GPS receiver permits the determination of the geodetic coordinates at a chosen reference system and, in addition, provides accurate timing information. The astronomical observations are performed through a digital total station with electronic registering of angles and time. The required accuracy of the values of the coordinates is achieved in about four hours of fieldwork. In addition, the instrumentation is lightweight, easily transportable and can be setup in the field very quickly. Combined with a stream-lined data reduction procedure and the use of up-to-date astrometric data, the values of the components xi, eta of the deflection of the vertical and, eventually, the changes ΔN of the geoid undulation are determined easily and accurately. In conclusion, this work demonstrates that it is quite feasible to create an accurate map of the geoid undulation, especially in areas that present large geoid variations and other methods are not capable to give accurate and reliable results.

High-accurate optical fiber liquid level sensor

NASA Astrophysics Data System (ADS)

Sun, Dexing; Chen, Shouliu; Pan, Chao; Jin, Henghuan

1991-08-01

A highly accurate optical fiber liquid level sensor is presented. The single-chip microcomputer is used to process and control the signal. This kind of sensor is characterized by self-security and is explosion-proof, so it can be applied in any liquid level detecting areas, especially in the oil and chemical industries. The theories and experiments about how to improve the measurement accuracy are described. The relative error for detecting the measurement range 10 m is up to 0.01%.

A rapid method of accurate detection and differentiation of Newcastle disease virus pathotypes by demonstrating multiple bands in degenerate primer based nested RT-PCR.

PubMed

Desingu, P A; Singh, S D; Dhama, K; Kumar, O R Vinodh; Singh, R; Singh, R K

2015-02-01

A rapid and accurate method of detection and differentiation of virulent and avirulent Newcastle disease virus (NDV) pathotypes was developed. The NDV detection was carried out for different domestic avian field isolates and pigeon paramyxo virus-1 (25 field isolates and 9 vaccine strains) by using APMV-I "fusion" (F) gene Class II specific external primer A and B (535bp), internal primer C and D (238bp) based reverses transcriptase PCR (RT-PCR). The internal degenerative reverse primer D is specific for F gene cleavage position of virulent strain of NDV. The nested RT-PCR products of avirulent strains showed two bands (535bp and 424bp) while virulent strains showed four bands (535bp, 424bp, 349bp and 238bp) on agar gel electrophoresis. This is the first report regarding development and use of degenerate primer based nested RT-PCR for accurate detection and differentiation of NDV pathotypes by demonstrating multiple PCR band patterns. Being a rapid, simple, and economical test, the developed method could serve as a valuable alternate diagnostic tool for characterizing NDV isolates and carrying out molecular epidemiological surveillance studies for this important pathogen of poultry. Copyright © 2014 Elsevier B.V. All rights reserved.

An accurate method of extracting fat droplets in liver images for quantitative evaluation

NASA Astrophysics Data System (ADS)

Ishikawa, Masahiro; Kobayashi, Naoki; Komagata, Hideki; Shinoda, Kazuma; Yamaguchi, Masahiro; Abe, Tokiya; Hashiguchi, Akinori; Sakamoto, Michiie

2015-03-01

The steatosis in liver pathological tissue images is a promising indicator of nonalcoholic fatty liver disease (NAFLD) and the possible risk of hepatocellular carcinoma (HCC). The resulting values are also important for ensuring the automatic and accurate classification of HCC images, because the existence of many fat droplets is likely to create errors in quantifying the morphological features used in the process. In this study we propose a method that can automatically detect, and exclude regions with many fat droplets by using the feature values of colors, shapes and the arrangement of cell nuclei. We implement the method and confirm that it can accurately detect fat droplets and quantify the fat droplet ratio of actual images. This investigation also clarifies the effective characteristics that contribute to accurate detection.

A novel method of adverse event detection can accurately identify venous thromboembolisms (VTEs) from narrative electronic health record data.

PubMed

Rochefort, Christian M; Verma, Aman D; Eguale, Tewodros; Lee, Todd C; Buckeridge, David L

2015-01-01

Venous thromboembolisms (VTEs), which include deep vein thrombosis (DVT) and pulmonary embolism (PE), are associated with significant mortality, morbidity, and cost in hospitalized patients. To evaluate the success of preventive measures, accurate and efficient methods for monitoring VTE rates are needed. Therefore, we sought to determine the accuracy of statistical natural language processing (NLP) for identifying DVT and PE from electronic health record data. We randomly sampled 2000 narrative radiology reports from patients with a suspected DVT/PE in Montreal (Canada) between 2008 and 2012. We manually identified DVT/PE within each report, which served as our reference standard. Using a bag-of-words approach, we trained 10 alternative support vector machine (SVM) models predicting DVT, and 10 predicting PE. SVM training and testing was performed with nested 10-fold cross-validation, and the average accuracy of each model was measured and compared. On manual review, 324 (16.2%) reports were DVT-positive and 154 (7.7%) were PE-positive. The best DVT model achieved an average sensitivity of 0.80 (95% CI 0.76 to 0.85), specificity of 0.98 (98% CI 0.97 to 0.99), positive predictive value (PPV) of 0.89 (95% CI 0.85 to 0.93), and an area under the curve (AUC) of 0.98 (95% CI 0.97 to 0.99). The best PE model achieved sensitivity of 0.79 (95% CI 0.73 to 0.85), specificity of 0.99 (95% CI 0.98 to 0.99), PPV of 0.84 (95% CI 0.75 to 0.92), and AUC of 0.99 (95% CI 0.98 to 1.00). Statistical NLP can accurately identify VTE from narrative radiology reports. © The Author 2014. Published by Oxford University Press on behalf of the American Medical Informatics Association.

A novel method of adverse event detection can accurately identify venous thromboembolisms (VTEs) from narrative electronic health record data

PubMed Central

Rochefort, Christian M; Verma, Aman D; Eguale, Tewodros; Lee, Todd C; Buckeridge, David L

2015-01-01

Background Venous thromboembolisms (VTEs), which include deep vein thrombosis (DVT) and pulmonary embolism (PE), are associated with significant mortality, morbidity, and cost in hospitalized patients. To evaluate the success of preventive measures, accurate and efficient methods for monitoring VTE rates are needed. Therefore, we sought to determine the accuracy of statistical natural language processing (NLP) for identifying DVT and PE from electronic health record data. Methods We randomly sampled 2000 narrative radiology reports from patients with a suspected DVT/PE in Montreal (Canada) between 2008 and 2012. We manually identified DVT/PE within each report, which served as our reference standard. Using a bag-of-words approach, we trained 10 alternative support vector machine (SVM) models predicting DVT, and 10 predicting PE. SVM training and testing was performed with nested 10-fold cross-validation, and the average accuracy of each model was measured and compared. Results On manual review, 324 (16.2%) reports were DVT-positive and 154 (7.7%) were PE-positive. The best DVT model achieved an average sensitivity of 0.80 (95% CI 0.76 to 0.85), specificity of 0.98 (98% CI 0.97 to 0.99), positive predictive value (PPV) of 0.89 (95% CI 0.85 to 0.93), and an area under the curve (AUC) of 0.98 (95% CI 0.97 to 0.99). The best PE model achieved sensitivity of 0.79 (95% CI 0.73 to 0.85), specificity of 0.99 (95% CI 0.98 to 0.99), PPV of 0.84 (95% CI 0.75 to 0.92), and AUC of 0.99 (95% CI 0.98 to 1.00). Conclusions Statistical NLP can accurately identify VTE from narrative radiology reports. PMID:25332356

Optimal pcr primers for rapid and accurate detection of Aspergillus flavus isolates.

PubMed

Al-Shuhaib, Mohammed Baqur S; Albakri, Ali H; Alwan, Sabah H; Almandil, Noor B; AbdulAzeez, Sayed; Borgio, J Francis

2018-03-01

Aspergillus flavus is among the most devastating opportunistic pathogens of several food crops including rice, due to its high production of carcinogenic aflatoxins. The presence of these organisms in economically important rice strip farming is a serious food safety concern. Several polymerase chain reaction (PCR) primers have been designed to detect this species; however, a comparative assessment of their accuracy has not been conducted. This study aims to identify the optimal diagnostic PCR primers for the identification of A. flavus, among widely available primers. We isolated 122 A. flavus native isolates from randomly collected rice strips (N = 300). We identified 109 isolates to the genus level using universal fungal PCR primer pairs. Nine pairs of primers were examined for their PCR diagnostic specificity on the 109 isolates. FLA PCR was found to be the optimal PCR primer pair for specific identification of the native isolates, over aflP(1), aflM, aflA, aflD, aflP(3), aflP(2), and aflR. The PEP primer pair was found to be the most unsuitable for A. flavus identification. In conclusion, the present study indicates the powerful specificity of the FLA PCR primer over other commonly available diagnostic primers for accurate, rapid, and large-scale identification of A. flavus native isolates. This study provides the first simple, practical comparative guide to PCR-based screening of A. flavus infection in rice strips. Copyright © 2018 Elsevier Ltd. All rights reserved.

Convolution neural networks for real-time needle detection and localization in 2D ultrasound.

PubMed

Mwikirize, Cosmas; Nosher, John L; Hacihaliloglu, Ilker

2018-05-01

We propose a framework for automatic and accurate detection of steeply inserted needles in 2D ultrasound data using convolution neural networks. We demonstrate its application in needle trajectory estimation and tip localization. Our approach consists of a unified network, comprising a fully convolutional network (FCN) and a fast region-based convolutional neural network (R-CNN). The FCN proposes candidate regions, which are then fed to a fast R-CNN for finer needle detection. We leverage a transfer learning paradigm, where the network weights are initialized by training with non-medical images, and fine-tuned with ex vivo ultrasound scans collected during insertion of a 17G epidural needle into freshly excised porcine and bovine tissue at depth settings up to 9 cm and [Formula: see text]-[Formula: see text] insertion angles. Needle detection results are used to accurately estimate needle trajectory from intensity invariant needle features and perform needle tip localization from an intensity search along the needle trajectory. Our needle detection model was trained and validated on 2500 ex vivo ultrasound scans. The detection system has a frame rate of 25 fps on a GPU and achieves 99.6% precision, 99.78% recall rate and an [Formula: see text] score of 0.99. Validation for needle localization was performed on 400 scans collected using a different imaging platform, over a bovine/porcine lumbosacral spine phantom. Shaft localization error of [Formula: see text], tip localization error of [Formula: see text] mm, and a total processing time of 0.58 s were achieved. The proposed method is fully automatic and provides robust needle localization results in challenging scanning conditions. The accurate and robust results coupled with real-time detection and sub-second total processing make the proposed method promising in applications for needle detection and localization during challenging minimally invasive ultrasound-guided procedures.

Ultrasound image edge detection based on a novel multiplicative gradient and Canny operator.

PubMed

Zheng, Yinfei; Zhou, Yali; Zhou, Hao; Gong, Xiaohong

2015-07-01

To achieve the fast and accurate segmentation of ultrasound image, a novel edge detection method for speckle noised ultrasound images was proposed, which was based on the traditional Canny and a novel multiplicative gradient operator. The proposed technique combines a new multiplicative gradient operator of non-Newtonian type with the traditional Canny operator to generate the initial edge map, which is subsequently optimized by the following edge tracing step. To verify the proposed method, we compared it with several other edge detection methods that had good robustness to noise, with experiments on the simulated and in vivo medical ultrasound image. Experimental results showed that the proposed algorithm has higher speed for real-time processing, and the edge detection accuracy could be 75% or more. Thus, the proposed method is very suitable for fast and accurate edge detection of medical ultrasound images. © The Author(s) 2014.

An Accurate Projector Calibration Method Based on Polynomial Distortion Representation

PubMed Central

Liu, Miao; Sun, Changku; Huang, Shujun; Zhang, Zonghua

2015-01-01

In structure light measurement systems or 3D printing systems, the errors caused by optical distortion of a digital projector always affect the precision performance and cannot be ignored. Existing methods to calibrate the projection distortion rely on calibration plate and photogrammetry, so the calibration performance is largely affected by the quality of the plate and the imaging system. This paper proposes a new projector calibration approach that makes use of photodiodes to directly detect the light emitted from a digital projector. By analyzing the output sequence of the photoelectric module, the pixel coordinates can be accurately obtained by the curve fitting method. A polynomial distortion representation is employed to reduce the residuals of the traditional distortion representation model. Experimental results and performance evaluation show that the proposed calibration method is able to avoid most of the disadvantages in traditional methods and achieves a higher accuracy. This proposed method is also practically applicable to evaluate the geometric optical performance of other optical projection system. PMID:26492247

Accurate dosimetry with GafChromic EBT film of a 6 MV photon beam in water: what level is achievable?

PubMed

van Battum, L J; Hoffmans, D; Piersma, H; Heukelom, S

2008-02-01

This paper focuses on the accuracy, in absolute dose measurements, with GafChromicTM EBT film achievable in water for a 6 MV photon beam up to a dose of 2.3 Gy. Motivation is to get an absolute dose detection system to measure up dose distributions in a (water) phantom, to check dose calculations. An Epson 1680 color (red green blue) transmission flatbed scanner has been used as film scanning system, where the response in the red color channel has been extracted and used for the analyses. The influence of the flatbed film scanner on the film based dose detection process was investigated. The scan procedure has been optimized; i.e. for instance a lateral correction curve was derived to correct the scan value, up to 10%, as a function of optical density and lateral position. Sensitometric curves of different film batches were evaluated in portrait and landscape scan mode. Between various batches important variations in sensitometric curve were observed. Energy dependence of the film is negligible, while a slight variation in dose response is observed for very large angles between film surface and incident photon beam. Improved accuracy in absolute dose detection can be obtained by repetition of a film measurement to tackle at least the inherent presence of film inhomogeneous construction. We state that the overall uncertainty is random in absolute EBT film dose detection and of the order of 1.3% (1 SD) under the condition that the film is scanned in a limited centered area on the scanner and at least two films have been applied. At last we advise to check a new film batch on its characteristics compared to available information, before using that batch for absolute dose measurements.

Fixed-Wing Micro Aerial Vehicle for Accurate Corridor Mapping

NASA Astrophysics Data System (ADS)

Rehak, M.; Skaloud, J.

2015-08-01

In this study we present a Micro Aerial Vehicle (MAV) equipped with precise position and attitude sensors that together with a pre-calibrated camera enables accurate corridor mapping. The design of the platform is based on widely available model components to which we integrate an open-source autopilot, customized mass-market camera and navigation sensors. We adapt the concepts of system calibration from larger mapping platforms to MAV and evaluate them practically for their achievable accuracy. We present case studies for accurate mapping without ground control points: first for a block configuration, later for a narrow corridor. We evaluate the mapping accuracy with respect to checkpoints and digital terrain model. We show that while it is possible to achieve pixel (3-5 cm) mapping accuracy in both cases, precise aerial position control is sufficient for block configuration, the precise position and attitude control is required for corridor mapping.

A method for the rapid detection of urinary tract infections.

PubMed

Olsson, Carl; Kapoor, Deepak; Howard, Glenn

2012-04-01

To determine the reliability of a rapid detection method compared with the reference standard streaked agar plate in diagnosing the presence of urinary tract infection (UTI). De-identified clean catch urine specimens from 980 office visit patients were processed during a 30-day period. Classic 1-μL and 10-μL streaked agar plates were used in parallel with the new CultureStat Rapid UTI Detection System (CSRUDS). Urine results were evaluated using the CSRUDS at 30 and 90 minutes after collection. A comparative analysis of the subsequent plate results versus the CSRUDS results was achieved for 973 of these samples. Positive UTI conditions were accurately identified by both CSRUDS and agar streak plate methods. CSRUDS accurately identified UTI negative conditions with 99.3% reliability at 90 minutes. The negative predictive value of CSRUDS was 99.2% at 30 minutes. Current agar plating for first-round UTI screening has substantial documented problems that can negatively affect an accurate and timely UTI diagnosis. A novel rapid detection system, the CSRUDS provides UTI negative/positive same-day results in ≤ 90 minutes from the start of test. Such rapidly available results will enable more accurate and timely clinical decisions to be made in the urology office, particularly regarding infection status before urologic instrumentation. Copyright © 2012 Elsevier Inc. All rights reserved.

Object Detection and Classification by Decision-Level Fusion for Intelligent Vehicle Systems.

PubMed

Oh, Sang-Il; Kang, Hang-Bong

2017-01-22

To understand driving environments effectively, it is important to achieve accurate detection and classification of objects detected by sensor-based intelligent vehicle systems, which are significantly important tasks. Object detection is performed for the localization of objects, whereas object classification recognizes object classes from detected object regions. For accurate object detection and classification, fusing multiple sensor information into a key component of the representation and perception processes is necessary. In this paper, we propose a new object-detection and classification method using decision-level fusion. We fuse the classification outputs from independent unary classifiers, such as 3D point clouds and image data using a convolutional neural network (CNN). The unary classifiers for the two sensors are the CNN with five layers, which use more than two pre-trained convolutional layers to consider local to global features as data representation. To represent data using convolutional layers, we apply region of interest (ROI) pooling to the outputs of each layer on the object candidate regions generated using object proposal generation to realize color flattening and semantic grouping for charge-coupled device and Light Detection And Ranging (LiDAR) sensors. We evaluate our proposed method on a KITTI benchmark dataset to detect and classify three object classes: cars, pedestrians and cyclists. The evaluation results show that the proposed method achieves better performance than the previous methods. Our proposed method extracted approximately 500 proposals on a 1226 × 370 image, whereas the original selective search method extracted approximately 10 6 × n proposals. We obtained classification performance with 77.72% mean average precision over the entirety of the classes in the moderate detection level of the KITTI benchmark dataset.

Object Detection and Classification by Decision-Level Fusion for Intelligent Vehicle Systems

PubMed Central

Oh, Sang-Il; Kang, Hang-Bong

2017-01-01

To understand driving environments effectively, it is important to achieve accurate detection and classification of objects detected by sensor-based intelligent vehicle systems, which are significantly important tasks. Object detection is performed for the localization of objects, whereas object classification recognizes object classes from detected object regions. For accurate object detection and classification, fusing multiple sensor information into a key component of the representation and perception processes is necessary. In this paper, we propose a new object-detection and classification method using decision-level fusion. We fuse the classification outputs from independent unary classifiers, such as 3D point clouds and image data using a convolutional neural network (CNN). The unary classifiers for the two sensors are the CNN with five layers, which use more than two pre-trained convolutional layers to consider local to global features as data representation. To represent data using convolutional layers, we apply region of interest (ROI) pooling to the outputs of each layer on the object candidate regions generated using object proposal generation to realize color flattening and semantic grouping for charge-coupled device and Light Detection And Ranging (LiDAR) sensors. We evaluate our proposed method on a KITTI benchmark dataset to detect and classify three object classes: cars, pedestrians and cyclists. The evaluation results show that the proposed method achieves better performance than the previous methods. Our proposed method extracted approximately 500 proposals on a 1226×370 image, whereas the original selective search method extracted approximately 106×n proposals. We obtained classification performance with 77.72% mean average precision over the entirety of the classes in the moderate detection level of the KITTI benchmark dataset. PMID:28117742

Fast and Accurate Microplate Method (Biolog MT2) for Detection of Fusarium Fungicides Resistance/Sensitivity.

PubMed

Frąc, Magdalena; Gryta, Agata; Oszust, Karolina; Kotowicz, Natalia

2016-01-01

The need for finding fungicides against Fusarium is a key step in the chemical plant protection and using appropriate chemical agents. Existing, conventional methods of evaluation of Fusarium isolates resistance to fungicides are costly, time-consuming and potentially environmentally harmful due to usage of high amounts of potentially toxic chemicals. Therefore, the development of fast, accurate and effective detection methods for Fusarium resistance to fungicides is urgently required. MT2 microplates (Biolog(TM)) method is traditionally used for bacteria identification and the evaluation of their ability to utilize different carbon substrates. However, to the best of our knowledge, there is no reports concerning the use of this technical tool to determine fungicides resistance of the Fusarium isolates. For this reason, the objectives of this study are to develop a fast method for Fusarium resistance to fungicides detection and to validate the effectiveness approach between both traditional hole-plate and MT2 microplates assays. In presented study MT2 microplate-based assay was evaluated for potential use as an alternative resistance detection method. This was carried out using three commercially available fungicides, containing following active substances: triazoles (tebuconazole), benzimidazoles (carbendazim) and strobilurins (azoxystrobin), in six concentrations (0, 0.0005, 0.005, 0.05, 0.1, 0.2%), for nine selected Fusarium isolates. In this study, the particular concentrations of each fungicides was loaded into MT2 microplate wells. The wells were inoculated with the Fusarium mycelium suspended in PM4-IF inoculating fluid. Before inoculation the suspension was standardized for each isolates into 75% of transmittance. Traditional hole-plate method was used as a control assay. The fungicides concentrations in control method were the following: 0, 0.0005, 0.005, 0.05, 0.5, 1, 2, 5, 10, 25, and 50%. Strong relationships between MT2 microplate and traditional hole

Fast and Accurate Microplate Method (Biolog MT2) for Detection of Fusarium Fungicides Resistance/Sensitivity

PubMed Central

Frąc, Magdalena; Gryta, Agata; Oszust, Karolina; Kotowicz, Natalia

2016-01-01

The need for finding fungicides against Fusarium is a key step in the chemical plant protection and using appropriate chemical agents. Existing, conventional methods of evaluation of Fusarium isolates resistance to fungicides are costly, time-consuming and potentially environmentally harmful due to usage of high amounts of potentially toxic chemicals. Therefore, the development of fast, accurate and effective detection methods for Fusarium resistance to fungicides is urgently required. MT2 microplates (BiologTM) method is traditionally used for bacteria identification and the evaluation of their ability to utilize different carbon substrates. However, to the best of our knowledge, there is no reports concerning the use of this technical tool to determine fungicides resistance of the Fusarium isolates. For this reason, the objectives of this study are to develop a fast method for Fusarium resistance to fungicides detection and to validate the effectiveness approach between both traditional hole-plate and MT2 microplates assays. In presented study MT2 microplate-based assay was evaluated for potential use as an alternative resistance detection method. This was carried out using three commercially available fungicides, containing following active substances: triazoles (tebuconazole), benzimidazoles (carbendazim) and strobilurins (azoxystrobin), in six concentrations (0, 0.0005, 0.005, 0.05, 0.1, 0.2%), for nine selected Fusarium isolates. In this study, the particular concentrations of each fungicides was loaded into MT2 microplate wells. The wells were inoculated with the Fusarium mycelium suspended in PM4-IF inoculating fluid. Before inoculation the suspension was standardized for each isolates into 75% of transmittance. Traditional hole-plate method was used as a control assay. The fungicides concentrations in control method were the following: 0, 0.0005, 0.005, 0.05, 0.5, 1, 2, 5, 10, 25, and 50%. Strong relationships between MT2 microplate and traditional hole

Path Searching Based Crease Detection for Large Scale Scanned Document Images

NASA Astrophysics Data System (ADS)

Zhang, Jifu; Li, Yi; Li, Shutao; Sun, Bin; Sun, Jun

2017-12-01

Since the large size documents are usually folded for preservation, creases will occur in the scanned images. In this paper, a crease detection method is proposed to locate the crease pixels for further processing. According to the imaging process of contactless scanners, the shading on both sides of the crease usually varies a lot. Based on this observation, a convex hull based algorithm is adopted to extract the shading information of the scanned image. Then, the possible crease path can be achieved by applying the vertical filter and morphological operations on the shading image. Finally, the accurate crease is detected via Dijkstra path searching. Experimental results on the dataset of real scanned newspapers demonstrate that the proposed method can obtain accurate locations of the creases in the large size document images.

Automated sleep scoring and sleep apnea detection in children

NASA Astrophysics Data System (ADS)

Baraglia, David P.; Berryman, Matthew J.; Coussens, Scott W.; Pamula, Yvonne; Kennedy, Declan; Martin, A. James; Abbott, Derek

2005-12-01

This paper investigates the automated detection of a patient's breathing rate and heart rate from their skin conductivity as well as sleep stage scoring and breathing event detection from their EEG. The software developed for these tasks is tested on data sets obtained from the sleep disorders unit at the Adelaide Women's and Children's Hospital. The sleep scoring and breathing event detection tasks used neural networks to achieve signal classification. The Fourier transform and the Higuchi fractal dimension were used to extract features for input to the neural network. The filtered skin conductivity appeared visually to bear a similarity to the breathing and heart rate signal, but a more detailed evaluation showed the relation was not consistent. Sleep stage classification was achieved with and accuracy of around 65% with some stages being accurately scored and others poorly scored. The two breathing events hypopnea and apnea were scored with varying degrees of accuracy with the highest scores being around 75% and 30%.

Oriented regions grouping based candidate proposal for infrared pedestrian detection

NASA Astrophysics Data System (ADS)

Wang, Jiangtao; Zhang, Jingai; Li, Huaijiang

2018-04-01

Effectively and accurately locating the positions of pedestrian candidates in image is a key task for the infrared pedestrian detection system. In this work, a novel similarity measuring metric is designed. Based on the selective search scheme, the developed similarity measuring metric is utilized to yield the possible locations for pedestrian candidate. Besides this, corresponding diversification strategies are also provided according to the characteristics of the infrared thermal imaging system. Experimental results indicate that the presented scheme can achieve more efficient outputs than the traditional selective search methodology for the infrared pedestrian detection task.

A novel method for the detection of R-peaks in ECG based on K-Nearest Neighbors and Particle Swarm Optimization

NASA Astrophysics Data System (ADS)

He, Runnan; Wang, Kuanquan; Li, Qince; Yuan, Yongfeng; Zhao, Na; Liu, Yang; Zhang, Henggui

2017-12-01

Cardiovascular diseases are associated with high morbidity and mortality. However, it is still a challenge to diagnose them accurately and efficiently. Electrocardiogram (ECG), a bioelectrical signal of the heart, provides crucial information about the dynamical functions of the heart, playing an important role in cardiac diagnosis. As the QRS complex in ECG is associated with ventricular depolarization, therefore, accurate QRS detection is vital for interpreting ECG features. In this paper, we proposed a real-time, accurate, and effective algorithm for QRS detection. In the algorithm, a proposed preprocessor with a band-pass filter was first applied to remove baseline wander and power-line interference from the signal. After denoising, a method combining K-Nearest Neighbor (KNN) and Particle Swarm Optimization (PSO) was used for accurate QRS detection in ECGs with different morphologies. The proposed algorithm was tested and validated using 48 ECG records from MIT-BIH arrhythmia database (MITDB), achieved a high averaged detection accuracy, sensitivity and positive predictivity of 99.43, 99.69, and 99.72%, respectively, indicating a notable improvement to extant algorithms as reported in literatures.

The Effect of Error Correction vs. Error Detection on Iranian Pre-Intermediate EFL Learners' Writing Achievement

ERIC Educational Resources Information Center

Abedi, Razie; Latifi, Mehdi; Moinzadeh, Ahmad

2010-01-01

This study tries to answer some ever-existent questions in writing fields regarding approaching the most effective ways to give feedback to students' errors in writing by comparing the effect of error correction and error detection on the improvement of students' writing ability. In order to achieve this goal, 60 pre-intermediate English learners…

An automated method for accurate vessel segmentation.

PubMed

Yang, Xin; Liu, Chaoyue; Le Minh, Hung; Wang, Zhiwei; Chien, Aichi; Cheng, Kwang-Ting Tim

2017-05-07

Vessel segmentation is a critical task for various medical applications, such as diagnosis assistance of diabetic retinopathy, quantification of cerebral aneurysm's growth, and guiding surgery in neurosurgical procedures. Despite technology advances in image segmentation, existing methods still suffer from low accuracy for vessel segmentation in the two challenging while common scenarios in clinical usage: (1) regions with a low signal-to-noise-ratio (SNR), and (2) at vessel boundaries disturbed by adjacent non-vessel pixels. In this paper, we present an automated system which can achieve highly accurate vessel segmentation for both 2D and 3D images even under these challenging scenarios. Three key contributions achieved by our system are: (1) a progressive contrast enhancement method to adaptively enhance contrast of challenging pixels that were otherwise indistinguishable, (2) a boundary refinement method to effectively improve segmentation accuracy at vessel borders based on Canny edge detection, and (3) a content-aware region-of-interests (ROI) adjustment method to automatically determine the locations and sizes of ROIs which contain ambiguous pixels and demand further verification. Extensive evaluation of our method is conducted on both 2D and 3D datasets. On a public 2D retinal dataset (named DRIVE (Staal 2004 IEEE Trans. Med. Imaging 23 501-9)) and our 2D clinical cerebral dataset, our approach achieves superior performance to the state-of-the-art methods including a vesselness based method (Frangi 1998 Int. Conf. on Medical Image Computing and Computer-Assisted Intervention) and an optimally oriented flux (OOF) based method (Law and Chung 2008 European Conf. on Computer Vision). An evaluation on 11 clinical 3D CTA cerebral datasets shows that our method can achieve 94% average accuracy with respect to the manual segmentation reference, which is 23% to 33% better than the five baseline methods (Yushkevich 2006 Neuroimage 31 1116-28; Law and Chung 2008

Automated particle correspondence and accurate tilt-axis detection in tilted-image pairs

DOE PAGES

Shatsky, Maxim; Arbelaez, Pablo; Han, Bong-Gyoon; ...

2014-07-01

Tilted electron microscope images are routinely collected for an ab initio structure reconstruction as a part of the Random Conical Tilt (RCT) or Orthogonal Tilt Reconstruction (OTR) methods, as well as for various applications using the "free-hand" procedure. These procedures all require identification of particle pairs in two corresponding images as well as accurate estimation of the tilt-axis used to rotate the electron microscope (EM) grid. Here we present a computational approach, PCT (particle correspondence from tilted pairs), based on tilt-invariant context and projection matching that addresses both problems. The method benefits from treating the two problems as a singlemore » optimization task. It automatically finds corresponding particle pairs and accurately computes tilt-axis direction even in the cases when EM grid is not perfectly planar.« less

Accurate and precise determination of isotopic ratios by MC-ICP-MS: a review.

PubMed

Yang, Lu

2009-01-01

For many decades the accurate and precise determination of isotope ratios has remained a very strong interest to many researchers due to its important applications in earth, environmental, biological, archeological, and medical sciences. Traditionally, thermal ionization mass spectrometry (TIMS) has been the technique of choice for achieving the highest accuracy and precision. However, recent developments in multi-collector inductively coupled plasma mass spectrometry (MC-ICP-MS) have brought a new dimension to this field. In addition to its simple and robust sample introduction, high sample throughput, and high mass resolution, the flat-topped peaks generated by this technique provide for accurate and precise determination of isotope ratios with precision reaching 0.001%, comparable to that achieved with TIMS. These features, in combination with the ability of the ICP source to ionize nearly all elements in the periodic table, have resulted in an increased use of MC-ICP-MS for such measurements in various sample matrices. To determine accurate and precise isotope ratios with MC-ICP-MS, utmost care must be exercised during sample preparation, optimization of the instrument, and mass bias corrections. Unfortunately, there are inconsistencies and errors evident in many MC-ICP-MS publications, including errors in mass bias correction models. This review examines "state-of-the-art" methodologies presented in the literature for achievement of precise and accurate determinations of isotope ratios by MC-ICP-MS. Some general rules for such accurate and precise measurements are suggested, and calculations of combined uncertainty of the data using a few common mass bias correction models are outlined.

Quasi-closed phase forward-backward linear prediction analysis of speech for accurate formant detection and estimation.

PubMed

Gowda, Dhananjaya; Airaksinen, Manu; Alku, Paavo

2017-09-01

Recently, a quasi-closed phase (QCP) analysis of speech signals for accurate glottal inverse filtering was proposed. However, the QCP analysis which belongs to the family of temporally weighted linear prediction (WLP) methods uses the conventional forward type of sample prediction. This may not be the best choice especially in computing WLP models with a hard-limiting weighting function. A sample selective minimization of the prediction error in WLP reduces the effective number of samples available within a given window frame. To counter this problem, a modified quasi-closed phase forward-backward (QCP-FB) analysis is proposed, wherein each sample is predicted based on its past as well as future samples thereby utilizing the available number of samples more effectively. Formant detection and estimation experiments on synthetic vowels generated using a physical modeling approach as well as natural speech utterances show that the proposed QCP-FB method yields statistically significant improvements over the conventional linear prediction and QCP methods.

Evaluation of a novel immunochromatographic device for rapid and accurate clinical detection of Porphyromonas gingivalis in subgingival plaque.

PubMed

Imamura, K; Takayama, S; Saito, A; Inoue, E; Nakayama, Y; Ogata, Y; Shirakawa, S; Nagano, T; Gomi, K; Morozumi, T; Akiishi, K; Watanabe, K; Yoshie, H

2015-10-01

An important goal for the improved diagnosis and management of infectious and inflammatory diseases, such as periodontitis, is the development of rapid and accurate technologies for the decentralized detection of bacterial pathogens. The aim of this prospective multicenter study was to evaluate the clinical use of a novel immunochromatographic device with monoclonal antibodies for the rapid point-of-care detection and semi-quantification of Porphyromonas gingivalis in subgingival plaque. Sixty-three patients with chronic periodontitis and 28 periodontally healthy volunteers were subjected to clinical and microbiological examinations. Subgingival plaque samples were analyzed for the presence of P. gingivalis using a novel immunochromatography based device DK13-PG-001, designed to detect the 40k-outer membrane protein of P. gingivalis, and compared with a PCR-Invader method. In the periodontitis group, a significant strong positive correlation in detection results was found between the test device score and the PCR-Invader method (Spearman rank correlation, r=0.737, p<0.0001). The sensitivity, specificity, and positive and negative predictive values of the test device were 96.2%, 91.8%, 90.4% and 96.7%, respectively. The detection threshold of the test device was determined to be approximately 10(4) (per two paper points). There were significant differences in the bacterial counts by the PCR-Invader method among groups with different ranges of device scores. With a cut-off value of ≥0.25 in device score, none of periodontally healthy volunteers were tested positive for the subgingival presence of P. gingivalis, whereas 76% (n=48) of periodontitis subjects were tested positive. There was a significant positive correlation between device scores for P. gingivalis and periodontal parameters including probing pocket depth and clinical attachment level (r=0.317 and 0.281, respectively, p<0.01). The results suggested that the DK13-PG-001 device kit can be effectively used

Comparative Associations Between Achieved Bicultural Identity, Achieved Ego Identity, and Achieved Religious Identity and Adaptation Among Australian Adolescent Muslims.

PubMed

Abu-Rayya, Hisham M; Abu-Rayya, Maram H; White, Fiona A; Walker, Richard

2018-04-01

This study examined the comparative roles of biculturalism, ego identity, and religious identity in the adaptation of Australian adolescent Muslims. A total of 504 high school Muslim students studying at high schools in metropolitan Sydney and Melbourne, Australia, took part in this study which required them to complete a self-report questionnaire. Analyses indicated that adolescent Muslims' achieved religious identity seems to play a more important role in shaping their psychological and socio-cultural adaptation compared to adolescents' achieved bicultural identity. Adolescents' achieved ego identity tended also to play a greater role in their psychological and socio-cultural adaptation than achieved bicultural identity. The relationships between the three identities and negative indicators of psychological adaptation were consistently indifferent. Based on these findings, we propose that the three identity-based forces-bicultural identity development, religious identity attainment, and ego identity formation-be amalgamated into one framework in order for researchers to more accurately examine the adaptation of Australian adolescent Muslims.

Multiscale peak detection in wavelet space.

PubMed

Zhang, Zhi-Min; Tong, Xia; Peng, Ying; Ma, Pan; Zhang, Ming-Jin; Lu, Hong-Mei; Chen, Xiao-Qing; Liang, Yi-Zeng

2015-12-07

Accurate peak detection is essential for analyzing high-throughput datasets generated by analytical instruments. Derivatives with noise reduction and matched filtration are frequently used, but they are sensitive to baseline variations, random noise and deviations in the peak shape. A continuous wavelet transform (CWT)-based method is more practical and popular in this situation, which can increase the accuracy and reliability by identifying peaks across scales in wavelet space and implicitly removing noise as well as the baseline. However, its computational load is relatively high and the estimated features of peaks may not be accurate in the case of peaks that are overlapping, dense or weak. In this study, we present multi-scale peak detection (MSPD) by taking full advantage of additional information in wavelet space including ridges, valleys, and zero-crossings. It can achieve a high accuracy by thresholding each detected peak with the maximum of its ridge. It has been comprehensively evaluated with MALDI-TOF spectra in proteomics, the CAMDA 2006 SELDI dataset as well as the Romanian database of Raman spectra, which is particularly suitable for detecting peaks in high-throughput analytical signals. Receiver operating characteristic (ROC) curves show that MSPD can detect more true peaks while keeping the false discovery rate lower than MassSpecWavelet and MALDIquant methods. Superior results in Raman spectra suggest that MSPD seems to be a more universal method for peak detection. MSPD has been designed and implemented efficiently in Python and Cython. It is available as an open source package at .

Multidimensional gas chromatography in combination with accurate mass, tandem mass spectrometry, and element-specific detection for identification of sulfur compounds in tobacco smoke.

PubMed

Ochiai, Nobuo; Mitsui, Kazuhisa; Sasamoto, Kikuo; Yoshimura, Yuta; David, Frank; Sandra, Pat

2014-09-05

A method is developed for identification of sulfur compounds in tobacco smoke extract. The method is based on large volume injection (LVI) of 10μL of tobacco smoke extract followed by selectable one-dimensional ((1)D) or two-dimensional ((2)D) gas chromatography (GC) coupled to a hybrid quadrupole time-of-flight mass spectrometer (Q-TOF-MS) using electron ionization (EI) and positive chemical ionization (PCI), with parallel sulfur chemiluminescence detection (SCD). In order to identify each individual sulfur compound, sequential heart-cuts of 28 sulfur fractions from (1)D GC to (2)D GC were performed with the three MS detection modes (SCD/EI-TOF-MS, SCD/PCI-TOF-MS, and SCD/PCI-Q-TOF-MS). Thirty sulfur compounds were positively identified by MS library search, linear retention indices (LRI), molecular mass determination using PCI accurate mass spectra, formula calculation using EI and PCI accurate mass spectra, and structure elucidation using collision activated dissociation (CAD) of the protonated molecule. Additionally, 11 molecular formulas were obtained for unknown sulfur compounds. The determined values of the identified and unknown sulfur compounds were in the range of 10-740ngmg total particulate matter (TPM) (RSD: 1.2-12%, n=3). Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.

Application of DBNs for concerned internet information detecting

NASA Astrophysics Data System (ADS)

Wang, Yanfang; Gao, Song

2017-03-01

In recent years, deep learning has achieved great success in many fields, ranging from voice recognition and image classification to computer vision. In this study we apply DBNs to concerned internet information in Chinese detecting problem, since there are inherent differences between English and Chinese. Contrastive divergence (CD) is employed in the DBNs to learn a multi-layer generative model from numerous unlabeled data. The features obtained by this model are used to initialize the feed-forward neural network, which can be fine-tuned with backpropagation. Experiment results indicate that, the model and training method we proposed can be used to detect the concerned internet information effectively and accurately.

Manifold structure preservative for hyperspectral target detection

NASA Astrophysics Data System (ADS)

Imani, Maryam

2018-05-01

A nonparametric method termed as manifold structure preservative (MSP) is proposed in this paper for hyperspectral target detection. MSP transforms the feature space of data to maximize the separation between target and background signals. Moreover, it minimizes the reconstruction error of targets and preserves the topological structure of data in the projected feature space. MSP does not need to consider any distribution for target and background data. So, it can achieve accurate results in real scenarios due to avoiding unreliable assumptions. The proposed MSP detector is compared to several popular detectors and the experiments on a synthetic data and two real hyperspectral images indicate the superior ability of it in target detection.

MIDAS robust trend estimator for accurate GPS station velocities without step detection

NASA Astrophysics Data System (ADS)

Blewitt, Geoffrey; Kreemer, Corné; Hammond, William C.; Gazeaux, Julien

2016-03-01

Automatic estimation of velocities from GPS coordinate time series is becoming required to cope with the exponentially increasing flood of available data, but problems detectable to the human eye are often overlooked. This motivates us to find an automatic and accurate estimator of trend that is resistant to common problems such as step discontinuities, outliers, seasonality, skewness, and heteroscedasticity. Developed here, Median Interannual Difference Adjusted for Skewness (MIDAS) is a variant of the Theil-Sen median trend estimator, for which the ordinary version is the median of slopes vij = (xj-xi)/(tj-ti) computed between all data pairs i > j. For normally distributed data, Theil-Sen and least squares trend estimates are statistically identical, but unlike least squares, Theil-Sen is resistant to undetected data problems. To mitigate both seasonality and step discontinuities, MIDAS selects data pairs separated by 1 year. This condition is relaxed for time series with gaps so that all data are used. Slopes from data pairs spanning a step function produce one-sided outliers that can bias the median. To reduce bias, MIDAS removes outliers and recomputes the median. MIDAS also computes a robust and realistic estimate of trend uncertainty. Statistical tests using GPS data in the rigid North American plate interior show ±0.23 mm/yr root-mean-square (RMS) accuracy in horizontal velocity. In blind tests using synthetic data, MIDAS velocities have an RMS accuracy of ±0.33 mm/yr horizontal, ±1.1 mm/yr up, with a 5th percentile range smaller than all 20 automatic estimators tested. Considering its general nature, MIDAS has the potential for broader application in the geosciences.

Learning to predict where human gaze is using quaternion DCT based regional saliency detection

NASA Astrophysics Data System (ADS)

Li, Ting; Xu, Yi; Zhang, Chongyang

2014-09-01

Many current visual attention approaches used semantic features to accurately capture human gaze. However, these approaches demand high computational cost and can hardly be applied to daily use. Recently, some quaternion-based saliency detection models, such as PQFT (phase spectrum of Quaternion Fourier Transform), QDCT (Quaternion Discrete Cosine Transform), have been proposed to meet real-time requirement of human gaze tracking tasks. However, current saliency detection methods used global PQFT and QDCT to locate jump edges of the input, which can hardly detect the object boundaries accurately. To address the problem, we improved QDCT-based saliency detection model by introducing superpixel-wised regional saliency detection mechanism. The local smoothness of saliency value distribution is emphasized to distinguish noises of background from salient regions. Our algorithm called saliency confidence can distinguish the patches belonging to the salient object and those of the background. It decides whether the image patches belong to the same region. When an image patch belongs to a region consisting of other salient patches, this patch should be salient as well. Therefore, we use saliency confidence map to get background weight and foreground weight to do the optimization on saliency map obtained by QDCT. The optimization is accomplished by least square method. The optimization approach we proposed unifies local and global saliency by combination of QDCT and measuring the similarity between each image superpixel. We evaluate our model on four commonly-used datasets (Toronto, MIT, OSIE and ASD) using standard precision-recall curves (PR curves), the mean absolute error (MAE) and area under curve (AUC) measures. In comparison with most state-of-art models, our approach can achieve higher consistency with human perception without training. It can get accurate human gaze even in cluttered background. Furthermore, it achieves better compromise between speed and accuracy.

Achievable Strength-Based Signal Detection in Quantity-Constrained PAM OOK Concentration-Encoded Molecular Communication.

PubMed

Mahfuz, Mohammad Upal

2016-10-01

In this paper, the expressions of achievable strength-based detection probabilities of concentration-encoded molecular communication (CEMC) system have been derived based on finite pulsewidth (FP) pulse-amplitude modulated (PAM) on-off keying (OOK) modulation scheme and strength threshold. An FP-PAM system is characterized by its duty cycle α that indicates the fraction of the entire symbol duration the transmitter remains on and transmits the signal. Results show that the detection performance of an FP-PAM OOK CEMC system significantly depends on the statistical distribution parameters of diffusion-based propagation noise and intersymbol interference (ISI). Analytical detection performance of an FP-PAM OOK CEMC system under ISI scenario has been explained and compared based on receiver operating characteristics (ROC) for impulse (i.e., spike)-modulated (IM) and FP-PAM CEMC schemes. It is shown that the effects of diffusion noise and ISI on ROC can be explained separately based on their communication range-dependent statistics. With full duty cycle, an FP-PAM scheme provides significantly worse performance than an IM scheme. The paper also analyzes the performance of the system when duty cycle, transmission data rate, and quantity of molecules vary.

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.

Sensitive, accurate and rapid detection of trace aliphatic amines in environmental samples with ultrasonic-assisted derivatization microextraction using a new fluorescent reagent for high performance liquid chromatography.

PubMed

Chen, Guang; Liu, Jianjun; Liu, Mengge; Li, Guoliang; Sun, Zhiwei; Zhang, Shijuan; Song, Cuihua; Wang, Hua; Suo, Yourui; You, Jinmao

2014-07-25

A new fluorescent reagent, 1-(1H-imidazol-1-yl)-2-(2-phenyl-1H-phenanthro[9,10-d]imidazol-1-yl)ethanone (IPPIE), is synthesized, and a simple pretreatment based on ultrasonic-assisted derivatization microextraction (UDME) with IPPIE is proposed for the selective derivatization of 12 aliphatic amines (C1: methylamine-C12: dodecylamine) in complex matrix samples (irrigation water, river water, waste water, cultivated soil, riverbank soil and riverbed soil). Under the optimal experimental conditions (solvent: ACN-HCl, catalyst: none, molar ratio: 4.3, time: 8 min and temperature: 80°C), micro amount of sample (40 μL; 5mg) can be pretreated in only 10 min, with no preconcentration, evaporation or other additional manual operations required. The interfering substances (aromatic amines, aliphatic alcohols and phenols) get the derivatization yields of <5%, causing insignificant matrix effects (<4%). IPPIE-analyte derivatives are separated by high performance liquid chromatography (HPLC) and quantified by fluorescence detection (FD). The very low instrumental detection limits (IDL: 0.66-4.02 ng/L) and method detection limits (MDL: 0.04-0.33 ng/g; 5.96-45.61 ng/L) are achieved. Analytes are further identified from adjacent peaks by on-line ion trap mass spectrometry (MS), thereby avoiding additional operations for impurities. With this UDME-HPLC-FD-MS method, the accuracy (-0.73-2.12%), precision (intra-day: 0.87-3.39%; inter-day: 0.16-4.12%), recovery (97.01-104.10%) and sensitivity were significantly improved. Successful applications in environmental samples demonstrate the superiority of this method in the sensitive, accurate and rapid determination of trace aliphatic amines in micro amount of complex samples. Copyright © 2014 Elsevier B.V. All rights reserved.

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

Accurate Quantitative Sensing of Intracellular pH based on Self-ratiometric Upconversion Luminescent Nanoprobe.

PubMed

Li, Cuixia; Zuo, Jing; Zhang, Li; Chang, Yulei; Zhang, Youlin; Tu, Langping; Liu, Xiaomin; Xue, Bin; Li, Qiqing; Zhao, Huiying; Zhang, Hong; Kong, Xianggui

2016-12-09

Accurate quantitation of intracellular pH (pH i ) is of great importance in revealing the cellular activities and early warning of diseases. A series of fluorescence-based nano-bioprobes composed of different nanoparticles or/and dye pairs have already been developed for pH i sensing. Till now, biological auto-fluorescence background upon UV-Vis excitation and severe photo-bleaching of dyes are the two main factors impeding the accurate quantitative detection of pH i . Herein, we have developed a self-ratiometric luminescence nanoprobe based on förster resonant energy transfer (FRET) for probing pH i , in which pH-sensitive fluorescein isothiocyanate (FITC) and upconversion nanoparticles (UCNPs) were served as energy acceptor and donor, respectively. Under 980 nm excitation, upconversion emission bands at 475 nm and 645 nm of NaYF 4 :Yb 3+ , Tm 3+ UCNPs were used as pH i response and self-ratiometric reference signal, respectively. This direct quantitative sensing approach has circumvented the traditional software-based subsequent processing of images which may lead to relatively large uncertainty of the results. Due to efficient FRET and fluorescence background free, a highly-sensitive and accurate sensing has been achieved, featured by 3.56 per unit change in pH i value 3.0-7.0 with deviation less than 0.43. This approach shall facilitate the researches in pH i related areas and development of the intracellular drug delivery systems.

Accurate Quantitative Sensing of Intracellular pH based on Self-ratiometric Upconversion Luminescent Nanoprobe

NASA Astrophysics Data System (ADS)

Li, Cuixia; Zuo, Jing; Zhang, Li; Chang, Yulei; Zhang, Youlin; Tu, Langping; Liu, Xiaomin; Xue, Bin; Li, Qiqing; Zhao, Huiying; Zhang, Hong; Kong, Xianggui

2016-12-01

Accurate quantitation of intracellular pH (pHi) is of great importance in revealing the cellular activities and early warning of diseases. A series of fluorescence-based nano-bioprobes composed of different nanoparticles or/and dye pairs have already been developed for pHi sensing. Till now, biological auto-fluorescence background upon UV-Vis excitation and severe photo-bleaching of dyes are the two main factors impeding the accurate quantitative detection of pHi. Herein, we have developed a self-ratiometric luminescence nanoprobe based on förster resonant energy transfer (FRET) for probing pHi, in which pH-sensitive fluorescein isothiocyanate (FITC) and upconversion nanoparticles (UCNPs) were served as energy acceptor and donor, respectively. Under 980 nm excitation, upconversion emission bands at 475 nm and 645 nm of NaYF4:Yb3+, Tm3+ UCNPs were used as pHi response and self-ratiometric reference signal, respectively. This direct quantitative sensing approach has circumvented the traditional software-based subsequent processing of images which may lead to relatively large uncertainty of the results. Due to efficient FRET and fluorescence background free, a highly-sensitive and accurate sensing has been achieved, featured by 3.56 per unit change in pHi value 3.0-7.0 with deviation less than 0.43. This approach shall facilitate the researches in pHi related areas and development of the intracellular drug delivery systems.

Holevo Capacity Achieving Joint Detection Receiver

DTIC Science & Technology

2013-12-31

DETECTION RECEIVER (75) Inventor: Saikat Guha, Everett , MA (US) (73) Assignee: Raytheon BBN Technologies Corp., Cambridge, MA (US) ( * ) Notice...tum Operations”, Oct. 18, 2011, 11 pages. Shannon, “The Bell System Technical Journal”, A Mathematical Theory of Communication, vol. XXVII, No. 3, Jul

A sensitive and accurate quantification method for the detection of hepatitis B virus covalently closed circular DNA by the application of a droplet digital polymerase chain reaction amplification system.

PubMed

Mu, Di; Yan, Liang; Tang, Hui; Liao, Yong

2015-10-01

To develop a sensitive and accurate assay system for the quantification of covalently closed circular HBV DNA (cccDNA) for future clinical monitoring of cccDNA fluctuation during antiviral therapy in the liver of infected patients. A droplet digital PCR (ddPCR)-based assay system detected template DNA input at the single copy level (or ~10(-5) pg of plasmid HBV DNA) by using serially diluted plasmid HBV DNA samples. Compared with the conventional quantitative PCR assay in the detection of cccDNA, which required at least 50 ng of template DNA input, a parallel experiment applying a ddPCR system demonstrates that the lowest detection limit of cccDNA from HepG2.215 cellular DNA samples is around 1 ng, which is equivalent to 0.54 ± 0.94 copies of cccDNA. In addition, we demonstrated that the addition of cccDNA-safe exonuclease and utilization of cccDNA-specific primers in the ddPCR assay system significantly improved the detection accuracy of HBV cccDNA from HepG2.215 cellular DNA samples. The ddPCR-based cccDNA detection system is a sensitive and accurate assay for the quantification of cccDNA in HBV-transfected HepG2.215 cellular DNA samples and may represent an important method for future application in monitoring cccDNA fluctuation during antiviral therapy.

High Order Schemes in Bats-R-US for Faster and More Accurate Predictions

NASA Astrophysics Data System (ADS)

Chen, Y.; Toth, G.; Gombosi, T. I.

2014-12-01

BATS-R-US is a widely used global magnetohydrodynamics model that originally employed second order accurate TVD schemes combined with block based Adaptive Mesh Refinement (AMR) to achieve high resolution in the regions of interest. In the last years we have implemented fifth order accurate finite difference schemes CWENO5 and MP5 for uniform Cartesian grids. Now the high order schemes have been extended to generalized coordinates, including spherical grids and also to the non-uniform AMR grids including dynamic regridding. We present numerical tests that verify the preservation of free-stream solution and high-order accuracy as well as robust oscillation-free behavior near discontinuities. We apply the new high order accurate schemes to both heliospheric and magnetospheric simulations and show that it is robust and can achieve the same accuracy as the second order scheme with much less computational resources. This is especially important for space weather prediction that requires faster than real time code execution.

Detecting nonsense for Chinese comments based on logistic regression

NASA Astrophysics Data System (ADS)

Zhuolin, Ren; Guang, Chen; Shu, Chen

2016-07-01

To understand cyber citizens' opinion accurately from Chinese news comments, the clear definition on nonsense is present, and a detection model based on logistic regression (LR) is proposed. The detection of nonsense can be treated as a binary-classification problem. Besides of traditional lexical features, we propose three kinds of features in terms of emotion, structure and relevance. By these features, we train an LR model and demonstrate its effect in understanding Chinese news comments. We find that each of proposed features can significantly promote the result. In our experiments, we achieve a prediction accuracy of 84.3% which improves the baseline 77.3% by 7%.

Autofocusing and Polar Body Detection in Automated Cell Manipulation.

PubMed

Wang, Zenan; Feng, Chen; Ang, Wei Tech; Tan, Steven Yih Min; Latt, Win Tun

2017-05-01

Autofocusing and feature detection are two essential processes for performing automated biological cell manipulation tasks. In this paper, we have introduced a technique capable of focusing on a holding pipette and a mammalian cell under a bright-field microscope automatically, and a technique that can detect and track the presence and orientation of the polar body of an oocyte that is rotated at the tip of a micropipette. Both algorithms were evaluated by using mouse oocytes. Experimental results show that both algorithms achieve very high success rates: 100% and 96%. As robust and accurate image processing methods, they can be widely applied to perform various automated biological cell manipulations.

Aptamer-conjugated live human immune cell based biosensors for the accurate detection of C-reactive protein

NASA Astrophysics Data System (ADS)

Hwang, Jangsun; Seo, Youngmin; Jo, Yeonho; Son, Jaewoo; Choi, Jonghoon

2016-10-01

C-reactive protein (CRP) is a pentameric protein that is present in the bloodstream during inflammatory events, e.g., liver failure, leukemia, and/or bacterial infection. The level of CRP indicates the progress and prognosis of certain diseases; it is therefore necessary to measure CRP levels in the blood accurately. The normal concentration of CRP is reported to be 1-3 mg/L. Inflammatory events increase the level of CRP by up to 500 times; accordingly, CRP is a biomarker of acute inflammatory disease. In this study, we demonstrated the preparation of DNA aptamer-conjugated peripheral blood mononuclear cells (Apt-PBMCs) that specifically capture human CRP. Live PBMCs functionalized with aptamers could detect different levels of human CRP by producing immune complexes with reporter antibody. The binding behavior of Apt-PBMCs toward highly concentrated CRP sites was also investigated. The immune responses of Apt-PBMCs were evaluated by measuring TNF-alpha secretion after stimulating the PBMCs with lipopolysaccharides. In summary, engineered Apt-PBMCs have potential applications as live cell based biosensors and for in vitro tracing of CRP secretion sites.

MIDAS robust trend estimator for accurate GPS station velocities without step detection

PubMed Central

Kreemer, Corné; Hammond, William C.; Gazeaux, Julien

2016-01-01

Abstract Automatic estimation of velocities from GPS coordinate time series is becoming required to cope with the exponentially increasing flood of available data, but problems detectable to the human eye are often overlooked. This motivates us to find an automatic and accurate estimator of trend that is resistant to common problems such as step discontinuities, outliers, seasonality, skewness, and heteroscedasticity. Developed here, Median Interannual Difference Adjusted for Skewness (MIDAS) is a variant of the Theil‐Sen median trend estimator, for which the ordinary version is the median of slopes vij = (xj–xi)/(tj–ti) computed between all data pairs i > j. For normally distributed data, Theil‐Sen and least squares trend estimates are statistically identical, but unlike least squares, Theil‐Sen is resistant to undetected data problems. To mitigate both seasonality and step discontinuities, MIDAS selects data pairs separated by 1 year. This condition is relaxed for time series with gaps so that all data are used. Slopes from data pairs spanning a step function produce one‐sided outliers that can bias the median. To reduce bias, MIDAS removes outliers and recomputes the median. MIDAS also computes a robust and realistic estimate of trend uncertainty. Statistical tests using GPS data in the rigid North American plate interior show ±0.23 mm/yr root‐mean‐square (RMS) accuracy in horizontal velocity. In blind tests using synthetic data, MIDAS velocities have an RMS accuracy of ±0.33 mm/yr horizontal, ±1.1 mm/yr up, with a 5th percentile range smaller than all 20 automatic estimators tested. Considering its general nature, MIDAS has the potential for broader application in the geosciences. PMID:27668140

MIDAS robust trend estimator for accurate GPS station velocities without step detection.

PubMed

Blewitt, Geoffrey; Kreemer, Corné; Hammond, William C; Gazeaux, Julien

2016-03-01

Automatic estimation of velocities from GPS coordinate time series is becoming required to cope with the exponentially increasing flood of available data, but problems detectable to the human eye are often overlooked. This motivates us to find an automatic and accurate estimator of trend that is resistant to common problems such as step discontinuities, outliers, seasonality, skewness, and heteroscedasticity. Developed here, Median Interannual Difference Adjusted for Skewness (MIDAS) is a variant of the Theil-Sen median trend estimator, for which the ordinary version is the median of slopes v ij  = ( x j -x i )/( t j -t i ) computed between all data pairs i  >  j . For normally distributed data, Theil-Sen and least squares trend estimates are statistically identical, but unlike least squares, Theil-Sen is resistant to undetected data problems. To mitigate both seasonality and step discontinuities, MIDAS selects data pairs separated by 1 year. This condition is relaxed for time series with gaps so that all data are used. Slopes from data pairs spanning a step function produce one-sided outliers that can bias the median. To reduce bias, MIDAS removes outliers and recomputes the median. MIDAS also computes a robust and realistic estimate of trend uncertainty. Statistical tests using GPS data in the rigid North American plate interior show ±0.23 mm/yr root-mean-square (RMS) accuracy in horizontal velocity. In blind tests using synthetic data, MIDAS velocities have an RMS accuracy of ±0.33 mm/yr horizontal, ±1.1 mm/yr up, with a 5th percentile range smaller than all 20 automatic estimators tested. Considering its general nature, MIDAS has the potential for broader application in the geosciences.

An automated method for accurate vessel segmentation

NASA Astrophysics Data System (ADS)

Yang, Xin; Liu, Chaoyue; Le Minh, Hung; Wang, Zhiwei; Chien, Aichi; (Tim Cheng, Kwang-Ting

2017-05-01

Vessel segmentation is a critical task for various medical applications, such as diagnosis assistance of diabetic retinopathy, quantification of cerebral aneurysm’s growth, and guiding surgery in neurosurgical procedures. Despite technology advances in image segmentation, existing methods still suffer from low accuracy for vessel segmentation in the two challenging while common scenarios in clinical usage: (1) regions with a low signal-to-noise-ratio (SNR), and (2) at vessel boundaries disturbed by adjacent non-vessel pixels. In this paper, we present an automated system which can achieve highly accurate vessel segmentation for both 2D and 3D images even under these challenging scenarios. Three key contributions achieved by our system are: (1) a progressive contrast enhancement method to adaptively enhance contrast of challenging pixels that were otherwise indistinguishable, (2) a boundary refinement method to effectively improve segmentation accuracy at vessel borders based on Canny edge detection, and (3) a content-aware region-of-interests (ROI) adjustment method to automatically determine the locations and sizes of ROIs which contain ambiguous pixels and demand further verification. Extensive evaluation of our method is conducted on both 2D and 3D datasets. On a public 2D retinal dataset (named DRIVE (Staal 2004 IEEE Trans. Med. Imaging 23 501-9)) and our 2D clinical cerebral dataset, our approach achieves superior performance to the state-of-the-art methods including a vesselness based method (Frangi 1998 Int. Conf. on Medical Image Computing and Computer-Assisted Intervention) and an optimally oriented flux (OOF) based method (Law and Chung 2008 European Conf. on Computer Vision). An evaluation on 11 clinical 3D CTA cerebral datasets shows that our method can achieve 94% average accuracy with respect to the manual segmentation reference, which is 23% to 33% better than the five baseline methods (Yushkevich 2006 Neuroimage 31 1116-28; Law and Chung 2008

Detecting Children's Lies: Are Parents Accurate Judges of Their Own Children's Lies?

ERIC Educational Resources Information Center

Talwar, Victoria; Renaud, Sarah-Jane; Conway, Lauryn

2015-01-01

The current study investigated whether parents are accurate judges of their own children's lie-telling behavior. Participants included 250 mother-child dyads. Children were between three and 11 years of age. A temptation resistance paradigm was used to elicit a minor transgressive behavior from the children involving peeking at a forbidden toy and…

Cloud Detection by Fusing Multi-Scale Convolutional Features

NASA Astrophysics Data System (ADS)

Li, Zhiwei; Shen, Huanfeng; Wei, Yancong; Cheng, Qing; Yuan, Qiangqiang

2018-04-01

Clouds detection is an important pre-processing step for accurate application of optical satellite imagery. Recent studies indicate that deep learning achieves best performance in image segmentation tasks. Aiming at boosting the accuracy of cloud detection for multispectral imagery, especially for those that contain only visible and near infrared bands, in this paper, we proposed a deep learning based cloud detection method termed MSCN (multi-scale cloud net), which segments cloud by fusing multi-scale convolutional features. MSCN was trained on a global cloud cover validation collection, and was tested in more than ten types of optical images with different resolution. Experiment results show that MSCN has obvious advantages over the traditional multi-feature combined cloud detection method in accuracy, especially when in snow and other areas covered by bright non-cloud objects. Besides, MSCN produced more detailed cloud masks than the compared deep cloud detection convolution network. The effectiveness of MSCN make it promising for practical application in multiple kinds of optical imagery.

Accurate RNA consensus sequencing for high-fidelity detection of transcriptional mutagenesis-induced epimutations.

PubMed

Reid-Bayliss, Kate S; Loeb, Lawrence A

2017-08-29

Transcriptional mutagenesis (TM) due to misincorporation during RNA transcription can result in mutant RNAs, or epimutations, that generate proteins with altered properties. TM has long been hypothesized to play a role in aging, cancer, and viral and bacterial evolution. However, inadequate methodologies have limited progress in elucidating a causal association. We present a high-throughput, highly accurate RNA sequencing method to measure epimutations with single-molecule sensitivity. Accurate RNA consensus sequencing (ARC-seq) uniquely combines RNA barcoding and generation of multiple cDNA copies per RNA molecule to eliminate errors introduced during cDNA synthesis, PCR, and sequencing. The stringency of ARC-seq can be scaled to accommodate the quality of input RNAs. We apply ARC-seq to directly assess transcriptome-wide epimutations resulting from RNA polymerase mutants and oxidative stress.

DB2: a probabilistic approach for accurate detection of tandem duplication breakpoints using paired-end reads.

PubMed

Yavaş, Gökhan; Koyutürk, Mehmet; Gould, Meetha P; McMahon, Sarah; LaFramboise, Thomas

2014-03-05

With the advent of paired-end high throughput sequencing, it is now possible to identify various types of structural variation on a genome-wide scale. Although many methods have been proposed for structural variation detection, most do not provide precise boundaries for identified variants. In this paper, we propose a new method, Distribution Based detection of Duplication Boundaries (DB2), for accurate detection of tandem duplication breakpoints, an important class of structural variation, with high precision and recall. Our computational experiments on simulated data show that DB2 outperforms state-of-the-art methods in terms of finding breakpoints of tandem duplications, with a higher positive predictive value (precision) in calling the duplications' presence. In particular, DB2's prediction of tandem duplications is correct 99% of the time even for very noisy data, while narrowing down the space of possible breakpoints within a margin of 15 to 20 bps on the average. Most of the existing methods provide boundaries in ranges that extend to hundreds of bases with lower precision values. Our method is also highly robust to varying properties of the sequencing library and to the sizes of the tandem duplications, as shown by its stable precision, recall and mean boundary mismatch performance. We demonstrate our method's efficacy using both simulated paired-end reads, and those generated from a melanoma sample and two ovarian cancer samples. Newly discovered tandem duplications are validated using PCR and Sanger sequencing. Our method, DB2, uses discordantly aligned reads, taking into account the distribution of fragment length to predict tandem duplications along with their breakpoints on a donor genome. The proposed method fine tunes the breakpoint calls by applying a novel probabilistic framework that incorporates the empirical fragment length distribution to score each feasible breakpoint. DB2 is implemented in Java programming language and is freely available

Accurate lithography simulation model based on convolutional neural networks

NASA Astrophysics Data System (ADS)

Watanabe, Yuki; Kimura, Taiki; Matsunawa, Tetsuaki; Nojima, Shigeki

2017-07-01

Lithography simulation is an essential technique for today's semiconductor manufacturing process. In order to calculate an entire chip in realistic time, compact resist model is commonly used. The model is established for faster calculation. To have accurate compact resist model, it is necessary to fix a complicated non-linear model function. However, it is difficult to decide an appropriate function manually because there are many options. This paper proposes a new compact resist model using CNN (Convolutional Neural Networks) which is one of deep learning techniques. CNN model makes it possible to determine an appropriate model function and achieve accurate simulation. Experimental results show CNN model can reduce CD prediction errors by 70% compared with the conventional model.

ARCOCT: Automatic detection of lumen border in intravascular OCT images.

PubMed

Cheimariotis, Grigorios-Aris; Chatzizisis, Yiannis S; Koutkias, Vassilis G; Toutouzas, Konstantinos; Giannopoulos, Andreas; Riga, Maria; Chouvarda, Ioanna; Antoniadis, Antonios P; Doulaverakis, Charalambos; Tsamboulatidis, Ioannis; Kompatsiaris, Ioannis; Giannoglou, George D; Maglaveras, Nicos

2017-11-01

Intravascular optical coherence tomography (OCT) is an invaluable tool for the detection of pathological features on the arterial wall and the investigation of post-stenting complications. Computational lumen border detection in OCT images is highly advantageous, since it may support rapid morphometric analysis. However, automatic detection is very challenging, since OCT images typically include various artifacts that impact image clarity, including features such as side branches and intraluminal blood presence. This paper presents ARCOCT, a segmentation method for fully-automatic detection of lumen border in OCT images. ARCOCT relies on multiple, consecutive processing steps, accounting for image preparation, contour extraction and refinement. In particular, for contour extraction ARCOCT employs the transformation of OCT images based on physical characteristics such as reflectivity and absorption of the tissue and, for contour refinement, local regression using weighted linear least squares and a 2nd degree polynomial model is employed to achieve artifact and small-branch correction as well as smoothness of the artery mesh. Our major focus was to achieve accurate contour delineation in the various types of OCT images, i.e., even in challenging cases with branches and artifacts. ARCOCT has been assessed in a dataset of 1812 images (308 from stented and 1504 from native segments) obtained from 20 patients. ARCOCT was compared against ground-truth manual segmentation performed by experts on the basis of various geometric features (e.g. area, perimeter, radius, diameter, centroid, etc.) and closed contour matching indicators (the Dice index, the Hausdorff distance and the undirected average distance), using standard statistical analysis methods. The proposed method was proven very efficient and close to the ground-truth, exhibiting non statistically-significant differences for most of the examined metrics. ARCOCT allows accurate and fully-automated lumen border

Fault detection and multiclassifier fusion for unmanned aerial vehicles (UAVs)

NASA Astrophysics Data System (ADS)

Yan, Weizhong

2001-03-01

UAVs demand more accurate fault accommodation for their mission manager and vehicle control system in order to achieve a reliability level that is comparable to that of a pilot aircraft. This paper attempts to apply multi-classifier fusion techniques to achieve the necessary performance of the fault detection function for the Lockheed Martin Skunk Works (LMSW) UAV Mission Manager. Three different classifiers that meet the design requirements of the fault detection of the UAAV are employed. The binary decision outputs from the classifiers are then aggregated using three different classifier fusion schemes, namely, majority vote, weighted majority vote, and Naieve Bayes combination. All of the three schemes are simple and need no retraining. The three fusion schemes (except the majority vote that gives an average performance of the three classifiers) show the classification performance that is better than or equal to that of the best individual. The unavoidable correlation between the classifiers with binary outputs is observed in this study. We conclude that it is the correlation between the classifiers that limits the fusion schemes to achieve an even better performance.

An Integrated Tool to Study MHC Region: Accurate SNV Detection and HLA Genes Typing in Human MHC Region Using Targeted High-Throughput Sequencing

PubMed Central

Liu, Xiao; Xu, Yinyin; Liang, Dequan; Gao, Peng; Sun, Yepeng; Gifford, Benjamin; D’Ascenzo, Mark; Liu, Xiaomin; Tellier, Laurent C. A. M.; Yang, Fang; Tong, Xin; Chen, Dan; Zheng, Jing; Li, Weiyang; Richmond, Todd; Xu, Xun; Wang, Jun; Li, Yingrui

2013-01-01

The major histocompatibility complex (MHC) is one of the most variable and gene-dense regions of the human genome. Most studies of the MHC, and associated regions, focus on minor variants and HLA typing, many of which have been demonstrated to be associated with human disease susceptibility and metabolic pathways. However, the detection of variants in the MHC region, and diagnostic HLA typing, still lacks a coherent, standardized, cost effective and high coverage protocol of clinical quality and reliability. In this paper, we presented such a method for the accurate detection of minor variants and HLA types in the human MHC region, using high-throughput, high-coverage sequencing of target regions. A probe set was designed to template upon the 8 annotated human MHC haplotypes, and to encompass the 5 megabases (Mb) of the extended MHC region. We deployed our probes upon three, genetically diverse human samples for probe set evaluation, and sequencing data show that ∼97% of the MHC region, and over 99% of the genes in MHC region, are covered with sufficient depth and good evenness. 98% of genotypes called by this capture sequencing prove consistent with established HapMap genotypes. We have concurrently developed a one-step pipeline for calling any HLA type referenced in the IMGT/HLA database from this target capture sequencing data, which shows over 96% typing accuracy when deployed at 4 digital resolution. This cost-effective and highly accurate approach for variant detection and HLA typing in the MHC region may lend further insight into immune-mediated diseases studies, and may find clinical utility in transplantation medicine research. This one-step pipeline is released for general evaluation and use by the scientific community. PMID:23894464

Washington State Student Achievement Initiative Policy Study: Final Report

ERIC Educational Resources Information Center

Jenkins, Davis; Wachen, John; Moore, Colleen; Shulock, Nancy

2012-01-01

In 2007, the Washington State Board for Community and Technical Colleges launched a performance funding policy called the Student Achievement Initiative (SAI) both to improve public accountability by more accurately describing what students achieve from enrolling in community colleges and to provide incentives to colleges through financial rewards…

Using Temporal Covariance of Motion and Geometric Features via Boosting for Human Fall Detection.

PubMed

Ali, Syed Farooq; Khan, Reamsha; Mahmood, Arif; Hassan, Malik Tahir; Jeon, And Moongu

2018-06-12

Fall induced damages are serious incidences for aged as well as young persons. A real-time automatic and accurate fall detection system can play a vital role in timely medication care which will ultimately help to decrease the damages and complications. In this paper, we propose a fast and more accurate real-time system which can detect people falling in videos captured by surveillance cameras. Novel temporal and spatial variance-based features are proposed which comprise the discriminatory motion, geometric orientation and location of the person. These features are used along with ensemble learning strategy of boosting with J48 and Adaboost classifiers. Experiments have been conducted on publicly available standard datasets including Multiple Cameras Fall ( with 2 classes and 3 classes ) and UR Fall Detection achieving percentage accuracies of 99.2, 99.25 and 99.0, respectively. Comparisons with nine state-of-the-art methods demonstrate the effectiveness of the proposed approach on both datasets.

Accurate and fast multiple-testing correction in eQTL studies.

PubMed

Sul, Jae Hoon; Raj, Towfique; de Jong, Simone; de Bakker, Paul I W; Raychaudhuri, Soumya; Ophoff, Roel A; Stranger, Barbara E; Eskin, Eleazar; Han, Buhm

2015-06-04

In studies of expression quantitative trait loci (eQTLs), it is of increasing interest to identify eGenes, the genes whose expression levels are associated with variation at a particular genetic variant. Detecting eGenes is important for follow-up analyses and prioritization because genes are the main entities in biological processes. To detect eGenes, one typically focuses on the genetic variant with the minimum p value among all variants in cis with a gene and corrects for multiple testing to obtain a gene-level p value. For performing multiple-testing correction, a permutation test is widely used. Because of growing sample sizes of eQTL studies, however, the permutation test has become a computational bottleneck in eQTL studies. In this paper, we propose an efficient approach for correcting for multiple testing and assess eGene p values by utilizing a multivariate normal distribution. Our approach properly takes into account the linkage-disequilibrium structure among variants, and its time complexity is independent of sample size. By applying our small-sample correction techniques, our method achieves high accuracy in both small and large studies. We have shown that our method consistently produces extremely accurate p values (accuracy > 98%) for three human eQTL datasets with different sample sizes and SNP densities: the Genotype-Tissue Expression pilot dataset, the multi-region brain dataset, and the HapMap 3 dataset. Copyright © 2015 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

Can a surgeon drill accurately at a specified angle?

PubMed Central

Brioschi, Valentina; Cook, Jodie; Arthurs, Gareth I

2016-01-01

Objectives To investigate whether a surgeon can drill accurately a specified angle and whether surgeon experience, task repetition, drill bit size and perceived difficulty influence drilling angle accuracy. Methods The sample population consisted of final-year students (n=25), non-specialist veterinarians (n=22) and board-certified orthopaedic surgeons (n=8). Each participant drilled a hole twice in a horizontal oak plank at 30°, 45°, 60°, 80°, 85° and 90° angles with either a 2.5  or a 3.5 mm drill bit. Participants then rated the perceived difficulty to drill each angle. The true angle of each hole was measured using a digital goniometer. Results Greater drilling accuracy was achieved at angles closer to 90°. An error of ≤±4° was achieved by 84.5 per cent of participants drilling a 90° angle compared with approximately 20 per cent of participants drilling a 30–45° angle. There was no effect of surgeon experience, task repetition or drill bit size on the mean error for intended versus achieved angle. Increased perception of difficulty was associated with the more acute angles and decreased accuracy, but not experience level. Clinical significance This study shows that surgeon ability to drill accurately (within ±4° error) is limited, particularly at angles ≤60°. In situations where drill angle is critical, use of computer-assisted navigation or custom-made drill guides may be preferable. PMID:27547423

UWB pulse detection and TOA estimation using GLRT

NASA Astrophysics Data System (ADS)

Xie, Yan; Janssen, Gerard J. M.; Shakeri, Siavash; Tiberius, Christiaan C. J. M.

2017-12-01

In this paper, a novel statistical approach is presented for time-of-arrival (TOA) estimation based on first path (FP) pulse detection using a sub-Nyquist sampling ultra-wide band (UWB) receiver. The TOA measurement accuracy, which cannot be improved by averaging of the received signal, can be enhanced by the statistical processing of a number of TOA measurements. The TOA statistics are modeled and analyzed for a UWB receiver using threshold crossing detection of a pulse signal with noise. The detection and estimation scheme based on the Generalized Likelihood Ratio Test (GLRT) detector, which captures the full statistical information of the measurement data, is shown to achieve accurate TOA estimation and allows for a trade-off between the threshold level, the noise level, the amplitude and the arrival time of the first path pulse, and the accuracy of the obtained final TOA.

Radio interferometric measurements for accurate planetary orbiter navigation

NASA Technical Reports Server (NTRS)

Poole, S. R.; Ananda, M.; Hildebrand, C. E.

1979-01-01

The use of narrowband delta-VLBI to achieve accurate orbit determination is presented by viewing a spacecraft from widely separated stations followed by viewing a nearby quasar from the same stations. Current analysis is examined that establishes the orbit determination accuracy achieved with data arcs spanning up to 3.5 d. Strategies for improving prediction accuracy are given, and the performance of delta-VLBI is compared with conventional radiometric tracking data. It is found that accuracy 'within the fit' is on the order of 0.5 km for data arcs having delta-VLBI on the ends of the arcs and for arc lengths varying from one baseline to 3.5 d. The technique is discussed with reference to the proposed Venus Orbiting Imaging Radar mission.

Guidance to Achieve Accurate Aggregate Quantitation in Biopharmaceuticals by SV-AUC.

PubMed

Arthur, Kelly K; Kendrick, Brent S; Gabrielson, John P

2015-01-01

The levels and types of aggregates present in protein biopharmaceuticals must be assessed during all stages of product development, manufacturing, and storage of the finished product. Routine monitoring of aggregate levels in biopharmaceuticals is typically achieved by size exclusion chromatography (SEC) due to its high precision, speed, robustness, and simplicity to operate. However, SEC is error prone and requires careful method development to ensure accuracy of reported aggregate levels. Sedimentation velocity analytical ultracentrifugation (SV-AUC) is an orthogonal technique that can be used to measure protein aggregation without many of the potential inaccuracies of SEC. In this chapter, we discuss applications of SV-AUC during biopharmaceutical development and how characteristics of the technique make it better suited for some applications than others. We then discuss the elements of a comprehensive analytical control strategy for SV-AUC. Successful implementation of these analytical control elements ensures that SV-AUC provides continued value over the long time frames necessary to bring biopharmaceuticals to market. © 2015 Elsevier Inc. All rights reserved.

How to achieve more accurate comparisons in organ donation activity: time to effectiveness indicators.

PubMed

Deulofeu, R; Bodí, M A; Twose, J; López, P

2010-06-01

We are used to comparisons of activity using donation or transplantation population (pmp) rates between regions or countries, without a further evaluation of the process. But crude pmp rates do not clearly reflect real transplantation capacity, because organ procurement does not finish with the donation step; it is also necessary to know the utilization of the obtained organs. The objective of this study was to present methods and indicators deemed necessary to evaluate the effectiveness of the process. We have proposed the use of simple definitions and indicators to more accurately measure and compare the effectiveness of the total organ procurement process. To illustrate the use and performance of these indicators, we have presented the donation and transplantation activity in Catalonia from 2002 to 2007.

A deep-learning based automatic pulmonary nodule detection system

NASA Astrophysics Data System (ADS)

Zhao, Yiyuan; Zhao, Liang; Yan, Zhennan; Wolf, Matthias; Zhan, Yiqiang

2018-02-01

Lung cancer is the deadliest cancer worldwide. Early detection of lung cancer is a promising way to lower the risk of dying. Accurate pulmonary nodule detection in computed tomography (CT) images is crucial for early diagnosis of lung cancer. The development of computer-aided detection (CAD) system of pulmonary nodules contributes to making the CT analysis more accurate and with more efficiency. Recent studies from other groups have been focusing on lung cancer diagnosis CAD system by detecting medium to large nodules. However, to fully investigate the relevance between nodule features and cancer diagnosis, a CAD that is capable of detecting nodules with all sizes is needed. In this paper, we present a deep-learning based automatic all size pulmonary nodule detection system by cascading two artificial neural networks. We firstly use a U-net like 3D network to generate nodule candidates from CT images. Then, we use another 3D neural network to refine the locations of the nodule candidates generated from the previous subsystem. With the second sub-system, we bring the nodule candidates closer to the center of the ground truth nodule locations. We evaluate our system on a public CT dataset provided by the Lung Nodule Analysis (LUNA) 2016 grand challenge. The performance on the testing dataset shows that our system achieves 90% sensitivity with an average of 4 false positives per scan. This indicates that our system can be an aid for automatic nodule detection, which is beneficial for lung cancer diagnosis.

Crowdsourcing seizure detection: algorithm development and validation on human implanted device recordings.

PubMed

Baldassano, Steven N; Brinkmann, Benjamin H; Ung, Hoameng; Blevins, Tyler; Conrad, Erin C; Leyde, Kent; Cook, Mark J; Khambhati, Ankit N; Wagenaar, Joost B; Worrell, Gregory A; Litt, Brian

2017-06-01

There exist significant clinical and basic research needs for accurate, automated seizure detection algorithms. These algorithms have translational potential in responsive neurostimulation devices and in automatic parsing of continuous intracranial electroencephalography data. An important barrier to developing accurate, validated algorithms for seizure detection is limited access to high-quality, expertly annotated seizure data from prolonged recordings. To overcome this, we hosted a kaggle.com competition to crowdsource the development of seizure detection algorithms using intracranial electroencephalography from canines and humans with epilepsy. The top three performing algorithms from the contest were then validated on out-of-sample patient data including standard clinical data and continuous ambulatory human data obtained over several years using the implantable NeuroVista seizure advisory system. Two hundred teams of data scientists from all over the world participated in the kaggle.com competition. The top performing teams submitted highly accurate algorithms with consistent performance in the out-of-sample validation study. The performance of these seizure detection algorithms, achieved using freely available code and data, sets a new reproducible benchmark for personalized seizure detection. We have also shared a 'plug and play' pipeline to allow other researchers to easily use these algorithms on their own datasets. The success of this competition demonstrates how sharing code and high quality data results in the creation of powerful translational tools with significant potential to impact patient care. © The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

High Resolution Viscosity Measurement by Thermal Noise Detection

PubMed Central

Aguilar Sandoval, Felipe; Sepúlveda, Manuel; Bellon, Ludovic; Melo, Francisco

2015-01-01

An interferometric method is implemented in order to accurately assess the thermal fluctuations of a micro-cantilever sensor in liquid environments. The power spectrum density (PSD) of thermal fluctuations together with Sader’s model of the cantilever allow for the indirect measurement of the liquid viscosity with good accuracy. The good quality of the deflection signal and the characteristic low noise of the instrument allow for the detection and corrections of drawbacks due to both the cantilever shape irregularities and the uncertainties on the position of the laser spot at the fluctuating end of the cantilever. Variation of viscosity below 0.03 mPa·s was detected with the alternative to achieve measurements with a volume as low as 50 μL. PMID:26540061

High Resolution Viscosity Measurement by Thermal Noise Detection.

PubMed

Sandoval, Felipe Aguilar; Sepúlveda, Manuel; Bellon, Ludovic; Melo, Francisco

2015-11-03

An interferometric method is implemented in order to accurately assess the thermal fluctuations of a micro-cantilever sensor in liquid environments. The power spectrum density (PSD) of thermal fluctuations together with Sader's model of the cantilever allow for the indirect measurement of the liquid viscosity with good accuracy. The good quality of the deflection signal and the characteristic low noise of the instrument allow for the detection and corrections of drawbacks due to both the cantilever shape irregularities and the uncertainties on the position of the laser spot at the fluctuating end of the cantilever. Variation of viscosity below 0:03mPa·s was detected with the alternative to achieve measurements with a volume as low as 50 µL.

Podiatry Ankle Duplex Scan: Readily Learned and Accurate in Diabetes.

PubMed

Normahani, Pasha; Powezka, Katarzyna; Aslam, Mohammed; Standfield, Nigel J; Jaffer, Usman

2018-03-01

We aimed to train podiatrists to perform a focused duplex ultrasound scan (DUS) of the tibial vessels at the ankle in diabetic patients; podiatry ankle (PodAnk) duplex scan. Thirteen podiatrists underwent an intensive 3-hour long simulation training session. Participants were then assessed performing bilateral PodAnk duplex scans of 3 diabetic patients with peripheral arterial disease. Participants were assessed using the duplex ultrasound objective structured assessment of technical skills (DUOSATS) tool and an "Imaging Score". A total of 156 vessel assessments were performed. All patients had abnormal waveforms with a loss of triphasic flow. Loss of triphasic flow was accurately detected in 145 (92.9%) vessels; the correct waveform was identified in 139 (89.1%) cases. Participants achieved excellent DUOSATS scores (median 24 [interquartile range: 23-25], max attainable score of 26) as well as "Imaging Scores" (8 [8-8], max attainable score of 8) indicating proficiency in technical skills. The mean time taken for each bilateral ankle assessment was 20.4 minutes (standard deviation ±6.7). We have demonstrated that a focused DUS for the purpose of vascular assessment of the diabetic foot is readily learned using intensive simulation training.

Low-dimensional, morphologically accurate models of subthreshold membrane potential

PubMed Central

Kellems, Anthony R.; Roos, Derrick; Xiao, Nan; Cox, Steven J.

2009-01-01

The accurate simulation of a neuron’s ability to integrate distributed synaptic input typically requires the simultaneous solution of tens of thousands of ordinary differential equations. For, in order to understand how a cell distinguishes between input patterns we apparently need a model that is biophysically accurate down to the space scale of a single spine, i.e., 1 μm. We argue here that one can retain this highly detailed input structure while dramatically reducing the overall system dimension if one is content to accurately reproduce the associated membrane potential at a small number of places, e.g., at the site of action potential initiation, under subthreshold stimulation. The latter hypothesis permits us to approximate the active cell model with an associated quasi-active model, which in turn we reduce by both time-domain (Balanced Truncation) and frequency-domain (ℋ2 approximation of the transfer function) methods. We apply and contrast these methods on a suite of typical cells, achieving up to four orders of magnitude in dimension reduction and an associated speed-up in the simulation of dendritic democratization and resonance. We also append a threshold mechanism and indicate that this reduction has the potential to deliver an accurate quasi-integrate and fire model. PMID:19172386

Achieving fast and stable failure detection in WDM Networks

NASA Astrophysics Data System (ADS)

Gao, Donghui; Zhou, Zhiyu; Zhang, Hanyi

2005-02-01

In dynamic networks, the failure detection time takes a major part of the convergence time, which is an important network performance index. To detect a node or link failure in the network, traditional protocols, like Hello protocol in OSPF or RSVP, exchanges keep-alive messages between neighboring nodes to keep track of the link/node state. But by default settings, it can get a minimum detection time in the measure of dozens of seconds, which can not meet the demands of fast network convergence and failure recovery. When configuring the related parameters to reduce the detection time, there will be notable instability problems. In this paper, we analyzed the problem and designed a new failure detection algorithm to reduce the network overhead of detection signaling. Through our experiment we found it is effective to enhance the stability by implicitly acknowledge other signaling messages as keep-alive messages. We conducted our proposal and the previous approaches on the ASON test-bed. The experimental results show that our algorithm gives better performances than previous schemes in about an order magnitude reduction of both false failure alarms and queuing delay to other messages, especially under light traffic load.

An improvement in rollover detection of articulated vehicles using the grey system theory

NASA Astrophysics Data System (ADS)

Chou, Tao; Chu, Tzyy-Wen

2014-05-01

A Rollover Index combined with the grey system theory, called a Grey Rollover Index (GRI), is proposed to assess the rollover threat for articulated vehicles with a tractor-semitrailer combination. This index can predict future trends of vehicle dynamics based on current vehicle motion; thus, it is suitable for vehicle-rollover detection. Two difficulties are encountered when applying the GRI for rollover detection. The first difficulty is effectively predicting the rollover threat of the vehicles, and the second difficulty is achieving a definite definition of the real rollover timing of a vehicle. The following methods are used to resolve these problems. First, a nonlinear mathematical model is constructed to accurately describe the vehicle dynamics of articulated vehicles. This model is combined with the GRI to predict rollover propensity. Finally, TruckSim™ software is used to determine the real rollover timing and facilitate the accurate supply of information to the rollover detection system through the GRI. This index is used to verify the simulation based on the common manoeuvres that cause rollover accidents to reduce the occurrence of false signals and effectively increase the efficiency of the rollover detection system.

Accurate segmentation of lung fields on chest radiographs using deep convolutional networks

NASA Astrophysics Data System (ADS)

Arbabshirani, Mohammad R.; Dallal, Ahmed H.; Agarwal, Chirag; Patel, Aalpan; Moore, Gregory

2017-02-01

Accurate segmentation of lung fields on chest radiographs is the primary step for computer-aided detection of various conditions such as lung cancer and tuberculosis. The size, shape and texture of lung fields are key parameters for chest X-ray (CXR) based lung disease diagnosis in which the lung field segmentation is a significant primary step. Although many methods have been proposed for this problem, lung field segmentation remains as a challenge. In recent years, deep learning has shown state of the art performance in many visual tasks such as object detection, image classification and semantic image segmentation. In this study, we propose a deep convolutional neural network (CNN) framework for segmentation of lung fields. The algorithm was developed and tested on 167 clinical posterior-anterior (PA) CXR images collected retrospectively from picture archiving and communication system (PACS) of Geisinger Health System. The proposed multi-scale network is composed of five convolutional and two fully connected layers. The framework achieved IOU (intersection over union) of 0.96 on the testing dataset as compared to manual segmentation. The suggested framework outperforms state of the art registration-based segmentation by a significant margin. To our knowledge, this is the first deep learning based study of lung field segmentation on CXR images developed on a heterogeneous clinical dataset. The results suggest that convolutional neural networks could be employed reliably for lung field segmentation.

Targeted Analyte Detection by Standard Addition Improves Detection Limits in MALDI Mass Spectrometry

PubMed Central

Eshghi, Shadi Toghi; Li, Xingde; Zhang, Hui

2014-01-01

Matrix-assisted laser desorption/ionization has proven an effective tool for fast and accurate determination of many molecules. However, the detector sensitivity and chemical noise compromise the detection of many invaluable low-abundance molecules from biological and clinical samples. To challenge this limitation, we developed a targeted analyte detection (TAD) technique. In TAD, the target analyte is selectively elevated by spiking a known amount of that analyte into the sample, thereby raising its concentration above the noise level, where we take advantage of the improved sensitivity to detect the presence of the endogenous analyte in the sample. We assessed TAD on three peptides in simple and complex background solutions with various exogenous analyte concentrations in two MALDI matrices. TAD successfully improved the limit of detection (LOD) of target analytes when the target peptides were added to the sample in a concentration close to optimum concentration. The optimum exogenous concentration was estimated through a quantitative method to be approximately equal to the original LOD for each target. Also, we showed that TAD could achieve LOD improvements on an average of 3-fold in a simple and 2-fold in a complex sample. TAD provides a straightforward assay to improve the LOD of generic target analytes without the need for costly hardware modifications. PMID:22877355

OpenMC In Situ Source Convergence Detection

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

Aldrich, Garrett Allen; Dutta, Soumya; Woodring, Jonathan Lee

2016-05-07

We designed and implemented an in situ version of particle source convergence for the OpenMC particle transport simulator. OpenMC is a Monte Carlo based-particle simulator for neutron criticality calculations. For the transport simulation to be accurate, source particles must converge on a spatial distribution. Typically, convergence is obtained by iterating the simulation by a user-settable, fixed number of steps, and it is assumed that convergence is achieved. We instead implement a method to detect convergence, using the stochastic oscillator for identifying convergence of source particles based on their accumulated Shannon Entropy. Using our in situ convergence detection, we are ablemore » to detect and begin tallying results for the full simulation once the proper source distribution has been confirmed. Our method ensures that the simulation is not started too early, by a user setting too optimistic parameters, or too late, by setting too conservative a parameter.« less

A practical and highly sensitive C3N4-TYR fluorescent probe for convenient detection of dopamine

NASA Astrophysics Data System (ADS)

Li, Hao; Yang, Manman; Liu, Juan; Zhang, Yalin; Yang, Yanmei; Huang, Hui; Liu, Yang; Kang, Zhenhui

2015-07-01

The C3N4-tyrosinase (TYR) hybrid is a highly accurate, sensitive and simple fluorescent probe for the detection of dopamine (DOPA). Under optimized conditions, the relative fluorescence intensity of C3N4-TYR is proportional to the DOPA concentration in the range from 1 × 10-3 to 3 × 10-8 mol L-1 with a correlation coefficient of 0.995. In the present system, the detection limit achieved is as low as 3 × 10-8 mol L-1. Notably, these quantitative detection results for clinical samples are comparable to those of high performance liquid chromatography. Moreover, the enzyme-encapsulated C3N4 sensing arrays on both glass slide and test paper were evaluated, which revealed sensitive detection and excellent stability. The results reported here provide a new approach for the design of a multifunctional nanosensor for the detection of bio-molecules.The C3N4-tyrosinase (TYR) hybrid is a highly accurate, sensitive and simple fluorescent probe for the detection of dopamine (DOPA). Under optimized conditions, the relative fluorescence intensity of C3N4-TYR is proportional to the DOPA concentration in the range from 1 × 10-3 to 3 × 10-8 mol L-1 with a correlation coefficient of 0.995. In the present system, the detection limit achieved is as low as 3 × 10-8 mol L-1. Notably, these quantitative detection results for clinical samples are comparable to those of high performance liquid chromatography. Moreover, the enzyme-encapsulated C3N4 sensing arrays on both glass slide and test paper were evaluated, which revealed sensitive detection and excellent stability. The results reported here provide a new approach for the design of a multifunctional nanosensor for the detection of bio-molecules. Electronic supplementary information (ESI) available. See DOI: 10.1039/c5nr03316k

Accurately controlled sequential self-folding structures by polystyrene film

NASA Astrophysics Data System (ADS)

Deng, Dongping; Yang, Yang; Chen, Yong; Lan, Xing; Tice, Jesse

2017-08-01

Four-dimensional (4D) printing overcomes the traditional fabrication limitations by designing heterogeneous materials to enable the printed structures evolve over time (the fourth dimension) under external stimuli. Here, we present a simple 4D printing of self-folding structures that can be sequentially and accurately folded. When heated above their glass transition temperature pre-strained polystyrene films shrink along the XY plane. In our process silver ink traces printed on the film are used to provide heat stimuli by conducting current to trigger the self-folding behavior. The parameters affecting the folding process are studied and discussed. Sequential folding and accurately controlled folding angles are achieved by using printed ink traces and angle lock design. Theoretical analyses are done to guide the design of the folding processes. Programmable structures such as a lock and a three-dimensional antenna are achieved to test the feasibility and potential applications of this method. These self-folding structures change their shapes after fabrication under controlled stimuli (electric current) and have potential applications in the fields of electronics, consumer devices, and robotics. Our design and fabrication method provides an easy way by using silver ink printed on polystyrene films to 4D print self-folding structures for electrically induced sequential folding with angular control.

Detection and quantitation of trace phenolphthalein (in pharmaceutical preparations and in forensic exhibits) by liquid chromatography-tandem mass spectrometry, a sensitive and accurate method.

PubMed

Sharma, Kakali; Sharma, Shiba P; Lahiri, Sujit C

2013-01-01

Phenolphthalein, an acid-base indicator and laxative, is important as a constituent of widely used weight-reducing multicomponent food formulations. Phenolphthalein is an useful reagent in forensic science for the identification of blood stains of suspected victims and for apprehending erring officials accepting bribes in graft or trap cases. The pink-colored alkaline hand washes originating from the phenolphthalein-smeared notes can easily be determined spectrophotometrically. But in many cases, colored solution turns colorless with time, which renders the genuineness of bribe cases doubtful to the judiciary. No method is known till now for the detection and identification of phenolphthalein in colorless forensic exhibits with positive proof. Liquid chromatography-tandem mass spectrometry had been found to be most sensitive, accurate method capable of detection and quantitation of trace phenolphthalein in commercial formulations and colorless forensic exhibits with positive proof. The detection limit of phenolphthalein was found to be 1.66 pg/L or ng/mL, and the calibration curve shows good linearity (r(2) = 0.9974). © 2012 American Academy of Forensic Sciences.

Accurate quantum chemical calculations

NASA Technical Reports Server (NTRS)

Bauschlicher, Charles W., Jr.; Langhoff, Stephen R.; Taylor, Peter R.

1989-01-01

An important goal of quantum chemical calculations is to provide an understanding of chemical bonding and molecular electronic structure. A second goal, the prediction of energy differences to chemical accuracy, has been much harder to attain. First, the computational resources required to achieve such accuracy are very large, and second, it is not straightforward to demonstrate that an apparently accurate result, in terms of agreement with experiment, does not result from a cancellation of errors. Recent advances in electronic structure methodology, coupled with the power of vector supercomputers, have made it possible to solve a number of electronic structure problems exactly using the full configuration interaction (FCI) method within a subspace of the complete Hilbert space. These exact results can be used to benchmark approximate techniques that are applicable to a wider range of chemical and physical problems. The methodology of many-electron quantum chemistry is reviewed. Methods are considered in detail for performing FCI calculations. The application of FCI methods to several three-electron problems in molecular physics are discussed. A number of benchmark applications of FCI wave functions are described. Atomic basis sets and the development of improved methods for handling very large basis sets are discussed: these are then applied to a number of chemical and spectroscopic problems; to transition metals; and to problems involving potential energy surfaces. Although the experiences described give considerable grounds for optimism about the general ability to perform accurate calculations, there are several problems that have proved less tractable, at least with current computer resources, and these and possible solutions are discussed.

Raman Computational and Experimental Studies of Dopamine Detection

PubMed Central

Ciubuc, John D.; Bennet, Kevin E.; Qiu, Chao; Alonzo, Matthew; Durrer, William G.; Manciu, Felicia S.

2017-01-01

A combined theoretical and experimental analysis of dopamine (DA) is presented in this work with the objective of achieving more accurate detection and monitoring of this neurotransmitter at very low concentrations, specific to physiological levels. Surface-enhanced Raman spectroscopy on silver nanoparticles was employed for recording DA concentrations as low as 10−11 molar. Quantum chemical density functional calculations were carried out using Gaussian-09 analytical suite software. Relatively good agreement between the simulated and experimentally determined results indicates the presence of different DA molecular forms, such as uncharged DA±, anionic DA−, and dopaminequinone. Disappearance of the strongest bands of dopamine around 750 cm−1 and 790 cm−1, which suggests its adsorption onto the metallic surface, is not only consistent with all of these DA configurations, but also provides additional information about the analyte’s redox process and voltammetric detection. On the other hand, occurrence of the abovementioned Raman lines could indicate the formation of multilayers of DA or its presence in a cationic DA+ form. Thus, through coordinated experiment and theory, valuable insights into changes observed in the vibrational signatures of this important neurotransmitter can be achieved for a better understanding of its detection at physiological levels, which is crucial if further optovoltammetric medical device development is envisioned. PMID:28956820

Rats track odour trails accurately using a multi-layered strategy with near-optimal sampling.

PubMed

Khan, Adil Ghani; Sarangi, Manaswini; Bhalla, Upinder Singh

2012-02-28

Tracking odour trails is a crucial behaviour for many animals, often leading to food, mates or away from danger. It is an excellent example of active sampling, where the animal itself controls how to sense the environment. Here we show that rats can track odour trails accurately with near-optimal sampling. We trained rats to follow odour trails drawn on paper spooled through a treadmill. By recording local field potentials (LFPs) from the olfactory bulb, and sniffing rates, we find that sniffing but not LFPs differ between tracking and non-tracking conditions. Rats can track odours within ~1 cm, and this accuracy is degraded when one nostril is closed. Moreover, they show path prediction on encountering a fork, wide 'casting' sweeps on encountering a gap and detection of reappearance of the trail in 1-2 sniffs. We suggest that rats use a multi-layered strategy, and achieve efficient sampling and high accuracy in this complex task.

Ethnicity and High School Student Achievement across Rural and Urban Districts.

ERIC Educational Resources Information Center

Maestas, Leo Carlos

1981-01-01

Cultural values must be identified and cultural orientations must be accurately reflected in school evaluations. The U.S. Commission on Civil Rights shows that in five achievement areas--school holding power, reading achievement, grade repetition, overageness, and participation in extra-curricular activities--Mexican Americans performed…

Ensembles of radial basis function networks for spectroscopic detection of cervical precancer

NASA Technical Reports Server (NTRS)

Tumer, K.; Ramanujam, N.; Ghosh, J.; Richards-Kortum, R.

1998-01-01

The mortality related to cervical cancer can be substantially reduced through early detection and treatment. However, current detection techniques, such as Pap smear and colposcopy, fail to achieve a concurrently high sensitivity and specificity. In vivo fluorescence spectroscopy is a technique which quickly, noninvasively and quantitatively probes the biochemical and morphological changes that occur in precancerous tissue. A multivariate statistical algorithm was used to extract clinically useful information from tissue spectra acquired from 361 cervical sites from 95 patients at 337-, 380-, and 460-nm excitation wavelengths. The multivariate statistical analysis was also employed to reduce the number of fluorescence excitation-emission wavelength pairs required to discriminate healthy tissue samples from precancerous tissue samples. The use of connectionist methods such as multilayered perceptrons, radial basis function (RBF) networks, and ensembles of such networks was investigated. RBF ensemble algorithms based on fluorescence spectra potentially provide automated and near real-time implementation of precancer detection in the hands of nonexperts. The results are more reliable, direct, and accurate than those achieved by either human experts or multivariate statistical algorithms.

Radiometrically accurate scene-based nonuniformity correction for array sensors.

PubMed

Ratliff, Bradley M; Hayat, Majeed M; Tyo, J Scott

2003-10-01

A novel radiometrically accurate scene-based nonuniformity correction (NUC) algorithm is described. The technique combines absolute calibration with a recently reported algebraic scene-based NUC algorithm. The technique is based on the following principle: First, detectors that are along the perimeter of the focal-plane array are absolutely calibrated; then the calibration is transported to the remaining uncalibrated interior detectors through the application of the algebraic scene-based algorithm, which utilizes pairs of image frames exhibiting arbitrary global motion. The key advantage of this technique is that it can obtain radiometric accuracy during NUC without disrupting camera operation. Accurate estimates of the bias nonuniformity can be achieved with relatively few frames, which can be fewer than ten frame pairs. Advantages of this technique are discussed, and a thorough performance analysis is presented with use of simulated and real infrared imagery.

Ship detection in optical remote sensing images based on deep convolutional neural networks

NASA Astrophysics Data System (ADS)

Yao, Yuan; Jiang, Zhiguo; Zhang, Haopeng; Zhao, Danpei; Cai, Bowen

2017-10-01

Automatic ship detection in optical remote sensing images has attracted wide attention for its broad applications. Major challenges for this task include the interference of cloud, wave, wake, and the high computational expenses. We propose a fast and robust ship detection algorithm to solve these issues. The framework for ship detection is designed based on deep convolutional neural networks (CNNs), which provide the accurate locations of ship targets in an efficient way. First, the deep CNN is designed to extract features. Then, a region proposal network (RPN) is applied to discriminate ship targets and regress the detection bounding boxes, in which the anchors are designed by intrinsic shape of ship targets. Experimental results on numerous panchromatic images demonstrate that, in comparison with other state-of-the-art ship detection methods, our method is more efficient and achieves higher detection accuracy and more precise bounding boxes in different complex backgrounds.

Compressive sensing for efficient health monitoring and effective damage detection of structures

NASA Astrophysics Data System (ADS)

Jayawardhana, Madhuka; Zhu, Xinqun; Liyanapathirana, Ranjith; Gunawardana, Upul

2017-02-01

Real world Structural Health Monitoring (SHM) systems consist of sensors in the scale of hundreds, each sensor generating extremely large amounts of data, often arousing the issue of the cost associated with data transfer and storage. Sensor energy is a major component included in this cost factor, especially in Wireless Sensor Networks (WSN). Data compression is one of the techniques that is being explored to mitigate the effects of these issues. In contrast to traditional data compression techniques, Compressive Sensing (CS) - a very recent development - introduces the means of accurately reproducing a signal by acquiring much less number of samples than that defined by Nyquist's theorem. CS achieves this task by exploiting the sparsity of the signal. By the reduced amount of data samples, CS may help reduce the energy consumption and storage costs associated with SHM systems. This paper investigates CS based data acquisition in SHM, in particular, the implications of CS on damage detection and localization. CS is implemented in a simulation environment to compress structural response data from a Reinforced Concrete (RC) structure. Promising results were obtained from the compressed data reconstruction process as well as the subsequent damage identification process using the reconstructed data. A reconstruction accuracy of 99% could be achieved at a Compression Ratio (CR) of 2.48 using the experimental data. Further analysis using the reconstructed signals provided accurate damage detection and localization results using two damage detection algorithms, showing that CS has not compromised the crucial information on structural damages during the compression process.

Highly specific detection of genetic modification events using an enzyme-linked probe hybridization chip.

PubMed

Zhang, M Z; Zhang, X F; Chen, X M; Chen, X; Wu, S; Xu, L L

2015-08-10

The enzyme-linked probe hybridization chip utilizes a method based on ligase-hybridizing probe chip technology, with the principle of using thio-primers for protection against enzyme digestion, and using lambda DNA exonuclease to cut multiple PCR products obtained from the sample being tested into single-strand chains for hybridization. The 5'-end amino-labeled probe was fixed onto the aldehyde chip, and hybridized with the single-stranded PCR product, followed by addition of a fluorescent-modified probe that was then enzymatically linked with the adjacent, substrate-bound probe in order to achieve highly specific, parallel, and high-throughput detection. Specificity and sensitivity testing demonstrated that enzyme-linked probe hybridization technology could be applied to the specific detection of eight genetic modification events at the same time, with a sensitivity reaching 0.1% and the achievement of accurate, efficient, and stable results.

Accurate Detection of Adenylation Domain Functions in Nonribosomal Peptide Synthetases by an Enzyme-linked Immunosorbent Assay System Using Active Site-directed Probes for Adenylation Domains.

PubMed

Ishikawa, Fumihiro; Miyamoto, Kengo; Konno, Sho; Kasai, Shota; Kakeya, Hideaki

2015-12-18

A significant gap exists between protein engineering and enzymes used for the biosynthesis of natural products, largely because there is a paucity of strategies that rapidly detect active-site phenotypes of the enzymes with desired activities. Herein, we describe a proof-of-concept study of an enzyme-linked immunosorbent assay (ELISA) system for the adenylation (A) domains in nonribosomal peptide synthetases (NRPSs) using a combination of active site-directed probes coupled to a 5'-O-N-(aminoacyl)sulfamoyladenosine scaffold with a biotin functionality that immobilizes probe molecules onto a streptavidin-coated solid support. The recombinant NRPSs have a C-terminal His-tag motif that is targeted by an anti-6×His mouse antibody as the primary antibody and a horseradish peroxidase-linked goat antimouse antibody as the secondary antibody. These probes can selectively capture the cognate A domains by ligand-directed targeting. In addition, the ELISA technique detected A domains in the crude cell-free homogenates from the Escherichia coli expression systems. When coupled with a chromogenic substrate, the antibody-based ELISA technique can visualize probe-protein binding interactions, which provides accurate readouts of the A-domain functions in NRPS enzymes. To assess the ELISA-based engineering of the A domains of NRPSs, we reprogramed 2,3-dihydroxybenzoic acid (DHB)-activating enzyme EntE toward salicylic acid (Sal)-activating enzymes and investigated a correlation between binding properties for probe molecules and enzyme catalysts. We generated a mutant of EntE that displayed negligible loss in the kcat/Km value with the noncognate substrate Sal and a corresponding 48-fold decrease in the kcat/Km value with the cognate substrate DHB. The resulting 26-fold switch in substrate specificity was achieved by the replacement of a Ser residue in the active site of EntE with a Cys toward the nonribosomal codes of Sal-activating enzymes. Bringing a laboratory ELISA technique

Study on high power ultraviolet laser oil detection system

NASA Astrophysics Data System (ADS)

Jin, Qi; Cui, Zihao; Bi, Zongjie; Zhang, Yanchao; Tian, Zhaoshuo; Fu, Shiyou

2018-03-01

Laser Induce Fluorescence (LIF) is a widely used new telemetry technology. It obtains information about oil spill and oil film thickness by analyzing the characteristics of stimulated fluorescence and has an important application in the field of rapid analysis of water composition. A set of LIF detection system for marine oil pollution is designed in this paper, which uses 355nm high-energy pulsed laser as the excitation light source. A high-sensitivity image intensifier is used in the detector. The upper machine sends a digital signal through a serial port to achieve nanoseconds range-gated width control for image intensifier. The target fluorescence spectrum image is displayed on the image intensifier by adjusting the delay time and the width of the pulse signal. The spectral image is coupled to CCD by lens imaging to achieve spectral display and data analysis function by computer. The system is used to detect the surface of the floating oil film in the distance of 25m to obtain the fluorescence spectra of different oil products respectively. The fluorescence spectra of oil products are obvious. The experimental results show that the system can realize high-precision long-range fluorescence detection and reflect the fluorescence characteristics of the target accurately, with broad application prospects in marine oil pollution identification and oil film thickness detection.

Accurate detection for a wide range of mutation and editing sites of microRNAs from small RNA high-throughput sequencing profiles

PubMed Central

Zheng, Yun; Ji, Bo; Song, Renhua; Wang, Shengpeng; Li, Ting; Zhang, Xiaotuo; Chen, Kun; Li, Tianqing; Li, Jinyan

2016-01-01

Various types of mutation and editing (M/E) events in microRNAs (miRNAs) can change the stabilities of pre-miRNAs and/or complementarities between miRNAs and their targets. Small RNA (sRNA) high-throughput sequencing (HTS) profiles can contain many mutated and edited miRNAs. Systematic detection of miRNA mutation and editing sites from the huge volume of sRNA HTS profiles is computationally difficult, as high sensitivity and low false positive rate (FPR) are both required. We propose a novel method (named MiRME) for an accurate and fast detection of miRNA M/E sites using a progressive sequence alignment approach which refines sensitivity and improves FPR step-by-step. From 70 sRNA HTS profiles with over 1.3 billion reads, MiRME has detected thousands of statistically significant M/E sites, including 3′-editing sites, 57 A-to-I editing sites (of which 32 are novel), as well as some putative non-canonical editing sites. We demonstrated that a few non-canonical editing sites were not resulted from mutations in genome by integrating the analysis of genome HTS profiles of two human cell lines, suggesting the existence of new editing types to further diversify the functions of miRNAs. Compared with six existing studies or methods, MiRME has shown much superior performance for the identification and visualization of the M/E sites of miRNAs from the ever-increasing sRNA HTS profiles. PMID:27229138

Mass spectrometry-based protein identification with accurate statistical significance assignment.

PubMed

Alves, Gelio; Yu, Yi-Kuo

2015-03-01

Assigning statistical significance accurately has become increasingly important as metadata of many types, often assembled in hierarchies, are constructed and combined for further biological analyses. Statistical inaccuracy of metadata at any level may propagate to downstream analyses, undermining the validity of scientific conclusions thus drawn. From the perspective of mass spectrometry-based proteomics, even though accurate statistics for peptide identification can now be achieved, accurate protein level statistics remain challenging. We have constructed a protein ID method that combines peptide evidences of a candidate protein based on a rigorous formula derived earlier; in this formula the database P-value of every peptide is weighted, prior to the final combination, according to the number of proteins it maps to. We have also shown that this protein ID method provides accurate protein level E-value, eliminating the need of using empirical post-processing methods for type-I error control. Using a known protein mixture, we find that this protein ID method, when combined with the Sorić formula, yields accurate values for the proportion of false discoveries. In terms of retrieval efficacy, the results from our method are comparable with other methods tested. The source code, implemented in C++ on a linux system, is available for download at ftp://ftp.ncbi.nlm.nih.gov/pub/qmbp/qmbp_ms/RAId/RAId_Linux_64Bit. Published by Oxford University Press 2014. This work is written by US Government employees and is in the public domain in the US.

Does High School Homework Increase Academic Achievement?

ERIC Educational Resources Information Center

Kalenkoski, Charlene Marie; Pabilonia, Sabrina Wulff

2017-01-01

Although previous research has shown that homework improves students' academic achievement, the majority of these studies use data on students' homework time from retrospective questionnaires, which may be less accurate than time-diary data. We use data from the combined Child Development Supplement (CDS) and the Transition to Adulthood Survey…

Fast algorithm for probabilistic bone edge detection (FAPBED)

NASA Astrophysics Data System (ADS)

Scepanovic, Danilo; Kirshtein, Joshua; Jain, Ameet K.; Taylor, Russell H.

2005-04-01

The registration of preoperative CT to intra-operative reality systems is a crucial step in Computer Assisted Orthopedic Surgery (CAOS). The intra-operative sensors include 3D digitizers, fiducials, X-rays and Ultrasound (US). FAPBED is designed to process CT volumes for registration to tracked US data. Tracked US is advantageous because it is real time, noninvasive, and non-ionizing, but it is also known to have inherent inaccuracies which create the need to develop a framework that is robust to various uncertainties, and can be useful in US-CT registration. Furthermore, conventional registration methods depend on accurate and absolute segmentation. Our proposed probabilistic framework addresses the segmentation-registration duality, wherein exact segmentation is not a prerequisite to achieve accurate registration. In this paper, we develop a method for fast and automatic probabilistic bone surface (edge) detection in CT images. Various features that influence the likelihood of the surface at each spatial coordinate are combined using a simple probabilistic framework, which strikes a fair balance between a high-level understanding of features in an image and the low-level number crunching of standard image processing techniques. The algorithm evaluates different features for detecting the probability of a bone surface at each voxel, and compounds the results of these methods to yield a final, low-noise, probability map of bone surfaces in the volume. Such a probability map can then be used in conjunction with a similar map from tracked intra-operative US to achieve accurate registration. Eight sample pelvic CT scans were used to extract feature parameters and validate the final probability maps. An un-optimized fully automatic Matlab code runs in five minutes per CT volume on average, and was validated by comparison against hand-segmented gold standards. The mean probability assigned to nonzero surface points was 0.8, while nonzero non-surface points had a mean

Phone camera detection of glucose blood level based on magnetic particles entrapped inside bubble wrap.

PubMed

Martinkova, Pavla; Pohanka, Miroslav

2016-12-18

Glucose is an important diagnostic biochemical marker of diabetes but also for organophosphates, carbamates, acetaminophens or salicylates poisoning. Hence, innovation of accurate and fast detection assay is still one of priorities in biomedical research. Glucose sensor based on magnetic particles (MPs) with immobilized enzymes glucose oxidase (GOx) and horseradish peroxidase (HRP) was developed and the GOx catalyzed reaction was visualized by a smart-phone-integrated camera. Exponential decay concentration curve with correlation coefficient 0.997 and with limit of detection 0.4 mmol/l was achieved. Interfering and matrix substances were measured due to possibility of assay influencing and no effect of the tested substances was observed. Spiked plasma samples were also measured and no influence of plasma matrix on the assay was proved. The presented assay showed complying results with reference method (standard spectrophotometry based on enzymes glucose oxidase and peroxidase inside plastic cuvettes) with linear dependence and correlation coefficient 0.999 in concentration range between 0 and 4 mmol/l. On the grounds of measured results, method was considered as highly specific, accurate and fast assay for detection of glucose.

Mass spectrometry-based targeted quantitative proteomics: achieving sensitive and reproducible detection of proteins.

PubMed

Boja, Emily S; Rodriguez, Henry

2012-04-01

Traditional shotgun proteomics used to detect a mixture of hundreds to thousands of proteins through mass spectrometric analysis, has been the standard approach in research to profile protein content in a biological sample which could lead to the discovery of new (and all) protein candidates with diagnostic, prognostic, and therapeutic values. In practice, this approach requires significant resources and time, and does not necessarily represent the goal of the researcher who would rather study a subset of such discovered proteins (including their variations or posttranslational modifications) under different biological conditions. In this context, targeted proteomics is playing an increasingly important role in the accurate measurement of protein targets in biological samples in the hope of elucidating the molecular mechanism of cellular function via the understanding of intricate protein networks and pathways. One such (targeted) approach, selected reaction monitoring (or multiple reaction monitoring) mass spectrometry (MRM-MS), offers the capability of measuring multiple proteins with higher sensitivity and throughput than shotgun proteomics. Developing and validating MRM-MS-based assays, however, is an extensive and iterative process, requiring a coordinated and collaborative effort by the scientific community through the sharing of publicly accessible data and datasets, bioinformatic tools, standard operating procedures, and well characterized reagents. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Development of a lab-on-chip electrochemical immunosensor for detection of Polycyclic Aromatic Hydrocarbons (PAH) in environmental water

NASA Astrophysics Data System (ADS)

Felemban, Shifa; Vazquez, Patricia; Dehnert, Jan; Goridko, Vadim; Tijero, Maria; Moore, Eric

2017-06-01

The work described in this manuscript focuses on how the integration of immunoassay techniques in combination with electrochemical detection can provide a portable and very accurate solution for detection of water pollutants that are detrimental for human health. In particular, we focus our work on the quantification of polycyclic aromatic hydrocarbons (PAHs) in polluted water. Our integrative approach facilitates a real-time detection of this family of organic compounds, by reducing the time of analysis to less than one hour. Additionally, the use of a lab-on-a-chip platform delivers a portable solution that could be used in situ. Optimization of a displacement assay that investigates the presence and concentration of Benzo[a]pyrene in water, allows with the miniaturization of the standard ELISA format into a highly accurate system that provides fast results. The limits of detection obtained are comparable to those of available state-of-the art tools, and achieve the values set by European Drinking Water Directive, 0.10ng/l, as the limit for PAHs in drinking water.

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

Accurate detection of low prevalence AKT1 E17K mutation in tissue or plasma from advanced cancer patients

PubMed Central

de Bruin, Elza C.; Whiteley, Jessica L.; Corcoran, Claire; Kirk, Pauline M.; Fox, Jayne C.; Armisen, Javier; Lindemann, Justin P. O.; Schiavon, Gaia; Ambrose, Helen J.; Kohlmann, Alexander

2017-01-01

Personalized healthcare relies on accurate companion diagnostic assays that enable the most appropriate treatment decision for cancer patients. Extensive assay validation prior to use in a clinical setting is essential for providing a reliable test result. This poses a challenge for low prevalence mutations with limited availability of appropriate clinical samples harboring the mutation. To enable prospective screening for the low prevalence AKT1 E17K mutation, we have developed and validated a competitive allele-specific TaqMan® PCR (castPCR™) assay for mutation detection in formalin-fixed paraffin-embedded (FFPE) tumor tissue. Analysis parameters of the castPCR™ assay were established using an FFPE DNA reference standard and its analytical performance was assessed using 338 breast cancer and gynecological cancer FFPE samples. With recent technical advances for minimally invasive mutation detection in circulating tumor DNA (ctDNA), we subsequently also evaluated the OncoBEAM™ assay to enable plasma specimens as additional diagnostic opportunity for AKT1 E17K mutation testing. The analysis performance of the OncoBEAM™ test was evaluated using a novel AKT1 E17K ctDNA reference standard consisting of sheared genomic DNA spiked into human plasma. Both assays are employed at centralized testing laboratories operating according to quality standards for prospective identification of the AKT1 E17K mutation in ER+ breast cancer patients in the context of a clinical trial evaluating the AKT inhibitor AZD5363 in combination with endocrine (fulvestrant) therapy. PMID:28472036

Robust High-Resolution Cloth Using Parallelism, History-Based Collisions and Accurate Friction

PubMed Central

Selle, Andrew; Su, Jonathan; Irving, Geoffrey; Fedkiw, Ronald

2015-01-01

In this paper we simulate high resolution cloth consisting of up to 2 million triangles which allows us to achieve highly detailed folds and wrinkles. Since the level of detail is also influenced by object collision and self collision, we propose a more accurate model for cloth-object friction. We also propose a robust history-based repulsion/collision framework where repulsions are treated accurately and efficiently on a per time step basis. Distributed memory parallelism is used for both time evolution and collisions and we specifically address Gauss-Seidel ordering of repulsion/collision response. This algorithm is demonstrated by several high-resolution and high-fidelity simulations. PMID:19147895

Lamb mode selection for accurate wall loss estimation via guided wave tomography

NASA Astrophysics Data System (ADS)

Huthwaite, P.; Ribichini, R.; Lowe, M. J. S.; Cawley, P.

2014-02-01

Guided wave tomography offers a method to accurately quantify wall thickness losses in pipes and vessels caused by corrosion. This is achieved using ultrasonic waves transmitted over distances of approximately 1-2m, which are measured by an array of transducers and then used to reconstruct a map of wall thickness throughout the inspected region. To achieve accurate estimations of remnant wall thickness, it is vital that a suitable Lamb mode is chosen. This paper presents a detailed evaluation of the fundamental modes, S0 and A0, which are of primary interest in guided wave tomography thickness estimates since the higher order modes do not exist at all thicknesses, to compare their performance using both numerical and experimental data while considering a range of challenging phenomena. The sensitivity of A0 to thickness variations was shown to be superior to S0, however, the attenuation from A0 when a liquid loading was present was much higher than S0. A0 was less sensitive to the presence of coatings on the surface of than S0.

These Shoes Are Made for Walking: Sensitivity Performance Evaluation of Commercial Activity Monitors under the Expected Conditions and Circumstances Required to Achieve the International Daily Step Goal of 10,000 Steps

PubMed Central

O’Connell, Sandra; ÓLaighin, Gearóid; Kelly, Lisa; Murphy, Elaine; Beirne, Sorcha; Burke, Niall; Kilgannon, Orlaith; Quinlan, Leo R.

2016-01-01

Introduction Physical activity is a vitally important part of a healthy lifestyle, and is of major benefit to both physical and mental health. A daily step count of 10,000 steps is recommended globally to achieve an appropriate level of physical activity. Accurate quantification of physical activity during conditions reflecting those needed to achieve the recommended daily step count of 10,000 steps is essential. As such, we aimed to assess four commercial activity monitors for their sensitivity/accuracy in a prescribed walking route that reflects a range of surfaces that would typically be used to achieve the recommended daily step count, in two types of footwear expected to be used throughout the day when aiming to achieve the recommended daily step count, and in a timeframe required to do so. Methods Four commercial activity monitors were worn simultaneously by participants (n = 15) during a prescribed walking route reflective of surfaces typically encountered while achieving the daily recommended 10,000 steps. Activity monitors tested were the Garmin Vivofit ™, New Lifestyles’ NL-2000 ™ pedometer, Withings Smart Activity Monitor Tracker (Pulse O2) ™, and Fitbit One ™. Results All activity monitors tested were accurate in their step detection over the variety of different surfaces tested (natural lawn grass, gravel, ceramic tile, tarmacadam/asphalt, linoleum), when wearing both running shoes and hard-soled dress shoes. Conclusion All activity monitors tested were accurate in their step detection sensitivity and are valid monitors for physical activity quantification over the variety of different surfaces tested, when wearing both running shoes and hard-soled dress shoes, and over a timeframe necessary for accumulating the recommended daily step count of 10,000 steps. However, it is important to consider the accuracy of activity monitors, particularly when physical activity in the form of stepping activities is prescribed as an intervention in the

Extracting Time-Accurate Acceleration Vectors From Nontrivial Accelerometer Arrangements.

PubMed

Franck, Jennifer A; Blume, Janet; Crisco, Joseph J; Franck, Christian

2015-09-01

Sports-related concussions are of significant concern in many impact sports, and their detection relies on accurate measurements of the head kinematics during impact. Among the most prevalent recording technologies are videography, and more recently, the use of single-axis accelerometers mounted in a helmet, such as the HIT system. Successful extraction of the linear and angular impact accelerations depends on an accurate analysis methodology governed by the equations of motion. Current algorithms are able to estimate the magnitude of acceleration and hit location, but make assumptions about the hit orientation and are often limited in the position and/or orientation of the accelerometers. The newly formulated algorithm presented in this manuscript accurately extracts the full linear and rotational acceleration vectors from a broad arrangement of six single-axis accelerometers directly from the governing set of kinematic equations. The new formulation linearizes the nonlinear centripetal acceleration term with a finite-difference approximation and provides a fast and accurate solution for all six components of acceleration over long time periods (>250 ms). The approximation of the nonlinear centripetal acceleration term provides an accurate computation of the rotational velocity as a function of time and allows for reconstruction of a multiple-impact signal. Furthermore, the algorithm determines the impact location and orientation and can distinguish between glancing, high rotational velocity impacts, or direct impacts through the center of mass. Results are shown for ten simulated impact locations on a headform geometry computed with three different accelerometer configurations in varying degrees of signal noise. Since the algorithm does not require simplifications of the actual impacted geometry, the impact vector, or a specific arrangement of accelerometer orientations, it can be easily applied to many impact investigations in which accurate kinematics need

Detection of periods of food intake using Support Vector Machines.

PubMed

Lopez-Meyer, Paulo; Schuckers, Stephanie; Makeyev, Oleksandr; Sazonov, Edward

2010-01-01

Studies of obesity and eating disorders need objective tools of Monitoring of Ingestive Behavior (MIB) that can detect and characterize food intake. In this paper we describe detection of food intake by a Support Vector Machine classifier trained on time history of chews and swallows. The training was performed on data collected from 18 subjects in 72 experiments involving eating and other activities (for example, talking). The highest accuracy of detecting food intake (94%) was achieved in configuration where both chews and swallows were used as predictors. Using only swallowing as a predictor resulted in 80% accuracy. Experimental results suggest that these two predictors may be used for differentiation between periods of resting and food intake with a resolution of 30 seconds. Proposed methods may be utilized for development of an accurate, inexpensive, and non-intrusive methodology to objectively monitor food intake in free living conditions.

FMR1 CGG repeat expansion mutation detection and linked haplotype analysis for reliable and accurate preimplantation genetic diagnosis of fragile X syndrome.

PubMed

Rajan-Babu, Indhu-Shree; Lian, Mulias; Cheah, Felicia S H; Chen, Min; Tan, Arnold S C; Prasath, Ethiraj B; Loh, Seong Feei; Chong, Samuel S

2017-07-19

Fragile X mental retardation 1 (FMR1) full-mutation expansion causes fragile X syndrome. Trans-generational fragile X syndrome transmission can be avoided by preimplantation genetic diagnosis (PGD). We describe a robust PGD strategy that can be applied to virtually any couple at risk of transmitting fragile X syndrome. This novel strategy utilises whole-genome amplification, followed by triplet-primed polymerase chain reaction (TP-PCR) for robust detection of expanded FMR1 alleles, in parallel with linked multi-marker haplotype analysis of 13 highly polymorphic microsatellite markers located within 1 Mb of the FMR1 CGG repeat, and the AMELX/Y dimorphism for gender identification. The assay was optimised and validated on single lymphoblasts isolated from fragile X reference cell lines, and applied to a simulated PGD case and a clinical in vitro fertilisation (IVF)-PGD case. In the simulated PGD case, definitive diagnosis of the expected results was achieved for all 'embryos'. In the clinical IVF-PGD case, delivery of a healthy baby girl was achieved after transfer of an expansion-negative blastocyst. FMR1 TP-PCR reliably detects presence of expansion mutations and obviates reliance on informative normal alleles for determining expansion status in female embryos. Together with multi-marker haplotyping and gender determination, misdiagnosis and diagnostic ambiguity due to allele dropout is minimised, and couple-specific assay customisation can be avoided.

A dual-process account of auditory change detection.

PubMed

McAnally, Ken I; Martin, Russell L; Eramudugolla, Ranmalee; Stuart, Geoffrey W; Irvine, Dexter R F; Mattingley, Jason B

2010-08-01

Listeners can be "deaf" to a substantial change in a scene comprising multiple auditory objects unless their attention has been directed to the changed object. It is unclear whether auditory change detection relies on identification of the objects in pre- and post-change scenes. We compared the rates at which listeners correctly identify changed objects with those predicted by change-detection models based on signal detection theory (SDT) and high-threshold theory (HTT). Detected changes were not identified as accurately as predicted by models based on either theory, suggesting that some changes are detected by a process that does not support change identification. Undetected changes were identified as accurately as predicted by the HTT model but much less accurately than predicted by the SDT models. The process underlying change detection was investigated further by determining receiver-operating characteristics (ROCs). ROCs did not conform to those predicted by either a SDT or a HTT model but were well modeled by a dual-process that incorporated HTT and SDT components. The dual-process model also accurately predicted the rates at which detected and undetected changes were correctly identified.

Contact detection for nanomanipulation in a scanning electron microscope.

PubMed

Ru, Changhai; To, Steve

2012-07-01

Nanomanipulation systems require accurate knowledge of the end-effector position in all three spatial coordinates, XYZ, for reliable manipulation of nanostructures. Although the images acquired by a scanning electron microscope (SEM) provide high resolution XY information, the lack of depth information in the Z-direction makes 3D nanomanipulation time-consuming. Existing approaches for contact detection of end-effectors inside SEM typically utilize fragile touch sensors that are difficult to integrate into a nanomanipulation system. This paper presents a method for determining the contact between an end-effector and a target surface during nanomanipulation inside SEM, purely based on the processing of SEM images. A depth-from-focus method is used in the fast approach of the end-effector to the substrate, followed by fine contact detection. Experimental results demonstrate that the contact detection approach is capable of achieving an accuracy of 21.5 nm at 50,000× magnification while inducing little end-effector damage. Copyright © 2012 Elsevier B.V. All rights reserved.

Funnel metadynamics as accurate binding free-energy method

PubMed Central

Limongelli, Vittorio; Bonomi, Massimiliano; Parrinello, Michele

2013-01-01

A detailed description of the events ruling ligand/protein interaction and an accurate estimation of the drug affinity to its target is of great help in speeding drug discovery strategies. We have developed a metadynamics-based approach, named funnel metadynamics, that allows the ligand to enhance the sampling of the target binding sites and its solvated states. This method leads to an efficient characterization of the binding free-energy surface and an accurate calculation of the absolute protein–ligand binding free energy. We illustrate our protocol in two systems, benzamidine/trypsin and SC-558/cyclooxygenase 2. In both cases, the X-ray conformation has been found as the lowest free-energy pose, and the computed protein–ligand binding free energy in good agreement with experiments. Furthermore, funnel metadynamics unveils important information about the binding process, such as the presence of alternative binding modes and the role of waters. The results achieved at an affordable computational cost make funnel metadynamics a valuable method for drug discovery and for dealing with a variety of problems in chemistry, physics, and material science. PMID:23553839

Water quality real-time monitoring system via biological detection based on video analysis

NASA Astrophysics Data System (ADS)

Xin, Chen; Fei, Yuan

2017-11-01

With the development of society, water pollution has become the most serious problem in China. Therefore, real-time water quality monitoring is an important part of human activities and water pollution prevention. In this paper, the behavior of zebrafish was monitored by computer vision. Firstly, the moving target was extracted by the method of saliency detection, and tracked by fitting the ellipse model. Then the motion parameters were extracted by optical flow method, and the data were monitored in real time by means of Hinkley warning and threshold warning. We achieved classification warning through a number of dimensions by comprehensive toxicity index. The experimental results show that the system can achieve more accurate real-time monitoring.

A source-attractor approach to network detection of radiation sources

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

Wu, Qishi; Barry, M. L..; Grieme, M.

Radiation source detection using a network of detectors is an active field of research for homeland security and defense applications. We propose Source-attractor Radiation Detection (SRD) method to aggregate measurements from a network of detectors for radiation source detection. SRD method models a potential radiation source as a magnet -like attractor that pulls in pre-computed virtual points from the detector locations. A detection decision is made if a sufficient level of attraction, quantified by the increase in the clustering of the shifted virtual points, is observed. Compared with traditional methods, SRD has the following advantages: i) it does not requiremore » an accurate estimate of the source location from limited and noise-corrupted sensor readings, unlike the localizationbased methods, and ii) its virtual point shifting and clustering calculation involve simple arithmetic operations based on the number of detectors, avoiding the high computational complexity of grid-based likelihood estimation methods. We evaluate its detection performance using canonical datasets from Domestic Nuclear Detection Office s (DNDO) Intelligence Radiation Sensors Systems (IRSS) tests. SRD achieves both lower false alarm rate and false negative rate compared to three existing algorithms for network source detection.« less

Functional magnetic resonance imaging activation detection: fuzzy cluster analysis in wavelet and multiwavelet domains.

PubMed

Jahanian, Hesamoddin; Soltanian-Zadeh, Hamid; Hossein-Zadeh, Gholam-Ali

2005-09-01

To present novel feature spaces, based on multiscale decompositions obtained by scalar wavelet and multiwavelet transforms, to remedy problems associated with high dimension of functional magnetic resonance imaging (fMRI) time series (when they are used directly in clustering algorithms) and their poor signal-to-noise ratio (SNR) that limits accurate classification of fMRI time series according to their activation contents. Using randomization, the proposed method finds wavelet/multiwavelet coefficients that represent the activation content of fMRI time series and combines them to define new feature spaces. Using simulated and experimental fMRI data sets, the proposed feature spaces are compared to the cross-correlation (CC) feature space and their performances are evaluated. In these studies, the false positive detection rate is controlled using randomization. To compare different methods, several points of the receiver operating characteristics (ROC) curves, using simulated data, are estimated and compared. The proposed features suppress the effects of confounding signals and improve activation detection sensitivity. Experimental results show improved sensitivity and robustness of the proposed method compared to the conventional CC analysis. More accurate and sensitive activation detection can be achieved using the proposed feature spaces compared to CC feature space. Multiwavelet features show superior detection sensitivity compared to the scalar wavelet features. (c) 2005 Wiley-Liss, Inc.

Physical activity monitoring: addressing the difficulties of accurately detecting slow walking speeds.

PubMed

Harrison, Samantha L; Horton, Elizabeth J; Smith, Robert; Sandland, Carolyn J; Steiner, Michael C; Morgan, Mike D L; Singh, Sally J

2013-01-01

To test the accuracy of a multi-sensor activity monitor (SWM) in detecting slow walking speeds in patients with chronic obstructive pulmonary disease (COPD). Concerns have been expressed regarding the use of pedometers in patient populations. Although activity monitors are more sophisticated devices, their accuracy at detecting slow walking speeds common in patients with COPD has yet to be proven. A prospective observational study design was employed. An incremental shuttle walk test (ISWT) was completed by 57 patients with COPD wearing an SWM. The ISWT was repeated by 20 patients wearing the same SWM. Differences were identified between metabolic equivalents (METS) and between step-count across five levels of the ISWT (p < 0.001). Good within monitor reproducibility between two ISWT was identified for total energy expenditure and step-count (p < 0.001). The SWM is able to detect slow (standardized) speeds of walking and is an acceptable method for measuring physical activity in individuals disabled by COPD. Copyright © 2013 Elsevier Inc. All rights reserved.

Towards Sensor-Free Affect Detection in Cognitive Tutor Algebra

ERIC Educational Resources Information Center

Baker, Ryan S. J. d.; Gowda, Sujith M.; Wixon, Michael; Kalka, Jessica; Wagner, Angela Z.; Salvi, Aatish; Aleven, Vincent; Kusbit, Gail W.; Ocumpaugh, Jaclyn; Rossi, Lisa

2012-01-01

In recent years, the usefulness of affect detection for educational software has become clear. Accurate detection of student affect can support a wide range of interventions with the potential to improve student affect, increase engagement, and improve learning. In addition, accurate detection of student affect could play an essential role in…

Fast Detection of Airports on Remote Sensing Images with Single Shot MultiBox Detector

NASA Astrophysics Data System (ADS)

Xia, Fei; Li, HuiZhou

2018-01-01

This paper introduces a method for fast airport detection on remote sensing images (RSIs) using Single Shot MultiBox Detector (SSD). To our knowledge, this could be the first study which introduces an end-to-end detection model into airport detection on RSIs. Based on the common low-level features between natural images and RSIs, a convolution neural network trained on large amounts of natural images was transferred to tackle the airport detection problem with limited annotated data. To deal with the specific characteristics of RSIs, some related parameters in the SSD, such as the scales and layers, were modified for more accurate and rapider detection. The experiments show that the proposed method could achieve 83.5% Average Recall at 8 FPS on RSIs with the size of 1024*1024. In contrast to Faster R-CNN, an improvement on AP and speed could be obtained.

Targeted analyte detection by standard addition improves detection limits in matrix-assisted laser desorption/ionization mass spectrometry.

PubMed

Toghi Eshghi, Shadi; Li, Xingde; Zhang, Hui

2012-09-18

Matrix-assisted laser desorption/ionization (MALDI) has proven an effective tool for fast and accurate determination of many molecules. However, the detector sensitivity and chemical noise compromise the detection of many invaluable low-abundance molecules from biological and clinical samples. To challenge this limitation, we developed a targeted analyte detection (TAD) technique. In TAD, the target analyte is selectively elevated by spiking a known amount of that analyte into the sample, thereby raising its concentration above the noise level, where we take advantage of the improved sensitivity to detect the presence of the endogenous analyte in the sample. We assessed TAD on three peptides in simple and complex background solutions with various exogenous analyte concentrations in two MALDI matrices. TAD successfully improved the limit of detection (LOD) of target analytes when the target peptides were added to the sample in a concentration close to optimum concentration. The optimum exogenous concentration was estimated through a quantitative method to be approximately equal to the original LOD for each target. Also, we showed that TAD could achieve LOD improvements on an average of 3-fold in a simple and 2-fold in a complex sample. TAD provides a straightforward assay to improve the LOD of generic target analytes without the need for costly hardware modifications.

Detection of food intake from swallowing sequences by supervised and unsupervised methods.

PubMed

Lopez-Meyer, Paulo; Makeyev, Oleksandr; Schuckers, Stephanie; Melanson, Edward L; Neuman, Michael R; Sazonov, Edward

2010-08-01

Studies of food intake and ingestive behavior in free-living conditions most often rely on self-reporting-based methods that can be highly inaccurate. Methods of Monitoring of Ingestive Behavior (MIB) rely on objective measures derived from chewing and swallowing sequences and thus can be used for unbiased study of food intake with free-living conditions. Our previous study demonstrated accurate detection of food intake in simple models relying on observation of both chewing and swallowing. This article investigates methods that achieve comparable accuracy of food intake detection using only the time series of swallows and thus eliminating the need for the chewing sensor. The classification is performed for each individual swallow rather than for previously used time slices and thus will lead to higher accuracy in mass prediction models relying on counts of swallows. Performance of a group model based on a supervised method (SVM) is compared to performance of individual models based on an unsupervised method (K-means) with results indicating better performance of the unsupervised, self-adapting method. Overall, the results demonstrate that highly accurate detection of intake of foods with substantially different physical properties is possible by an unsupervised system that relies on the information provided by the swallowing alone.

Detection of Food Intake from Swallowing Sequences by Supervised and Unsupervised Methods

PubMed Central

Lopez-Meyer, Paulo; Makeyev, Oleksandr; Schuckers, Stephanie; Melanson, Edward L.; Neuman, Michael R.; Sazonov, Edward

2010-01-01

Studies of food intake and ingestive behavior in free-living conditions most often rely on self-reporting-based methods that can be highly inaccurate. Methods of Monitoring of Ingestive Behavior (MIB) rely on objective measures derived from chewing and swallowing sequences and thus can be used for unbiased study of food intake with free-living conditions. Our previous study demonstrated accurate detection of food intake in simple models relying on observation of both chewing and swallowing. This article investigates methods that achieve comparable accuracy of food intake detection using only the time series of swallows and thus eliminating the need for the chewing sensor. The classification is performed for each individual swallow rather than for previously used time slices and thus will lead to higher accuracy in mass prediction models relying on counts of swallows. Performance of a group model based on a supervised method (SVM) is compared to performance of individual models based on an unsupervised method (K-means) with results indicating better performance of the unsupervised, self-adapting method. Overall, the results demonstrate that highly accurate detection of intake of foods with substantially different physical properties is possible by an unsupervised system that relies on the information provided by the swallowing alone. PMID:20352335

Engineering surface states of carbon dots to achieve controllable luminescence for solid-luminescent composites and sensitive Be2+ detection

PubMed Central

Li, Xiaoming; Zhang, Shengli; Kulinich, Sergei A.; Liu, Yanli; Zeng, Haibo

2014-01-01

Luminescent carbon dots (L-CDs) with high quantum yield value (44.7%) and controllable emission wavelengths were prepared via a facile hydrothermal method. Importantly, the surface states of the materials could be engineered so that their photoluminescence was either excitation-dependent or distinctly independent. This was achieved by changing the density of amino-groups on the L-CD surface. The above materials were successfully used to prepare multicolor L-CDs/polymer composites, which exhibited blue, green, and even white luminescence. In addition, the excellent excitation-independent luminescence of L-CDs prepared at low temperature was tested for detecting various metal ions. As an example, the detection limit of toxic Be2+ ions, tested for the first time, was as low as 23 μM.

Accurate optical vector network analyzer based on optical single-sideband modulation and balanced photodetection.

PubMed

Xue, Min; Pan, Shilong; Zhao, Yongjiu

2015-02-15

A novel optical vector network analyzer (OVNA) based on optical single-sideband (OSSB) modulation and balanced photodetection is proposed and experimentally demonstrated, which can eliminate the measurement error induced by the high-order sidebands in the OSSB signal. According to the analytical model of the conventional OSSB-based OVNA, if the optical carrier in the OSSB signal is fully suppressed, the measurement result is exactly the high-order-sideband-induced measurement error. By splitting the OSSB signal after the optical device-under-test (ODUT) into two paths, removing the optical carrier in one path, and then detecting the two signals in the two paths using a balanced photodetector (BPD), high-order-sideband-induced measurement error can be ideally eliminated. As a result, accurate responses of the ODUT can be achieved without complex post-signal processing. A proof-of-concept experiment is carried out. The magnitude and phase responses of a fiber Bragg grating (FBG) measured by the proposed OVNA with different modulation indices are superimposed, showing that the high-order-sideband-induced measurement error is effectively removed.

Accelerometer and Camera-Based Strategy for Improved Human Fall Detection.

PubMed

Zerrouki, Nabil; Harrou, Fouzi; Sun, Ying; Houacine, Amrane

2016-12-01

In this paper, we address the problem of detecting human falls using anomaly detection. Detection and classification of falls are based on accelerometric data and variations in human silhouette shape. First, we use the exponentially weighted moving average (EWMA) monitoring scheme to detect a potential fall in the accelerometric data. We used an EWMA to identify features that correspond with a particular type of fall allowing us to classify falls. Only features corresponding with detected falls were used in the classification phase. A benefit of using a subset of the original data to design classification models minimizes training time and simplifies models. Based on features corresponding to detected falls, we used the support vector machine (SVM) algorithm to distinguish between true falls and fall-like events. We apply this strategy to the publicly available fall detection databases from the university of Rzeszow's. Results indicated that our strategy accurately detected and classified fall events, suggesting its potential application to early alert mechanisms in the event of fall situations and its capability for classification of detected falls. Comparison of the classification results using the EWMA-based SVM classifier method with those achieved using three commonly used machine learning classifiers, neural network, K-nearest neighbor and naïve Bayes, proved our model superior.

Sub-millimeter detected z ~ 2 radio-quiet QSOs. Accurate redshifts, black hole masses, and inflow/outflow velocities

NASA Astrophysics Data System (ADS)

Orellana, G.; Nagar, N. M.; Isaak, K. G.; Priddey, R.; Maiolino, R.; McMahon, R.; Marconi, A.; Oliva, E.

2011-07-01

Context. We present near-IR spectroscopy of a sample of luminous (MB - 27.5; Lbol > 1014 L⊙), sub-millimeter-detected, dusty (Md ~ 109 M⊙), radio-quiet quasi-stellar objects (QSOs) at z ~ 2. Aims: A primary aim is to provide a more accurate QSO redshift determination in order to trace kinematics and inflows/outflows in these sub-mm bright QSOs. Additionally, the Hα and continuum properties allow an estimation of the black hole mass and accretion rate, offering insights into the starburst-AGN connection in sub-mm bright QSOs. Methods: We measure the redshift, width, and luminosity of the Hα line, and the continuum luminosity near Hα. Relative velocity differences between Hα and rest-frame UV emission lines are used to study the presence and strength of outflows/inflows. Luminosities and line widths are used to estimate the black hole masses, bolometric luminosities, Eddington fractions, and accretion rates; these are compared to the star-formation-rate (SFR), estimated from the sub-mm derived far-infrared (FIR) luminosity. Finally our sub-mm-bright QSO sample is compared with other QSO samples at similar redshifts. Results: The Hα emission line was strongly detected in all sources. Two components - a very broad (≳5000 km s-1) Gaussian and an intermediate-width (≳1500 km s-1) Gaussian, were required to fit the Hα profile of all observed QSOs. Narrow (≲1000 km s-1) lines were not detected in the sample QSOs. The rest-frame UV emission lines in these sub-mm bright QSOs show larger than average blue-shifted velocities, potentially tracing strong - up to 3000 km s-1 - outflows in the broad line region. With the exception of the one QSO which shows exceptionally broad Hα lines, the black hole masses of the QSO sample are in the range log MBH = 9.0-9.7 and the Eddington fractions are between 0.5 and ~1. In black hole mass and accretion rate, this sub-mm bright QSO sample is indistinguishable from the Shemmer et al. (2004, ApJ, 614, 547) optically

Accurate Mobile Urban Mapping via Digital Map-Based SLAM †

PubMed Central

Roh, Hyunchul; Jeong, Jinyong; Cho, Younggun; Kim, Ayoung

2016-01-01

This paper presents accurate urban map generation using digital map-based Simultaneous Localization and Mapping (SLAM). Throughout this work, our main objective is generating a 3D and lane map aiming for sub-meter accuracy. In conventional mapping approaches, achieving extremely high accuracy was performed by either (i) exploiting costly airborne sensors or (ii) surveying with a static mapping system in a stationary platform. Mobile scanning systems recently have gathered popularity but are mostly limited by the availability of the Global Positioning System (GPS). We focus on the fact that the availability of GPS and urban structures are both sporadic but complementary. By modeling both GPS and digital map data as measurements and integrating them with other sensor measurements, we leverage SLAM for an accurate mobile mapping system. Our proposed algorithm generates an efficient graph SLAM and achieves a framework running in real-time and targeting sub-meter accuracy with a mobile platform. Integrated with the SLAM framework, we implement a motion-adaptive model for the Inverse Perspective Mapping (IPM). Using motion estimation derived from SLAM, the experimental results show that the proposed approaches provide stable bird’s-eye view images, even with significant motion during the drive. Our real-time map generation framework is validated via a long-distance urban test and evaluated at randomly sampled points using Real-Time Kinematic (RTK)-GPS. PMID:27548175

Simultaneous detection and determination of mercury (II) and lead (II) ions through the achievement of novel functional nucleic acid-based biosensors.

PubMed

Khoshbin, Zahra; Housaindokht, Mohammad Reza; Verdian, Asma; Bozorgmehr, Mohammad Reza

2018-09-30

The serious threats of mercury (Hg 2+ ) and lead (Pb 2+ ) ions for the public health makes it important to achieve the detection methods of the ions with high affinity and specificity. Metal ions usually coexist in some environment and foodstuff or clinical samples. Therefore, it is very necessary to develop a fast and simple method for simultaneous monitoring the amount of metal ions, especially when Hg 2+ and Pb 2+ coexist. DNAzyme-based biosensors and aptasensors have been highly regarded for this purpose as two main groups of the functional nucleic acid (FNA)-based biosensors. In this review, we summarize the recent achievements of functional nucleic acid-based biosensors for the simultaneous detection of Hg 2+ and Pb 2+ ions in two main optical and electrochemical groups. The tremendous interest in utilizing the various nanomaterials is also highlighted in the fabrication of the FNA-based biosensors. Finally, some results are presented based on the advantages and disadvantages of the studied FNA-based biosensors to compare their validation. Copyright © 2018 Elsevier B.V. All rights reserved.

Accurate HLA type inference using a weighted similarity graph.

PubMed

Xie, Minzhu; Li, Jing; Jiang, Tao

2010-12-14

, achieving an accuracy of 96% for gene HLA-A, 95% for HLA-B, 97% for HLA-C, 84% for HLA-DRB1, 98% for HLA-DQA1 and 97% for HLA-DQB1 in a leave-one-out test. Our algorithm can infer HLA gene types from neighboring SNP genotype data accurately. Compared with a recent approach on the same input data, our algorithm achieved a higher accuracy. The code of our algorithm is available to the public for free upon request to the corresponding authors.

Accurate sub-millimetre rest frequencies for HOCO+ and DOCO+ ions

NASA Astrophysics Data System (ADS)

Bizzocchi, L.; Lattanzi, V.; Laas, J.; Spezzano, S.; Giuliano, B. M.; Prudenzano, D.; Endres, C.; Sipilä, O.; Caselli, P.

2017-06-01

Context. HOCO+ is a polar molecule that represents a useful proxy for its parent molecule CO2, which is not directly observable in the cold interstellar medium. This cation has been detected towards several lines of sight, including massive star forming regions, protostars, and cold cores. Despite the obvious astrochemical relevance, protonated CO2 and its deuterated variant, DOCO+, still lack an accurate spectroscopic characterisation. Aims: The aim of this work is to extend the study of the ground-state pure rotational spectra of HOCO+ and DOCO+ well into the sub-millimetre region. Methods: Ground-state transitions have been recorded in the laboratory using a frequency-modulation absorption spectrometer equipped with a free-space glow-discharge cell. The ions were produced in a low-density, magnetically confined plasma generated in a suitable gas mixture. The ground-state spectra of HOCO+ and DOCO+ have been investigated in the 213-967 GHz frequency range; 94 new rotational transitions have been detected. Additionally, 46 line positions taken from the literature have been accurately remeasured. Results: The newly measured lines have significantly enlarged the available data sets for HOCO+ and DOCO+, thus enabling the determination of highly accurate rotational and centrifugal distortion parameters. Our analysis shows that all HOCO+ lines with Ka ≥ 3 are perturbed by a ro-vibrational interaction that couples the ground state with the v5 = 1 vibrationally excited state. This resonance has been explicitly treated in the analysis in order to obtain molecular constants with clear physical meaning. Conclusions: The improved sets of spectroscopic parameters provide enhanced lists of very accurate sub-millimetre rest frequencies of HOCO+ and DOCO+ for astrophysical applications. These new data challenge a recent tentative identification of DOCO+ towards a pre-stellar core. Supplementary tables are only available at the CDS via anonymous ftp to http

Freezing of Gait Detection in Parkinson's Disease: A Subject-Independent Detector Using Anomaly Scores.

PubMed

Pham, Thuy T; Moore, Steven T; Lewis, Simon John Geoffrey; Nguyen, Diep N; Dutkiewicz, Eryk; Fuglevand, Andrew J; McEwan, Alistair L; Leong, Philip H W

2017-11-01

Freezing of gait (FoG) is common in Parkinsonian gait and strongly relates to falls. Current clinical FoG assessments are patients' self-report diaries and experts' manual video analysis. Both are subjective and yield moderate reliability. Existing detection algorithms have been predominantly designed in subject-dependent settings. In this paper, we aim to develop an automated FoG detector for subject independent. After extracting highly relevant features, we apply anomaly detection techniques to detect FoG events. Specifically, feature selection is performed using correlation and clusterability metrics. From a list of 244 feature candidates, 36 candidates were selected using saliency and robustness criteria. We develop an anomaly score detector with adaptive thresholding to identify FoG events. Then, using accuracy metrics, we reduce the feature list to seven candidates. Our novel multichannel freezing index was the most selective across all window sizes, achieving sensitivity (specificity) of (). On the other hand, freezing index from the vertical axis was the best choice for a single input, achieving sensitivity (specificity) of () for ankle and () for back sensors. Our subject-independent method is not only significantly more accurate than those previously reported, but also uses a much smaller window (e.g., versus ) and/or lower tolerance (e.g., versus ).Freezing of gait (FoG) is common in Parkinsonian gait and strongly relates to falls. Current clinical FoG assessments are patients' self-report diaries and experts' manual video analysis. Both are subjective and yield moderate reliability. Existing detection algorithms have been predominantly designed in subject-dependent settings. In this paper, we aim to develop an automated FoG detector for subject independent. After extracting highly relevant features, we apply anomaly detection techniques to detect FoG events. Specifically, feature selection is performed using correlation and clusterability metrics. From

Accurate means of detecting and characterizing abnormal patterns of ventricular activation by phase image analysis

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

Botvinick, E.H.; Frais, M.A.; Shosa, D.W.

1982-08-01

The ability of scintigraphic phase image analysis to characterize patterns of abnormal ventricular activation was investigated. The pattern of phase distribution and sequential phase changes over both right and left ventricular regions of interest were evaluated in 16 patients with normal electrical activation and wall motion and compared with those in 8 patients with an artificial pacemaker and 4 patients with sinus rhythm with the Wolff-Parkinson-White syndrome and delta waves. Normally, the site of earliest phase angle was seen at the base of the interventricular septum, with sequential change affecting the body of the septum and the cardiac apex andmore » then spreading laterally to involve the body of both ventricles. The site of earliest phase angle was located at the apex of the right ventricle in seven patients with a right ventricular endocardial pacemaker and on the lateral left ventricular wall in one patient with a left ventricular epicardial pacemaker. In each case the site corresponded exactly to the position of the pacing electrode as seen on posteroanterior and left lateral chest X-ray films, and sequential phase changes spread from the initial focus to affect both ventricles. In each of the patients with the Wolff-Parkinson-White syndrome, the site of earliest ventricular phase angle was located, and it corresponded exactly to the site of the bypass tract as determined by endocardial mapping. In this way, four bypass pathways, two posterior left paraseptal, one left lateral and one right lateral, were correctly localized scintigraphically. On the basis of the sequence of mechanical contraction, phase image analysis provides an accurate noninvasive method of detecting abnormal foci of ventricular activation.« less

External Quality Assessment for Rubella Virus RNA Detection Using Armored RNA in China.

PubMed

Zhang, D; Lin, G; Yi, L; Hao, M; Fan, G; Yang, X; Peng, R; Ding, J; Zhang, K; Zhang, R; Li, J

2017-02-01

Although tremendous efforts have been made to reduce rubella incidence, there are still 300 new cases of congenital rubella syndrome daily; thus, rubella infections remain one of the leading causes of preventable congenital birth defects. An effective surveillance system, which could be achieved and maintained by using an external quality assessment program, is critical for prevention and control of this disease. Armored RNAs, which are noninfectious and RNase-resistant, were used for encapsulation of the E1 gene of rubella virus and for preparation of a 10-specimen panel for external quality assessment. Thirty-two laboratories across mainland China that used nucleic acid tests for rubella virus RNA detection were included in the external quality assessment program organized by the National Center for Clinical Laboratories of China. Different kinds of commercial kits were used by the laboratories for nucleic acid extraction and TaqMan real-time reverse-transcription PCR for rubella virus RNA detection; 99.2% sensitivity and 100% specificity were achieved in this external quality assessment program. Most of the participating laboratories obtained accurate results for rubella nucleic acid tests, thereby achieving the quality required for regional rubella and congenital rubella syndrome elimination.

Muver, a computational framework for accurately calling accumulated mutations.

PubMed

Burkholder, Adam B; Lujan, Scott A; Lavender, Christopher A; Grimm, Sara A; Kunkel, Thomas A; Fargo, David C

2018-05-09

Identification of mutations from next-generation sequencing data typically requires a balance between sensitivity and accuracy. This is particularly true of DNA insertions and deletions (indels), that can impart significant phenotypic consequences on cells but are harder to call than substitution mutations from whole genome mutation accumulation experiments. To overcome these difficulties, we present muver, a computational framework that integrates established bioinformatics tools with novel analytical methods to generate mutation calls with the extremely low false positive rates and high sensitivity required for accurate mutation rate determination and comparison. Muver uses statistical comparison of ancestral and descendant allelic frequencies to identify variant loci and assigns genotypes with models that include per-sample assessments of sequencing errors by mutation type and repeat context. Muver identifies maximally parsimonious mutation pathways that connect these genotypes, differentiating potential allelic conversion events and delineating ambiguities in mutation location, type, and size. Benchmarking with a human gold standard father-son pair demonstrates muver's sensitivity and low false positive rates. In DNA mismatch repair (MMR) deficient Saccharomyces cerevisiae, muver detects multi-base deletions in homopolymers longer than the replicative polymerase footprint at rates greater than predicted for sequential single-base deletions, implying a novel multi-repeat-unit slippage mechanism. Benchmarking results demonstrate the high accuracy and sensitivity achieved with muver, particularly for indels, relative to available tools. Applied to an MMR-deficient Saccharomyces cerevisiae system, muver mutation calls facilitate mechanistic insights into DNA replication fidelity.

Unenhanced breast MRI (STIR, T2-weighted TSE, DWIBS): An accurate and alternative strategy for detecting and differentiating breast lesions.

PubMed

Telegrafo, Michele; Rella, Leonarda; Stabile Ianora, Amato Antonio; Angelelli, Giuseppe; Moschetta, Marco

2015-10-01

To assess the role of STIR, T2-weighted TSE and DWIBS sequences for detecting and characterizing breast lesions and to compare unenhanced (UE)-MRI results with contrast-enhanced (CE)-MRI and histological findings, having the latter as the reference standard. Two hundred eighty consecutive patients (age range, 27-73 years; mean age±standard deviation (SD), 48.8±9.8years) underwent MR examination with a diagnostic protocol including STIR, T2-weighted TSE, THRIVE and DWIBS sequences. Two radiologists blinded to both dynamic sequences and histological findings evaluated in consensus STIR, T2-weighted TSE and DWIBS sequences and after two weeks CE-MRI images searching for breast lesions. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy for UE-MRI and CE-MRI were calculated. UE-MRI results were also compared with CE- MRI. UE-MRI sequences obtained sensitivity, specificity, diagnostic accuracy, PPV and NPV values of 94%, 79%, 86%, 79% and 94%, respectively. CE-MRI sequences obtained sensitivity, specificity, diagnostic accuracy, PPV and NPV values of 98%, 83%, 90%, 84% and 98%, respectively. No statistically significant difference between UE-MRI and CE-MRI was found. Breast UE-MRI could represent an accurate diagnostic tool and a valid alternative to CE-MRI for evaluating breast lesions. STIR and DWIBS sequences allow to detect breast lesions while T2-weighted TSE sequences and ADC values could be useful for lesion characterization. Copyright © 2015 Elsevier Inc. All rights reserved.

Determining accurate distances to nearby galaxies

NASA Astrophysics Data System (ADS)

Bonanos, Alceste Zoe

2005-11-01

Determining accurate distances to nearby or distant galaxies is a very simple conceptually, yet complicated in practice, task. Presently, distances to nearby galaxies are only known to an accuracy of 10-15%. The current anchor galaxy of the extragalactic distance scale is the Large Magellanic Cloud, which has large (10-15%) systematic uncertainties associated with it, because of its morphology, its non-uniform reddening and the unknown metallicity dependence of the Cepheid period-luminosity relation. This work aims to determine accurate distances to some nearby galaxies, and subsequently help reduce the error in the extragalactic distance scale and the Hubble constant H 0 . In particular, this work presents the first distance determination of the DIRECT Project to M33 with detached eclipsing binaries. DIRECT aims to obtain a new anchor galaxy for the extragalactic distance scale by measuring direct, accurate (to 5%) distances to two Local Group galaxies, M31 and M33, with detached eclipsing binaries. It involves a massive variability survey of these galaxies and subsequent photometric and spectroscopic follow-up of the detached binaries discovered. In this work, I also present a catalog of variable stars discovered in one of the DIRECT fields, M31Y, which includes 41 eclipsing binaries. Additionally, we derive the distance to the Draco Dwarf Spheroidal galaxy, with ~100 RR Lyrae found in our first CCD variability study of this galaxy. A "hybrid" method of discovering Cepheids with ground-based telescopes is described next. It involves applying the image subtraction technique on the images obtained from ground-based telescopes and then following them up with the Hubble Space Telescope to derive Cepheid period-luminosity distances. By re-analyzing ESO Very Large Telescope data on M83 (NGC 5236), we demonstrate that this method is much more powerful for detecting variability, especially in crowded fields. I finally present photometry for the Wolf-Rayet binary WR 20a

Accurate CT-MR image registration for deep brain stimulation: a multi-observer evaluation study

NASA Astrophysics Data System (ADS)

Rühaak, Jan; Derksen, Alexander; Heldmann, Stefan; Hallmann, Marc; Meine, Hans

2015-03-01

Since the first clinical interventions in the late 1980s, Deep Brain Stimulation (DBS) of the subthalamic nucleus has evolved into a very effective treatment option for patients with severe Parkinson's disease. DBS entails the implantation of an electrode that performs high frequency stimulations to a target area deep inside the brain. A very accurate placement of the electrode is a prerequisite for positive therapy outcome. The assessment of the intervention result is of central importance in DBS treatment and involves the registration of pre- and postinterventional scans. In this paper, we present an image processing pipeline for highly accurate registration of postoperative CT to preoperative MR. Our method consists of two steps: a fully automatic pre-alignment using a detection of the skull tip in the CT based on fuzzy connectedness, and an intensity-based rigid registration. The registration uses the Normalized Gradient Fields distance measure in a multilevel Gauss-Newton optimization framework and focuses on a region around the subthalamic nucleus in the MR. The accuracy of our method was extensively evaluated on 20 DBS datasets from clinical routine and compared with manual expert registrations. For each dataset, three independent registrations were available, thus allowing to relate algorithmic with expert performance. Our method achieved an average registration error of 0.95mm in the target region around the subthalamic nucleus as compared to an inter-observer variability of 1.12 mm. Together with the short registration time of about five seconds on average, our method forms a very attractive package that can be considered ready for clinical use.

Screening of 439 Pesticide Residues in Fruits and Vegetables by Gas Chromatography-Quadrupole-Time-of-Flight Mass Spectrometry Based on TOF Accurate Mass Database and Q-TOF Spectrum Library.

PubMed

Li, Jian-Xun; Li, Xiao-Ying; Chang, Qiao-Ying; Li, Yan; Jin, Ling-He; Pang, Guo-Fang; Fan, Chun-Lin

2018-05-03

Because of its unique characteristics of accurate mass full-spectrum acquisition, high resolution, and fast acquisition rates, GC-quadrupole-time-of-flight MS (GC-Q-TOF/MS) has become a powerful tool for pesticide residue analysis. In this study, a TOF accurate mass database and Q-TOF spectrum library of 439 pesticides were established, and the parameters of the TOF database were optimized. Through solid-phase extraction (SPE), whereby pesticides are extracted from fruit and vegetable substrates by using 40 mL 1% acetic acid in acetonitrile (v/v), purified by the Carbon/NH₂ SPE cartridge, and finally detected by GC-Q-TOF/MS, the rapid analysis of 439 pesticides in fruits and vegetables can be achieved. The methodology verification results show that more than 70 and 91% of pesticides, spiked in fruits and vegetables with concentrations of 10 and 100 μg/kg, respectively, saw recoveries that conform to the European Commission's criterion of between 70 and 120% with RSD ≤20%. Eighty-one percent of pesticides have screening detection limits lower than 10 μg/kg, which makes this a reliable analysis technology for the monitoring of pesticide residues in fruits and vegetables. This technology was further validated for its characteristics of high precision, high speed, and high throughput through successful detection of 9817 samples during 2013-2015.

Dynamic sensing model for accurate delectability of environmental phenomena using event wireless sensor network

NASA Astrophysics Data System (ADS)

Missif, Lial Raja; Kadhum, Mohammad M.

2017-09-01

Wireless Sensor Network (WSN) has been widely used for monitoring where sensors are deployed to operate independently to sense abnormal phenomena. Most of the proposed environmental monitoring systems are designed based on a predetermined sensing range which does not reflect the sensor reliability, event characteristics, and the environment conditions. Measuring of the capability of a sensor node to accurately detect an event within a sensing field is of great important for monitoring applications. This paper presents an efficient mechanism for even detection based on probabilistic sensing model. Different models have been presented theoretically in this paper to examine their adaptability and applicability to the real environment applications. The numerical results of the experimental evaluation have showed that the probabilistic sensing model provides accurate observation and delectability of an event, and it can be utilized for different environment scenarios.

Diagnostic performance of direct traction MR arthrography of the hip: detection of chondral and labral lesions with arthroscopic comparison.

PubMed

Schmaranzer, Florian; Klauser, Andrea; Kogler, Michael; Henninger, Benjamin; Forstner, Thomas; Reichkendler, Markus; Schmaranzer, Ehrenfried

2015-06-01

To assess diagnostic performance of traction MR arthrography of the hip in detection and grading of chondral and labral lesions with arthroscopic comparison. Seventy-five MR arthrograms obtained ± traction of 73 consecutive patients (mean age, 34.5 years; range, 14-54 years) who underwent arthroscopy were included. Traction technique included weight-adapted traction (15-23 kg), a supporting plate for the contralateral leg, and intra-articular injection of 18-27 ml (local anaesthetic and contrast agent). Patients reported on neuropraxia and on pain. Two blinded readers independently assessed femoroacetabular cartilage and labrum lesions which were correlated with arthroscopy. Interobserver agreement was calculated using κ values. Joint distraction ± traction was evaluated in consensus. No procedure had to be stopped. There were no cases of neuropraxia. Accuracy for detection of labral lesions was 92 %/93 %, 91 %/83 % for acetabular lesions, and 92 %/88 % for femoral cartilage lesions for reader 1/reader 2, respectively. Interobserver agreement was moderate (κ = 0.58) for grading of labrum lesions and substantial (κ = 0.7, κ = 0.68) for grading of acetabular and femoral cartilage lesions. Joint distraction was achieved in 72/75 and 14/75 hips with/without traction, respectively. Traction MR arthrography safely enabled accurate detection and grading of labral and chondral lesions. • The used traction technique was well tolerated by most patients. • The used traction technique almost consistently achieved separation of cartilage layers. • Traction MR arthrography enabled accurate detection of chondral and labral lesions.

Near real time, accurate, and sensitive microbiological safety monitoring using an all-fibre spectroscopic fluorescence system

NASA Astrophysics Data System (ADS)

Vanholsbeeck, F.; Swift, S.; Cheng, M.; Bogomolny, E.

2013-11-01

Enumeration of microorganisms is an essential microbiological task for many industrial sectors and research fields. Various tests for detection and counting of microorganisms are used today. However most of the current methods to enumerate bacteria require either long incubation time for limited accuracy, or use complicated protocols along with bulky equipment. We have developed an accurate, all-fibre spectroscopic system to measure fluorescence signal in-situ. In this paper, we examine the potential of this setup for near real time bacteria enumeration in aquatic environment. The concept is based on a well-known phenomenon that the fluorescence quantum yields of some nucleic acid stains significantly increase upon binding with nucleic acids of microorganisms. In addition we have used GFP labeled organisms. The fluorescence signal increase can be correlated to the amount of nucleic acid present in the sample. In addition we have used GFP labeled organisms. Our results show that we are able to detect a wide range of bacteria concentrations without dilution or filtration (1-108 CFU/ml) using different optical probes we designed. This high sensitivity is due to efficient light delivery with an appropriate collection volume and in situ fluorescence detection as well as the use of a sensitive CCD spectrometer. By monitoring the laser power, we can account for laser fluctuations while measuring the fluorescence signal which improves as well the system accuracy. A synchronized laser shutter allows us to achieve a high SNR with minimal integration time, thereby reducing the photobleaching effect. In summary, we conclude that our optical setup may offer a robust method for near real time bacterial detection in aquatic environment.

Positive Biases in Self-Assessment of Mathematics Competence, Achievement Goals, and Mathematics Performance

ERIC Educational Resources Information Center

Dupeyrat, Caroline; Escribe, Christian; Huet, Nathalie; Regner, Isabelle

2011-01-01

The study examined how biases in self-evaluations of math competence relate to achievement goals and progress in math achievement. It was expected that performance goals would be related to overestimation and mastery goals to accurate self-assessments. A sample of French high-school students completed a questionnaire measuring their math…

An automatic and accurate method of full heart segmentation from CT image based on linear gradient model

NASA Astrophysics Data System (ADS)

Yang, Zili

2017-07-01

Heart segmentation is an important auxiliary method in the diagnosis of many heart diseases, such as coronary heart disease and atrial fibrillation, and in the planning of tumor radiotherapy. Most of the existing methods for full heart segmentation treat the heart as a whole part and cannot accurately extract the bottom of the heart. In this paper, we propose a new method based on linear gradient model to segment the whole heart from the CT images automatically and accurately. Twelve cases were tested in order to test this method and accurate segmentation results were achieved and identified by clinical experts. The results can provide reliable clinical support.

Fast and Accurate Circuit Design Automation through Hierarchical Model Switching.

PubMed

Huynh, Linh; Tagkopoulos, Ilias

2015-08-21

In computer-aided biological design, the trifecta of characterized part libraries, accurate models and optimal design parameters is crucial for producing reliable designs. As the number of parts and model complexity increase, however, it becomes exponentially more difficult for any optimization method to search the solution space, hence creating a trade-off that hampers efficient design. To address this issue, we present a hierarchical computer-aided design architecture that uses a two-step approach for biological design. First, a simple model of low computational complexity is used to predict circuit behavior and assess candidate circuit branches through branch-and-bound methods. Then, a complex, nonlinear circuit model is used for a fine-grained search of the reduced solution space, thus achieving more accurate results. Evaluation with a benchmark of 11 circuits and a library of 102 experimental designs with known characterization parameters demonstrates a speed-up of 3 orders of magnitude when compared to other design methods that provide optimality guarantees.

An Accurate Link Correlation Estimator for Improving Wireless Protocol Performance

PubMed Central

Zhao, Zhiwei; Xu, Xianghua; Dong, Wei; Bu, Jiajun

2015-01-01

Wireless link correlation has shown significant impact on the performance of various sensor network protocols. Many works have been devoted to exploiting link correlation for protocol improvements. However, the effectiveness of these designs heavily relies on the accuracy of link correlation measurement. In this paper, we investigate state-of-the-art link correlation measurement and analyze the limitations of existing works. We then propose a novel lightweight and accurate link correlation estimation (LACE) approach based on the reasoning of link correlation formation. LACE combines both long-term and short-term link behaviors for link correlation estimation. We implement LACE as a stand-alone interface in TinyOS and incorporate it into both routing and flooding protocols. Simulation and testbed results show that LACE: (1) achieves more accurate and lightweight link correlation measurements than the state-of-the-art work; and (2) greatly improves the performance of protocols exploiting link correlation. PMID:25686314

In situ, accurate, surface-enhanced Raman scattering detection of cancer cell nucleus with synchronous location by an alkyne-labeled biomolecular probe.

PubMed

Zhang, Jing; Liang, Lijia; Guan, Xin; Deng, Rong; Qu, Huixin; Huang, Dianshuai; Xu, Shuping; Liang, Chongyang; Xu, Weiqing

2018-01-01

A surface-enhanced Raman scattering (SERS) method for in situ detection and analysis of the intranuclear biomolecular information of a cell has been developed based on a small, biocompatible, nuclear-targeting alkyne-tagged deoxyribonucleic acid (DNA) probe (5-ethynyl-2'-deoxyuridine, EDU) that can specially accumulate in the cell nucleus during DNA replications to precisely locate the nuclear region without disturbance in cell biological activities and functions. Since the specific alkyne group shows a Raman peak in the Raman-silent region of cells, it is an interior label to visualize the nuclear location synchronously in real time when measuring the SERS spectra of a cell. Because no fluorescent-labeled dyes were used for locating cell nuclei, this method is simple, nondestructive, non- photobleaching, and valuable for the in situ exploration of vital physiological processes with DNA participation in cell organelles. Graphical abstract A universal strategy was developed to accurately locate the nuclear region and obtain precise molecular information of cell nuclei by SERS.

Accurate high-speed liquid handling of very small biological samples.

PubMed

Schober, A; Günther, R; Schwienhorst, A; Döring, M; Lindemann, B F

1993-08-01

Molecular biology techniques require the accurate pipetting of buffers and solutions with volumes in the microliter range. Traditionally, hand-held pipetting devices are used to fulfill these requirements, but many laboratories have also introduced robotic workstations for the handling of liquids. Piston-operated pumps are commonly used in manually as well as automatically operated pipettors. These devices cannot meet the demands for extremely accurate pipetting of very small volumes at the high speed that would be necessary for certain applications (e.g., in sequencing projects with high throughput). In this paper we describe a technique for the accurate microdispensation of biochemically relevant solutions and suspensions with the aid of a piezoelectric transducer. It is suitable for liquids of a viscosity between 0.5 and 500 milliPascals. The obtainable drop sizes range from 5 picoliters to a few nanoliters with up to 10,000 drops per second. Liquids can be dispensed in single or accumulated drops to handle a wide volume range. The system proved to be excellently suitable for the handling of biological samples. It did not show any detectable negative impact on the biological function of dissolved or suspended molecules or particles.

The relation between children's accuracy estimates of their physical competence and achievement-related characteristics.

PubMed

Weiss, M R; Horn, T S

1990-09-01

The relationship between perceptions of competence and control, achievement, and motivated behavior in youth sport has been a topic of considerable interest. The purpose of this study was to examine whether children who are under-, accurate, or overestimators of their physical competence differ in their achievement characteristics. Children (N = 133), 8 to 13 years of age, who were attending a summer sport program, completed a series of questionnaires designed to assess perceptions of competence and control, motivational orientation, and competitive trait anxiety. Measures of physical competence were obtained by teachers' ratings that paralleled the children's measure of perceived competence. Perceived competence and teachers' ratings were standardized by grade level, and an accuracy score was computed from the difference between these scores. Children were then categorized as underestimators, accurate raters, or overestimators according to upper and lower quartiles of this distribution. A 2 x 2 x 3 (age level by gender by accuracy) MANCOVA revealed a significant gender by accuracy interaction. Underestimating girls were lower in challenge motivation, higher in trait anxiety, and more external in their control perceptions than accurate or overestimators. Underestimating boys were higher in perceived unknown control than accurate and overestimating boys. It was concluded that children who seriously underestimate their perceived competence may be likely candidates for discontinuation of sport activities or low levels of physical achievement.

Nonlinear detection for a high rate extended binary phase shift keying system.

PubMed

Chen, Xian-Qing; Wu, Le-Nan

2013-03-28

The algorithm and the results of a nonlinear detector using a machine learning technique called support vector machine (SVM) on an efficient modulation system with high data rate and low energy consumption is presented in this paper. Simulation results showed that the performance achieved by the SVM detector is comparable to that of a conventional threshold decision (TD) detector. The two detectors detect the received signals together with the special impacting filter (SIF) that can improve the energy utilization efficiency. However, unlike the TD detector, the SVM detector concentrates not only on reducing the BER of the detector, but also on providing accurate posterior probability estimates (PPEs), which can be used as soft-inputs of the LDPC decoder. The complexity of this detector is considered in this paper by using four features and simplifying the decision function. In addition, a bandwidth efficient transmission is analyzed with both SVM and TD detector. The SVM detector is more robust to sampling rate than TD detector. We find that the SVM is suitable for extended binary phase shift keying (EBPSK) signal detection and can provide accurate posterior probability for LDPC decoding.

Nonlinear Detection for a High Rate Extended Binary Phase Shift Keying System

PubMed Central

Chen, Xian-Qing; Wu, Le-Nan

2013-01-01

The algorithm and the results of a nonlinear detector using a machine learning technique called support vector machine (SVM) on an efficient modulation system with high data rate and low energy consumption is presented in this paper. Simulation results showed that the performance achieved by the SVM detector is comparable to that of a conventional threshold decision (TD) detector. The two detectors detect the received signals together with the special impacting filter (SIF) that can improve the energy utilization efficiency. However, unlike the TD detector, the SVM detector concentrates not only on reducing the BER of the detector, but also on providing accurate posterior probability estimates (PPEs), which can be used as soft-inputs of the LDPC decoder. The complexity of this detector is considered in this paper by using four features and simplifying the decision function. In addition, a bandwidth efficient transmission is analyzed with both SVM and TD detector. The SVM detector is more robust to sampling rate than TD detector. We find that the SVM is suitable for extended binary phase shift keying (EBPSK) signal detection and can provide accurate posterior probability for LDPC decoding. PMID:23539034

Accurate and ergonomic method of registration for image-guided neurosurgery

NASA Astrophysics Data System (ADS)

Henderson, Jaimie M.; Bucholz, Richard D.

1994-05-01

There has been considerable interest in the development of frameless stereotaxy based upon scalp mounted fiducials. In practice we have experienced difficulty in relating markers to the image data sets in our series of 25 frameless cases, as well as inaccuracy due to scalp movement and the size of the markers. We have developed an alternative system for accurately and conveniently achieving surgical registration for image-guided neurosurgery based on alignment and matching of patient forehead contours. The system consists of a laser contour digitizer which is used in the operating room to acquire forehead contours, editing software for extracting contours from patient image data sets, and a contour-match algorithm for aligning the two contours and performing data set registration. The contour digitizer is tracked by a camera array which relates its position with respect to light emitting diodes placed on the head clamp. Once registered, surgical instrument can be tracked throughout the procedure. Contours can be extracted from either CT or MRI image datasets. The system has proven to be robust in the laboratory setting. Overall error of registration is 1 - 2 millimeters in routine use. Image to patient registration can therefore be achieved quite easily and accurately, without the need for fixation of external markers to the skull, or manually finding markers on the scalp and image datasets. The system is unobtrusive and imposes little additional effort on the neurosurgeon, broadening the appeal of image-guided surgery.

Generic protease detection technology for monitoring periodontal disease.

PubMed

Zheng, Xinwei; Cook, Joseph P; Watkinson, Michael; Yang, Shoufeng; Douglas, Ian; Rawlinson, Andrew; Krause, Steffi

2011-01-01

Periodontal diseases are inflammatory conditions that affect the supporting tissues of teeth and can lead to destruction of the bone support and ultimately tooth loss if untreated. Progression of periodontitis is usually site specific but not uniform, and currently there are no accurate clinical methods for distinguishing sites where there is active disease progression from sites that are quiescent. Consequently, unnecessary and costly treatment of periodontal sites that are not progressing may occur. Three proteases have been identified as suitable markers for distinguishing sites with active disease progression and quiescent sites: human neutrophil elastase, cathepsin G and MMP8. Generic sensor materials for the detection of these three proteases have been developed based on thin dextran hydrogel films cross-linked with peptides. Degradation of the hydrogel films was monitored using impedance measurements. The target proteases were detected in the clinically relevant range within a time frame of 3 min. Good specificity for different proteases was achieved by choosing appropriate peptide cross-linkers.

MSeq-CNV: accurate detection of Copy Number Variation from Sequencing of Multiple samples.

PubMed

Malekpour, Seyed Amir; Pezeshk, Hamid; Sadeghi, Mehdi

2018-03-05

Currently a few tools are capable of detecting genome-wide Copy Number Variations (CNVs) based on sequencing of multiple samples. Although aberrations in mate pair insertion sizes provide additional hints for the CNV detection based on multiple samples, the majority of the current tools rely only on the depth of coverage. Here, we propose a new algorithm (MSeq-CNV) which allows detecting common CNVs across multiple samples. MSeq-CNV applies a mixture density for modeling aberrations in depth of coverage and abnormalities in the mate pair insertion sizes. Each component in this mixture density applies a Binomial distribution for modeling the number of mate pairs with aberration in the insertion size and also a Poisson distribution for emitting the read counts, in each genomic position. MSeq-CNV is applied on simulated data and also on real data of six HapMap individuals with high-coverage sequencing, in 1000 Genomes Project. These individuals include a CEU trio of European ancestry and a YRI trio of Nigerian ethnicity. Ancestry of these individuals is studied by clustering the identified CNVs. MSeq-CNV is also applied for detecting CNVs in two samples with low-coverage sequencing in 1000 Genomes Project and six samples form the Simons Genome Diversity Project.

Image edge detection based tool condition monitoring with morphological component analysis.

PubMed

Yu, Xiaolong; Lin, Xin; Dai, Yiquan; Zhu, Kunpeng

2017-07-01

The measurement and monitoring of tool condition are keys to the product precision in the automated manufacturing. To meet the need, this study proposes a novel tool wear monitoring approach based on the monitored image edge detection. Image edge detection has been a fundamental tool to obtain features of images. This approach extracts the tool edge with morphological component analysis. Through the decomposition of original tool wear image, the approach reduces the influence of texture and noise for edge measurement. Based on the target image sparse representation and edge detection, the approach could accurately extract the tool wear edge with continuous and complete contour, and is convenient in charactering tool conditions. Compared to the celebrated algorithms developed in the literature, this approach improves the integrity and connectivity of edges, and the results have shown that it achieves better geometry accuracy and lower error rate in the estimation of tool conditions. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

DNA extraction techniques compared for accurate detection of genetically modified organisms (GMOs) in maize food and feed products.

PubMed

Turkec, Aydin; Kazan, Hande; Karacanli, Burçin; Lucas, Stuart J

2015-08-01

In this paper, DNA extraction methods have been evaluated to detect the presence of genetically modified organisms (GMOs) in maize food and feed products commercialised in Turkey. All the extraction methods tested performed well for the majority of maize foods and feed products analysed. However, the highest DNA content was achieved by the Wizard, Genespin or the CTAB method, all of which produced optimal DNA yield and purity for different maize food and feed products. The samples were then screened for the presence of GM elements, along with certified reference materials. Of the food and feed samples, 8 % tested positive for the presence of one GM element (NOS terminator), of which half (4 % of the total) also contained a second element (the Cauliflower Mosaic Virus 35S promoter). The results obtained herein clearly demonstrate the presence of GM maize in the Turkish market, and that the Foodproof GMO Screening Kit provides reliable screening of maize food and feed products.

STFT or CWT for the detection of Doppler ultrasound embolic signals.

PubMed

Gonçalves, Ivo B; Leiria, Ana; Moura, M M M

2013-09-01

Aiming reliable detection and localization of cerebral blood flow and emboli, embolic signals were added to simulated middle cerebral artery Doppler signals and analysed. Short-time Fourier transform (STFT) and continuous wavelet transform (CWT) were used in the evaluation. The following parameters were used in this study: the powers of the embolic signals added were 5, 6, 6.5, 7, 7.5, 8 and 9 dB; the mother wavelets for CWT analysis were Morlet, Mexican hat, Meyer, Gaussian (order 4) and Daubechies (orders 4 and 8); and the thresholds for detection (equated in terms of false positive, false negative and sensitivity) were 2 and 3.5 dB for the CWT and STFT, respectively. The results indicate that although the STFT allows accurately detecting emboli, better time localization can be achieved with the CWT. Among the CWT, the current best overall results were obtained with Mexican Hat mother wavelet, with optimal results for sensitivity (100% detection rate) for nearly all emboli power values studied. Copyright © 2013 John Wiley & Sons, Ltd.

Accurate segmentation framework for the left ventricle wall from cardiac cine MRI

NASA Astrophysics Data System (ADS)

Sliman, H.; Khalifa, F.; Elnakib, A.; Soliman, A.; Beache, G. M.; Gimel'farb, G.; Emam, A.; Elmaghraby, A.; El-Baz, A.

2013-10-01

We propose a novel, fast, robust, bi-directional coupled parametric deformable model to segment the left ventricle (LV) wall borders using first- and second-order visual appearance features. These features are embedded in a new stochastic external force that preserves the topology of LV wall to track the evolution of the parametric deformable models control points. To accurately estimate the marginal density of each deformable model control point, the empirical marginal grey level distributions (first-order appearance) inside and outside the boundary of the deformable model are modeled with adaptive linear combinations of discrete Gaussians (LCDG). The second order visual appearance of the LV wall is accurately modeled with a new rotationally invariant second-order Markov-Gibbs random field (MGRF). We tested the proposed segmentation approach on 15 data sets in 6 infarction patients using the Dice similarity coefficient (DSC) and the average distance (AD) between the ground truth and automated segmentation contours. Our approach achieves a mean DSC value of 0.926±0.022 and AD value of 2.16±0.60 compared to two other level set methods that achieve 0.904±0.033 and 0.885±0.02 for DSC; and 2.86±1.35 and 5.72±4.70 for AD, respectively.

Automated accident detection at intersections.

DOT National Transportation Integrated Search

2004-03-01

This research aims to provide a timely and accurate accident detection method at intersections, which is : very important for the Traffic Management System(TMS). This research uses acoustic signals to detect : accident at intersections. A system is c...

Accurate Semilocal Density Functional for Condensed-Matter Physics and Quantum Chemistry.

PubMed

Tao, Jianmin; Mo, Yuxiang

2016-08-12

Most density functionals have been developed by imposing the known exact constraints on the exchange-correlation energy, or by a fit to a set of properties of selected systems, or by both. However, accurate modeling of the conventional exchange hole presents a great challenge, due to the delocalization of the hole. Making use of the property that the hole can be made localized under a general coordinate transformation, here we derive an exchange hole from the density matrix expansion, while the correlation part is obtained by imposing the low-density limit constraint. From the hole, a semilocal exchange-correlation functional is calculated. Our comprehensive test shows that this functional can achieve remarkable accuracy for diverse properties of molecules, solids, and solid surfaces, substantially improving upon the nonempirical functionals proposed in recent years. Accurate semilocal functionals based on their associated holes are physically appealing and practically useful for developing nonlocal functionals.

Rapid detection, classification and accurate alignment of up to a million or more related protein sequences.

PubMed

Neuwald, Andrew F

2009-08-01

The patterns of sequence similarity and divergence present within functionally diverse, evolutionarily related proteins contain implicit information about corresponding biochemical similarities and differences. A first step toward accessing such information is to statistically analyze these patterns, which, in turn, requires that one first identify and accurately align a very large set of protein sequences. Ideally, the set should include many distantly related, functionally divergent subgroups. Because it is extremely difficult, if not impossible for fully automated methods to align such sequences correctly, researchers often resort to manual curation based on detailed structural and biochemical information. However, multiply-aligning vast numbers of sequences in this way is clearly impractical. This problem is addressed using Multiply-Aligned Profiles for Global Alignment of Protein Sequences (MAPGAPS). The MAPGAPS program uses a set of multiply-aligned profiles both as a query to detect and classify related sequences and as a template to multiply-align the sequences. It relies on Karlin-Altschul statistics for sensitivity and on PSI-BLAST (and other) heuristics for speed. Using as input a carefully curated multiple-profile alignment for P-loop GTPases, MAPGAPS correctly aligned weakly conserved sequence motifs within 33 distantly related GTPases of known structure. By comparison, the sequence- and structurally based alignment methods hmmalign and PROMALS3D misaligned at least 11 and 23 of these regions, respectively. When applied to a dataset of 65 million protein sequences, MAPGAPS identified, classified and aligned (with comparable accuracy) nearly half a million putative P-loop GTPase sequences. A C++ implementation of MAPGAPS is available at http://mapgaps.igs.umaryland.edu. Supplementary data are available at Bioinformatics online.

Contour-Based Corner Detection and Classification by Using Mean Projection Transform

PubMed Central

Kahaki, Seyed Mostafa Mousavi; Nordin, Md Jan; Ashtari, Amir Hossein

2014-01-01

Image corner detection is a fundamental task in computer vision. Many applications require reliable detectors to accurately detect corner points, commonly achieved by using image contour information. The curvature definition is sensitive to local variation and edge aliasing, and available smoothing methods are not sufficient to address these problems properly. Hence, we propose Mean Projection Transform (MPT) as a corner classifier and parabolic fit approximation to form a robust detector. The first step is to extract corner candidates using MPT based on the integral properties of the local contours in both the horizontal and vertical directions. Then, an approximation of the parabolic fit is calculated to localize the candidate corner points. The proposed method presents fewer false-positive (FP) and false-negative (FN) points compared with recent standard corner detection techniques, especially in comparison with curvature scale space (CSS) methods. Moreover, a new evaluation metric, called accuracy of repeatability (AR), is introduced. AR combines repeatability and the localization error (Le) for finding the probability of correct detection in the target image. The output results exhibit better repeatability, localization, and AR for the detected points compared with the criteria in original and transformed images. PMID:24590354

Contour-based corner detection and classification by using mean projection transform.

PubMed

Kahaki, Seyed Mostafa Mousavi; Nordin, Md Jan; Ashtari, Amir Hossein

2014-02-28

Image corner detection is a fundamental task in computer vision. Many applications require reliable detectors to accurately detect corner points, commonly achieved by using image contour information. The curvature definition is sensitive to local variation and edge aliasing, and available smoothing methods are not sufficient to address these problems properly. Hence, we propose Mean Projection Transform (MPT) as a corner classifier and parabolic fit approximation to form a robust detector. The first step is to extract corner candidates using MPT based on the integral properties of the local contours in both the horizontal and vertical directions. Then, an approximation of the parabolic fit is calculated to localize the candidate corner points. The proposed method presents fewer false-positive (FP) and false-negative (FN) points compared with recent standard corner detection techniques, especially in comparison with curvature scale space (CSS) methods. Moreover, a new evaluation metric, called accuracy of repeatability (AR), is introduced. AR combines repeatability and the localization error (Le) for finding the probability of correct detection in the target image. The output results exhibit better repeatability, localization, and AR for the detected points compared with the criteria in original and transformed images.

The effects of a science intervention program on the attitudes and achievement of high school girls in science

NASA Astrophysics Data System (ADS)

Steakley, Carrie Capers

This study investigated the effects of a high school science intervention program that included hands-on activities, science-related career information and exposure, and real-world experiences on girls' attitudes and achievement in science. Eighty-four girls, 44 ninth-graders and 40 tenth-graders, and 105 parents participated in the study. Survey data was collected to assess the girls' attitudes toward science in seven distinct areas: social implications of science, normality of scientists, attitude toward scientific inquiry, adoption of scientific attitudes, enjoyment of science lessons, leisure interest in science, and career interest in science. Additional questionnaires were used to determine the extent of the girls' participation in sports and the attitudes of their parents toward science. The girls' cumulative science semester grade point averages since the seventh grade were used to assess academic science achievement. This study found no evidence that participation in the program improved the girls' attitudes or achievement in science. Parent attitudes and years of participation in sports were not accurate predictors of science achievement. Additionally, no significant relationship was detected between the girls' and their parents' perceptions of science. However, the study did suggest that extended participation in sports may positively affect science achievement for girls. This study holds implications for educational stakeholders who seek to implement intervention methods and programs that may improve student attitudes and achievement in science and attract more youth to future science-related careers.

Hydrogen atoms can be located accurately and precisely by x-ray crystallography.

PubMed

Woińska, Magdalena; Grabowsky, Simon; Dominiak, Paulina M; Woźniak, Krzysztof; Jayatilaka, Dylan

2016-05-01

Precise and accurate structural information on hydrogen atoms is crucial to the study of energies of interactions important for crystal engineering, materials science, medicine, and pharmacy, and to the estimation of physical and chemical properties in solids. However, hydrogen atoms only scatter x-radiation weakly, so x-rays have not been used routinely to locate them accurately. Textbooks and teaching classes still emphasize that hydrogen atoms cannot be located with x-rays close to heavy elements; instead, neutron diffraction is needed. We show that, contrary to widespread expectation, hydrogen atoms can be located very accurately using x-ray diffraction, yielding bond lengths involving hydrogen atoms (A-H) that are in agreement with results from neutron diffraction mostly within a single standard deviation. The precision of the determination is also comparable between x-ray and neutron diffraction results. This has been achieved at resolutions as low as 0.8 Å using Hirshfeld atom refinement (HAR). We have applied HAR to 81 crystal structures of organic molecules and compared the A-H bond lengths with those from neutron measurements for A-H bonds sorted into bonds of the same class. We further show in a selection of inorganic compounds that hydrogen atoms can be located in bridging positions and close to heavy transition metals accurately and precisely. We anticipate that, in the future, conventional x-radiation sources at in-house diffractometers can be used routinely for locating hydrogen atoms in small molecules accurately instead of large-scale facilities such as spallation sources or nuclear reactors.

Hydrogen atoms can be located accurately and precisely by x-ray crystallography

PubMed Central

Woińska, Magdalena; Grabowsky, Simon; Dominiak, Paulina M.; Woźniak, Krzysztof; Jayatilaka, Dylan

2016-01-01

Precise and accurate structural information on hydrogen atoms is crucial to the study of energies of interactions important for crystal engineering, materials science, medicine, and pharmacy, and to the estimation of physical and chemical properties in solids. However, hydrogen atoms only scatter x-radiation weakly, so x-rays have not been used routinely to locate them accurately. Textbooks and teaching classes still emphasize that hydrogen atoms cannot be located with x-rays close to heavy elements; instead, neutron diffraction is needed. We show that, contrary to widespread expectation, hydrogen atoms can be located very accurately using x-ray diffraction, yielding bond lengths involving hydrogen atoms (A–H) that are in agreement with results from neutron diffraction mostly within a single standard deviation. The precision of the determination is also comparable between x-ray and neutron diffraction results. This has been achieved at resolutions as low as 0.8 Å using Hirshfeld atom refinement (HAR). We have applied HAR to 81 crystal structures of organic molecules and compared the A–H bond lengths with those from neutron measurements for A–H bonds sorted into bonds of the same class. We further show in a selection of inorganic compounds that hydrogen atoms can be located in bridging positions and close to heavy transition metals accurately and precisely. We anticipate that, in the future, conventional x-radiation sources at in-house diffractometers can be used routinely for locating hydrogen atoms in small molecules accurately instead of large-scale facilities such as spallation sources or nuclear reactors. PMID:27386545

Accurate upwind methods for the Euler equations

NASA Technical Reports Server (NTRS)

Huynh, Hung T.

1993-01-01

A new class of piecewise linear methods for the numerical solution of the one-dimensional Euler equations of gas dynamics is presented. These methods are uniformly second-order accurate, and can be considered as extensions of Godunov's scheme. With an appropriate definition of monotonicity preservation for the case of linear convection, it can be shown that they preserve monotonicity. Similar to Van Leer's MUSCL scheme, they consist of two key steps: a reconstruction step followed by an upwind step. For the reconstruction step, a monotonicity constraint that preserves uniform second-order accuracy is introduced. Computational efficiency is enhanced by devising a criterion that detects the 'smooth' part of the data where the constraint is redundant. The concept and coding of the constraint are simplified by the use of the median function. A slope steepening technique, which has no effect at smooth regions and can resolve a contact discontinuity in four cells, is described. As for the upwind step, existing and new methods are applied in a manner slightly different from those in the literature. These methods are derived by approximating the Euler equations via linearization and diagonalization. At a 'smooth' interface, Harten, Lax, and Van Leer's one intermediate state model is employed. A modification for this model that can resolve contact discontinuities is presented. Near a discontinuity, either this modified model or a more accurate one, namely, Roe's flux-difference splitting. is used. The current presentation of Roe's method, via the conceptually simple flux-vector splitting, not only establishes a connection between the two splittings, but also leads to an admissibility correction with no conditional statement, and an efficient approximation to Osher's approximate Riemann solver. These reconstruction and upwind steps result in schemes that are uniformly second-order accurate and economical at smooth regions, and yield high resolution at discontinuities.

Turning State Data and Research into Information: An Example from Washington State's Student Achievement Initiative

ERIC Educational Resources Information Center

Prince, David; Seppanen, Loretta; Stephens, Deborah; Stewart, Carmen

2010-01-01

This chapter discusses Washington State's Student Achievement Initiative, a new performance funding system for community and technical colleges. Its purposes are to improve public accountability by more accurately describing what students achieve from enrolling in state colleges each year and provide incentives through financial rewards to…

Accurate Reading with Sequential Presentation of Single Letters

PubMed Central

Price, Nicholas S. C.; Edwards, Gemma L.

2012-01-01

Rapid, accurate reading is possible when isolated, single words from a sentence are sequentially presented at a fixed spatial location. We investigated if reading of words and sentences is possible when single letters are rapidly presented at the fovea under user-controlled or automatically controlled rates. When tested with complete sentences, trained participants achieved reading rates of over 60 wpm and accuracies of over 90% with the single letter reading (SLR) method and naive participants achieved average reading rates over 30 wpm with greater than 90% accuracy. Accuracy declined as individual letters were presented for shorter periods of time, even when the overall reading rate was maintained by increasing the duration of spaces between words. Words in the lexicon that occur more frequently were identified with higher accuracy and more quickly, demonstrating that trained participants have lexical access. In combination, our data strongly suggest that comprehension is possible and that SLR is a practicable form of reading under conditions in which normal scanning of text is not possible, or for scenarios with limited spatial and temporal resolution such as patients with low vision or prostheses. PMID:23115548

Security Applications Of Computer Motion Detection

NASA Astrophysics Data System (ADS)

Bernat, Andrew P.; Nelan, Joseph; Riter, Stephen; Frankel, Harry

1987-05-01

An important area of application of computer vision is the detection of human motion in security systems. This paper describes the development of a computer vision system which can detect and track human movement across the international border between the United States and Mexico. Because of the wide range of environmental conditions, this application represents a stringent test of computer vision algorithms for motion detection and object identification. The desired output of this vision system is accurate, real-time locations for individual aliens and accurate statistical data as to the frequency of illegal border crossings. Because most detection and tracking routines assume rigid body motion, which is not characteristic of humans, new algorithms capable of reliable operation in our application are required. Furthermore, most current detection and tracking algorithms assume a uniform background against which motion is viewed - the urban environment along the US-Mexican border is anything but uniform. The system works in three stages: motion detection, object tracking and object identi-fication. We have implemented motion detection using simple frame differencing, maximum likelihood estimation, mean and median tests and are evaluating them for accuracy and computational efficiency. Due to the complex nature of the urban environment (background and foreground objects consisting of buildings, vegetation, vehicles, wind-blown debris, animals, etc.), motion detection alone is not sufficiently accurate. Object tracking and identification are handled by an expert system which takes shape, location and trajectory information as input and determines if the moving object is indeed representative of an illegal border crossing.

Automatic QRS complex detection using two-level convolutional neural network.

PubMed

Xiang, Yande; Lin, Zhitao; Meng, Jianyi

2018-01-29

The QRS complex is the most noticeable feature in the electrocardiogram (ECG) signal, therefore, its detection is critical for ECG signal analysis. The existing detection methods largely depend on hand-crafted manual features and parameters, which may introduce significant computational complexity, especially in the transform domains. In addition, fixed features and parameters are not suitable for detecting various kinds of QRS complexes under different circumstances. In this study, based on 1-D convolutional neural network (CNN), an accurate method for QRS complex detection is proposed. The CNN consists of object-level and part-level CNNs for extracting different grained ECG morphological features automatically. All the extracted morphological features are used by multi-layer perceptron (MLP) for QRS complex detection. Additionally, a simple ECG signal preprocessing technique which only contains difference operation in temporal domain is adopted. Based on the MIT-BIH arrhythmia (MIT-BIH-AR) database, the proposed detection method achieves overall sensitivity Sen = 99.77%, positive predictivity rate PPR = 99.91%, and detection error rate DER = 0.32%. In addition, the performance variation is performed according to different signal-to-noise ratio (SNR) values. An automatic QRS detection method using two-level 1-D CNN and simple signal preprocessing technique is proposed for QRS complex detection. Compared with the state-of-the-art QRS complex detection approaches, experimental results show that the proposed method acquires comparable accuracy.

A Highly Accurate Face Recognition System Using Filtering Correlation

NASA Astrophysics Data System (ADS)

Watanabe, Eriko; Ishikawa, Sayuri; Kodate, Kashiko

2007-09-01

The authors previously constructed a highly accurate fast face recognition optical correlator (FARCO) [E. Watanabe and K. Kodate: Opt. Rev. 12 (2005) 460], and subsequently developed an improved, super high-speed FARCO (S-FARCO), which is able to process several hundred thousand frames per second. The principal advantage of our new system is its wide applicability to any correlation scheme. Three different configurations were proposed, each depending on correlation speed. This paper describes and evaluates a software correlation filter. The face recognition function proved highly accurate, seeing that a low-resolution facial image size (64 × 64 pixels) has been successfully implemented. An operation speed of less than 10 ms was achieved using a personal computer with a central processing unit (CPU) of 3 GHz and 2 GB memory. When we applied the software correlation filter to a high-security cellular phone face recognition system, experiments on 30 female students over a period of three months yielded low error rates: 0% false acceptance rate and 2% false rejection rate. Therefore, the filtering correlation works effectively when applied to low resolution images such as web-based images or faces captured by a monitoring camera.

Effort Analysis: Individual Score Validation of Achievement Test Data

ERIC Educational Resources Information Center

Wise, Steven L.

2015-01-01

Whenever the purpose of measurement is to inform an inference about a student's achievement level, it is important that we be able to trust that the student's test score accurately reflects what that student knows and can do. Such trust requires the assumption that a student's test event is not unduly influenced by construct-irrelevant factors…

Detection of heavy metal ions in drinking water using a high-resolution differential surface plasmon resonance sensor.

PubMed

Forzani, Erica S; Zhang, Haiqian; Chen, Wilfred; Tao, Nongjian

2005-03-01

We have built a high-resolution differential surface plasmon resonance (SPR) sensor for heavy metal ion detection. The sensor surface is divided into a reference and sensing areas, and the difference in the SPR angles from the two areas is detected with a quadrant cell photodetector as a differential signal. In the presence of metal ions, the differential signal changes due to specific binding of the metal ions onto the sensing area coated with properly selected peptides, which provides an accurate real-time measurement and quantification of the metal ions. Selective detection of Cu2+ and Ni2+ in the ppt-ppb range was achieved by coating the sensing surface with peptides NH2-Gly-Gly-His-COOH and NH2-(His)6-COOH. Cu2+ in drinking water was tested using this sensor.

A Dual-Mode Large-Arrayed CMOS ISFET Sensor for Accurate and High-Throughput pH Sensing in Biomedical Diagnosis.

PubMed

Huang, Xiwei; Yu, Hao; Liu, Xu; Jiang, Yu; Yan, Mei; Wu, Dongping

2015-09-01

The existing ISFET-based DNA sequencing detects hydrogen ions released during the polymerization of DNA strands on microbeads, which are scattered into microwell array above the ISFET sensor with unknown distribution. However, false pH detection happens at empty microwells due to crosstalk from neighboring microbeads. In this paper, a dual-mode CMOS ISFET sensor is proposed to have accurate pH detection toward DNA sequencing. Dual-mode sensing, optical and chemical modes, is realized by integrating a CMOS image sensor (CIS) with ISFET pH sensor, and is fabricated in a standard 0.18-μm CIS process. With accurate determination of microbead physical locations with CIS pixel by contact imaging, the dual-mode sensor can correlate local pH for one DNA slice at one location-determined microbead, which can result in improved pH detection accuracy. Moreover, toward a high-throughput DNA sequencing, a correlated-double-sampling readout that supports large array for both modes is deployed to reduce pixel-to-pixel nonuniformity such as threshold voltage mismatch. The proposed CMOS dual-mode sensor is experimentally examined to show a well correlated pH map and optical image for microbeads with a pH sensitivity of 26.2 mV/pH, a fixed pattern noise (FPN) reduction from 4% to 0.3%, and a readout speed of 1200 frames/s. A dual-mode CMOS ISFET sensor with suppressed FPN for accurate large-arrayed pH sensing is proposed and demonstrated with state-of-the-art measured results toward accurate and high-throughput DNA sequencing. The developed dual-mode CMOS ISFET sensor has great potential for future personal genome diagnostics with high accuracy and low cost.

Accurate Vehicle Location System Using RFID, an Internet of Things Approach.

PubMed

Prinsloo, Jaco; Malekian, Reza

2016-06-04

Modern infrastructure, such as dense urban areas and underground tunnels, can effectively block all GPS signals, which implies that effective position triangulation will not be achieved. The main problem that is addressed in this project is the design and implementation of an accurate vehicle location system using radio-frequency identification (RFID) technology in combination with GPS and the Global system for Mobile communication (GSM) technology, in order to provide a solution to the limitation discussed above. In essence, autonomous vehicle tracking will be facilitated with the use of RFID technology where GPS signals are non-existent. The design of the system and the results are reflected in this paper. An extensive literature study was done on the field known as the Internet of Things, as well as various topics that covered the integration of independent technology in order to address a specific challenge. The proposed system is then designed and implemented. An RFID transponder was successfully designed and a read range of approximately 31 cm was obtained in the low frequency communication range (125 kHz to 134 kHz). The proposed system was designed, implemented, and field tested and it was found that a vehicle could be accurately located and tracked. It is also found that the antenna size of both the RFID reader unit and RFID transponder plays a critical role in the maximum communication range that can be achieved.

Accurate Vehicle Location System Using RFID, an Internet of Things Approach

PubMed Central

Prinsloo, Jaco; Malekian, Reza

2016-01-01

Modern infrastructure, such as dense urban areas and underground tunnels, can effectively block all GPS signals, which implies that effective position triangulation will not be achieved. The main problem that is addressed in this project is the design and implementation of an accurate vehicle location system using radio-frequency identification (RFID) technology in combination with GPS and the Global system for Mobile communication (GSM) technology, in order to provide a solution to the limitation discussed above. In essence, autonomous vehicle tracking will be facilitated with the use of RFID technology where GPS signals are non-existent. The design of the system and the results are reflected in this paper. An extensive literature study was done on the field known as the Internet of Things, as well as various topics that covered the integration of independent technology in order to address a specific challenge. The proposed system is then designed and implemented. An RFID transponder was successfully designed and a read range of approximately 31 cm was obtained in the low frequency communication range (125 kHz to 134 kHz). The proposed system was designed, implemented, and field tested and it was found that a vehicle could be accurately located and tracked. It is also found that the antenna size of both the RFID reader unit and RFID transponder plays a critical role in the maximum communication range that can be achieved. PMID:27271638

A stereo-vision hazard-detection algorithm to increase planetary lander autonomy

NASA Astrophysics Data System (ADS)

Woicke, Svenja; Mooij, Erwin

2016-05-01

For future landings on any celestial body, increasing the lander autonomy as well as decreasing risk are primary objectives. Both risk reduction and an increase in autonomy can be achieved by including hazard detection and avoidance in the guidance, navigation, and control loop. One of the main challenges in hazard detection and avoidance is the reconstruction of accurate elevation models, as well as slope and roughness maps. Multiple methods for acquiring the inputs for hazard maps are available. The main distinction can be made between active and passive methods. Passive methods (cameras) have budgetary advantages compared to active sensors (radar, light detection and ranging). However, it is necessary to proof that these methods deliver sufficiently good maps. Therefore, this paper discusses hazard detection using stereo vision. To facilitate a successful landing not more than 1% wrong detections (hazards that are not identified) are allowed. Based on a sensitivity analysis it was found that using a stereo set-up at a baseline of ≤ 2 m is feasible at altitudes of ≤ 200 m defining false positives of less than 1%. It was thus shown that stereo-based hazard detection is an effective means to decrease the landing risk and increase the lander autonomy. In conclusion, the proposed algorithm is a promising candidate for future landers.

Trace detection of specific viable bacteria using tetracysteine-tagged bacteriophages.

PubMed

Wu, Lina; Luan, Tian; Yang, Xiaoting; Wang, Shuo; Zheng, Yan; Huang, Tianxun; Zhu, Shaobin; Yan, Xiaomei

2014-01-07

Advanced methods are urgently needed to determine the identity and viability of trace amounts of pathogenic bacteria in a short time. Existing approaches either fall short in the accurate assessment of microbial viability or lack specificity in bacterial identification. Bacteriophages (or phages for short) are viruses that exclusively infect bacterial host cells with high specificity. As phages infect and replicate only in living bacterial hosts, here we exploit the strategy of using tetracysteine (TC)-tagged phage in combination with biarsenical dye to the discriminative detection of viable target bacteria from dead target cells and other viable but nontarget bacterial cells. Using recombinant M13KE-TC phage and Escherichia coli ER2738 as a model system, distinct differentiation between individual viable target cells from dead target cells was demonstrated by flow cytometry and fluorescence microscopy. As few as 1% viable E. coli ER2738 can be accurately quantified in a mix with dead E. coli ER2738 by flow cytometry. With fluorescence microscopic measurement, specific detection of as rare as 1 cfu/mL original viable target bacteria was achieved in the presence of a large excess of dead target cells and other viable but nontarget bacterial cells in 40 mL artificially contaminated drinking water sample in less than 3 h. This TC-phage-FlAsH approach is sensitive, specific, rapid, and simple, and thus shows great potential in water safety monitoring, health surveillance, and clinical diagnosis of which trace detection and identification of viable bacterial pathogens is highly demanded.

Highly accurate articulated coordinate measuring machine

DOEpatents

Bieg, Lothar F.; Jokiel, Jr., Bernhard; Ensz, Mark T.; Watson, Robert D.

2003-12-30

Disclosed is a highly accurate articulated coordinate measuring machine, comprising a revolute joint, comprising a circular encoder wheel, having an axis of rotation; a plurality of marks disposed around at least a portion of the circumference of the encoder wheel; bearing means for supporting the encoder wheel, while permitting free rotation of the encoder wheel about the wheel's axis of rotation; and a sensor, rigidly attached to the bearing means, for detecting the motion of at least some of the marks as the encoder wheel rotates; a probe arm, having a proximal end rigidly attached to the encoder wheel, and having a distal end with a probe tip attached thereto; and coordinate processing means, operatively connected to the sensor, for converting the output of the sensor into a set of cylindrical coordinates representing the position of the probe tip relative to a reference cylindrical coordinate system.

Accurate Drift Time Determination by Traveling Wave Ion Mobility Spectrometry: The Concept of the Diffusion Calibration.

PubMed

Kune, Christopher; Far, Johann; De Pauw, Edwin

2016-12-06

Ion mobility spectrometry (IMS) is a gas phase separation technique, which relies on differences in collision cross section (CCS) of ions. Ionic clouds of unresolved conformers overlap if the CCS difference is below the instrumental resolution expressed as CCS/ΔCCS. The experimental arrival time distribution (ATD) peak is then a superimposition of the various contributions weighted by their relative intensities. This paper introduces a strategy for accurate drift time determination using traveling wave ion mobility spectrometry (TWIMS) of poorly resolved or unresolved conformers. This method implements through a calibration procedure the link between the peak full width at half-maximum (fwhm) and the drift time of model compounds for wide range of settings for wave heights and velocities. We modified a Gaussian equation, which achieves the deconvolution of ATD peaks where the fwhm is fixed according to our calibration procedure. The new fitting Gaussian equation only depends on two parameters: The apex of the peak (A) and the mean drift time value (μ). The standard deviation parameter (correlated to fwhm) becomes a function of the drift time. This correlation function between μ and fwhm is obtained using the TWIMS calibration procedure which determines the maximum instrumental ion beam diffusion under limited and controlled space charge effect using ionic compounds which are detected as single conformers in the gas phase. This deconvolution process has been used to highlight the presence of poorly resolved conformers of crown ether complexes and peptides leading to more accurate CCS determinations in better agreement with quantum chemistry predictions.

Polarization speckle imaging as a potential technique for in vivo skin cancer detection.

PubMed

Tchvialeva, Lioudmila; Dhadwal, Gurbir; Lui, Harvey; Kalia, Sunil; Zeng, Haishan; McLean, David I; Lee, Tim K

2013-06-01

Skin cancer is the most common cancer in the Western world. In order to accurately detect the disease, especially malignant melanoma-the most fatal form of skin cancer-at an early stage when the prognosis is excellent, there is an urgent need to develop noninvasive early detection methods. We believe that polarization speckle patterns, defined as a spatial distribution of depolarization ratio of traditional speckle patterns, can be an important tool for skin cancer detection. To demonstrate our technique, we conduct a large in vivo clinical study of 214 skin lesions, and show that statistical moments of the polarization speckle pattern could differentiate different types of skin lesions, including three common types of skin cancers, malignant melanoma, squamous cell carcinoma, basal cell carcinoma, and two benign lesions, melanocytic nevus and seborrheic keratoses. In particular, the fourth order moment achieves better or similar sensitivity and specificity than many well-known and accepted optical techniques used to differentiate melanoma and seborrheic keratosis.

Polarization speckle imaging as a potential technique for in vivo skin cancer detection

NASA Astrophysics Data System (ADS)

Tchvialeva, Lioudmila; Dhadwal, Gurbir; Lui, Harvey; Kalia, Sunil; Zeng, Haishan; McLean, David I.; Lee, Tim K.

2013-06-01

Skin cancer is the most common cancer in the Western world. In order to accurately detect the disease, especially malignant melanoma-the most fatal form of skin cancer-at an early stage when the prognosis is excellent, there is an urgent need to develop noninvasive early detection methods. We believe that polarization speckle patterns, defined as a spatial distribution of depolarization ratio of traditional speckle patterns, can be an important tool for skin cancer detection. To demonstrate our technique, we conduct a large in vivo clinical study of 214 skin lesions, and show that statistical moments of the polarization speckle pattern could differentiate different types of skin lesions, including three common types of skin cancers, malignant melanoma, squamous cell carcinoma, basal cell carcinoma, and two benign lesions, melanocytic nevus and seborrheic keratoses. In particular, the fourth order moment achieves better or similar sensitivity and specificity than many well-known and accepted optical techniques used to differentiate melanoma and seborrheic keratosis.

Adiabatic Quantum Anomaly Detection and Machine Learning

NASA Astrophysics Data System (ADS)

Pudenz, Kristen; Lidar, Daniel

2012-02-01

We present methods of anomaly detection and machine learning using adiabatic quantum computing. The machine learning algorithm is a boosting approach which seeks to optimally combine somewhat accurate classification functions to create a unified classifier which is much more accurate than its components. This algorithm then becomes the first part of the larger anomaly detection algorithm. In the anomaly detection routine, we first use adiabatic quantum computing to train two classifiers which detect two sets, the overlap of which forms the anomaly class. We call this the learning phase. Then, in the testing phase, the two learned classification functions are combined to form the final Hamiltonian for an adiabatic quantum computation, the low energy states of which represent the anomalies in a binary vector space.

A new accurate pill recognition system using imprint information

NASA Astrophysics Data System (ADS)

Chen, Zhiyuan; Kamata, Sei-ichiro

2013-12-01

Great achievements in modern medicine benefit human beings. Also, it has brought about an explosive growth of pharmaceuticals that current in the market. In daily life, pharmaceuticals sometimes confuse people when they are found unlabeled. In this paper, we propose an automatic pill recognition technique to solve this problem. It functions mainly based on the imprint feature of the pills, which is extracted by proposed MSWT (modified stroke width transform) and described by WSC (weighted shape context). Experiments show that our proposed pill recognition method can reach an accurate rate up to 92.03% within top 5 ranks when trying to classify more than 10 thousand query pill images into around 2000 categories.

Supramolecular assembly affording a ratiometric two-photon fluorescent nanoprobe for quantitative detection and bioimaging.

PubMed

Wang, Peng; Zhang, Cheng; Liu, Hong-Wen; Xiong, Mengyi; Yin, Sheng-Yan; Yang, Yue; Hu, Xiao-Xiao; Yin, Xia; Zhang, Xiao-Bing; Tan, Weihong

2017-12-01

Fluorescence quantitative analyses for vital biomolecules are in great demand in biomedical science owing to their unique detection advantages with rapid, sensitive, non-damaging and specific identification. However, available fluorescence strategies for quantitative detection are usually hard to design and achieve. Inspired by supramolecular chemistry, a two-photon-excited fluorescent supramolecular nanoplatform ( TPSNP ) was designed for quantitative analysis with three parts: host molecules (β-CD polymers), a guest fluorophore of sensing probes (Np-Ad) and a guest internal reference (NpRh-Ad). In this strategy, the TPSNP possesses the merits of (i) improved water-solubility and biocompatibility; (ii) increased tissue penetration depth for bioimaging by two-photon excitation; (iii) quantitative and tunable assembly of functional guest molecules to obtain optimized detection conditions; (iv) a common approach to avoid the limitation of complicated design by adjustment of sensing probes; and (v) accurate quantitative analysis by virtue of reference molecules. As a proof-of-concept, we utilized the two-photon fluorescent probe NHS-Ad-based TPSNP-1 to realize accurate quantitative analysis of hydrogen sulfide (H 2 S), with high sensitivity and good selectivity in live cells, deep tissues and ex vivo -dissected organs, suggesting that the TPSNP is an ideal quantitative indicator for clinical samples. What's more, TPSNP will pave the way for designing and preparing advanced supramolecular sensors for biosensing and biomedicine.

Unsupervised EEG analysis for automated epileptic seizure detection

NASA Astrophysics Data System (ADS)

Birjandtalab, Javad; Pouyan, Maziyar Baran; Nourani, Mehrdad

2016-07-01

Epilepsy is a neurological disorder which can, if not controlled, potentially cause unexpected death. It is extremely crucial to have accurate automatic pattern recognition and data mining techniques to detect the onset of seizures and inform care-givers to help the patients. EEG signals are the preferred biosignals for diagnosis of epileptic patients. Most of the existing pattern recognition techniques used in EEG analysis leverage the notion of supervised machine learning algorithms. Since seizure data are heavily under-represented, such techniques are not always practical particularly when the labeled data is not sufficiently available or when disease progression is rapid and the corresponding EEG footprint pattern will not be robust. Furthermore, EEG pattern change is highly individual dependent and requires experienced specialists to annotate the seizure and non-seizure events. In this work, we present an unsupervised technique to discriminate seizures and non-seizures events. We employ power spectral density of EEG signals in different frequency bands that are informative features to accurately cluster seizure and non-seizure events. The experimental results tried so far indicate achieving more than 90% accuracy in clustering seizure and non-seizure events without having any prior knowledge on patient's history.

PROCEDURES FOR ACCURATE PRODUCTION OF COLOR IMAGES FROM SATELLITE OR AIRCRAFT MULTISPECTRAL DIGITAL DATA.

USGS Publications Warehouse

Duval, Joseph S.

1985-01-01

Because the display and interpretation of satellite and aircraft remote-sensing data make extensive use of color film products, accurate reproduction of the color images is important. To achieve accurate color reproduction, the exposure and chemical processing of the film must be monitored and controlled. By using a combination of sensitometry, densitometry, and transfer functions that control film response curves, all of the different steps in the making of film images can be monitored and controlled. Because a sensitometer produces a calibrated exposure, the resulting step wedge can be used to monitor the chemical processing of the film. Step wedges put on film by image recording machines provide a means of monitoring the film exposure and color balance of the machines.

Accurate radiation temperature and chemical potential from quantitative photoluminescence analysis of hot carrier populations.

PubMed

Gibelli, François; Lombez, Laurent; Guillemoles, Jean-François

2017-02-15

In order to characterize hot carrier populations in semiconductors, photoluminescence measurement is a convenient tool, enabling us to probe the carrier thermodynamical properties in a contactless way. However, the analysis of the photoluminescence spectra is based on some assumptions which will be discussed in this work. We especially emphasize the importance of the variation of the material absorptivity that should be considered to access accurate thermodynamical properties of the carriers, especially by varying the excitation power. The proposed method enables us to obtain more accurate results of thermodynamical properties by taking into account a rigorous physical description and finds direct application in investigating hot carrier solar cells, which are an adequate concept for achieving high conversion efficiencies with a relatively simple device architecture.

Accurate Prediction of Motor Failures by Application of Multi CBM Tools: A Case Study

NASA Astrophysics Data System (ADS)

Dutta, Rana; Singh, Veerendra Pratap; Dwivedi, Jai Prakash

2018-02-01

Motor failures are very difficult to predict accurately with a single condition-monitoring tool as both electrical and the mechanical systems are closely related. Electrical problem, like phase unbalance, stator winding insulation failures can, at times, lead to vibration problem and at the same time mechanical failures like bearing failure, leads to rotor eccentricity. In this case study of a 550 kW blower motor it has been shown that a rotor bar crack was detected by current signature analysis and vibration monitoring confirmed the same. In later months in a similar motor vibration monitoring predicted bearing failure and current signature analysis confirmed the same. In both the cases, after dismantling the motor, the predictions were found to be accurate. In this paper we will be discussing the accurate predictions of motor failures through use of multi condition monitoring tools with two case studies.

Accurate interlaminar stress recovery from finite element analysis

NASA Technical Reports Server (NTRS)

Tessler, Alexander; Riggs, H. Ronald

1994-01-01

The accuracy and robustness of a two-dimensional smoothing methodology is examined for the problem of recovering accurate interlaminar shear stress distributions in laminated composite and sandwich plates. The smoothing methodology is based on a variational formulation which combines discrete least-squares and penalty-constraint functionals in a single variational form. The smoothing analysis utilizes optimal strains computed at discrete locations in a finite element analysis. These discrete strain data are smoothed with a smoothing element discretization, producing superior accuracy strains and their first gradients. The approach enables the resulting smooth strain field to be practically C1-continuous throughout the domain of smoothing, exhibiting superconvergent properties of the smoothed quantity. The continuous strain gradients are also obtained directly from the solution. The recovered strain gradients are subsequently employed in the integration o equilibrium equations to obtain accurate interlaminar shear stresses. The problem is a simply-supported rectangular plate under a doubly sinusoidal load. The problem has an exact analytic solution which serves as a measure of goodness of the recovered interlaminar shear stresses. The method has the versatility of being applicable to the analysis of rather general and complex structures built of distinct components and materials, such as found in aircraft design. For these types of structures, the smoothing is achieved with 'patches', each patch covering the domain in which the smoothed quantity is physically continuous.

Sensitive detection of C-reactive protein using optical fiber Bragg gratings.

PubMed

Sridevi, S; Vasu, K S; Asokan, S; Sood, A K

2015-03-15

An accurate and highly sensitive sensor platform has been demonstrated for the detection of C-reactive protein (CRP) using optical fiber Bragg gratings (FBGs). The CRP detection has been carried out by monitoring the shift in Bragg wavelength (ΔλB) of an etched FBG (eFBG) coated with an anti-CRP antibody (aCRP)-graphene oxide (GO) complex. The complex is characterized by Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy and atomic force microscopy. A limit of detection of 0.01mg/L has been achieved with a linear range of detection from 0.01mg/L to 100mg/L which includes clinical range of CRP. The eFBG sensor coated with only aCRP (without GO) show much less sensitivity than that of aCRP-GO complex coated eFBG. The eFBG sensors show high specificity to CRP even in the presence of other interfering factors such as urea, creatinine and glucose. The affinity constant of ∼1.1×10(10)M(-1) has been extracted from the data of normalized shift (ΔλB/λB) as a function of CRP concentration. Copyright © 2014 Elsevier B.V. All rights reserved.

Accurate simulation of backscattering spectra in the presence of sharp resonances

NASA Astrophysics Data System (ADS)

Barradas, N. P.; Alves, E.; Jeynes, C.; Tosaki, M.

2006-06-01

In elastic backscattering spectrometry, the shape of the observed spectrum due to resonances in the nuclear scattering cross-section is influenced by many factors. If the energy spread of the beam before interaction is larger than the resonance width, then a simple convolution with the energy spread on exit and with the detection system resolution will lead to a calculated spectrum with a resonance much sharper than the observed signal. Also, the yield from a thin layer will not be calculated accurately. We have developed an algorithm for the accurate simulation of backscattering spectra in the presence of sharp resonances. Albeit approximate, the algorithm leads to dramatic improvements in the quality and accuracy of the simulations. It is simple to implement and leads to only small increases of the calculation time, being thus suitable for routine data analysis. We show different experimental examples, including samples with roughness and porosity.

POLLUTION DETECTION DOGS: PROOF OF CONCEPT

EPA Science Inventory

Dogs have been used extensively in law enforcement and military applications to detect narcotics and explosives for over thirty years. Dogs are regularly used in arson investigations to detect accelerants since they are much more accurate at discriminating between accelerants an...

Advances in Multicollector ICPMS for precise and accurate isotope ratio measurements of Uranium isotopes

NASA Astrophysics Data System (ADS)

Bouman, C.; Lloyd, N. S.; Schwieters, J.

2011-12-01

The accurate and precise determination of uranium isotopes is challenging, because of the large dynamic range posed by the U isotope abundances and the limited available sample material. Various mass spectrometric techniques are used for the measurement of U isotopes, where TIMS is the most accepted and accurate one. Multicollector inductively coupled plasma mass spectrometry (MC-ICPMS) can offer higher productivity compared to TIMS, but is traditionally limited by low efficiency of sample utilisation. This contribution will discuss progress in MC-ICPMS for detecting 234U, 235U, 236U and 238U in various uranium reference materials from IRMM and NBL. The Thermo Scientific NEPTUNE Plus with Jet Interface offers a modified dry plasma ICP interface using a large interface pump combined with a special set of sample and skimmer cones giving ultimate sensitivity for all elements across the mass range. For uranium, an ion yield of > 3 % was reported previously [1]. The NEPTUNE Plus also offers Multi Ion Counting using discrete dynode electron multipliers as well as two high abundance-sensitivity filters to discriminate against peak tailing effects on 234U and 236U originating from the major uranium beams. These improvements in sensitivity and dynamic range allow accurate measurements of 234U, 235U and 236U abundances on very small samples and at low concentration. In our approach, minor U isotopes 234U and 236U were detected on ion counters with high abundance sensitivity filters, whereas 235U and 238U were detected on Faraday Cups using a high gain current amplifier (10e12 Ohm) for 235U. Precisions and accuracies for 234U and 236U were down to ~1%. For 235U, subpermil levels were reached.

Application of fast Fourier transform cross-correlation and mass spectrometry data for accurate alignment of chromatograms.

PubMed

Zheng, Yi-Bao; Zhang, Zhi-Min; Liang, Yi-Zeng; Zhan, De-Jian; Huang, Jian-Hua; Yun, Yong-Huan; Xie, Hua-Lin

2013-04-19

Chromatography has been established as one of the most important analytical methods in the modern analytical laboratory. However, preprocessing of the chromatograms, especially peak alignment, is usually a time-consuming task prior to extracting useful information from the datasets because of the small unavoidable differences in the experimental conditions caused by minor changes and drift. Most of the alignment algorithms are performed on reduced datasets using only the detected peaks in the chromatograms, which means a loss of data and introduces the problem of extraction of peak data from the chromatographic profiles. These disadvantages can be overcome by using the full chromatographic information that is generated from hyphenated chromatographic instruments. A new alignment algorithm called CAMS (Chromatogram Alignment via Mass Spectra) is present here to correct the retention time shifts among chromatograms accurately and rapidly. In this report, peaks of each chromatogram were detected based on Continuous Wavelet Transform (CWT) with Haar wavelet and were aligned against the reference chromatogram via the correlation of mass spectra. The aligning procedure was accelerated by Fast Fourier Transform cross correlation (FFT cross correlation). This approach has been compared with several well-known alignment methods on real chromatographic datasets, which demonstrates that CAMS can preserve the shape of peaks and achieve a high quality alignment result. Furthermore, the CAMS method was implemented in the Matlab language and available as an open source package at http://www.github.com/matchcoder/CAMS. Copyright © 2013. Published by Elsevier B.V.

The Automated Assessment of Postural Stability: Balance Detection Algorithm.

PubMed

Napoli, Alessandro; Glass, Stephen M; Tucker, Carole; Obeid, Iyad

2017-12-01

Impaired balance is a common indicator of mild traumatic brain injury, concussion and musculoskeletal injury. Given the clinical relevance of such injuries, especially in military settings, it is paramount to develop more accurate and reliable on-field evaluation tools. This work presents the design and implementation of the automated assessment of postural stability (AAPS) system, for on-field evaluations following concussion. The AAPS is a computer system, based on inexpensive off-the-shelf components and custom software, that aims to automatically and reliably evaluate balance deficits, by replicating a known on-field clinical test, namely, the Balance Error Scoring System (BESS). The AAPS main innovation is its balance error detection algorithm that has been designed to acquire data from a Microsoft Kinect ® sensor and convert them into clinically-relevant BESS scores, using the same detection criteria defined by the original BESS test. In order to assess the AAPS balance evaluation capability, a total of 15 healthy subjects (7 male, 8 female) were required to perform the BESS test, while simultaneously being tracked by a Kinect 2.0 sensor and a professional-grade motion capture system (Qualisys AB, Gothenburg, Sweden). High definition videos with BESS trials were scored off-line by three experienced observers for reference scores. AAPS performance was assessed by comparing the AAPS automated scores to those derived by three experienced observers. Our results show that the AAPS error detection algorithm presented here can accurately and precisely detect balance deficits with performance levels that are comparable to those of experienced medical personnel. Specifically, agreement levels between the AAPS algorithm and the human average BESS scores ranging between 87.9% (single-leg on foam) and 99.8% (double-leg on firm ground) were detected. Moreover, statistically significant differences in balance scores were not detected by an ANOVA test with alpha equal to 0

Bat detective-Deep learning tools for bat acoustic signal detection.

PubMed

Mac Aodha, Oisin; Gibb, Rory; Barlow, Kate E; Browning, Ella; Firman, Michael; Freeman, Robin; Harder, Briana; Kinsey, Libby; Mead, Gary R; Newson, Stuart E; Pandourski, Ivan; Parsons, Stuart; Russ, Jon; Szodoray-Paradi, Abigel; Szodoray-Paradi, Farkas; Tilova, Elena; Girolami, Mark; Brostow, Gabriel; Jones, Kate E

2018-03-01

Passive acoustic sensing has emerged as a powerful tool for quantifying anthropogenic impacts on biodiversity, especially for echolocating bat species. To better assess bat population trends there is a critical need for accurate, reliable, and open source tools that allow the detection and classification of bat calls in large collections of audio recordings. The majority of existing tools are commercial or have focused on the species classification task, neglecting the important problem of first localizing echolocation calls in audio which is particularly problematic in noisy recordings. We developed a convolutional neural network based open-source pipeline for detecting ultrasonic, full-spectrum, search-phase calls produced by echolocating bats. Our deep learning algorithms were trained on full-spectrum ultrasonic audio collected along road-transects across Europe and labelled by citizen scientists from www.batdetective.org. When compared to other existing algorithms and commercial systems, we show significantly higher detection performance of search-phase echolocation calls with our test sets. As an example application, we ran our detection pipeline on bat monitoring data collected over five years from Jersey (UK), and compared results to a widely-used commercial system. Our detection pipeline can be used for the automatic detection and monitoring of bat populations, and further facilitates their use as indicator species on a large scale. Our proposed pipeline makes only a small number of bat specific design decisions, and with appropriate training data it could be applied to detecting other species in audio. A crucial novelty of our work is showing that with careful, non-trivial, design and implementation considerations, state-of-the-art deep learning methods can be used for accurate and efficient monitoring in audio.

A novel sensitive pathogen detection system based on Microbead Quantum Dot System.

PubMed

Wu, Tzong-Yuan; Su, Yi-Yu; Shu, Wei-Hsien; Mercado, Augustus T; Wang, Shi-Kwun; Hsu, Ling-Yi; Tsai, Yow-Fu; Chen, Chung-Yung

2016-04-15

A fast and accurate detection system for pathogens can provide immediate measurements for the identification of infectious agents. Therefore, the Microbead Quantum-dots Detection System (MQDS) was developed to identify and measure target DNAs of pathogenic microorganisms and eliminated the need of PCR amplifications. This nanomaterial-based technique can detect different microorganisms by flow cytometry measurements. In MQDS, pathogen specific DNA probes were designed to form a hairpin structure and conjugated on microbeads. In the presence of the complementary target DNA sequence, the probes will compete for binding with the reporter probes but will not interfere with the binding between the probe and internal control DNA. To monitor the binding process by flow cytometry, both the reporter probes and internal control probes were conjugated with Quantum dots that fluoresce at different emission wavelengths using the click reaction. When MQDS was used to detect the pathogens in environmental samples, a high correlation coefficient (R=0.994) for Legionella spp., with a detection limit of 0.1 ng of the extracted DNAs and 10 CFU/test, can be achieved. Thus, this newly developed technique can also be applied to detect other pathogens, particularly viruses and other genetic diseases. Copyright © 2015 Elsevier B.V. All rights reserved.

NNLOPS accurate associated HW production

NASA Astrophysics Data System (ADS)

Astill, William; Bizon, Wojciech; Re, Emanuele; Zanderighi, Giulia

2016-06-01

We present a next-to-next-to-leading order accurate description of associated HW production consistently matched to a parton shower. The method is based on reweighting events obtained with the HW plus one jet NLO accurate calculation implemented in POWHEG, extended with the MiNLO procedure, to reproduce NNLO accurate Born distributions. Since the Born kinematics is more complex than the cases treated before, we use a parametrization of the Collins-Soper angles to reduce the number of variables required for the reweighting. We present phenomenological results at 13 TeV, with cuts suggested by the Higgs Cross section Working Group.

High-accurate optical vector analysis based on optical single-sideband modulation

NASA Astrophysics Data System (ADS)

Xue, Min; Pan, Shilong

2016-11-01

Most of the efforts devoted to the area of optical communications were on the improvement of the optical spectral efficiency. Varies innovative optical devices are thus developed to finely manipulate the optical spectrum. Knowing the spectral responses of these devices, including the magnitude, phase and polarization responses, is of great importance for their fabrication and application. To achieve high-resolution characterization, optical vector analyzers (OVAs) based on optical single-sideband (OSSB) modulation have been proposed and developed. Benefiting from the mature and highresolution microwave technologies, the OSSB-based OVA can potentially achieve a resolution of sub-Hz. However, the accuracy is restricted by the measurement errors induced by the unwanted first-order sideband and the high-order sidebands in the OSSB signal, since electrical-to-optical conversion and optical-to-electrical conversion are essentially required to achieve high-resolution frequency sweeping and extract the magnitude and phase information in the electrical domain. Recently, great efforts have been devoted to improve the accuracy of the OSSB-based OVA. In this paper, the influence of the unwanted-sideband induced measurement errors and techniques for implementing high-accurate OSSB-based OVAs are discussed.

Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks

NASA Astrophysics Data System (ADS)

Abdeljaber, Osama; Avci, Onur; Kiranyaz, Serkan; Gabbouj, Moncef; Inman, Daniel J.

2017-02-01

Structural health monitoring (SHM) and vibration-based structural damage detection have been a continuous interest for civil, mechanical and aerospace engineers over the decades. Early and meticulous damage detection has always been one of the principal objectives of SHM applications. The performance of a classical damage detection system predominantly depends on the choice of the features and the classifier. While the fixed and hand-crafted features may either be a sub-optimal choice for a particular structure or fail to achieve the same level of performance on another structure, they usually require a large computation power which may hinder their usage for real-time structural damage detection. This paper presents a novel, fast and accurate structural damage detection system using 1D Convolutional Neural Networks (CNNs) that has an inherent adaptive design to fuse both feature extraction and classification blocks into a single and compact learning body. The proposed method performs vibration-based damage detection and localization of the damage in real-time. The advantage of this approach is its ability to extract optimal damage-sensitive features automatically from the raw acceleration signals. Large-scale experiments conducted on a grandstand simulator revealed an outstanding performance and verified the computational efficiency of the proposed real-time damage detection method.

Towards Discovery and Targeted Peptide Biomarker Detection Using nanoESI-TIMS-TOF MS

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

Garabedian, Alyssa; Benigni, Paolo; Ramirez, Cesar E.

Abstract. In the present work, the potential of trapped ion mobility spectrometry coupled to TOF mass spectrometry (TIMS-TOF MS) for discovery and targeted monitoring of peptide biomarkers from human-in-mouse xenograft tumor tissue was evaluated. In particular, a TIMS-MS workflow was developed for the detection and quantification of peptide biomarkers using internal heavy analogs, taking advantage of the high mobility resolution (R = 150–250) prior to mass analysis. Five peptide biomarkers were separated, identified, and quantified using offline nanoESI-TIMSCID- TOF MS; the results were in good agreement with measurements using a traditional LC-ESI-MS/MS proteomics workflow. The TIMS-TOF MS analysis permitted peptidemore » biomarker detection based on accurate mobility, mass measurements, and high sequence coverage for concentrations in the 10–200 nM range, while simultaneously achieving discovery measurements« less

Description of a nanobody-based competitive immunoassay to detect tsetse fly exposure.

PubMed

Caljon, Guy; Hussain, Shahid; Vermeiren, Lieve; Van Den Abbeele, Jan

2015-02-01

Tsetse flies are the main vectors of human and animal African trypanosomes. The Tsal proteins in tsetse fly saliva were previously identified as suitable biomarkers of bite exposure. A new competitive assay was conceived based on nanobody (Nb) technology to ameliorate the detection of anti-Tsal antibodies in mammalian hosts. A camelid-derived Nb library was generated against the Glossina morsitans morsitans sialome and exploited to select Tsal specific Nbs. One of the three identified Nb families (family III, TsalNb-05 and TsalNb-11) was found suitable for anti-Tsal antibody detection in a competitive ELISA format. The competitive ELISA was able to detect exposure to a broad range of tsetse species (G. morsitans morsitans, G. pallidipes, G. palpalis gambiensis and G. fuscipes) and did not cross-react with the other hematophagous insects (Stomoxys calcitrans and Tabanus yao). Using a collection of plasmas from tsetse-exposed pigs, the new test characteristics were compared with those of the previously described G. m. moristans and rTsal1 indirect ELISAs, revealing equally good specificities (> 95%) and positive predictive values (> 98%) but higher negative predictive values and hence increased sensitivity (> 95%) and accuracy (> 95%). We have developed a highly accurate Nb-based competitive immunoassay to detect specific anti-Tsal antibodies induced by various tsetse fly species in a range of hosts. We propose that this competitive assay provides a simple serological indicator of tsetse fly presence without the requirement of test adaptation to the vertebrate host species. In addition, the use of monoclonal Nbs for antibody detection is innovative and could be applied to other tsetse fly salivary biomarkers in order to achieve a multi-target immunoprofiling of hosts. In addition, this approach could be broadened to other pathogenic organisms for which accurate serological diagnosis remains a bottleneck.

Description of a Nanobody-based Competitive Immunoassay to Detect Tsetse Fly Exposure

PubMed Central

Caljon, Guy; Hussain, Shahid; Vermeiren, Lieve; Van Den Abbeele, Jan

2015-01-01

Background Tsetse flies are the main vectors of human and animal African trypanosomes. The Tsal proteins in tsetse fly saliva were previously identified as suitable biomarkers of bite exposure. A new competitive assay was conceived based on nanobody (Nb) technology to ameliorate the detection of anti-Tsal antibodies in mammalian hosts. Methodology/Principal Findings A camelid-derived Nb library was generated against the Glossina morsitans morsitans sialome and exploited to select Tsal specific Nbs. One of the three identified Nb families (family III, TsalNb-05 and TsalNb-11) was found suitable for anti-Tsal antibody detection in a competitive ELISA format. The competitive ELISA was able to detect exposure to a broad range of tsetse species (G. morsitans morsitans, G. pallidipes, G. palpalis gambiensis and G. fuscipes) and did not cross-react with the other hematophagous insects (Stomoxys calcitrans and Tabanus yao). Using a collection of plasmas from tsetse-exposed pigs, the new test characteristics were compared with those of the previously described G. m. moristans and rTsal1 indirect ELISAs, revealing equally good specificities (> 95%) and positive predictive values (> 98%) but higher negative predictive values and hence increased sensitivity (> 95%) and accuracy (> 95%). Conclusion/Significance We have developed a highly accurate Nb-based competitive immunoassay to detect specific anti-Tsal antibodies induced by various tsetse fly species in a range of hosts. We propose that this competitive assay provides a simple serological indicator of tsetse fly presence without the requirement of test adaptation to the vertebrate host species. In addition, the use of monoclonal Nbs for antibody detection is innovative and could be applied to other tsetse fly salivary biomarkers in order to achieve a multi-target immunoprofiling of hosts. In addition, this approach could be broadened to other pathogenic organisms for which accurate serological diagnosis remains a

Novel fMRI working memory paradigm accurately detects cognitive impairment in Multiple Sclerosis

PubMed Central

Nelson, Flavia; Akhtar, Mohammad A.; Zúñiga, Edward; Perez, Carlos A.; Hasan, Khader M.; Wilken, Jeffrey; Wolinsky, Jerry S.; Narayana, Ponnada A.; Steinberg, Joel L.

2016-01-01

Background Cognitive impairment (CI) cannot be diagnosed by MRI. Functional MRI (fMRI) paradigms such as the immediate/delayed memory task (I/DMT), detect varying degrees of working memory. Preliminary findings using I/DMT, showed differences in Blood Oxygenation Level Dependent (BOLD) activation between impaired (MSCI, n=12) and non-impaired (MSNI, n=9) MS patients. Objectives To confirm CI detection based on I/DMT’ BOLD activation in a larger cohort of MS patients. The role of T2 lesion volume (LV) and EDSS in magnitude of BOLD signal were also sought. Methods Fifty patients [EDSS mean (m) = 3.2, DD m =12 yr., age m =40yr.] underwent the Minimal Assessment of Cognitive Function in MS (MACFIMS) and the I/DMT. Working-memory activation (WMa) represents BOLD signal during DMT minus signal during IMT. CI was based on MACFIMS. Results 10 MSNI, 30 MSCI and 4 borderline patients were included in analyses. ANOVA showed MSNI had significantly greater WMa than MSCI, in the left (L) prefrontal cortex and L supplementary motor area (p = 0.032). Regression analysis showed significant inverse correlations between WMa and T2 LV/EDSS in similar areas (p = 0.005, 0.004 respectively). Conclusion I/DMT-based BOLD activation detects CI in MS, larger studies are needed to confirm these findings. PMID:27613119

Distributed Fault Detection Based on Credibility and Cooperation for WSNs in Smart Grids.

PubMed

Shao, Sujie; Guo, Shaoyong; Qiu, Xuesong

2017-04-28

Due to the increasingly important role in monitoring and data collection that sensors play, accurate and timely fault detection is a key issue for wireless sensor networks (WSNs) in smart grids. This paper presents a novel distributed fault detection mechanism for WSNs based on credibility and cooperation. Firstly, a reasonable credibility model of a sensor is established to identify any suspicious status of the sensor according to its own temporal data correlation. Based on the credibility model, the suspicious sensor is then chosen to launch fault diagnosis requests. Secondly, the sending time of fault diagnosis request is discussed to avoid the transmission overhead brought about by unnecessary diagnosis requests and improve the efficiency of fault detection based on neighbor cooperation. The diagnosis reply of a neighbor sensor is analyzed according to its own status. Finally, to further improve the accuracy of fault detection, the diagnosis results of neighbors are divided into several classifications to judge the fault status of the sensors which launch the fault diagnosis requests. Simulation results show that this novel mechanism can achieve high fault detection ratio with a small number of fault diagnoses and low data congestion probability.

Distributed Fault Detection Based on Credibility and Cooperation for WSNs in Smart Grids

PubMed Central

Shao, Sujie; Guo, Shaoyong; Qiu, Xuesong

2017-01-01

Due to the increasingly important role in monitoring and data collection that sensors play, accurate and timely fault detection is a key issue for wireless sensor networks (WSNs) in smart grids. This paper presents a novel distributed fault detection mechanism for WSNs based on credibility and cooperation. Firstly, a reasonable credibility model of a sensor is established to identify any suspicious status of the sensor according to its own temporal data correlation. Based on the credibility model, the suspicious sensor is then chosen to launch fault diagnosis requests. Secondly, the sending time of fault diagnosis request is discussed to avoid the transmission overhead brought about by unnecessary diagnosis requests and improve the efficiency of fault detection based on neighbor cooperation. The diagnosis reply of a neighbor sensor is analyzed according to its own status. Finally, to further improve the accuracy of fault detection, the diagnosis results of neighbors are divided into several classifications to judge the fault status of the sensors which launch the fault diagnosis requests. Simulation results show that this novel mechanism can achieve high fault detection ratio with a small number of fault diagnoses and low data congestion probability. PMID:28452925

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.

Automatic cardiac cycle determination directly from EEG-fMRI data by multi-scale peak detection method.

PubMed

Wong, Chung-Ki; Luo, Qingfei; Zotev, Vadim; Phillips, Raquel; Chan, Kam Wai Clifford; Bodurka, Jerzy

2018-03-31

In simultaneous EEG-fMRI, identification of the period of cardioballistic artifact (BCG) in EEG is required for the artifact removal. Recording the electrocardiogram (ECG) waveform during fMRI is difficult, often causing inaccurate period detection. Since the waveform of the BCG extracted by independent component analysis (ICA) is relatively invariable compared to the ECG waveform, we propose a multiple-scale peak-detection algorithm to determine the BCG cycle directly from the EEG data. The algorithm first extracts the high contrast BCG component from the EEG data by ICA. The BCG cycle is then estimated by band-pass filtering the component around the fundamental frequency identified from its energy spectral density, and the peak of BCG artifact occurrence is selected from each of the estimated cycle. The algorithm is shown to achieve a high accuracy on a large EEG-fMRI dataset. It is also adaptive to various heart rates without the needs of adjusting the threshold parameters. The cycle detection remains accurate with the scan duration reduced to half a minute. Additionally, the algorithm gives a figure of merit to evaluate the reliability of the detection accuracy. The algorithm is shown to give a higher detection accuracy than the commonly used cycle detection algorithm fmrib_qrsdetect implemented in EEGLAB. The achieved high cycle detection accuracy of our algorithm without using the ECG waveforms makes possible to create and automate pipelines for processing large EEG-fMRI datasets, and virtually eliminates the need for ECG recordings for BCG artifact removal. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Communication: Accurate higher-order van der Waals coefficients between molecules from a model dynamic multipole polarizability

DOE PAGES

Tao, Jianmin; Rappe, Andrew M.

2016-01-20

Due to the absence of the long-range van der Waals (vdW) interaction, conventional density functional theory (DFT) often fails in the description of molecular complexes and solids. In recent years, considerable progress has been made in the development of the vdW correction. However, the vdW correction based on the leading-order coefficient C 6 alone can only achieve limited accuracy, while accurate modeling of higher-order coefficients remains a formidable task, due to the strong non-additivity effect. Here, we apply a model dynamic multipole polarizability within a modified single-frequency approximation to calculate C 8 and C 10 between small molecules. We findmore » that the higher-order vdW coefficients from this model can achieve remarkable accuracy, with mean absolute relative deviations of 5% for C 8 and 7% for C 10. As a result, inclusion of accurate higher-order contributions in the vdW correction will effectively enhance the predictive power of DFT in condensed matter physics and quantum chemistry.« less

An accurate algorithm to match imperfectly matched images for lung tumor detection without markers

PubMed Central

Rozario, Timothy; Bereg, Sergey; Yan, Yulong; Chiu, Tsuicheng; Liu, Honghuan; Kearney, Vasant; Jiang, Lan

2015-01-01

implanted and used as ground truth for tumor positions. Although other organs and bony structures introduced strong signals superimposed on tumors at some angles, this method accurately located tumors on every projection over 12 gantry angles. The maximum error was less than 2.2 mm, while the total average error was less than 0.9 mm. This algorithm was capable of detecting tumors without markers, despite strong background signals. PACS numbers: 87.57.cj, 87.57.cp87.57.nj, 87.57.np, 87.57.Q‐, 87.59.bf, 87.63.lm

Detection of BCG bacteria using a magnetoresistive biosensor: A step towards a fully electronic platform for tuberculosis point-of-care detection.

PubMed

Barroso, Teresa G; Martins, Rui C; Fernandes, Elisabete; Cardoso, Susana; Rivas, José; Freitas, Paulo P

2018-02-15

Tuberculosis is one of the major public health concerns. This highly contagious disease affects more than 10.4 million people, being a leading cause of morbidity by infection. Tuberculosis is diagnosed at the point-of-care by the Ziehl-Neelsen sputum smear microscopy test. Ziehl-Neelsen is laborious, prone to human error and infection risk, with a limit of detection of 10 4 cells/mL. In resource-poor nations, a more practical test, with lower detection limit, is paramount. This work uses a magnetoresistive biosensor to detect BCG bacteria for tuberculosis diagnosis. Herein we report: i) nanoparticle assembly method and specificity for tuberculosis detection; ii) demonstration of proportionality between BCG cell concentration and magnetoresistive voltage signal; iii) application of multiplicative signal correction for systematic effects removal; iv) investigation of calibration effectiveness using chemometrics methods; and v) comparison with state-of-the-art point-of-care tuberculosis biosensors. Results present a clear correspondence between voltage signal and cell concentration. Multiplicative signal correction removes baseline shifts within and between biochip sensors, allowing accurate and precise voltage signal between different biochips. The corrected signal was used for multivariate regression models, which significantly decreased the calibration standard error from 0.50 to 0.03log 10 (cells/mL). Results show that Ziehl-Neelsen detection limits and below are achievable with the magnetoresistive biochip, when pre-processing and chemometrics are used. Copyright © 2017 Elsevier B.V. All rights reserved.

Accurate high-throughput structure mapping and prediction with transition metal ion FRET

PubMed Central

Yu, Xiaozhen; Wu, Xiongwu; Bermejo, Guillermo A.; Brooks, Bernard R.; Taraska, Justin W.

2013-01-01

Mapping the landscape of a protein’s conformational space is essential to understanding its functions and regulation. The limitations of many structural methods have made this process challenging for most proteins. Here, we report that transition metal ion FRET (tmFRET) can be used in a rapid, highly parallel screen, to determine distances from multiple locations within a protein at extremely low concentrations. The distances generated through this screen for the protein Maltose Binding Protein (MBP) match distances from the crystal structure to within a few angstroms. Furthermore, energy transfer accurately detects structural changes during ligand binding. Finally, fluorescence-derived distances can be used to guide molecular simulations to find low energy states. Our results open the door to rapid, accurate mapping and prediction of protein structures at low concentrations, in large complex systems, and in living cells. PMID:23273426

Accurate quantification of microRNA via single strand displacement reaction on DNA origami motif.

PubMed

Zhu, Jie; Feng, Xiaolu; Lou, Jingyu; Li, Weidong; Li, Sheng; Zhu, Hongxin; Yang, Lun; Zhang, Aiping; He, Lin; Li, Can

2013-01-01

DNA origami is an emerging technology that assembles hundreds of staple strands and one single-strand DNA into certain nanopattern. It has been widely used in various fields including detection of biological molecules such as DNA, RNA and proteins. MicroRNAs (miRNAs) play important roles in post-transcriptional gene repression as well as many other biological processes such as cell growth and differentiation. Alterations of miRNAs' expression contribute to many human diseases. However, it is still a challenge to quantitatively detect miRNAs by origami technology. In this study, we developed a novel approach based on streptavidin and quantum dots binding complex (STV-QDs) labeled single strand displacement reaction on DNA origami to quantitatively detect the concentration of miRNAs. We illustrated a linear relationship between the concentration of an exemplary miRNA as miRNA-133 and the STV-QDs hybridization efficiency; the results demonstrated that it is an accurate nano-scale miRNA quantifier motif. In addition, both symmetrical rectangular motif and asymmetrical China-map motif were tested. With significant linearity in both motifs, our experiments suggested that DNA Origami motif with arbitrary shape can be utilized in this method. Since this DNA origami-based method we developed owns the unique advantages of simple, time-and-material-saving, potentially multi-targets testing in one motif and relatively accurate for certain impurity samples as counted directly by atomic force microscopy rather than fluorescence signal detection, it may be widely used in quantification of miRNAs.

Accurate Quantification of microRNA via Single Strand Displacement Reaction on DNA Origami Motif

PubMed Central

Lou, Jingyu; Li, Weidong; Li, Sheng; Zhu, Hongxin; Yang, Lun; Zhang, Aiping; He, Lin; Li, Can

2013-01-01

DNA origami is an emerging technology that assembles hundreds of staple strands and one single-strand DNA into certain nanopattern. It has been widely used in various fields including detection of biological molecules such as DNA, RNA and proteins. MicroRNAs (miRNAs) play important roles in post-transcriptional gene repression as well as many other biological processes such as cell growth and differentiation. Alterations of miRNAs' expression contribute to many human diseases. However, it is still a challenge to quantitatively detect miRNAs by origami technology. In this study, we developed a novel approach based on streptavidin and quantum dots binding complex (STV-QDs) labeled single strand displacement reaction on DNA origami to quantitatively detect the concentration of miRNAs. We illustrated a linear relationship between the concentration of an exemplary miRNA as miRNA-133 and the STV-QDs hybridization efficiency; the results demonstrated that it is an accurate nano-scale miRNA quantifier motif. In addition, both symmetrical rectangular motif and asymmetrical China-map motif were tested. With significant linearity in both motifs, our experiments suggested that DNA Origami motif with arbitrary shape can be utilized in this method. Since this DNA origami-based method we developed owns the unique advantages of simple, time-and-material-saving, potentially multi-targets testing in one motif and relatively accurate for certain impurity samples as counted directly by atomic force microscopy rather than fluorescence signal detection, it may be widely used in quantification of miRNAs. PMID:23990889

Automated detection of dark and bright lesions in retinal images for early detection of diabetic retinopathy.

PubMed

Akram, Usman M; Khan, Shoab A

2012-10-01

There is an ever-increasing interest in the development of automatic medical diagnosis systems due to the advancement in computing technology and also to improve the service by medical community. The knowledge about health and disease is required for reliable and accurate medical diagnosis. Diabetic Retinopathy (DR) is one of the most common causes of blindness and it can be prevented if detected and treated early. DR has different signs and the most distinctive are microaneurysm and haemorrhage which are dark lesions and hard exudates and cotton wool spots which are bright lesions. Location and structure of blood vessels and optic disk play important role in accurate detection and classification of dark and bright lesions for early detection of DR. In this article, we propose a computer aided system for the early detection of DR. The article presents algorithms for retinal image preprocessing, blood vessel enhancement and segmentation and optic disk localization and detection which eventually lead to detection of different DR lesions using proposed hybrid fuzzy classifier. The developed methods are tested on four different publicly available databases. The presented methods are compared with recently published methods and the results show that presented methods outperform all others.

A real time metabolomic profiling approach to detecting fish fraud using rapid evaporative ionisation mass spectrometry.

PubMed

Black, Connor; Chevallier, Olivier P; Haughey, Simon A; Balog, Julia; Stead, Sara; Pringle, Steven D; Riina, Maria V; Martucci, Francesca; Acutis, Pier L; Morris, Mike; Nikolopoulos, Dimitrios S; Takats, Zoltan; Elliott, Christopher T

2017-01-01

Fish fraud detection is mainly carried out using a genomic profiling approach requiring long and complex sample preparations and assay running times. Rapid evaporative ionisation mass spectrometry (REIMS) can circumvent these issues without sacrificing a loss in the quality of results. To demonstrate that REIMS can be used as a fast profiling technique capable of achieving accurate species identification without the need for any sample preparation. Additionally, we wanted to demonstrate that other aspects of fish fraud other than speciation are detectable using REIMS. 478 samples of five different white fish species were subjected to REIMS analysis using an electrosurgical knife. Each sample was cut 8-12 times with each one lasting 3-5 s and chemometric models were generated based on the mass range m/z 600-950 of each sample. The identification of 99 validation samples provided a 98.99% correct classification in which species identification was obtained near-instantaneously (≈ 2 s) unlike any other form of food fraud analysis. Significant time comparisons between REIMS and polymerase chain reaction (PCR) were observed when analysing 6 mislabelled samples demonstrating how REIMS can be used as a complimentary technique to detect fish fraud. Additionally, we have demonstrated that the catch method of fish products is capable of detection using REIMS, a concept never previously reported. REIMS has been proven to be an innovative technique to help aid the detection of fish fraud and has the potential to be utilised by fisheries to conduct their own quality control (QC) checks for fast accurate results.

Accurate Arabic Script Language/Dialect Classification

DTIC Science & Technology

2014-01-01

Army Research Laboratory Accurate Arabic Script Language/Dialect Classification by Stephen C. Tratz ARL-TR-6761 January 2014 Approved for public...1197 ARL-TR-6761 January 2014 Accurate Arabic Script Language/Dialect Classification Stephen C. Tratz Computational and Information Sciences...Include area code) Standard Form 298 (Rev. 8/98) Prescribed by ANSI Std. Z39.18 January 2014 Final Accurate Arabic Script Language/Dialect Classification

A Novel Zero Velocity Interval Detection Algorithm for Self-Contained Pedestrian Navigation System with Inertial Sensors

PubMed Central

Tian, Xiaochun; Chen, Jiabin; Han, Yongqiang; Shang, Jianyu; Li, Nan

2016-01-01

Zero velocity update (ZUPT) plays an important role in pedestrian navigation algorithms with the premise that the zero velocity interval (ZVI) should be detected accurately and effectively. A novel adaptive ZVI detection algorithm based on a smoothed pseudo Wigner–Ville distribution to remove multiple frequencies intelligently (SPWVD-RMFI) is proposed in this paper. The novel algorithm adopts the SPWVD-RMFI method to extract the pedestrian gait frequency and to calculate the optimal ZVI detection threshold in real time by establishing the function relationships between the thresholds and the gait frequency; then, the adaptive adjustment of thresholds with gait frequency is realized and improves the ZVI detection precision. To put it into practice, a ZVI detection experiment is carried out; the result shows that compared with the traditional fixed threshold ZVI detection method, the adaptive ZVI detection algorithm can effectively reduce the false and missed detection rate of ZVI; this indicates that the novel algorithm has high detection precision and good robustness. Furthermore, pedestrian trajectory positioning experiments at different walking speeds are carried out to evaluate the influence of the novel algorithm on positioning precision. The results show that the ZVI detected by the adaptive ZVI detection algorithm for pedestrian trajectory calculation can achieve better performance. PMID:27669266

Toward accurate and fast iris segmentation for iris biometrics.

PubMed

He, Zhaofeng; Tan, Tieniu; Sun, Zhenan; Qiu, Xianchao

2009-09-01

Iris segmentation is an essential module in iris recognition because it defines the effective image region used for subsequent processing such as feature extraction. Traditional iris segmentation methods often involve an exhaustive search of a large parameter space, which is time consuming and sensitive to noise. To address these problems, this paper presents a novel algorithm for accurate and fast iris segmentation. After efficient reflection removal, an Adaboost-cascade iris detector is first built to extract a rough position of the iris center. Edge points of iris boundaries are then detected, and an elastic model named pulling and pushing is established. Under this model, the center and radius of the circular iris boundaries are iteratively refined in a way driven by the restoring forces of Hooke's law. Furthermore, a smoothing spline-based edge fitting scheme is presented to deal with noncircular iris boundaries. After that, eyelids are localized via edge detection followed by curve fitting. The novelty here is the adoption of a rank filter for noise elimination and a histogram filter for tackling the shape irregularity of eyelids. Finally, eyelashes and shadows are detected via a learned prediction model. This model provides an adaptive threshold for eyelash and shadow detection by analyzing the intensity distributions of different iris regions. Experimental results on three challenging iris image databases demonstrate that the proposed algorithm outperforms state-of-the-art methods in both accuracy and speed.

Group discussion improves lie detection

PubMed Central

Klein, Nadav; Epley, Nicholas

2015-01-01

Groups of individuals can sometimes make more accurate judgments than the average individual could make alone. We tested whether this group advantage extends to lie detection, an exceptionally challenging judgment with accuracy rates rarely exceeding chance. In four experiments, we find that groups are consistently more accurate than individuals in distinguishing truths from lies, an effect that comes primarily from an increased ability to correctly identify when a person is lying. These experiments demonstrate that the group advantage in lie detection comes through the process of group discussion, and is not a product of aggregating individual opinions (a “wisdom-of-crowds” effect) or of altering response biases (such as reducing the “truth bias”). Interventions to improve lie detection typically focus on improving individual judgment, a costly and generally ineffective endeavor. Our findings suggest a cheap and simple synergistic approach of enabling group discussion before rendering a judgment. PMID:26015581

When Dijkstra Meets Vanishing Point: A Stereo Vision Approach for Road Detection.

PubMed

Zhang, Yigong; Su, Yingna; Yang, Jian; Ponce, Jean; Kong, Hui

2018-05-01

In this paper, we propose a vanishing-point constrained Dijkstra road model for road detection in a stereo-vision paradigm. First, the stereo-camera is used to generate the u- and v-disparity maps of road image, from which the horizon can be extracted. With the horizon and ground region constraints, we can robustly locate the vanishing point of road region. Second, a weighted graph is constructed using all pixels of the image, and the detected vanishing point is treated as the source node of the graph. By computing a vanishing-point constrained Dijkstra minimum-cost map, where both disparity and gradient of gray image are used to calculate cost between two neighbor pixels, the problem of detecting road borders in image is transformed into that of finding two shortest paths that originate from the vanishing point to two pixels in the last row of image. The proposed approach has been implemented and tested over 2600 grayscale images of different road scenes in the KITTI data set. The experimental results demonstrate that this training-free approach can detect horizon, vanishing point, and road regions very accurately and robustly. It can achieve promising performance.

Detection of aflatoxin B₁ with immunochromatographic test strips: Enhanced signal sensitivity using gold nanoflowers.

PubMed

Ji, Yanwei; Ren, Meiling; Li, Yanping; Huang, Zhibing; Shu, Mei; Yang, Hongwei; Xiong, Yonghua; Xu, Yang

2015-09-01

Immunochromatographic test strips (ICTS) are commonly limited to higher concentrations of analytes. This limitation stems from the relatively low sensitivity of conventional gold nanospheres (AuNSs with a diameter of 20 nm) to emit detectable brightness values. The larger multi-branched gold nanoflowers (AuNFs) with a higher optical brightness as well as good colloidal stability exhibit significant improvements over conventional AuNSs for enhanced sensitivity of ICTS. In this study, blue AuNFs with an average diameter of 75±5 nm were synthetized and employed as a signal amplification probe for ultrasensitive and quantitative detection of aflatoxin B1 (AFB1) in rice. A portable optical strip reader was used to record the optical densities of test and control lines of the strip. Under the optimal conditions, the AuNF based ICTS system accurately detected AFB1 linearly and dynamically over the range of 0.5-25 pg/mL with a half maximal inhibitory concentration at 4.17 pg/mL. The inhibitory concentration was achieved 10 times lower than that of the traditional AuNS based ICTS systems (41.25 pg/mL). The limit of detection for AFB1 in rice extract was achieved at 0.32 pg/mL. In summary, AuNFs are a novel probe that exhibited excellent sensitivity in the ICTS system and could be used for ultrasensitive detection of other analytes in food safety monitoring, and even medical diagnostics. Copyright © 2015 Elsevier B.V. All rights reserved.

An Accurate Transmitting Power Control Method in Wireless Communication Transceivers

NASA Astrophysics Data System (ADS)

Zhang, Naikang; Wen, Zhiping; Hou, Xunping; Bi, Bo

2018-01-01

Power control circuits are widely used in transceivers aiming at stabilizing the transmitted signal power to a specified value, thereby reducing power consumption and interference to other frequency bands. In order to overcome the shortcomings of traditional modes of power control, this paper proposes an accurate signal power detection method by multiplexing the receiver and realizes transmitting power control in the digital domain. The simulation results show that this novel digital power control approach has advantages of small delay, high precision and simplified design procedure. The proposed method is applicable to transceivers working at large frequency dynamic range, and has good engineering practicability.

Accurate Simulation and Detection of Coevolution Signals in Multiple Sequence Alignments

PubMed Central

Ackerman, Sharon H.; Tillier, Elisabeth R.; Gatti, Domenico L.

2012-01-01

Background While the conserved positions of a multiple sequence alignment (MSA) are clearly of interest, non-conserved positions can also be important because, for example, destabilizing effects at one position can be compensated by stabilizing effects at another position. Different methods have been developed to recognize the evolutionary relationship between amino acid sites, and to disentangle functional/structural dependencies from historical/phylogenetic ones. Methodology/Principal Findings We have used two complementary approaches to test the efficacy of these methods. In the first approach, we have used a new program, MSAvolve, for the in silico evolution of MSAs, which records a detailed history of all covarying positions, and builds a global coevolution matrix as the accumulated sum of individual matrices for the positions forced to co-vary, the recombinant coevolution, and the stochastic coevolution. We have simulated over 1600 MSAs for 8 protein families, which reflect sequences of different sizes and proteins with widely different functions. The calculated coevolution matrices were compared with the coevolution matrices obtained for the same evolved MSAs with different coevolution detection methods. In a second approach we have evaluated the capacity of the different methods to predict close contacts in the representative X-ray structures of an additional 150 protein families using only experimental MSAs. Conclusions/Significance Methods based on the identification of global correlations between pairs were found to be generally superior to methods based only on local correlations in their capacity to identify coevolving residues using either simulated or experimental MSAs. However, the significant variability in the performance of different methods with different proteins suggests that the simulation of MSAs that replicate the statistical properties of the experimental MSA can be a valuable tool to identify the coevolution detection method that is most

Analytical method for the accurate determination of tricothecenes in grains using LC-MS/MS: a comparison between MRM transition and MS3 quantitation.

PubMed

Lim, Chee Wei; Tai, Siew Hoon; Lee, Lin Min; Chan, Sheot Harn

2012-07-01

The current food crisis demands unambiguous determination of mycotoxin contamination in staple foods to achieve safer food for consumption. This paper describes the first accurate LC-MS/MS method developed to analyze tricothecenes in grains by applying multiple reaction monitoring (MRM) transition and MS(3) quantitation strategies in tandem. The tricothecenes are nivalenol, deoxynivalenol, deoxynivalenol-3-glucoside, fusarenon X, 3-acetyl-deoxynivalenol, 15-acetyldeoxynivalenol, diacetoxyscirpenol, and HT-2 and T-2 toxins. Acetic acid and ammonium acetate were used to convert the analytes into their respective acetate adducts and ammonium adducts under negative and positive MS polarity conditions, respectively. The mycotoxins were separated by reversed-phase LC in a 13.5-min run, ionized using electrospray ionization, and detected by tandem mass spectrometry. Analyte-specific mass-to-charge (m/z) ratios were used to perform quantitation under MRM transition and MS(3) (linear ion trap) modes. Three experiments were made for each quantitation mode and matrix in batches over 6 days for recovery studies. The matrix effect was investigated at concentration levels of 20, 40, 80, 120, 160, and 200 μg kg(-1) (n = 3) in 5 g corn flour and rice flour. Extraction with acetonitrile provided a good overall recovery range of 90-108% (n = 3) at three levels of spiking concentration of 40, 80, and 120 μg kg(-1). A quantitation limit of 2-6 μg kg(-1) was achieved by applying an MRM transition quantitation strategy. Under MS(3) mode, a quantitation limit of 4-10 μg kg(-1) was achieved. Relative standard deviations of 2-10% and 2-11% were reported for MRM transition and MS(3) quantitation, respectively. The successful utilization of MS(3) enabled accurate analyte fragmentation pattern matching and its quantitation, leading to the development of analytical methods in fields that demand both analyte specificity and fragmentation fingerprint-matching capabilities that are

Accurate Sample Time Reconstruction of Inertial FIFO Data.

PubMed

Stieber, Sebastian; Dorsch, Rainer; Haubelt, Christian

2017-12-13

In the context of modern cyber-physical systems, the accuracy of underlying sensor data plays an increasingly important role in sensor data fusion and feature extraction. The raw events of multiple sensors have to be aligned in time to enable high quality sensor fusion results. However, the growing number of simultaneously connected sensor devices make the energy saving data acquisition and processing more and more difficult. Hence, most of the modern sensors offer a first-in-first-out (FIFO) interface to store multiple data samples and to relax timing constraints, when handling multiple sensor devices. However, using the FIFO interface increases the negative influence of individual clock drifts-introduced by fabrication inaccuracies, temperature changes and wear-out effects-onto the sampling data reconstruction. Furthermore, additional timing offset errors due to communication and software latencies increases with a growing number of sensor devices. In this article, we present an approach for an accurate sample time reconstruction independent of the actual clock drift with the help of an internal sensor timer. Such timers are already available in modern sensors, manufactured in micro-electromechanical systems (MEMS) technology. The presented approach focuses on calculating accurate time stamps using the sensor FIFO interface in a forward-only processing manner as a robust and energy saving solution. The proposed algorithm is able to lower the overall standard deviation of reconstructed sampling periods below 40 μ s, while run-time savings of up to 42% are achieved, compared to single sample acquisition.

Hemorrhage Detection and Segmentation in Traumatic Pelvic Injuries

PubMed Central

Davuluri, Pavani; Wu, Jie; Tang, Yang; Cockrell, Charles H.; Ward, Kevin R.; Najarian, Kayvan; Hargraves, Rosalyn H.

2012-01-01

Automated hemorrhage detection and segmentation in traumatic pelvic injuries is vital for fast and accurate treatment decision making. Hemorrhage is the main cause of deaths in patients within first 24 hours after the injury. It is very time consuming for physicians to analyze all Computed Tomography (CT) images manually. As time is crucial in emergence medicine, analyzing medical images manually delays the decision-making process. Automated hemorrhage detection and segmentation can significantly help physicians to analyze these images and make fast and accurate decisions. Hemorrhage segmentation is a crucial step in the accurate diagnosis and treatment decision-making process. This paper presents a novel rule-based hemorrhage segmentation technique that utilizes pelvic anatomical information to segment hemorrhage accurately. An evaluation measure is used to quantify the accuracy of hemorrhage segmentation. The results show that the proposed method is able to segment hemorrhage very well, and the results are promising. PMID:22919433

38 CFR 4.46 - Accurate measurement.

Code of Federal Regulations, 2011 CFR

2011-07-01

... RATING DISABILITIES Disability Ratings The Musculoskeletal System § 4.46 Accurate measurement. Accurate... indispensable in examinations conducted within the Department of Veterans Affairs. Muscle atrophy must also be...

38 CFR 4.46 - Accurate measurement.

Code of Federal Regulations, 2014 CFR

2014-07-01

... RATING DISABILITIES Disability Ratings The Musculoskeletal System § 4.46 Accurate measurement. Accurate... indispensable in examinations conducted within the Department of Veterans Affairs. Muscle atrophy must also be...

38 CFR 4.46 - Accurate measurement.

Code of Federal Regulations, 2010 CFR

2010-07-01

... RATING DISABILITIES Disability Ratings The Musculoskeletal System § 4.46 Accurate measurement. Accurate... indispensable in examinations conducted within the Department of Veterans Affairs. Muscle atrophy must also be...

38 CFR 4.46 - Accurate measurement.

Code of Federal Regulations, 2013 CFR

2013-07-01

... RATING DISABILITIES Disability Ratings The Musculoskeletal System § 4.46 Accurate measurement. Accurate... indispensable in examinations conducted within the Department of Veterans Affairs. Muscle atrophy must also be...

38 CFR 4.46 - Accurate measurement.

Code of Federal Regulations, 2012 CFR

2012-07-01

... RATING DISABILITIES Disability Ratings The Musculoskeletal System § 4.46 Accurate measurement. Accurate... indispensable in examinations conducted within the Department of Veterans Affairs. Muscle atrophy must also be...

TreeShrink: fast and accurate detection of outlier long branches in collections of phylogenetic trees.

PubMed

Mai, Uyen; Mirarab, Siavash

2018-05-08

Sequence data used in reconstructing phylogenetic trees may include various sources of error. Typically errors are detected at the sequence level, but when missed, the erroneous sequences often appear as unexpectedly long branches in the inferred phylogeny. We propose an automatic method to detect such errors. We build a phylogeny including all the data then detect sequences that artificially inflate the tree diameter. We formulate an optimization problem, called the k-shrink problem, that seeks to find k leaves that could be removed to maximally reduce the tree diameter. We present an algorithm to find the exact solution for this problem in polynomial time. We then use several statistical tests to find outlier species that have an unexpectedly high impact on the tree diameter. These tests can use a single tree or a set of related gene trees and can also adjust to species-specific patterns of branch length. The resulting method is called TreeShrink. We test our method on six phylogenomic biological datasets and an HIV dataset and show that the method successfully detects and removes long branches. TreeShrink removes sequences more conservatively than rogue taxon removal and often reduces gene tree discordance more than rogue taxon removal once the amount of filtering is controlled. TreeShrink is an effective method for detecting sequences that lead to unrealistically long branch lengths in phylogenetic trees. The tool is publicly available at https://github.com/uym2/TreeShrink .

Feedback about More Accurate versus Less Accurate Trials: Differential Effects on Self-Confidence and Activation

ERIC Educational Resources Information Center

Badami, Rokhsareh; VaezMousavi, Mohammad; Wulf, Gabriele; Namazizadeh, Mahdi

2012-01-01

One purpose of the present study was to examine whether self-confidence or anxiety would be differentially affected by feedback from more accurate rather than less accurate trials. The second purpose was to determine whether arousal variations (activation) would predict performance. On Day 1, participants performed a golf putting task under one of…

On-Site Molecular Detection of Soil-Borne Phytopathogens Using a Portable Real-Time PCR System

PubMed Central

DeShields, Joseph B.; Bomberger, Rachel A.; Woodhall, James W.; Wheeler, David L.; Moroz, Natalia; Johnson, Dennis A.; Tanaka, Kiwamu

2018-01-01

On-site diagnosis of plant diseases can be a useful tool for growers for timely decisions enabling the earlier implementation of disease management strategies that reduce the impact of the disease. Presently in many diagnostic laboratories, the polymerase chain reaction (PCR), particularly real-time PCR, is considered the most sensitive and accurate method for plant pathogen detection. However, laboratory-based PCRs typically require expensive laboratory equipment and skilled personnel. In this study, soil-borne pathogens of potato are used to demonstrate the potential for on-site molecular detection. This was achieved using a rapid and simple protocol comprising of magnetic bead-based nucleic acid extraction, portable real-time PCR (fluorogenic probe-based assay). The portable real-time PCR approach compared favorably with a laboratory-based system, detecting as few as 100 copies of DNA from Spongospora subterranea. The portable real-time PCR method developed here can serve as an alternative to laboratory-based approaches and a useful on-site tool for pathogen diagnosis. PMID:29553557

An ensemble of dynamic neural network identifiers for fault detection and isolation of gas turbine engines.

PubMed

Amozegar, M; Khorasani, K

2016-04-01

In this paper, a new approach for Fault Detection and Isolation (FDI) of gas turbine engines is proposed by developing an ensemble of dynamic neural network identifiers. For health monitoring of the gas turbine engine, its dynamics is first identified by constructing three separate or individual dynamic neural network architectures. Specifically, a dynamic multi-layer perceptron (MLP), a dynamic radial-basis function (RBF) neural network, and a dynamic support vector machine (SVM) are trained to individually identify and represent the gas turbine engine dynamics. Next, three ensemble-based techniques are developed to represent the gas turbine engine dynamics, namely, two heterogeneous ensemble models and one homogeneous ensemble model. It is first shown that all ensemble approaches do significantly improve the overall performance and accuracy of the developed system identification scheme when compared to each of the stand-alone solutions. The best selected stand-alone model (i.e., the dynamic RBF network) and the best selected ensemble architecture (i.e., the heterogeneous ensemble) in terms of their performances in achieving an accurate system identification are then selected for solving the FDI task. The required residual signals are generated by using both a single model-based solution and an ensemble-based solution under various gas turbine engine health conditions. Our extensive simulation studies demonstrate that the fault detection and isolation task achieved by using the residuals that are obtained from the dynamic ensemble scheme results in a significantly more accurate and reliable performance as illustrated through detailed quantitative confusion matrix analysis and comparative studies. Copyright © 2016 Elsevier Ltd. All rights reserved.

An efficient and accurate 3D displacements tracking strategy for digital volume correlation

NASA Astrophysics Data System (ADS)

Pan, Bing; Wang, Bo; Wu, Dafang; Lubineau, Gilles

2014-07-01

Owing to its inherent computational complexity, practical implementation of digital volume correlation (DVC) for internal displacement and strain mapping faces important challenges in improving its computational efficiency. In this work, an efficient and accurate 3D displacement tracking strategy is proposed for fast DVC calculation. The efficiency advantage is achieved by using three improvements. First, to eliminate the need of updating Hessian matrix in each iteration, an efficient 3D inverse compositional Gauss-Newton (3D IC-GN) algorithm is introduced to replace existing forward additive algorithms for accurate sub-voxel displacement registration. Second, to ensure the 3D IC-GN algorithm that converges accurately and rapidly and avoid time-consuming integer-voxel displacement searching, a generalized reliability-guided displacement tracking strategy is designed to transfer accurate and complete initial guess of deformation for each calculation point from its computed neighbors. Third, to avoid the repeated computation of sub-voxel intensity interpolation coefficients, an interpolation coefficient lookup table is established for tricubic interpolation. The computational complexity of the proposed fast DVC and the existing typical DVC algorithms are first analyzed quantitatively according to necessary arithmetic operations. Then, numerical tests are performed to verify the performance of the fast DVC algorithm in terms of measurement accuracy and computational efficiency. The experimental results indicate that, compared with the existing DVC algorithm, the presented fast DVC algorithm produces similar precision and slightly higher accuracy at a substantially reduced computational cost.

Novel fMRI working memory paradigm accurately detects cognitive impairment in multiple sclerosis.

PubMed

Nelson, Flavia; Akhtar, Mohammad A; Zúñiga, Edward; Perez, Carlos A; Hasan, Khader M; Wilken, Jeffrey; Wolinsky, Jerry S; Narayana, Ponnada A; Steinberg, Joel L

2017-05-01

Cognitive impairment (CI) cannot be diagnosed by magnetic resonance imaging (MRI). Functional magnetic resonance imaging (fMRI) paradigms, such as the immediate/delayed memory task (I/DMT), detect varying degrees of working memory (WM). Preliminary findings using I/DMT showed differences in blood oxygenation level dependent (BOLD) activation between impaired (MSCI, n = 12) and non-impaired (MSNI, n = 9) multiple sclerosis (MS) patients. The aim of the study was to confirm CI detection based on I/DMT BOLD activation in a larger cohort of MS patients. The role of T2 lesion volume (LV) and Expanded Disability Status Scale (EDSS) in magnitude of BOLD signal was also sought. A total of 50 patients (EDSS mean ( m) = 3.2, disease duration (DD) m = 12 years, and age m = 40 years) underwent the Minimal Assessment of Cognitive Function in Multiple Sclerosis (MACFIMS) and I/DMT. Working memory activation (WMa) represents BOLD signal during DMT minus signal during IMT. CI was based on MACFIMS. A total of 10 MSNI, 30 MSCI, and 4 borderline patients were included in the analyses. Analysis of variance (ANOVA) showed MSNI had significantly greater WMa than MSCI, in the left prefrontal cortex and left supplementary motor area ( p = 0.032). Regression analysis showed significant inverse correlations between WMa and T2 LV/EDSS in similar areas ( p = 0.005, 0.004, respectively). I/DMT-based BOLD activation detects CI in MS. Larger studies are needed to confirm these findings.

Cascaded Segmentation-Detection Networks for Word-Level Text Spotting.

PubMed

Qin, Siyang; Manduchi, Roberto

2017-11-01

We introduce an algorithm for word-level text spotting that is able to accurately and reliably determine the bounding regions of individual words of text "in the wild". Our system is formed by the cascade of two convolutional neural networks. The first network is fully convolutional and is in charge of detecting areas containing text. This results in a very reliable but possibly inaccurate segmentation of the input image. The second network (inspired by the popular YOLO architecture) analyzes each segment produced in the first stage, and predicts oriented rectangular regions containing individual words. No post-processing (e.g. text line grouping) is necessary. With execution time of 450 ms for a 1000 × 560 image on a Titan X GPU, our system achieves good performance on the ICDAR 2013, 2015 benchmarks [2], [1].

Performance of a Micro-Strip Gas Chamber for event wise, high rate thermal neutron detection with accurate 2D position determination

NASA Astrophysics Data System (ADS)

Mindur, B.; Alimov, S.; Fiutowski, T.; Schulz, C.; Wilpert, T.

2014-12-01

A two-dimensional (2D) position sensitive detector for neutron scattering applications based on low-pressure gas amplification and micro-strip technology was built and tested with an innovative readout electronics and data acquisition system. This detector contains a thin solid neutron converter and was developed for time- and thus wavelength-resolved neutron detection in single-event counting mode, which improves the image contrast in comparison with integrating detectors. The prototype detector of a Micro-Strip Gas Chamber (MSGC) was built with a solid natGd/CsI thermal neutron converter for spatial resolutions of about 100 μm and counting rates up to 107 neutrons/s. For attaining very high spatial resolutions and counting rates via micro-strip readout with centre-of-gravity evaluation of the signal amplitude distributions, a fast, channel-wise, self-triggering ASIC was developed. The front-end chips (MSGCROCs), which are very first signal processing components, are read out into powerful ADC-FPGA boards for on-line data processing and thereafter via Gigabit Ethernet link into the data receiving PC. The workstation PC is controlled by a modular, high performance dedicated software suite. Such a fast and accurate system is crucial for efficient radiography/tomography, diffraction or imaging applications based on high flux thermal neutron beam. In this paper a brief description of the detector concept with its operation principles, readout electronics requirements and design together with the signals processing stages performed in hardware and software are presented. In more detail the neutron test beam conditions and measurement results are reported. The focus of this paper is on the system integration, two dimensional spatial resolution, the time resolution of the readout system and the imaging capabilities of the overall setup. The detection efficiency of the detector prototype is estimated as well.

Stop! border ahead: Automatic detection of subthalamic exit during deep brain stimulation surgery.

PubMed

Valsky, Dan; Marmor-Levin, Odeya; Deffains, Marc; Eitan, Renana; Blackwell, Kim T; Bergman, Hagai; Israel, Zvi

2017-01-01

Microelectrode recordings along preplanned trajectories are often used for accurate definition of the subthalamic nucleus (STN) borders during deep brain stimulation (DBS) surgery for Parkinson's disease. Usually, the demarcation of the STN borders is performed manually by a neurophysiologist. The exact detection of the borders is difficult, especially detecting the transition between the STN and the substantia nigra pars reticulata. Consequently, demarcation may be inaccurate, leading to suboptimal location of the DBS lead and inadequate clinical outcomes. We present machine-learning classification procedures that use microelectrode recording power spectra and allow for real-time, high-accuracy discrimination between the STN and substantia nigra pars reticulata. A support vector machine procedure was tested on microelectrode recordings from 58 trajectories that included both STN and substantia nigra pars reticulata that achieved a 97.6% consistency with human expert classification (evaluated by 10-fold cross-validation). We used the same data set as a training set to find the optimal parameters for a hidden Markov model using both microelectrode recording features and trajectory history to enable real-time classification of the ventral STN border (STN exit). Seventy-three additional trajectories were used to test the reliability of the learned statistical model in identifying the exit from the STN. The hidden Markov model procedure identified the STN exit with an error of 0.04 ± 0.18 mm and detection reliability (error < 1 mm) of 94%. The results indicate that robust, accurate, and automatic real-time electrophysiological detection of the ventral STN border is feasible. © 2016 International Parkinson and Movement Disorder Society. © 2016 International Parkinson and Movement Disorder Society.

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

Pairagon: a highly accurate, HMM-based cDNA-to-genome aligner.

PubMed

Lu, David V; Brown, Randall H; Arumugam, Manimozhiyan; Brent, Michael R

2009-07-01

The most accurate way to determine the intron-exon structures in a genome is to align spliced cDNA sequences to the genome. Thus, cDNA-to-genome alignment programs are a key component of most annotation pipelines. The scoring system used to choose the best alignment is a primary determinant of alignment accuracy, while heuristics that prevent consideration of certain alignments are a primary determinant of runtime and memory usage. Both accuracy and speed are important considerations in choosing an alignment algorithm, but scoring systems have received much less attention than heuristics. We present Pairagon, a pair hidden Markov model based cDNA-to-genome alignment program, as the most accurate aligner for sequences with high- and low-identity levels. We conducted a series of experiments testing alignment accuracy with varying sequence identity. We first created 'perfect' simulated cDNA sequences by splicing the sequences of exons in the reference genome sequences of fly and human. The complete reference genome sequences were then mutated to various degrees using a realistic mutation simulator and the perfect cDNAs were aligned to them using Pairagon and 12 other aligners. To validate these results with natural sequences, we performed cross-species alignment using orthologous transcripts from human, mouse and rat. We found that aligner accuracy is heavily dependent on sequence identity. For sequences with 100% identity, Pairagon achieved accuracy levels of >99.6%, with one quarter of the errors of any other aligner. Furthermore, for human/mouse alignments, which are only 85% identical, Pairagon achieved 87% accuracy, higher than any other aligner. Pairagon source and executables are freely available at http://mblab.wustl.edu/software/pairagon/

DR-TAMAS: Diffeomorphic Registration for Tensor Accurate Alignment of Anatomical Structures.

PubMed

Irfanoglu, M Okan; Nayak, Amritha; Jenkins, Jeffrey; Hutchinson, Elizabeth B; Sadeghi, Neda; Thomas, Cibu P; Pierpaoli, Carlo

2016-05-15

In this work, we propose DR-TAMAS (Diffeomorphic Registration for Tensor Accurate alignMent of Anatomical Structures), a novel framework for intersubject registration of Diffusion Tensor Imaging (DTI) data sets. This framework is optimized for brain data and its main goal is to achieve an accurate alignment of all brain structures, including white matter (WM), gray matter (GM), and spaces containing cerebrospinal fluid (CSF). Currently most DTI-based spatial normalization algorithms emphasize alignment of anisotropic structures. While some diffusion-derived metrics, such as diffusion anisotropy and tensor eigenvector orientation, are highly informative for proper alignment of WM, other tensor metrics such as the trace or mean diffusivity (MD) are fundamental for a proper alignment of GM and CSF boundaries. Moreover, it is desirable to include information from structural MRI data, e.g., T1-weighted or T2-weighted images, which are usually available together with the diffusion data. The fundamental property of DR-TAMAS is to achieve global anatomical accuracy by incorporating in its cost function the most informative metrics locally. Another important feature of DR-TAMAS is a symmetric time-varying velocity-based transformation model, which enables it to account for potentially large anatomical variability in healthy subjects and patients. The performance of DR-TAMAS is evaluated with several data sets and compared with other widely-used diffeomorphic image registration techniques employing both full tensor information and/or DTI-derived scalar maps. Our results show that the proposed method has excellent overall performance in the entire brain, while being equivalent to the best existing methods in WM. Copyright © 2016 Elsevier Inc. All rights reserved.

Heterogeneous boiling-up of superheated liquid at achievable superheat threshold.

PubMed

Ermakov, G V; Lipnyagov, E V; Perminov, S A; Gurashkin, A L

2009-07-21

The classical theory of homogeneous nucleation describes well the superheat threshold observed in experiments. It may be assumed therefore that homogeneous boiling-up of a liquid takes place in experiments, and the theory has been verified experimentally well. The streak photography used in this study showed that boiling-up of a superheated liquid at the threshold of the achievable superheat occurs at a limited number of surface fluctuation centers in a vessel, rather than in the bulk as one would expect with homogeneous nucleation. Thus, the homogeneous theory, which rather accurately describes the heterogeneous threshold of the achievable superheat, obviously is not confirmed in experiments.

An Accurate Co-registration Method for Airborne Repeat-pass InSAR

NASA Astrophysics Data System (ADS)

Dong, X. T.; Zhao, Y. H.; Yue, X. J.; Han, C. M.

2017-10-01

Interferometric Synthetic Aperture Radar (InSAR) technology plays a significant role in topographic mapping and surface deformation detection. Comparing with spaceborne repeat-pass InSAR, airborne repeat-pass InSAR solves the problems of long revisit time and low-resolution images. Due to the advantages of flexible, accurate, and fast obtaining abundant information, airborne repeat-pass InSAR is significant in deformation monitoring of shallow ground. In order to getting precise ground elevation information and interferometric coherence of deformation monitoring from master and slave images, accurate co-registration must be promised. Because of side looking, repeat observing path and long baseline, there are very different initial slant ranges and flight heights between repeat flight paths. The differences of initial slant ranges and flight height lead to the pixels, located identical coordinates on master and slave images, correspond to different size of ground resolution cells. The mismatching phenomenon performs very obvious on the long slant range parts of master image and slave image. In order to resolving the different sizes of pixels and getting accurate co-registration results, a new method is proposed based on Range-Doppler (RD) imaging model. VV-Polarization C-band airborne repeat-pass InSAR images were used in experiment. The experiment result shows that the proposed method leads to superior co-registration accuracy.

Toward achieving flexible and high sensitivity hexagonal boron nitride neutron detectors

NASA Astrophysics Data System (ADS)

Maity, A.; Grenadier, S. J.; Li, J.; Lin, J. Y.; Jiang, H. X.

2017-07-01

Hexagonal boron nitride (h-BN) detectors have demonstrated the highest thermal neutron detection efficiency to date among solid-state neutron detectors at about 51%. We report here the realization of h-BN neutron detectors possessing one order of magnitude enhancement in the detection area but maintaining an equal level of detection efficiency of previous achievement. These 3 mm × 3 mm detectors were fabricated from 50 μm thick freestanding and flexible 10B enriched h-BN (h-10BN) films, grown by metal organic chemical vapor deposition followed by mechanical separation from sapphire substrates. Mobility-lifetime results suggested that holes are the majority carriers in unintentionally doped h-BN. The detectors were tested under thermal neutron irradiation from californium-252 (252Cf) moderated by a high density polyethylene moderator. A thermal neutron detection efficiency of ˜53% was achieved at a bias voltage of 200 V. Conforming to traditional solid-state detectors, the realization of h-BN epilayers with enhanced electrical transport properties is the key to enable scaling up the device sizes. More specifically, the present results revealed that achieving an electrical resistivity of greater than 1014 Ωṡcm and a leakage current density of below 3 × 10-10 A/cm2 is needed to fabricate large area h-BN detectors and provided guidance for achieving high sensitivity solid state neutron detectors based on h-BN.

Detecting of foreign object debris on airfield pavement using convolution neural network

NASA Astrophysics Data System (ADS)

Cao, Xiaoguang; Gu, Yufeng; Bai, Xiangzhi

2017-11-01

It is of great practical significance to detect foreign object debris (FOD) timely and accurately on the airfield pavement, because the FOD is a fatal threaten for runway safety in airport. In this paper, a new FOD detection framework based on Single Shot MultiBox Detector (SSD) is proposed. Two strategies include making the detection network lighter and using dilated convolution, which are proposed to better solve the FOD detection problem. The advantages mainly include: (i) the network structure becomes lighter to speed up detection task and enhance detection accuracy; (ii) dilated convolution is applied in network structure to handle smaller FOD. Thus, we get a faster and more accurate detection system.

Automatic arrival time detection for earthquakes based on Modified Laplacian of Gaussian filter

NASA Astrophysics Data System (ADS)

Saad, Omar M.; Shalaby, Ahmed; Samy, Lotfy; Sayed, Mohammed S.

2018-04-01

Precise identification of onset time for an earthquake is imperative in the right figuring of earthquake's location and different parameters that are utilized for building seismic catalogues. P-wave arrival detection of weak events or micro-earthquakes cannot be precisely determined due to background noise. In this paper, we propose a novel approach based on Modified Laplacian of Gaussian (MLoG) filter to detect the onset time even in the presence of very weak signal-to-noise ratios (SNRs). The proposed algorithm utilizes a denoising-filter algorithm to smooth the background noise. In the proposed algorithm, we employ the MLoG mask to filter the seismic data. Afterward, we apply a Dual-threshold comparator to detect the onset time of the event. The results show that the proposed algorithm can detect the onset time for micro-earthquakes accurately, with SNR of -12 dB. The proposed algorithm achieves an onset time picking accuracy of 93% with a standard deviation error of 0.10 s for 407 field seismic waveforms. Also, we compare the results with short and long time average algorithm (STA/LTA) and the Akaike Information Criterion (AIC), and the proposed algorithm outperforms them.

Glue detection based on teaching points constraint and tracking model of pixel convolution

NASA Astrophysics Data System (ADS)

Geng, Lei; Ma, Xiao; Xiao, Zhitao; Wang, Wen

2018-01-01

On-line glue detection based on machine version is significant for rust protection and strengthening in car production. Shadow stripes caused by reflect light and unevenness of inside front cover of car reduce the accuracy of glue detection. In this paper, we propose an effective algorithm to distinguish the edges of the glue and shadow stripes. Teaching points are utilized to calculate slope between the two adjacent points. Then a tracking model based on pixel convolution along motion direction is designed to segment several local rectangular regions using distance. The distance is the height of rectangular region. The pixel convolution along the motion direction is proposed to extract edges of gules in local rectangular region. A dataset with different illumination and complexity shape stripes are used to evaluate proposed method, which include 500 thousand images captured from the camera of glue gun machine. Experimental results demonstrate that the proposed method can detect the edges of glue accurately. The shadow stripes are distinguished and removed effectively. Our method achieves the 99.9% accuracies for the image dataset.

Are Fluency Measures Accurate Predictors of Reading Achievement?

ERIC Educational Resources Information Center

Schilling, Stephen G.; Carlisle, Joanne F.; Scott, Sarah E.; Zeng, Ji

2007-01-01

This study focused on the predictive validity of fluency measures that comprise Dynamic Indicators of Basic Early Literacy Skills (DIBELS). Data were gathered from first through third graders attending 44 schools in 9 districts or local educational agencies that made up the first Reading First cohort in Michigan. Students were administered DIBELS…

Accurate electromagnetic modeling of terahertz detectors

NASA Technical Reports Server (NTRS)

Focardi, Paolo; McGrath, William R.

2004-01-01

Twin slot antennas coupled to superconducting devices have been developed over the years as single pixel detectors in the terahertz (THz) frequency range for space-based and astronomy applications. Used either for mixing or direct detection, they have been object of several investigations, and are currently being developed for several missions funded or co-funded by NASA. Although they have shown promising performance in terms of noise and sensitivity, so far they have usually also shown a considerable disagreement in terms of performance between calculations and measurements, especially when considering center frequency and bandwidth. In this paper we present a thorough and accurate electromagnetic model of complete detector and we compare the results of calculations with measurements. Starting from a model of the embedding circuit, the effect of all the other elements in the detector in the coupled power have been analyzed. An extensive variety of measured and calculated data, as presented in this paper, demonstrates the effectiveness and reliability of the electromagnetic model at frequencies between 600 GHz and 2.5THz.

GridMass: a fast two-dimensional feature detection method for LC/MS.

PubMed

Treviño, Victor; Yañez-Garza, Irma-Luz; Rodriguez-López, Carlos E; Urrea-López, Rafael; Garza-Rodriguez, Maria-Lourdes; Barrera-Saldaña, Hugo-Alberto; Tamez-Peña, José G; Winkler, Robert; Díaz de-la-Garza, Rocío-Isabel

2015-01-01

One of the initial and critical procedures for the analysis of metabolomics data using liquid chromatography and mass spectrometry is feature detection. Feature detection is the process to detect boundaries of the mass surface from raw data. It consists of detected abundances arranged in a two-dimensional (2D) matrix of mass/charge and elution time. MZmine 2 is one of the leading software environments that provide a full analysis pipeline for these data. However, the feature detection algorithms provided in MZmine 2 are based mainly on the analysis of one-dimension at a time. We propose GridMass, an efficient algorithm for 2D feature detection. The algorithm is based on landing probes across the chromatographic space that are moved to find local maxima providing accurate boundary estimations. We tested GridMass on a controlled marker experiment, on plasma samples, on plant fruits, and in a proteome sample. Compared with other algorithms, GridMass is faster and may achieve comparable or better sensitivity and specificity. As a proof of concept, GridMass has been implemented in Java under the MZmine 2 environment and is available at http://www.bioinformatica.mty.itesm.mx/GridMass and MASSyPup. It has also been submitted to the MZmine 2 developing community. Copyright © 2015 John Wiley & Sons, Ltd.

Multiscale-Driven approach to detecting change in Synthetic Aperture Radar (SAR) imagery

NASA Astrophysics Data System (ADS)

Gens, R.; Hogenson, K.; Ajadi, O. A.; Meyer, F. J.; Myers, A.; Logan, T. A.; Arnoult, K., Jr.

2017-12-01

Detecting changes between Synthetic Aperture Radar (SAR) images can be a useful but challenging exercise. SAR with its all-weather capabilities can be an important resource in identifying and estimating the expanse of events such as flooding, river ice breakup, earthquake damage, oil spills, and forest growth, as it can overcome shortcomings of optical methods related to cloud cover. However, detecting change in SAR imagery can be impeded by many factors including speckle, complex scattering responses, low temporal sampling, and difficulty delineating boundaries. In this presentation we use a change detection method based on a multiscale-driven approach. By using information at different resolution levels, we attempt to obtain more accurate change detection maps in both heterogeneous and homogeneous regions. Integrated within the processing flow are processes that 1) improve classification performance by combining Expectation-Maximization algorithms with mathematical morphology, 2) achieve high accuracy in preserving boundaries using measurement level fusion techniques, and 3) combine modern non-local filtering and 2D-discrete stationary wavelet transform to provide robustness against noise. This multiscale-driven approach to change detection has recently been incorporated into the Alaska Satellite Facility (ASF) Hybrid Pluggable Processing Pipeline (HyP3) using radiometrically terrain corrected SAR images. Examples primarily from natural hazards are presented to illustrate the capabilities and limitations of the change detection method.

DR-TAMAS: Diffeomorphic Registration for Tensor Accurate alignMent of Anatomical Structures

PubMed Central

Irfanoglu, M. Okan; Nayak, Amritha; Jenkins, Jeffrey; Hutchinson, Elizabeth B.; Sadeghi, Neda; Thomas, Cibu P.; Pierpaoli, Carlo

2016-01-01

In this work, we propose DR-TAMAS (Diffeomorphic Registration for Tensor Accurate alignMent of Anatomical Structures), a novel framework for intersubject registration of Diffusion Tensor Imaging (DTI) data sets. This framework is optimized for brain data and its main goal is to achieve an accurate alignment of all brain structures, including white matter (WM), gray matter (GM), and spaces containing cerebrospinal fluid (CSF). Currently most DTI-based spatial normalization algorithms emphasize alignment of anisotropic structures. While some diffusion-derived metrics, such as diffusion anisotropy and tensor eigenvector orientation, are highly informative for proper alignment of WM, other tensor metrics such as the trace or mean diffusivity (MD) are fundamental for a proper alignment of GM and CSF boundaries. Moreover, it is desirable to include information from structural MRI data, e.g., T1-weighted or T2-weighted images, which are usually available together with the diffusion data. The fundamental property of DR-TAMAS is to achieve global anatomical accuracy by incorporating in its cost function the most informative metrics locally. Another important feature of DR-TAMAS is a symmetric time-varying velocity-based transformation model, which enables it to account for potentially large anatomical variability in healthy subjects and patients. The performance of DR-TAMAS is evaluated with several data sets and compared with other widely-used diffeomorphic image registration techniques employing both full tensor information and/or DTI-derived scalar maps. Our results show that the proposed method has excellent overall performance in the entire brain, while being equivalent to the best existing methods in WM. PMID:26931817

Exploratory Movement Generates Higher-Order Information That Is Sufficient for Accurate Perception of Scaled Egocentric Distance

PubMed Central

Mantel, Bruno; Stoffregen, Thomas A.; Campbell, Alain; Bardy, Benoît G.

2015-01-01

Body movement influences the structure of multiple forms of ambient energy, including optics and gravito-inertial force. Some researchers have argued that egocentric distance is derived from inferential integration of visual and non-visual stimulation. We suggest that accurate information about egocentric distance exists in perceptual stimulation as higher-order patterns that extend across optics and inertia. We formalize a pattern that specifies the egocentric distance of a stationary object across higher-order relations between optics and inertia. This higher-order parameter is created by self-generated movement of the perceiver in inertial space relative to the illuminated environment. For this reason, we placed minimal restrictions on the exploratory movements of our participants. We asked whether humans can detect and use the information available in this higher-order pattern. Participants judged whether a virtual object was within reach. We manipulated relations between body movement and the ambient structure of optics and inertia. Judgments were precise and accurate when the higher-order optical-inertial parameter was available. When only optic flow was available, judgments were poor. Our results reveal that participants perceived egocentric distance from the higher-order, optical-inertial consequences of their own exploratory activity. Analysis of participants’ movement trajectories revealed that self-selected movements were complex, and tended to optimize availability of the optical-inertial pattern that specifies egocentric distance. We argue that accurate information about egocentric distance exists in higher-order patterns of ambient energy, that self-generated movement can generate these higher-order patterns, and that these patterns can be detected and used to support perception of egocentric distance that is precise and accurate. PMID:25856410

A novel adaptive, real-time algorithm to detect gait events from wearable sensors.

PubMed

Chia Bejarano, Noelia; Ambrosini, Emilia; Pedrocchi, Alessandra; Ferrigno, Giancarlo; Monticone, Marco; Ferrante, Simona

2015-05-01

A real-time, adaptive algorithm based on two inertial and magnetic sensors placed on the shanks was developed for gait-event detection. For each leg, the algorithm detected the Initial Contact (IC), as the minimum of the flexion/extension angle, and the End Contact (EC) and the Mid-Swing (MS), as minimum and maximum of the angular velocity, respectively. The algorithm consisted of calibration, real-time detection, and step-by-step update. Data collected from 22 healthy subjects (21 to 85 years) walking at three self-selected speeds were used to validate the algorithm against the GaitRite system. Comparable levels of accuracy and significantly lower detection delays were achieved with respect to other published methods. The algorithm robustness was tested on ten healthy subjects performing sudden speed changes and on ten stroke subjects (43 to 89 years). For healthy subjects, F1-scores of 1 and mean detection delays lower than 14 ms were obtained. For stroke subjects, F1-scores of 0.998 and 0.944 were obtained for IC and EC, respectively, with mean detection delays always below 31 ms. The algorithm accurately detected gait events in real time from a heterogeneous dataset of gait patterns and paves the way for the design of closed-loop controllers for customized gait trainings and/or assistive devices.

Flexibility in Visual Working Memory: Accurate Change Detection in the Face of Irrelevant Variations in Position

PubMed Central

Woodman, Geoffrey F.; Vogel, Edward K.; Luck, Steven J.

2012-01-01

Many recent studies of visual working memory have used change-detection tasks in which subjects view sequential displays and are asked to report whether they are identical or if one object has changed. A key question is whether the memory system used to perform this task is sufficiently flexible to detect changes in object identity independent of spatial transformations, but previous research has yielded contradictory results. To address this issue, the present study compared standard change-detection tasks with tasks in which the objects varied in size or position between successive arrays. Performance was nearly identical across the standard and transformed tasks unless the task implicitly encouraged spatial encoding. These results resolve the discrepancies in prior studies and demonstrate that the visual working memory system can detect changes in object identity across spatial transformations. PMID:22287933

Action change detection in video using a bilateral spatial-temporal constraint

NASA Astrophysics Data System (ADS)

Tian, Jing; Chen, Li

2016-08-01

Action change detection in video aims to detect action discontinuity in video. The silhouettes-based features are desirable for action change detection. This paper studies the problem of silhouette-quality assessment. For that, a non-reference approach without the need for ground truth is proposed in this paper to evaluate the quality of silhouettes, by exploiting both the boundary contrast of the silhouettes in the spatial domain and the consistency of the silhouettes in the temporal domain. This is in contrast to that either only spatial information or only temporal information of silhouettes is exploited in conventional approaches. Experiments are conducted using artificially generated degraded silhouettes to show that the proposed approach outperforms conventional approaches to achieve more accurate quality assessment. Furthermore, experiments are performed to show that the proposed approach is able to improve the accuracy performance of conventional action change approaches in two human action video data-sets. The average runtime of the proposed approach for Weizmann action video data-set is 0.08 second for one frame using Matlab programming language. It is computationally efficient and potential to real-time implementations.

Molecular methods for pathogen detection and quantification

USDA-ARS?s Scientific Manuscript database

Ongoing interest in convenient, inexpensive, fast, sensitive and accurate techniques for detecting and/or quantifying the presence of soybean pathogens has resulted in increased usage of molecular tools. The method of extracting a molecular target (usually DNA or RNA) for detection depends wholly up...

A fast and accurate frequency estimation algorithm for sinusoidal signal with harmonic components

NASA Astrophysics Data System (ADS)

Hu, Jinghua; Pan, Mengchun; Zeng, Zhidun; Hu, Jiafei; Chen, Dixiang; Tian, Wugang; Zhao, Jianqiang; Du, Qingfa

2016-10-01

Frequency estimation is a fundamental problem in many applications, such as traditional vibration measurement, power system supervision, and microelectromechanical system sensors control. In this paper, a fast and accurate frequency estimation algorithm is proposed to deal with low efficiency problem in traditional methods. The proposed algorithm consists of coarse and fine frequency estimation steps, and we demonstrate that it is more efficient than conventional searching methods to achieve coarse frequency estimation (location peak of FFT amplitude) by applying modified zero-crossing technique. Thus, the proposed estimation algorithm requires less hardware and software sources and can achieve even higher efficiency when the experimental data increase. Experimental results with modulated magnetic signal show that the root mean square error of frequency estimation is below 0.032 Hz with the proposed algorithm, which has lower computational complexity and better global performance than conventional frequency estimation methods.

Sampling of the anterior apical region results in increased cancer detection and upgrading in transrectal repeat saturation biopsy of the prostate.

PubMed

Seles, Maximilian; Gutschi, Thomas; Mayrhofer, Katrin; Fischereder, Katja; Ehrlich, Georg; Gallé, Guenter; Gutschi, Stefan; Pachernegg, Oliver; Pummer, Karl; Augustin, Herbert

2016-04-01

To evaluate whether biopsy cores taken via a transrectal approach from the anterior apical region of the prostate in a repeat-biopsy population can result in an increased overall cancer detection rate and in more accurate assessment of the Gleason score. The study was a prospective, randomised (end-fire vs side-fire ultrasound probe) evaluation of 288 men by repeat transrectal saturation biopsy with 28 cores taken from the transition zone, base, mid-lobar, anterior and the anterior apical region located ventro-laterally to the urethra of the peripheral zone. The overall prostate cancer detection rate was 44.4%. Improvement of the overall detection rate by 7.8% could be achieved with additional biopsies of the anterior apical region. Two tumours featuring a Gleason score 7 could only be detected in the anterior apical region. In three cases (2.34%) Gleason score upgrading was achieved by separate analysis of each positive core of the anterior apical region. A five-fold higher cancer detection rate in the anterior apical region compared with the transition zone could be shown. Sampling of the anterior apical region results in higher overall cancer detection rate in repeat transrectal saturation biopsies of the prostate. Specimens from this region can detect clinically significant cancer, improve accuracy of the Gleason Scoring and therefore may alter therapy. © 2015 The Authors BJU International © 2015 BJU International Published by John Wiley & Sons Ltd.

Duplex microfluidic SERS detection of pathogen antigens with nanoyeast single-chain variable fragments.

PubMed

Wang, Yuling; Rauf, Sakandar; Grewal, Yadveer S; Spadafora, Lauren J; Shiddiky, Muhammad J A; Cangelosi, Gerard A; Schlücker, Sebastian; Trau, Matt

2014-10-07

Quantitative and accurate detection of multiple biomarkers would allow for the rapid diagnosis and treatment of diseases induced by pathogens. Monoclonal antibodies are standard affinity reagents applied for biomarkers detection; however, their production is expensive and labor-intensive. Herein, we report on newly developed nanoyeast single-chain variable fragments (NYscFv) as an attractive alternative to monoclonal antibodies, which offers the unique advantage of a cost-effective production, stability in solution, and target-specificity. By combination of surface-enhanced Raman scattering (SERS) microspectroscopy using glass-coated, highly purified SERS nanoparticle clusters as labels, with a microfluidic device comprising multiple channels, a robust platform for the sensitive duplex detection of pathogen antigens has been developed. Highly sensitive detection for individual Entamoeba histolytica antigen EHI_115350 (limit of detection = 1 pg/mL, corresponding to 58.8 fM) and EHI_182030 (10 pg/mL, corresponding 453 fM) with high specificity has been achieved, employing the newly developed corresponding NYscFv as probe in combination with SERS microspectroscopy at a single laser excitation wavelength. Our first report on SERS-based immunoassays using the novel NYscFv affinity reagent demonstrates the flexibility of NYscFv fragments as viable alternatives to monoclonal antibodies in a range of bioassay platforms and paves the way for further applications.

EEG-based driver fatigue detection using hybrid deep generic model.

PubMed

Phyo Phyo San; Sai Ho Ling; Rifai Chai; Tran, Yvonne; Craig, Ashley; Hung Nguyen

2016-08-01

Classification of electroencephalography (EEG)-based application is one of the important process for biomedical engineering. Driver fatigue is a major case of traffic accidents worldwide and considered as a significant problem in recent decades. In this paper, a hybrid deep generic model (DGM)-based support vector machine is proposed for accurate detection of driver fatigue. Traditionally, a probabilistic DGM with deep architecture is quite good at learning invariant features, but it is not always optimal for classification due to its trainable parameters are in the middle layer. Alternatively, Support Vector Machine (SVM) itself is unable to learn complicated invariance, but produces good decision surface when applied to well-behaved features. Consolidating unsupervised high-level feature extraction techniques, DGM and SVM classification makes the integrated framework stronger and enhance mutually in feature extraction and classification. The experimental results showed that the proposed DBN-based driver fatigue monitoring system achieves better testing accuracy of 73.29 % with 91.10 % sensitivity and 55.48 % specificity. In short, the proposed hybrid DGM-based SVM is an effective method for the detection of driver fatigue in EEG.

Early detection of lung cancer from CT images: nodule segmentation and classification using deep learning

NASA Astrophysics Data System (ADS)

Sharma, Manu; Bhatt, Jignesh S.; Joshi, Manjunath V.

2018-04-01

Lung cancer is one of the most abundant causes of the cancerous deaths worldwide. It has low survival rate mainly due to the late diagnosis. With the hardware advancements in computed tomography (CT) technology, it is now possible to capture the high resolution images of lung region. However, it needs to be augmented by efficient algorithms to detect the lung cancer in the earlier stages using the acquired CT images. To this end, we propose a two-step algorithm for early detection of lung cancer. Given the CT image, we first extract the patch from the center location of the nodule and segment the lung nodule region. We propose to use Otsu method followed by morphological operations for the segmentation. This step enables accurate segmentation due to the use of data-driven threshold. Unlike other methods, we perform the segmentation without using the complete contour information of the nodule. In the second step, a deep convolutional neural network (CNN) is used for the better classification (malignant or benign) of the nodule present in the segmented patch. Accurate segmentation of even a tiny nodule followed by better classification using deep CNN enables the early detection of lung cancer. Experiments have been conducted using 6306 CT images of LIDC-IDRI database. We achieved the test accuracy of 84.13%, with the sensitivity and specificity of 91.69% and 73.16%, respectively, clearly outperforming the state-of-the-art algorithms.

A Semi-implicit Method for Time Accurate Simulation of Compressible Flow

NASA Astrophysics Data System (ADS)

Wall, Clifton; Pierce, Charles D.; Moin, Parviz

2001-11-01

A semi-implicit method for time accurate simulation of compressible flow is presented. The method avoids the acoustic CFL limitation, allowing a time step restricted only by the convective velocity. Centered discretization in both time and space allows the method to achieve zero artificial attenuation of acoustic waves. The method is an extension of the standard low Mach number pressure correction method to the compressible Navier-Stokes equations, and the main feature of the method is the solution of a Helmholtz type pressure correction equation similar to that of Demirdžić et al. (Int. J. Num. Meth. Fluids, Vol. 16, pp. 1029-1050, 1993). The method is attractive for simulation of acoustic combustion instabilities in practical combustors. In these flows, the Mach number is low; therefore the time step allowed by the convective CFL limitation is significantly larger than that allowed by the acoustic CFL limitation, resulting in significant efficiency gains. Also, the method's property of zero artificial attenuation of acoustic waves is important for accurate simulation of the interaction between acoustic waves and the combustion process. The method has been implemented in a large eddy simulation code, and results from several test cases will be presented.

A visible light imaging device for cardiac rate detection with reduced effect of body movement

NASA Astrophysics Data System (ADS)

Jiang, Xiaotian; Liu, Ming; Zhao, Yuejin

2014-09-01

A visible light imaging system to detect human cardiac rate is proposed in this paper. A color camera and several LEDs, acting as lighting source, were used to avoid the interference of ambient light. From people's forehead, the cardiac rate could be acquired based on photoplethysmography (PPG) theory. The template matching method was used after the capture of video. The video signal was discomposed into three signal channels (RGB) and the region of interest was chosen to take the average gray value. The green channel signal could provide an excellent waveform of pulse wave on the account of green lights' absorptive characteristics of blood. Through the fast Fourier transform, the cardiac rate was exactly achieved. But the research goal was not just to achieve the cardiac rate accurately. With the template matching method, the effects of body movement are reduced to a large extent, therefore the pulse wave can be detected even while people are in the moving state and the waveform is largely optimized. Several experiments are conducted on volunteers, and the results are compared with the ones gained by a finger clamped pulse oximeter. The contrast results between these two ways are exactly agreeable. This method to detect the cardiac rate and the pulse wave largely reduces the effects of body movement and can probably be widely used in the future.

Addressing variability in the acoustic startle reflex for accurate gap detection assessment.

PubMed

Longenecker, Ryan J; Kristaponyte, Inga; Nelson, Gregg L; Young, Jesse W; Galazyuk, Alexander V

2018-06-01

The acoustic startle reflex (ASR) is subject to substantial variability. This inherent variability consequently shapes the conclusions drawn from gap-induced prepulse inhibition of the acoustic startle reflex (GPIAS) assessments. Recent studies have cast doubt as to the efficacy of this methodology as it pertains to tinnitus assessment, partially, due to variability in and between data sets. The goal of this study was to examine the variance associated with several common data collection variables and data analyses with the aim to improve GPIAS reliability. To study this the GPIAS tests were conducted in adult male and female CBA/CaJ mice. Factors such as inter-trial interval, circadian rhythm, sex differences, and sensory adaptation were each evaluated. We then examined various data analysis factors which influence GPIAS assessment. Gap-induced facilitation, data processing options, and assessments of tinnitus were studied. We found that the startle reflex is highly variable in CBA/CaJ mice, but this can be minimized by certain data collection factors. We also found that careful consideration of temporal fluctuations of the ASR and controlling for facilitation can lead to more accurate GPIAS results. This study provides a guide for reducing variance in the GPIAS methodology - thereby improving the diagnostic power of the test. Copyright © 2018 Elsevier B.V. All rights reserved.

Salient man-made structure detection in infrared images

NASA Astrophysics Data System (ADS)

Li, Dong-jie; Zhou, Fu-gen; Jin, Ting

2013-09-01

Target detection, segmentation and recognition is a hot research topic in the field of image processing and pattern recognition nowadays, among which salient area or object detection is one of core technologies of precision guided weapon. Many theories have been raised in this paper; we detect salient objects in a series of input infrared images by using the classical feature integration theory and Itti's visual attention system. In order to find the salient object in an image accurately, we present a new method to solve the edge blur problem by calculating and using the edge mask. We also greatly improve the computing speed by improving the center-surround differences method. Unlike the traditional algorithm, we calculate the center-surround differences through rows and columns separately. Experimental results show that our method is effective in detecting salient object accurately and rapidly.

Accurate FRET Measurements within Single Diffusing Biomolecules Using Alternating-Laser Excitation

PubMed Central

Lee, Nam Ki; Kapanidis, Achillefs N.; Wang, You; Michalet, Xavier; Mukhopadhyay, Jayanta; Ebright, Richard H.; Weiss, Shimon

2005-01-01

Fluorescence resonance energy transfer (FRET) between a donor (D) and an acceptor (A) at the single-molecule level currently provides qualitative information about distance, and quantitative information about kinetics of distance changes. Here, we used the sorting ability of confocal microscopy equipped with alternating-laser excitation (ALEX) to measure accurate FRET efficiencies and distances from single molecules, using corrections that account for cross-talk terms that contaminate the FRET-induced signal, and for differences in the detection efficiency and quantum yield of the probes. ALEX yields accurate FRET independent of instrumental factors, such as excitation intensity or detector alignment. Using DNA fragments, we showed that ALEX-based distances agree well with predictions from a cylindrical model of DNA; ALEX-based distances fit better to theory than distances obtained at the ensemble level. Distance measurements within transcription complexes agreed well with ensemble-FRET measurements, and with structural models based on ensemble-FRET and x-ray crystallography. ALEX can benefit structural analysis of biomolecules, especially when such molecules are inaccessible to conventional structural methods due to heterogeneity or transient nature. PMID:15653725

Accurate metacognition for visual sensory memory representations.

PubMed

Vandenbroucke, Annelinde R E; Sligte, Ilja G; Barrett, Adam B; Seth, Anil K; Fahrenfort, Johannes J; Lamme, Victor A F

2014-04-01

The capacity to attend to multiple objects in the visual field is limited. However, introspectively, people feel that they see the whole visual world at once. Some scholars suggest that this introspective feeling is based on short-lived sensory memory representations, whereas others argue that the feeling of seeing more than can be attended to is illusory. Here, we investigated this phenomenon by combining objective memory performance with subjective confidence ratings during a change-detection task. This allowed us to compute a measure of metacognition--the degree of knowledge that subjects have about the correctness of their decisions--for different stages of memory. We show that subjects store more objects in sensory memory than they can attend to but, at the same time, have similar metacognition for sensory memory and working memory representations. This suggests that these subjective impressions are not an illusion but accurate reflections of the richness of visual perception.

Development of a High Throughput Assay for Rapid and Accurate 10-Plex Detection of Citrus Pathogens

USDA-ARS?s Scientific Manuscript database

The need to reliably detect and identify multiple plant pathogens simultaneously, especially in woody perennial hosts, has led to development of new molecular diagnostic approaches. In this study, a Luminex-based system was developed that provided a robust and sensitive test for simultaneous detect...

The New Aptima HCV Quant Dx Real-time TMA Assay Accurately Quantifies Hepatitis C Virus Genotype 1-6 RNA.

PubMed

Chevaliez, Stéphane; Dubernet, Fabienne; Dauvillier, Claude; Hézode, Christophe; Pawlotsky, Jean-Michel

2017-06-01

Sensitive and accurate hepatitis C virus (HCV) RNA detection and quantification is essential for the management of chronic hepatitis C therapy. Currently available platforms and assays are usually batched and require at least 5hours of work to complete the analyses. The aim of this study was to evaluate the ability of the newly developed Aptima HCV Quant Dx assay that eliminates the need for batch processing and automates all aspects of nucleic acid testing in a single step, to accurately detect and quantify HCV RNA in a large series of patients infected with different HCV genotypes. The limit of detection was estimated to be 2.3 IU/mL. The specificity of the assay was 98.6% (95% confidence interval: 96.1%-99.5%). Intra-assay and inter-assay coefficients of variation ranged from 0.09% to 5.61%, and 1.05% to 3.65%, respectively. The study of serum specimens from patients infected with HCV genotypes 1 to 6 showed a satisfactory relationship between HCV RNA levels measured by the Aptima HCV Quant Dx assay, and both real-time PCR comparators (Abbott RealTime HCV and Cobas AmpliPrep/Cobas TaqMan HCV Test, version 2.0, assays). the new Aptima HCV Quant Dx assay is rapid, sensitive, reasonably specific and reproducible and accurately quantifies HCV RNA in serum samples from patients with chronic HCV infection, including patients on antiviral treatment. The Aptima HCV Quant Dx assay can thus be confidently used to detect and quantify HCV RNA in both clinical trials with new anti-HCV drugs and clinical practice in Europe and the US. Copyright © 2017 Elsevier B.V. All rights reserved.

Toward achieving flexible and high sensitivity hexagonal boron nitride neutron detectors

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

Maity, A.; Grenadier, S. J.; Li, J.

Hexagonal boron nitride (h-BN) detectors have demonstrated the highest thermal neutron detection efficiency to date among solid-state neutron detectors at about 51%. We report here the realization of h-BN neutron detectors possessing one order of magnitude enhancement in the detection area but maintaining an equal level of detection efficiency of previous achievement.

Toward achieving flexible and high sensitivity hexagonal boron nitride neutron detectors

DOE PAGES

Maity, A.; Grenadier, S. J.; Li, J.; ...

2017-07-17

Hexagonal boron nitride (h-BN) detectors have demonstrated the highest thermal neutron detection efficiency to date among solid-state neutron detectors at about 51%. We report here the realization of h-BN neutron detectors possessing one order of magnitude enhancement in the detection area but maintaining an equal level of detection efficiency of previous achievement.

detectIR: a novel program for detecting perfect and imperfect inverted repeats using complex numbers and vector calculation.

PubMed

Ye, Congting; Ji, Guoli; Li, Lei; Liang, Chun

2014-01-01

Inverted repeats are present in abundance in both prokaryotic and eukaryotic genomes and can form DNA secondary structures--hairpins and cruciforms that are involved in many important biological processes. Bioinformatics tools for efficient and accurate detection of inverted repeats are desirable, because existing tools are often less accurate and time consuming, sometimes incapable of dealing with genome-scale input data. Here, we present a MATLAB-based program called detectIR for the perfect and imperfect inverted repeat detection that utilizes complex numbers and vector calculation and allows genome-scale data inputs. A novel algorithm is adopted in detectIR to convert the conventional sequence string comparison in inverted repeat detection into vector calculation of complex numbers, allowing non-complementary pairs (mismatches) in the pairing stem and a non-palindromic spacer (loop or gaps) in the middle of inverted repeats. Compared with existing popular tools, our program performs with significantly higher accuracy and efficiency. Using genome sequence data from HIV-1, Arabidopsis thaliana, Homo sapiens and Zea mays for comparison, detectIR can find lots of inverted repeats missed by existing tools whose outputs often contain many invalid cases. detectIR is open source and its source code is freely available at: https://sourceforge.net/projects/detectir.

Toward optimizing patient-specific IMRT QA techniques in the accurate detection of dosimetrically acceptable and unacceptable patient plans

PubMed Central

McKenzie, Elizabeth M.; Balter, Peter A.; Stingo, Francesco C.; Jones, Jimmy; Followill, David S.; Kry, Stephen F.

2014-01-01

was no significant difference in the performance of any device between gamma criteria of 2%/2 mm, 3%/3 mm, and 5%/3 mm. Finally, optimal cutoffs (e.g., percent of pixels passing gamma) were determined for each device and while clinical practice commonly uses a threshold of 90% of pixels passing for most cases, these results showed variability in the optimal cutoff among devices. Conclusions: IMRT QA devices have differences in their ability to accurately detect dosimetrically acceptable and unacceptable plans. Field-by-field analysis with a MapCheck device and use of the MapCheck with a MapPhan phantom while delivering at planned rotational gantry angles resulted in a significantly poorer ability to accurately sort acceptable and unacceptable plans compared with the other techniques examined. Patient-specific IMRT QA techniques in general should be thoroughly evaluated for their ability to correctly differentiate acceptable and unacceptable plans. Additionally, optimal agreement thresholds should be identified and used as common clinical thresholds typically worked very poorly to identify unacceptable plans. PMID:25471949

Toward optimizing patient-specific IMRT QA techniques in the accurate detection of dosimetrically acceptable and unacceptable patient plans.

PubMed

McKenzie, Elizabeth M; Balter, Peter A; Stingo, Francesco C; Jones, Jimmy; Followill, David S; Kry, Stephen F

2014-12-01

in the performance of any device between gamma criteria of 2%/2 mm, 3%/3 mm, and 5%/3 mm. Finally, optimal cutoffs (e.g., percent of pixels passing gamma) were determined for each device and while clinical practice commonly uses a threshold of 90% of pixels passing for most cases, these results showed variability in the optimal cutoff among devices. IMRT QA devices have differences in their ability to accurately detect dosimetrically acceptable and unacceptable plans. Field-by-field analysis with a MapCheck device and use of the MapCheck with a MapPhan phantom while delivering at planned rotational gantry angles resulted in a significantly poorer ability to accurately sort acceptable and unacceptable plans compared with the other techniques examined. Patient-specific IMRT QA techniques in general should be thoroughly evaluated for their ability to correctly differentiate acceptable and unacceptable plans. Additionally, optimal agreement thresholds should be identified and used as common clinical thresholds typically worked very poorly to identify unacceptable plans.

A drone detection with aircraft classification based on a camera array

NASA Astrophysics Data System (ADS)

Liu, Hao; Qu, Fangchao; Liu, Yingjian; Zhao, Wei; Chen, Yitong

2018-03-01

In recent years, because of the rapid popularity of drones, many people have begun to operate drones, bringing a range of security issues to sensitive areas such as airports and military locus. It is one of the important ways to solve these problems by realizing fine-grained classification and providing the fast and accurate detection of different models of drone. The main challenges of fine-grained classification are that: (1) there are various types of drones, and the models are more complex and diverse. (2) the recognition test is fast and accurate, in addition, the existing methods are not efficient. In this paper, we propose a fine-grained drone detection system based on the high resolution camera array. The system can quickly and accurately recognize the detection of fine grained drone based on hd camera.

Road detection and buried object detection in elevated EO/IR imagery

NASA Astrophysics Data System (ADS)

Kennedy, Levi; Kolba, Mark P.; Walters, Joshua R.

2012-06-01

To assist the warfighter in visually identifying potentially dangerous roadside objects, the U.S. Army RDECOM CERDEC Night Vision and Electronic Sensors Directorate (NVESD) has developed an elevated video sensor system testbed for data collection. This system provides color and mid-wave infrared (MWIR) imagery. Signal Innovations Group (SIG) has developed an automated processing capability that detects the road within the sensor field of view and identifies potentially threatening buried objects within the detected road. The road detection algorithm leverages system metadata to project the collected imagery onto a flat ground plane, allowing for more accurate detection of the road as well as the direct specification of realistic physical constraints in the shape of the detected road. Once the road has been detected in an image frame, a buried object detection algorithm is applied to search for threatening objects within the detected road space. The buried object detection algorithm leverages textural and pixel intensity-based features to detect potential anomalies and then classifies them as threatening or non-threatening objects. Both the road detection and the buried object detection algorithms have been developed to facilitate their implementation in real-time in the NVESD system.

Associations between Student Achievement and Student Learning: Implications for Value-Added School Accountability Models

ERIC Educational Resources Information Center

Ready, Douglas David

2013-01-01

Accountability systems that measure student learning rather than student achievement have the potential to more accurately evaluate school quality. However, one methodological concern has remained surprisingly absent from discussions of value-added modeling. Standardized assessments that exhibit either positive or negative correlations between…

A Sea-Sky Line Detection Method for Unmanned Surface Vehicles Based on Gradient Saliency.

PubMed

Wang, Bo; Su, Yumin; Wan, Lei

2016-04-15

Special features in real marine environments such as cloud clutter, sea glint and weather conditions always result in various kinds of interference in optical images, which make it very difficult for unmanned surface vehicles (USVs) to detect the sea-sky line (SSL) accurately. To solve this problem a saliency-based SSL detection method is proposed. Through the computation of gradient saliency the line features of SSL are enhanced effectively, while other interference factors are relatively suppressed, and line support regions are obtained by a region growing method on gradient orientation. The SSL identification is achieved according to region contrast, line segment length and orientation features, and optimal state estimation of SSL detection is implemented by introducing a cubature Kalman filter (CKF). In the end, the proposed method is tested on a benchmark dataset from the "XL" USV in a real marine environment, and the experimental results demonstrate that the proposed method is significantly superior to other state-of-the-art methods in terms of accuracy rate and real-time performance, and its accuracy and stability are effectively improved by the CKF.

DeepFruits: A Fruit Detection System Using Deep Neural Networks.

PubMed

Sa, Inkyu; Ge, Zongyuan; Dayoub, Feras; Upcroft, Ben; Perez, Tristan; McCool, Chris

2016-08-03

This paper presents a novel approach to fruit detection using deep convolutional neural networks. The aim is to build an accurate, fast and reliable fruit detection system, which is a vital element of an autonomous agricultural robotic platform; it is a key element for fruit yield estimation and automated harvesting. Recent work in deep neural networks has led to the development of a state-of-the-art object detector termed Faster Region-based CNN (Faster R-CNN). We adapt this model, through transfer learning, for the task of fruit detection using imagery obtained from two modalities: colour (RGB) and Near-Infrared (NIR). Early and late fusion methods are explored for combining the multi-modal (RGB and NIR) information. This leads to a novel multi-modal Faster R-CNN model, which achieves state-of-the-art results compared to prior work with the F1 score, which takes into account both precision and recall performances improving from 0 . 807 to 0 . 838 for the detection of sweet pepper. In addition to improved accuracy, this approach is also much quicker to deploy for new fruits, as it requires bounding box annotation rather than pixel-level annotation (annotating bounding boxes is approximately an order of magnitude quicker to perform). The model is retrained to perform the detection of seven fruits, with the entire process taking four hours to annotate and train the new model per fruit.

DeepFruits: A Fruit Detection System Using Deep Neural Networks

PubMed Central

Sa, Inkyu; Ge, Zongyuan; Dayoub, Feras; Upcroft, Ben; Perez, Tristan; McCool, Chris

2016-01-01

This paper presents a novel approach to fruit detection using deep convolutional neural networks. The aim is to build an accurate, fast and reliable fruit detection system, which is a vital element of an autonomous agricultural robotic platform; it is a key element for fruit yield estimation and automated harvesting. Recent work in deep neural networks has led to the development of a state-of-the-art object detector termed Faster Region-based CNN (Faster R-CNN). We adapt this model, through transfer learning, for the task of fruit detection using imagery obtained from two modalities: colour (RGB) and Near-Infrared (NIR). Early and late fusion methods are explored for combining the multi-modal (RGB and NIR) information. This leads to a novel multi-modal Faster R-CNN model, which achieves state-of-the-art results compared to prior work with the F1 score, which takes into account both precision and recall performances improving from 0.807 to 0.838 for the detection of sweet pepper. In addition to improved accuracy, this approach is also much quicker to deploy for new fruits, as it requires bounding box annotation rather than pixel-level annotation (annotating bounding boxes is approximately an order of magnitude quicker to perform). The model is retrained to perform the detection of seven fruits, with the entire process taking four hours to annotate and train the new model per fruit. PMID:27527168

Simple Adaptive Single Differential Coherence Detection of BPSK Signals in IEEE 802.15.4 Wireless Sensor Networks

PubMed Central

Wen, Hong; Wang, Longye; Xie, Ping; Song, Liang; Tang, Jie; Liao, Runfa

2017-01-01

In this paper, we propose an adaptive single differential coherent detection (SDCD) scheme for the binary phase shift keying (BPSK) signals in IEEE 802.15.4 Wireless Sensor Networks (WSNs). In particular, the residual carrier frequency offset effect (CFOE) for differential detection is adaptively estimated, with only linear operation, according to the changing channel conditions. It was found that the carrier frequency offset (CFO) and chip signal-to-noise ratio (SNR) conditions do not need a priori knowledge. This partly benefits from that the combination of the trigonometric approximation sin−1(x)≈x and a useful assumption, namely, the asymptotic or high chip SNR, is considered for simplification of the full estimation scheme. Simulation results demonstrate that the proposed algorithm can achieve an accurate estimation and the detection performance can completely meet the requirement of the IEEE 802.15.4 standard, although with a little loss of reliability and robustness as compared with the conventional optimal single-symbol detector. PMID:29278404

Simple Adaptive Single Differential Coherence Detection of BPSK Signals in IEEE 802.15.4 Wireless Sensor Networks.

PubMed

Zhang, Gaoyuan; Wen, Hong; Wang, Longye; Xie, Ping; Song, Liang; Tang, Jie; Liao, Runfa

2017-12-26

In this paper, we propose an adaptive single differential coherent detection (SDCD) scheme for the binary phase shift keying (BPSK) signals in IEEE 802.15.4 Wireless Sensor Networks (WSNs). In particular, the residual carrier frequency offset effect (CFOE) for differential detection is adaptively estimated, with only linear operation, according to the changing channel conditions. It was found that the carrier frequency offset (CFO) and chip signal-to-noise ratio (SNR) conditions do not need a priori knowledge. This partly benefits from that the combination of the trigonometric approximation sin - 1 ( x ) ≈ x and a useful assumption, namely, the asymptotic or high chip SNR, is considered for simplification of the full estimation scheme. Simulation results demonstrate that the proposed algorithm can achieve an accurate estimation and the detection performance can completely meet the requirement of the IEEE 802.15.4 standard, although with a little loss of reliability and robustness as compared with the conventional optimal single-symbol detector.

Accurate quantitation of circulating cell-free mitochondrial DNA in plasma by droplet digital PCR.

PubMed

Ye, Wei; Tang, Xiaojun; Liu, Chu; Wen, Chaowei; Li, Wei; Lyu, Jianxin

2017-04-01

To establish a method for accurate quantitation of circulating cell-free mitochondrial DNA (ccf-mtDNA) in plasma by droplet digital PCR (ddPCR), we designed a ddPCR method to determine the copy number of ccf-mtDNA by amplifying mitochondrial ND1 (MT-ND1). To evaluate the sensitivity and specificity of the method, a recombinant pMD18-T plasmid containing MT-ND1 sequences and mtDNA-deleted (ρ 0 ) HeLa cells were used, respectively. Subsequently, different plasma samples were prepared for ddPCR to evaluate the feasibility of detecting plasma ccf-mtDNA. In the results, the ddPCR method showed high sensitivity and specificity. When the DNA was extracted from plasma prior to ddPCR, the ccf-mtDNA copy number was higher than that measured without extraction. This difference was not due to a PCR inhibitor, such as EDTA-Na 2 , an anti-coagulant in plasma, because standard EDTA-Na 2 concentration (5 mM) did not significantly inhibit ddPCR reactions. The difference might be attributable to plasma exosomal mtDNA, which was 4.21 ± 0.38 copies/μL of plasma, accounting for ∼19% of plasma ccf-mtDNA. Therefore, ddPCR can quickly and reliably detect ccf-mtDNA from plasma with a prior DNA extraction step, providing for a more accurate detection of ccf-mtDNA. The direct use of plasma as a template in ddPCR is suitable for the detection of exogenous cell-free nucleic acids within plasma, but not of nucleic acids that have a vesicle-associated form, such as exosomal mtDNA. Graphical Abstract Designs of the present work. *: Module 1, #: Module 2, &: Module 3.

Varying face occlusion detection and iterative recovery for face recognition

NASA Astrophysics Data System (ADS)

Wang, Meng; Hu, Zhengping; Sun, Zhe; Zhao, Shuhuan; Sun, Mei

2017-05-01

In most sparse representation methods for face recognition (FR), occlusion problems were usually solved via removing the occlusion part of both query samples and training samples to perform the recognition process. This practice ignores the global feature of facial image and may lead to unsatisfactory results due to the limitation of local features. Considering the aforementioned drawback, we propose a method called varying occlusion detection and iterative recovery for FR. The main contributions of our method are as follows: (1) to detect an accurate occlusion area of facial images, an image processing and intersection-based clustering combination method is used for occlusion FR; (2) according to an accurate occlusion map, the new integrated facial images are recovered iteratively and put into a recognition process; and (3) the effectiveness on recognition accuracy of our method is verified by comparing it with three typical occlusion map detection methods. Experiments show that the proposed method has a highly accurate detection and recovery performance and that it outperforms several similar state-of-the-art methods against partial contiguous occlusion.

Towards Discovery and Targeted Peptide Biomarker Detection Using nanoESI-TIMS-TOF MS

NASA Astrophysics Data System (ADS)

Garabedian, Alyssa; Benigni, Paolo; Ramirez, Cesar E.; Baker, Erin S.; Liu, Tao; Smith, Richard D.; Fernandez-Lima, Francisco

2018-05-01

In the present work, the potential of trapped ion mobility spectrometry coupled to TOF mass spectrometry (TIMS-TOF MS) for discovery and targeted monitoring of peptide biomarkers from human-in-mouse xenograft tumor tissue was evaluated. In particular, a TIMS-MS workflow was developed for the detection and quantification of peptide biomarkers using internal heavy analogs, taking advantage of the high mobility resolution (R = 150-250) prior to mass analysis. Five peptide biomarkers were separated, identified, and quantified using offline nanoESI-TIMS-CID-TOF MS; the results were in good agreement with measurements using a traditional LC-ESI-MS/MS proteomics workflow. The TIMS-TOF MS analysis permitted peptide biomarker detection based on accurate mobility, mass measurements, and high sequence coverage for concentrations in the 10-200 nM range, while simultaneously achieving discovery measurements of not initially targeted peptides as markers from the same proteins and, eventually, other proteins. [Figure not available: see fulltext.

Improving battery safety by early detection of internal shorting with a bifunctional separator

NASA Astrophysics Data System (ADS)

Wu, Hui; Zhuo, Denys; Kong, Desheng; Cui, Yi

2014-10-01

Lithium-based rechargeable batteries have been widely used in portable electronics and show great promise for emerging applications in transportation and wind-solar-grid energy storage, although their safety remains a practical concern. Failures in the form of fire and explosion can be initiated by internal short circuits associated with lithium dendrite formation during cycling. Here we report a new strategy for improving safety by designing a smart battery that allows internal battery health to be monitored in situ. Specifically, we achieve early detection of lithium dendrites inside batteries through a bifunctional separator, which offers a third sensing terminal in addition to the cathode and anode. The sensing terminal provides unique signals in the form of a pronounced voltage change, indicating imminent penetration of dendrites through the separator. This detection mechanism is highly sensitive, accurate and activated well in advance of shorting and can be applied to many types of batteries for improved safety.

Pedestrian Detection and Tracking from Low-Resolution Unmanned Aerial Vehicle Thermal Imagery

PubMed Central

Ma, Yalong; Wu, Xinkai; Yu, Guizhen; Xu, Yongzheng; Wang, Yunpeng

2016-01-01

Driven by the prominent thermal signature of humans and following the growing availability of unmanned aerial vehicles (UAVs), more and more research efforts have been focusing on the detection and tracking of pedestrians using thermal infrared images recorded from UAVs. However, pedestrian detection and tracking from the thermal images obtained from UAVs pose many challenges due to the low-resolution of imagery, platform motion, image instability and the relatively small size of the objects. This research tackles these challenges by proposing a pedestrian detection and tracking system. A two-stage blob-based approach is first developed for pedestrian detection. This approach first extracts pedestrian blobs using the regional gradient feature and geometric constraints filtering and then classifies the detected blobs by using a linear Support Vector Machine (SVM) with a hybrid descriptor, which sophisticatedly combines Histogram of Oriented Gradient (HOG) and Discrete Cosine Transform (DCT) features in order to achieve accurate detection. This research further proposes an approach for pedestrian tracking. This approach employs the feature tracker with the update of detected pedestrian location to track pedestrian objects from the registered videos and extracts the motion trajectory data. The proposed detection and tracking approaches have been evaluated by multiple different datasets, and the results illustrate the effectiveness of the proposed methods. This research is expected to significantly benefit many transportation applications, such as the multimodal traffic performance measure, pedestrian behavior study and pedestrian-vehicle crash analysis. Future work will focus on using fused thermal and visual images to further improve the detection efficiency and effectiveness. PMID:27023564

Pedestrian Detection and Tracking from Low-Resolution Unmanned Aerial Vehicle Thermal Imagery.

PubMed

Ma, Yalong; Wu, Xinkai; Yu, Guizhen; Xu, Yongzheng; Wang, Yunpeng

2016-03-26

Driven by the prominent thermal signature of humans and following the growing availability of unmanned aerial vehicles (UAVs), more and more research efforts have been focusing on the detection and tracking of pedestrians using thermal infrared images recorded from UAVs. However, pedestrian detection and tracking from the thermal images obtained from UAVs pose many challenges due to the low-resolution of imagery, platform motion, image instability and the relatively small size of the objects. This research tackles these challenges by proposing a pedestrian detection and tracking system. A two-stage blob-based approach is first developed for pedestrian detection. This approach first extracts pedestrian blobs using the regional gradient feature and geometric constraints filtering and then classifies the detected blobs by using a linear Support Vector Machine (SVM) with a hybrid descriptor, which sophisticatedly combines Histogram of Oriented Gradient (HOG) and Discrete Cosine Transform (DCT) features in order to achieve accurate detection. This research further proposes an approach for pedestrian tracking. This approach employs the feature tracker with the update of detected pedestrian location to track pedestrian objects from the registered videos and extracts the motion trajectory data. The proposed detection and tracking approaches have been evaluated by multiple different datasets, and the results illustrate the effectiveness of the proposed methods. This research is expected to significantly benefit many transportation applications, such as the multimodal traffic performance measure, pedestrian behavior study and pedestrian-vehicle crash analysis. Future work will focus on using fused thermal and visual images to further improve the detection efficiency and effectiveness.

Gender differences in science attitude-achievement relationships over time among white middle-school students

NASA Astrophysics Data System (ADS)

Mattern, Nancy; Schau, Candace

2002-04-01

Four causal models describing the longitudinal relationships between attitudes and achievement have been proposed in the literature. These models feature: (a) cross-effects over time between attitudes and achievement, (b) influence of achievement predominant over time, (c) influence of attitudes predominant over time, or (d) no cross-effects over time between attitudes and achievement. In an examin-ation of the causal relationships over time between attitudes toward science and science achievement for White rural seventh- and eighth-grade students, the cross-effects model was the best fitting model form for students overall. However, when examined by gender, the no cross-effects model exhibited the most accurate fit for White rural middle-school girls, whereas a new model called the no attitudes-path model exhibited the best fit for these boys.

Detection of myasthenia gravis using electrooculography signals.

PubMed

Liang, T; Boulos, M I; Murray, B J; Krishnan, S; Katzberg, H; Umapathy, K

2016-08-01

Myasthenia gravis (MG) is an autoimmune neuromuscular disorder resulting from skeletal muscle weakness and fatigue. An early common symptom is fatigable weakness of the extrinsic ocular muscles; if symptoms remain confined to the ocular muscles after a few years, this is classified as ocular myasthenia gravis (OMG). Diagnosis of MG when there are mild, isolated ocular symptoms can be difficult, and currently available diagnostic techniques are insensitive, non-specific or technically cumbersome. In addition, there are no accurate biomarkers to follow severity of ocular dysfunction in MG over time. Single-fiber electromyography (SFEMG) and repetitive nerve stimulation (RNS) offers a way of detecting and measuring ocular muscle dysfunction in MG, however, challenges of these methods include a poor signal to noise ratio in quantifying eye muscle weakness especially in mild cases. This paper presents one of the attempts to use the electric potentials from the eyes or electrooculography (EOG) signals but obtained from three different forms of sleep testing to differentiate MG patients from age- and gender-matched controls. We analyzed 8 MG patients and 8 control patients and demonstrated a difference in the average eye movements detected between the groups. A classification accuracy as high as 68.8% was achieved using a linear discriminant analysis based classifier.

Detection of ventricular fibrillation from multiple sensors

NASA Astrophysics Data System (ADS)

Lindsley, Stephanie A.; Ludeman, Lonnie C.

1992-07-01

Ventricular fibrillation is a potentially fatal medical condition in which the flow of blood through the body is terminated due to the lack of an organized electric potential in the heart. Automatic implantable defibrillators are becoming common as a means for helping patients confronted with repeated episodes of ventricular fibrillation. Defibrillators must first accurately detect ventricular fibrillation and then provide an electric shock to the heart to allow a normal sinus rhythm to resume. The detection of ventricular fibrillation by using an array of multiple sensors to distinguish between signals recorded from single (normal sinus rhythm) or multiple (ventricular fibrillation) sources is presented. An idealistic model is presented and the analysis of data generated by this model suggests that the method is promising as a method for accurately and quickly detecting ventricular fibrillation from signals recorded from sensors placed on the epicardium.

Detection of Wildfires with Artificial Neural Networks

NASA Astrophysics Data System (ADS)

Umphlett, B.; Leeman, J.; Morrissey, M. L.

2011-12-01

Currently fire detection for the National Oceanic and Atmospheric Administration (NOAA) using satellite data is accomplished with algorithms and error checking human analysts. Artificial neural networks (ANNs) have been shown to be more accurate than algorithms or statistical methods for applications dealing with multiple datasets of complex observed data in the natural sciences. ANNs also deal well with multiple data sources that are not all equally reliable or equally informative to the problem. An ANN was tested to evaluate its accuracy in detecting wildfires utilizing polar orbiter numerical data from the Advanced Very High Resolution Radiometer (AVHRR). Datasets containing locations of known fires were gathered from the NOAA's polar orbiting satellites via the Comprehensive Large Array-data Stewardship System (CLASS). The data was then calibrated and navigation corrected using the Environment for Visualizing Images (ENVI). Fires were located with the aid of shapefiles generated via ArcGIS. Afterwards, several smaller ten pixel by ten pixel datasets were created for each fire (using the ENVI corrected data). Several datasets were created for each fire in order to vary fire position and avoid training the ANN to look only at fires in the center of an image. Datasets containing no fires were also created. A basic pattern recognition neural network was established with the MATLAB neural network toolbox. The datasets were then randomly separated into categories used to train, validate, and test the ANN. To prevent over fitting of the data, the mean squared error (MSE) of the network was monitored and training was stopped when the MSE began to rise. Networks were tested using each channel of the AVHRR data independently, channels 3a and 3b combined, and all six channels. The number of hidden neurons for each input set was also varied between 5-350 in steps of 5 neurons. Each configuration was run 10 times, totaling about 4,200 individual network evaluations. Thirty

An accurate reactive power control study in virtual flux droop control

NASA Astrophysics Data System (ADS)

Wang, Aimeng; Zhang, Jia

2017-12-01

This paper investigates the problem of reactive power sharing based on virtual flux droop method. Firstly, flux droop control method is derived, where complicated multiple feedback loops and parameter regulation are avoided. Then, the reasons for inaccurate reactive power sharing are theoretically analyzed. Further, a novel reactive power control scheme is proposed which consists of three parts: compensation control, voltage recovery control and flux droop control. Finally, the proposed reactive power control strategy is verified in a simplified microgrid model with two parallel DGs. The simulation results show that the proposed control scheme can achieve accurate reactive power sharing and zero deviation of voltage. Meanwhile, it has some advantages of simple control and excellent dynamic and static performance.

Accurate modeling and evaluation of microstructures in complex materials

NASA Astrophysics Data System (ADS)

Tahmasebi, Pejman

2018-02-01

Accurate characterization of heterogeneous materials is of great importance for different fields of science and engineering. Such a goal can be achieved through imaging. Acquiring three- or two-dimensional images under different conditions is not, however, always plausible. On the other hand, accurate characterization of complex and multiphase materials requires various digital images (I) under different conditions. An ensemble method is presented that can take one single (or a set of) I(s) and stochastically produce several similar models of the given disordered material. The method is based on a successive calculating of a conditional probability by which the initial stochastic models are produced. Then, a graph formulation is utilized for removing unrealistic structures. A distance transform function for the Is with highly connected microstructure and long-range features is considered which results in a new I that is more informative. Reproduction of the I is also considered through a histogram matching approach in an iterative framework. Such an iterative algorithm avoids reproduction of unrealistic structures. Furthermore, a multiscale approach, based on pyramid representation of the large Is, is presented that can produce materials with millions of pixels in a matter of seconds. Finally, the nonstationary systems—those for which the distribution of data varies spatially—are studied using two different methods. The method is tested on several complex and large examples of microstructures. The produced results are all in excellent agreement with the utilized Is and the similarities are quantified using various correlation functions.

Balancing the Assessment "of" Learning and "for" Learning in Support of Student Literacy Achievement

ERIC Educational Resources Information Center

Edwards, Patricia A.; Turner, Jennifer D.; Mokhtari, Kouider

2008-01-01

There is a delicate balance between the assessment of learning and assessment for learning. The recommendations included in this Assessment department may be useful for teachers working to achieve this balance and find a more accurate and complete understandings of students' literacy strengths and needs.

Approaching system equilibrium with accurate or not accurate feedback information in a two-route system

NASA Astrophysics Data System (ADS)

Zhao, Xiao-mei; Xie, Dong-fan; Li, Qi

2015-02-01

With the development of intelligent transport system, advanced information feedback strategies have been developed to reduce traffic congestion and enhance the capacity. However, previous strategies provide accurate information to travelers and our simulation results show that accurate information brings negative effects, especially in delay case. Because travelers prefer to the best condition route with accurate information, and delayed information cannot reflect current traffic condition but past. Then travelers make wrong routing decisions, causing the decrease of the capacity and the increase of oscillations and the system deviating from the equilibrium. To avoid the negative effect, bounded rationality is taken into account by introducing a boundedly rational threshold BR. When difference between two routes is less than the BR, routes have equal probability to be chosen. The bounded rationality is helpful to improve the efficiency in terms of capacity, oscillation and the gap deviating from the system equilibrium.

A New Generation of Evidence: The Family is Critical to Student Achievement.

ERIC Educational Resources Information Center

Henderson, Anne T., Ed.; Berla, Nancy, Ed.

This report covers 66 studies, reviews, reports, analyses, and books. Of these 39 are new; 27 have been carried over from previous editions. An ERIC search was conducted to identify relevant studies. Noting that the most accurate predictor of student achievement is the extent to which the family is involved in his or her education, this report…

Vehicle Localization by LIDAR Point Correlation Improved by Change Detection

NASA Astrophysics Data System (ADS)

Schlichting, A.; Brenner, C.

2016-06-01

LiDAR sensors are proven sensors for accurate vehicle localization. Instead of detecting and matching features in the LiDAR data, we want to use the entire information provided by the scanners. As dynamic objects, like cars, pedestrians or even construction sites could lead to wrong localization results, we use a change detection algorithm to detect these objects in the reference data. If an object occurs in a certain number of measurements at the same position, we mark it and every containing point as static. In the next step, we merge the data of the single measurement epochs to one reference dataset, whereby we only use static points. Further, we also use a classification algorithm to detect trees. For the online localization of the vehicle, we use simulated data of a vertical aligned automotive LiDAR sensor. As we only want to use static objects in this case as well, we use a random forest classifier to detect dynamic scan points online. Since the automotive data is derived from the LiDAR Mobile Mapping System, we are able to use the labelled objects from the reference data generation step to create the training data and further to detect dynamic objects online. The localization then can be done by a point to image correlation method using only static objects. We achieved a localization standard deviation of about 5 cm (position) and 0.06° (heading), and were able to successfully localize the vehicle in about 93 % of the cases along a trajectory of 13 km in Hannover, Germany.

Segmentation of the Clustered Cells with Optimized Boundary Detection in Negative Phase Contrast Images

PubMed Central

Wang, Yuliang; Zhang, Zaicheng; Wang, Huimin; Bi, Shusheng

2015-01-01

Cell image segmentation plays a central role in numerous biology studies and clinical applications. As a result, the development of cell image segmentation algorithms with high robustness and accuracy is attracting more and more attention. In this study, an automated cell image segmentation algorithm is developed to get improved cell image segmentation with respect to cell boundary detection and segmentation of the clustered cells for all cells in the field of view in negative phase contrast images. A new method which combines the thresholding method and edge based active contour method was proposed to optimize cell boundary detection. In order to segment clustered cells, the geographic peaks of cell light intensity were utilized to detect numbers and locations of the clustered cells. In this paper, the working principles of the algorithms are described. The influence of parameters in cell boundary detection and the selection of the threshold value on the final segmentation results are investigated. At last, the proposed algorithm is applied to the negative phase contrast images from different experiments. The performance of the proposed method is evaluated. Results show that the proposed method can achieve optimized cell boundary detection and highly accurate segmentation for clustered cells. PMID:26066315

Accurate airway segmentation based on intensity structure analysis and graph-cut

NASA Astrophysics Data System (ADS)

Meng, Qier; Kitsaka, Takayuki; Nimura, Yukitaka; Oda, Masahiro; Mori, Kensaku

2016-03-01

This paper presents a novel airway segmentation method based on intensity structure analysis and graph-cut. Airway segmentation is an important step in analyzing chest CT volumes for computerized lung cancer detection, emphysema diagnosis, asthma diagnosis, and pre- and intra-operative bronchoscope navigation. However, obtaining a complete 3-D airway tree structure from a CT volume is quite challenging. Several researchers have proposed automated algorithms basically based on region growing and machine learning techniques. However these methods failed to detect the peripheral bronchi branches. They caused a large amount of leakage. This paper presents a novel approach that permits more accurate extraction of complex bronchial airway region. Our method are composed of three steps. First, the Hessian analysis is utilized for enhancing the line-like structure in CT volumes, then a multiscale cavity-enhancement filter is employed to detect the cavity-like structure from the previous enhanced result. In the second step, we utilize the support vector machine (SVM) to construct a classifier for removing the FP regions generated. Finally, the graph-cut algorithm is utilized to connect all of the candidate voxels to form an integrated airway tree. We applied this method to sixteen cases of 3D chest CT volumes. The results showed that the branch detection rate of this method can reach about 77.7% without leaking into the lung parenchyma areas.

Cognitive Predictors of Achievement Growth in Mathematics: A Five Year Longitudinal Study

PubMed Central

Geary, David C.

2011-01-01

The study's goal was to identify the beginning of first grade quantitative competencies that predict mathematics achievement start point and growth through fifth grade. Measures of number, counting, and arithmetic competencies were administered in early first grade and used to predict mathematics achievement through fifth (n = 177), while controlling for intelligence, working memory, and processing speed. Multilevel models revealed intelligence, processing speed, and the central executive component of working memory predicted achievement or achievement growth in mathematics and, as a contrast domain, word reading. The phonological loop was uniquely predictive of word reading and the visuospatial sketch pad of mathematics. Early fluency in processing and manipulating numerical set size and Arabic numerals, accurate use of sophisticated counting procedures for solving addition problems, and accuracy in making placements on a mathematical number line were uniquely predictive of mathematics achievement. Use of memory-based processes to solve addition problems predicted mathematics and reading achievement but in different ways. The results identify the early quantitative competencies that uniquely contribute to mathematics learning. PMID:21942667

SU-E-J-23: An Accurate Algorithm to Match Imperfectly Matched Images for Lung Tumor Detection Without Markers

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

Rozario, T; Bereg, S; Chiu, T

Purpose: In order to locate lung tumors on projection images without internal markers, digitally reconstructed radiograph (DRR) is created and compared with projection images. Since lung tumors always move and their locations change on projection images while they are static on DRRs, a special DRR (background DRR) is generated based on modified anatomy from which lung tumors are removed. In addition, global discrepancies exist between DRRs and projections due to their different image originations, scattering, and noises. This adversely affects comparison accuracy. A simple but efficient comparison algorithm is reported. Methods: This method divides global images into a matrix ofmore » small tiles and similarities will be evaluated by calculating normalized cross correlation (NCC) between corresponding tiles on projections and DRRs. The tile configuration (tile locations) will be automatically optimized to keep the tumor within a single tile which has bad matching with the corresponding DRR tile. A pixel based linear transformation will be determined by linear interpolations of tile transformation results obtained during tile matching. The DRR will be transformed to the projection image level and subtracted from it. The resulting subtracted image now contains only the tumor. A DRR of the tumor is registered to the subtracted image to locate the tumor. Results: This method has been successfully applied to kV fluoro images (about 1000 images) acquired on a Vero (Brainlab) for dynamic tumor tracking on phantom studies. Radiation opaque markers are implanted and used as ground truth for tumor positions. Although, other organs and bony structures introduce strong signals superimposed on tumors at some angles, this method accurately locates tumors on every projection over 12 gantry angles. The maximum error is less than 2.6 mm while the total average error is 1.0 mm. Conclusion: This algorithm is capable of detecting tumor without markers despite strong background

Transition from High School to University: A Person-Centered Approach to Academic Achievement

ERIC Educational Resources Information Center

De Clercq, Mikaël; Galand, Benoît; Frenay, Mariane

2017-01-01

Although a vast body of studies regarding the variables related to students' achievement exists, only a handful has investigated how these variables combine and interact together. Such an investigation might make it possible to more accurately illustrate the heterogeneity of students enrolling in university and assess the impact of this diversity…

Simultaneous Nonrigid Registration, Segmentation, and Tumor Detection in MRI Guided Cervical Cancer Radiation Therapy

PubMed Central

Lu, Chao; Chelikani, Sudhakar; Jaffray, David A.; Milosevic, Michael F.; Staib, Lawrence H.; Duncan, James S.

2013-01-01

External beam radiation therapy (EBRT) for the treatment of cancer enables accurate placement of radiation dose on the cancerous region. However, the deformation of soft tissue during the course of treatment, such as in cervical cancer, presents significant challenges for the delineation of the target volume and other structures of interest. Furthermore, the presence and regression of pathologies such as tumors may violate registration constraints and cause registration errors. In this paper, automatic segmentation, nonrigid registration and tumor detection in cervical magnetic resonance (MR) data are addressed simultaneously using a unified Bayesian framework. The proposed novel method can generate a tumor probability map while progressively identifying the boundary of an organ of interest based on the achieved nonrigid transformation. The method is able to handle the challenges of significant tumor regression and its effect on surrounding tissues. The new method was compared to various currently existing algorithms on a set of 36 MR data from six patients, each patient has six T2-weighted MR cervical images. The results show that the proposed approach achieves an accuracy comparable to manual segmentation and it significantly outperforms the existing registration algorithms. In addition, the tumor detection result generated by the proposed method has a high agreement with manual delineation by a qualified clinician. PMID:22328178

Insulation detection of electric vehicle batteries

NASA Astrophysics Data System (ADS)

Dai, Qiqi; Zhu, Zhongwen; Huang, Denggao; Du, Mingxing; Wei, Kexin

2018-06-01

In this paper, an electric vehicle insulation detection method with single side switching fixed resistance is designed, and the hardware and software design of the system are given. The experiment proves that the insulation detection system can detect the insulation resistance in a wide range of resistance values, and accurately report the fault level. This system can effectively monitor the insulation fault between the car body and the high voltage line and avoid the passengers from being injured.

Posture Detection Based on Smart Cushion for Wheelchair Users

PubMed Central

Ma, Congcong; Li, Wenfeng; Gravina, Raffaele; Fortino, Giancarlo

2017-01-01

The postures of wheelchair users can reveal their sitting habit, mood, and even predict health risks such as pressure ulcers or lower back pain. Mining the hidden information of the postures can reveal their wellness and general health conditions. In this paper, a cushion-based posture recognition system is used to process pressure sensor signals for the detection of user’s posture in the wheelchair. The proposed posture detection method is composed of three main steps: data level classification for posture detection, backward selection of sensor configuration, and recognition results compared with previous literature. Five supervised classification techniques—Decision Tree (J48), Support Vector Machines (SVM), Multilayer Perceptron (MLP), Naive Bayes, and k-Nearest Neighbor (k-NN)—are compared in terms of classification accuracy, precision, recall, and F-measure. Results indicate that the J48 classifier provides the highest accuracy compared to other techniques. The backward selection method was used to determine the best sensor deployment configuration of the wheelchair. Several kinds of pressure sensor deployments are compared and our new method of deployment is shown to better detect postures of the wheelchair users. Performance analysis also took into account the Body Mass Index (BMI), useful for evaluating the robustness of the method across individual physical differences. Results show that our proposed sensor deployment is effective, achieving 99.47% posture recognition accuracy. Our proposed method is very competitive for posture recognition and robust in comparison with other former research. Accurate posture detection represents a fundamental basic block to develop several applications, including fatigue estimation and activity level assessment. PMID:28353684

Fundamentals, achievements and challenges in the electrochemical sensing of pathogens.

PubMed

Monzó, Javier; Insua, Ignacio; Fernandez-Trillo, Francisco; Rodriguez, Paramaconi

2015-11-07

Electrochemical sensors are powerful tools widely used in industrial, environmental and medical applications. The versatility of electrochemical methods allows for the investigation of chemical composition in real time and in situ. Electrochemical detection of specific biological molecules is a powerful means for detecting disease-related markers. In the last 10 years, highly-sensitive and specific methods have been developed to detect waterborne and foodborne pathogens. In this review, we classify the different electrochemical techniques used for the qualitative and quantitative detection of pathogens. The robustness of electrochemical methods allows for accurate detection even in heterogeneous and impure samples. We present a fundamental description of the three major electrochemical sensing methods used in the detection of pathogens and the advantages and disadvantages of each of these methods. In each section, we highlight recent breakthroughs, including the utilisation of microfluidics, immunomagnetic separation and multiplexing for the detection of multiple pathogens in a single device. We also include recent studies describing new strategies for the design of future immunosensing systems and protocols. The high sensitivity and selectivity, together with the portability and the cost-effectiveness of the instrumentation, enhances the demand for further development in the electrochemical detection of microbes.

Rapid Methods for the Detection of General Fecal Indicators

EPA Science Inventory

Specified that EPA should develop: appropriate and effective indicators for improving detection in a timely manner of pathogens in coastal waters appropriate, accurate, expeditious and cost-effective methods for the timely detection of pathogens in coastal waters

Modification of CMV DNA detection from dried blood spots for diagnosing congenital CMV infection.

PubMed

Binda, Sandro; Caroppo, Simona; Didò, Patrizia; Primache, Valeria; Veronesi, Licia; Calvario, Agata; Piana, Andrea; Barbi, Maria

2004-07-01

Detection of viral DNA in dried blood spots using the Guthrie card (DBS test) is a reliable and practical method of diagnosing congenital cytomegalovirus (CMV) infection. The test lends itself to epidemiological studies to establish the prevalence of the infection, but also to neonatal screening for secondary prevention of sequelae. These applications would be facilitated if it were possible to use smaller samples and do the test on pools of individual cases. To ascertain whether doing the test on smaller, pooled samples still accurately identifies neonates with congenital CMV infection. We tested DBS from: (A) 39 laboratory reference cases; (B) 156 neonates suspected of having congenital CMV infection; (C) 119 children examined for the retrospective diagnosis of congenital CMV; (D) mock specimens prepared with known amounts of viral DNA. The test using only one third of the usual amount of dried blood was 100% sensitive and specific compared to the standard DBS test (A) and to viral isolation (A and B). Pools of three single cases gave the same results as viral isolation (B) and the small-sample test (B and C). All the versions of the test gave a detection limit of 400 copies/ml. The modified procedure can accurately diagnose congenital CMV infection. It achieves savings in both the patient material and the costs of testing.

Detection Copy Number Variants from NGS with Sparse and Smooth Constraints.

PubMed

Zhang, Yue; Cheung, Yiu-Ming; Xu, Bo; Su, Weifeng

2017-01-01

It is known that copy number variations (CNVs) are associated with complex diseases and particular tumor types, thus reliable identification of CNVs is of great potential value. Recent advances in next generation sequencing (NGS) data analysis have helped manifest the richness of CNV information. However, the performances of these methods are not consistent. Reliably finding CNVs in NGS data in an efficient way remains a challenging topic, worthy of further investigation. Accordingly, we tackle the problem by formulating CNVs identification into a quadratic optimization problem involving two constraints. By imposing the constraints of sparsity and smoothness, the reconstructed read depth signal from NGS is anticipated to fit the CNVs patterns more accurately. An efficient numerical solution tailored from alternating direction minimization (ADM) framework is elaborated. We demonstrate the advantages of the proposed method, namely ADM-CNV, by comparing it with six popular CNV detection methods using synthetic, simulated, and empirical sequencing data. It is shown that the proposed approach can successfully reconstruct CNV patterns from raw data, and achieve superior or comparable performance in detection of the CNVs compared to the existing counterparts.

Crowdsourcing lung nodules detection and annotation

NASA Astrophysics Data System (ADS)

Boorboor, Saeed; Nadeem, Saad; Park, Ji Hwan; Baker, Kevin; Kaufman, Arie

2018-03-01

We present crowdsourcing as an additional modality to aid radiologists in the diagnosis of lung cancer from clinical chest computed tomography (CT) scans. More specifically, a complete work flow is introduced which can help maximize the sensitivity of lung nodule detection by utilizing the collective intelligence of the crowd. We combine the concept of overlapping thin-slab maximum intensity projections (TS-MIPs) and cine viewing to render short videos that can be outsourced as an annotation task to the crowd. These videos are generated by linearly interpolating overlapping TS-MIPs of CT slices through the depth of each quadrant of a patient's lung. The resultant videos are outsourced to an online community of non-expert users who, after a brief tutorial, annotate suspected nodules in these video segments. Using our crowdsourcing work flow, we achieved a lung nodule detection sensitivity of over 90% for 20 patient CT datasets (containing 178 lung nodules with sizes between 1-30mm), and only 47 false positives from a total of 1021 annotations on nodules of all sizes (96% sensitivity for nodules>4mm). These results show that crowdsourcing can be a robust and scalable modality to aid radiologists in screening for lung cancer, directly or in combination with computer-aided detection (CAD) algorithms. For CAD algorithms, the presented work flow can provide highly accurate training data to overcome the high false-positive rate (per scan) problem. We also provide, for the first time, analysis on nodule size and position which can help improve CAD algorithms.

Fourier transform profilometry (FTP) using an innovative band-pass filter for accurate 3-D surface reconstruction

NASA Astrophysics Data System (ADS)

Chen, Liang-Chia; Ho, Hsuan-Wei; Nguyen, Xuan-Loc

2010-02-01

This article presents a novel band-pass filter for Fourier transform profilometry (FTP) for accurate 3-D surface reconstruction. FTP can be employed to obtain 3-D surface profiles by one-shot images to achieve high-speed measurement. However, its measurement accuracy has been significantly influenced by the spectrum filtering process required to extract the phase information representing various surface heights. Using the commonly applied 2-D Hanning filter, the measurement errors could be up to 5-10% of the overall measuring height and it is unacceptable to various industrial application. To resolve this issue, the article proposes an elliptical band-pass filter for extracting the spectral region possessing essential phase information for reconstructing accurate 3-D surface profiles. The elliptical band-pass filter was developed and optimized to reconstruct 3-D surface models with improved measurement accuracy. Some experimental results verify that the accuracy can be effectively enhanced by using the elliptical filter. The accuracy improvement of 44.1% and 30.4% can be achieved in 3-D and sphericity measurement, respectively, when the elliptical filter replaces the traditional filter as the band-pass filtering method. Employing the developed method, the maximum measured error can be kept within 3.3% of the overall measuring range.

The New Aptima HBV Quant Real-Time TMA Assay Accurately Quantifies Hepatitis B Virus DNA from Genotypes A to F

PubMed Central

Dauvillier, Claude; Dubernet, Fabienne; Poveda, Jean-Dominique; Laperche, Syria; Hézode, Christophe; Pawlotsky, Jean-Michel

2017-01-01

ABSTRACT Sensitive and accurate hepatitis B virus (HBV) DNA detection and quantification are essential to diagnose HBV infection, establish the prognosis of HBV-related liver disease, and guide the decision to treat and monitor the virological response to antiviral treatment and the emergence of resistance. Currently available HBV DNA platforms and assays are generally designed for batching multiple specimens within an individual run and require at least one full day of work to complete the analyses. The aim of this study was to evaluate the ability of the newly developed, fully automated, one-step Aptima HBV Quant assay to accurately detect and quantify HBV DNA in a large series of patients infected with different HBV genotypes. The limit of detection of the assay was estimated to be 4.5 IU/ml. The specificity of the assay was 100%. Intra-assay and interassay coefficients of variation ranged from 0.29% to 5.07% and 4.90% to 6.85%, respectively. HBV DNA levels from patients infected with HBV genotypes A to F measured with the Aptima HBV Quant assay strongly correlated with those measured by two commercial real-time PCR comparators (Cobas AmpliPrep/Cobas TaqMan HBV test, version 2.0, and Abbott RealTime HBV test). In conclusion, the Aptima HBV Quant assay is sensitive, specific, and reproducible and accurately quantifies HBV DNA in plasma samples from patients with chronic HBV infections of all genotypes, including patients on antiviral treatment with nucleoside or nucleotide analogues. The Aptima HBV Quant assay can thus confidently be used to detect and quantify HBV DNA in both clinical trials with new anti-HBV drugs and clinical practice. PMID:28202793

Separation and dual detection of prostate cancer cells and protein biomarkers using a microchip device.

PubMed

Huang, Wanfeng; Chang, Chun-Li; Brault, Norman D; Gur, Onur; Wang, Zhe; Jalal, Shadia I; Low, Philip S; Ratliff, Timothy L; Pili, Roberto; Savran, Cagri A

2017-01-31

Current efforts for the detection of prostate cancer using only prostate specific antigen are not ideal and indicate a need to develop new assays - using multiple targets - that can more accurately stratify disease states. We previously introduced a device capable of the concurrent detection of cellular and molecular markers from a single sample fluid. Here, an improved design, which achieves affinity as well as size-based separation of captured targets using antibody-conjugated magnetic beads and a silicon chip containing micro-apertures, is presented. Upon injection of the sample, the integration of magnetic attraction with the micro-aperture chip permits larger cell-bead complexes to be isolated in an upper chamber with the smaller protein-bead complexes and remaining beads passing through the micro-apertures into the lower chamber. This enhances captured cell purity for on chip quantification, allows the separate retrieval of captured cells and proteins for downstream analysis, and enables higher bead concentrations for improved multiplexed ligand targeting. Using LNCaP cells and prostate specific membrane antigen (PSMA) to model prostate cancer, the device was able to detect 34 pM of spiked PSMA and achieve a cell capture efficiency of 93% from culture media. LNCaP cells and PSMA were then spiked into diluted healthy human blood to mimic a cancer patient. The device enabled the detection of spiked PSMA (relative to endogenous PSMA) while recovering 85-90% of LNCaP cells which illustrated the potential of new assays for the diagnosis of prostate cancer.

Integrated signal probe based aptasensor for dual-analyte detection.

PubMed

Xiang, Juan; Pi, Xiaomei; Chen, Xiaoqing; Xiang, Lei; Yang, Minghui; Ren, Hao; Shen, Xiaojuan; Qi, Ning; Deng, Chunyan

2017-10-15

For the multi-analyte detection, although the sensitivity has commonly met the practical requirements, the reliability, reproducibility and stability need to be further improved. In this work, two different aptamer probes labeled with redox tags were used as signal probe1 (sP1) and signal probe2 (sP2), which were integrated into one unity DNA architecture to develop the integrated signal probe (ISP). Comparing with the conventional independent signal probes for the simultaneous multi-analyte detection, the proposed ISP was more reproducible and accurate. This can be due to that ISP in one DNA structure can ensure the completely same modification condition and an equal stoichiometric ratio between sP1 and sP2, and furthermore the cross interference between sP1 and sP2 can be successfully prevented by regulating the complementary position of sP1 and sP2. The ISP-based assay system would be a great progress for the dual-analyte detection. Combining with gold nanoparticles (AuNPs) signal amplification, the ISP/AuNPs-based aptasensor for the sensitive dual-analyte detection was explored. Based on DNA structural switching induced by targets binding to aptamer, the simultaneous dual-analyte detection was simply achieved by monitoring the electrochemical responses of methylene blue (MB) and ferrocene (Fc) This proposed detection system possesses such advantages as simplicity in design, easy operation, good reproducibility and accuracy, high sensitivity and selectivity, which indicates the excellent application of this aptasensor in the field of clinical diagnosis or other molecular sensors. Copyright © 2017 Elsevier B.V. All rights reserved.

Enzymic colorimetry-based DNA chip: a rapid and accurate assay for detecting mutations for clarithromycin resistance in the 23S rRNA gene of Helicobacter pylori.

PubMed

Xuan, Shi-Hai; Zhou, Yu-Gui; Shao, Bo; Cui, Ya-Lin; Li, Jian; Yin, Hong-Bo; Song, Xiao-Ping; Cong, Hui; Jing, Feng-Xiang; Jin, Qing-Hui; Wang, Hui-Min; Zhou, Jie

2009-11-01

Macrolide drugs, such as clarithromycin (CAM), are a key component of many combination therapies used to eradicate Helicobacter pylori. However, resistance to CAM is increasing in H. pylori and is becoming a serious problem in H. pylori eradication therapy. CAM resistance in H. pylori is mostly due to point mutations (A2142G/C, A2143G) in the peptidyltransferase-encoding region of the 23S rRNA gene. In this study an enzymic colorimetry-based DNA chip was developed to analyse single-nucleotide polymorphisms of the 23S rRNA gene to determine the prevalence of mutations in CAM-related resistance in H. pylori-positive patients. The results of the colorimetric DNA chip were confirmed by direct DNA sequencing. In 63 samples, the incidence of the A2143G mutation was 17.46 % (11/63). The results of the colorimetric DNA chip were concordant with DNA sequencing in 96.83 % of results (61/63). The colorimetric DNA chip could detect wild-type and mutant signals at every site, even at a DNA concentration of 1.53 x 10(2) copies microl(-1). Thus, the colorimetric DNA chip is a reliable assay for rapid and accurate detection of mutations in the 23S rRNA gene of H. pylori that lead to CAM-related resistance, directly from gastric tissues.

The Shuttle Orbital Maneuvering System P-V-T Propellant Quantity Gaging Accuracy and Leak Detection Allowance for Four Instrumentation Conditions

NASA Technical Reports Server (NTRS)

Duhon, D. D.

1975-01-01

The shuttle orbital maneuvering system (OMS) pressure-volume-temperature (P-V-T) propellant gaging module computes the quantity of usable OMS propellant remaining based on the real gas P-V-T relationship for the propellant tank pressurant, helium. The OMS P-V-T propellant quantity gaging error was determined for four sets of instrumentation configurations and accuracies with the propellant tank operating in the normal constant pressure mode and in the blowdown mode. The instrumentation inaccuracy allowance for propellant leak detection was also computed for these same four sets of instrumentation. These gaging errors and leak detection allowances are presented in tables designed to permit a direct comparison of the effectiveness of the four instrumentation sets. The results show the magnitudes of the improvements in propellant quantity gaging accuracies and propellant leak detection allowances which can be achieved by employing more accurate pressure and temperature instrumentation.

Accurate inclusion mass screening: a bridge from unbiased discovery to targeted assay development for biomarker verification.

PubMed

Jaffe, Jacob D; Keshishian, Hasmik; Chang, Betty; Addona, Theresa A; Gillette, Michael A; Carr, Steven A

2008-10-01

Verification of candidate biomarker proteins in blood is typically done using multiple reaction monitoring (MRM) of peptides by LC-MS/MS on triple quadrupole MS systems. MRM assay development for each protein requires significant time and cost, much of which is likely to be of little value if the candidate biomarker is below the detection limit in blood or a false positive in the original discovery data. Here we present a new technology, accurate inclusion mass screening (AIMS), designed to provide a bridge from unbiased discovery to MS-based targeted assay development. Masses on the software inclusion list are monitored in each scan on the Orbitrap MS system, and MS/MS spectra for sequence confirmation are acquired only when a peptide from the list is detected with both the correct accurate mass and charge state. The AIMS experiment confirms that a given peptide (and thus the protein from which it is derived) is present in the plasma. Throughput of the method is sufficient to qualify up to a hundred proteins/week. The sensitivity of AIMS is similar to MRM on a triple quadrupole MS system using optimized sample preparation methods (low tens of ng/ml in plasma), and MS/MS data from the AIMS experiments on the Orbitrap can be directly used to configure MRM assays. The method was shown to be at least 4-fold more efficient at detecting peptides of interest than undirected LC-MS/MS experiments using the same instrumentation, and relative quantitation information can be obtained by AIMS in case versus control experiments. Detection by AIMS ensures that a quantitative MRM-based assay can be configured for that protein. The method has the potential to qualify large number of biomarker candidates based on their detection in plasma prior to committing to the time- and resource-intensive steps of establishing a quantitative assay.

Compression-based distance (CBD): a simple, rapid, and accurate method for microbiota composition comparison

PubMed Central

2013-01-01

Background Perturbations in intestinal microbiota composition have been associated with a variety of gastrointestinal tract-related diseases. The alleviation of symptoms has been achieved using treatments that alter the gastrointestinal tract microbiota toward that of healthy individuals. Identifying differences in microbiota composition through the use of 16S rRNA gene hypervariable tag sequencing has profound health implications. Current computational methods for comparing microbial communities are usually based on multiple alignments and phylogenetic inference, making them time consuming and requiring exceptional expertise and computational resources. As sequencing data rapidly grows in size, simpler analysis methods are needed to meet the growing computational burdens of microbiota comparisons. Thus, we have developed a simple, rapid, and accurate method, independent of multiple alignments and phylogenetic inference, to support microbiota comparisons. Results We create a metric, called compression-based distance (CBD) for quantifying the degree of similarity between microbial communities. CBD uses the repetitive nature of hypervariable tag datasets and well-established compression algorithms to approximate the total information shared between two datasets. Three published microbiota datasets were used as test cases for CBD as an applicable tool. Our study revealed that CBD recaptured 100% of the statistically significant conclusions reported in the previous studies, while achieving a decrease in computational time required when compared to similar tools without expert user intervention. Conclusion CBD provides a simple, rapid, and accurate method for assessing distances between gastrointestinal tract microbiota 16S hypervariable tag datasets. PMID:23617892

Compression-based distance (CBD): a simple, rapid, and accurate method for microbiota composition comparison.

PubMed

Yang, Fang; Chia, Nicholas; White, Bryan A; Schook, Lawrence B

2013-04-23

Perturbations in intestinal microbiota composition have been associated with a variety of gastrointestinal tract-related diseases. The alleviation of symptoms has been achieved using treatments that alter the gastrointestinal tract microbiota toward that of healthy individuals. Identifying differences in microbiota composition through the use of 16S rRNA gene hypervariable tag sequencing has profound health implications. Current computational methods for comparing microbial communities are usually based on multiple alignments and phylogenetic inference, making them time consuming and requiring exceptional expertise and computational resources. As sequencing data rapidly grows in size, simpler analysis methods are needed to meet the growing computational burdens of microbiota comparisons. Thus, we have developed a simple, rapid, and accurate method, independent of multiple alignments and phylogenetic inference, to support microbiota comparisons. We create a metric, called compression-based distance (CBD) for quantifying the degree of similarity between microbial communities. CBD uses the repetitive nature of hypervariable tag datasets and well-established compression algorithms to approximate the total information shared between two datasets. Three published microbiota datasets were used as test cases for CBD as an applicable tool. Our study revealed that CBD recaptured 100% of the statistically significant conclusions reported in the previous studies, while achieving a decrease in computational time required when compared to similar tools without expert user intervention. CBD provides a simple, rapid, and accurate method for assessing distances between gastrointestinal tract microbiota 16S hypervariable tag datasets.

Three Decades of Precision Orbit Determination Progress, Achievements, Future Challenges and its Vital Contribution to Oceanography and Climate Research

NASA Technical Reports Server (NTRS)

Luthcke, Scott; Rowlands, David; Lemoine, Frank; Zelensky, Nikita; Beckley, Brian; Klosko, Steve; Chinn, Doug

2006-01-01

Although satellite altimetry has been around for thirty years, the last fifteen beginning with the launch of TOPEX/Poseidon (TP) have yielded an abundance of significant results including: monitoring of ENS0 events, detection of internal tides, determination of accurate global tides, unambiguous delineation of Rossby waves and their propagation characteristics, accurate determination of geostrophic currents, and a multi-decadal time series of mean sea level trend and dynamic ocean topography variability. While the high level of accuracy being achieved is a result of both instrument maturity and the quality of models and correction algorithms applied to the data, improving the quality of the Climate Data Records produced from altimetry is highly dependent on concurrent progress being made in fields such as orbit determination. The precision orbits form the reference frame from which the radar altimeter observations are made. Therefore, the accuracy of the altimetric mapping is limited to a great extent by the accuracy to which a satellite orbit can be computed. The TP mission represents the first time that the radial component of an altimeter orbit was routinely computed with an accuracy of 2-cm. Recently it has been demonstrated that it is possible to compute the radial component of Jason orbits with an accuracy of better than 1-cm. Additionally, still further improvements in TP orbits are being achieved with new techniques and algorithms largely developed from combined Jason and TP data analysis. While these recent POD achievements are impressive, the new accuracies are now revealing subtle systematic orbit error that manifest as both intra and inter annual ocean topography errors. Additionally the construction of inter-decadal time series of climate data records requires the removal of systematic differences across multiple missions. Current and future efforts must focus on the understanding and reduction of these errors in order to generate a complete and

GUIDANCE2: accurate detection of unreliable alignment regions accounting for the uncertainty of multiple parameters.

PubMed

Sela, Itamar; Ashkenazy, Haim; Katoh, Kazutaka; Pupko, Tal

2015-07-01

Inference of multiple sequence alignments (MSAs) is a critical part of phylogenetic and comparative genomics studies. However, from the same set of sequences different MSAs are often inferred, depending on the methodologies used and the assumed parameters. Much effort has recently been devoted to improving the ability to identify unreliable alignment regions. Detecting such unreliable regions was previously shown to be important for downstream analyses relying on MSAs, such as the detection of positive selection. Here we developed GUIDANCE2, a new integrative methodology that accounts for: (i) uncertainty in the process of indel formation, (ii) uncertainty in the assumed guide tree and (iii) co-optimal solutions in the pairwise alignments, used as building blocks in progressive alignment algorithms. We compared GUIDANCE2 with seven methodologies to detect unreliable MSA regions using extensive simulations and empirical benchmarks. We show that GUIDANCE2 outperforms all previously developed methodologies. Furthermore, GUIDANCE2 also provides a set of alternative MSAs which can be useful for downstream analyses. The novel algorithm is implemented as a web-server, available at: http://guidance.tau.ac.il. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

A fast and accurate dihedral interpolation loop subdivision scheme

NASA Astrophysics Data System (ADS)

Shi, Zhuo; An, Yalei; Wang, Zhongshuai; Yu, Ke; Zhong, Si; Lan, Rushi; Luo, Xiaonan

2018-04-01

In this paper, we propose a fast and accurate dihedral interpolation Loop subdivision scheme for subdivision surfaces based on triangular meshes. In order to solve the problem of surface shrinkage, we keep the limit condition unchanged, which is important. Extraordinary vertices are handled using modified Butterfly rules. Subdivision schemes are computationally costly as the number of faces grows exponentially at higher levels of subdivision. To address this problem, our approach is to use local surface information to adaptively refine the model. This is achieved simply by changing the threshold value of the dihedral angle parameter, i.e., the angle between the normals of a triangular face and its adjacent faces. We then demonstrate the effectiveness of the proposed method for various 3D graphic triangular meshes, and extensive experimental results show that it can match or exceed the expected results at lower computational cost.

An Improved Sparse Representation over Learned Dictionary Method for Seizure Detection.

PubMed

Li, Junhui; Zhou, Weidong; Yuan, Shasha; Zhang, Yanli; Li, Chengcheng; Wu, Qi

2016-02-01

Automatic seizure detection has played an important role in the monitoring, diagnosis and treatment of epilepsy. In this paper, a patient specific method is proposed for seizure detection in the long-term intracranial electroencephalogram (EEG) recordings. This seizure detection method is based on sparse representation with online dictionary learning and elastic net constraint. The online learned dictionary could sparsely represent the testing samples more accurately, and the elastic net constraint which combines the 11-norm and 12-norm not only makes the coefficients sparse but also avoids over-fitting problem. First, the EEG signals are preprocessed using wavelet filtering and differential filtering, and the kernel function is applied to make the samples closer to linearly separable. Then the dictionaries of seizure and nonseizure are respectively learned from original ictal and interictal training samples with online dictionary optimization algorithm to compose the training dictionary. After that, the test samples are sparsely coded over the learned dictionary and the residuals associated with ictal and interictal sub-dictionary are calculated, respectively. Eventually, the test samples are classified as two distinct categories, seizure or nonseizure, by comparing the reconstructed residuals. The average segment-based sensitivity of 95.45%, specificity of 99.08%, and event-based sensitivity of 94.44% with false detection rate of 0.23/h and average latency of -5.14 s have been achieved with our proposed method.

Detection of defects in formed sheet metal using medial axis transformation

NASA Astrophysics Data System (ADS)

Murmu, Naresh C.; Velgan, Roman

2003-05-01

In the metal forming processes, the sheet metals are often prone to various defects such as thinning, dents, wrinkles etc. In the present manufacturing environments with ever increasing demand of higher quality, detecting the defects of formed sheet metal using an effective and objective inspection system is the foremost norm to remain competitive in market. The defect detection using optical techniques aspire to satisfy its needs to be non-contact and fast. However, the main difficulties to achieve this goal remain essentially on the development of efficient evaluation technique and accurate interpretation of extracted data. The defect like thinning is detected by evaluating the deviations of the thickness in the formed sheet metal against its nominal value. The present evaluation procedure for determination of thickness applied on the measurements data is not without deficiency. To improve this procedure, a new evaluation approach based on medial axis transformation is proposed here. The formed sheet metals are digitized using fringe projection systems in different orientations, and afterwards registered into one coordinate frame. The medial axis transformation (MAT) is applied on the point clouds, generating the point clouds of MAT. This data is further processed and medial surface is determined. The thinning defect is detected by evaluating local wall thickness and other defects like wrinkles are determined using the shape recognition on the medial surface. The applied algorithm is simple, fast and robust.

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.

Accurate seismic phase identification and arrival time picking of glacial icequakes

NASA Astrophysics Data System (ADS)

Jones, G. A.; Doyle, S. H.; Dow, C.; Kulessa, B.; Hubbard, A.

2010-12-01

A catastrophic lake drainage event was monitored continuously using an array of 6, 4.5 Hz 3 component geophones in the Russell Glacier catchment, Western Greenland. Many thousands of events and arrival time phases (e.g., P- or S-wave) were recorded, often with events occurring simultaneously but at different locations. In addition, different styles of seismic events were identified from 'classical' tectonic earthquakes to tremors usually observed in volcanic regions. The presence of such a diverse and large dataset provides insight into the complex system of lake drainage. One of the most fundamental steps in seismology is the accurate identification of a seismic event and its associated arrival times. However, the collection of such a large and complex dataset makes the manual identification of a seismic event and picking of the arrival time phases time consuming with variable results. To overcome the issues of consistency and manpower, a number of different methods have been developed including short-term and long-term averages, spectrograms, wavelets, polarisation analyses, higher order statistics and auto-regressive techniques. Here we propose an automated procedure which establishes the phase type and accurately determines the arrival times. The procedure combines a number of different automated methods to achieve this, and is applied to the recently acquired lake drainage data. Accurate identification of events and their arrival time phases are the first steps in gaining a greater understanding of the extent of the deformation and the mechanism of such drainage events. A good knowledge of the propagation pathway of lake drainage meltwater through a glacier will have significant consequences for interpretation of glacial and ice sheet dynamics.

Mild Depression Detection of College Students: an EEG-Based Solution with Free Viewing Tasks.

PubMed

Li, Xiaowei; Hu, Bin; Shen, Ji; Xu, Tingting; Retcliffe, Martyn

2015-12-01

Depression is a common mental disorder with growing prevalence; however current diagnoses of depression face the problem of patient denial, clinical experience and subjective biases from self-report. By using a combination of linear and nonlinear EEG features in our research, we aim to develop a more accurate and objective approach to depression detection that supports the process of diagnosis and assists the monitoring of risk factors. By classifying EEG features during free viewing task, an accuracy of 99.1%, which is the highest to our knowledge by far, was achieved using kNN classifier to discriminate depressed and non-depressed subjects. Furthermore, through correlation analysis, comparisons of performance on each electrode were discussed on the availability of single channel EEG recording depression detection system. Combined with wearable EEG collecting devices, our method offers the possibility of cost effective wearable ubiquitous system for doctors to monitor their patients with depression, and for normal people to understand their mental states in time.

How Accurately Can the Google Web Speech API Recognize and Transcribe Japanese L2 English Learners' Oral Production?

ERIC Educational Resources Information Center

Ashwell, Tim; Elam, Jesse R.

2017-01-01

The ultimate aim of our research project was to use the Google Web Speech API to automate scoring of elicited imitation (EI) tests. However, in order to achieve this goal, we had to take a number of preparatory steps. We needed to assess how accurate this speech recognition tool is in recognizing native speakers' production of the test items; we…

An FPGA-Based People Detection System

NASA Astrophysics Data System (ADS)

Nair, Vinod; Laprise, Pierre-Olivier; Clark, James J.

2005-12-01

This paper presents an FPGA-based system for detecting people from video. The system is designed to use JPEG-compressed frames from a network camera. Unlike previous approaches that use techniques such as background subtraction and motion detection, we use a machine-learning-based approach to train an accurate detector. We address the hardware design challenges involved in implementing such a detector, along with JPEG decompression, on an FPGA. We also present an algorithm that efficiently combines JPEG decompression with the detection process. This algorithm carries out the inverse DCT step of JPEG decompression only partially. Therefore, it is computationally more efficient and simpler to implement, and it takes up less space on the chip than the full inverse DCT algorithm. The system is demonstrated on an automated video surveillance application and the performance of both hardware and software implementations is analyzed. The results show that the system can detect people accurately at a rate of about[InlineEquation not available: see fulltext.] frames per second on a Virtex-II 2V1000 using a MicroBlaze processor running at[InlineEquation not available: see fulltext.], communicating with dedicated hardware over FSL links.

Carbene footprinting accurately maps binding sites in protein-ligand and protein-protein interactions

NASA Astrophysics Data System (ADS)

Manzi, Lucio; Barrow, Andrew S.; Scott, Daniel; Layfield, Robert; Wright, Timothy G.; Moses, John E.; Oldham, Neil J.

2016-11-01

Specific interactions between proteins and their binding partners are fundamental to life processes. The ability to detect protein complexes, and map their sites of binding, is crucial to understanding basic biology at the molecular level. Methods that employ sensitive analytical techniques such as mass spectrometry have the potential to provide valuable insights with very little material and on short time scales. Here we present a differential protein footprinting technique employing an efficient photo-activated probe for use with mass spectrometry. Using this methodology the location of a carbohydrate substrate was accurately mapped to the binding cleft of lysozyme, and in a more complex example, the interactions between a 100 kDa, multi-domain deubiquitinating enzyme, USP5 and a diubiquitin substrate were located to different functional domains. The much improved properties of this probe make carbene footprinting a viable method for rapid and accurate identification of protein binding sites utilizing benign, near-UV photoactivation.

a Landsat Time-Series Stacks Model for Detection of Cropland Change

NASA Astrophysics Data System (ADS)

Chen, J.; Chen, J.; Zhang, J.

2017-09-01

Global, timely, accurate and cost-effective cropland monitoring with a fine spatial resolution will dramatically improve our understanding of the effects of agriculture on greenhouse gases emissions, food safety, and human health. Time-series remote sensing imagery have been shown particularly potential to describe land cover dynamics. The traditional change detection techniques are often not capable of detecting land cover changes within time series that are severely influenced by seasonal difference, which are more likely to generate pseuso changes. Here,we introduced and tested LTSM ( Landsat time-series stacks model), an improved Continuous Change Detection and Classification (CCDC) proposed previously approach to extract spectral trajectories of land surface change using a dense Landsat time-series stacks (LTS). The method is expected to eliminate pseudo changes caused by phenology driven by seasonal patterns. The main idea of the method is that using all available Landsat 8 images within a year, LTSM consisting of two term harmonic function are estimated iteratively for each pixel in each spectral band .LTSM can defines change area by differencing the predicted and observed Landsat images. The LTSM approach was compared with change vector analysis (CVA) method. The results indicated that the LTSM method correctly detected the "true change" without overestimating the "false" one, while CVA pointed out "true change" pixels with a large number of "false changes". The detection of change areas achieved an overall accuracy of 92.37 %, with a kappa coefficient of 0.676.

Structural Damage Detection Using Changes in Natural Frequencies: Theory and Applications

NASA Astrophysics Data System (ADS)

He, K.; Zhu, W. D.

2011-07-01

A vibration-based method that uses changes in natural frequencies of a structure to detect damage has advantages over conventional nondestructive tests in detecting various types of damage, including loosening of bolted joints, using minimum measurement data. Two major challenges associated with applications of the vibration-based damage detection method to engineering structures are addressed: accurate modeling of structures and the development of a robust inverse algorithm to detect damage, which are defined as the forward and inverse problems, respectively. To resolve the forward problem, new physics-based finite element modeling techniques are developed for fillets in thin-walled beams and for bolted joints, so that complex structures can be accurately modeled with a reasonable model size. To resolve the inverse problem, a logistical function transformation is introduced to convert the constrained optimization problem to an unconstrained one, and a robust iterative algorithm using a trust-region method, called the Levenberg-Marquardt method, is developed to accurately detect the locations and extent of damage. The new methodology can ensure global convergence of the iterative algorithm in solving under-determined system equations and deal with damage detection problems with relatively large modeling error and measurement noise. The vibration-based damage detection method is applied to various structures including lightning masts, a space frame structure and one of its components, and a pipeline. The exact locations and extent of damage can be detected in the numerical simulation where there is no modeling error and measurement noise. The locations and extent of damage can be successfully detected in experimental damage detection.

Development of a triage engine enabling behavior recognition and lethal arrhythmia detection for remote health care system.

PubMed

Sugano, Hiroto; Hara, Shinsuke; Tsujioka, Tetsuo; Inoue, Tadayuki; Nakajima, Shigeyoshi; Kozaki, Takaaki; Namkamura, Hajime; Takeuchi, Kazuhide

2011-01-01

For ubiquitous health care systems which continuously monitor a person's vital signs such as electrocardiogram (ECG), body surface temperature and three-dimensional (3D) acceleration by wireless, it is important to accurately detect the occurrence of an abnormal event in the data and immediately inform a medical doctor of its detail. In this paper, we introduce a remote health care system, which is composed of a wireless vital sensor, multiple receivers and a triage engine installed in a desktop personal computer (PC). The middleware installed in the receiver, which was developed in C++, supports reliable data handling of vital data to the ethernet port. On the other hand, the human interface of the triage engine, which was developed in JAVA, shows graphics on his/her ECG data, 3D acceleration data, body surface temperature data and behavior status in the display of the desktop PC and sends an urgent e-mail containing the display data to a pre-registered medical doctor when it detects the occurrence of an abnormal event. In the triage engine, the lethal arrhythmia detection algorithm based on short time Fourier transform (STFT) analysis can achieve 100 % sensitivity and 99.99 % specificity, and the behavior recognition algorithm based on the combination of the nearest neighbor method and the Naive Bayes method can achieve more than 71 % classification accuracy.

USING CANINES IN SOURCE DETECTION OF INDOOR AIR POLLUTANTS

EPA Science Inventory

Dogs have been used extensively in law enforcement and military applications to detect narcotics and explosives for over thirty years. Dogs are regularly used in arson investigations to detect accelerants since they are much more accurate at discriminating between accelerants an...

Mammalian choices: combining fast-but-inaccurate and slow-but-accurate decision-making systems.

PubMed

Trimmer, Pete C; Houston, Alasdair I; Marshall, James A R; Bogacz, Rafal; Paul, Elizabeth S; Mendl, Mike T; McNamara, John M

2008-10-22

Empirical findings suggest that the mammalian brain has two decision-making systems that act at different speeds. We represent the faster system using standard signal detection theory. We represent the slower (but more accurate) cortical system as the integration of sensory evidence over time until a certain level of confidence is reached. We then consider how two such systems should be combined optimally for a range of information linkage mechanisms. We conclude with some performance predictions that will hold if our representation is realistic.

Is 50 Hz high enough ECG sampling frequency for accurate HRV analysis?

PubMed

Mahdiani, Shadi; Jeyhani, Vala; Peltokangas, Mikko; Vehkaoja, Antti

2015-01-01

With the worldwide growth of mobile wireless technologies, healthcare services can be provided at anytime and anywhere. Usage of wearable wireless physiological monitoring system has been extensively increasing during the last decade. These mobile devices can continuously measure e.g. the heart activity and wirelessly transfer the data to the mobile phone of the patient. One of the significant restrictions for these devices is usage of energy, which leads to requiring low sampling rate. This article is presented in order to investigate the lowest adequate sampling frequency of ECG signal, for achieving accurate enough time domain heart rate variability (HRV) parameters. For this purpose the ECG signals originally measured with high 5 kHz sampling rate were down-sampled to simulate the measurement with lower sampling rate. Down-sampling loses information, decreases temporal accuracy, which was then restored by interpolating the signals to their original sampling rates. The HRV parameters obtained from the ECG signals with lower sampling rates were compared. The results represent that even when the sampling rate of ECG signal is equal to 50 Hz, the HRV parameters are almost accurate with a reasonable error.

Robust snow avalanche detection using machine learning on infrasonic array data

NASA Astrophysics Data System (ADS)

Thüring, Thomas; Schoch, Marcel; van Herwijnen, Alec; Schweizer, Jürg

2014-05-01

Snow avalanches may threaten people and infrastructure in mountain areas. Automated detection of avalanche activity would be highly desirable, in particular during times of poor visibility, to improve hazard assessment, but also to monitor the effectiveness of avalanche control by explosives. In the past, a variety of remote sensing techniques and instruments for the automated detection of avalanche activity have been reported, which are based on radio waves (radar), seismic signals (geophone), optical signals (imaging sensor) or infrasonic signals (microphone). Optical imagery enables to assess avalanche activity with very high spatial resolution, however it is strongly weather dependent. Radar and geophone-based detection typically provide robust avalanche detection for all weather conditions, but are very limited in the size of the monitoring area. On the other hand, due to the long propagation distance of infrasound through air, the monitoring area of infrasonic sensors can cover a large territory using a single sensor (or an array). In addition, they are by far more cost effective than radars or optical imaging systems. Unfortunately, the reliability of infrasonic sensor systems has so far been rather low due to the strong variation of ambient noise (e.g. wind) causing a high false alarm rate. We analyzed the data collected by a low-cost infrasonic array system consisting of four sensors for the automated detection of avalanche activity at Lavin in the eastern Swiss Alps. A comparably large array aperture (~350m) allows highly accurate time delay estimations of signals which arrive at different times at the sensors, enabling precise source localization. An array of four sensors is sufficient for the time resolved source localization of signals in full 3D space, which is an excellent method to anticipate true avalanche activity. Robust avalanche detection is then achieved by using machine learning methods such as support vector machines. The system is initially

Evaluating the Reference Interview: A Theoretical Discussion of the Desirability and Achievability of Evaluation.

ERIC Educational Resources Information Center

Smith, Lisa L.

1991-01-01

Review and examination of the current literature on reference interview evaluation explores the degree to which such evaluative practices are both desirable and achievable. It is concluded that, if both quantitative and qualitative techniques are appropriately used, accurate mechanisms of evaluation are possible and desirable. (17 references) (LRW)

A Portable, Shock-Proof, Surface-Heated Droplet PCR System for Escherichia coli Detection

PubMed Central

Angus, Scott V.; Cho, Soohee; Harshman, Dustin K.; Song, Jae-Young; Yoon, Jeong-Yeol

2015-01-01

A novel polymerase chain reaction (PCR) device was developed that uses wire-guided droplet manipulation (WDM) to guide a droplet over three different heating chambers. After PCR amplification, end-point detection is achieved using a smartphone-based fluorescence microscope. The device was tested for identification of the 16S rRNA gene V3 hypervariable region from Escherichia coli genomic DNA. The lower limit of detection was 103 genome copies per sample. The device is portable with smartphone-based end-point detection and provides the assay results quickly (15 min for a 30-cycle amplification) and accurately. The system is also shock and vibration resistant, due to the multiple points of contact between the droplet and the thermocouple and the Teflon film on the heater surfaces. The thermocouple also provides realtime droplet temperature feedback to ensure it reaches the set temperature before moving to the next chamber/step in PCR. The device is equipped to use either silicone oil or coconut oil. Coconut oil provides additional portability and ease of transportation by eliminating spilling because its high melting temperature means it is solid at room temperature. PMID:26164008